Volume 13 Issue 3

Solar Energy Challenges And Future Role In India: A Review

Authors: Kuraganti Syam Kumar, Palineti Karthik, Thodindala Siva Teja, Shaik Anwar Mohiddeen, Syed Mohammad Waseem

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Secure Electronic Voting System

Authors: Abdul Huq, Ankit Pandey, Sonam Bajpai, Vinay Tiwari, Professor (Dr.) Sunil Dhore

Abstract: The implementation of electronic voting systems presents the opportunity to enhance accessibility and efficiency in democracy. However, security matters such as authentication of voters, ensuring the integrity of votes cast, and protection against data tampering still pose serious problems. Voting car- ried out through paper ballots and centralized computerized voting systems suffer from voting fraud and manipulation of votes. This paper proposes a Secure Electronic Voting System which addresses these gaps and improves transparency and trust in elections. The system incorporates distributed storage, cryptographic hash functions, and multi-factor authentication. To guarantee the integrity of votes, cryptographic hash functions are incorporated to make them un-changeable. With multi-factor authentication, the authorized voters are verified. Utilization of blockchain technology for distributed storage protects the system from single point of failure. Voter information and confidentiality of vote is encrypted with AES and RSA, while tallying the votes is conducted through homomorphic encryption, enabling counting without decryption. The results of performance assessment showed that processing efficiency remains at a high level while enhancing security of the system significantly, thus creating the possibility for clear, verifiable and tamper-proof elections.

Next-Gen Cricket: Leveraging IOT And AI For Real-Time Health Monitoring, Injury Prevention, And Player Enhancement

Authors: Vishal Ramkumar Rajbhar, K P Agarwal

Abstract: As cricket continues to integrate more data-driven approaches, player health and safety have become of paramount importance. This paper presents an innovative health monitoring system for cricket players, utilizing Internet of Things (IoT) technology and Artificial Intelligence (AI) to enhance player safety, predict potential injuries and optimize players’ performance. The proposed system incorporates wearable sensors to monitor key physiological and biomechanical parameters, including cardiac activity, hydration levels, body temperature, and impact forces. Through the use of AI-driven Predictive Analytics, the system provides real-time alerts for health anomalies such as irregular heart rhythms, dehydration, and overexertion. Additionally, the system also enables long-term health trend analysis, offering personalized fitness recommendations and injury risk predictions based on patterns of players movement. The integration of these technologies’ shifts player health management from reactive to proactive care, ensuring timely interventions and reducing the risk of injuries and health emergencies. This research demonstrates the potential integration of IoT and AI in revolutionizing athlete care, providing a comprehensive framework that enhances both player safety and performance mainly in the sport of cricket.

DOI: http://doi.org/

Disaster Recovery In Cloud Environments: A Theoretical Review

Authors: Vaishanavi Rajgopal Sitalgeri

Abstract: Disaster Recovery (DR) in cloud environments has emerged as a critical priority in the digital era, as data becomes the foundation of business operations. With the rise of cloud computing, businesses increasingly rely on distributed infrastructure to store, manage, and process critical data. This shift, while offering scalability and cost-efficiency, also introduces unique vulnerabilities such as system outages, cyberattacks, natural disasters, and hardware failures. Consequently, developing robust, adaptive, and affordable disaster recovery mechanisms has emerged as a critical priority. Despite significant technological progress, many existing DR strategies still fall short in addressing the evolving demands of cloud- native architectures, particularly in areas like cross-platform compatibility, energy sustainability, and real-time recovery. This work offers an extensive theoretical analysis of disaster recovery strategies in cloud systems. It evaluates existing models and identifies critical gaps related to automation, energy usage, and resilience under dynamic workloads. Drawing from concepts in resilience engineering, distributed systems, and green computing, the study proposes a new direction for DR frameworks—one that emphasizes flexibility, multi-cloud support, and ecological sustainability. By synthesizing current academic and industry literature, the research offers foundational insights that can inform both future experimental work and the development of next- generation cloud resilience strategies.

Performance And Scalability Optimization In “MeetShield”: A Java-Based Safe Learning Platform Using Multithreading, WebSockets, And Mobile-Centric Enhancements

Authors: Dr. Krishn Kumar,, bShristi Srivastava, Kriti Ramani, Tripti Srivastava

Abstract: – This paper presents a comprehensive study of optimization techniques applied to a Safe Learning Platform developed in Java. The platform’s performance and scalability are significantly enhanced through the implementation of Java concurrency mechanisms , multithreading asynchronous processin, Web-Sockets for real-time communication , efficient data structures and algorithms (DSA) , GPU acceleration , and caching strategies . The system architecture integrates modern technologies, including Spring Boot , WebRTC , Redis , OpenCV , MySQL, and Hibernate [, to support robust, real-time, and scalable learning experiences. Performance testing and benchmarking results validate the effectiveness of these optimizations, demonstrating measurable improvements in task execution speed and overall system efficiency .

DOI: http://doi.org/

Performance And Scalability Optimization In “MeetShield”: A Java-Based Safe Learning Platform Using Multithreading, WebSockets, And Mobile-Centric Enhancements

Authors:

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DOI: http://doi.org/

Performance And Scalability Optimization In “MeetShield”: A Java-Based Safe Learning Platform Using Multithreading, WebSockets, And Mobile-Centric Enhancements

Authors:

Abstract:

DOI: http://doi.org/

Music Genre Classification Using Convolutional Neural Networks

Authors: Siddhartha T Hadimani

Abstract: Automatically classifying music into genres is a challenging task that has seen significant improvement through the use of deep learning. In this paper, we present a Convolutional Neural Network (CNN)-based model for music genre classifi- cation using Mel-Frequency Cepstral Coefficients (MFCCs) ex- tracted from audio files. Our model was trained and evaluated on the GTZAN dataset, achieving a solid accuracy of 91.2%. These results highlight the potential of deep learning to understand and categorize audio content effectively.

Artificial Intelligence And Paradigm Shift In Indian Agricultural Sector

Authors: Bhaskar Banerjee

Abstract: Artificial intelligence (A.I.) is a multidisciplinary ground aimed at mechanising works that Presently required human intelligence. Despite its deficiency of general awareness, artificial Intelligence (AI) is taking care each and every aspect of today’s life. This article aims to educate About AI and inspire us to utilize various AI Applications in Indian Agricultural Sector.

DOI: http://doi.org/

Event Sphere

Authors: ARUN, SANTHOSHKUMAR., SIVAKUMAR., MADHANKUMAR

Abstract: Event management is a complex and time-sensitive process that involves the coordination of multiple activities such as scheduling, registration, resource allocation, and feedback analysis. Manual event planning is often inefficient, error-prone, and lacks real-time coordination. This paper presents the design and implementation of a web-based Event Management System (EMS) aimed at automating and streamlining the event planning lifecycle. The proposed EMS enables administrators and event organizers to create and manage events, monitor registrations, communicate with attendees, and generate reports. Developed using modern web technologies such as HTML, CSS, JavaScript, and a backend framework (Node.js/Django), the system ensures scalability, usability, and performance. Evaluation through user feedback and load testing demonstrates the effectiveness of the EMS in enhancing the efficiency of event coordination

DOI: http://doi.org/

AI-Powered De Novo Motif Discovery System For Genomic Sequence Analysis

Authors: Khushi S Shukla, Prof. Shilpa M, Mohammed Viqar, Mohd Shahnawaz Khan, Prateeksha R Y

Abstract: Accurately identifying regulatory DNA motifs—short, recurring sequences that influence gene expression—is challenging due to their short length, sequence variability, and dependence on surrounding genomic context. Conventional experimental methods to identify motifs are time-consuming and not scalable. This study describes a computational workflow for de novo motif discovery that utilizes statistical and AI methods, such as Expectation Maximization, Gibbs Sampling, and deep learning algorithms, to recognize conserved sequence motifs from genomic data. By avoiding the pre-existing knowledge of motifs, the system identifies prospective transcription factor binding sites and other regulatory factors and deepens our understanding of gene regulation. The method is validated against benchmark datasets and visualized by sequence logos, providing a scalable and understandable solution for research in functional genomics.

DOI: http://doi.org/

Optimized Low Power 8-bit Array Multiplier With CSA And CLA

Authors: Rallabandi Shruthi, Dasania Prashanth, Nittala Sesha HariHara kumar, K. Thrisandhya

Abstract: This paper presents an optimized design of an 8-bit array multiplier optimized for superior performance and reduced power usage. While conventional array multipliers are straightforward to design, they often suffer from high propagation delays and excessive power demands. The proposed design integrates Carry Save Adders (CSA) for parallel partial product summation and Carry Look-Ahead Adders (CLA) for efficient final addition. Simulations confirm the design’s improved speed, lower power consumption, and better area efficiency, making it an ideal choice for mobile and embedded applications. The architecture was implemented using Verilog HDL and validated with Xilinx Vivado tools on the Artix7 platform, demonstrating its practicality The design is implemented in Verilog HDL and simulated using Xilinx Vivado, showcasing the practical viability of the architecture.

DOI: http://doi.org/

The Application Of Bio-Based And Recyclable Materials In Manufacturing

Authors: Okpala Charles Chikwendu, Udu Chukwudi Emeka, Joseph Ajaefobi

Abstract: The integration of bio-based and recyclable materials into manufacturing represents a significant step towards achieving sustainable industrial practices. As concerns over environmental impact and circular economy principles grow, this study explores the applications, benefits, and challenges associated with these materials across various industries. It emphasizes reducing ecological footprints, enhancing resource efficiency, and promoting long-term sustainability. The research examines bio-based materials, including biopolymers, natural fibers, and bio-composites, alongside recyclable materials such as metals, plastics, and paper-based composites. Their mechanical properties, environmental advantages, and economic feasibility are analyzed. Additionally, the study identifies key challenges, including high production costs, durability limitations, processing constraints, and regulatory compliance issues. By reviewing literature and case studies, it assesses technological advancements, innovative processing techniques, and industrial applications. The study also considers the role of government policies, industry regulations, and consumer demand in fostering adoption. Emerging trends like biofabrication, additive manufacturing, and nanotechnology-enhanced bio-materials are explored as catalysts for sustainable production. While bio-based and recyclable materials offer significant environmental and economic benefits, widespread implementation requires advancements in material engineering, cost reduction strategies, and stronger regulatory support. Future research should focus on optimizing material performance, improving recycling systems, and fostering industry collaboration to accelerate sustainable manufacturing.

SMART TRAFFIC MANAGEMENT SYETEM USING SIMULATION

Authors: Ujjwal Mishra, Mukesh Maurya, Krishna Yadav, Prateek Tiwari, Dr.B.K. Sharma, Assistant professor Nitin Sharma

Abstract: This study has been undertaken to build a Smart Traffic Management System using two modern technology and tools. Smart Traffic Management System (STMS) is a live Python/Pygame simulation of an urban intersection that models adaptive traffic control. It provides a graphical user interface (GUI) where users can adjust the vehicle spawn rate and the probability of emergency vehicles to create light or heavy traffic scenarios. Unlike fixed-schedule signals, the STMS dynamically alters signal durations in response to simulated real-time traffic conditions. Our simulation similarly uses virtual detectors (vehicle counts) to extend green lights when queues grow and shorten them when approaches clear, mimicking how modern controllers reduce delays and improve travel times tracking system at intersections of roads. The system detects the arrival of emergency vehicles such as ambulance, fire truck, etc. and adjusts traffic lights to speed up their passage, shortening response time. The goal of this STMS simulation is to demonstrate how intelligent signal control can reduce congestion and improve emergency response in a city setting. This conceptual model captures key aspects of smart traffic management in a simulated environment. (Note: this is a simulation– it does not use live camera or sensor feeds, but is designed to mimic their effect.)

Title: Greenvoy: A Survey On Smart Ambulance Routing Through Green AI And Edge Intelligence_585

Authors: Shalini S, C. Nandini, Geetha Shree R, M. Yasaswini, Neetha Jain, Anusha P

Abstract: Emergency medical services (EMS) play a critical role in saving lives during critical situations such as accidents and cardiac arrests. However, conventional ambulance systems face significant challenges including traffic delays, poor route planning, and the lack of real-time patient monitoring. This survey paper explores the evolution and integration of Smart Ambulance Systems that combine Internet of Things (IoT) sensors for patient vitals monitoring and Machine Learning (ML) algorithms for intelligent route optimization. The paper reviews various technologies such as GPS, GSM modules, biomedical sensors, and cloud-based APIs that enable real-time data acquisition and transmission to hospitals. It also investigates recent advances in ML-based routing using algorithms like Reinforcement Learning for dynamic, energy-efficient, and time-optimized ambulance navigation. The survey highlights key contributions from current literature, compares system architectures, and discusses open challenges and future directions for deploying scalable, reliable, and AI-driven EMS solutions

DOI: http://doi.org/

Title: Greenvoy: A Survey On Smart Ambulance Routing Through Green AI And Edge Intelligence_585

Authors:

Abstract: – Emergency medical services (EMS) play a critical role in saving lives during critical situations such as accidents and cardiac arrests. However, conventional ambulance systems face significant challenges including traffic delays, poor route planning, and the lack of real-time patient monitoring. This survey paper explores the evolution and integration of Smart Ambulance Systems that combine Internet of Things (IoT) sensors for patient vitals monitoring and Machine Learning (ML) algorithms for intelligent route optimization. The paper reviews various technologies such as GPS, GSM modules, biomedical sensors, and cloud-based APIs that enable real-time data acquisition and transmission to hospitals. It also investigates recent advances in ML-based routing using algorithms like Reinforcement Learning for dynamic, energy-efficient, and time-optimized ambulance navigation. The survey highlights key contributions from current literature, compares system architectures, and discusses open challenges and future directions for deploying scalable, reliable, and AI-driven EMS solutions.

DOI: http://doi.org/

Security Management For Internet Of Things

Authors: Assistent Professor Ashadeepa.S.N

Abstract: The Internet of Things (IoT) is an emerging technology that has gained widespread attention across industries due to its potential to revolutionize how we interact with the world. The central goal of IoT is to enable seamless communication between physical objects of all sizes, allowing them to exchange data autonomously over the Internet without human intervention. These devices are equipped with sensors to collect data and actuators to take actions based on that data, driving intelligent decision-making processes. IoT has already had a significant impact in numerous fields, such as home automation, smart cities, healthcare, agriculture, and manufacturing, as well as the development of wearables and smart devices. It has become a cornerstone for innovation, enabling the creation of smart environments that improve efficiency and convenience for individuals and businesses alike. However, the widespread adoption of IoT also introduces critical challenges, particularly concerning connectivity, compatibility, longevity, and, most importantly, security and privacy. The inherent heterogeneity and dynamism of IoT systems complicate the effective management of security risks, with sensitive data being vulnerable to various cyber threats. This paper reviews existing security frameworks and assessment standards in the context of IoT, highlighting the challenges in securing IoT-based smart environments. It emphasizes the importance of addressing these security concerns to ensure the continued growth and safe adoption of IoT technologies.

Secure Real-Time File Sharing Using Blockchain Technology

Authors: Sampath .M, Adhwaith Anilkumar, Akshay Ajay, Arjun Balagopalan

Abstract: In the modern digital age, secure and efficient file sharing is paramount. This paper presents a blockchain-based real-time file sharing application that ensures data integrity, confidentiality, and decentralized access. The system is developed using Python and JavaScript, with Flask as the backend framework and AES encryption for data security. Users can sign up, upload files, share them via links, and download them, all while transactions are logged immutably on a blockchain. This application offers a reliable, transparent, and tamper-proof solution for personal and organizational data exchange.

DOI: 10.61463/ijset.vol.13.issue3.181

REVIEW PAPER ON THE IMPROVEMENT IN MECHANICAL PROPERTIES AND DURABILITY OF CONCRETE BY THE IN-CORPORATION OF GRANULATED SLAG AND METAKAOLIN

Authors: TAMBIR HUSSAIN1, MD., ANZAR RABBANI

Abstract: The use of granulated slag and metakaolin as partial substitutes for cement has shown considerable promise in improving the mechanical characteristics and durability of concrete. Granulated slag, a byproduct of steel production, boosts long-term strength development and chemical resistance, while metakaolin, a thermally activated aluminosilicate, improves early-age strength and microstructural densification. This review methodically assesses current studies on the synergistic effects of supplemental cementitious materials (SCMs), concentrating on their influence on compressive strength, flexural performance, and durability against harsh conditions. Research demonstrates that ideal proportions of slag and metakaolin may enhance pore structure, decrease permeability, and alleviate sulphate attack and chloride ingress, thereby prolonging the lifespan of concrete buildings. Moreover, the pozzolanic reactivity of these materials facilitates the generation of secondary C-S-H gel, hence improving binding capacity and dimensional stability. The results highlight the feasibility of these sustainable additions in creating high-performance concrete with a reduced carbon impact. The durability improvements realised by including slag and metakaolin are due to their filler effect and pore-blocking capacity, which impede the penetration of detrimental ions and moisture. Experimental data indicates that slag-metakaolin hybrid systems provide enhanced resistance to acid corrosion, alkali-silica reaction (ASR), and carbonation relative to traditional concrete. The thermal stability of these composites renders them appropriate for harsh exposure circumstances. This paper examines the rheological features and workability issues related to various SCMs, suggesting admixture methods to ensure ease of installation. The research elucidates the environmental advantages of decreasing clinker concentration, as shown by life-cycle assessment (LCA) studies, without compromising performance. The integration of existing information offers a thorough foundation for enhancing mix designs and promoting sustainable building techniques, in accordance with worldwide initiatives for green infrastructure development.

DOI: http://doi.org/

REVIEW PAPER ON THE IMPROVEMENT IN MECHANICAL PROPERTIES AND DURABILITY OF CONCRETE BY THE IN-CORPORATION OF GRANULATED SLAG AND METAKAOLIN

Authors: TAMBIR HUSSAIN,, MD. ANZAR RABBANI

Abstract: The use of granulated slag and metakaolin as partial substitutes for cement has shown considerable promise in improving the mechanical characteristics and durability of concrete. Granulated slag, a byproduct of steel production, boosts long-term strength development and chemical resistance, while metakaolin, a thermally activated aluminosilicate, improves early-age strength and microstructural densification. This review methodically assesses current studies on the synergistic effects of supplemental cementitious materials (SCMs), concentrating on their influence on compressive strength, flexural performance, and durability against harsh conditions. Research demonstrates that ideal proportions of slag and metakaolin may enhance pore structure, decrease permeability, and alleviate sulphate attack and chloride ingress, thereby prolonging the lifespan of concrete buildings. Moreover, the pozzolanic reactivity of these materials facilitates the generation of secondary C-S-H gel, hence improving binding capacity and dimensional stability. The results highlight the feasibility of these sustainable additions in creating high-performance concrete with a reduced carbon impact. The durability improvements realised by including slag and metakaolin are due to their filler effect and pore-blocking capacity, which impede the penetration of detrimental ions and moisture. Experimental data indicates that slag-metakaolin hybrid systems provide enhanced resistance to acid corrosion, alkali-silica reaction (ASR), and carbonation relative to traditional concrete. The thermal stability of these composites renders them appropriate for harsh exposure circumstances. This paper examines the rheological features and workability issues related to various SCMs, suggesting admixture methods to ensure ease of installation. The research elucidates the environmental advantages of decreasing clinker concentration, as shown by life-cycle assessment (LCA) studies, without compromising performance. The integration of existing information offers a thorough foundation for enhancing mix designs and promoting sustainable building techniques, in accordance with worldwide initiatives for green infrastructure development

DOI: http://doi.org/

AI IN PREDICTIVE ANALYTICS WITH BIG DATA

Authors: Purvi Singh, Sagar Saxena, Shagun Singh, Miss Neha Patel, Mr. Subodh Kumar

Abstract: : As the amount of data in the world keeps growing, traditional methods of predictive analytics are struggling to keep up. This paper looks at how artificial intelligence (AI), especially self-learning systems like reinforcement learning and generative models, can work with big, messy, and unstructured data to make smarter predictions. Unlike older systems that need clean and well-organized data, these new AI models can learn from raw and real-time data to find patterns, predict trends, and help in better decision-making. We explore real-life examples in areas like city traffic and supply chain planning to show how these AI systems improve over time as they get more data. We also discuss some important challenges, such as fairness, data privacy, and the heavy computing power needed. Overall, this paper shows how AI is changing predictive analytics into a more dynamic and intelligent process.

Impact Of Level Design On Player Engagement In Horror Games.

Authors: Animesh Gabhane

Abstract: Level design has a very important role in games that make the player feel present, especially in horror games where the setting, suspense, and the tempo of the game are the basis of players’ engagement. The present study investigates what the specific elements of level design for horror games are and how they affect the player engagement. Using the existing literature and player feedback the research is centered on the organization and coherence of space, lighting, environmental storytelling, and pacing as the main components of the design. The mixed-method research design that presented in the paper involved different approaches such as gameplay observation, survey data, and player interviews all of which were used to analyse emotion and player experiences from various design choices. The results show that the prison-like passage, changing the lighting, and the plot were all strong contributors to the fear and the feeling of being in the game; and these lead to very high engagement. The research paper closes with the presentation of the recommendations for the design that will make horror environments challenging and loaded with emotion.

DOI: http://doi.org/

AI IN PREDICTIVE ANALYTICS WITH BIG DATA

Authors: Purvi Singh, Sagar Saxena, Shagun Singh, Miss Neha Patel, Mr. Subodh Kumar

Abstract: As the amount of data in the world keeps growing, traditional methods of predictive analytics are struggling to keep up. This paper looks at how artificial intelligence (AI), especially self-learning systems these AI systems improve over time as they get more data. We also discuss some important challenges like reinforcement learning and generative models, can work with big, messy, and unstructured data to make smarter predictions. Unlike older systems that need clean and well-organized data, these new AI models can learn from raw and real-time data to find patterns, predict trends, and help in better decision-making. We explore real-life examples in areas like city traffic and supply chain planning to show how, such as fairness, data privacy, and the heavy computing power needed. Overall, this paper shows how AI is changing predictive analytics into a more dynamic and intelligent process.

DOI: http://doi.org/

AI, IoT, And Nanotech: Converging Technologies Shaping Future Businesses

Authors: Sahana, Manoj Kumar, Prabhu Prasad

Abstract: The convergence of Artificial Intelligence (AI), the Internet of Things (IoT), and Nanotechnology (Nanotech) is driving unprecedented transformations across global business landscapes. Individually powerful, these technologies together enable smarter data processing, real-time connectivity, and nanoscale innovation, fostering new opportunities for product development, operational efficiency, and customer engagement. This article examines the characteristics of each technology, their synergistic effects, and the profound impact on diverse industries such as healthcare, manufacturing, agriculture, and energy. It also explores emerging business models, challenges related to integration, data security, and ethical considerations, while offering strategic recommendations for companies seeking to harness these converging technologies. As AI, IoT, and Nanotech continue to evolve and integrate, businesses that proactively adapt and innovate will be positioned to gain sustainable competitive advantages in a rapidly digitizing world. This synthesis underscores the critical role of these technologies in shaping the future of business innovation, resilience, and sustainability.

DOI: http://doi.org/10.61463/ijset.vol.12.issue6.970

Email Spam Detection With Machine Learning

Authors: Rajnish Kumar Chauhan, Praveen Yadav, Vishakha Kashyap, Assistant Professor Dr. Chhaya Singh

Abstract: Email remains one of the most widely used communication tools, but the increasing volume of spam messages has become a persistent issue for both individuals and organizations. Traditional rule-based filtering methods struggle to keep up with the ever-changing techniques used by spammers, leading to inefficiencies in detection. To address this challenge, this study explores a machine learning-based approach to improve spam classification and enhance email security. The research applies algorithms such as Random Forest, Logistic Regression, and K-Nearest Neighbors (KNN) to differentiate between spam and legitimate emails. By analyzing key features like email content, subject lines, and sender details, the model learns to identify patterns commonly found in spam messages. Performance evaluation using standard datasets demonstrates that machine learning significantly improves detection accuracy, speed, and adaptability compared to conventional methods. The findings suggest that machine learning offers a robust and scalable solution to the growing problem of email spam.

Email Spam Detection With Machine Learning

Authors: Rajnish Kumar Chauhan, Praveen Yadav, Vishakha Kashyap, Assistant Professor Dr. Chhaya Singh

Abstract: Email remains one of the most widely used communication tools, but the increasing volume of spam messages has become a persistent issue for both individuals and organizations. Traditional rule-based filtering methods struggle to keep up with the ever-changing techniques used by spammers, leading to inefficiencies in detection. To address this challenge, this study explores a machine learning-based approach to improve spam classification and enhance email security. The research applies algorithms such as Random Forest, Logistic Regression, and K-Nearest Neighbors (KNN) to differentiate between spam and legitimate emails. By analyzing key features like email content, subject lines, and sender details, the model learns to identify patterns commonly found in spam messages. Performance evaluation using standard datasets demonstrates that machine learning significantly improves detection accuracy, speed, and adaptability compared to conventional methods. The findings suggest that machine learning offers a robust and scalable solution to the growing problem of email spam.

Blockchain, Nanotech, And Transparency In Global Supply Chains

Authors: Nandan Kumar, Bhagya, Prabhu Prasad

Abstract: This article delves into the significant role that blockchain technology and nanotechnology are playing in revolutionizing transparency across global supply chains, a critical need in today’s increasingly interconnected and complex market environments. Modern supply chains span multiple countries, suppliers, and intermediaries, often making it challenging to verify the authenticity, origin, and condition of products as they move from source to consumer. Ensuring transparency is vital for maintaining consumer trust, adhering to regulatory requirements, and promoting ethical and sustainable sourcing practices. Blockchain technology offers a groundbreaking solution by creating a decentralized, tamper-resistant ledger that records every transaction or movement of goods immutably. This feature enables all participants in the supply chain—from manufacturers and logistics providers to retailers and consumers—to access a single, trustworthy source of truth. It enhances traceability by making it possible to verify the provenance of raw materials, track product handling, and confirm compliance with labor and environmental standards. Moreover, blockchain reduces the risk of fraud, counterfeiting, and data manipulation, which are major concerns in sectors such as pharmaceuticals, luxury goods, and food. Complementing blockchain’s data integrity, nanotechnology introduces nanoscale sensors and smart materials capable of monitoring products in real time. These nanosensors can detect temperature, humidity, exposure to contaminants, or physical stress, providing granular data on the environmental conditions and integrity of products throughout transit and storage. This molecular-level monitoring ensures that sensitive goods, like medicines or perishable foods, meet quality standards and regulatory guidelines, reducing waste and enhancing consumer safety.

DOI: http://doi.org/110.61463/ijset.vol.12.issue6.971

Narrative Of Population And Economic Growth On Wastes Disposal And Reclamations On Environment

Authors: Okorun Ambrose Ali, Agbadua Segun Afokhainu, Ehormhanyin Ehigie Michael, Ebisintei Samuel

Abstract: – Strive for conformability created quest in man to investigate the best alternatives of living in the live that is provided by the environment around him. Activities of man lead to creation of wastes which can be bio degradable, on biodegradable, and a non-refillable in terms of quarry activities. All these are function of the environment man find himself. Population growth is increasing vastly hence the trend of demand for goods and services as it affect the growth of the economy. Nature provides man with diverse opportunities that are unequal over the surface of the earth hence the law of comparative advantage is ushered as we have producing nation and consuming nation. The developing nations of the world which African countries found herself are consuming nations with high potentials for generating wastes. These wastes generation are function of population and economic growth as they deal with importation of finished goods, plastics products, abandoned vehicles called’ tokunbo’ ,vehicles parts and tyres, scraps and used fabrics .Nigeria with her land mass and population as well as her weak political reforms provides favorable environment for these businesses to thrive. This work analyses the proliferation of these waste, areas of concentrations of these wastes, environmental impact assessment on dumps of these waste and their recycling, suggestions and solution to curbs waste dumps and live a free environment.

DOI: http://doi.org/

New Technologies And Trends In Web Development

Authors: Ashish Kumar, Assistant Professor Pooja Sharma, Dr. Rajendra Khatana

Abstract: Web development refers to the process of designing, building, and maintaining websites and web applications that are accessed via the internet or a private intranet. It encompasses a wide range of tasks, from creating static web pages with simple content to developing complex, interactive applications that support real-time user interactions, data processing, and cloud integration.Web development is a constantly evolving field, influenced by user expectations, device diversity, and emerging technologies like WebAssembly, AI, and Web3. As the demand for seamless digital experiences grows, so does the need for skilled developers who understand both the technical and user-centric aspects of building for the web.It examines key innovations including Progressive Web Apps (PWAs), WebAssembly, and Web3 technologies, while addressing challenges related to security, performance optimization, and cross-platform compatibility. Drawing insights from academic literature and real-world case studies—such as Spotify, Airbnb, and GitHub—this paper highlights how emerging tools and frameworks have redefined the user experience and developer workflow. The study concludes with a forward-looking analysis of future trends, emphasizing the growing importance of accessibility, decentralization, and performance in shaping the web of tomorrow. The paper concludes with a forward-looking analysis of the web development landscape, highlighting the increasing importance of accessibility, sustainability, and ethical development practices in shaping the next generation of digital experiences.

IMPROVEMENT IN MECHANICAL PROPERTIES AND DURABILITY OF CONCRETE BY THE IN-CORPORATION OF GRANULATED SLAG AND METAKAOLIN

Authors: Assistant Professor Md. Anzar Rabbani, Tambir Hussain

Abstract: This work investigates the complex interplay between blast furnace slag (BFS), metakaolin, and the compressive strength of concrete, with the objective of clarifying their synergistic effects as supplemental cementitious materials (SCMs). The study examines the pozzolanic characteristics of metakaolin, obtained from calcined kaolin clay, and the latent hydraulic reactivity of blast furnace slag, a byproduct of iron manufacturing. Controlled laboratory studies were conducted on concrete samples with varied amounts of SCM substitution (0–40%) to measure their compressive strength development at 7, 28, and 90 days. The inquiry examines microstructural development, water absorption rates, and calcium hydroxide consumption to evaluate hydration kinetics. This work elucidates how supplementary cementitious materials (SCMs) improve pore structure and augment binding capacity when compared to traditional Portland cement systems, establishing a clear correlation between their chemical composition, particle size distribution, and mechanical performance. The thesis also examines the fundamental processes influencing strength variations, highlighting the significance of BFS in long-term strength enhancement and metakaolin in early-age microstructural densification. Advanced characterisation methods, including as SEM-EDS and XRD, elucidate how these materials enhance the interfacial transition zone (ITZ) and diminish permeability. The research determines ideal replacement ratios (e.g., 20–30% BFS + 10% metakaolin) that optimise workability, durability, and compressive strength while reducing clinker use. Fluid absorption studies indicate a 30–50% decrease in capillary porosity for SCM-blended concretes, clearly associating their pore-blocking capacity with enhanced sulphate and chloride resistance. These results highlight the environmental and technological benefits of SCMs, endorsing their use under harsh exposure circumstances.

DESIGN OF 8-BIT SUCCESSIVE APPROXIMATION REGISTER ANALOG TO DIGITAL CONVERTER USING CADENCE TOOL

Authors: Assistant Professor Deepak Sharma, SRISTHI S SUGATE, POOJITHA J N, SUSHMEETA M BADLI, VARSHA U HEGADI

Abstract: This project proposes a 1V 8-bit asynchronous successive approximation register (SAR) analog-to-digital converter (ADC) implemented in 45nm CMOS technology. The asynchronous SAR ADC system consists of an internal-clock generator, a bootstrapped sample-and-hold switch, a capacitive digital-to-analog converter (DAC), a dynamic comparator, and a SAR logic. Taking a 64-point FFT on the output of the SAR ADC and with an input signal of 1.2V differentially, the maximum ENOB achieved at 20 MHz. This SAR ADC system can be used in systems that mainly require low power with medium resolution and medium speed like in computing-in-memory cores for AI applications and in sensors for biomedical applications.

Forward Error Correction Control For 5G Small Cell Network

Authors: Assistant Professor Harsha Gv, N C Charan, Ajay Kumar M N, Gururaj

Abstract: The fifth generation (5G) wireless networks utilize small cell deployments to satisfy the growing need for high data rates, ultra-reliable communications, and low-latency services. Dense small cell environments bring along challenging conditions such as high interference, elevated mobility, and fluctuating channel conditions, which compromise the reliability of the transmission. Forward Error Correction (FEC) methods, specifically Low-Density Parity-Check (LDPC) codes and Polar codes, are crucial to combating transmission errors without the need for retransmissions. In this paper, an adaptive FEC control mechanism designed for 5G small cell networks is introduced. The system dynamically varies coding rates and block lengths according to real-time channel feedback, maximizing the trade-off between throughput, latency, and error correction ability. Extensive simulation outcomes illustrate that adaptive FEC performs well above conventional static coding techniques, improving link reliability, lowering latency, and sustaining quality of service (QoS) under fluctuating network scenarios. This mechanism aids in creating robust and effective next-generation wireless systems.

River Cleaning Robot Using Solar Power

Authors: Assistant Professor Pavitra M. Badiger, Ms. Saipooja S. Sunagar, Ms. Vaishnavi B. Wadageri, Ms. Swati R. Patil, Ms. Veronica

Abstract: River pollution caused by floating waste is a critical environmental issue. Manual cleanup is time-consuming, expensive, and often ineffective for large water bodies. This study presents a solar-powered robotic system designed to collect floating debris from river surfaces. The robot operates using renewable energy and includes a conveyor mechanism for waste collection, DC motors for propulsion, and Bluetooth for remote control. The prototype demonstrates an eco-friendly, low-cost solution for continuous surface waste management in rivers. Future versions aim to incorporate autonomous navigation and AI-based classification to avoid harming aquatic life.

Design And Implementation Of Self Balancing Robot Using PID Controller

Authors: Assistant Professor Anita M. Hanchinal, Mr. Harish R Savadatti, Mr. Kevin S Anthony, Mr. Mahamed Yasin Baig M Savanur, Mr. Vinodkumar V Jaladi

Abstract: Accomplishing stability in transportable mechanical technology, mainly in two- wheeled setups, is essential as it mirrors the factors of an changed pendulum framework. This contemplate offers the development of a cost- powerful selfbalancing robotic that makes use of a Proportional-Integral-Derivative (PID) controller along side an MPU6050 inertial estimation unit (IMU). The manage aspect leverages real-time sensor statistics to determine the robotic`s tilt and modify motor reactions in like way to hold upright modify. The look into lines the mechanical system, integration of sensors, flag sifting strategies, and PID tuning methodologies. Actualized using an Arduino microcontroller, L298N engine driver, and general DC engines, the version gives a common sense and affordable association capin a position of assisting vertical advent with negligible misKeywords— CAM, IOT, WIFI.

DOI: http://doi.org/

Handwriting Recognition And Information Retrieval System

Authors: Assistant Professor Dr. Madhusudan Kulkarni, Mr. Abdulrahman Shaikh, Ms. Ankita A Chavan, Mr. Atiqur Rehman Sayed, Mr. Rahul Madiwal

Abstract: This project is a full-stack handwriting recognition app that blends machine learning with modern web technologies to turn handwritten text into digital content. The frontend is built using React and JavaScript, while the backend runs on Python and Django. At its core, the app uses a pre-trained machine learning model based on the EMNIST dataset to recognize handwritten digits, uppercase letters, and unique lowercase letters. To tell similar characters apart, it even uses a clever height-based approach to accurately detect letter casing.Designed with real-world use in mind—like in education, healthcare, and business—the app helps quickly digitize handwritten notes and forms. It also features ChatGPT integration, so users can get instant explanations and context for the text they've scanned, making the experience more interactive and insightful.By combining deep learning with AI, the app delivers accurate handwriting recognition, quick info retrieval, and a clean, user-friendly interface. It’s a powerful tool for boosting productivity and simplifying the move from paper to digital.

PERSON RECOGNITION USING FINGER VEIN BIOLOGICAL TRAIT

Authors: Ritik Singh, Sri Saumya, Sameer Khan

Abstract: – Currently, one of the newest areas of study in biometric recognition is finger vein recognition. Although the Gabor filter's settings are hard to modify, it has been widely employed for vein and finger recognition. Here, an adaptive-learning Gabor filter is proposed to address this issue. Based on the goal function, the gradient of the Gabor-filter parameters is calculated, we merge convolutional neural networks with a Gabor filter. Then, we optimize its parameters by back-propagation. The Gabor filter's θ parameter can be learned at same angle as the vein texture in an image of a finger vein. There is a relationship between the Gabor filter's σ and λ parameters, and the latter can converge to ideal value. With this method, we not only select appropriate and effective Gabor filter parameters for filter bank construction, but we also consider the interrelationships between those parameters. Lastly, we conduct tests on four publicly available finger vein datasets. According to experimental results, our approach performs better in finger vein classification than the most advanced techniques.

DOI: http://doi.org/

CRITICAL ANALYSIS OF THE WASTE LAND

Authors: Dr. Mrs. Vibha Singh Thakur, Mr. Rohan Chouhan,

Abstract: T.S. Eliot's "The Waste Land" (1922) is analyzed as a profound spiritual and social commentary on the pervasive disillusionment of the post-World War I era. This study delves into the poem's deep intertextual dialogue with ancient Indian wisdom, particularly exploring its integration of the Brihadaranyaka Upanishad's "Da, Da, Da" mantra (Datta, Damyata, Dayadhvam) and the Bhagavad Gita's philosophy of "death and rebirth" as a pathway to spiritual transformation. Furthermore, the article draws significant parallels between the societal breakdown and spiritual emptiness depicted in Eliot's poem and the aftermath of the Kurukshetra War in the Mahabharata, highlighting a shared narrative of loss of dharma and the enduring psychological scars of conflict. Eliot's extensive academic engagement with Sanskrit and Hindu philosophy is presented as foundational to the poem's unique synthesis of Eastern and Western thought, ultimately arguing that "The Waste Land" transcends pessimism to offer a hopeful vision of redemption through spiritual introspection. It champions ancient wisdom as a perennial guide for navigating modern chaos towards a revitalized existence, a "fresh land.

