Volume 13 Issue 1

Improvement of Electrical Power Load Demand and Stability in Electrical Power System with Incorporation of Wind Power Generation

Authors- Abass Balogun, Isaiah Gbadegeshin Adebayo

Abstract-Nowadays, the penetration level of wind generation in power system is one of the most evolving among renewable generation (solar and hydro generation) with a least cost of installation to support the power system expansion planning. Thus, this study aimed to improve the Nigerian power system stability and meet the load demand from customers during contingency with application of wind power generators. The new generation location was obtained by converting one of the load buses in Nigerian 31-bus power system to generator bus and a new generator admittance matrix using Y bus matrix was formed. Swing equation was employed and the behavior of generator during contingency was determined. Then, the L-VSI for each new addition generation was obtained and used to optimally identify new location for the placement of the wind generator in the power system. Simulation was done in MATLAB R2023a. The generator damping ratio, total active power losses and total cost of the generator were determined. It was revealed that the result verified the accuracy of effective placement of wind generator in the power system to meet the load growth.

DOI: /10.61463/ijset.vol.12.issue5.240

Simulation Model to Reduce the Fuel Consumption through Efficient Road Traffic Modelling

Authors- Md Muneer Alam, Dr. Sunil Sugandhi

Abstract-This thesis considers the effect of active speed management on traffic-induced emissions. In particular, the traffic emissions caused by acceleration and deceleration of vehicles are modelled based on an instantaneous emission model integrated with a microscopic traffic simulation