DOI: http://doi.org/

DESIGN AND VERIFICATION OF LOW DROPOUT REGULATOR USING 45nm IN CADENCE

Authors: Assistant Professor Mr. Deepak Sharma, Shreedevi Sidnal, Sandhya Naik, Bhoomika Hiremath, Asfiya Sheikh

Abstract: This project involves the design and verification of a Low Dropout (LDO) Regulator using 45nm CMOS technology within the Cadence design environment. The main goal is to develop a power-efficient and high-performance voltage regulator suitable for modern low-power integrated circuits. The design focuses on achieving low quiescent current, high power supply rejection, and stability across different operating conditions. Key performance parameters such as line regulation, load regulation, and transient response were analyzed in detail through simulations. The LDO was tested under various input voltages and load currents to evaluate its performance and robustness. Compensation techniques were applied to ensure stability over process, voltage, and temperature variations. Simulation results show that the LDO performs reliably, with fast response and minimal voltage deviations. Overall, the design is well-suited for applications in nanoscale, low-power electronic systems like mobile devices.

DOI: http://doi.org/

Design And Analysis Of 4:2 Priority Encoder In 45nm Using Cadence

Authors: Assistant Professor Deepak Sharma, Ms. Narayani Shanbhag, Ms. Rashmi Kalkapur, Ms. Sanjana Mahantesh. Koladur, Ms. Varshini Fakkirgouda. Patil

Abstract: In modern digital systems, priority encoders play a vital role in resolving multiple simultaneous requests by assigning a priority to each input. This work focuses on the design and implementation of a 4:2 Priority Encoder using 45nm CMOS technology, targeting low-power and high-performance applications. The encoder accepts four input lines and provides a 2-bit binary code corresponding to the highest-priority active input, along with a valid output signal to indicate the presence of any high inputs. The design process was carried out using the Cadence design suite, leveraging Virtuoso for schematic capture, and Spectre for circuit simulation. Transistor-level design methodologies were applied to optimize power, delay. The design achieved minimal propagation delay and power consumption, making it suitable for integration in processors, interrupt controllers, and other digital subsystems where fast and efficient signal prioritization is required. Furthermore, the physical design was verified through. The successful implementation of the 4:2 Priority Encoder using 45nm technology showcases the viability of compact digital logic design in advanced nodes and provides a reference for future low-power VLSI designs.

CRITICAL ANALYSIS OF THE WASTE LAND

Authors: Suday Naidu

Abstract: T.S. Eliot's "The Waste Land" (1922) is analyzed as a profound spiritual and social commentary on the pervasive disillusionment of the post-World War I era. This study delves into the poem's deep intertextual dialogue with ancient Indian wisdom, particularly exploring its integration of the Brihadaranyaka Upanishad's "Da, Da, Da" mantra (Datta, Damyata, Dayadhvam) and the Bhagavad Gita's philosophy of "death and rebirth" as a pathway to spiritual transformation. Furthermore, the article draws significant parallels between the societal breakdown and spiritual emptiness depicted in Eliot's poem and the aftermath of the Kurukshetra War in the Mahabharata, highlighting a shared narrative of loss of dharma and the enduring psychological scars of conflict. Eliot's extensive academic engagement with Sanskrit and Hindu philosophy is presented as foundational to the poem's unique synthesis of Eastern and Western thought, ultimately arguing that "The Waste Land" transcends pessimism to offer a hopeful vision of redemption through spiritual introspection. It champions ancient wisdom as a perennial guide for navigating modern chaos towards a revitalized existence, a "fresh land.

DOI: http://doi.org/

Real-Time Fire And Smoke Detection System_339

Authors: Assistant Professor Vaishali Shende, Sejal Kumbhare, Khushbu Katakwar

Abstract: Around the world, fire events continue to rank among the top causes of property loss, injuries, and fatalities. Traditional fire detection systems mostly rely on smoke alarms and heat sensors, which frequently identify fires at an advanced stage with little time for evacuation and response. The likelihood of uncontrollable fires, which cause extensive damage and health risks, especially respiratory and skin-related illnesses from extended exposure to toxic smoke, is greatly increased by delayed detection. In order to overcome these obstacles, we suggest a cutting-edge Fire and Smoke Detection System that makes use of artificial intelligence, sensor-based environmental monitoring, and contemporary computer vision techniques to guarantee early fire detection and risk assessment.

Growth Of Potassium Titanyl Phosphate (KTP) Crystals By Flux Method: Investigation Of Crucible Adhesion Issues

Authors: Pankaj R. Uikey, Ramesh M. Thombare, Ratan S. Meshram, Vijay R. Raghorte, Roshan Tadurwar

Abstract: Potassium titanyl phosphate (KTiOPO4, KTP) is a nonlinear optical material widely used in laser applications due to its high optical damage threshold and efficient frequency conversion. This study investigates the synthesis of KTP crystals via the flux growth method, utilizing a mixture of 10 g KH2PO4, 3.5 g TiO2, and an additional 10 g KH2PO4 as a flux, heated at 1100 ◦C for 4 hours in a platinum crucible. The experiment resulted in the material adhering strongly to the crucible, preventing effective crystal extraction. This paper systematically analyzes the experimental conditions, potential causes of crucible adhesion, and proposes modifications to improve KTP crystal growth. The results suggest that excessive temperature, improper flux composition, and crucible interactions contributed to the observed adhesion.

A Study On Order Processing, Packing And Customer Satisfaction

Authors: Assistant Professor Mr. A. Prasanth, Mr. VN Mohamed harris

Abstract: This study investigates the efficiency of logistics processes and the level of customer satisfaction at Shakthi Knitting Pvt. Ltd. A quantitative approach using structured questionnaires was employed to gather data from stakeholders. The findings reveal a positive correlation between efficient logistics management and customer satisfaction. However, specific areas such as delivery timelines, product quality consistency, and communication responsiveness were identified as needing improvement.

HEALTHCARE RESOURCE PORTAL FOR STREAMLINED PATIENT AND MEDICAL FACILITY MANAGEMENT

Authors: Assistant Professor S. Hibbathullah Jasim, H. Jayamangala

Abstract: The Healthcare Resource Portal is an innovative web-based solution designed to streamline patient management and optimize medical facility operations. Developed using a secure and scalable architecture with Spring Boot and MSSQL, the portal offers features such as patient registration, appointment scheduling, electronic health records (EHR) management, billing, and real-time resource tracking. Emphasizing data security, the system integrates role-based access control (RBAC) and AES encryption to safeguard sensitive medical information. The platform enhances communication between patients, doctors, and administrators, reduces manual errors, and supports efficient healthcare service delivery.

DOI: http://doi.org/10.61463/ijset.vol.13.issue3.176

A Survey :Federated Learning Driven Decentralized Security In Digital Payment Systems

Authors: Professor Shylaja B, Panuganti Snigdha, Reekanksha Prakash, Repakula Tharuni, Rithika Shankar

Abstract: The financial sector is undergoing rapid digital transformation, accompanied by a surge in cyber threats and fraud. Traditional centralized machine learning approaches for fraud detection are increasingly limited by privacy concerns, data-sharing restrictions, and regulatory compliance issues. Federated Learning (FL) offers a decentralized alternative by enabling collaborative model training across institutions without sharing sensitive data. This survey explores the application of FL in financial security, focusing on its foundations, privacy-preserving mechanisms, and real-world use cases such as fraud detection, credit scoring, and customer behavior analysis. We compare FL with existing centralized techniques in terms of accuracy, privacy, adaptability, and scalability. Additionally, we examine how FL integrates with emerging technologies like blockchain, Explainable AI (XAI), and Secure Multi-Party Computation (SMPC). The paper highlights key challenges, research gaps, and future directions, providing a comprehensive overview of FL's potential to revolutionize secure and intelligent financial systems.

REVIEW PAPER ON OPTIMIZING STEEL FIBER PARAMETERS FOR ENHANCED CONCRETE PERFORMANCE AT ROOM AND ELEVATED TEMERATURES

Authors: NAVREEN, Ms. SHEELA MALIK

Abstract: The use of steel fibres into concrete has garnered considerable interest for its capacity to improve mechanical characteristics and durability at both ambient and increased temperatures. This review study methodically analyses the impact of critical steel fibre parameters—namely fibre quantity, aspect ratio, morphology, and distribution—on the efficacy of fibre-reinforced concrete (FRC). At ambient temperature, steel fibres enhance tensile strength, fracture toughness, and crack resistance by bridging microcracks and redistributing stress. The efficacy of these fibres is contingent upon optimum parameter selection, since high fibre content or inappropriate aspect ratios may result in workability challenges and uneven distribution. The report assesses current research to provide criteria for optimising FRC function while preserving structural integrity and facilitating application. At high temperatures, steel fibre-reinforced concrete shows intricate behaviour owing to thermal deterioration and the potential for spalling. This review examines the impact of fibre characteristics on residual strength, thermal conductivity, and fire resistance. Fibres with elevated melting temperatures and refined geometries may reduce strength degradation by preserving matrix integrity under thermal stress. The interaction between fibres and additives, such as polypropylene fibres, is examined to improve fire-resistant qualities. The research emphasises the need of balanced fibre doses to avert detrimental impacts on the thermal stability of concrete while enhancing its mechanical performance after exposure to fire. This analysis ultimately delineates research deficiencies and prospective avenues for enhancing steel fibre characteristics in fibre-reinforced concrete (FRC). Advanced computational modelling and experimental investigations are crucial for optimising fibre selection across varying temperature conditions. Potential options for eco-friendly building include sustainable and cost-effective fibre alternatives, such as recycled steel fibres. This study consolidates existing information to provide a complete framework for engineers and researchers to develop high-performance fibre-reinforced composites (FRC) suitable for both ambient and elevated temperature applications, therefore assuring durability and safety under harsh conditions.

DOI: http://doi.org/

Voice Command Door Lock System _548

Authors: Prem Narwekar, Shubham Nannware, Aditya Gupta, Sohan Londhe

Abstract: This project presents the design and implementation of a Voice-Activated Door Lock System integrated into a portable door unit constructed from glass fiber-reinforced polymer (GFRP), including a matching lintel beam. The primary objective is to enhance security, accessibility, and portability while maintaining structural durability and aesthetic appeal. The voice-controlled locking mechanism utilizes speech recognition technology to grant or deny access based on authorized voice commands. This hands- free approach to security offers a modern alternative to traditional key-based or keypad systems, ideal for users with mobility impairments or for smart home integration. The door and lintel beam are fabricated using glass fiber, chosen for its lightweight nature, high strength- to-weight ratio, corrosion resistance, and ease of transportation, making the system suitable for both temporary installations and permanent structures. The portability of the door unit allows flexible deployment in residential, commercial, or construction site environments. The system is powered by a microcontroller (e.g., Arduino) interfaced with a microphone module, voice recognition module, and electronic lock. Security is further enhanced through multi-level authentication protocols and real-time status feedback via a mobile app or local display. This project merges advanced materials with intelligent control systems, providing a robust, user-friendly, and portable access control solution for modern smart environments.

Home Service Providing System Using Machine Learning

Authors: Prof. Samish N. Kambale, Mr. Suraj Shivaji Patil, Mr. Sushant Anandrao Pawar, Mr. Tanvir Mansoor Fakir, Mr. Aditya Suresh Shikhare, Mr. Pratik Rajendra Kumbhar

Abstract: The on-demand home service application developed using Flutter offers a convenient and efficient solution for individuals by delivering a wide range of household services across multiple categories such as plumbing, gas appliance maintenance, house cleaning, gardening, tailoring, vehicle repair, and more. In today’s fast-paced world, where relocation and time management have become increasingly significant, such a platform helps users address their household issues promptly and effortlessly. This system is especially advantageous for maintaining a clean, hygienic, and organized living environment. It provides users with seamless access to professionals for tasks like pest control, electrical repairs, painting, cooking assistance, and housekeeping—all available under one unified platform. The application also incorporates real-time worker availability through a time slot booking feature, enabling users to choose service providers based on ratings and reviews. Additionally, secure payment options such as online transactions and Cash on Delivery (COD) are integrated for ease of use. By leveraging modern technologies, the system ensures a dynamic and intuitive user interface and experience (UI/UX). The core objective is to enhance the quality of life by making homes safer, cleaner, and healthier environments to live in.

DOI: http://doi.org/

Spam Email Detection Using Machine Learning

Authors: Shivam Devidas Gawade, Professor Nishant Rathod

Abstract: The rapid development of digital communication has resulted in a huge volume of email including unsolicited spam, which can cause serious problems such as criminal fraud, time wastage and difficulty in identifying useful emails The aim of this study is to develop pattern-based machine learning that accurately detects and filters spam emails It can do that. By leveraging algorithms to analyze email content, sender information, and metadata attributes, we address the growing need for an efficient, scalable solution to this problem. Our approach involves pre-processing email data through tokenization, stopword extraction, stemming, and vectorization, followed by feature extraction focusing on content-based, metadata, behavioral attributes. We look at how different machine learning models some including Naive Bayes, Random Forest, Gradient Boosting are performed Model performance is evaluated using , and F1-scores The study concludes that clustering methods, especially random forests, provide solutions that are difficult for, balances accuracy and computational efficiency. Although deep learning models such as CNN and NLP-based transformers provide good detection capabilities, their inherent robustness limits their practical application in small-scale applications Future work should focus on nature further integration of advanced language processing techniques to improve the effectiveness and efficiency of spam email detection.

DOI: 10.61463/ijset.vol.13.issue3.177

AN ANALYSIS OF POSTGRADUATE STUDENT’S DEMOGRAPHICS AND DIGITAL LITERACY IN THE USE OF ONLINE DATABASES ACROSS SIX SELECTED UNIVERSITY LIBRARIES IN SOUTHWESTERN NIGERIA.

Authors: Olatunji Austine Kehinde, Zahidah Zulkifli, Nur Leyni Nilam Putri Junurham, Murni Mahmud Ibrahim Ismail Isa

Abstract: In the digital era, postgraduate students' ability to effectively access and utilize online databases is critical to their academic success. This study investigates the relationship between demographic characteristics and digital literacy skills among postgraduate students across six selected university libraries in Southwestern Nigeria. Grounded in the Theory of Planned Behaviour Ajzen, (1991), the research explores how factors such as gender, age, academic year, educational level, and state of origin influence students’ digital resource usage. Quantitative research design was employed, utilizing a stratified random sampling technique to gather data from 358 respondents. Data collection was carried out using a structured questionnaire segmented into five key demographic areas. The results revealed that the majority of participants were male (56.7%), within the age range of 25–54 (89%), predominantly masters degree holders (69.0%), and primarily in their first or second year of study (70.4%). Osun State had the highest state-level representation (37.4%). The findings indicate significant demographic influences on digital literacy, echoing theoretical assumptions that personal and contextual variables shape behavioral intentions and actual use of digital technologies. Consistent with studies by Okafor & Ajibola (2020), Adebayo & Hassan (2022), and Smith & Nwankwo (2017), early-year students and middle-aged learners were more active users of digital resources, highlighting the need for targeted support strategies. Based on the analysis, the study recommends improved digital literacy programs tailored to underrepresented groups, more balanced sampling in future research, and enhanced institutional efforts to bridge demographic gaps in access and competence. Overall, the research underscores the importance of integrating demographic insights into library services and educational planning to foster inclusive and effective digital learning environments in higher education.

DOI: http://doi.org/10.5281/zenodo.15726599

Web-Based Firewall Automation System Using Flask

Authors: Assistant Professor R.Kalavani Jobin K Easo, Abhiram.S, Ageesh Lal N.G

Abstract: In today's cybersecurity landscape, managing firewall rules effectively and securely is essential in both educational and professional settings. This project introduces a Python Flask-based web application for automating and managing Linux firewall rules through an intuitive web interface. The platform enables secure user login, rule validation, history tracking, and firewall interaction via REST API. It supports both iptables and nftables and includes features such as user role control, dry-run simulation, multi-host management using Ansible, and Docker deployment for portability. Additional capabilities like notifications, rule import/export, and a responsive UI make it ideal for cybersecurity labs, hands-on training, and lightweight operational use

DOI: http://doi.org/

VISION-BASED WOMEN SAFETY SYSTEM: DETECTING HIGH-RISK SITUATIONS USING DEEP LEARNING

Authors: Associate Professor Dr. M. J. Salunkhe, Prajyot Patil, Prathamesh Sawant, Akshay Kumar Powar, Harshada Patil, Pratik Chavan

Abstract: Women’s safety is a major concern, especially in crowded public places. This project introduces a smart surveillance system that uses Artificial Intelligence (AI) to help detect potentially unsafe situations for women in real time. The system uses a camera feed and combines object detection (to find people in the frame) with a gender classification model (to identify if a person is male or female). It then checks if a female is surrounded by four or more males. If such a situation is found, the system draws a red box around the female and triggers an alert to notify authorities or take action. This tool can be used in public places like colleges, bus stations, parks, or offices to monitor surroundings and act early to prevent possible threats. By using technologies like computer vision and deep learning, the system helps improve safety and provide faster responses in risky situations. By combining computer vision, deep learning, and real-time analysis, this project offers a proactive tool for improving public safety. The solution can be deployed in universities, transport hubs, workplaces, and other sensitive areas, enhancing monitoring capabilities and supporting preventive action against potential harassment or assault scenarios.

DOI: 10.61463/ijset.vol.13.issue3.180

Smart Attendance System

Authors: Professor Dr S. Swarnalatha, Mr. K.Subramanyam, Mudireddy Hari Sahasree, Canchi Ramaswamy Shashank, Gutthi Indra, Talari Ashok Kumar Thejesh

Abstract: This project, titled " SMART ATTENDANCE SYSTEM: Facial Recognition and Gesture Control using Python," presents an innovative and contactless approach to employee login and attendance management by integrating facial recognition as the primary method of authentication. The system is developed using Python and leverages powerful libraries and frameworks including Flask for web development, OpenCV for real-time video processing, and the Face recognition library for accurate facial identification. The core functionality of the system allows users to register their personal details along with facial data through an intuitive web interface. During each login attempt, the system captures live video feed from a connected camera and compares it against the stored facial dataset to authenticate users. Upon successful verification, the system records attendance along with precise timestamps, ensuring reliable, tamper-proof attendance tracking and minimizing the risk of proxy attendance or manual errors. To push the boundaries of traditional attendance systems, a secondary feature—a hand gesture- controlled cursor—has been added as a novel enhancement. This feature, implemented using MediaPipe, pyautogui, and pynput, allows users to interact with the system using simple hand movements, eliminating the need for physical contact with input devices. While this gesture control is not the central component of the project, it represents a forward-thinking step toward building fully touchless systems, especially relevant in the context of health-conscious and hygiene-sensitive environments. Overall, the Smart Attendance System aims to offer a secure, efficient, and user-friendly alternative to traditional attendance mechanisms, combining the reliability of facial recognition with the emerging potential of gesture-based interaction.

DOI: http://doi.org/

Investigating The Doping Effects On The Optical, Electrical, Structural, Morphological, Elemental Composition, And Magnetic Properties Of Electrodeposited Ti-doped CuS Thin Films

Authors: Emmanuel O. Okechukwu, Azubuike J. Ekpunobi, Azubogu, A. C. O, Onuigbo, E. N, Overcomer Anusiuba, Adline Nwodo, Diemiruaye M. Jeroh, Chukwudi B. Muomeliri, Chiedozie Okafor, Lynda A. Ozobialu

Abstract: Successfully, thin films of copper (II) sulfide (CuS) and titanium-doped copper (II) sulfide (Ti:CuS) have been deposited on fluorine tin-doped oxide (FTO) glass substrates, using electrodeposition technique, at room temperature. The films were characterized to investigate their optical, structural, morphological, compositional, electrical, and magnetic properties, using UV-Vis spectrophotometer (at wavelength range of 300 nm – 1100 nm), x-ray diffractometer machine, scanning electron microscope equipped with energy dispersive x-ray spectroscope, four-point probe technique, and vibrating sample magnetometer (VSM), respectively. Thickness of the films was obtained using a profilometer, and thickness values of 109.16 nm, 113.17 nm, 121.11 nm, and 131.79 nm were obtained for the undoped CuS thin film, 2 % Ti doped, 6 % Ti doped, and 10 % Ti doped thin films, respectively. Optical bandgap of the films range between 2.40 eV and 2.60 eV. Structural analysis of the films confirmed hexagonal phase of CuS with lattice constant, a₌b ₌ 3.7920 Å and c ₌ 16.3440 Å.

DOI: http://doi.org/10.61463/ijset.vol.13.issue3.179

A Novel Real-Time System For Translating Sign Language Into Braille Through Text Intermediation

Authors: Jeevitha M, Sai Ananya J, Shweta Y Bhajantri, Tejashwini V, Vinutha H V

Abstract: Communication between the hearing-impaired and visually-impaired communities is a significant accessibility challenge due to the lack of a common mode of interaction. This paper presents a novel real-time system that translates sign language into Braille through an intermediate textual representation, with the output displayed visually on a screen. The system uses computer vision and deep learning algorithms to accurately recognize sign language gestures, converts them into text, and then maps the text to corresponding Braille patterns using a visual Braille representation. Unlike traditional systems that require tactile Braille devices, this solution offers a screen-based output, making it cost- effective and accessible for learning and communication.

DOI: http://doi.org/

A Novel Real-Time System For Translating Sign Language Into Braille Through Text Intermediation

Authors: Jeevitha M, Sai Ananya J, Shweta Y Bhajantri, Tejashwini V, Vinutha H V

Abstract: Communication between the hearing-impaired and visually-impaired communities is a significant accessibility challenge due to the lack of a common mode of interaction. This paper presents a novel real-time system that translates sign language into Braille through an intermediate textual representation, with the output displayed visually on a screen. The system uses computer vision and deep learning algorithms to accurately recognize sign language gestures, converts them into text, and then maps the text to corresponding Braille patterns using a visual Braille representation. Unlike traditional systems that require tactile Braille devices, this solution offers a screen-based output, making it cost- effective and accessible for learning and communication.

DOI: http://doi.org/

Secure Real-Time File Sharing Using Blockchain Technology

Authors: Sampath .M, Adhwaith Anilkumar, Akshay Ajay, Arjun Balagopalan

Abstract: In the modern digital age, secure and efficient file sharing is paramount. This paper presents a blockchain-based real-time file sharing application that ensures data integrity, confidentiality, and decentralized access. The system is developed using Python and JavaScript, with Flask as the backend framework and AES encryption for data security. Users can sign up, upload files, share them via links, and download them, all while transactions are logged immutably on a blockchain. This application offers a reliable, transparent, and tamper-proof solution for personal and organizational data exchange.

DOI: 10.61463/ijset.vol.13.issue3.181

A SMART LEGAL ASSISTANT FOR INDIAN LAWS

Authors: Assistant Professor Ms. K.Nivetha, Uthraa Ujjwal pu

Abstract: This project introduces a chatbot solution that combines Retrieval-Augmented Generation (RAG) with a Knowledge Graph (KG) to respond to inquiries related to Indian law. The system intelligently adjusts to the complexity of user questions to maximize both efficiency and accuracy. For direct and simple queries, the RAG module fetches pertinent legal documents and delivers concise responses. In contrast, for intricate and layered questions, the system utilizes a multi-hop reasoning technique through the knowledge graph to produce precise and context-rich answers. To broaden reach and inclusiveness, the chatbot is equipped with multilingual capabilities, allowing individuals from various linguistic backgrounds across India to engage in their native language. This blended architecture promotes computational efficiency by activating the knowledge graph module only when required, thereby conserving system resources while maintaining high response quality. The chatbot ensures smooth and intuitive access to legal information—offering fast replies for basic questions and thorough explanations for more complex legal matters. By harnessing the complementary advantages of RAG and KG, this system seeks to transform legal support services by enhancing availability, reliability, and user-friendliness for both legal experts and the general.

DOI: http://doi.org/10.61463/ijset.vol.13.issue3.182

Deep CNN Architecture For Automated Identification And Severity Grading Of Diabetic Retinopathy

Authors: Vaishnavi S. Kulkarni, Suhasini A. Phatak, Sneha P. Balaki, Nikhil A. Kulkarni

Abstract: Diabetic Retinopathy (DR) is among the most prevalent microvascular diabetic complications and a major cause of avoidable blindness in the world. Early identification and correct grading of DR are critical for the initiation of prompt treatment and prevention of vision loss. Nonetheless, expert ophthalmologist-dependent retinal fundus image manual evaluation is time-consuming, prone to subjectivity, and highly reliant on skilled ophthalmologists. In order to deal with these issues, in this research, an automated, deep learning approach and Convolutional Neural Networks (CNN) is suggested for detecting the severity of Diabetic Retinopathy using retinal fundus images. The CNN architecture classifies these retinal scan images into five clinically accepted stages: NO DR, Mild Stage DR, Moderate Stage DR, Severe Stage DR, and Proliferative Stage DR. The architecture includes several convolutional layers followed by Batch Normalizationh,aRrdeLU Aectxiuvadtaiotens,, and Msoaftx Pooleinxugdoapteesr,ationsantod extract hierarchical features of retinal abnormalities. nAeFouvlalysccuolanrnizeacttieodn.layer approach is used to avoid overfitting and improve generalization. The last softmax layer gives probabilistic classification output. The model is learned and being tested on a vast annotated dataset of retinal images with high classification accuracy and sensitivity for all DR grades. Experimental results have proven that the suggested CNN model efficiently detects the DR stages with great reliability. The system provides a scalable and non-invasive method to aid ophthalmologists in early screening and diagnosis, thus facilitating better treatment, particularly for resource-poor areas.

DOI: 10.61463/ijset.vol.13.issue3.184

Web Analyzer For Private Networks

Authors: Professor Dr.A. Neelamadheswari, Assistant Professor Mr.K.S.Arun, M.E. Ph.D Aloysius Rosario K, Aasif Ahameed S, Arunkumar M

Abstract: In the current digital landscape, real-time monitoring and assessment of network domain safety are essential for proactive cybersecurity defense. This project introduces a Python-based live domain safety monitoring tool that leverages network packet analysis to evaluate and visualize the security posture of domains accessed within a network. The tool integrates the power of tshark, the command-line interface of Wireshark, to capture live DNS, HTTP, and SSL/TLS traffic, extracting relevant protocol and domain information for immediate analysis. At the core of the system is a dynamic scoring mechanism that assigns and adjusts safety scores to each detected domain. Domains are initially assigned a neutral score, which is then modified based on a set of heuristic rules. For instance, domains with suspicious characteristics—such as those starting with "malware" or containing the substring "phish"—are penalized, reflecting their higher likelihood of being malicious. The tool also evaluates the security of the communication protocol: traffic over HTTP results in score deductions due to its inherent insecurity, while HTTPS and SSH connections are rewarded for their stronger security guarantees. This flexible scoring approach allows the system to adapt to evolving threat patterns and user behavior. To further enhance situational awareness, the tool incorporates a Man-in-the-Middle (MITM) risk assessment for each domain. By considering both the protocol in use and the domain’s current safety score, the system categorizes MITM risk as High, Medium, or Low. Domains accessed via insecure protocols, those with low safety scores, or those containing phishing indicators are flagged as high risk, enabling rapid identification of potential attack vectors. Visualization is a key feature of the tool, achieved through the rich Python library. The console interface displays a continuously updating table of observed domains, their protocols, safety scores, and MITM risks, all color-coded for quick interpretation. This real-time feedback loop empowers network administrators and security analysts to take immediate action in response to emerging threats, such as isolating compromised hosts or blocking access to dangerous domains.

DOI: http://doi.org/10.61463/ijset.vol.13.issue3.183

Optimizing 3D Character Creation For Game Performance

Authors: Abhay Urkude

Abstract: This paper will take a look at the optimization of 3D character creation for game performance and will focus on low-poly modeling as well as the most efficient texturing techniques. It explores a method that allows artists to keep the visual quality while reducing the polygon count and texture resolution, which permits the high performance of games, particularly mobile and low-end devices. The work makes use of real-world examples and project experience to discuss practical workflows and industry-standard tools.

Investigating the Causes of Poor Performance in Physics among Female Students: A Case Study in Kasama District

Authors: Musonda Patricia

Abstract: This journal seeks to explore the underlying factors contributing to the poor performance of girls in Physics within the context of three educational institutions in Kasama District, Northern Province: Ituna Secondary School, Kasama Girls Secondary School, and Lukashya Secondary School. The study aims to identify academic, social, and environmental constraints that hinder female students' performance in Physics, analyze the feedback from teachers and students, and suggest practical interventions to improve outcomes.

Movie Recommendation Using Content-based Filtering.

Authors: Assistant Professor Akhilesh kumar singh, Naman kumar Maurya, Sejal Verma

Abstract: Recommendation System used to predict and suggest movies based on user preferences.The technique applied here tries to predict user preferences using an information filtering technique that improves the user experience through timely and pertinent recommendations. In particular, movie suggestions are vital for enhancing interpersonal relationships since they may provide users with entertainment options based on their tastes or the current popularity of films. Data filtering systems often use these to help people locate content that meet their needs by going through large databases and making recommendations on what to buy or watch. These filtering systems, at times referred to as recommender systems, recommendation engines, or platforms, are designed to predict how a user might rank or favor an item. They are mainly used in the business sector. The primary purpose of this project is to produce a content- based model for film recommendations that involve cosine similarity and vectorization to provide the consumer with general recommendations regarding the popularity of the films.

DOI: http://doi.org/

Movie Recommendation Using Content-based Filtering.

Authors: Assistant Professor Akhilesh kumar singh, Naman kumar Maurya, Sejal Verma

Abstract: Recommendation System used to predict and suggest movies based on user preferences.The technique applied here tries to predict user preferences using an information filtering technique that improves the user experience through timely and pertinent recommendations. In particular, movie suggestions are vital for enhancing interpersonal relationships since they may provide users with entertainment options based on their tastes or the current popularity of films. Data filtering systems often use these to help people locate content that meet their needs by going through large databases and making recommendations on what to buy or watch. These filtering systems, at times referred to as recommender systems, recommendation engines, or platforms, are designed to predict how a user might rank or favor an item. They are mainly used in the business sector. The primary purpose of this project is to produce a content- based model for film recommendations that involve cosine similarity and vectorization to provide the consumer with general recommendations regarding the popularity of the films.

DOI: http://doi.org/

The Effect of Mathematics Teachers’ Attitudes on Learners’ Performance in Mathematics: A Case Study of Five Selected Secondary Schools in Kaputa District of Northern Province of Zambia

Authors: Chikachi Cheelo

Abstract: This study explores the impact of mathematics teachers’ attitudes on learners’ performance in mathematics within five secondary schools in Kaputa District, Northern Province, Zambia. Using a mixed-methods approach, data were collected from 10 teachers and 200 learners through surveys, standardized tests, interviews, and classroom observations. The findings indicate a significant positive correlation between positive teacher attitudes and improved learner performance. Key influencing factors included professional development, school environment, and teaching experience. The study underscores the importance of cultivating positive attitudes among teachers to enhance student engagement, motivation, and achievement in mathematics.

ROLE OF KOTAH STONE WASTE IN ROAD CONTRUCTIONS

Authors: Dr. Venkata Hanumantha Rao Chittem, Dhvani Patel

Abstract: Kota Stone, a naturally available material, is increasingly used in construction for its strength and aesthetic appeal. This study explores the reuse potential of Kota Stone waste, generated during construction of the Government Engineering College building, in various geotechnical applications such as flexible pavements, embankments, earthen dams, reinforced earth walls, and gabion retaining walls. The investigation began with the determination of basic geotechnical properties—specific gravity, grain size distribution, liquid limit, shrinkage limit—along with engineering parameters like optimum moisture content, maximum dry density, and California Bearing Ratio (CBR). The suitability of Kotah Stone Waste in flexible pavement construction was further evaluated by performing CBR tests at curing periods of 3, 7, 14, and 28 days under both soaked and unsoaked conditions. To enhance strength, Kotah Stone Waste was modified with 2%, 4%, 6%, and 8% cement by weight, followed by similar testing. Results indicated that both the unmodified and cement-modified samples cured for 14 and 28 days exhibited higher strength values. Notably, 8% cement addition yielded optimum CBR values. The findings align with IRC-37 (2018) and BIS standards, supporting the use of Kotah Stone Waste and its cement-modified forms in sub-base and base courses of flexible pavements and other geotechnical structures.

TO SHOWCASE THE DANGERS OF TAKING OF THE PROHIBITED DRUGS BY LEARNERS AT CHILUBI MAINLAND BOARDING SECONDARY SCHOOL OCCURED

Authors: CHISALA EVANS

Abstract: In reality, this research study significantly seeks to assess the challenges of taking of the prohibited drugs on the performance of a learner in the selected public secondary school in Chilubi district of Northern Province of Zambia. The study specifically explores the taking of the forbidden substances as a contributing factor to poor performance of learners at Chilubi Mainland Boarding Secondary school in Chilubi district of Northern Province of Zambia. Most of the challenges encountered in learning institutions are taking of illicit substances or drug addiction related, (World Health Organization report, 2014). This study therefore aims to actually highlight the prevalence and factors associated with taking of the forbidden substances among secondary school learners in the selected potential learning institution in Chilubi district at Chilubi Boarding Secondary school . By definition, a drug is any product other than food or water that affects the way people feel, think, see, and behave. It is also a substance which by its chemical nature affects physical, mental and emotional functioning, (Rang et al, 2011). It can enter the body through chewing, inhaling, smoking, drinking, rubbing on the skin or injection. The research target population is 400 respondents consisting of one public secondary school , in this case Chilubi Mainland Boarding Secondary school The sample size is 63 respondents, quantitatively characterized by 33 pupils, 3 school administrators 12 teachers, 3 guidance and counseling patrons or matrons, 12 parents and (2) Officers from the Department of DEC are also included in the sample because they represent providers or facilitators of drug and alcohol preventive education in learning institution. The data will be precisely collected by employment of personal questionnaires, individual interviews and through collective participations and engagements Data analysis will be done using descriptive statistics after data clearing and coding. Quantitative data will be analyzed using variable distribution, Bar graphs, mode, mean, and percentages as averages whereas qualitative data will be analyzed by tallying the numbers of none numerical responses. In view of this, the data variable tabulations will be presented using frequency distribution tables and bar graphs respectively.

DOI:

Multiagents AI Systems In Healthcare

Authors: Simrandzhit Kaur, Konstantin Koschechkin

Abstract: Multiagent AI systems represent a sophisticated solution to complex healthcare challenges by enabling coordinated action among autonomous agents. These systems can enhance diagnostic accuracy, optimize resource allocation, and support treatment planning through collaborative decision-making. This article examines the technical foundations of multiagent AI systems, including system architecture, communication protocols, and decision-making mechanisms. A prototype framework was developed using cooperative multiagent reinforcement learning (MARL) and Distributed Constraint Optimization Problems (DCOP), implemented in a simulated emergency department environment. Results showed improved task completion, faster convergence of learning strategies, and more efficient staff scheduling compared to rule-based systems. Communication efficiency was enhanced through the use of FIPA-ACL protocols and adaptive throttling. Multiagent AI systems hold significant promise for transforming healthcare delivery by increasing efficiency, robustness, and personalization in clinical work flows.

DOI: https://www.doi.org/10.61463/ijset.vol.13.issue3.272

PRIVACY AND SECURITY IN THE WORLD OF ARTIFICIAL INTELLIGENCE

Authors: D. K. Tripathi, Anjali Sharma, S. Nandi

Abstract: – Many technical companies and organization are relying to artificial intelligence (AI) for a variety of advantages – problem solving or making a decision. Not only in organizations, we can see the tremendous use of AI, but also in healthcare, education, electronics, e-commerce, software development, pharmacies, games, engineering, communication and development. AI makes our work more accurate and saves our time on the other hand there is always a possibility of leaking of personal data to third party with whom we are not associated by any means. In spite of much advancement in AI, the privacy and security are not addressed to appreciable extent. In this paper the privacy and security issues associated with artificial intelligence is critically examined. The challenges related to the privacy and security is investigated on the basis of available literature data. This paper not only discusses the problems in using AI but also suggests an optimized solution regarding user privacy and their personal data.

DOI: http://doi.org/

FINGERPRINT-BASED BLOOD GROUP PREDICTION USING MACHINE LEARNING

Authors: Rajesh Vasant Jondhale, Nayan Sunil Sonawane, Yuvraj Rajendra Rasal, Nilesh Prakash Pawar, Prasad Yadav, Dr. Madhav J. Salunkhe

Abstract: Determining an individual's blood group is a vital step in medical diagnostics, traditionally conducted through laboratory-based blood tests. These methods, however, require blood samples, specialized equipment, and trained personnel, making them time-consuming and resource-intensive. This study introduces an innovative, non-invasive approach for blood group prediction using deep learning techniques applied to fingerprint images. Convolutional Neural Networks (CNNs) are utilized to extract and analyze distinct fingerprint features that correlate with blood group classification. The dataset comprises 6000 fingerprint samples, which are preprocessed using OpenCV techniques to enhance image quality and standardization. The CNN model is trained using the Adam optimizer over 25 epochs, ensuring effective learning while maintaining minimal training loss. Additionally, a web-based system is developed with Flask for front-end interaction and SQL Server for secure data management. This proposed framework offers a rapid, cost-efficient, and accessible alternative to conventional blood group detection methods, potentially benefiting remote healthcare services and emergency medical scenarios. The findings highlight the potential of biometric-based artificial intelligence in medical applications, paving the way for further research in non-invasive diagnostic techniques.

DOI: http://doi.org/

Pest Net-X: Vision Transformer AI For Real-Time Multispectral Pest Detection

Authors: Dr. C Nandini, Professor Rajesh M, Shashikala B Thakur, Srikumar L, Varshitha G C, Yashaswini M A

Abstract: Pest Net-X presents an innovative AI-driven solution for real-time, multispectral pest detection in agriculture, leveraging a hybrid Vision Transformer (ViT) architecture optimized for edge deployment. Unlike conventional CNN-based approaches, Pest Net-X integrates RGB and near-infrared (NIR) spectral analysis to identify pests at early developmental stages (egg/nymph phases) with 95.1% accuracy—surpassing existing tools like Plantix by 8.8%. The system features a farmer-centric mobile app with bilingual (Kannada/English) support, offline functionality, and explainable AI (Grad-CAM++ heatmaps) to deliver actionable pest advisories. Field trials with 50 South Indian farmers demonstrated a 27% reduction in crop losses and ₹5,800/acre cost savings through precision pesticide use. Pest Net-X’s lightweight TensorFlow Lite implementation achieves 47ms inference latency, making it viable for low-end smartphones in resource-limited settings. This work bridges critical gaps in agricultural AI by combining multispectral ViT technology, edge computing, and vernacular accessibility to empower sustainable farming practices.

LUNG CANCER PREDICTION AND CLASSIFICATION USING DEEP LEARNING TECHNIQUES

Authors: Asscoiate Professor Mrs.C.Radha, Mr.R.Midunkumar, Mr.C.Mani, Mr.B.Mohanraj

Abstract: Lung cancer remains one of the leading causes of cancer-related mortality worldwide, necessitating the development of effective diagnostic and predictive tools. This paper explores the application of deep learning techniques for the prediction and classification of lung cancer, leveraging advancements in artificial intelligence to enhance early detection and improve patient outcomes. We provide a comprehensive overview of various deep learning architectures, particularly Convolutional Neural Networks (CNNs), and their efficacy in analyzing medical imaging modalities such as computed tomography (CT) scans and chest X-rays. The study highlights preprocessing methods, feature extraction techniques, and evaluation metrics that are critical for model performance. Finally, we discuss future directions for research, emphasizing the integration of deep learning with emerging technologies to further enhance diagnostic capabilities in oncology. This work aims to contribute to the ongoing efforts in utilizing artificial intelligence for improving lung cancer detection and management.

DOI: http://doi.org/10.61463/ijset.vol.13.issue3.184

AI-Powered Digital Hoarding Cleaner

Authors: Asscoiate Professor K Deepa Shree, Sanjana S, Shreya R, Sinchana Adiga, Sharanya

Abstract: In today’s data-driven world, individuals and organizations generate vast amounts of digital data, often leading to inefficient storage and digital clutter—commonly known as digital hoarding. Traditional storage cleaners primarily focus on file size or duplication and lack the intelligence to make context-aware decisions. This paper proposes an AI-powered Digital Hoarding Cleaner that leverages advanced Natural Language Processing (NLP) and Machine Learning (ML) to analyze file content and user behavior. The system integrates models such as BERT, GPT, and BART for file summarization and semantic understanding. It offers personalized recommendations to retain, delete, or reorganize files based on relevance, using techniques like semantic similarity detection, fuzzy matching, and behavioral analytics. Cloud support for services such as Google Drive and OneDrive ensures seamless integration, while a user-friendly web dashboard provides insights into storage patterns and suggested actions. Emphasizing data privacy, the tool enables local processing and secure communication. Overall, the system aims to optimize storage usage, reduce cognitive overload, and deliver intelligent file management beyond traditional cleaning methods.

DOI: http://doi.org/

Geoscience Education for Energy Transition: A Critical Need for a Sustainable Future

Authors: Akharia, Kenneth Itoya, Okujagu Diepiriye Chenaboso

Abstract: The commitment to energy transition, the reduction of greenhouse gas emissions that has been observed globally means that there has to be a drastic change in the energy production structure, necessitating faster rate of innovation, enhance on existing technology and fundamental installation of carbon-free energy systems. To accomplish all these goals of sustainability, many more geoscientists are expected to be hired to practice in the energy industry. However, the emerging global electricity demand attributed to demographic growth, urbanization, and shift in the wealthy nations’ income per capita presents a major challenge in common effort to decommission carbon. To this effect, it is imperative that effort be made towards replenishing the stock of geoscientists that are available to work in the energy sector with more manpower that would focus on the sustainable and safe exploitation of the subsurface resources. This article emphasizes the importance and necessity of geoscience education and awareness amongst academicians as well as the general public to support sustainable energy solutions.

Surveillance In Smart Cities: A Threat To Privacy Or A Tool For Public Safety?

Authors: Naveen Talawar

Abstract: The use of enhanced surveillance systems in the smart cities provides a great opportunity in enhancing the secure society, efficient urban planning, and development. However, it broadens some significant questions concerning the rights of individual privacy and the feasible manipulative use of data. While most proponents of smart cities claim that surveillance is one of the essential tools for keeping citizens safe, this paper will explore whether it is in fact the only means for protecting people in urban environments or just another invasive method to violate our rights to privacy. The research employs the doctrinal research method to analyse the existing legal provisions on surveillance in India and other jurisdictions. It discusses the importance of privacy as a human right and legal concerns arising from the rising adoption of AI, facial recognition technology, as well as data analysis in the management of cities. It also provides an analytical discussion of current privacy protections to ascertain their efficacy. The selection of this theme is based on the fact that smart technologies are increasingly extending over cities, and therefore, the privacy concerns arising from these technologies need to be tackled. It is therefore important to fully grasp the meanings of surveillance within smart cities in order to come up with fair policies that would safeguard the privacy of the citizens and at the same time enhance security. The goal of the study is to outline a fair approach that the cities may follow when putting in place the surveillance technologies and still uphold the democratic governance as well as privacy of a person. Finally, the paper concludes by presenting the guidelines for policymakers and urban planners to undertake surveillance ethically by providing recommendations that include the need to embrace transparency and accountability in the use of the technology.

DOI: http://doi.org/

Surveillance In Smart Cities: A Threat To Privacy Or A Tool For Public Safety?

Authors: Naveen Talawar

Abstract: The use of enhanced surveillance systems in the smart cities provides a great opportunity in enhancing the secure society, efficient urban planning, and development. However, it broadens some significant questions concerning the rights of individual privacy and the feasible manipulative use of data. While most proponents of smart cities claim that surveillance is one of the essential tools for keeping citizens safe, this paper will explore whether it is in fact the only means for protecting people in urban environments or just another invasive method to violate our rights to privacy. The research employs the doctrinal research method to analyse the existing legal provisions on surveillance in India and other jurisdictions. It discusses the importance of privacy as a human right and legal concerns arising from the rising adoption of AI, facial recognition technology, as well as data analysis in the management of cities. It also provides an analytical discussion of current privacy protections to ascertain their efficacy. The selection of this theme is based on the fact that smart technologies are increasingly extending over cities, and therefore, the privacy concerns arising from these technologies need to be tackled. It is therefore important to fully grasp the meanings of surveillance within smart cities in order to come up with fair policies that would safeguard the privacy of the citizens and at the same time enhance security. The goal of the study is to outline a fair approach that the cities may follow when putting in place the surveillance technologies and still uphold the democratic governance as well as privacy of a person. Finally, the paper concludes by presenting the guidelines for policymakers and urban planners to undertake surveillance ethically by providing recommendations that include the need to embrace transparency and accountability in the use of the technology.

DOI: http://doi.org/

Augmenting Data Integrity With Blockchain Technology

Authors: Mammu’an Titus Alams, Godwin A. Thomas, Stephen Mallo Jr, David E. Ogwuche, Betty T. Dimka

Abstract: In many critical sectors such as finance, healthcare, and supply-chain management, data security is important because data breaches or errors can cause severe damage. Blockchain technology promises that data cannot be altered or deleted once recorded making it free from forgery or unauthorized modifications. This is in contrast to traditional databases that allow data to be changed or removed without any trace which could result in potentially integrity problems. Unlike conventional centralized databases which are typically prone to manipulation, blockchain uses cryptographic algorithms where each block in the blockchain is directly linked with cryptographic algorithms making it impossible for anyone to change the records without being detected thereby ensuring that stored information is highly reliable. While there are obstacles such as scalability as well as energy consumption, blockchain has features unique that position it as a transformative technology to secure and ensure the integrity of stored records. Especially in decentralized, distributed, and untrusted environments, blockchain technologies present qualifying characteristics that allow it to uphold the authenticity and integrity of data in different applications. This paper explores the prospects of integrating Blockchain into database technologies to enhance their ability to maintain data integrity and validity. The research employs a comparative analysis of the traditional databases and blockchain approaches to data integrity to output the findings in terms of their strengths and limitations.

DOI: http://doi.org/

DATA RETRIEVAL DURING FAILURE OF VIRTUAL MACHINE

Authors: LAKSHMI M R, SHASHANK SHANKAR M,, VINAY KURDEKAR, KARTHIK D

Abstract: Virtualization has become a cornerstone of modern computing, enabling multiple Virtual Machines (VMs) to operate on a single physical server, thereby enhancing resource utilization and scalability. However, one of the significant challenges in virtualized environments is the retrieval of data from VMs that are in a shut-down state. Traditional recovery methods are often inefficient, prone to data corruption, or unable to maintain system integrity. This project aims to explore and implement effective techniques for accessing and recovering data from VMs in various shutdown conditions, considering factors such as storage formats, hypervisor types, and security configurations. By analyzing existing approaches and developing optimized recovery workflows, the project seeks to ensure data integrity, reduce recovery time, and minimize system impact. The outcomes will contribute to improving virtual data resilience in enterprise and cloud environments, offering valuable solutions for system administrators, forensic analysts, and disaster recovery teams.

DOI: http://doi.org/

ResNet-50 Based Intelligent System For Brain Tumor Detection

Authors: Dr. Nandini C, Prof. Mangala H S, Apoorva Patil, Athiya Syed, B Suhas, Harshitha J

Abstract: Detection of brain tumors via MRI scans is a healthcare imperative complicated by the enormous variation in the shapes and locations of the tumors. This project proposes the application of deep learning using the ResNet-50 convolutional neural network to automatically classify brain tumors from MRI scans. The system not only aims for high diagnostic accuracy but also incorporates Grad-CAM heatmaps to provide visual interpretation of the model’s predictions, enhancing transparency in clinical decision-making. Through extensive preprocessing, data augmentation during model training, and a user-friendly web interface, the system is designed to be both powerful and practical for real-world medical use.

DOI: 10.61463/ijset.vol.13.issue3.185

Utilization Of Cow Fat-Derived Biodiesel As A Sustainable Base Fluid In Oil-Based Drilling Muds

Authors: Ndubuisi, Elizabeth Chinyerem, Mormah, Progress Chuka

Abstract: Oil and gas production is capital-intensive, with drilling operations accounting for a significant portion of overall costs. Drilling fluids are essential to address downhole challenges such as hole cleaning, bit cooling, pressure control, and lubrication. Among these, drilling muds are classified into water-based and oil-based types. Oil-based muds (OBMs) typically use diesel or synthetic oils as the continuous phase, but the potential of waste-derived oils remains underexplored. This study investigates the feasibility of using biodiesel derived from cow fat as an alternative continuous phase in OBMs. Biodiesel was extracted through transesterification and used to formulate mud samples, alongside conventional diesel-based samples. Both mud types were evaluated for rheological properties plastic viscosity (PV), yield point (YP), and gel strength at temperatures of 80°F, 120°F, and 150°F, along with electrical stability (ES). At 80°F, the PV of diesel-based and biodiesel-based muds were 57.51 cP and 37.27 cP, respectively. As temperature increased, diesel-based PV dropped more sharply than the biodiesel-based mud, indicating better thermal stability in the latter. Biodiesel-based mud also exhibited superior yield point values, suggesting enhanced carrying capacity. However, its electrical stability (67.33 V) was significantly lower than that of the diesel-based mud (413 V), limiting its effectiveness in electrical stability performance. In conclusion, biodiesel-based drilling fluid shows promise as a rheological modifier with favorable thermal behavior but is not a suitable replacement for electrical stability enhancers in OBMs.

Virtual Air Board

Authors: Associate professor Dr. Priya Nandihal, B R Aditya Vardhan Reddy, Gopineedi Sahaj,Bhuvan AM, Annam Sri Nimisha Reddy

Abstract: Virtual Air Board is a webcam-based system that allows users to write in the air using finger movements, without the need for styluses or special hardware. It uses computer vision to track the hand and draw on a virtual canvas, while integrated OCR converts handwriting into editable text. Simple hand gestures are also used for commands like backspace and cursor movement, making digital writing more accessible and intuitive. The system runs in real time using only a standard laptop webcam, making it highly portable and cost-effective. It eliminates the dependency on external writing tools, promoting accessibility for a wide range of users. The interface supports a natural writing experience with smooth stroke rendering and gesture feedback. This project demonstrates the potential of vision-based input systems for creative and practical applications.

INTEGRATING INFORMATION COMMUNICATION TECHNOLOGY IN MATHEMATICS AT SECONDARY LEVEL: A STUDY OF THREE SELECTED SECONDARY SCHOOLS IN CHILUBI DISTRICT OF NORTHERN PROVINCE

Authors: Edgar chongo Mphil

Abstract: The purpose of this study was to explore the various challenges and opportunities influencing integration of ICT in teaching and learning Mathematics in secondary schools in Chilubi District. The study sought to: Determine the levels of ICT integration in teaching and learning Mathematics; identify the challenges and opportunities of ICT use in teaching and learning Mathematics; and identify best pedagogical practices used in teaching Mathematics using ICT. The study adopted Rogers’s diffusion theory, whereby the user or adopter is critical in the whole process. The study also adopted a descriptive survey research design hence data was largely descriptive in nature. Three instruments were used to determine the results in the study: teachers and student questionnaires, a structured interview schedule for the deputy heads of department and an observation checklist. The study was carried out in three public secondary schools in Chilubi District.The schools included Chaba Day,Matipa day and Chilubi mainland boarding school. The study adopted purposive sampling to select teachers, while simple random sampling to select schools, head of departments and Grade nine students. The sample comprised of two thousand seven hundred and seven five (2775) respondents. The sample of the study included three secondary schools, twelve Mathematics teachers, two hundred and seventy five (275) grade twelve students and three heads of Mathematics department. Data analysis was done using Statistical Package for Social Sciences (SPSS) version 20, which involved the use of percentages and frequency tables. The findings indicated that there were low levels of ICT integration; Mathematics teachers are not well prepared to integrate ICT in teaching Mathematics.

Codified Likness Utility

Authors: Jyothis K P, Madalam Dhanush, Kiran R B, Preetham M V, Prajwal

Abstract: CLU (Codified Likeness Utility) is an AI- powered web development platform designed to streamline the creation of full-stack web and mobile applications directly within the browser. By integrating advanced AI models with browser-based container technology, CLU enables developers to prompt, run, edit, and deploy applications without requiring local setup or installations.

DOI: http://doi.org/

Evaluating Models For Movie Recommendation: A Comparative Study To Enhance User Experience

Authors: Nikil Kumar, Uttam, Priya Chauhan, Anurag Gupta

Abstract: In the rapidly growing personal entertainment space, providing great video recommendations on a wide range of topics is vital to amplify customer satisfaction. This study compares various machine learning algorithms to evaluate their effectiveness in predicting video preferences based on user behavior and historical data. The analysis uses techniques like user-based filtering, item- based filtering, hybrid models, decision trees, and neural networks. The methods were evaluated using performance metrics including metrics like accuracy, sensitivity, F1 measure, and root mean squared deviation (RMSD). The results show that there is a significant difference between the algorithms in terms of accuracy and computational efficiency, with the hybrid model outperforming other models in capturing user preferences. Artificial neural networks also show potential in managing customer interactions, although they require more investment. This research provides a good understanding of the capabilities and limitations of different machine learning methods, laying the foundation for future developments and practical applications of recommendations.

Guardian-AI: On-Device AI Security For Sensitive Data

Authors: Prof. Bhavya V, Jayanthan P, Kiran K G, M Jathin Reddy, Manu S

Abstract:

DOI: http://doi.org/

AI-Powered Cyber Risk Management System Using IoT And BiLSTM-Based Threat Intelligence

Authors: Shakeeb Ahmed, Syed Zubair Yuneeb, Tejas BN, Sneha Singh, Dr.C Nandini

Abstract: – In today's hyper-connected digital landscape, organizations are confronting an escalating tide of increasingly complex and rapidly evolving cyber threats. This challenge is profoundly exacerbated across the vast and distributed systems enabled by the Internet of Things (IoT), where the sheer volume of devices and their constant communication create an expansive and often vulnerable attack surface. Traditional cybersecurity solutions, typically reliant on static, signature-based detection methods, inherently struggle to adapt and respond in real time to novel or polymorphic threats, leaving critical IoT infrastructure and sensitive data highly susceptible to exploitation. This project introduces the comprehensive design and development of an innovative AI-powered automated tool, meticulously engineered for seamless integration with heterogeneous IoT devices, specifically to address these emerging and dynamic cyber risks more effectively. By leveraging the continuous, real-time streams of operational and behavioral data generated from diverse IoT sensors and network endpoints, the system applies advanced deep learning—specifically Bidirectional Long Short-Term Memory (BiLSTM) networks. These networks are uniquely capable of analyzing intricate temporal sequences and learning complex behavioral baselines, allowing them to precisely detect subtle anomalies and assess potential vulnerabilities across interconnected networks. Unlike conventional static detection methods, BiLSTM models possess the intelligence to understand contextual patterns over time, identifying nuanced changes in device behavior or network traffic that could signify a nascent cyberattack or a compromised system.

DOI: http://doi.org/10.61463/ijset.vol.13.issue3.186

SAAS – Based Notion Clone With AI Integration

Authors: Professor Shreenidhi B S, Shirisha S, Sri Vidhya MJ, Varshini A, Soujanya Ratnakar Naik

Abstract: This literature review discusses the latest developments in creating smart, full-stack SaaS applications with artificial intelligence (AI) integration to improve user experience and automation. As there is a rising demand for responsive, collaborative, and personalized digital experiences, developers have been turning towards contemporary frameworks such as Next.js, React, and Supabase in combination with AI models to provide more intelligent functionality. This research is a real-world application of a workspace-centric platform developed with Next.js 13, Tailwind CSS, Drizzle ORM, and Supabase, along with AI integrated to support features such as intelligent UI recommendations, automatic tagging, and improved content processing. The platform has support for real-time editing, tracking user presence, authentication, and subscription management through Stripe. By integrating AI with a solid tech stack, the system illustrates how intelligent applications can be built effectively, providing valuable insights into the future of AI- driven web development.

FOOT TEMPERATURE MONITORING SMART SOCKS FOR DIABETIC PATIENTS

Authors: Professor Gayathri K, Jothika D, Saltina V, Sarathy k, Deepa K

Abstract: Diabetes mellitus is a chronic disorder impacting millions globally, with diabetic foot issues being a considerable issue due to peripheral neuropathy and inadequate circulation. Foot ulcers and infections are typical complications that can lead to serious outcomes, including amputations. Early detection and prevention are keys in addressing these problems. This project intends to design and build revolutionary smart socks that combine temperature sensors to monitor foot temperature in real-time. The smart socks will enable continuous monitoring of foot temperature, enabling for early detection of potential foot issues. The temperature data will be delivered wireless to a mobile application or cloud platform, where it will be examined and alarms will be created if any abnormal temperature swings are discovered. Patients with diabetes may be able to take proactive steps to avoid foot issues and enhance their general quality of life thanks to this creative solution, which has the potential to completely transform foot care. These smart socks can lower the risk of amputations and enhance the health of diabetic patients by enabling prompt interventions and delivering early warnings. In order to guarantee that the product satisfies the needs of the intended audience and effectively prevents diabetic foot issues, the development of these smart socks will entail cooperation with patients, engineers, and healthcare professionals. All things considered, this project has the potential to significantly enhance the lives of millions of people globally and manage diabetic foot care.

A Review On Deep Learning Based Breast Cancer Classification For Histopathology Images

Authors: Assistant professor Manasa Sandeep, Dr. C Nandini,Bhargavi S.R, Disha.A,Fida.K.S, Harshitha.B.K

Abstract: Breast cancer is one of the most common and life-threatening cancers in women worldwide. The clinical gold standard for diagnosis is still histopathological examination, but it is time-consuming, subject to human expertise, and susceptible to human error. This project presents a deep learning system based on DenseNet201 architecture for automated and enhanced accuracy of breast cancer detection from histopathology images. The system is developed using the BreaKHis dataset, employing state-of-the-art preprocessing and data augmentation methods for enhancing robustness. Performance metrics such as accuracy, precision, recall, and AUC-ROC results reflect the system's performance as a sound diagnostic tool in clinical settings. Histopathological diagnosis, while critical, entails a number of challenges including inter-observer variability, workload burden on pathologists, and risk of delayed treatment decisions. Convolutional Neural Networks (CNNs), specifically DenseNet201, have proven to be useful tools for extracting complex visual patterns in medical images. In this research, transfer learning, reuse of features, and a well- designed classification pipeline are utilized to separate benign from malignant samples successfully. The application of artificial intelligence to pathology is not just a means of improving diagnostic correctness but also of broadening access to healthcare through making sound diagnostics available in low-resource environments. By providing a speedy and reproducible second opinion, the model described here is an advance toward real-time, AI-augmented cancer diagnosis that can revolutionize conventional clinical practice.

DOI: 10.61463/ijset.vol.13.issue3.188

Enhancing Multi-Tenant SaaS Billing Systems With Real-Time Automation And Adaptive Financial Intelligence

Authors: Keerthana Shankar, Adarsh P Thomson, Bhargav S, Divyesh Kumar Mahanta, Haripriyaa G B

Abstract:

DOI: http://doi.org/ 10.61463/ijset.vol.13.issue3.189

AI Applications In Healthcare Fraud Detection, Diagnostic Support, And Legal Analytics

Authors: Samarth Nayak, Anurag Sharma, Tanishq Raj, Suvi Yadav, Professor Keerthi Mohan

Abstract: The increasing complexity of the healthcare and legal sectors stems from the growing number of fraudulent insurance claims, diagnostic inaccuracies, and the rising incidence of malpractice litigation. These issues have serious implications for patient trust, financial stability, and legal accountability These challenges demand innovative solutions that go beyond traditional, manual approaches, which are often time-consuming, error-prone, and inefficient at scale. Recent advancements in Artificial Intelligence (AI) have offered promising tools to address these persistent issues. This paper presents a comprehensive review of recent literature focused on the application of Machine Learning (ML), Deep Learning (DL), and Natural Language Processing (NLP) in healthcare and legal contexts. Specifically, ML models like Random Forest and XGBoost have demonstrated effectiveness in detecting abnormal patterns in insurance claims, helping to reduce fraud. Similarly, DL techniques, particularly Convolutional Neural Networks (CNNs), have significantly enhanced diagnostic accuracy in fields such as radiology and pathology. Meanwhile, NLP models like BERT have enabled efficient extraction and interpretation of complex legal language, thus streamlining legal analytics and document processing. Despite these technological advancements, notable gaps still persist in the literature. There is a limited number of interdisciplinary systems that integrate healthcare and legal AI applications cohesively. Additionally, the majority of studies lack real-time implementation capabilities and fail to emphasize critical aspects like explainability, fairness, and compliance with legal and ethical standards. This review not only synthesizes key findings but also critically analyses existing limitations and proposes future directions. These include developing real-time AI systems, enhancing interpretability of black-box models, and fostering collaboration between AI developers, medical professionals, and legal experts. Such advancements could revolutionize the way these high-stakes sectors operate, ensuring better outcomes for both patients and institutions. This paper reviews and synthesizes recent research across these three areas to identify how AI is being applied, where it is succeeding, and where further work is needed. These include building interdisciplinary AI frameworks, deploying explainable and ethically aligned models, and enabling real-time intelligent systems that are both robust and compliant. The convergence of AI with healthcare and legal expertise holds the potential to revolutionize both industries by enhancing decision-making accuracy, reducing administrative burden, and ultimately improving patient and institutional outcomes.

5G TECHNOLOGY AND NETWORKING : CHALLENGES ,APPLICATIONS AND CHARACTERISTICS

Authors: Professor K.M.Jadhav, Ms.Anjali lad, Ms.Sonali Patil, Ms.Tanuja kolekar, Ms.Ritika Pawar, Ms.Priyanka Sabnis, Ms.Pratiksha Shinde, Ms.Pooja Pawar, Ms.Anuja Shendage, Ms.Pradnya Keskar

Abstract: This paper presents the concept of 5G technology. First, we will review the evolution of 5G technology and then find out how the 5G networks works. After the up rise of 4G wireless mobile technology takes place; researchers, mobile operator industries representative, academic institutions have started to look into the advancement (technological) towards 5G communication networks due to some main demands that are meliorated data rates, better capacity, minimized latency and better QoS (Quality of Service). In 5G research is being made on development of World Wide Wireless Web (WWWW), Dynamic Adhoc Wireless Networks (DAWN) and Real Wireless World. The advancement of remote access innovations is going to achieve 5G mobile systems will focus on the improvement of the client stations anywhere the stations. The 5G network technology can be used to support Vehicle-to-Everything (V2X) communications and applications on autonomous vehicles Additionally, 5G introduces additional radio upgrades for new service requirements and IoT enabling technologies. With a primary focus on 5G mobile networks, which are expected to handle the exponential growth in traffic for enabling the Internet of Things, this paper provides a thorough assessment of upcoming and enabling technologies. In order to develop an effective context-aware congestion control mechanism, the difficulties and unexplored research avenues related to the deployment of huge to critical IoT applications are also discussed. Students would be able to feel and operate physical things remotely thanks to the 5G cellular network's exceptional latency and reliability performance. Universities started competing with one another for academic prestige as a result of the expansion of international markets for contemporary technical education and the variety of programs offered to meet the demands of the regional and international

DOI: http://doi.org/

The Psychology Behind Social Media

Authors: Prof.T.S.Patil, Mr.Shubham Jagtap, Ms.Riya Mahadik, Mr.Omkar Lotake, Ms.Riya Gawade, Mr.Harshvardhan Lole, Ms.Samruddhi Kulkarni, Mr.Sahil Chavan, Ms.Pranoti Patil, Ms.Amruta Shinde

Abstract: This research explores the psychological mechanisms behind the rise and spread of social media trends. By examining social influence, emotional contagion, and digital behavior, the paper identifies the roles of conformity, validation, and algorithm-driven engagement. While trends can build communities and promote awareness, they also contribute to digital addiction and mental health challenges. This paper offers insights and psychological strategies to foster healthy social media use.

DOI: http://doi.org/

Assessing The Impact Of Hybrid Energy Storage Systems On Grid Stability And Renewable Energy Integration: A Comprehensive Review

Authors: Emmanuel U. Usen, Olamide O. Olusanya, Titilayo A. Kuku

Abstract: The increasing reliance on renewable energy sources such as wind and solar has introduced variability and intermittency into power grids, challenging grid stability. Hybrid Energy Storage Systems (HESS) offer a promising solution by integrating complementary energy storage technologies. This review assesses the synergistic impact of HESS on grid stability and renewable energy integration, highlighting how combinations of technologies like batteries, supercapacitors, flywheels, and compressed air energy storage (CAES) improve response times, frequency regulation, voltage control, load leveling, and peak shaving—essential for reliable grid operations. The review demonstrates how hybrid systems outperform single-technology solutions by enhancing operational flexibility, energy efficiency, and response to power fluctuations. Case studies from various countries illustrate the practical benefits of HESS in renewable grids, including cost savings, improved reliability, and reduced carbon emissions. Additionally, the paper examines the economic and environmental implications of HESS, offering insights into their role in advancing the global transition to sustainable energy. This review critically evaluates performance metrics, technological advancements, and real-world applications, laying a foundation for future research and development in optimizing HESS for grid resilience and renewable energy use.

Mathematical Foundations And Cryptographic Algorithms In Blockchain Technology

Authors: Prof. P.S.Sutar, Ms.Tanishka Shinde, Mr.Shubham Jare, Ms.Minal Jadhav, Mr.Swanpjeet Bhandare, Ms.Sakshi Lugade, Mr. Vijay Jadhav,, Mr. Subhan Maner,, Mr. Dipak Honshetti, Mr. Parth Amane,

Abstract: – This paper explores the essential fine and cryptographic principles that bolster blockchain technology. It highlights the significance of hash functions, digital autographs, and agreement mechanisms in securing, validating, and operating decentralized systems. Hash functions are examined for their places in maintaining data invariability and verification, while digital autographs are anatomized in relation to authentication and icing responsibility in peer- to- peer networks. The study also evaluates major agreement protocols similar as Proof of Work( PoW) and evidence of Stake( PoS), fastening on their effectiveness in achieving distributed agreement and precluding double- spending. In addition, arising pitfalls particularly from amount computing — are bandied, as they challenge the security of conventional cryptographic styles. The paper assesses current advancements inpost-quantum cryptography and proposes unborn exploration directions involving chassis- grounded, hash- grounded, and multivariate cryptographic schemes. By addressing both being and arising security challenges, this exploration aims to strengthen the adaptability of decentralized networks against evolving pitfalls.

DOI: http://doi.org/10.61463/ijset.vol.13.issue3.193

Impact Of Technology On Agriculture Through Smart Irrigation And Control Systems

Authors: Professor H.S. Bhore, Ms. Vaishnavi Pawar, Ms. Riya Yadav, Ms. Prajakta Chavan, Ms. Shravani Londhe, Ms.Siddhali Kshirsagar, Ms. Komal Mahadik, Ms. Safina Mulani, Mr. Shivdeep Ugalmugale, Mr. Pravin Sonur

Abstract: Agriculture is the backbone of many economies, but traditional farming practices often suffer from inefficiencies such as excessive water usage and unpredictable climate conditions. The integration of technology into agriculture, specifically through smart irrigation and control systems, has opened new avenues for increasing productivity and sustainability. This paper explores the fundamentals, components, advantages, and limitations of smart irrigation systems, emphasizing their role in improving agricultural outcomes while conserving vital resources like water.

DOI: 10.61463/ijset.vol.13.issue3.192

Power Losses In Transformer And Transmission Lines

Authors: Professor P.E.Pawar, Mr.Sumit Patole, Mr.Ramchandra Karennavar, Mr.Pushkar Kulkarni, Mr.viraj Raskar, Mr.Sourabh Bhosale, Mr.Sangram Patil, Mr.Indrajeet Salunkhe, Mr.Rahid Shaikh, Mr.Madgonda Pargond

Abstract: This research comprehensively examines the persistent issue of energy losses in transmission and distribution (T&D) systems, which pose a significant challenge to the efficiency and reliability of electrical power delivery. Losses are broadly divided into technical losses— originating from physical and electrical characteristics of the grid infrastructure—and non- technical losses, which are largely administrative or socio-political in nature. To address these issues, the study introduces a globally adaptable statistical model that predicts loss behaviour based on economic indicators (GDP per capita), socio-political factors (corruption index), geographical characteristics, ambient climate conditions, and the structural organization of the electrical grid. On the technical side, the study evaluates the impact of reactive power on transmission efficiency and explores the effectiveness of capacitor-based compensation methods for loss mitigation. The integration of Phasor Measurement Units (PMUs) and smart metering technologies allows for a more granular and synchronized analysis of power flows, enhancing the real-time detection and classification of loss types, including Joule losses, corona discharge, and insulation leakage. The proposed methodologies offer a holistic and scalable approach to minimizing power losses, improving grid stability, and guiding policy decisions in both developed and developing regions. The findings provide a robust framework for engineers, system operators, and policymakers aiming to modernize electrical infrastructure and promote sustainable energy practices.

DOI: 10.61463/ijset.vol.13.issue3.194

Role Of Data Science In Improving Climate Change Model

Authors: Prof.P.E.Pawarl, Ms. Mansi Mali, Ms.Anushka Salunkhe, Ms. Harshada Shinde, Ms.Sanika Yadav, Ms. Prachi Kharat, Ms. Prachi Davri, Ms.Shardha Gaokar, Ms.Gaytri Chavan, Ms. Kajal Pawar

Abstract: – Data science has gained prominence in various countries as they explore how it can aid in addressing challenges related to climate change. Through data -driven analysis, it is to shape effective policies and interventions that benefit communities affected by climate- related issues. This paper explores the expanding role of data science not only in measuring human-induced climate change but also in guiding impact assessments and strategic actions across sectors sensitive to environmental shifts. As the convergence of artificial intelligence (AI), machine learning (ML), and climate change research continues to evolve, ongoing interdisciplinary collaboration is vital to fully leverage these technologies in protecting our planet. Environmental impact assessment is a key component of climate research, and AI and ML are playing a crucial role in enhancing its accuracy and effectiveness. Given the global magnitude of climate change, precise modeling and forecasting are essential for minimizing its adverse effects.

DOI: http://doi.org/

Green AI: Advancing Environmentally Sustainable Artificial Intelligence

Authors: Professor P.E.Pawar, Ms. Sanjeevani Yadav, Mr. Aditya Jagtap, Ms. Mahi Deshmukh, Ms. Nikita Jankar, Ms. Anuja Nikam, Ms. Shravani Ghadge, Ms. Snehal Mahadik

Abstract: Artificial Intelligence (AI) has become a transformative force across various sectors, yet its environmental impact—particularly from energy consumption and carbon emissions—is increasingly concerning. Training and deploying large-scale models like BERT and GPT require immense computational resources, contributing to significant power use and environmental degradation. In response, Green AI has emerged to promote energy-efficient and environmentally sustainable AI development. This paper explores the core principles, techniques, and applications of Green AI. It highlights the environmental costs of conventional AI, including the carbon footprint of training, data center energy demands, and hardware lifecycle impacts. Green AI promotes efficiency alongside accuracy, transparency in reporting energy and carbon metrics, and lifecycle-based evaluations. Key techniques such as model pruning, quantization, and knowledge distillation are discussed for their role in reducing computational complexity. Efficient architectures like Mobile Net and Tiny ML, and innovations in edge computing and low-power hardware (e.g., TPUs, FPGAs) are examined for their sustainability benefits. Tools and metrics like ML CO2 Impact and performance-per-watt benchmarks support the evaluation of sustainable AI.Real-world applications in smart agriculture, energy management, and urban planning illustrate the practical relevance of Green AI. The paper concludes by addressing ethical and policy considerations, advocating for responsible, low-impact AI as both a technical necessity and a moral imperative.

DOI: 10.61463/ijset.vol.13.issue3.194

MIND OVER MACHINE: ELON MUSKS NEURALINK BRAIN CHIP

Authors: Assistant Professor H.S. Bhore, Ms. Janhvi Samudre, Mr.Shahabaj Pirjade, Ms. Sakshi Gaikwad, Ms.Tanishka Suryavanshi, Ms. Madhura Takale, Ms. Shradha Vaidya, Ms. Vaishnavi Sutar, Ms. Vaishnavi Patil, Mr.Jeeshan sande

Abstract: As the days pass by, we come across new and latest inventions that utilize Artificial Intelligence to enhance our device usage. Neuralink is a neurotechnology venture founded by Elon Musk, focusing on the development of implantable brain-machine interface (BMI) systems. The core device is a coin-sized chip, known as the Link, which contains ultra- thin electrode threads designed to record and stimulate neural activity. These threads are surgically implanted into specific regions of the cerebral cortex using a high-precision surgical robot. The device wirelessly transmits neural signals to an external application, enabling real-time interaction between the brain and digital devices. Neuralink’s primary goal is to assist individuals with neurological disorders, such as paralysis, ALS, or spinal cord injuries, by enabling direct neural control of computers and prosthetics. Long-term objectives include enhancing cognitive function, treating neurodegenerative diseases, and facilitating human-AI symbiosis.

DOI: http://doi.org/ijset.vol.13.issue3.196

Role Of Data Science In Improving Climate Change Model

Authors: Prof.P.E.Pawarl, Ms. Mansi Mali, Ms.Anushka Salunkhe, Ms. Harshada Shinde, Ms.Sanika Yadav, Ms. Prachi Kharat, Ms. Prachi Davri, Ms.Shardha Gaokar, Ms.Gaytri Chavan, Ms. Kajal Pawar

Abstract: – Data science has gained prominence in various countries as they explore how it can aid in addressing challenges related to climate change. Through data -driven analysis, it is to shape effective policies and interventions that benefit communities affected by climate- related issues. This paper explores the expanding role of data science not only in measuring human-induced climate change but also in guiding impact assessments and strategic actions across sectors sensitive to environmental shifts. As the convergence of artificial intelligence (AI), machine learning (ML), and climate change research continues to evolve, ongoing interdisciplinary collaboration is vital to fully leverage these technologies in protecting our planet. Environmental impact assessment is a key component of climate research, and AI and ML are playing a crucial role in enhancing its accuracy and effectiveness. Given the global magnitude of climate change, precise modeling and forecasting are essential for minimizing its adverse effects.

DOI: http://doi.org/

The Shopping Mall Website

Authors: Professor T.S.Patil, Mr. Aryan Ghadge, Mr.Omkar Jadhav, Mr.Samadhan Ghutukde, MR.Vikas Hajare, Mr.Pritam Patil, Mr.Nikhil Kadam, Mr.Nishant Jadhav, Mr.Sandesh Bhosale, Mr,Vinayak Kadam

Abstract: According to the fast-changing of the business environment nowadays, we have to be more effective and fast in responding to customers' needs to make them able to access to our products instantly. This can be done by designing an E-commerce web application for online shopping, which sells variant fashions and goods to the customers either by instant payment or by payment on delivery. Many business houses carry out commercial transactions using websites. This makes the shopping process on the web familiar and makes E-commerce an accepted paradigm. To implement online shopping, a virtual store on the Internet is needed which allows customers to seek for products and select them from a catalog. The customer needs to fill some fields to order a specific product. The purpose of this paper is designing and implementation of online shopping website of clothes. This E-commerce shopping website needs to be designed and developed by studying and understanding the server and client techniques, Adobe Dreamweaver application, relational databases and many programming languages such as HTML, CSS, JAVA, JAVASCRIPT, and PHP.

DOI: http://doi.org/10.61463/ijset.vol.13.issue3.195

Paper On Voice-Controlled Web Application

Authors: Prof. H. S. Bhore, Mr.Soham Kadam, Mr. Abuzar Mulla, Mr. Arman Shikalgar, Mr. Rushikesh More, Ms. Shraddha Sable, Mr. Yash Suryavanshi, Mr. Bharat Shitole, Mr. Parth Shinde, Mr. Ashitosh Nikam

Abstract: – This study presents a voice-enabled web application developed using Alan Studio, the News API, and React, designed to offer users an intuitive and engaging way to access and interact with news content. By leveraging speech recognition and natural language processing (NLP), the system allows users to access news hands-free based on location, source, theme, and interest. The proposed solution enhances accessibility, particularly for visually impaired users, and demonstrates the potential of voice- controlled interfaces in web applications and e-commerce. Utilizing IBM Watson Speech-to-Text and neural network-based speech recognition, the application efficiently processes voice commands to deliver real-time, relevant information. This AI-driven system improves user engagement and convenience by enabling voice-assisted navigation. The study highlights the impact of speech-to-text technology in modern web applications, offering a dynamic, user-friendly, and accessible digital experience. Future applications include government services, healthcare, and automated customer interactions..

DOI: http://doi.org/

Review On Artificial Intelligence Based Framework On Sustainable Agriculture With Blockchain With Inclusion Of IDP

Authors: Arpitha Vasudev, B Nitin Reddy, A Rithesh Reddy, B Jagadeeshwar Reddy, Gagan KN,

Abstract: The agricultural sector is grappling with critical challenges driven by climate instability, dwindling natural resources, and supply chain inefficiencies. This paper presents a novel framework—IDP (Intelligent Document Processing)—that integrates Artificial Intelligence (AI), Internet of Things (IoT), and Blockchain to create a sustainable and intelligent agriculture ecosystem. Leveraging machine learning for crop prediction, IoT sensors for real-time field data, and blockchain for secure, transparent transactions, the proposed system enhances decision-making, ensures data integrity, and builds trust among stakeholders. By addressing issues such as price manipulation, quality fraud, and irrigation inefficiencies, this multi-layered approach advances fair, transparent, and eco-conscious farming practices.

Review Paper On Solar Cell

Authors: Assistant Professor K. M. Jadhav, Ms.Nikita Jadhav, Ms. Rinkal Ravatale, Ms. Saloni Mane, Ms.Twinkal Salunkhe, Ms.Sanskruti Nikam, Ms.Ribafatima Shaikh, Ms.Shrushti Sawant, Ms.Sakshi Jagtap, Ms.Neha Thombare

Abstract: The light from the Sun is a non-vanishing renewable source of energy which is free from enviromental pollution and noise. It can easily compensate the energy drawn from the non-renewable sources of energy such as fossil fuels and petroleum deposits inside the earth. The fabrication of solar cells has passed through a large number of improvement steps from one generation to another. Silicon based solar cells were the first generation solar cells grown on Si wafers, mainly single crystals. Further development to thin films, dye sensitized solar cells and organic solar cells enhanced the cell efficiency. The development is basically hindered by the cost and efficiency. In order to choose the right solar cell for a specific geographic location, we are required to understand fundamental mechanisms and functions of several solar technologies that are widely studied. In this article, we have reviewed a progressive development in the solar cell research from one generation to another, and discussed about their future trends and aspects. The article also tries to emphasize the various practices and methods to promote the benefits of solar energy.

Review Paper On Scoping Of Artificial Intelligence In Health Care Technology

Authors: Professor K.M.Jadhav, Miss. Shreya Ugale, Miss. Rutuja Sagare, Miss. Shweta Sutar, Miss. Parnali Shinde, Miss. Vaishnavi Pawar, Miss. Sanskruti Pol, Miss. Sanika Shinde, Miss. Daneshwari Susalad, Miss. Unmesha Shinde

Abstract: Artificial Intelligence concept is becoming popular in search engines, voice recognition software’s, biometric recognition software’s, automatic vehicles, healthcare electronic device. With the assistance of Artificial Intelligence, clinicians can sort out the relevant information about the management of a disease in order to take right decisions. Moreover, clinicians could consult e-books, websites, e-journals for collecting updated information. As technology is evolving day by day, Medical researchers also use this technology in setting up appropriate modalities and algorithm for several diseases. Artificial Intelligence methods excel at recognizing tumor’s at stages, complex images, exact pathologies behind several disease and tumors. AI also reserved its importance in robotics, by which several complicated surgeries can be performed easily. Here, we also explore the emerging potential of Artificial Intelligence in research, in which, AI can extract the relevant information from huge data towards taking perfect clinical decision in healthcare system. Artificial Intelligence has got its application in several branches of medicine. In this review, we tried to establish a general understanding and scope of Artificial Intelligence in medicine. The application of Artificial Intelligence (A.I.) in healthcare has led to significant advancements and transformative developments in various areas. Medical imaging and diagnostics benefit from A.I.'s ability to analyze and interpret complex imaging data, enabling more accurate and timely diagnoses. Electronic health records (E.H.R.) are streamlined through A.I., facilitating efficient data management and retrieval for better patient care. In robot�assisted surgery, A.I. enhances surgical precision and safety.

DOI: http://doi.org/

Applications Of Matrices In Engineering

Authors: Professor P.S. Sutar, Ms. Anjali Bhosale, Ms.Neha Yadav, Ms.Shraddha Mane, Ms.Samrudhi Chavan, Ms. Kanchan Agalave, Ms. Pratiksha Suryawanshi, Ms.Nikata Jadhav, Mr. Shreyash Patil, Mr. Shubham Pawar

Abstract: Matrices are foundational tools in engineering, enabling efficient solutions to complex problems across multiple domains. This paper explores key applications in electrical circuits, structural analysis, computer graphics, and network theory. In electrical engineering, matrices support circuit analysis using nodal and mesh methods. In structural engineering, they form the basis of the finite element method, facilitating stress and deformation analysis. Computer graphics utilizes matrices for geometric transformations, while network theory employs them for flow and connectivity analysis. Advanced techniques like eigenvalue and singular value decomposition further enhance computational efficiency and system analysis. This paper presents a comprehensive overview of how matrices underpin modern engineering solutions.

A Comprehensive Review On Design And Analysis Of High Current Water Cooled Feed Through For Furnaces

Authors: Professor P.V.R.Ravindra Reddy, D.V.Sai Praneeth Yadav

Abstract: High current electrical feedthroughs are critical components in industrial furnaces operating under vacuum or controlled atmospheres, enabling processes like sintering, melting, crystal growth, and heat treatment. The transmission of thousands of amperes through feedthroughs presents significant challenges in thermal management, electrical efficiency, structural integrity, and sealing. Water cooling has emerged as the dominant solution for managing the intense Joule heating generated. This review synthesizes key literature on the design principles, analytical approaches, material selection, cooling system optimization, failure mechanisms, and performance validation of high-current water-cooled feedthroughs for furnace applications. The analysis highlights the multidisciplinary nature of this engineering challenge, encompassing electromagnetics, thermodynamics, fluid dynamics, structural mechanics, and materials science.

DOI: http://doi.org/

Automated Fabric Density Measurement Via FFT And Intensity Gray Profile Analysis

Authors: Rofidatunnissa, Kusworo Adi, Catur Edi Widodo

Abstract: Woven fabrics are formed by the interlacing of warp (vertical) and weft (horizontal) yarns, with yarn density (threads per inch) being a critical parameter in textile quality assessment. This study aims to develop an automated yarn density measurement system using the Fast Fourier Transform (FFT), which analyzes periodic texture patterns in the frequency domain. The method involves frequency filtering and density estimation based on gray line profile intensity and was tested on 25 images for each weave type. Results show high accuracy for plain (0.96% warp error; 1.14% weft error) and twill (1.02% warp error; 1.57% weft error) weaves. However, satin weave exhibits a significant discrepancy between warp (31.98%) and weft (1.99%) errors, attributed to its unique structural characteristics—high yarn density, overlapping warp threads that obscure the weft, and a glossy surface that causes uneven light reflections, which affect image acquisition. While the method proves effective for most fabrics, accurately measuring warp density in satin remains a challenge. Nonetheless, the proposed approach has potential for industrial application to improve production efficiency in the textile industry.

DOI: http://doi.org/

Quantum Computing

Authors: Professor P.S.Sutar, Mr.Pranav Uthale, Mr.Santosh Shelake, Mr.Prashant Athavale, Mr.Shubham Pawar, Mr.Shubham Pawar, Mr. Shubham Pawar, Mr.Jay Tingre, Mr.Rushikesh Linge, Mr.Aryan tupsaundary

Abstract: Quantum computing is an emerging field that leverages the principles of quantum mechanics to process information in ways that classical computers cannot. By exploiting quantum bits or qubits, quantum computers can represent and manipulate complex data structures more efficiently. This paper presents a comprehensive review of the fundamentals, development, and potential applications of quantum computing. It discusses recent literature in the field, elaborates on the theoretical framework and methodologies, and illustrates the distinct advantages and limitations of quantum computation. The paper concludes with insights into future directions, emphasizing the transformative potential of quantum technologies across various sectors.

DOI: http://doi.org/

Communication Skills, Virtual Sources And Challenges

Authors: Assistant Professor Tai. S. Patil, Mr. Rushikesh P. Kayande, Ms. Vaishnavi S. Bagal, Ms. Bhagyashree V. Lugade, Ms. Purva G. Kamble, Mr Sumit G. Jagtap, Ms. Shreya C. More, Ms Shruti M. Atkari, Ms Nilam S. Gaikwad, Ms Pratiksha A. Mote

Abstract: The increasing reliance on virtual communication necessitates a deeper understanding of the unique challenges and opportunities it presents for communication skills. This paper examines the significance of communication in virtual environments, exploring how digital platforms like online meetings, instant messaging, and project management tools can either enhance or hinder effective interaction. We will analyse the specific challenges posed by virtual communication, including the absence of non-verbal cues, potential for misinterpretations, and the need for clear and concise language. Furthermore, we will explore the opportunities that virtual platforms offer, such as wider reach, asynchronous communication, and access to diverse perspectives. The study will also address the importance of training and development initiatives to improve virtual communication skills, including the use of technology-based learning tools and the development of specific strategies for online interaction. Ultimately, this paper aims to provide insights into the evolution of communication skills in a digital age, emphasizing the need for adaptability and the effective utilization of virtual tools to foster positive and productive relationships, even in the absence of face-to-face interaction.

DOI: http://doi.org/10.61463/ijset.vol.13.issue3.199

Email Spam And Phishing Classifier With Pretrained Language Models

Authors: Prof. Nethra H L, Vedant Sachin Nagare, Tejas Nag NK, Shivam Raj, Mokshit S,

Abstract: – The proliferation of spam and phishing emails poses a significant threat to digital security, necessitating advanced filtering mechanisms to protect users before malicious emails reach their inboxes. This study proposes an email spam and phishing classifier leveraging pretrained language models (PLMs) such as BERT, Roberta, and GPT-4, integrated into a pre-inbox filtering system. By employing transformer-based architectures and hybrid feature engineering, the system achieves high precision in classifying emails as spam, phishing, or ham. The methodology incorporates semantic embeddings, metadata analysis, and concept drift detection to ensure robust performance against evolving threats. Experimental results indicate an accuracy of up to 99.8% on benchmark datasets, with real-time filtering capabilities suitable for integration with email servers. This work highlights the efficacy of PLMs in proactive email security and addresses challenges such as adversarial attacks and multilingual spam detection, offering a scalable solution for modern cybersecurity needs.

Hydrothermal Synthesis Of Titanium Dioxide

Authors: Professor P.E.Pawar, Mr.Pavankumar Jadhav, Mr.Aditya Rasal, Mr.Harshal Dhadas, Mr.Parth Shinde, Mr.Ashitosh Pawar, Mr.Rudra Tonpe, Mr.Omkar sargar, Mr.Parth Patil, Mr.Rohit Didwagh

Abstract: Hydrothermal synthesis has emerged as a versatile and efficient method for preparing titanium dioxide (TiO₂) nanomaterials with tailored properties, including controlled morphology, crystallinity, and phase composition. This technique enables the formation of highly crystalline TiO₂ nanoparticles at relatively low temperatures and pressures, making it an attractive approach for scalable production. The synthesized TiO₂ exhibits remarkable photocatalytic activity, chemical stability, and non-toxicity, which make it suitable for various applications such as environmental remediation, self-cleaning surfaces, and energy conversion devices like dye-sensitized solar cells. Several studies highlight the influence of parameters such as temperature, time, and precursor concentration on the structural and functional properties of TiO₂. The potential to engineer specific anatase or rutile phases further enhances its performance in targeted applications. Overall, hydrothermal synthesis offers a green, cost-effective route to produce high-performance TiO₂ nanostructures, supporting its continued research and application in environmental and energy related technologies.

DOI: http://doi.org/

Paper On Sustainable Development

Authors: Assistant Professor P.S.Sutar, Mr.Saptesh Tatpuje, Mr.Amit Ilageri, Mr.Rajvardhan Pawar, Mr.Akash Nandikol, Mr.Shankar Vankhande, Mr. Harshad Loni, Mr.Raj Lohar, Mr. Shubham Jadhav, Mr. Vinayak Suryawanshi

Abstract: Sustainable development is a multidimensional approach aimed at fostering economic growth, social inclusion, and environmental protection in a balanced and integrated manner. As the global community faces mounting challenges such as climate change, resource depletion, and social inequality, sustainable development offers a framework to ensure that current needs are met without compromising the well-being of future generations. This paper examines the core principles of sustainable development, including its environmental, economic, and social dimensions. It also explores the role of international frameworks like the United Nations Sustainable Development Goals (SDGs), identifies key challenges in implementation, and discusses strategies for achieving sustainability at global, national, and local levels. Through an interdisciplinary lens, the paper highlights the need for collaborative efforts, innovative technologies, and policy reform to achieve long-term sustainability. The findings underscore the urgency of integrating sustainability into all sectors to create a more resilient and equitable future.

DOI: http://doi.org/10.61463/ijset.vol.13.issue3.202

A Novel Approach For Pneumonia Detection Using AI And ML

Authors: Dr. C. Nandini, Assistant Professor Shreenidhi, B. S, Madhu. S, Lohit, Gowda. T, Mokshit. S, Raju

Abstract: Pneumonia remains a significant global health threat, especially in developing nations where access to skilled radiologists and medical equipment is limited. This paper explores traditional and state-of-the-art methods for automated pneumonia detection using artificial intelligence (AI) and machine learning (ML). It includes a comparative survey of classical machine learning algorithms and modern deep learning architectures like CNNs, transfer learning with pretrained models, and hybrid methods. We discuss various medical image datasets, feature extraction methods, model performance metrics, and the ethical issues in deploying AI in healthcare. The proposed approach aims to improve diagnosis speed and accuracy while ensuring transparency and privacy.

DOI: http://doi.org/

Transformation And Trends In The Global Automobile Industry: Innovation, Sustainability, And Future Mobility

Authors: Assistant Professor T.S.Patil, Mr. Pranav Yenpekar, Ms. Shravni Jadhav, Mr. Sahil Kamble, Ms. Geetanjali Nikam, Mr. Shreyas Ghadage, Ms. Pooja Jadhav, Mr. Viraj Panase, Mr Sushant Patil, Mr. Pruthviraj Padage

Abstract: The global automobile industry is undergoing rapid transformation driven by innovation, sustainability goals, and evolving consumer demands. This paper explores major developments shaping the future of mobility, including electric vehicles, autonomous driving, digital connectivity, and sustainable manufacturing. It examines both the positive aspects and challenges, such as digital addiction to automotive technology, and highlights key results and future directions. The aim is to present a holistic view of how the automobile industry is redefining transportation in the 21st century, This research paper explores the revolutionary transformation of the global automobile industry in the 21st century, emphasizing innovation, sustainability, and future mobility. Key areas examined include advancements in electric vehicles (EVs), autonomous driving technology, smart connectivity, and green manufacturing practices. While the industry offers numerous benefits such as cleaner transportation and enhanced safety, it also faces modern challenges like digital dependency and global supply chain vulnerabilities. This paper provides a comprehensive overview of the shifting dynamics, positive impacts, and emerging threats within the sector, offering insights into future trends that will shape mobility across the globe.

DOI: http://doi.org/10.61463/ijset.vol.13.issue3.203

Review Paper On AI And Its Applications

Authors: Professor K.M.Jadhav, Mr.Wasim Shikalgar, Mr.Yash Satarkar, Mr.Abhijeet Patil, Mr.Abhijeet Patil, Mr.Viraj Mohite, Mr.Shreyas Kadam, Mr.Yashwant Bhosale, Mr.Shivam Sutar, Mr.Mayur Ransing

Abstract: Artificial Intelligence (AI) has emerged as a transformative technology with the potential to revolutionize nearly every sector of modern society. This paper explores the core principles of AI, including machine learning, deep learning, and natural language processing, and examines their practical implementations across various domains. We investigate AI applications in healthcare, finance, education, transportation, manufacturing, and cybersecurity, highlighting both the current impact and future possibilities. The paper also discusses the challenges associated with AI adoption, such as ethical concerns, data privacy, algorithmic bias, and the need for regulatory frameworks. By analyzing both technical advancements and societal implications, this study aims to provide a comprehensive overview of how AI is shaping the future of human interaction with technology and decision-making processes. The findings underscore the necessity for interdisciplinary collaboration to ensure the responsible and equitable integration of AI into global systems.

DOI: http://doi.org/

Role Of IOT In Traffic And Waste Management : A Smart City Approach

Authors: Assistant Professor H.S.Bhore, Ms. Akshada Pawar, Ms. Joya Mujawar, Ms. Saloni Mulani, Ms. Nikhita Karpe, Ms. Saniya Attar, Mr. Sujal Lad, Mr. Prathmesh Waghmode, Ms. Nandini Kadam, Ms. Rutuja Bhise

Abstract: The growing population in city areas has resulted in significant difficulties in effectively managing traffic congestion and waste disposal .The internet of thing (IOT)has surfaced as a crucial factor in devloping smarter cities by upgrading traditional infrastructure into smart systems. This document offrers a detailed look at the role of IOT in traffic and waste management. It emphasizes the application of sensors, data analysis, and automated systems for real-time tracking, adaptive control, and improved resource allocation. By integrating IOT technologies, cities can minimize traffic delays, enhance public safety, streamline waste collection, and support environmental sustainability.

DOI: http://doi.org/

Enhancing Operational Efficiency Using Method Study And Time Study Techniques: A Case Study

Authors: Professor P.V.R.Ravindra Reddy, B. Hariram Reddy, B.Krishna Mourya, N.Nishanth

Abstract: This research explores the application of method study and time study techniques to enhance operational efficiency in the manufacturing of compressor shells. Through systematic observation, time measurement, and workflow analysis, the study identifies key inefficiencies in the existing production process.Method study was used to streamline work procedures by eliminating unnecessary motions and improving task sequencing, while time study helped establish standard times and identify delays. Based on these insights, the study introduced targeted interventions, including machine modifications, improved material handling, and resequenced operations.These measures led to a 15% increase in production efficiency for compressor shells, with noticeable reductions in cycle time and better resource utilization. The results validate the effectiveness of industrial engineering tools as drivers of productivity and operational improvement in manufacturing environments.

ARTIFICIAL INTELLIGENCE BASED SKIN DISEASE RECOGNITION SYSTEM

Authors: Professor Jayachandiran. V,, Sweety Swanthika. M, Yash Mallya, Dr, Dr. Jeya Prabha, A

Abstract: Timely medical intervention and effective treatment rely heavily on the early identification of skin diseases. This project introduces a real-time skin disease recognition system utilizing a live camera on a Raspberry Pi, designed to detect and classify three common skin conditions: Actinic Keratosis, Pigmented Benign Keratosis, and Melanoma. A deep learning model, trained on a diverse dataset of skin images, is deployed on the Raspberry Pi to provide efficient and low-cost diagnosis, capturing and processing live skin images to deliver instant classification results, this system provides 96% accuracy and 58%, 12% and 36% sensitivity for each disease respectively, enabling early detection and facilitating prompt medical consultation, and enhancing accessibility to dermatological analysis, particularly in remote areas.

Streaming The World: A Comprehensive Analysis Of Global YouTube Usage And Content Trends

Authors: Mohammed Zaid, Abdul Muqeet, Ethesham Uddin, Prof P Lavanya

Abstract: Professor, Department of Computer Science and Engineering, Methodist College of Engineering and Technology, Hyderabad, InThis research utilizes the Global YouTube Statistics dataset to analyze and visualize the performance and distribution of YouTube content across various countries. By exploring key metrics such as subscriber counts, video views, average likes, comments, and video counts, the study aims to uncover regional trends, identify top-performing content categories, and understand how digital engagement varies globally. Through the use of dimensions like country and channel type, and quantitative measures like viewership and engagement statistics, the research establishes a comprehensive framework for examining the influence and reach of YouTube as a digital platform. Data visualization techniques are employed to enhance the interpretability of patterns and relationships, thereby transforming raw numerical data into actionable insights. The significance of this study lies in its ability to demonstrate the growing impact of digital media consumption in different parts of the world, and its implications on cultural, economic, and social levels. The findings of this research not only provide a clear view of global digital engagement but also serve as a reference point for content creators, marketers, and media strategists seeking to optimize their presence on the platform. Ultimately, this study bridges the gap between data-driven insights and real-world digital influence, offering a holistic perspective on the global YouTube ecosystem.

DOI: http://doi.org/

Comparative Effect of Artisinal Crude Oil Products’ Storage on the Soils of Ijalla and Okere-Urhobo Communities, Warri, Delta State, Nigeria

Authors: Dorcas Ufuoma. Adams, IBE. Kenneth. Abara

Abstract: Illegal (Artisanal) refining of petroleum products in Niger Delta creeks has been a great concern over the years as it has debilitating effects on the environment. This study aimed at determining the effects of artisanal products on the geochemical status of the soil of Ijalla and Okere-Urhobo community. To achieve this, 10 soil samples each were collected from both communities. Back titration was used to determine the TOC in the samples while n-hexane was used to extract the oil and grease from measured samples. Biomarkers of AHCs were detected using GC-FID after the extracts were collected by the soxhlet extraction method using dichloromethane as the solvent for extraction and then fractionated into AHCs, Aromatics Hydrocarbon and NSOs using Column Chromatography. Elution of fractions in the column was achieved using n- hexane, Dichloromethane and chloroform of AHCs, Aromatics and NSOs respectively. Aliphatic Hydrocarbons in both sites shows that more AHCs were present in IJalla sites than Okere-Urhobo. A pristane/phytane ratio that is near to 1 in the context of the okere-urhobo assumed non-petroleum soil indicates that the organic matter contained in the soil is probably of mixed origin while some samples of Ijalla has a CPI close to 1 suggests minimal microbial degradation and a predominantly terrestrial source. The TOC results shows that all soil samples have POC greater than 0.2%, which indicates that both soil has other source carbon. In comparison, Ijalla site is much higher than that of Okere-Urhobo. On analysis of O%G, the samples from Ijalla have an average concentration of oil and grease of 5440mg/kg with soil JA having 7460mg/kg, which far exceeds the DPR, (Department of Pesticides Regulation), California, 2007 states the maximum permissible concentration of oil and grease is 1000mg/kg in an average of 30cm depths.

Personalized Knowledge Extraction And Query Answering Via Semantic Search

Authors: Deepanshu Bhati, Surya Prakash, Surat Singh, Mr. Ibrar Ahmad

Abstract: This paper proposes a novel approach to enhancing personal knowledge management through a webbased system that integrates semantic search and future Retrieval-Augmented Generation (RAG) capabilities. By leveraging large language models (LLMs) and embedding techniques, the system aims to improve the efficiency and accuracy of retrieving relevant documents and links from personal collections. The system addresses the limitations of traditional keyword-based search methods by providing context-aware query understanding and retrieval.

DOI: http://doi.org/

Gesture And Voice Controlled Home Automation

Authors: Divya H N, Ishika Keshri, Kavyashree R, Mandara R, Pushpa PB

Abstract: Home automation systems are gaining more attention due to technological advances. These smart devices and sensors help us collect or capture physical experiences and convert them into informational data. This project aims to develop a system that helps users to interact with appliances using voice and gesture commands to provide a more interactive and user friendly home experience. The Raspberry Pi takes voice commands through a microphone or gesture commands from the camera module and interprets them to manage appliances through the relay, which turns the house on and off based on the user’s request. This system could also be a perfect solution for people with disabilities who want access to home devices. For example, voice commands are useful for the visually impaired, and the deaf can use hand gestures to operate the appliances.

Nexa – A Cloud-Native AI Chatbot For Scalable, Context Aware Conversations

Authors: Deepashree K, Karthik G Sharma, Abhishek, Chandrashekar, Preeti B Hosur

Abstract: – Nexa is a cloud-native, AI-enabled chatbot designed to address the inherent limitations of traditional rule-based conversational systems. By employing Deep Learning (DL) and Natural Language Processing (NLP), it enables intelligent, adaptive, and real-time interactions. Built using Python and frameworks such as Flask, PyTorch, and NLTK, Nexa integrates core AWS services—Amazon Lex, Lambda, Bedrock, API Gateway, and DynamoDB—to support seamless deployment, scalable architecture, and robust security. This paper presents the motivations behind Nexa’s creation, particularly the need for responsive, intelligent virtual assistants in sectors such as education, customer support, and enterprise communication. The system’s design is modular, allowing for easy integration of additional features like multilingual support, biometric authentication, and voice-based interactions. The proposed methodology includes a multi-layered system architecture comprising a web-based frontend, Flask-based backend, NLP-driven intent classifier, and a cloud services layer. Performance metrics indicate high classification accuracy, fast response times, and scalability for thousands of concurrent users. By combining modern machine learning techniques with a fully serverless cloud infrastructure, Nexa demonstrates a forward-thinking approach to building next-generation AI-driven chat interfaces. Its real-time capabilities, flexible design, and potential for future enhancements make it a strong foundation for scalable and context-aware conversational systems.

DOI: http://doi.org/

Skin Cancer Detection Using Deep Learning_855

Authors: Shama S Dessai, Samrudhi Polawar, Sila Rakshita Patro, Swathi Cheralu, Sowmya N

Abstract: Cybersecurity threats continue to rise, posing significant risks to organizations by exploiting human vulnerabilities, particularly among employees. Traditional security awareness programs, relying on slideshows, classroom training, and videos, often fail to sufficiently engage or educate users. This research introduces a gamified cybersecurity awareness program known as the Zero-Day Awareness Program (ZDAP), designed to enhance employees' knowledge and response to cyber threats through interactive learning. This study reinforces the importance of experiential learning in cybersecurity education and encourages organizations to adopt gamified methods for security training.

DOI: http://doi.org/

Epidemiology And Diagnosis Of Mucormycosis: An Update

Authors: Tamanna Tandon, Pranay Jain

Abstract: Mucormycosis is a fatal fungal infection caused by filamentous molds belongs to order Mucorales.. Mucormycosis is observed to be more prominent in immunocompromised patients. In certain developed economies, mucormycosis is seen to be an associated infection with a bad prognosis regulated diabetes mellitus (DM). Mucormycosis or commonly known as black fungus, has a strong tendency of invading blood, causing nacrosis, thrombosis, and tissue infraction. Mucormycosis is found to be predisposed in co-morbidity or in the non-diabetic patients of COVID-19 especially in those who were at high dosage of steroids for a longer period of time or on ventilator support. Additionally poor hygienic conditions or disturbed diabetic management provides favorable conditions for the pathogenic fungal infection. Other reasons which are responsible for mucormycosis can be excess of uncontrolled conventional precautions. One of them is regular steaming, which may cause affliction in the nasal tracts’ beneficial microbiota and virome. Early diagnosis is crucial to initiate the therapeutic interventions necessary for preventing progressive tissue invasion and its devastating sequelae, minimizing the effect of disfiguring corrective surgery and improving outcome and survival. In this review, the black fungus causes and cures of Mucormycosis have been highlighted. It contains cases from Mucormycosis outbreak, the pathogenesis and diagnosis of the respective disease along with its available treatments. This review also suggests some natural treatments for black fungus disease.

PSS_FAC : MLP Based Instrument Recognition By Using Feature Extraction In Polyphonic Music

Authors: Archana Kale, Durga Prasad, Om Badgujar, Sumedh Waghchoure, Nikhil Waghmode

Abstract: As the days pass by, we come across new and latest inventions that utilize Artificial Intelligence to enhance our device usage. Neuralink is a neurotechnology venture founded by Elon Musk, focusing on the development of implantable brain-machine interface (BMI) systems. The core device is a coin-sized chip, known as the Link, which contains ultra- thin electrode threads designed to record and stimulate neural activity. These threads are surgically implanted into specific regions of the cerebral cortex using a high-precision surgical robot. The device wirelessly transmits neural signals to an external application, enabling real-time interaction between the brain and digital devices. Neuralink’s primary goal is to assist individuals with neurological disorders, such as paralysis, ALS, or spinal cord injuries, by enabling direct neural control of computers and prosthetics. Long-term objectives include enhancing cognitive function, treating neurodegenerative diseases, and facilitating human-AI symbiosis.

Self-Charging EV Ecosystem Using Urban Thermoelectric Scavening Surface

Authors: Laxmi N Baraker

Abstract: As the global demand for sustainable transportation grows,electric vehicles(EVs)have emergedas a pivotal solution to reduce greenhouse gas emission and fossil fuel dependence.The rapid rise of electric vehicles(EVs)present an urgent need for innovative,sustainable,and decentralized energy solution. This proposes a novel self-charging EV ecosystem leveraging urban thermoelectric scavenging surfaces(UTSS) to convertambient heat differentials in urban infrastructure into electrical energy. By intrgrating thermoelectric generators(TEGs) into high-exposure urban surface-such as roads,sidewalks,buildingexteriors,and vehicle bodies-this system continuously harvests lowgrade thermal energy from solar radiation, waste heat, and temperatur gradient between materials and the atmosphere.The ecosystem is designed to support a distributed, passive energy generation netework that operates continuously with minimal manitenance.Energy-efficient routing algorithms,AI-based thermal mapping,and realtime demand-supply balancing mechanisms are incorporated to ensure option energy tranfer to EVs.This approach minimizes reliance on tradition grid-based charging stations and enhances urban energy resilience by turning cities into decentralized power sources.

DOI: http://doi.org/

Predictive Analysis And Transparency In Network Traffic

Authors: Assistant Professor Usha C R, Pratibha, Preeti B K, Ranjana N J, Rashmi R H

Abstract: Network traffic analysis is a critical process in cybersecurity and network management that involves monitoring, capturing, and examining data packets transmitted across a network. The primary goal is to understand network behavior, identify anomalies, and detect malicious activities such as intrusions, data breaches, or distributed denial-of-service (DDoS) attacks. Modern traffic analysis employs techniques from machine learning, deep packet inspection, and statistical modeling to uncover hidden patterns and predict threats. By analyzing traffic in real-time or through historical logs, organizations can enhance their network performance, enforce security policies, and ensure compliance with regulatory standards. This paper explores key methodologies, tools, and challenges in network traffic analysis, emphasizing its pivotal role in securing modern digital infrastructures.

Deep Fake Detection Of Videos

Authors: Thejashwini M, Thejaswini K P, Vismitha K, Vivek A M, Shylaja B

Abstract: Deep fake videos—synthetically manipulated visual content created by deep learning techniques—pose an alarming threat issues in sectors involving from media and politics to healthcare. This study aims to develop a robust deep fake detec- tion framework using deep learning methods and algorithms, focusing on generalization across datasets and video types. Our approach is informed by an extensive literature review of recent advancements, including methods using xception, Mobile Net, Mask R-CNN, and affective cue-based models. The review highlights challenges such as dataset diversity, generalization issues, and real-time detection limitations. Our proposed deep learning system will address these challenges by integrating temporal and spatial video analysis, leveraging convolutional and recurrent neural networks to detect subtle manipulative traces.

DOI: http://doi.org/

A Data Driven Game Based Framework For Autism Spectrum Disorder Assessment

Authors: Nirupama K, Vivitha R, Mrs. Babisha A, Mrs. Swagatha J P, Dr. Suma Christal Mary S

Abstract: This project is a Data-Driven Game-Based Framework for the Assessment of Autism Spectrum Disorder proposes the construction of a state-of-the-art framework to measure autism spectrum disorder (ASD) in children by combining machine learning and game-based methods. By incorporating interactive cognitive games, checks and MRI interpretation, this work takes advantage of artificial intelligence in order to enhance the diagnostic accuracy of ASD. At the first stage, information is obtained from different public data, such as behavioural research, neuroimaging repositories and validated ASD diagnostic questionnaires. The data has been gathered after preprocessing so that it is clean and structured in a manner that is ready for future analysis. The framework uses supervised learning algorithms, in particular, deep learning architectures such as VGG16 for the analysis of MRI scans and to discover ASD-related neurological features. Whereas game-based tests (and quizzes) are utilized to assess cognitive abilities, social skills and behavioural patterns. The models are also compared based on their quality (e.g. accuracy, precision, recall) to ensure that they have reliability. This study utilizes diverse data sources, such as facial expressions, cognitive responses, and neuroimaging scans, to improve the accuracy of assessments. The developed system demonstrates strong generalization to unseen data, making it suitable for real-world Autism Spectrum Disorder (ASD) diagnosis. These tools enhance accessibility, enabling early detection and timely intervention. Machine learning aids in analyzing behavioral patterns, improving diagnostic accuracy. Clinicians and caregivers can leverage AI-driven insights to support informed decision-making. Overall, this approach holds promise for advancing ASD assessment and intervention strategies.

Title: Quality Assessment Using Large Language Models and Prompt Engineering

Authors: Mukesh Kumar A, Gokulnath G, Mrs. A. Jeyanthi

Abstract: We present a novel and intelligent framework that integrates web scraping, domain-specific sentiment classification, and Large Language Models (LLMs) to evaluate product quality based on customer reviews in the e-commerce domain. The proposed system is designed to be fully automated and scalable, capable of extracting and analyzing user-generated content from popular platforms such as Flipkart. Using customized web scraping modules, the system collects real-time reviews and filters them through a preprocessing pipeline. These reviews are then contextually categorized into predefined product domains such as durability, comfort, performance, and usability, thereby enabling a more granular understanding of customer sentiment. For sentiment classification, we employ DeepSeek-R1, a state-of-the-art open-source LLM hosted and accelerated on Groq— a high-speed cloud infrastructure optimized for inference workloads. This allows for efficient and context-aware sentiment analysis that outperforms traditional approaches in terms of speed, scalability, and accuracy. The sentiment outcomes are further transformed into structured data representations, which are visualized through interactive dashboards built using PyQt5 and Matplotlib. These dashboards support real-time filtering, trend analysis, and comparison across products, offering stakeholders actionable insights into customer satisfaction. The system was empirically evaluated on a manually annotated dataset comprising 50 reviews across three product categories. The classification pipeline achieved an accuracy of 98%, precision of 0.99, recall of 0.97, and an F1 score of 0.98. Additionally, a Cohen’s Kappa score of 0.953 was recorded, indicating near-perfect agreement with human annotations. These results demonstrate the robustness and reliability of our LLM-based approach in real-world e-commerce scenarios, setting a new benchmark for intelligent review analysis systems.

DOI: http://doi.org/

Twist Angle Effect On Convective Heat Transfer In A Different Pin Fin Cross-section

Authors: Assistant Professor Ch.Indira Priyadarsini, V Sairam

Abstract: The objective of this study is to evaluate the thermal performance of various enhanced fin configurations under steady-state forced convection, using air as the working fluid. Numerical simulations were performed for Reynolds numbers up to 19,945.4, with a constant heat flux boundary condition applied at the heat sink base. The fin geometries analyzed include conventional cylindrical fins, straight HPPF (high-performance pin fins), 30° twisted HPPF. The simulations were carried out in ANSYS Fluent using the realizable k–ε turbulence model. Mesh independence and model validation were confirmed using benchmark data from the literature. Temperature contour analysis indicated that cylindrical fins exhibited limited thermal gradients and retained higher base temperatures due to insufficient airflow disruption and surface interaction. In contrast, the straight and twisted HPPFs demonstrated superior heat dissipation, with the 55° twisted perforated HPPF showing the highest thermal performance by maintaining the lowest base and tip temperatures.

DOI: http://doi.org/ijset.vol.13.issue3.204

Age-Hardening Of Aluminium Alloys: A Review

Authors: Professor P.V.R.Ravindra Reddy, Bodanapu Sharon Geetha, Chimmula Sreeja Reddy, Bethu Gowrishankar sai sri

Abstract: Age-hardening, or precipitation hardening, is a critical heat treatment process that enhances the mechanical properties of aluminum alloys by forming finely dispersed precipitates within the matrix. This review comprehensively examines the mechanisms, kinetics, and influencing factors of age-hardening in aluminium alloys, with a focus on the role of alloying elements, aging temperatures, and time. Recent advancements in characterization techniques, computational modelling, and novel aging processes are also discussed. The review consolidates findings from over 50 studies to provide a detailed understanding of precipitation sequences, hardening effects, and industrial applications.

DOI: http://doi.org/ijset.vol.13.issue3.205

ADVANCED TRAIN ACCIDENT AVOIDANCE SYSTEM

Authors: Professor Sushant Pawar, Shubham n. Sonawane, Dr.Bhagwat kakde, Arjun Gudade, Jay Dhande

Abstract: Railways are the backbone of inter-city connectivity for goods and people. Train accidents are a major concern, leading to loss of life, infrastructure damage, and economic losses. It is widely believed that These accidents are due to human errors, signal failures, over speeding, track switching issue. With traditional railway safety systems like manual signalling or standard track monitoring failing the test due to delays in action or unrefined systems, the need of the hour is a remedy like AITS. We propose to avert such challenges with an Advanced Train Accident Avoidance System using LoRa communication with RFID based track monitoring along with STM32 microcontroller processing which will lead to enhanced safety in railways. Such a system allows the detection of hazards in real-time as well as automated accident aversion systems which require minimal input from the driver. The RFID module tracks the train location and the speed sensor constantly checks for velocity to avoid over-speeding. Implementing a LoRa module – a wireless data transmission system – helps the train send alerts to the central control station. If there is a potential collision or track mismatch, the STM-32 microcontroller initiates an emergency braking mechanism, ensuring immediate response to avoid accidents.

DOI: http://doi.org/10.5281/zenodo.15681608

Enhancing Biodiesel Yields From Animal Fat Waste For Sustainable Energy Solutions

Authors: Ndubuisi, Elizabeth Chinyerem, Achadu, Michael A., Odunna, Evans Chibuzor

Abstract: This study focuses on the production and optimization of biodiesel derived from waste animal fat, specifically cow fat (beef tallow). The tallow was processed using a dry rendering method to extract oil, which served as the feedstock for transesterification. To optimize biodiesel yield, various alcohol-to-oil molar ratios (5:1, 6:1, and 9:1) were investigated. The physicochemical properties of the rendered oil were assessed to evaluate its suitability for biodiesel production. The final biodiesel product was characterized and benchmarked against the European Standard Specification for unblended biodiesel (EN14214, B100), with results indicating compliance across key parameters. Gas Chromatography analysis revealed that the biodiesel primarily consisted of saturated fatty acid methyl esters, including pentadecanoic acid 14-methyl ester, methyl stearate, methyl tetradecanoate, methyl 13-methyl tetradecanoate, and 9-octadecanoate methyl ester. The prevalence of these saturated compounds significantly influenced the fuel's properties, notably increasing its cloud and pour points. Among the tested conditions, a molar ratio of 9:1 yielded the highest biodiesel output. Overall, the study demonstrates that beef tallow is a viable and compliant feedstock for biodiesel production when processed under optimized conditions.

EFFECTS OF ARTIFICIAL INTELLIGENCE PACKAGE ON MECHANICAL TECHNOLOGY EDUCATION STUDENTS’ ACADEMIC PERFORMANCE IN MILLING MACHINE OPERATION IN TERTIARY INTITUTIONS IN RIVERS STATE, NIGERIA

Authors: Prince Maduabuchukwu, Clinton Nwachukwu, Ozioma

Abstract: – This study investigated the effects of artificial intelligence package on mechanical technology education students’ academic performance in Milling Machine operation in Tertiary Institutions in Rivers State, Nigeria. The aspect of Artificial intelligence investigated were perceived course mastery, research complexity and functionality. Three research questions were raised and answered, while three null hypotheses formulated and tested at 0.05 level of significance. A quasi-experimental design guided the study. The population of the study was 188 year 1-IV mechanical technology education students from three tertiary institutions in Rivers State that offers mechanical technology education programmes, year three with a population of 72 was sampled. The researcher collected data for the study using teacher made test. The instrument was validated by two lecturers in the department of Industrial Technology Education (mechanical technology option). The reliability of the instrument was established using test, re-test method. The data achieved were analyzed with PPMC. The coefficient achieved was .84. Analysis of Covariance (ANCOVA) statistics were used to test the hypotheses at .05 levels of significance. Based on the findings of the study, it was concluded that experimental group taught with artificial intelligence package performed better than the control group taught with white-board. It was recommended that government should train lecturers on how to develop and use AI packages in addition to white-board (conventional) teaching method since it has proven to be effective.

DOI: http://doi.org/10.61463/ijset.vol.13.issue3.207

System Core And Mesh Analyser

Authors: Hariharan R,, Mrs V.Roopa,, Anbarasu P, Eraiamudhan VD.

Abstract: The Python programs serve as comprehensive system diagnostic and security auditing tools. The first script utilizes the psutil and platform libraries to collect and display detailed system information, including operating system details, CPU specifications, memory usage, storage data, and boot time. It ensures the required psutil package is installed dynamically, making the script portable and robust. This tool is useful for monitoring resource usage and understanding system performance in real time.The second script focuses on basic security auditing and vulnerability scanning. It checks for the presence of known vulnerable packages (e.g., older versions of gitpython), scans the filesystem for common misconfigurations such as hardcoded secrets and insecure file permissions, and categorizes the associated risk levels. Additionally, it ensures that the pkg_resources module is available by managing the installation or upgrade of setuptools if needed.

DOI: http://doi.org/10.61463/ijset.vol.13.issue3.208

A DEEP DRIVE INTO QUANTUM COMPUTING: PRINCIPLES, POTENTIAL AND CHALLENGES

Authors: Simi Singh, Chhaya kumari

Abstract: Unlike classical computers that operate using binary logic, quantum computers process information in qubits, enabling them to perform complex calculations at unprecedented speeds. Quantum computing represents a transformative leap in computational paradigms by leveraging the principles of quantum mechanics – superpositions, entanglement, and quantum interference. This paper explores the foundational concepts and technological advancements shaping the fields, including quantum gates, quantum circuits, and quantum algorithms such as Shor’s and Grover’s It also addresses the current challenges in Scalability error correction, and decoherence, as well as the promising applications in cryptography, optimization, and material science. The main theoretical concepts and principles of quantum mechanics that are needed to grasp the basic ideas, models and theoretical method of quantum computing are simple elegant and powerful.

DOI: http://doi.org/

A DEEP DRIVE INTO QUANTUM COMPUTING: PRINCIPLES, POTENTIAL AND CHALLENGES

Authors: Chhaya kumari, Simi singh

Abstract: Unlike classical computers that operate using binary logic, quantum computers process information in qubits, enabling them to perform complex calculations at unprecedented speeds. Quantum computing represents a transformative leap in computational paradigms by leveraging the principles of quantum mechanics – superpositions, entanglement, and quantum interference. This paper explores the foundational concepts and technological advancements shaping the fields, including quantum gates, quantum circuits, and quantum algorithms such as Shor’s and Grover’s It also addresses the current challenges in Scalability error correction, and decoherence, as well as the promising applications in cryptography, optimization, and material science. The main theoretical concepts and principles of quantum mechanics that are needed to grasp the basic ideas, models and theoretical method of quantum computing are simple elegant and powerful.

DOI: http://doi.org/

Thermal Effect On Vibration Of Non- Homogeneous Orthotropic Trapezoidal Plate With Thickness Varies Parabolically In Both Directions

Authors: Amit Sharma, Pragati Sharma

Abstract: The objective of present paper is to study the thermal effect on vibration of non- homogeneous orthotropic trapezoidal plate with thickness varies parabolically in both directions. For non-homogeneity of the plate density is assumed linearly in x-direction.Using Rayleigh-Ritz method governing differential equation has been attained by taking two term deflection function corresponding to clamped-simply supported clamped-simply supported (C-S-C-S) boundary conditions. The effects of structural parameters such as taper constant, non-homogeneity constant, aspect ratio and thermal gradient have also been studied.Results are calculated with great accuracy and compare the present model with the other in literature with the help of tables

DOI: 10.61463/ijset.vol.13.issue3.213

An Analytical Study Of Photography In Forensic Science

Authors: Baby Rashmi Rani

Abstract: Crime Scene Photography is basically based on Photograghy. The word photograph has derived from the Greek word ‘Photo’ which means light and ‘Graphia’ which means writing. It is an art , science and practice of creating images through the recording of light-sensitive material. Crime scene photography is an part of forensic photography. Forensic photography is the field that deals with the use of various photography techniques that helps to find various important evidences on the scene of crime for further documentation. Items of trace evidences are photographed in situ, commonly with rulers or other objects to scale the size , distance from corpus. Photomicrography involves taking pictures of the evidences which are not visible from naked eyes like blood, other body fluids, bullet markings, etc.

Towards A Substantially Autonomous Robot As A Personal Assistant: An Overview

Authors: Akshaya E, Dharshini S1, Pratheeba P, Chandru M

Abstract: An autonomous robot is a of machine equipped with sensors, actuators and processors, that empower it to detect its surroundings, handle information, and undertake tasks without human involvement. These robots are a demonstration of AI technology, as they depend on machine learning and deep learning algorithms to carry out tasks. Personal assistant robots are robots formulated to guide individuals with assorted tasks and activities. From automating household activities and repetitive tasks, robots rise above their traditional roles, by serving as companions and support systems. In essence, Autonomous robots beckon us forward to be used as personal assistant due to their potential to solve daily tasks and enhance productivity with human capabilities. These robots aim to strengthen the quality of life by automating regular duties and delivering personalized guidance. By closing the divide between humans and machines, this advancement opens up a world of eternal avenues.

DOI: http://doi.org/10.61463/ijset.vol.13.issue3.210

CRDT-based Distributed Rate Limiter

Authors: Souvik Sarkar, Professor Sanchita Ghosh

Abstract: In contemporary large-scale distributed systems, the challenge of handling user request rates across multiple servers without centralized bottlenecks is a core problem. This project introduces the design and implementation of a scalable, decentralized, distributed rate limiter based on the Token Bucket algorithm and CRDT (Conflict-Free Replicated Data Types) principles to provide eventual consistency between nodes. The system uniquely identifies users, applies configurable rate limits, and synchronizes token states across multiple instances of the server without depending on a central database or coordinator. Kafka, in KRaft (Kafka Raft Metadata mode) mode, serves as the decentralized message bus for state propagation between services with low synchronization latency while handling millions of concurrent users. To provide high availability and fault tolerance, several instances of the rate limiter service are run behind an NGINX load balancer on Docker containers, supporting dynamic scaling and automatic traffic routing. The architecture supports temporary divergence in token values, but CRDT merging guarantees that the system automatically corrects itself without over-permitting requests above the specified rate limits. A stress testing suite is also implemented to ensure the system's performance under high concurrency conditions. This project efficiently showcases the achievement of decentralized rate limiting at scale with eventual consistency guarantees through contemporary concepts in distributed systems, containerization, and message-driven architecture and hence making it fit for deployment in real-world scenarios such as API rate limiting, distributed authentication throttling, and multi-region request control systems

DOI: http://doi.org/

Real-Time IoT-Based Driver Monitoring System For Health And Drowsiness Detection Using Eye Tracking And Pulse Sensors.

Authors: Professor Harshitha B.K, Navyashree.R, Ruhinaaz, Sadhana T.R

Abstract: Driver fatigue & health issues are major causes of traffic accidents. This research introduces a real-time Internet of Things (IoT) framework that checks driver alertness by looking at eye movements & monitoring heart rates. The system sends early warnings when it spots signs of tiredness or unusual heart rates. If needed, it can also take over vehicle controls. Moreover, it performs reliably in low-light conditions thanks to infrared night vision, which boosts road safety.

DOI: http://doi.org/ijset.vol.13.issue3.211

Survey on Maritime Navigation Using AI

Authors: Shalini S, Nandini C, K.Mahesh Babu, M.Uday Kiran, R.Siva Karthik Reddy, K.Lakshmi Narayana

Abstract: With the increasing complexity of global maritime logistics and the rising impact of unpredictable oceanic weather, there is a critical need for intelligent systems that support safer and more efficient sea navigation. This research addresses that need by offering a data-driven solution that integrates real-time marine conditions into voyage planning. By focusing on ocean-only navigation paths and incorporating dynamic environmental awareness, it helps identify hazardous zones and enables proactive decision-making to avoid risks such as severe weather or navigational obstacles. The system promotes operational safety, reduces fuel consumption by optimizing routes, and enhances overall voyage reliability. It also improves accessibility through interactive visual tools, making it valuable not only for shipping companies but also for port authorities, academic researchers, and disaster management agencies. The research lays the groundwork for future advancements in smart maritime technologies, ensuring that sea travel evolves with greater intelligence, adaptability, and sustainability.

A Comprehensive Review On The Socio-Economic And Environmental Dynamics Of Global Shrimp Aquaculture

Authors: Karuna Bamel

Abstract: Shrimp is a globally popular seafood, and its cultivation significantly contributes to the sustainability and socioeconomic well-being of shrimp farming communities. However, infectious diseases pose a major challenge to shrimp aquaculture worldwide. This review examines the status of shrimp aquaculture, particularly in India, where an estimated 11.91 lakh hectares across 10 states and union territories are suitable for cultivation, though only about 1.6 lakh hectares are currently utilized. Notably, Andhra Pradesh and West Bengal lead in shrimp cultivation land. The article also delves into the socio-economic aspects of shrimp farming, including living standards, occupational status, and women's participation in fisheries. It highlights the shift from agriculture to shrimp farming, often driven by economic benefits and increasing salinity. While shrimp farming has significantly increased farmers' incomes and purchasing power, it has also led to a reduction in livestock raising and tree production. Furthermore, the review discusses the adoption of improved aquaculture practices and the critical role of water quality parameters such as salinity, temperature, pH, dissolved oxygen, alkalinity, hardness, carbon dioxide, ammonia, nitrite, and nitrate for optimal shrimp growth and health.

“QuantumKnight”: AI-Powered Chess On Blockchain

Authors: Professor Bhasker Rao, Mohammad Haseeb Mir

Abstract: Abstract – Traditional online chess platforms do not provide financial rewards, personalized training, or secure transactions. Blockchain-based games attempt to include decentralized finance, but they often overlook how artificial intelligence can help with skill development and strategy during matches. Players now want platforms that offer smart coaching, secure transactions, and the ability to use assets across different games. QuantumKnight is a new chess ecosystem that addresses these needs by combining artificial intelligence with blockchain technology. It creates a clear, decentralized, and rewarding space where players can improve their skills with AI-driven training, earn rewards through competitive play, and manage digital assets across the gaming world. By ensuring secure economic interactions and offering smart gameplay support, QuantumKnight transforms the online chess experience for today’s digital players. Keywords-Online Chess,Artificial Intelligence,Blockchain Technology,Decentralized Gaming,AI Coaching

DOI: http://doi.org/

Microstructure Analysis Of Heat Affected Zone During MIG Welding Using Mild Steel

Authors: Md Zaid Javed, Er. Shara Khursheed, Dr. Mohd Faizan Hasa

Abstract: Metal Inert Gas (MIG) welding is extensively employed for joining mild steel due to its efficiency and adaptability. However, the thermal cycles inherent in the welding process induce significant microstructural transformations, particularly within the Heat Affected Zone (HAZ), which can influence the mechanical properties of the welded joint. This study delves into the microstructural variations within the HAZ of MIG-welded mild steel, examining how different welding parameters affect grain morphology and phase distribution. Through metallographic analysis, the research identifies distinct sub-zones within the HAZ and correlates these findings with mechanical property alterations, offering insights for optimizing welding practices.

A Review on Virtual Try-On Systems Using Augmented Reality and Artificial Intelligence in Fashion Retail

Authors: Assistant Professor Kavyashree L, Kartik Sharma, Rishav Raj, Mahaling C Sarapali, P Midhu Chandana

Abstract: Online fashion retail faces persistent challenges in product fit and personalization. The Smart AR Wardrobe concept aims to enhance virtual shopping by integrating Augmented Reality (AR) with Artificial Intelligence (AI) to simulate clothing try-ons, offer accurate sizing, and suggest personalized styles. This paper surveys the underlying technologies enabling such systems, categorizing them by AR rendering, AI-based body modeling, and fashion recommendation engines. Current systems show promise but technical constraints, user privacy, and lack of data standardization remain barriers. This survey consolidates the latest research trends, identifies gaps, and outlines future directions toward more immersive and scalable AR wardrobe experiences.

Diseases In Shrimp Aquaculture

Authors: Karuna Bamel

Abstract: Shrimp aquaculture in India is a major economic activity, with significant production and export values in 2019-2020. However, the industry is severely impacted by various diseases, including microbial infections and viral outbreaks. Key emerging diseases include White Faecal Syndrome (WFS), often linked to the microsporidian parasite Enterocytozoon hepatopenaei (EHP), and increased occurrences of White Spot Syndrome Virus (WSSV) and Infectious Hypodermal and Haematopoietic Necrosis Virus (IHHNV). Vibrio species are prominent bacterial pathogens causing substantial economic losses, with transmission occurring through water, mucus surfaces, and the digestive tract. Other bacterial genera such as Erythrobacteraceae, Alteromonas, and Shewanella have also been isolated from shrimp ponds. Shewanella algae is of particular concern, with little information on its biochemical profiles and infection in aquaculture. Disease prevalence is influenced by water quality parameters like temperature, salinity, and pH, as well as factors such as increased stocking density and culture intensity. Histopathological studies show that pathogenic bacteria, such as Vibrio parahaemolyticus, rapidly target the hepatopancreas and intestinal epithelial cells of shrimp. Management strategies include the use of probiotics like Bacillus and lactic acid bacteria, which offer competitive exclusion, antiviral effects, and immune enhancement. Herbal extracts, such as those from Allium sativum and Thymus vulgaris, also demonstrate strong antibacterial properties. Other approaches include vaccination, bacteriophages, and immunostimulants. The presence of pathogenic bacteria in shrimp also poses a risk of seafood-borne illnesses to humans, underscoring the importance of proper refrigeration and monitoring from harvest to consumption.

Diagnosis Of Diabetic Retinopathy And Glaucoma From Retinal Images Using Deep Convolutional Neural Network

Authors: Usha C R, Kartik Narayan Bhat, Naveen Bhavi, Nikhil Patil, S.M Abhishek

Abstract: The rise in global cases of vision-threatening conditions such as Diabetic Retinopathy (DR) and Glaucoma underscores the urgent need for early and accurate diagnosis. However, traditional diagnostic approaches are often limited by accessibility, cost, and the requirement for specialized personnel. This research presents a novel, deep learning-based framework that utilizes retinal fundus images to simultaneously detect Diabetic Retinopathy and Glaucoma. Leveraging the power of Deep Convolutional Neural Networks (DCNN), the system analyzes ocular features and pathological markers with high precision, enabling efficient, non-invasive screening. The proposed model incorporates advanced image processing techniques and pre-trained CNN architectures to extract deep visual features from fundus photographs. It is trained and validated on publicly available retinal image datasets, ensuring robustness and generalizability. Furthermore, the system includes a smart triage mechanism that classifies patient severity levels to facilitate timely medical intervention. By integrating Artificial Intelligence, Machine Learning, and Computer Vision, this work aims to improve early detection rates, reduce diagnostic latency, and support overburdened healthcare systems, particularly in resource-constrained regions. The dual-disease focus enhances its clinical utility and scalability, making it a promising solution in the evolving field of AI-assisted ophthalmology.

Decentralized Rate Limiter

Authors: Souvik Sarkar

Abstract: As distributed systems operate at increasingly high throughput, enforcing fair and efficient request control becomes critical to safeguard service reliability and prevent misuse. This work introduces a decentralized rate limiting mechanism engineered for scalability and resilience without central oversight. The solution integrates Conflict-free Replicated Data Types (CRDTs) for consistent state sharing and employs the libp2p gossip protocol to synchronize nodes through peer-to-peer communication. Each peer manages an LRU-based in-memory cache for frequent users and offloads less active records to disk, balancing performance with persistence. Performance evaluations indicate that each node can independently process up to 3,000 requests per second, maintaining a 99th percentile latency below 2 milliseconds. CRDT-based synchronization across peers shows convergence latencies around 2 ms with compact gossip payloads averaging 3 KB. These findings validate the feasibility of a decentralized architecture for rate limiting, offering a robust alternative to traditional centralized techniques in modern cloud-native systems.

PixelProof : Uncovering The Truth In Images

Authors: Prof. Rinku Badgujar, Nagesh Kannure, Shriyog Borse

Abstract:

DOI: http://doi.org/

Smart Traffic Light Control System with Ambulance Detection

Authors: G.Sai Sreekanth, K.Hem Kumar, K.Subramanyam, L.Sharat Chandra, C.Darshan

Abstract: Traffic congestion is a major challenge in urban areas, leading to delays, fuel wastage, and increased pollution. This paper presents a Real-Time Traffic Control System using IoT nodes based on traffic density information to optimize signal timing and improve traffic flow. The system utilizes ultrasonic sensors (HC-SR04) to measure real-time traffic density and dynamically adjust traffic signals accordingly. An Arduino Mega acts as the central processing unit, while a NodeMCU is used to integrate the system with Adafruit IoT for remote monitoring and control. Additionally, an RFID-based vehicle authentication system using RC522 RFID cards and an RFID reader is implemented to prioritize emergency or authorized vehicles. The system also includes an LCD display to show real-time traffic information and LED indicators (red, yellow, and green) the proposed system enhances traffic management, reduces congestion, and improves overall urban mobility.

Artificial Intelligence And The Metaverse: Present And Future Aspects

Authors: Sahjad Ahamad, Sagar Chaudhary

Abstract: The two most vital twenty first century technologies are Artificial Intelligence (AI) and the metaverse. Both can enrich people’s lives in many ways, advance many industries, and improve the efficiency of numerous working processes. The metaverse encompasses all these realities; virtual, augmented, and physical. Though the term "metaverse" has been around a while, it remains an emerging technology, even as it becomes one of the hottest topics of conversation in an ever-growing presence. It will not be long until the metaverse will be a place to work, learn, shop, be entertained, and connect to others in ways we never believed could be done. Most metaverse experiences will not be possible without machine learning, which is a branch of AI that allows software applications to make more accurate predictions with outcomes, but not explicitly programmed charges. AI and data science will continue to lead this convergence of technology to revolutionize how people connect and interact globally. will yield innovations, revenue streams, and deeper connections. AI and the metaverse can be utilized in a number of industries including healthcare, gaming, management, marketing, education, and so forth. Generally, these technologies are examined independently without consideration of how each influence one another and are possible collaborators. This paper examines how artificial intelligence and metaverse work together, and their application in a number of industry, as well as their influence. Before the role AI plays in the metaverse can be described the concept of the metaverse should be described, where and how it is applied, and its possibilities.

DOI: http://doi.org/

Role Of School Social Worker In Psychosocial Issues With Children And Adolescents

Authors: Renu Chahal, Assistant Professor Deepali Mathur

Abstract: Aggression, low self-esteem, anxiety, depression, and anger are typical in school-age children and adolescents. These issues are a sign of mental health disorders and psychosocial problems. With 34.8% of the population between the ages of 0 and 19 in 2021, India has the highest percentage of children and adolescents globally. A WHO report states that 10–20% of all children suffer from one or more mental or behavioral issues. Parents spend their wealth beyond their means to support their children's education and demands because they want them to do well in school. However, parents lack the time to deal with hidden problems in their children's lives that affect their behavior, health, social skills, and academic performance. In raising children, all parties involved—parents, educators, and the community—are accountable, but the hidden problems that affect well-being and academic performance are not addressed. If proper care and attention are not provided during this transitional period, adolescents are at risk of developing several psychosocial issues that will affect their cognitive abilities, academic stress, problem-solving skills, depression symptoms, and general mental health for a long time. Programs for school-based support are crucial for recognizing and resolving these issues early. School social workers possess both theoretical understanding and practical experience at the levels of individuals, families, schools, and communities. This study explains the psychological problems and how they affect the lives of students. The results show that a person's emotional, behavioral, cognitive, and social aspects are influenced by a variety of socioeconomic, environmental, technological, social, and relationship factors. Thus, school social workers can provide coping mechanisms in a variety of ways.

DOI: https://doi.org/10.5281/zenodo.15726322

A Comprehensive Study of Chuck Palahniuk’s Fight Club: Analyzing Themes, Characters, and Cultural Impact

Authors: Mr. Rohan Chouhan, Dr.Mrs Vibha Singh Thakur, Hitesh Yadav, Mr Avinash Kujur

Abstract: This research paper provides an in-depth analysis of Chuck Palahniuk’s seminal novel Fight Club (1996). Through a multi-faceted exploration of its intricate themes, complex characters, distinctive narrative style, potent symbolism, and profound cultural impact, this paper demonstrates how the book serves as a penetrating reflection of contemporary society's pervasive anxieties regarding masculinity, consumerism, and the precarious nature of identity in a postmodern world. Drawing upon literary theory and sociological perspectives, the study examines the novel's biting critique of late-capitalist values and its unflinching portrayal of existential ennui. Furthermore, it scrutinizes the unique narrative architecture, the deliberate use of recurring symbolic motifs, and the profound psychological elements that collectively establish Fight Club as a trenchant and enduring commentary on the discontents of modern life. The paper additionally explores the transformative influence of the novel’s highly acclaimed adaptation into a film in 1999, analyzing how this cinematic translation both amplified its mainstream visibility and reshaped its legacy and reception. Finally, the research rigorously discusses the significant implications and enduring relevance of Fight Club within the broader context of postmodern literature, positing its status as a quintessential text that embodies the characteristics, concerns, and experimental spirit of the era.

EFFECT OF TOOL GEOMETRY AND WELDING PROCESS PARAMTERS ON THE PROPERTIES OF FRICTION STIR WELDED 1100 ALUMINIUM ALLOY

Authors: Navdeep Kumar, Tanvir Singh

Abstract: This research work deals with studying the effect of tool shoulder geometry and welding parameters on the mechanical and metallurgical properties of friction stir welded joints of AA1100 aluminum alloy. Two distinct shoulder geometries—the flat shoulder and the concentric circle shoulder—have been employed in friction stir welding tools. The surface temperature of the nugget zone for every sample was measured using FLIR A320 infrared camera. To assess the material behavior in various welding conditions, the Vicker's microhardness Test and tensile test was conducted on the cross-sections of welded joints. Results revealed that the concentric circle shoulder tool yields a greater ultimate tensile stress (101.76 MPa) than the flat shoulder tool (92.71 MPa). While the flat shoulder tool produces the maximum yield stress (85.26 MPa) under all sets of welding conditions. Optimal welding and rotating speeds for a concentric circle shoulder tool are 31.5 mm/min and 900 rpm providing the highest ultimate tensile stress and elongation percentage. More ductility is indicated by a high percentage of elongation (19.87) provided by a concentrated circle shoulder tool. In addition, the temperature differential is greater at low rpm (the tool's rotational speed), and it decreases as rotational speed rises for both tools. Such that the welded joint's surface temperature rises and falls with increasing and decreasing rotating and welding speeds, respectively. For every set of welding conditions, a concentric circle shoulder tool generates more heat. Good surface appearance and morphology were obtained when using the concentric circle shoulder tool.

DOI: http://doi.org/

Analysis Of Various Types Of Welding Defect Occured During The Welding Process Of Mild Steel Using TIG Process

Authors: Rafi Ahmad Beg, Dr. Mohd Faizan Hasan, Dr. Syed Ali Husain Jafri

Abstract: A common welding technique that produces precise and robust welds is tungsten inert gas (TIG) welding, also known as gas tungsten arc welding (GTAW). Incorrect settings, worker error, or disparities in the materials utilized can all lead to welding issues. These defects affect the overall strength, durability, and mechanical properties of welded structures. The various welding issues that can arise during TIG welding are examined in detail in this research report. Surface faults, internal defects, and metallurgical defects are the three categories into which it divides these issues. The article includes tabular data for easy understanding, as well as the causes, impacts, and preventative measures of each fault. The findings highlight how important proper welding techniques and quality control measures are to minimizing defects and ensuring the highest possible weld performance.

DOI: http://doi.org/

A Data Driven Game Based Framework For Autism Spectrum Disorder Assessment

Authors: Nirupama K, Vivitha R, Mrs. Babisha A, Dr. Suma Christal, Mrs. Swagatha J P, Mary S

Abstract: This project is a Data-Driven Game-Based Framework for the Assessment of Autism Spectrum Disorder proposes the construction of a state-of-the-art framework to measure autism spectrum disorder (ASD) in children by combining machine learning and game-based methods. By incorporating interactive cognitive games, checks and MRI interpretation, this work takes advantage of artificial intelligence in order to enhance the diagnostic accuracy of ASD. At the first stage, information is obtained from different public data, such as behavioural research, neuroimaging repositories and validated ASD diagnostic questionnaires. The data has been gathered after preprocessing so that it is clean and structured in a manner that is ready for future analysis. The framework uses supervised learning algorithms, in particular, deep learning architectures such as VGG16 for the analysis of MRI scans and to discover ASD-related neurological features. Whereas game-based tests (and quizzes) are utilized to assess cognitive abilities, social skills and behavioural patterns. The models are also compared based on their quality (e.g. accuracy, precision, recall) to ensure that they have reliability. This study utilizes diverse data sources, such as facial expressions, cognitive responses, and neuroimaging scans, to improve the accuracy of assessments. The developed system demonstrates strong generalization to unseen data, making it suitable for real-world Autism Spectrum Disorder (ASD) diagnosis. These tools enhance accessibility, enabling early detection and timely intervention. Machine learning aids in analyzing behavioral patterns, improving diagnostic accuracy. Clinicians and caregivers can leverage AI-driven insights to support informed decision-making. Overall, this approach holds promise for advancing ASD assessment and intervention strategies.

Blockchain Secured Tracking System

Authors: Assisstant Professor Bhavya V, Vinutha B R, Ashmita Chavan, Avni Ivy, Chaithra G

Abstract: Supply chain management plays a crucial role in delivering products efficiently and safely from origin to end-user. However, traditional supply chain systems face challenges such as lack of transparency, data tampering, product counterfeiting, and inefficiencies in traceability. These limitations often result in reduced consumer trust, delayed recalls, and regulatory compliance issues. To address these challenges, this project introduces a blockchain-based supply tracking system designed to enhance transparency, security, and accountability across the entire supply chain. Blockchain’s decentralized and immutable ledger ensures that each transaction or event—such as manufacturing, quality testing, packaging, transportation, and delivery—is securely recorded and cannot be altered. By assigning unique QR codes to products, stakeholders can track and verify the product’s journey in real time.

The Role of AI-Powered Chatbots in Enhancing Customer Service and Satisfaction in Online Retail Platforms

Authors: Research Scholoar K. Shameer, Associate Professor Dr. G. Jayalakshmi

Abstract: This study investigates the transformative impact of AI-powered chatbots on customer service and retention in the online retail sector in Tamil Nadu, India. As artificial intelligence (AI) technologies advance, businesses are increasingly adopting AI-powered chatbots to enhance customer service experiences and streamline interactions. The research examines how AI-driven chatbots influence customer behaviours such as service satisfaction, retention rates, and overall engagement. Additionally, the study explores the moderating effects of demographic factors like age, gender, and frequency of online shopping. A sample size of 300 respondents was surveyed using a structured questionnaire, and data were analyzed using statistical tools such as regression analysis and t-tests. The findings indicate that AI-powered chatbots have a significant positive effect on customer service satisfaction and retention, with demographic variables serving as key moderators. Based on these results, the study suggests that online retail companies implement tailored chatbot strategies to improve customer engagement and foster stronger retention. The insights gained from this study contribute to the growing body of research on the pivotal role of AI in transforming customer service dynamics in the e-commerce landscape.

EFFICIENT DESIGN AND IMPLEMENTATION OF LOW-POWER FULL ADDER CIRCUITS USING CMOS TECHNOLOGY

Authors: Barla Ganesh, MOHAMMED SAMEER, B. SIREESHA, SURESH KUMAR HARINI, PEDDAMODULA PRAVEEN KUMAR

Abstract:

DOI: http://doi.org/

Security In Wireless Sensor Networks By Using Machine Learning

Authors: Mainka Saharan, Professor Dr. Vishal Bharti

Abstract: Wireless sensor networks (WSNs) have emerged as a vast technology in almost every fields, enabling prevalent monitoring and data collection in various environments. However, inherent characteristics of WSNs, such as resource constraints, dynamic network topology, and vulnerability to various at- tacks, pose significant security challenges. This paper provides a comprehensive review of security issues in WSNs, including authentication, confidentiality, integrity, availability, and resilience against attacks. Various security mechanisms and protocols proposed to address these challenges are analyzed, highlighting their strengths, limitations, and suitability for different applications. Addition- ally, the paper discusses future research directions and emerging trends in WSN security, aiming to provide researchers and practitioners with insights to develop robust and secure WSN solutions.”

DOI: http://doi.org/

Human Stress Detection Based On Sleeping Habits Using Machine Learning Algorithms

Authors: Mrs.S.Kalaiselvi., Mrs.S. Praveena.

Abstract: Stress significantly impacts human health, with disrupted sleep patterns being a key contributor. This study introduces a deep learning-based system using Convolutional Neural Networks (CNN) to detect stress by analyzing various sleep-related parameters, including sleep duration, interruptions, heart rate variability, and body movements. Data is gathered through wearable devices and sleep monitoring applications, providing a real-world basis for analysis. The model incorporates time-series analysis and statistical feature selection techniques to enhance its predictive accuracy. Experimental results show that CNN effectively captures complex sleep behaviors linked to stress, enabling accurate classification of stress levels. This research offers an automated and reliable solution for stress detection, supporting improved mental health through better sleep analysis.

DOI: http://doi.org/

Green Cloud Computing For IoT: Energy Optimization, Virtualization, And Sustainable Data Management

Authors: Assistant Professor Mr. Suraj Kanal, Mr. Mohak Sawant, Mr. Sujit Shedge, Dr. Jasbir Kaur

Abstract: The rapid proliferation of the Internet of Things (IoT) has introduced significant challenges in energy consumption, operational scalability, and environmental sustainability. Traditional cloud infrastructures, while providing scalable computing resources, contribute substantially to global carbon emissions. In this context, Green Cloud Computing (GCC) has emerged as a transformative strategy, focusing on energy-efficient practices, virtualization, and sustainable data management to support IoT ecosystems. This paper presents a comprehensive study of GCC practices tailored for IoT environments, elaborates on state-of-the-art technologies, and proposes a conceptual framework integrating edge computing, dynamic virtualization, and AI- driven energy management. Through simulated environments using CloudSim and iFogSim, the study evaluates the performance of the proposed framework in reducing energy consumption and improving operational efficiency. The paper also discusses security challenges, practical applications, and future research directions to guide the development of eco-friendly IoT infrastructures.

DOI: http://doi.org/

Medium-Free Local Determination of Earth’s Gravitational Field: A Multi-Modal Laboratory Study across Quantum, Classical, and Relativistic Regimes

Authors: Mario Nascimento, Joao Silva, Mariana Alves

Abstract: We determine the local gravitational acceleration and potential with three indepen- dent ultra-high-vacuum (UHV) instruments: (i) a dual-species 87Rb / 39K Mach–Zehnder atom interferometer, (ii) laser-tracked free-fall of centimetre spheres whose bulk den- sities differ by a factor eight, and (iii) a vertically separated pair of 87Sr optical-lattice clocks. All experiments operate below 10−5 Pa, where hydrostatic forces are < 10−11 of the test weight. The three sensors yield g = 9.803 07 ± 0.000 07 m s−2 (atom interferom- eter, Rb), g = 9.803 02 ± 0.000 29 m s (free fall), and g = 9.804 ± 0.0017 m s (optical clocks), in mutual agreement at the 3 × 10 level. A pressure-variation study up to 10−1 Pa shows no discernible change in g (∂g/∂p = −0.8 ± 1.7 × 10−5 m s−2 Pa−1). The fractional differential acceleration between the two atomic species is η < 7.6×10 (95% C.L.), confirming the composition independence of free fall. Because the quantum phase (interferometer) and fractional frequency shift (clocks) cannot be generated by hydrostatic pressure, and the classical drop occurs where such pressure is negligible, the data establish gravity—not density sorting or buoyancy—as the unique driver of weight and free fall.

STUDY OF MACHINE LEARNING ALGORITHMS FOR PREDICTING CAR PURCHASE BASED ON CUSTOMER DEMANDS

Authors: M.Jayanth, H.Satish, N.Chaitanya, S.Srivani, S.Ishwaraya

Abstract: – This study explores the application of various machine learning algorithms to predict car purchases based on customer demands and preferences. With the growing volume of customer data in the automotive sector, predictive modeling has become a valuable tool for understanding consumer behavior. In this paper, we analyze and compare algorithms such as Decision Trees, Random Forest, and Support Vector Machines using a real-world dataset. The models are evaluated based on accuracy, precision, and recall to identify the most effective approach. The results demonstrate that machine learning can significantly enhance the ability to forecast purchase decisions, offering valuable insights for car manufacturers and dealerships.

DOI: http://doi.org/

STUDY OF MACHINE LEARNING ALGORITHMS FOR PREDICTING CAR PURCHASE BASED ON CUSTOMER DEMANDS

Authors: M.Jayanth, H.Satish, N.Chaitanya, S.Srivani, S.Ishwaraya

Abstract: – This study explores the application of various machine learning algorithms to predict car purchases based on customer demands and preferences. With the growing volume of customer data in the automotive sector, predictive modeling has become a valuable tool for understanding consumer behavior. In this paper, we analyze and compare algorithms such as Decision Trees, Random Forest, and Support Vector Machines using a real-world dataset. The models are evaluated based on accuracy, precision, and recall to identify the most effective approach. The results demonstrate that machine learning can significantly enhance the ability to forecast purchase decisions, offering valuable insights for car manufacturers and dealerships.

DOI: http://doi.org/

Deep Learning Based Blood Group Detection Using Fingerprint

Authors: Mrs.S.Kalaiselvi., Mrs.J.Sneha .

Abstract: – Blood group identification is vital in medical contexts like emergencies, transfusions, and organ transplants. Traditional detection methods typically require invasive blood sampling and lab analysis, which can be time-consuming. To address these challenges, this project introduces a deep learning-based system for determining blood groups using fingerprint images, eliminating the need for physical blood samples. Utilizing Convolutional Neural Networks (CNN) alongside EfficientNet-B0, the model captures subtle fingerprint features linked to specific blood types. Trained on a labeled dataset of fingerprint images, the system learns to associate unique fingerprint patterns with corresponding blood groups. EfficientNet-B0’s optimized architecture ensures precise feature extraction with minimal computational cost. Once a blood type is identified, the system offers tailored health insights, including dietary suggestions, donor compatibility, and preventive measures. This innovative, non-invasive solution enables fast and reliable blood group detection, especially valuable in rural or emergency settings, enhancing accessibility and efficiency in modern healthcare.

DOI: http://doi.org/

Customer Churn Prediction Using Machine Learning

Authors: Ujjwal Kumar, Tushar Tomar, Suyash Sharma, Ansh Jain, Mainka Saharan

Abstract: Avoidance of client renewal is a big problem for business organizations because the business owners suffer the loss of money and loyalty which they would have had the business enterprise if the client had permitted renewal of the portfolio within the business. Making key points of churn recognizable to adopt useful techniques in retaining consumers, this study is modeling technical processes to anticipate churn and help in designing strategies best for accomplishment of desired exposure.

DOI: http://doi.org/

Time Television: Realizing Temporal Imaging through Light Information Capture and Simulation Using Present-Day Technology

Authors: Amit Kumar

Abstract: Time Television is a theoretical and now pragmatically evolving concept centered around the idea of observing the past by capturing, storing, and reconstructing the light emitted or reflected by Earth. Rather than attempting to trap actual photons—which is currently beyond our engineering capabilities—this version proposes a feasible path using today's technology. It involves capturing detailed light-field information, digitally archiving it, and reconstructing the captured data into visual displays using advanced optics, holography, and artificial intelligence. This paper outlines the conceptual framework, implementation pathway, and scope for future development.

DOI: https://doi.org/10.5281/zenodo.16565637

Oral

Authors: Mrs. G. SANGEETHA LAKSHMI., Mrs. S. Hemalatha

Abstract: – Early and precise detection of oral cancer is critical for improving patient outcomes, yet conventional diagnostic methods often involve manual analysis, which can be slow and susceptible to human error. To overcome these limitations, this research introduces an automated detection system that combines deep learning for feature extraction with the Random Forest algorithm for classification. By analyzing medical images, the deep learning component identifies essential features such as texture, color inconsistencies, and irregular tissue structures. These features are then processed by the Random Forest classifier, which utilizes an ensemble of decision trees to enhance classification accuracy and minimize errors. Trained on a dedicated dataset of oral cancer images, the model effectively differentiates between malignant and benign tissues. Experimental findings reveal that this hybrid approach outperforms standard machine learning techniques, offering a faster and more dependable diagnostic tool to aid clinicians in early oral cancer detection and improve patient survival rates.

DOI: http://doi.org/

The Future Of Cyber Security: Opportunities, Challenges, And Ethical Implications

Authors: Ankit Kumar, Mr. Sunil Kaushik, Dr.Rajendra Khatana

Abstract: – Cybersecurity has become a critical concern in today's digital era as cyber threats grow in number, scale, and complexity. From ransomware attacks and data breaches to state-sponsored espionage, the need for secure digital systems has never been greater. This paper provides a clear overview of the present state of cybersecurity, the future trends that could transform it, and the challenges that come with progress. It also addresses ethical and societal issues like data privacy, surveillance, and the moral responsibility of cybersecurity professionals. The aim is to highlight how innovation in cybersecurity can be both a shield and a potential risk, depending on how it's managed. A future where digital safety is inclusive, transparent, and ethical will require cooperation between governments, businesses, and individuals.

DOI: http://doi.org/

AI in Mental Health: A Review on Early Detection of Depression and Anxiety Using AI Techniques Based on Voice and Text Analysis

Authors: Chirag Sharma, Aryan Kamble, Dr. Jasbir Kaur, Assistant Professor Mr. Suraj Kanal, Assistant Professor Ms. Ifrah Kampoo

Abstract: Mental health disorders, particularly depression and anxiety, are among the leading causes of disability worldwide. Yet, early and accurate detection remains challenging due to the shortage of trained professionals, societal stigma, and the subjective nature of traditional assessments. Artificial Intelligence (AI), powered by Machine Learning (ML), Natural Language Processing (NLP), and acoustic analysis, is emerging as a promising tool for early detection. This paper presents a data-driven framework for identifying early signs of depression and anxiety through voice and text analysis. Given the extensive research in this domain, we highlight comparative studies using various AI techniques, showcasing their effectiveness in mental health diagnostics. These studies explore text-based analysis, voice-based models, multimodal fusion systems, and ethical considerations in AI-driven mental health screening. Special attention is given to real-time social media analysis and conversational voice pattern recognition, demonstrating AI’s potential in developing scalable, intelligent, and ethical preclinical screening systems. Additionally, this review presents a comparative analysis of studies employing various AI techniques, evaluating their strengths and limitations. It also highlights the accuracy of multimodal fusion models, reinforcing their superiority over unimodal approaches in mental health assessments.

Analysis Of Air Quality Index Pre And Post Covid Using Machine Learning Technique

Authors: Mainka Saharan, Revansh Raizada, Vasu Tyagi

Abstract: – Air is one of the most crucial anticipated assets for the endurance and sustenance of all species on the planet. Air quality sensor devices increase the attention and focus of particles that have an anthropogenic source and cause hazardous consequences during or after a human being's gulp of air. Particles such as PM2.5, PM10, CO, O3, NO2, NO, and others degrade air quality. As technology progresses, researchers and environmental agencies have developed novel methods for combating and managing air pollution.Covid-19 is an epidemic that will influence people' lives and activities in unprecedented ways through the year 2020. This lockout led in a great recovery in ecosystem quality, with much lower levels of air contaminants. Air pollution and the COVID-19 pandemic are connected in two distinct phases— before the outbreak and after its emergence. In the pre-pandemic phase, many regions were already facing severe air quality issues, largely due to dense populations, heavy traffic, and industrial emissions. Aerosol could help to promote the virus at a faster rate, and air pollutants could adversely impact people's lungs, allowing the virus to attack patients more brutally. Public authorities utilize a standardized scale to inform citizens about present and forecasted air pollution conditions. A higher reading on this scale reflects increased health hazards. Many countries have developed their own air monitoring systems, each aligned with their specific environmental safety benchmarks.In this study I have focused on the levels of air quality index pre-covid and post covid and tried to analyze what future outcomes could be possible with either of the situations. Data of 5 cities of India has been processed (Agra, Anand Vihar ,Delhi,Faridabad,Gurgaon),missing value treatments, data validation, and data cleaning/preparation and have been used to predict future outcomes by following a pattern of consecutive years. There is no doubt that this problem has to be addressed with utmost focus.

DOI: http://doi.org/

Laboratory Access Management System

Authors: Professor Mohammed Juned, Shaikh Umar Sajid, Shaikh Siraj Ahmed Akbar Ali, Muskan Shahnawaz Khan, Mohammed Salim Shaikh

Abstract: The Laboratory Access Management System (LAMS) features robust user authentication protocols, ensuring that only authorized personnel can access sensitive resources. With time-based access control, the system limits access to designated hours, while comprehensive logging provides a detailed record of user interactions. Administrator’s benefit from real-time alerts for unauthorized access attempts. LAMS supports role-based access control, allowing customizable permissions that align with organizational policies. This flexibility not only improves security but also ensures compliance with regulatory standards. The user-friendly interface and efficient performance of LAMS, facilitate effortless maintenance and usability.

Enhanced Video Captioning Using A Hybrid Vision-Swin Transformer Technique With Semantic Feature Augmentation And Improved Optimization By Eurasian Oystercatcher Algorithm

Authors: Dr. R Muthuram Associate Professor, S.Sanjay,

Abstract: – As an important step of multimedia processing, video captioning requires natural language generation for video content, integrating state-of-art approaches in computer vision and NLP to describe unmanaged visual information with useful text. It's complex task, as leveraging the temporal progression and the structured connections between objects, actions, and events in video is quite challenging. Into our paper, we suggest novel hybrid transformer model, that effectively integrates ViT and Swin Transformer based classifier for video captioning. The MSVD dataset is utilised for work. Caption preprocessing comes after which applies spelling correction, tokenization, part of speech tagging, and stop word removal. Applying TF-IDF, N-Grams, and semantic web-based feature extraction techniques for building a richer representation over textual data A hybrid transformer model was then utilised for extracting visual features and produce captions, followed by hyperparameter optimization utilising Eurasian Oystercatcher Optimiser (EOO). These captions are scored against ground truths utilising metrics like BLEU, METEOR and CIDEr.

DOI: http://doi.org/

Integrating Deep Learning With Dermoscopy For Enhanced Detection Of Scalp And Hair Follicle Anomalies

Authors: Prateek Rohatgi

Abstract: Hair fall is the common issue for many people worldwide. Almost 50% of Indian men and 20-30% of Indian women experience hair loss in any form in their lifetime. Hair loss occurs due to many factors such as aging, stress, medication, etc. Hair fall and related diseases often go unnoticed in the beginning and patients find it difficult to differentiate between hair loss and a regular hair fall. When the situation gets worsened, then they get aware of the illness. When they consult a dermatologist, then the diagnosis gets delayed. Due to the latest Deep Learning (DL) technologies and its applications, it is easy to assist Dermatologists with faster disease detection and diagnosis. In this research, 10 diseases are used for detection namely Alopecia Areata, Contact Dermatitis, Folliculitis, Head Lice, Lichen Planus, Male Pattern Baldness, Psoriasis, Seborrheic Dermatitis, Telogen Effluvium and Tinea Capitis. 12000 images are divided into 10 classes containing 1200 images in each class. Images were preprocessed by denoising, enhancement and data augmentation. Hyperparameter tuning, fine tuning and regularization was also done to make the model more precise with learning rate of 0.0001, pretrained VGG16 model and Dropout probability of 0.5. Overall training accuracy of 99% with a validation accuracy of 93% is obtained.

DOI: http://doi.org/10.5281/zenodo.15702790

AI-Driven Mock Interviews: A New Era in Candidate Preparation

Authors: Mohd Adnan Ahmed, Shashank BS, Munmeeth K, Sanjay J Ganiga

Abstract: This paper introduces an innovative AI-driven mock interview platform designed to enhance interview preparedness by assessing candidates across three key dimensions: emotions, confidence, and knowledge. Utilizing deep learning convolutional neural networks, the system analyzes facial expressions to gauge emotional responses, while speech recognition and natural language processing evaluate the candidate’s confidence levels. Additionally, semantic analysis and keyword mapping assess the candidate’s knowledge by comparing responses with relevant online resources. This comprehensive approach aims to reduce pre-interview anxiety, boost confidence, and refine interview skills, providing a more effective preparation tool compared to traditional methods.

DOI: https://doi.org/10.5281/zenodo.15695043

SignMate: Sign Language Detection System

Authors: Om More Mrinaalini Shankar, Yogesh Pawar, Mrudula Kotgire, Vihar Motghare, Riyah More

Abstract: This paper outlines the designing and improvement of a real-time sign language recognition system that can precisely translate hand movements from a typical webcam. The basis of the system is YOLOv5 (Ultralytics), a cutting-edge object detection model, utilizing the PyTorch deep learning framework for implementation and training. Data acquisition entailed capturing personal Indian Sign Language (ISL) gestures with a webcam under normal illumination. These movements were carefully annotated and exported with Roboflow to produce a high-quality, YOLO-ready dataset. The system initially supported 7 sign classes. Through dramatic enhancements, the dataset grew to 15 classes: "Bye," "Congratulations," "How_Are_You," and "No_Worries," with 50-100 images taken per class to maximize diversity and balance. The YOLOv5s model was trained for 30 epochs with 16 batch size and an image input of 416×416 and achieved a remarkable average mAP@0.5 of more than 98%. Real-time inference is conducted using the detect.py script with accurate bounding box predictions and confidence scores. The whole project was implemented in Python, using Visual Studio Code and Anaconda for managing the environment, and is run locally on a CPU with OpenCV for video processing. The system thus shows much better real-world applicability, diversity of classes, and usability than its previous version and represents a great leap toward more effective gesture-based communication. Future projects involve building an Android app through a Flask API, enhancing the graphical user interface, testing out more larger YOLOv5 models, and incorporating temporal tracking for video-based identification.

DOI: http://doi.org/

Design and Analysis of Attachable Trailer for Electric Three- Wheeler

Authors: Assistant Professor Mr. C.K. Murugesan, Mr. K. Kathiresan

Abstract: This paper describes the design and analysis of three-wheeler electric vehicle with detachable loading trailer. The problem to be dealt for this work is to design and analysis of the chassis frame using the software. The design modelling was prepared using PRO-E and the analysis is carried out using Ansys. The chassis frame is considered as an overhanging beam with supports corresponding to rear wheels and single steering system for front wheel. The total load acting on the chassis is taken as the sum of the weight of the chassis frame, weight of the persons and the other components. This total load is considered as uniformly distributed load, bending stress, shear stress and deflection is evaluated using ANSYS. The load at the seat was considered 80KG and 250KG in the loading carrier. The maximum stress obtained in the vehicle was at the joint of steering wheel with chassis. Though, its acceptable stress it should be strengthen for better results. The total deformation also was high at the driver part which cause minimum effect in the damage for the vehicle.

A Survey Of Techniques, Methods And Approaches In The Field Of Natural Language Processing And Data Mining”

Authors: Assistant Professor Ms. Pooja, Ms. Neeharika Sengar

Abstract: This paper first describes the history of text mining technology, highlights its drawbacks, and then develops a text mining system based on natural language processing technology. The incessant generation of data has presented novel research obstacles because of its intricacy, variety, and magnitude. As a result, big data is gradually being acknowledged as a legitimate scientific discipline. An overview of the current state of big data science research is given in this article, with a focus on the theoretical underpinnings and applications of the field. Natural Language Processing (NLP) is one of the domains where data has a significant impact. The majority of NLP applications, including automatic speech recognition and machine translation, have not performed as well as they could in the past due to the proliferation of data. As such, a lot of NLP applications are regularly shifting from data-driven strategies to knowledge- and rule-based systems. On the other hand, gathered data that are based on vague design specifications or on forms that are not technically appropriate will be meaningless.

A Study On Supply Chain System Of Manufacturing Steel Industry

Authors: Shahnawaz Alam

Abstract: The manufacturing industry, the backbone of global economies, relies heavily on a sophisticated and well-orchestrated supply chain system. Far from a simple linear progression, today’s manufacturing supply chains are complex, interconnected networks that encompass every stage from raw material acquisition to the delivery of finished goods to the end consumer. An efficient and resilient supply chain is not merely an operational necessity; it is a critical differentiator that determines a manufacturer’s competitiveness, profitability, and ability to meet evolving customer demands in a dynamic global marketplace. A manufacturing supply chain involves a series of integrated activities that transform raw materials into sellable products. Planning involves forecasting demand, setting production goals, and developing strategies to align supply with anticipated market needs. Effective planning includes demand planning, supply planning, material requirements planning (MRP), and sales and operations planning. Procurement stage focuses on identifying, evaluating, and selecting suppliers for raw materials, components, and other necessary resources. Strong supplier relationships, contract negotiation, and quality assurance are crucial here. Production and Manufacturing is the heart of the supply chain, where raw materials are transformed into finished products through various processes. Efficient production schedules, quality control, and cost-effective manufacturing techniques are paramount. Logistics and Distribution component deals with the physical movement, storage, and delivery of goods. It includes inbound and outbound transportation management, fleet management, warehouse management, and inventory control, ensuring products reach customers in a timely and cost-effective manner.

Federated Machine Learning For Fraud Detection In IoT-Based Credit Card Systems

Authors: Mrs N.S.Kulkarni, Santosh S Kalshetty

Abstract: With the proliferation of Internet of Things (IoT)-based payment systems, credit card fraud has become more sophisticated and widespread. Traditional centralized fraud detection methods often fall short due to latency, data privacy concerns, and scalability limitations. This paper proposes a novel Federated Machine Learning (FML) approach for real-time credit card fraud detection within IoT ecosystems. By leveraging federated learning, data remains on edge devices (e.g., PoS terminals, mobile devices), ensuring privacy while enabling collaborative model training. The proposed framework integrates anomaly detection, ensemble learning, and cloud-edge orchestration. Experimental results on real-world datasets demonstrate superior accuracy, recall, and privacy compared to traditional methods.

DOI: http://doi.org/

Federated Machine Learning For Fraud Detection In IoT-Based Credit Card Systems

Authors: Mrs N.S.Kulkarni, Santosh S Kalshetty

Abstract: With the proliferation of Internet of Things (IoT)-based payment systems, credit card fraud has become more sophisticated and widespread. Traditional centralized fraud detection methods often fall short due to latency, data privacy concerns, and scalability limitations. This paper proposes a novel Federated Machine Learning (FML) approach for real-time credit card fraud detection within IoT ecosystems. By leveraging federated learning, data remains on edge devices (e.g., PoS terminals, mobile devices), ensuring privacy while enabling collaborative model training. The proposed framework integrates anomaly detection, ensemble learning, and cloud-edge orchestration. Experimental results on real-world datasets demonstrate superior accuracy, recall, and privacy compared to traditional methods.

DOI: http://doi.org/

Plant Pulse 2.0 – Multi-Crop Disease Detection_642

Authors: Rinku Badgujar, Sambhav Kothari, Dhiraj Tapkir, Abhijeet Prasad

Abstract: The agricultural industry is increasingly adopting Artificial Intelligence (AI) solutions to tackle the challenges of early disease identification and crop management. While previous approaches have primarily focused on single-crop disease classification, this paper presents an enhanced version of the Plant Pulse system— expanding its capabilities beyond apples to include a wide variety of crops such as cherry, corn, grape, orange, peach, pepper, potato, strawberry, and tomato. The proposed system employs a custom-designed Convolutional Neural Network (CNN) trained on over 61,000 images spanning 39 plant disease and healthy categories. Developed using PyTorch, the system demonstrates high performance, achieving 98.9% accuracy on the test set. The architecture is optimized for both accuracy and scalability, supporting real-time inference and future integration with field-deployable tools. This research builds upon our prior work [1], significantly extending its scope and applicability across diverse agricultural domains.

DOI: http://doi.org/

Cosmic Rays: Origin, Composition, And Detection Techniques, A Review

Authors: Jaza Anwar Sayyed, Ansari Novman Nabeel, Ansari Ammara Firdaus

Abstract: Cosmic rays are high-energy charged particles from astrophysical sources like supernovae, AGNs, and gamma-ray bursts, continuously bombarding Earth's atmosphere. Their study provides insights into high-energy astrophysics, particle acceleration, and fundamental physics beyond the Standard Model. Spanning energies from MeV to over 10201020eV, cosmic rays interact with the atmosphere, producing secondary particles that influence atmospheric ionization and space weather. Detection methods include space-based observatories (AMS-02, BESS, CREAM), ground-based arrays (Pierre Auger Observatory, Telescope Array), and Cherenkov telescopes (HESS, MAGIC, VERITAS), along with cloud chambers for direct particle visualization. Cosmic rays contribute to multi-messenger astronomy, linking observations with gravitational waves and neutrinos. Future experiments like CTA and JEM-EUSO aim to further explore their origins and extreme energy properties, advancing astrophysics and particle physics.

DOI: http://doi.org/

Psychology Chatbot: A Conversational AI for Mental Health Support

Authors: Ashwini Kale, Rahul Paul, Anant Tejale

Abstract: – This report discusses the design, development, and anticipated effects of a psychology chatbot app aimed at providing affordable and initial mental health guidance. As the prevalence of mental health issues rises and advancements in Natural Language Processing (NLP) and Artificial Intelligence (AI) continue, there's a unique opportunity to utilize conversational agents for preliminary assessments, psychoeducation, and connecting users with appropriate resources. This project details the structure of the chatbot, including its knowledge base, NLP engine, dialogue management system, and user interface. It also addresses ethical considerations, challenges, and possible future developments in the use of AI for mental health care. The focus is on creating an empathetic and user-friendly virtual assistant capable of engaging in meaningful conversations, offering coping strategies, and directing users to professional help when necessary. The evaluation plan includes user testing to assess the chatbot's usability, its effectiveness in providing support, and overall user satisfaction. This initiative contributes to the growing landscape of AI-driven mental health solutions by presenting a practical implementation and a thoughtful analysis of its opportunities and challenges.

Psychology Chatbot

Authors: Rahul Paul, Anant Tejale, Ashwini Kale

Abstract: – This report discusses the design, development, and anticipated effects of a psychology chatbot app aimed at providing affordable and initial mental health guidance. As the prevalence of mental health issues rises and advancements in Natural Language Processing (NLP) and Artificial Intelligence (AI) continue, there's a unique opportunity to utilize conversational agents for preliminary assessments, psychoeducation, and connecting users with appropriate resources. This project details the structure of the chatbot, including its knowledge base, NLP engine, dialogue management system, and user interface. It also addresses ethical considerations, challenges, and possible future developments in the use of AI for mental health care. The focus is on creating an empathetic and user-friendly virtual assistant capable of engaging in meaningful conversations, offering coping strategies, and directing users to professional help when necessary. The evaluation plan includes user testing to assess the chatbot's usability, its effectiveness in providing support, and overall user satisfaction. This initiative contributes to the growing landscape of AI-driven mental health solutions by presenting a practical implementation and a thoughtful analysis of its opportunities and challenges.

Enhancement of the Solar Energy System via Nano Fluid

Authors: Dr. Nikhilesh Sil, Avik Ghosh, Sakshi Singhania, Rimpa Ghsoh, Rittika Shaw

Abstract: Utilization of solar energy increased day by day due to the scarcity of natural resources such as fosil fuel (coal, oil, and natural gas) and the emission of Carbon dioxide (CO2)). Sun is the most important source of renewable energy on the earth as it is available free of cost and it is free of greenhouse gas such as carbon dioxide. Solar energy can be used for producing electricity, heating, cooling, and detoxification. It can be stored for future use also. But there is a problem in the proper utilization of solar energy and heat transfer process of the solar energy system. To overcome this situation the researcher designed the solar energy system in such a way that it can increase the heat transfer process. It is observed that solar energy system can be improved by the use of nano fluids. Nano fluid is nothing but the mixture nanoparticle together with the base fluid, which can store the solar energy and can be used in future. The major aim of this review article is to develop the renewable energy with the help of nano fluids and accelerate the production of renewable energy in several solar thermal systems.

DOI: https://doi.org/10.5281/zenodo.15720024

Impact Of Digital Payments On Consumer Behavior: A Study Of The 25–40 Age Group

Authors: Sabya Sharma

Abstract: Thiз зtudy exploreз the tranзformative influence of digital payment зyзtemз on the purchaзing behavior of India’з 25–40 age cohortз, emphaзizing pзychological driverз, tranзactional efficiency, and зocio-economic implicationз. By integrating claззical conзumer behavior frameworkз (e.g., Maзlow’з hierarchy) with contemporary caзe зtudieз, the reзearch identifieз trendз зuch aз impulзive buying, brand loyalty through perзonalized incentiveз, and privacy concernз. Mixed-method analyзiз—combining зurveyз (N=500) and interviewз (N=30)—revealз that platformз like UPI and mobile walletз reзhape financial autonomy while poзing ethical challengeз. The findingз advocate for data-driven, culturally nuanced marketing зtrategieз tailored to India’з digital economy.

DOI: http://doi.org/

The Study On Natural Dyes Extract Of Jamblang, Butterfly Pea Flower, Turmeric Powder, Henna Leaves, Pomegranate, & Hibiscus Testing Potency With Blood Cells Morphology

Authors: Assistant Professor V.Vinothkumar, K.Deepikadevi

Abstract: The staining efficacy of jamblang fruit, butterfly pea flower, turmeric powder, henna leaves, pomegranate seeds, & hibiscus flower crude extract freely and combined with natural acidic & basic dye extract, synthetic dye of methylene blue & eosin testing potency with blood cells morpholoqy as alternative stain for reqular stining chemical in blood smear preparation.

Performance Analysis of a Counter-Flow Jet Condenser with Optimized Nozzle Geometry

Authors: Abhilash Abhinandan, Soumyaranjan Behera, Santosh Sahoo, Arpit Kavi Satapathy, Rama Chandra Parida, Amar Kumar Das

Abstract: This study experimentally evaluates a laboratory-scale counter-flow jet condenser proposed for medium-capacity thermal power applications. Steam at 0.12 MPa is introduced from the base of the shell while sub-cooled cooling water is admitted through twin concentric spray nozzles mounted at the top.This setup creates opposing flow paths that maximize the local temperature gradient. Tests were performed with massflow rates of 0.03-0.06 kg s-1for steam and0.4-0.9 kg s-1for cooling water. Results show that increasing the water flow from 0.4 to 0.9 kg s-1 raises the volumetric heat-transfer rate by up to 38%, while the overall condensation efficiency improves from 83% to 92% before plateauing once the water approaches its thermal capacity. Nozzle optimization proved equally critical: reducing the orifice diameter from 2.0 mm to 1.2 mm generated finer droplets, shortened condensation time by 17%, and lifted the heat-transfer coefficient by 12% owing to enhanced inter-phase contact. Thermal-efficiency analysis further indicates that a 7 °C reduction in inlet-water temperature can yield an additional 6% gain in condenser effectiveness. These findings demonstrate that coupled control of cooling-water throughput and jet-nozzle geometry can substantially boost jet-condenser performance, offering a pragmatic pathway to lower specific steam-cycle energy consumption in compact power and refrigeration systems.

Experimental Study on effect of R134a Refrigerants in a Domestic Vapour Compression Refrigeration System

Authors: Chittaranjan Beshra, Lalatendu Paul, Chinmaya Behera, Aman Anand, Bidyut Ranjan Rout, Amar Kumar Das

Abstract: This study presents a comparative performance analysis of a vapor compression refrigeration system using two different refrigerants: R134a and hydrocarbon refrigerants (R600a/R290). The experiment was conducted on a standard domestic refrigerator setup under identical environmental and operational conditions. Parameters such as coefficient of performance, refrigeration effect, and environmental impact were used to evaluate performance. The results showed that the hydrocarbon refrigerant demonstrated a significantly higher COP and refrigeration effect than R134a. This improved performance is attributed to the lower molecular weight and superior thermodynamic compatibility of hydrocarbons, which resulted in faster temperature pull-down and greater energy efficiency. The study also highlights the environmental advantages of hydrocarbons, including zero ozone depletion potential and negligible global warming potential, in contrast to R134a’s higher GWP of approximately 1430.Despite flammability concerns associated with hydrocarbons, their performance and environmental benefits suggest they are promising alternatives to conventional refrigerants for domestic refrigeration applications. The findings support the suitability of hydrocarbon refrigerants in simple VCR systems as energy-efficient, eco-friendly replacements for high-GWP refrigerants.

Performance Analysis of Solar Parabolic Trough Collector

Authors: Subham Samal, Sachin Gouda, Sanjeeb Kumar Das, Raja Gouda, Sanjay Kumar Raj

Abstract: The growing demand for sustainable and renewable energy sources has led to significant interest in solar thermal technologies. This project focuses on the performance analysis of a solar parabolic trough collector (PTC), a concentrated solar power (CSP) system known for its efficiency in harnessing solar energy. Both theoretical and experimental evaluations were conducted under varying operational and environmental conditions. Key parameters such as solar irradiance, ambient temperature, heat transfer fluid (HTF) temperature, and flow rate were monitored to assess thermal performance. The analysis considered the influence of design elements such as reflector curvature, receiver tube characteristics, and tracking accuracy. The results showed that performance is highly influenced by solar intensity and fluid flow rate, with optimal efficiency observed during peak sunlight hours. This study provides insight into enhancing the operational parameters and system design of PTCs to improve solar thermal energy capture for practical applications.

Design and Experimental Evaluation of a Passive Float-Driven Mechanical Solar Tracker with Integrated Irrigation Reuse

Authors: Amlan Sahoo, Biswajit Mishra, Biswajit Padhi, K. Mohan Rao, G. Arun Manohar

Abstract: This study details the design, construction, and preliminary evaluation of a fully mechanical single-axis solar-tracking system that uses a sealed water tank and float ball to rotate a photovoltaic module in alignment with the sun. As the water gradually leaves the tank through a calibrated flow-control valve, the falling float actuates a linkage that turns the panel east-to-west. Then, the drained water is then channeled directly to adjacent crop rows, coupling energy harvesting with micro-irrigation in a single passive device. Day-long outdoor tests on the GIFT Autonomous, Bhubaneswar campus showed that the tracker increased array energy output by 20-40% compared with an identically rated fixed-tilt reference module. Panel pointing accuracy remained within ±2-5°, ensuring a consistently near-normal incidence angle and noticeably reducing shadowing and thermal build-up on the cell surface. Because panel motion is driven solely by buoyancy and gravity, the system requires no electrical actuation. Any auxiliary energy demand is limited to the small head loss across the valve, far below the 2-3% self-consumption typical of motorized trackers. The results highlight a cost-effective, maintenance-light pathway for boosting PV yield while re-using tracking water for irrigation, making the concept attractive for off-grid farming and other rural applications where both power and water are in short supply.

Design, Fabrication, and Analysis of A4-Dof Manually Controlled Pick-and-Place Robotic Arm

Authors: Sagar Bhuyan, Biswajit Barik, Priyaranjan Behera, Manas Kumar Sethy, Subhashree Naik

Abstract: This paper presents the design and fabrication of a manually controlled 4-DOF pick-and-place robotic arm mounted on a four-wheeled mobile platform. The robotic arm is actuated using servo motors and gear motors, with movement controlled directly via push buttons, potentiometers, toggle switches, and a servo tester, eliminating the need for microcontrollers or programmable controllers. The structure is fabricated from lightweight materials such as aluminum and foam board to ensure portability and mechanical stability.The robotic arm includes revolute joints at the shoulder, elbow, wrist, and gripper, each capable of independent and simultaneous motion. CAD modeling was performed using Creo software to visualize and validate the design before fabrication. Inverse kinematics analysis was conducted using RoboAnalyzer software to determine joint angles required for specific end-effector positions. Additionally, finite element analysis (FEA) was performed using ANSYS to evaluate stress and deformation across critical arm components. The resulting system provides a cost-effective and functional platform for basic automation tasks, academic demonstrations, and prototyping. Its simplicity and manual control make it especially suitable for learning environments focused on mechanical design, actuation, and robotic system integration.

The Global Path To Academic Excellence And Building A Knowledge-Based Society For The 21st Century And Beyond

Authors: Davendra Sharma

Abstract: In the rapidly evolving global landscape of the 21st century, academic excellence and the cultivation of knowledge-based societies have emerged as central imperatives for national development, global competitiveness, and sustainable human progress. This paper critically examines the transformation of education systems worldwide in response to the demands of the Fourth Industrial Revolution (4IR), digital globalization, and the shifting nature of knowledge creation, dissemination, and application. Academic excellence is reconceptualized not solely as achievement within traditional scholarly domains but as a multidimensional construct that includes inclusivity, innovation, critical thinking, technological fluency, and social impact (Trilling & Fadel, 2009; UNESCO, 2021). The research explores how educational institutions must reposition themselves as dynamic hubs of knowledge production and social transformation, committed to equity, relevance, and lifelong learning (OECD, 2018; Altbach& Salmi, 2011).The paper further interrogates global frameworks such as the United Nations Sustainable Development Goal 4 (SDG 4), which promotes inclusive and equitable quality education and lifelong learning opportunities for all, as a blueprint for building knowledge-based societies (UN, 2015). Drawing on interdisciplinary literature and global policy analyses, it identifies key strategies that underpin this transformation: investment in digital infrastructure, reform of curriculum and pedagogy, professional development for educators, culturally responsive education, and multi-stakeholder collaboration (World Economic Forum, 2020; World Bank, 2021). The abstract also considers challenges such as persistent digital divides, systemic inequalities, and colonial legacies in education, particularly in developing and postcolonial contexts. Ultimately, the study argues for a reimagining of education as a public good and a strategic catalyst for economic diversification, social resilience, ethical leadership, and innovation. The findings emphasize that building a knowledge-based society is not merely a technical or economic endeavor but a deeply ethical and political one, requiring inclusive policies, global solidarity, and transformative leadership in education (Marginson, 2016; UNESCO, 2005). As the world moves beyond the 21st century, the pursuit of academic excellence and knowledge equity will be essential in addressing global crises and enabling all learners to become agents of sustainable change.

DOI: http://doi.org/

Strengthening Educational Governance And Ethical Leadership In Fiji: Navigating The Fourth Industrial Revolution For Inclusive And Sustainable Reform

Authors: Davendra Sharma

Abstract: The Fourth Industrial Revolution (4IR) is rapidly transforming global education systems, compelling nations like Fiji to reconsider traditional approaches to leadership, governance, and ethics in schooling. While the discourse around education reform increasingly emphasizes innovation, digitalization, and future-readiness, the success of such reforms is contingent upon robust institutional governance and ethically grounded leadership (UNESCO, 2021; OECD, 2020). This paper critically examines how the 4IR is reshaping decision-making structures in education, with particular attention to the erosion of ethical frameworks and governance integrity in Pacific Island contexts. Drawing on evidence from regional policy documents, leadership theories, and international case studies, the paper argues that educational institutions in Fiji face growing vulnerabilities due to fragmented policy coordination, underdeveloped leadership pipelines, and insufficient digital ethics infrastructure (Lingam & Lingam, 2018; Zawacki-Richter et al., 2019). These systemic gaps risk amplifying educational inequities, particularly for marginalized and rural learners, unless they are countered by strategic investments in leadership capacity-building, culturally responsive governance, and coherent institutional support mechanisms. The analysis also highlights opportunities for policy coherence through frameworks such as the Pacific Regional Education Framework (PacREF) and the Sustainable Development Goals (SDG 4), which offer pathways for more equitable and ethically driven reforms (Pacific Community, 2018; United Nations, 2015). Ultimately, the study calls for a recalibration of reform narratives to foreground ethical leadership and governance as foundational pillars for navigating technological disruption and ensuring inclusive, sustainable educational transformation in Fiji and the wider Pacific.

DOI: http://doi.org/

Design and Implementation of a Trainer Board for Digital Logic Design Principles Lab

Authors: Mustafa O. Ali, Abeer A.S. Mohammed

Abstract: Educational institutions suffer from a scarcity of practical training due to the financial constraints imposed by the US sanctions on our country, Sudan, for more than three decades. Despite the availability and diversity of equipment and tools for practical educational applications on the global market, the economic reality of Sudanese educational institutions allows only training at the basic level in most cases. Our situation in the Digital Electronics Lab at Nile Valley University is similar to that of everyone else under the US sanctions situation. We used to train students on the basics with very simple equipment. We are barely able to implement 25% of the practical material covered in the course. This is because we use simple electronic components to implement the exercises, which complicates the application, consumes materials and effort, and takes a long time per the student. Through this paper, we aim to utilize the simple capabilities available in our lab and the local market to design and implement a laboratory board that isolates students from interacting with basic electronic components and enables them to directly apply digital logic. They then obtain the results of the exercise, enabling them to write the exercise report and achieve the desired objectives of the experiment. We also hope that the designed board will improve the achievement rate and raise up the percentage of practical work applied in the laboratory according to the course plan.

DOI: https://doi.org/10.5281/zenodo.15728089

Artificial Intelligence In Medical Science: Transforming Diagnosis, Treatment, and Healthcare Delivery

Authors: Mr. Suyash Mishra, Ms. Vertika Sood, Dr. Jasbir Kaur, Assistant Professor Ms. Sandhya Thakkar

Abstract: Artificial Intelligence (AI) is revolutionizing the landscape of medical science by enhancing diagnostic accuracy, personalizing treatment, accelerating drug discovery, and optimizing healthcare operations. This paper explores the diverse applications of AI in the medical field, including medical imaging, robotic surgery, virtual health assistants, and predictive analytics. The integration of AI technologies into clinical workflows enables faster decision-making, reduces human errors, and improves patient outcomes. Real-world case studies highlight the effectiveness of AI in early disease detection and personalized care. Despite its transformative potential, AI adoption also brings forth challenges such as data privacy, ethical concerns, and regulatory compliance. This paper presents a comprehensive overview of AI’s role in modern healthcare and discusses future prospects and recommendations for its responsible implementation.

Redone Bricks: An Innovative Low-Carbon, Lightweight Construction Material Synthesized from Industrial and Organic Waste

Authors: Kevadia Chirag Kantilal, Yash Gadhiya, Bhargav Gajera

Abstract: Redone Bricks are a novel, sustainable construction material engineered to address the urgent need for low-carbon alternatives in the building industry. Developed from a mix of quick lime, C&D waste, plastic waste, fly ash, aluminium powder, agricultural waste, and Indian Bedellium (Guggul), these bricks are designed to outperform conventional materials in both environmental impact and mechanical properties. A comparative study was conducted against six alternatives: Stabilized Mud Blocks (SMBs), Conventional Clay Bricks, Fly Ash Bricks, Autoclaved Aerated Concrete (AAC) Bricks, Fiber-Reinforced Composite (FRC) Bricks, and Plastic Bricks. Redone Bricks demonstrated superior performance with low weight (1.2 lbs), excellent thermal insulation (2–2.5 m²•K/W), reduced water absorption (20%), and high compressive strength (4.3 N/m²). Priced affordably at INR 8 per unit, they offer economic as well as environmental benefits. Critically, Redone Bricks exhibit a remarkably low CO₂ emission of just 0.00206 kg/kg, representing a >99% reduction compared to conventional materials: Fired Clay Bricks (0.335 kg/kg), AAC Blocks (0.23 kg/kg), SMBs and Plastic Bricks (0.48 kg/kg), and even Fly Ash Bricks (0.0228 kg/kg). This positions Redone Bricks as the most environmentally friendly option in the study. Overall, Redone Bricks offer a better path toward greener construction, combining sustainability, performance, and affordability—making them an ideal solution for climate-resilient infrastructure.

DOI: https://doi.org/10.5281/zenodo.15728812

Socio Demographic Correlates of Patients with Diabetes Mellitus at a Tertiary Care Center in Ahmedabad, Gujrat, India

Authors: Dr. Chirag Vaghela, Dr. Mrugdha Patel, Dr.Prabhudas Patel

Abstract: Background: India is experiencing a fast demographic and epidemiological conversion with Non-Communicable disease (NCDs), bookkeeping for two out of each three deaths (1). The most important cause causative to high incidence of diabetes mellitus is quick developmental urbanization, inactive lifestyle, and modify in dietary habits. Aim: To evaluate the relationship between socio demographic individualism and Diabetes Mellitus. Subject and Methods: All subjects were interviewed after obtaining consent from them. The study had approved by the Ethics Committee of IEC-BHR Dr.Jivraj Mehta Smarak Health Foundation, Ahmedabad, and Gujarat, India. A questionnaire have been used which includes the socio demographic, life style, family history of diabetes and the relationship with diabetic patients. A cross sectional study was conducted among 151 patients suffering with type 2 diabetes mellitus and 151 patients with non diabetes mellitus OPD of tertiary care hospital. Socio demographic correlates were assessed by brief questionnaire. Results: Our study reported 59.01% individuals had high fasting blood sugar, high post parandial blood sugar and high Hba1c. The highest prevalence was found in the age group 51-60 years (64.11%). A large figure of the study people (35%) was inactive. A significant proportion of the study subjects had associated co morbidities such as hypertension (21.19%), and hyperlipidemia (7.28%). Fasting blood glucose, postprandial blood glucose, and glycated hemoglobin levels were elevated in both gender.. The values were higher in males, but statistically, the difference was significant. Conclusions: The present study revealed that poor glycemic control, dyslipidemia, sedentary lifestyles, and hypertension were prevalent in T2DM patients. Hence, the overall risk profile in patients from Ahmedabad was very poor and needs improvement. These data can support health professionals’ actions to efficiently keep up and afford a more broad advance to organization

DOI: https://doi.org/10.5281/zenodo.15729038

Design, Mathematical Modeling and Simulation of an H-Bridge 3KVA Pure_Sine_Wave_Inverter

Authors: Gabriel Ebiowei Moses, David Ebregbe

Abstract: This study presents the design, mathematical modeling, and simulation of an H-bridge pure sine wave inverter. The proposed inverter utilizes a pulse-width modulation (PWM) technique to generate a high-quality sine wave output. A mathematical model is developed to analyze the inverter’s performance, and simulations are conducted using MATLAB/Simulink to validate the design. The results demonstrate the inverter’s ability to produce a pure sine wave output with low total harmonic distortion (THD), making it suitable for applications such as renewable energy systems, motor drives, and power supplies. The simulation results are presented and discussed, highlighting the inverter’s performance and potential for practical implementation. Cost-Effective and Practical Design, Thermal Management and Durability, Robust Protection and Control Features, Effective Hybrid Energy Integration, Efficient Power Conversion, High-Quality Pure Sine Wave Output are among the key findings of this research work.

DOI: https://doi.org/10.5281/zenodo.15729493

Effective Utilization of Electrical/Electronics Skills for Social Vices and Unemployment Reduction

Authors: Agu, P. M., Echara Wilson Sunday, Mbam, Arinze Raphael, Chimdia Abara Obasi, Joseph Odii, Ogbonnaya Orji Rex

Abstract: Curbing of social vices and unemployment in any society has never been an easy task. Unemployment is a major economic virus militating against the economic well-being of many countries in recent times, and this menace has resulted in increasing rate of social vices such as sexual harassment, prostitutions, arm robbery, thuggery, drugs, gambling, rape etc among youths. To solve the above problem revenging the society, this paper looked at effective utilization of electrical/electronics skills for social vices and unemployment reduction. The ultimate aim of Electrical-Electronics education is gainful employment and it is interesting to note that the discipline is dynamic, lucrative and versatile and as such have numerous employment opportunities for self-reliance. The concept of social vices, unemployment, skills in Electrical-Electronics Education; the goals of Electrical-Electronics Education in curbing social vices and unemployment rate were discussed. The paper also outlined the causes of social vices and unemployment. The skills in electrical/electronics discussed are radio and TV repair and servicing skills, house wiring skills, electrical installation skills, transformer installation and earthing skills, conduit wiring skills, battery charging skills, and electric machine coil winding skills. To this, the paper suggested that engaging youths in electrical/electronics skills or the like will go a long way in reducing the menace of social vices and unemployment among youths and that, the wide gap between the classroom and the industry should be bridged by skill acquisition policy in every ramification. And the conclusion was that, the ratio of theoretical to practical should be 30:70 because you learn what you see, but you remember what you touch.

DOI: https://doi.org/10.5281/zenodo.15735178

Aero-Mind: Intelligent UAV Navigation

Authors: Professor Dr. R.N.Patil, Mr.Siyang Prafulla Kamble

Abstract: The advancement of autonomous aerial systems is transforming military operations, offering enhanced situational awareness, precision, and safety. This project focuses on developing a comprehensive 3D simulation framework within the Godot engine to model UAVs operating in complex military environments. The primary objective is to enable these autonomous drones to navigate dynamically around obstacles, assess threats, and execute missions effectively in real-time scenarios. To achieve this, the simulation incorporates sophisticated algorithms powered by machine learning techniques for obstacle detection and decision-making, physics-based models for realistic flight behavior, and advanced path planning methods to chart optimal routes amidst challenging terrains. A crucial aspect of the framework involves simulating sensor inputs such as radar, LIDAR, and visual sensors, which allow UAVs to perceive and interpret their environment accurately. These sensor simulations are integrated with real-time data processing systems that facilitate immediate response and adaptive navigation. The simulation environment is designed to replicate realistic military scenarios, including urban landscapes, rugged terrains, and obstacle-rich zones, providing a robust platform for testing autonomous behaviors under various operational conditions. By leveraging Godot's capabilities for 3D visualization and physics, this project aims to deliver a scalable and flexible tool for developing and evaluating autonomous UAV technologies. The ultimate goal is to enhance the operational proficiency of military drones, ensuring safer and more efficient deployment in complex battlefield environments. Furthermore, this simulation framework can serve as a foundation for future research in defense technology, contributing to the development of autonomous systems that are capable of performing in high-stakes, real-world scenarios with minimal human intervention. Through this work, we seek to bridge the gap between simulation and real-world application, supporting the ongoing evolution of autonomous aerospace systems in defense.

Optimizing Fertilizer Application Using Machine Learning for Precision Agriculture

Authors: Dr. Pankaj Malik, Anmol Singh Tomar, Ayush Trivedi, Aniruddha Paliwal, Ansh Goyal

Abstract: Efficient and site-specific fertilizer application is a cornerstone of precision agriculture, aiming to enhance crop yield while minimizing environmental impact. Traditional fertilizer practices often lead to overuse or under-application, resulting in resource inefficiency, soil degradation, and reduced profitability. In this study, we propose a machine learning-based system for optimizing fertilizer application by analyzing key agronomic parameters such as soil nutrients (N, P, K), pH, organic carbon, weather conditions (temperature, rainfall), and crop type. We evaluated several machine learning models, including Random Forest, Artificial Neural Networks, and XGBoost, using the publicly available Soil and Crop Fertilizer Recommendation Dataset. The experimental results show that the XGBoost model achieved the best performance with an accuracy of 93.4%, F1-score of 0.92, and AUC of 0.96 in predicting the optimal fertilizer type and dosage. Field-level simulations further demonstrated a 17% increase in average crop yield and a 23% reduction in fertilizer usage compared to traditional application methods. These findings suggest that machine learning can play a significant role in advancing sustainable agricultural practices by delivering intelligent, data-driven fertilizer recommendations.

DOI: https://doi.org/10.5281/zenodo.15737088

Design and Performance Evaluation of an Energy-Efficient Biomass Cookstove Using Multiple Biomass Fuels

Authors: Hassan Raja, Abhishek Choudhary, Akul Kumar, Soumya Ranajan Parimanik, Radha Krushna Sahu

Abstract: This paper deals with the design and performance evaluation of an energy-efficient biomass cookstove suitable for a variety of biomass fuels including babul wood (Prosopis juliflora), groundnut shell briquettes (Arachis hypogaea), sawdust briquettes, and cashew nut (Anacardium occidentale) shells. The stove is constructed using refractory insulation (Insulyte-11U) to minimize heat losses and operates on the inverted downdraft gasification principle. Thermal performance testing revealed a thermal efficiency of approximately 35%, with a power output range of 1.53 to 1.76 kW depending on the fuel used. The maximum flame temperature recorded was 763°C for cashew nut shells. Emissions remained within acceptable indoor air quality limits, with CO ranging from 3–6 ppm and CO₂ from 17–25 ppm. The results confirm the stove's suitability for rural households and its potential for fuel savings, reduced emissions, and operational safety, thereby supporting wider adoption of biomass-based clean cooking technologies.

Design and Analysis of a Box-Type Solar Cooker

Authors: Ankit Kumar Maharana, Sagar Mallik, Radha Raman Padhi, Banamali Dalai

Abstract: This project involves the design, fabrication, and testing of a box-type solar cooker that utilizes solar energy for clean and cost-effective cooking. The cooker comprises a well-insulated wooden box, an aluminum inner lining, a glass lid, and reflective aluminum sheets to concentrate sunlight into the cooking chamber. Performance testing showed that the cooker is capable of reaching sufficient internal temperatures to boil water and cook standard food items under varying weather conditions. The solar cooker demonstrates practical viability for rural and off-grid regions, especially where conventional fuels and electricity are scarce. The study also outlines design constraints, including sunlight dependency and heat retention challenges, and proposes future enhancements to improve cooking performance and reliability.

Leveraging Graph Embeddings To Detect Fake Vendors In E-Commerce Supply Networks

Authors: Dr. Pankaj Malik, Tanvay Soni, Tanishq Sharma, Rashi Dongre, Sarthak Shrimali

Abstract: The rapid expansion of e-commerce platforms has introduced significant challenges in ensuring the authenticity of vendors and the integrity of supply chains. Traditional fraud detection techniques often fail to capture the complex, dynamic relationships among vendors, products, and transactions. In this study, we propose a novel graph-based machine learning framework that leverages graph embeddings to detect fake vendors in e-commerce supply networks. By modeling the supply ecosystem as a heterogeneous graph comprising vendors, products, transactions, and reviews, we employ node embedding techniques such as Node2Vec and GraphSAGE to learn low-dimensional representations of entities. These embeddings are then fed into supervised classifiers (e.g., Random Forest, XGBoost, and GCN) to identify fraudulent vendors. A labeled dataset was constructed using transaction logs and platform moderation records from a leading e-commerce platform, consisting of 12,000 vendors (1,500 labeled as fake). Our approach achieved a detection accuracy of 94.3%, with a precision of 91.8%, recall of 89.6%, and F1-score of 90.7%, outperforming baseline methods such as rule-based heuristics and traditional feature-based classifiers. Furthermore, the embedding visualizations revealed distinct clusters of suspicious vendor behavior, highlighting the interpretability of our model. The results demonstrate the effectiveness of graph embedding techniques in capturing relational patterns and structural anomalies, offering a scalable and intelligent solution for fraud detection in e-commerce supply chains.

DOI: http://doi.org/10.5281/zenodo.15737544

A Comparative Analysis Of DNA Degradation Patterns In Forensic Samples Involved In Sexual Assault Cases

Authors: Bhawna Sharma, Ritika Sinha

Abstract: – Sexual assault is a pressing global public health issue, with forensic experts recognising that the majority of such cases remain unreported. In India only, 31,677 rape cases are reported annually, averaging 86 daily, with only 3-5 solved each day. However, to identify suspected culprits, reported incidents require thorough investigations. DNA samples play a vital role in forensic investigations, particularly in cases of sexual assault, where they can provide critical evidence for identifying perpetrators. When collected from a crime scene or the victim's body, DNA evidence often serves as a vital source of clues, playing a pivotal role in the identification process and aiding in the resolution of cases. Extracting a genetic profile involves several stages: extraction, quantification, amplification, separation of STR fragments, and genotyping. While accurate profiling is possible when samples contain sufficient, high-quality genetic material, this ideal scenario is often challenging. Many cases involve samples with degraded or insufficient DNA from the perpetrator, complicating forensic analyses using current methodologies. DNA degradation, influenced by environmental factors, impacts samples from crime scenes, leading to varying degrees of integrity loss. By implementing proper methods and understanding degradation levels, we can prevent sample degradation, leading to faster and more reliable investigation results. Understanding the causes and mechanisms of DNA degradation is essential for improving analysis techniques. Degradation affects DNA universally, eroding its quality over time. Environmental factors such as light exposure, preservation techniques, and material type cause varying deterioration patterns in samples. This thesis investigates biological samples commonly encountered in sexual assault cases, analyzing factors that influence their condition and the rate of DNA degradation during the initial case days. It is a comparative study of degradation trends in forensic fluids, such as semen and vaginal fluid, under different environmental conditions. The study is intended to increase knowledge on DNA integrity to enhance forensic techniques and resolve challenges presented by degraded samples.

DOI: https://doi.org/10.5281/zenodo.15743994

The Possible Impact Of Quantum Computing On Cryptography

Authors: Anu Thind

Abstract: The purpose of this paper's abstract is to explain how quantum computing works from a current cryptographic perspective and to provide the reader with a basic understanding of post-quantum algorithms. The post-quantum cryptography section specifically discusses various mathematics-based quantum key stream techniques, lattice structure cryptography, multivariate structure cryptography, hash-based symbols, and code-based coding. Quantum computing is a new technology in today's society. The development of quantum computing applications is the focus of many communities and research institutions around the world. Another field that is gradually developing steadily is artificial intelligence. The main goal of this study is to determine the impact of the development of quantum computing research on artificial intelligence applications. Therefore, computational methods are used in the methodology of this study. In order to arrive at the findings of this study on the growing impact of quantum computing research on specific applications of artificial intelligence, this study also discusses the impact of quantum computing on the field of artificial intelligence and how quantum computing affects the discipline.

A FORECASTING FUTURE WATER REQUIREMENTS AND ASSESING STORAGE CAPACITIES IN RESERVOIRS

Authors: M.Sai deeraj, N.Rajesh, A.Praveen, Yash, Dr.G. Mary Swarnalatha

Abstract: Water scarcity is a growing concern due to population growth and climate variability. Accurate forecasting of future water requirements and adequate reservoir storage are essential for sustainable water management. This paper explores methods like statistical modeling, hydrological simulations, and machine learning to predict water demand and assess reservoir capacities. By analyzing existing studies, it proposes practical strategies to address challenges like sedimentation and climate uncertainty. The findings emphasize adaptive planning to ensure water security for communities.

DOI: http://doi.org/

Enhancing Social Media Experiences Through Recommendation Systems: Techniques, Applications, And Future Prospects

Authors: Onkar R. Malawade

Abstract: Recommendation systems play a pivotal role in transforming user engagement on social media platforms by curating personalized content feeds, friend suggestions, advertisements, and more. This paper presents a comprehensive review of recommendation techniques applied in social media, including collaborative filtering, content-based filtering, graph-based models, and transformer-powered algorithms. It explores how these systems enhance user satisfaction, interaction, and platform retention. Drawing on over 180 peer-reviewed studies and industry implementations, this paper examines the technological foundations of social media recommenders, with a special focus on trends like deep learning, user profiling, and real-time adaptation. Critical issues such as filter bubbles, algorithmic bias, privacy concerns, and limited interpretability are also discussed. Finally, the paper suggests pathways for ethical, explainable, and inclusive social recommendation systems.

Application of Fuzzy Controllers for Liquid Flow Control Process in Chemical Industries Using Sugeno Model

Authors: G. Vasanti

Abstract: The control of liquid flow in chemical industries is a critical aspect of ensuring process stability. In the chemical industry, precise liquid flow control is crucial for ensuring product quality, minimizing waste, and enhancing process efficiency. Traditional control systems often face challenges in handling the inherent uncertainties and non-linearity’s present in liquid flow processes. This paper investigates the application of fuzzy logic controllers, specifically the Sugeno model, for liquid flow control in chemical processes. A fuzzy inference system (FIS) – Sugeno Model is designed to model the complex relationship, enabling effective control under varying operating conditions, chosen for its computational efficiency and ease of implementation in real-time control systems. The results demonstrate that the fuzzy controller offers superior performance in handling disturbances, uncertainties, and non-linear behaviours compared to traditional PID controllers. This study highlights the potential of fuzzy controllers in achieving robust and adaptive flow control in the chemical industry.

Things That Think: Exploring IoT’s Role Across Different Sectors

Authors: Pournima P R

Abstract: – IoT stands for Internet of Things. It refers to the connection between physical devices, such as appliances and vehicles, that are embedded with software, sensors, and connectivity which enables these objects to connect and exchange data. This technology allows for the collection and sharing of data from a huge network of devices, creating opportunities for more efficient and automated systems. Internet of Things (IoT) is the networking of physical objects that contain electronics embedded within their architecture in order to communicate with the external environment. Advancements in medicine, power, gene therapies, agriculture, smart cities, and smart homes are just a few of the categorical examples where IoT is strongly established. The Internet of Things (IoT) can be used in many different aspects of life, in both the private as well as public sectors. Thanks to IoT, people can do anything from anywhere by using IoT in their daily life. Consumers can use the IoT to help them monitor their lifestyle, agriculture in smart ways, industrial productions, track logistics & supply chain, make their homes automated by smart home technologies and cities smarter by using technologies like smart parking, smart street light etc.

DOI: http://doi.org/

Oral Cancer Detection Using Deep Learning

Authors: Pallavi M R, Dr.Manjunath M, Dr. Evangelin Geetha D

Abstract: Oral cancer is a major health concern globally, and early detection plays a vital role in successful treatment and improved survival rates. This paper presents an innovative diagnostic system that employs advanced deep learning models to analyze images of the oral cavity alongside patient health data. By combining convolutional neural networks for image analysis with recurrent neural networks for sequential data processing, the system achieves enhanced precision in detecting early signs of oral cancer. Additionally, the use of explainable AI techniques provides transparency in decision-making, helping clinicians to better interpret results and build trust in the system. Designed to be adaptable, this solution supports mobile imaging devices and emphasizes patient data privacy, making it suitable for both well-equipped hospitals and resource-limited settings. Experimental results validate the system’s capability to deliver accurate, fast, and reliable diagnoses, which could significantly improve early intervention and patient care in oral oncology.

Flexural Behavior of CFST Beams with Plain and Sustainable Concrete Infill

Authors: Research Scholar Venugopal Burugupally, Dr Ajay Swarup

Abstract: This paper focused on flexural performance of concrete-filled steel tubular (CFST) beams with plain and green concrete infillings. The specimens, a total of thirty (30) square and rectangular hollow steel sections filled with normal mix concrete (NMC), fly ash concrete (FAC), quarry waste concrete (QWC) and low-strength concrete (LSC) were tested in basic ways, namely, under static and under cyclic bending mode. Comparative tests were drawn against unfilled hollow sections to determine the effect of composite action and of the type of concrete. It was found that NMC and FAC has the highest flexural capacity, stiffness and ductility with NMC performing better than the others by 42 percent in peak load compared to the hollow beams. Both FAC and QWC offered alternatives that were environmentally sustainable and acceptable in performance and LSC exhibited limited structural viability. Depending on the concrete type, the failure modes were characterized by ductile plastic hinging, buckling before reaching the core and core crushing. These results serve to emphasize that infill concrete characteristics are crucial to maximize flexural behavior of CFST structures and propose the green concrete mixes applicable in sustainable structures.

Enhancing Upper-Body Robotic Motion Through Human-Based Criteria: A Study Of Inverse Kinematics And Multi-Criteria Performance Analysis

Authors: Radha Krishan Yadav

Abstract: This study introduces a bio-inspired inverse kinematics (IK) framework for upper-body humanoid robots, integrating human biomechanical principles to improve motion naturalness, efficiency, and adaptability. By combining multi-objective optimization with human motion analysis, the framework addresses limitations of traditional IK solvers, such as rigid motion and poor task adaptability. Human upper-limb kinematics were analysed using motion capture and OpenSim, distilling features like energy minimization and joint comfort into dynamic cost functions. A hybrid Weighted Least Norm (WLN)-gradient IK solver achieved real-time performance (<100 ms latency) and outperformed classical methods by ~20% in human-likeness and ~50% in safety margins. Validation on 7-DOF humanoid arms showed 90–95% task success rates in Activities of Daily Living (ADLs). Applications in assistive robotics and industrial cobots highlight the framework’s potential for human-robot interaction (HRI). Future work includes reinforcement learning for adaptive IK and soft robotics integration.Key criteria, including metabolic cost, safety, coordination, and kinematic efficiency, are analyzed to optimize robotic upper body motion for human-like performance. A multi- criteria performance framework is proposed, integrating these factors to assess their impact on task-specific outcomes. The methodology involves computational modeling, simulation, and comparative analysis of robotic motion against human benchmarks. Results reveal that incorporating human-based criteria enhances the adaptability and efficiency of robotic systems, with notable improvements in safety and coordination during complex tasks. These findings contribute to the advancement of human-robot interaction and the design of next-generation robotic systems for applications requiring precise and natural upper body movements.

Green Synthesis Of Cellulose Nanocrystals From Banana Pseudo Stem And It’s Characterization

Authors: Vaidik Savaliya, Dr. Hemangi Desai

Abstract: Cellulose nanocrystasl were isolated from Banana Pseudo Stem, an under-utilized agriculture biomass. Raw Banana stem was collected from Abrama Farm, Kamrej, Surat. Which were then dried in the Hot Air Oven @ 105°C for 6 hours and after cooling and desiccating, crushed in a grinder and sieved to get fine powder particles. The chemical compositions of cellulose nanocrystals from banana stem after each treatment were determined according to Technical Association of the Pulp and Paper Industry (TAPPI) standard. The lignin content was determined according to the TAPPI norm T222 om-88. The Hemicellulose content was determined according to the TAPPI T257 om-09. Cellulose content was determined by extracting hemicellulose with the aqueous sodium hydroxide (17.5%) for 5 h before quenching the reaction with ice. The obtained white powder was washed with copious amount of water until filtrate (Whatman Filter Paper No. 42) becoming neutral. Cellulose nanocrystal (CNC) was obtained by sulfuric acid hydrolysis according to the reported method. The resultant mixture was first centrifuged at 1000 rpm for 10 min ice-cooling temperature (18°C) to remove large particles, and then centrifuged at 11,000 rpm for 15 min ice-cooling temperature (-15°C) to obtain cellulose nanocrystal. The obtained cellulose nanocrystal was washed and centrifuged repeatedly for 3 times before dialysis against distilled water for 2 days. The obtained CNC was processed by ultrasonic processor (VCX 500:500 W, Sonics & Materials, Newton, CT) to suspension better before further application. The composition of Banana Pseudo Stem found after research experiment was Cellulose – 69.9%, Hemicellulose – 19.6% and Lignin – 5.7%. The chemical pretreatment removed the non-cellulosic constituents of Banana Pseudo Stem was found to be an essential and fundamental procedure for the Cellulose Nanocrystal. The pretreatment process of Banana stem included Alkali Treatment, Delignification and Bleaching Treatment. FEG – TRANSMISSION ELECTRON MICROSCOPE (HR-TEM) was used to find the size of obtained CNC. The obtained Cellulose Nano Crystal exhibited a different Category of 500 nm, 200 nm, 100 nm, and 1µm. 500 nm scale observe the (331nm, 290 nm and 271nm) diameter size of Cellulose Nano Crystals. 200 nm scale observe the (95.2nm, 87.5nm, 81.6nm, and 67.9nm) diameter size of Cellulose Nano Crystals.

INDIA’S NATIONAL CYBERSECURITY STRATEGY (2020 DRAFT): GAPS BETWEEN POLICY AND LAW_605

Authors: Akshara Gupta

Abstract: The frequency, sophistication, and intensity of cyber-attacks have compelled strong national cybersecurity efforts worldwide. India being among the largest digital economies is confronting special cybersecurity challenges in terms of cybercrime, data breaches, vulnerabilities in infrastructure as well as issues regarding digital sovereignty. The Ministry of Electronics and Information Technology (MeitY), anticipating this challenge and responding to it, formulated the National Cybersecurity Strategy (NCS) in 2020. Though the draft gives an overarching vision including secure cyberspace, data privacy as well as institutional coordination, it shows gaps galore when tested against current legal norms and principles. This paper critically examines the 2020 draft National Cybersecurity Strategy (NCS) by contrasting its goals with India's existing legislative and regulatory framework, including the Information Technology Act 2000, the Personal Data Protection Act (DPDP), 2023, and industry-specific policies. The research reveals major policy-law disconnects including a lack of legally enforceable commitments, institutionally fragmented accountability, inadequate cyber deterrent provisions, and insufficient transparency regarding surveillance and privacy protections. Additionally, the draft strategy is lacking in specific timeframes, implementation mechanisms, and convergence with international norms and conventions on cybersecurity. Through doctrinal legal assessment and policy examination, the paper analyzes how gaps might jeopardize India’s cybersecurity posture and international digital credibility. The study culminates with policy proposals for legal reform, inter-agency coordination structures, and institutionalizing cybersecurity audits to implement the strategy efficaciously. Closing the gap between policy aspiration and enforceable law is necessary to maintain India’s cyberspace resilient, secure, and rights-respecting amidst rising digital threats.

DOI: http://doi.org/

Enhancing The Accuracy Of Candidate Selection For Hiring Using Natural Language Processing In Artificial Intelligence

Authors: Assistant Professor Shanmuga Priya K, Manikandan S, Karan Singh D, Bharanitharan S, Manikandan S

Abstract: To leverage towards better hiring and recruiting methods the traditional review of resumes is often carried out via simple keyword matching. It records only superficial abilities and credentials. Although candidates are shown the basic skills and qualifications, it misses the deeper competencies such as solving problems, personal development and contributions to projects. This paper proposes an artificially intelligent approach which combines machine learning (ML) for assessing abilities and potential, natural language processing (NLP) for reviewing resumes and job descriptions, and graph-based career mapping to visualize career progression. Compared with traditional resume scoring models, this proposed methodology presents more informed candidate evaluation on all factors including context, experience and growth potential. Professional network analysis, accuracy and quality of input data, as well as candidate skill alignment is one of the important aspects of the proposed model. The graph-based method presents some career paths and the practical contributions to the study via mapping abilities over time. By using our proposed technology approach, hiring decisions can be improved and position matching can be performed optimally. Moreover, context-aware analysis can provide an accurate evaluation of candidate potential. In the field of HR technology, this innovative method has a new standard in fair and intelligent talent evaluation

Enhancing Handwritten Text Recognition And Spelling Correction Using Auto-Encoding Language Models In Deep Learning

Authors: Dr. P. Meenakshi Devi, G. Sandhya S. Dharshini, K. Balambiga

Abstract: This paper presents an advanced deep learning system that takes handwritten text recognition and correction to the next level. It combines powerful computer vision and language modeling techniques to not only read messy or varied handwriting but also clean it up with smart, context-aware corrections. At the heart of the system is a BERT-based Transformer that uses self-attention to understand the structure and flow of handwriting. A CNN first processes the scanned image to pull out visual features, which are then passed through the Transformer to generate readable text. But it doesn’t stop there once the initial text is generated, a language model with autoencoding and contextual awareness steps in to refine the results. It fixes spelling mistakes and grammatical errors by analyzing the full sentence, much like how a human would understand and edit a paragraph. This approach outperforms traditional OCR tools and dictionary-based corrections by adapting to different handwriting styles, languages, and even noisy or low-quality scans. The system is fast, accurate, and flexible, making it a practical solution for tasks like digitizing educational materials, archiving historical documents, managing healthcare records, and streamlining office paperwork. By merging vision and language understanding in a single pipeline, this system offers a smarter way to process handwritten content in the digital age

Resilient Event-triggered Estimation Under Coordinated False Data Injection Attacks

Authors: Mr. Akshai Vinu. K, Dr. F. Ramesh Dhanaseelan Professor, Dr. M. Jeya Sutha Associate Professor

Abstract: Distributed state estimation in nonlinear systems faces critical security and efficiency challenges, particularly under stealthy cyber-attacks and energy constraints. This project introduces a detection strategy for nonlinear consensus filters, allowing sensor nodes to verify local state estimates and error covariances to identify subtle intrusions. To enhance resource efficiency, an event-triggered distributed Cubature Kalman filtering (DKF) algorithm is proposed. Unlike traditional methods that require continuous data transmission, this approach activates updates only when necessary, significantly reducing communication overhead while maintaining estimation accuracy. Stability analysis confirms the reliability of the algorithm, ensuring robust performance even in adversarial conditions. Practical implementation in sensor networks demonstrates its effectiveness in mitigating stealthy attacks and optimizing energy consumption. By integrating advanced detection mechanisms with event-driven filtering, this work provides a secure, efficient, and resilient solution for nonlinear state estimation in distributed systems

DOI: 10.5281/zenodo.15834124

The Lunar Cycle And Bitcoin: Empirical Evidence And Practical Implications For Crypto Trading

Authors: Monish Patil

Abstract: This research paper investigates whether lunar phases have a measurable impact on Bitcoin price movements, a hypothesis that remains controversial within both financial and scientific communities. By reviewing empirical backtests, academic literature, and real-world trading data, the study seeks to determine if moon phase trading strategies can consistently generate profits in the cryptocurrency market. Several backtests show that strategies aligned with lunar cycles—such as buying during new moons and selling during full moons—have, at times, outperformed simple buy-and-hold approaches, especially in bullish market conditions. For example, some studies report annualized returns exceeding 30% when applying moon phase filters to Bitcoin trades. However, these results are often accompanied by high volatility and significant drawdowns, raising questions about their practical reliability. Academic research in traditional markets has produced mixed findings, with some evidence of modest lunar effects on stock returns but little consensus on causality. In the context of Bitcoin, which is heavily influenced by investor sentiment and speculative trading, it is plausible that psychological factors tied to lunar cycles could temporarily affect market behavior. Nevertheless, the statistical significance of these patterns is debatable, and no robust causal mechanism has been established. As a result, while moon phase trading strategies may offer intriguing signals for swing traders, they should be viewed as supplementary tools rather than standalone systems. The broader evidence suggests that market fundamentals and technical indicators remain far more reliable for consistent trading performance.

DOI: https://doi.org/10.5281/zenodo.15782115

Camel Detection System over Optical Ground Wire among Highways between Cities and Villages

Authors: Ahmad Alotaibi, Mansour Alyami, Hussain Alsalman, Khaled Alsheddi

Abstract: Gulf countries such as Saudi Arabia, Kuwait, Qatar, and the UAE face recurring hazards due to camels crossing highways, often leading to fatal car accidents. The current mitigation strategy—constructing double fences on both sides of highways—is both capital- and labour-intensive, especially considering the region’s harsh climate and long highway stretches (e.g., the 380 km Riyadh–Dammam route). To address this issue more efficiently, this patent proposes an innovative, cost-effective monitoring system that leverages existing power transmission towers and infrastructure alongside highways. These power towers, connected via power lines and Optical Ground Wire (OPGW) fibres, are already used to transmit electricity between substations. The proposed system involves mounting a Local Access Network (LAN) on each tower, composed of an outdoor industrial switch and a long-range camera. Each switch is powered by a standalone DC system utilizing solar panels, Lithium Iron Phosphate batteries, and an Automatic Transfer Switch (ATS), ensuring operation under extreme environmental conditions. The long-range cameras, connected via PoE, continuously monitor the highway for camels. Captured video is analysed in real-time by a YOLO (You Only Look Once) object detection algorithm hosted in a centralized data centre. Once a camel is detected, an alert is automatically sent to the highway security team via SMS, with location data derived from the reporting camera's IP address. This solution eliminates the need for costly new infrastructure and extensive maintenance, offering a scalable, environmentally resilient system to prevent camel-related accidents across Gulf highways.

ExpressAI

Authors: Samay Gupta, Rishee Mulchandani, Riti Dodiya

Abstract: In natural language processing, sentiment analysis has grown in importance, especially for multilingual and code- mixed languages like Hinglish. Analyzing sentiments in movie subtitles is still largely unexplored, despite themajority of sentiment analysis research concentrating on social media and product evaluations. This work introduces a newmethod for sentiment analysis of movie subtitles that combines deep learning-based sentiment classification, Hinglish-to-Hindi transliteration, and optical character recognition (OCR)-based subtitle extraction. The retrieved subtitles go through a transformer-based model for emotion analysis, preprocessing to eliminate noise, and transliteration into Hindi using the Google Translator API. Accurate sentiment classification is made possible by the suggested methodology, which captures the emotional tone of subtitles. Results from experiments show how well our method works with transliterated, noisy, and code- mixed text. By providing insights into the emotional dynamics of cinematic storylines, this research helps close the gap between sentiment analysis and multimedia content understanding. As a result, the words sentiment analysis and text analysis developed their paths to becoming important computational linguistics and text analysis components. [1

DOI: http://doi.org/

RNN-Based Heartbeat Sound Analysis With Django Integration

Authors: Sri Mira P, Professor Dr. P. Sujatha, Head, Assistant Professor Dr.M.Sakthivanitha

Abstract: This research work presents an innovative approach to heartbeat audio classification using Recurrent Neural Networks (RNNs) integrated with the Django framework. The primary aim is to develop an efficient and accurate system for classifying heartbeat sounds to aid in the early detection and diagnosis of cardiac conditions. The system leverages RNNs, which are particularly suited for processing sequential data, to analyze and classify heartbeat audio recordings. The Django framework facilitates seamless integration, providing a robust and scalable web application for data management, model deployment, prediction. The RNN model is trained on a diverse dataset of heartbeat audio recordings, enabling it to recognize various cardiac anomalies. The proposed system demonstrates high accuracy and reliability, making it a valuable tool for healthcare professionals. Additionally, the integration with Django ensures that the system can be easily accessed and utilized in clinical settings, promoting widespread adoption and improving patient outcomes.

Climate Change and it’s impact on the Environment

Authors: Assistant Professor P.S.Sutar, Mr.Shrijeet Patole, Mr.Pruthaviraj Patil, Mr.Suyash Hajare, Mr.Vikrant Anuse

Abstract: Climate change stands as one of the most significant and urgent challenges of the 21st century. Far from being a future threat, its impacts are being felt today across every continent and ocean. From rising global temperatures to intensifying storms, from melting glaciers to shrinking biodiversity, the evidence is clear: our planet is undergoing rapid transformation. This paper delves into the multifaceted causes of climate change, including anthropogenic activities such as the burning of fossil fuels, deforestation, and unsustainable industrialization. It further explores the environmental, social, and economic repercussions of a warming world. By drawing on a range of scientific reports, global climate models, and empirical data, this paper presents an in-depth examination of climate change, advocating for collective mitigation strategies, adaptation mechanisms, and sustainable policy actions that can protect both the environment and human civilization.

Field Extensions And Galois Theory In Solving Higher-Degree Polynomials

Authors: Assistant Professor Rajkumar Soni, Assistant Professor Rahul Kaushik

Abstract: Early successes in solving polynomial equations up to degree four by radicals most famously Cardano’s solution to the cubic and Ferrari’s to the quartic demonstrate the power of adjoin-and-solve techniques in classical algebra (Dummit and Foote 765). However, the general quintic and higher-degree cases elude such formulas: Abel’s impossibility theorem proves that no expression in a finite combination of radicals can capture the roots of an arbitrary fifth-degree polynomial (Abel “Mémoire” 12). This elusion finds its true explanation in the language of field extensions and group theory. By considering a polynomial’s splitting field and the automorphisms that permute its roots, one constructs the Galois group a measure of the equation’s intrinsic symmetries (Stewart 34). The Fundamental Theorem of Galois Theory then establishes a one-to-one correspondence between intermediate fields and subgroups of this Galois group, yielding a precise criterion: a polynomial is solvable by radicals if and only if its Galois group is a solvable group (Rotman 216; Artin 52).This paper first reviews the foundations of field extensions and Abel’s theorem, then develops Galois’s structural framework. It next applies the Galois correspondence to characterize solvable cases, illustrating cubic and quartic examples before showing why the symmetric group S5S_5S5 defies solvability. Subsequent sections examine special higher-degree families such as cyclotomic and trinomial cases and modern algorithms for computing Galois groups and constructing number fields (Cohen; Neumann 142). Through case studies, we compare classical formulaic methods with contemporary computational approaches, highlight open problems, and discuss implications for number theory, cryptography, and algebraic geometry. In conclusion, we underscore the enduring relevance of Galois theory and outline future directions that integrate algorithmic techniques with group-theoretic insights.

Skin Cancer Prediction By Ml

Authors: Rohini A Zambare, N.S.Kulkarni

Abstract: Skin cancer is one of the most commonly diagnosed cancers worldwide, and early detection significantly improves treatment outcomes. Machine learning (ML) has emerged as a powerful tool in medical diagnostics, offering accurate, efficient, and scalable solutions for skin cancer prediction. This paper presents a comprehensive approach to classifying skin lesions using ML models such as Convolutional Neural Networks (CNNs), Support Vector Machines (SVMs), and Random Forests. Using datasets like HAM10000 and ISIC, we analyze performance metrics including accuracy, precision, recall, and F1-score. The experimental results show that CNN-based models outperform traditional ML algorithms in detecting melanoma and other skin cancers. This study demonstrates the potential of AI-assisted dermatological diagnosis, thereby contributing to improved clinical workflows and patient outcomes.

Remote Patient Monitoring In Healthcare: Leveraging IoT For Continuous Care

Authors: Nithin Nanchari

Abstract: The integration of the Internet of Things (IoT) in healthcare has redefined the landscape of patient care by enabling continuous health monitoring beyond traditional clinical environments. Remote Patient Monitoring (RPM) systems powered by IoT technologies use smart sensors, wearable devices, cloud computing, and artificial intelligence to collect and transmit real-time patient data. These systems improve care quality, reduce hospital admissions, and enhance operational efficiency by enabling timely medical interventions. This paper provides an in-depth overview of IoT-driven RPM systems, explores their critical applications, and highlights their potential to drive the future of smart healthcare infrastructure.

DOI: http://doi.org/

Optimizing Healthcare Costs And ROI Through IoT Integration: A Strategic Evaluation

Authors: Nithin Nanchari

Abstract: The implementation of IoT enables better and more affordable healthcare operations. IoT implements smart devices to deliver real-time patient observations coupled with automated processes and decision systems based on gathered data. The innovative applications cut down operational costs, hospital readmission rates, and staff management expenses, yielding financial savings (Kang et al., 2018). Implementing IoT devices leads to improved patient results, reduced operational expenses, and increased resource deployment, generating financial returns for hospitals. Data protection within IoT healthcare depends on software development, AI algorithms, cloud computing services, and cybersecurity methods. Healthcare technology receives its benefits from IoT automation in combination with predictive analytics. Organizations that implement IoT technology experience financial benefits that enhance their service quality. More AI integration with legislative measures will lead to the digital transformation of healthcare services that produce sustainable and cost-effective medical care delivery.

DOI: http://doi.org/

A Study on the Use of AI and Emerging Technoligies for Identification and Prevention of Digital Frauds With Reference to IDBI Bank in Thane District

Authors: Research Scholar Megha Joshi

Abstract: A digital transformation is the up-gradation of prevailing progressions or primer of new ways of resonant out professional activities using digital technologies that augment a customer’s experience and leads to higher conversion rates for the banks. The pandemic has completely reformed the way people accomplish things in their life, right from shopping & working to banking specifically. Many evolutionary vicissitudes are anticipated to come in the future of digital banking. Some consumers will expect completely sovereign banking progressions due to deficiency of time and acquaintance whereas a few will still vouch for high-level involvement. As the time conceded the traditional banking has enthused forward and welcomed numerous vicissitudes and technologies for affluence of Banking for everyone but meanwhile as the technology entered the banking sector the cyber-criminal also got access in the entire banking system. Here is the introduction of Artificial Intelligence grabbed a step forward. Banking has become cashless, faceless for customer expediency. This thesis explores the evolution, applications, and impact of AI, particularly in the banking and financial sectors. It provides a historical overview of AI, highlighting key milestones, deep learning, and robotics. showcasing its potential to enhance efficiency and security. A key focus of this research is the Indian banking sector, where AI is increasingly utilized for fraud detection, risk assessment, customer service, and operational automation. The study analyses major financial fraud cases, including cyber fraud, insider fraud, credit and debit card fraud, loan fraud, and money laundering, to understand the vulnerabilities in traditional banking systems. Through detailed survey studies, it demonstrates how AI-powered tools such as fraud detection algorithms, biometric authentication, and predictive analytics help mitigate risks and prevent fraudulent activities. By providing a comprehensive analysis of AI applications, it underscores the significance of AI in strengthening the banking ecosystem. It concludes recommendations on leveraging AI for improved fraud prevention, regulatory compliant sustainable financial growth, ensuring a secure and resilient banking infrastructure.

DOI: https://doi.org/10.5281/zenodo.15798162

Techniques to Enhance Production Quality, Safety, and Sustainability through the Use of Machine Learning in IIOT and Smart Production

Authors: Research Scholar Vennila P, Associate Professor Maniraj V

Abstract: Production has been transformed by Industrial IoT (IIoT), which makes data faster and more granularly available to stakeholders at various levels. The goal of evaluating the data gathered in smart manufacturing is often to increase overall efficiency, which entails raising output while reducing waste and energy consumption. Additionally, the IIoT's connectivity rise necessitates extra consideration for higher safety and security standards. Smart production has been impacted by the recent expansion of machine learning (ML) capabilities in a number of ways. The application of various machine learning approaches for IIoT, smart production, and maintenance is summarized in the current study, with a focus on safety, security, asset localization, quality assurance, and sustainability. Each domain—security and safety, asset localization, quality control, and maintenance—has its own chapter, with a final table on common ML techniques and the relevant references. This is because the paper's approach is to give a thorough overview of ML methods from an application point of view. Lessons learned are outlined in the study along with research gaps and future work areas.

A Hybrid SVM-Guided RVM Framework For Enhanced Sparse Classification

Authors: Pham Quoc Thang, Hoang Thi Lam

Abstract: Relevance Vector Machines (RVMs) are known for producing sparse probabilistic models, often with significantly fewer vectors than Support Vector Machines (SVMs). However, RVMs sometimes underperform in classification accuracy due to their reliance on Bayesian inference over the entire dataset, which may not emphasize decision boundary regions effectively. This paper proposes a novel hybrid framework—SVM-Guided RVM (SG-RVM)—which enhances the RVM by leveraging the support vectors of a pre-trained SVM to guide its training. Specifically, the SG-RVM model restricts RVM training to a subset of data points near the SVM decision boundary, thereby focusing learning effort where classification uncertainty is highest. Experiments on multiple benchmark datasets demonstrate that SG-RVM consistently outperforms traditional RVM in accuracy, while maintaining or improving model sparsity.

DOI: http://doi.org/10.5281/zenodo.15797525

Ai-Enhanced Learnig Approaches In English Language Teaching :A Review.

Authors: Mr.Billa Jaheer

Abstract: Artificial Intelligence (AI) has emerged as a transformative force in English Language Teaching (ELT), offering personalized and adaptive learning experiences. Research indicates that AI- driven platforms enhance individualized instruction by tailoring content to learners' proficiency levels and styles. AI chatbots provide interactive conversational practice with real-time feedback, while AI-powered assessment tools streamline evaluation processes, reducing teacher workload and ensuring objective grading. However, studies also highlight significant challenges, including AI's limitations in understanding idiomatic expressions, dialectal variations, and cultural nuances. Furthermore, while AI can complement traditional teaching methods, it cannot replace educators' essential roles in fostering critical thinking and creativity. Teacher training remains crucial, as many instructors lack the necessary skills to effectively integrate AI into pedagogical practices. This review underscores both the opportunities and challenges of AI in ELT, emphasizing the need for balanced and informed implementation to maximize its benefits in language education.

DOI: https://doi.org/10.5281/zenodo.15813019

Power System Surges: Causes, Effects, and Mitigation Strategies

Authors: Richeal Chinaeche Ijeoma, Elendu Victor Odu

Abstract: Electrical power system surges represent a significant challenge in both residential and industrial settings, affecting the reliability and safety of electrical networks. This paper examines the fundamental causes of electrical surges, the potential effects on power systems and sensitive equipment, and various mitigation strategies aimed at preventing surge-induced damages. With the increasing reliance on sophisticated electronics and the growing complexity of power systems, understanding the dynamics of surges is crucial for engineers and utility operators to maintain power quality. A Resistor-Inductor-Capacitor (RLC) circuit, Telegrapher’s Equation, Double-Exponential Function, and Laplace Transform were used as modelling techniques. Conclusively, connecting surge absorbers and surge diverters in parallel is the preferred method for enhancing the performance and reliability of a surge protection system and by adopting a multi-layered protection strategy including Surge Protection Devices (SPDs), proper grounding, and advanced monitoring technologies, power systems can maintain reliability and protect both equipment and infrastructure from surge-induced damage. As electrical systems continue to evolve, integrating smarter, more predictive technologies will further enhance surge protection and ensure the continued resilience of the power grid.

DOI: https://doi.org/10.5281/zenodo.15797841

The Contribution Of Youth In Nation Building : A Case Study On Young Indian’s Initiative_194

Authors: P Suneetha Naidu, Assistant Professors Dr M Praveena

Abstract: Department of Humanities & Sciences, Annamacharya University, Rajampet, Annamayya District, Andhra Pradesh

DOI: http://doi.org/

The Contribution Of Youth In Nation Building : A Case Study On Young Indian’s Initiative_194

Authors: P Suneetha Naidu, Assistant Professors Dr M Praveena

Abstract: The above quotation by Nelson Mandela clearly declares that the youth of any nation are the pillars of its future. Youth play a pivotal role in the dynamic transformation of society by driving social, cultural, and technological advancements. In the 21st century, young people are increasingly involving in reshaping the societal norms, contributing to economic development, and influencing political movements. Their inherent adaptability to change technological fluency, and innovative thinking make them as the key agents of progress in a rapidly evolving world. This paper examines the 'Young Indians’ Initiative' (We Can, We Will), launched by the Government of India in 2002, which serves as a platform for young leaders to actively participate in India's development." The program focussed on the specified areas that include education, skill development, health care and environment. Furthermore, the present study examines the impact of the Young Indians’ initiative, its limitations and challenges in navigating the current scenario in India.

The Impact of AI-Driven Personalized Learning Systems on Student Engagement in Music Education

Authors: Liang Qing, Chng Lay Kee

Abstract: This study investigates the influence of AI-driven personalized learning systems on student engagement within music education settings at higher education institutions in China. Employing a mixed-methods approach, the research utilized structured questionnaires for quantitative analysis and semi-structured interviews for qualitative insights. Findings indicate that AI-driven personalized learning systems significantly enhance student engagement by offering adaptive content, real-time feedback, and individualized learning pathways. These systems foster motivation, self-efficacy, and a sense of ownership in the learning process. However, challenges related to equitable access, data privacy, and the need for teacher training were identified. The study concludes that while AI-driven personalized learning systems hold transformative potential for music education, their integration must be accompanied by robust ethical guidelines and inclusive strategies to maximize benefits for all students.

DOI: https://doi.org/10.5281/zenodo.15798044

The Effect Of Music Education On Preschoolers’ Mental Health In Henan Province

Authors: Pham Quoc Thang, Hoang Thi Lam

Abstract: This paper explores the effect of music education on preschoolers’ mental health in Henan Province. The findings reveal a significant positive effect of music education quality and participation frequency on mental health indicators, with music education quality yielding a β = .437 (p < .001) and participation yielding a β = .382 (p < .001). The results highlight the role of well being (β = .274, p < .001) and emotion regulation (β = .236, p < .001) as mediators, underscoring music education’s transformative potential for early childhood mental health. Multiple regression analyses confirmed that together these factors accounted for over 51% of the variance in mental health outcomes (R² = .512). The results underscore the critical role music education can play in supporting psychological resilience and emotional stability in preschool environments across Henan Province.

DOI: http://doi.org/10.5281/zenodo.15806746

Mind Mate: AI-Powered Virtual Mental Health Assistant

Authors: Dr. Ujwala Sav, Ms. Seema Murkar, Ms. Gaargi Mohite, Ms. Raksha Tikone

Abstract: In this article, we present 'Mind Mate,' an artificial intelligence-driven virtual mental health counselor that aims to offer accessible, anonymous, and stigma-free care via web-based access. The system incorporates an AI chatbot, directory of therapists, self-assessment tools, journaling, and music therapy. Mind Mate seeks to fill the gap between the population and mental health services by leveraging artificial intelligence to offer personalized emotional support. This study presents the background, system design, methodologies, and implications of applying AI for solving mental health problems.

DOI: http://doi.org/

Automated Depression Assessment Using RLHF And RLAIF Instructions In Machine Learning

Authors: Ratnesh Kumar Sharma, Prof. (Dr.) Satya Singh

Abstract: In the domain of machine learning, a useful application of Reinforcement Learning from Human Feedback (RLHF) and Reinforcement Learning from AI Feedback (RLAIF) lies in the automated evaluation of depression in patients of all age groups. By leveraging natural language processing (NLP) techniques and machine learning algorithms, this paper aims to develop effective models for detecting depression through language patterns and assessing the severity of depression. The study demonstrates that RLHF significantly enhances model performance through the incorporation of expert feedback, while RLAIF offers a scalable solution that utilizes AI-driven insights. The findings suggest that both methodologies hold promise for improving automated mental health assessment tools, ultimately contributing to more effective diagnoses and follow up counselling/ treatment.

DOI: https://doi.org/10.5281/zenodo.15804092

Review Part 1: Handwriting Analysis, Forgery And Counterfeiting

Authors: Baraa K. Mohamed, Thamer A. Rehan, Mahmoud A.Hussein, Louay A.l M. Ali, Homam A. Mohammed, Firas H. Abdulrazzak, Ayad F Alkaim, Takialdin A. Himdan

Abstract: Handwriting is a distinctive characteristic of individuals, and has been used for a long time to identify individuals and continues to evolve. This study reviewed the characteristics of handwriting in terms of its advantages, characteristics, and factors influencing writing, including clarity, shape, and drawing, while taking into account the individual characteristics of individuals. Through handwriting characteristics, these characteristics were discussed in order to identify forgery and counterfeiting, which can occur intentionally or unintentionally. The study also included a review of writing characteristics and a comparison with samples diagnosed as forged or altered, with varying degrees of accuracy. The study demonstrated that there are several factors that control the persistence of the same writing style as a distinctive form for individuals. These factors include time and the health status of individuals, which may lead to a forced change in handwriting style.

DOI: https://doi.org/10.5281/zenodo.15805116

Automated Detection Of Psoriasis And Aging Biomarkers Using Machine Learning On Optoacoustic Mesoscopy Images

Authors: Jemimah, Dr. M. Jeyasutha, Associate Professor, Dr. F. Ramesh Dhanaseelan, Professor

Abstract: Ultra-wideband raster-scan optoacoustic mesoscopy (RSOM) is an advanced imaging modality that has shown exceptional capability in visualizing in-vivo epidermal and dermal structures with high resolution. Despite its promise, the automatic and quantitative analysis of three-dimensional RSOM datasets remains largely unaddressed. In this study, we introduce DeepRAP (Deep Learning RSOM Analysis Pipeline), a novel framework designed to analyze and quantify morphological skin features from RSOM images and extract clinically relevant imaging biomarkers for disease characterization. DeepRAP employs a multi-network segmentation strategy based on convolutional neural networks (CNNs) enhanced through transfer learning. This architecture facilitates the automatic identification of skin layers and precise segmentation of the dermal microvasculature, achieving performance on par with expert human annotation. The framework was validated against manual segmentation using RSOM data from 25 psoriasis patients undergoing treatment. The extracted biomarkers successfully characterized disease severity and progression, showing a strong correlation with physician assessments and histological data. In a distinct validation experiment, DeepRAP was applied to a timeseries dataset capturing occlusion-induced hyperemia in 10 healthy volunteers. The framework effectively tracked changes in microvascular biomarkers during occlusion and subsequent reperfusion, demonstrating both high accuracy and reproducibility. Additionally, analysis of a cohort of 75 individuals revealed a significant association between microvascular features in the dermal layer and age, with fine vascular patterns showing the strongest age-related correlation. These findings highlight DeepRAP’s potential to automate and accelerate in-vivo skin analysis, offering a non-invasive alternative to traditional biopsybased methods. The framework enhances the clinical and translational relevance of RSOM by enabling high-throughput, quantitative assessment of skin morphology and vascular health.

DOI: http://doi.org/10.5281/zenodo.15806478

A Study on Nuclear-Grade Boron Nitride and Nuclear Grade Boron Carbide Powder for use in Space Exploration , Atom Bomb and Brahmos Missile Development

Authors: Ajit Kumar Thakur

Abstract: The advancement of propulsion technologies for space exploration has led to the consideration of novel materials, such as nuclear-grade boron nitride (BN) and boron carbide (B4C) powders, as potential fuel and shielding materials. These compounds exhibit high-temperature stability, neutron absorption capabilities, and superior mechanical properties. This review explores the properties of BN and B4C in the context of nuclear thermal propulsion (NTP) and nuclear electric propulsion (NEP), analyzing their effectiveness in enhancing thrust efficiency, fuel longevity, and radiation shielding. Special emphasis is given to the optimal ratio of BN to B4C in nuclear fuel applications based on recent experimental and computational studies, along with a detailed examination of their mechanical properties. The Boron Carbide is also used for the manufacturing of Atom Bomb and Nuclear Bomb. India has made a strive forward in Nuclear technology by the manufacturing of nuclear capable Brahmos Missile using Boron Nitride powder technology.

Studying, Simulation And Analysis Of A Small-Scale Horizontal Axis Wind Turbine Using MATLAB And Ansys

Authors: Ahmed Abdalla, Aya Mohamed, Sama Mohamed, Seif Mamdouh, Mohamed Gamal, Dr. Mostafa Shawky Abdelmoez

Abstract: Wind energy has been one of the most essential parts of the trend of moving towards cleaner, more sustainable energy systems. This is mainly because of two primary reasons, reducing CO2 emissions and secure energy production. Unlike conventional large-scale, carbon-intensive power plants, modern wind farms provide noticeable reductions in greenhouse-gas emissions and enhanced energy security by harnessing an abundant natural resource: the wind. Among various models and configurations of wind turbines, Horizontal Axis Wind Turbines (HAWTs) are the most dominant in wind energy applications. This is primarily because they are the most efficient, the most scalable, and the most mature in terms of their technology. The study gives the reader a structured overview of the advancement, performance, and design features of HAWTs in this literature review. The work presents the main topics of standardized aerodynamic blade design, the operation of the elements of the control mechanism, and possibilities of the improvement of the efficiency of the wind system. Additionally, in the document the comparison between the two types, Horizontal Axis Wind Turbines (HAWTs) and Vertical Axis Wind Turbines (VAWTs), is carried out to show the advantages and limitations of HAWTs. This review identifies the current challenges as well as the possible future development in the wind energy sector, through a deeper analysis of recent research and technical improvements.

DOI:

Design Of Horizontal Axis Wind Turbine (HAWT)

Authors: Ahmed Tarek El-Sayed , Ahmed Mahmoud Makram, Ahmed Mamdouh Mohamed , Eslam Mohamed Waheed, Eslam Mohamed Yehia, Dr. Mostafa Shawky Abdelmoez

Abstract: Cairo University, Faculty of Engineering This research presents a detailed investigation into the design of a Horizontal Axis Wind Turbine (HAWT) blade, applying advanced methodologies such as Exergy Analysis, Fatigue Analysis, and Damping Analysis. The design process began with the development of the blade geometry using optimized performance equations, followed by the use of the Blade Element Momentum (BEM) method. This method effectively integrates the principles of blade element theory with momentum theory to provide an accurate representation of aerodynamic forces. After the design was completed, Exergy Analysis was conducted to evaluate the thermodynamic performance and efficiency of the system. The Fatigue Analysis was carried out to assess the blade's resistance to cyclic stresses over its operational life. Additionally, Damping Analysis was performed to study the stability and vibrational characteristics of the blade, ensuring its long-term functionality. The findings aim to contribute to the development of more efficient and sustainable wind turbine technologies.

Advancements In Wind Turbine Technology: A Comprehensive Study Of Design, Deployment, And Energy Storage

Authors: Ismael Ali, Abdul Rahman Hosny, Abdul Rahman Ali, Ali Antar, Mahmoud Sherif, Professor Dr. Mostafa Shawky Abdelmoez

Abstract: This paper explores key developments in wind turbine technology, focusing on blade innovations, the performance differences between horizontal and vertical axis turbines, and the distinctions between onshore and offshore wind installations. Additionally, the study examines the critical role of energy storage systems in enhancing the efficiency and reliability of wind energy. From advancements in materials and aerodynamics to comparisons of wind farm locations and energy storage methods, this research offers a comprehensive look into the technological, environmental, and economic dimensions shaping the future of wind power.

DOI: http://doi.org/10.5281/zenodo.15827519

Enhancement Of BLDC Parameters Using ANN Controller Across Electric Vehicle.

Authors: Akash Baghel, Professor Amit kumar Asthana

Abstract: The control of Brushless DC (BLDC) motors plays a vital role in various high-performance applications such as electric vehicles, robotics, aerospace, and industrial automation. Traditional control techniques like Proportional-Integral (PI) or Proportional-Integral-Derivative (PID) controllers often struggle to maintain optimal performance under nonlinear conditions, parameter variations, and dynamic load changes. To address these limitations, this study proposes an Artificial Neural Network (ANN)-based control method for BLDC motor operation. The proposed ANN controller is designed to learn the nonlinear dynamics of the motor and generate optimal control signals in real-time. Trained using supervised learning techniques, the ANN effectively predicts the appropriate voltage or PWM duty cycle required for desired speed and torque control. The controller adapts to disturbances and uncertainties, resulting in improved transient response, reduced steady-state error, and better disturbance rejection compared to conventional controllers. Simulation and experimental results demonstrate the superior performance of the ANN-based controller in terms of dynamic response, accuracy, and robustness. This approach enhances the efficiency and reliability of BLDC motor drives, making it highly suitable for intelligent motion control systems in modern applications

MODELING AND ANALYSIS OF LOAD FLOW ANALYSIS ACROSS SOLAR POWER GENERATOR

Authors: Rahul Chikane, Professor Amit kumar Asthana

Abstract: For problem of load flow and power flow analysis is variable stability index. Also, arrangement of different bus system and fault tolerance occurrence across the load. In this case, resolution of power quality and improvement of power distribution performance apply islanding protection method. This paper is resenting result and simulation section arrangement of 9 bus system and Reducing Fault effects.

Future Load Energy Forecast of Stone-City, Mgbede Community Rural Electrification Scheme

Authors: Richeal Chinaeche Ijeoma, Elendu Victor Odu

Abstract: Electrical power system consists of power generation, transmission, and distribution. Rural Electrification is the process of bringing electrical power to rural and remote areas. Rural communities are suffering from massive market failures as the national grids fall short of their electricity demand. This study is focused on the future load energy forecast, and rural electrification system for Stone-City, Mgbede community in Rivers State, Nigeria. The energy forecast for rural electrification indicates a promising trajectory toward increased energy access in underserved regions. The anticipated rise in energy demand, coupled with the deployment of renewable energy sources such as solar and wind, suggests a sustainable path forward. These advancements are poised to significantly enhance the quality of life, economic opportunities, and social services in rural areas. The load forecast for different areas in Stone-City Mgbede Community in Table 5 shows 10 years of energy forecast in the ratio of 5:3:2. However, achieving these goals is not without its challenges. Financial limitations, infrastructure gaps, and technical hurdles must be addressed to ensure the success of rural electrification initiatives. We recommend focusing on policy frameworks that incentivize renewable energy investments, developing robust financing models, and investing in capacity-building programs for local technicians and engineers. Additionally, fostering community engagement and ownership can drive the long-term sustainability of these projects. Looking ahead, the integration of smart grid technologies and energy storage solutions could further revolutionize rural electrification, making it more resilient and efficient.

DOI: https://doi.org/10.5281/zenodo.15827577

Preventing Phishing Attacks Through URL Detection Using HML Algorithm In Machine Learning

Authors: Sowmiya R,, Divakar P, Ragul M, Anbu D

Abstract: – Cyber attacks are among the most widespread and dangerous cyber threats, tricking users into revealing sensitive information by imitating legitimate websites. As these attacks become increasingly sophisticated, traditional detection methods often fail to identify them accurately. This project proposes a hybrid machine learning ML -based system that enhances phishing URL detection by analyzing both the structural and semantic features of URLs. The system extracts a rich set of features, including domain names, subdomains, URL paths, query parameters, and overall URL structure, which serve as critical indicators for identifying hidden phishing patterns. To optimize detection performance, the system integrates the capabilities of two robust ML models: Random Forest and Support Vector Machine . RF functions as both a feature selector and classifier, leveraging its ensemble learning mechanism to improve accuracy while minimizing overfitting. SVM, known for its effectiveness in handling high-dimensional data, constructs an optimal hyperplane to separate legitimate URLs from phishing ones. The hybrid approach of combining RF and SVM enhances the system’s precision, robustness, and overall detection capability. This dual-model system not only addresses the shortcomings of conventional and single-model techniques but also contributes significantly to preventing data breaches and financial losses. The proposed method demonstrates a scalable and efficient solution for real-world phishing detection by applying advanced machine learning techniques to analyze URL characteristics in depth.

Carbon Nanotube Polymer Interaction

Authors: Assistant Professor Er. Rajdeep Saharawat, Zainab Tyagi, Assistant Professor Ms. Meenal Maan

Abstract: This review critically examines the interactions between carbon nanotubes (CNTs) and polymers, highlighting the significance of interfacial dynamics on the mechanical, electrical, and thermal properties of polymer nanocomposites. Both experimental techniques and computational modeling approaches are discussed to understand CNT–polymer compatibility. The study addresses functionalization strategies to improve interaction, challenges of dispersion, processing techniques, toxicity concerns, and emerging applications, culminating in future directions for smart, sustainable materials development.

A Comprehensive Review of Sentiment Analysis Using Transformer Model

Authors: Assistant Professor Dr. Shivani Sharma, Assistant Professor Akhil Kumar, Nitin Yadav, Dilip Kumar

Abstract: In the last several years, the www has altered how individuals communicate with each other, share their thoughts and ideas on social media platforms, and give feedback on websites. Understanding the sentiments from people's ideas, feedback, and interactions is essential in the era of artificial intelligence. Recently, sentiment analysis has received a lot of attention. Sentiment analysis has been widely applied and utilized in a variety of industries, including business. With a focus on the function of Transformer models, this work offers a theoretical discussion of sentiment analysis. The paper will discuss the evolution from traditional approaches to deep learning techniques, delve into the architecture and advantages of Transformers, explore their applications across various domains, and address the challenges and limitations associated with their use.

Encrypted Cloud Storage Using A Dual System Encryption Better Architecture.

Authors: Ayesha, Akheel Mohammed, Sameera Khanam

Abstract: This research introduces a Secure Cloud Data architecture that secures cloud storage and data using dual-system encryption and selective-proofing. Traditional techniques have proven that the recommended solution, which permits any standard access structure inside a composite bilinear group, is adaptively CCA secure while preserving access policy expressiveness. This study aims to improve the model's key generation and re-encryption efficiency. Proxy Re-Encryption (PRE) allows data owners provide other companies access to encrypted cloud data without an innocent- looking cloud server prying. It streamlines data sharing by letting data owners with weaker hardware (like mobile devices) use the cloud for much of their work. Since its beginning, PRE has been proposed and supported. Sec RBAC-Based Proxy Re-Encryption (SecRBAC-ABPRE) uses PRE technology in the attribute-based encryption cryptographic architecture to allow the proxy to switch between access policies. PRE is Proxy Re-Encryption. Secure data exchange in network or cloud applications is one way CP-ABPRE may be utilised with real-time network devices.

CHEMICAL IMPREGNATION METHODS OPTIMIZED FOR SPENT ACTIVATED CARBON REGENERATION_704

Authors: Ravi Verma, Manish Choudhary

Abstract: One efficient method for recovering the adsorptive capacity of spent activated carbon is to regenerate it through chemical impregnation. To regenerate the carbon and eliminate adsorbed pollutants, the spent activated carbon is treated with specific chemicals. The purpose of this study was to examine the properties of the regenerated activated carbon to determine whether it might be used in water purification applications. Spent activated carbon was gathered from the water purification unit's spent activated carbon cartridge. It is easily accessible and contains more carbon. At the ideal activation period, 1N KOH was used to activate the wasted carbon. The elemental analysis of spent activated carbon both before and after activation is the focus of this work. SEM, CHNS (elemental analysis), and significant index iodine number studies were used in this work. The goal of the current project is to investigate the regeneration of spent activated carbon. Regenerated carbon has been found to have well-developed pores and activation sites.

DOI:

CHEMICAL IMPREGNATION METHODS OPTIMIZED FOR SPENT ACTIVATED CARBON REGENERATION

Authors: Ravi Verma, Manish Choudhary

Abstract: One efficient method for recovering the adsorptive capacity of spent activated carbon is to regenerate it through chemical impregnation. To regenerate the carbon and eliminate adsorbed pollutants, the spent activated carbon is treated with specific chemicals. The purpose of this study was to examine the properties of the regenerated activated carbon to determine whether it might be used in water purification applications. Spent activated carbon was gathered from the water purification unit's spent activated carbon cartridge. It is easily accessible and contains more carbon. At the ideal activation period, 1N KOH was used to activate the wasted carbon. The elemental analysis of spent activated carbon both before and after activation is the focus of this work. SEM, CHNS (elemental analysis), and significant index iodine number studies were used in this work. The goal of the current project is to investigate the regeneration of spent activated carbon. Regenerated carbon has been found to have well-developed pores and activation sites.

DOI: https://doi.org/10.5281/zenodo.16361674

Ai Powered Emission Control And Fuel Optimization In Ic Engine

Authors: Budh Sen, Khemraj Beragi

Abstract: As environmental regulations grow increasingly stringent and fuel efficiency becomes a central concern for both manufacturers and consumers, the automotive industry is undergoing a technological shift toward intelligent engine management systems. Internal combustion (IC) engines, though mature, continue to dominate the global vehicle fleet, particularly in developing economies. However, traditional emission control and fuel optimization methods, which rely on fixed calibration maps and rule-based logic, struggle to adapt to real-time driving conditions and evolving operational complexities. This paper explores the transformative role of Artificial Intelligence (AI) in enhancing emission control and fuel efficiency in IC engines through a comprehensive secondary analysis of literature, case studies, and industrial applications from 2015 to 2025. AI techniques such as machine learning, deep learning, and reinforcement learning are capable of analyzing real-time engine data from multiple sensors, enabling dynamic adjustment of parameters like fuel injection, ignition timing, and air-fuel ratios. Case studies from companies such as Bosch, Toyota, and Mahindra demonstrate tangible improvements in emission reduction (up to 18%) and fuel savings (up to 10%) through AI-powered systems. The paper also discusses emerging trends including edge AI in ECUs, hybrid control systems, digital twin modeling, and AI integration in hybrid and biofuel engines. While the potential is vast, challenges such as data noise, computational constraints, legacy system integration, and regulatory compliance must be addressed. The study concludes that AI-driven engine control systems offer a promising path toward cleaner, more adaptive, and efficient automotive technologies.

DOI: http://doi.org/

Recent Advances In GFRP Composite Bridge Decks: Materials, Fabrication Techniques, And Performance Evaluation

Authors: Mr. Atishay Singhai, Assistant Professor Dr. Bhagyashree Naik

Abstract: Glass Fibre Reinforced Polymer (GFRP) composites have emerged as a promising material alternative for bridge deck applications due to their high strength-to-weight ratio, corrosion resistance, and ease of installation. This review provides a comprehensive overview of the developments in GFRP composite bridge decks, focusing on material selection, fabrication methods, structural applications, and performance assessment. Emphasis is placed on the mechanical behavior of various GFRP configurations under static, fatigue, and environmental loading conditions. Analytical and numerical modeling techniques, particularly finite element methods (FEM), are discussed to highlight their role in predicting structural performance. The review also addresses current challenges, such as long-term durability, lack of standardized design guidelines, and cost-related limitations. By consolidating findings from experimental studies and simulations, this paper identifies key research gaps and provides future directions for optimizing GFRP bridge deck systems in civil infrastructure.

DOI: http://doi.org/10.5281/zenodo.16752900

Thermal Analysis Of Composite Materials

Authors: Aakash Muniya, Assistant Professor Khemraj Beragi

Abstract: Composite materials have emerged as critical components in the design and manufacturing of aerospace and automotive structures due to their superior mechanical strength, lightweight nature, and customizable thermal properties. This paper presents a secondary study of the thermal behavior of advanced composite materials, focusing on key parameters such as thermal conductivity, coefficient of thermal expansion, thermal degradation resistance, and insulation capabilities. Through a comprehensive review of peer-reviewed literature and technical reports published between 2015 and 2025, this study compares the performance of carbon fiber-reinforced polymers (CFRPs), glass fiber composites (GFRPs), and aramid fiber composites in thermally demanding applications. The analysis highlights how these materials are used in aircraft fuselages, engine components, electric vehicle battery enclosures, and high-performance braking systems. Innovation trends such as hybrid fiber systems, nanocomposite integration, and AI-based simulation tools are also discussed. The findings underscore the importance of selecting composites based on specific thermal requirements and application contexts. Additionally, the paper identifies challenges related to standardization, testing, and cost that continue to affect material adoption. This secondary analysis serves as a foundation for future research and development in the thermal optimization of composites for high-performance engineering sectors.

DOI: http://doi.org/10.5281/zenodo.16874906

Location And Edge Based Energy Efficient Reliable Approach For Teen Protocol In Wireless Sensore Network

Authors: Deepti Tripathi, Professor Amit Thakur

Abstract: Wireless Sensor Networks (WSNs) require efficient data routing and intelligent event detection to ensure energy conservation and timely response in critical applications. This paper proposes a hybrid method that integrates the Threshold-sensitive Energy Efficient sensor Network protocol (TEEN) with Support Vector Machine (SVM) classification for optimized event-driven data transmission and accurate decision-making. The TEEN protocol is employed to minimize energy consumption by transmitting data only when predefined hard and soft thresholds are crossed, thereby reducing redundant communication. The collected threshold-triggered data is then processed using SVM to classify events and detect anomalies with high precision. This hybrid approach enhances both the responsiveness and reliability of WSNs by ensuring that only relevant, high-quality data is analyzed, while SVM’s robust classification capability improves event detection accuracy. Simulation results indicate that the TEEN-SVM method significantly prolongs network lifetime, reduces communication overhead, and achieves superior detection rates compared to conventional routing or classification techniques alone, making it suitable for time-critical applications such as environmental monitoring, disaster management, and industrial automation.

DOI: http://doi.org/10.5281/zenodo.16925433

Novel Approach Of WSN Routing To Data Communication Between Sensor Node On Energy Warning

Authors: Pankaj kumar singh, Professor Amit Thakur

Abstract: Wireless Sensor Networks (WSNs) are widely used in environmental monitoring, healthcare, military, and smart city applications, but their resource-constrained nature makes security and energy efficiency critical challenges. Traditional cryptographic approaches are often unsuitable due to high computation and communication overheads. To address this, we propose a Secured Energy Efficient Key Management (SEEKM) scheme, which ensures confidentiality, authentication, and integrity of sensor data while prolonging network lifetime. In the proposed approach, nodes are preloaded with a small subset of cryptographic keys from a global pool, and secure communication is established only through authenticated neighbors that share common keys. Lightweight session key derivation and message authentication are applied to minimize computational cost, while a first-order radio energy model accounts for transmission and cryptographic energy consumption. Simulation in MATLAB evaluates network performance in terms of packet delivery ratio, alive nodes over time, first node death (FND), half node death (HND), end of network lifetime (EOL), and security drop rate under node compromise attacks. Results demonstrate that SEEKM achieves a favorable trade-off between security and energy efficiency, reducing unauthorized transmissions while extending overall network lifetime compared to conventional key management schemes.

DOI: http://doi.org/10.5281/zenodo.16925497

Assessing Multi Hop Performance Of Reactive Routing Protocol In Wireless Sensor Network

Authors: Isha Vyas, Professor Amit Thakur

Abstract: Wireless Sensor Networks (WSNs) are widely used for data collection and monitoring in large-scale environments, where efficient routing is essential to ensure energy conservation, reliability, and extended network lifetime. Traditional multihop routing approaches often face challenges such as uneven energy consumption, high latency, and reduced scalability. In this context, a K-Nearest Neighbors (KNN) based multihop routing strategy is analyzed to improve network performance. The KNN algorithm dynamically selects the most suitable forwarding nodes based on proximity, residual energy, and network topology, thereby balancing the load across the network. Simulation results highlight that the KNN-based routing scheme enhances packet delivery ratio, reduces average communication delay, and minimizes energy consumption compared to conventional protocols. The analysis confirms that KNN-driven multihop routing is a promising approach for optimizing data transmission in WSNs, particularly in dense and energy-constrained environments.

DOI: http://doi.org/10.5281/zenodo.16925532

Evolving Epidemiology and Early Diagnostic Advances in Mucormycosis

Authors: Tamanna Tandon, Pranay Jain

Abstract: Mucormycosis is a fatal fungal infection caused by filamentous molds belongs to order Mucorales.. Mucormycosis is observed to be more prominent in immunocompromised patients. In certain developed economies, mucormycosis is seen to be an associated infection with a bad prognosis regulated diabetes mellitus (DM). Mucormycosis or commonly known as black fungus, has a strong tendency of invading blood, causing nacrosis, thrombosis, and tissue infraction. Mucormycosis is found to be predisposed in co-morbidity or in the non-diabetic patients of COVID-19 especially in those who were at high dosage of steroids for a longer period of time or on ventilator support. Additionally poor hygienic conditions or disturbed diabetic management provides favorable conditions for the pathogenic fungal infection. Other reasons which are responsible for mucormycosis can be excess of uncontrolled conventional precautions. One of them is regular steaming, which may cause affliction in the nasal tracts’ beneficial microbiota and virome. Early diagnosis is crucial to initiate the therapeutic interventions necessary for preventing progressive tissue invasion and its devastating sequelae, minimizing the effect of disfiguring corrective surgery and improving outcome and survival. In this review, the black fungus causes and cures of Mucormycosis have been highlighted. It contains cases from Mucormycosis outbreak, the pathogenesis and diagnosis of the respective disease along with its available treatments. This review also suggests some natural treatments for black fungus disease.

Design And Analysis Of Shell And Tube Heat Exchanger

Authors: Sovaran Singh Yadav, Khemraj beragi

Abstract: Power generating, chemical processing, and oil refining are just a few of the many industries that rely on tube and shell heat exchangers because of their versatility, high-pressure handling capacity, and sturdy architecture. Limitations in thermal effectiveness and operational costs caused by problems including flow maldistribution, contamination, and high-pressure drop are common in traditional systems. Analytical and computational approaches to optimising the design and efficiency of shell and tube heat exchangers are investigated in this work. Emphasis is placed on key design considerations including baffle configuration, tube arrangement, material selection, and flow orientation. Analytical models using LMTD, NTU-effectiveness methods, and pressure drop equations are applied to evaluate thermal performance. Additionally, CFD simulations are used to validate and visualize improvements in flow and heat transfer behavior. Case studies highlight the advantages of baffle optimization and AI-assisted predictive diagnostics. The paper also discusses emerging innovations such as the use of nanofluids, additive manufacturing of complex geometries, and smart sensors for real-time monitoring. Challenges related to material constraints, maintenance access, and computational limitations are also addressed. The findings suggest that through integrated design and advanced analysis, tube and shell heat exchangers can be significantly enhanced for modern industrial applications.

DOI: http://doi.org/10.5281/zenodo.17104172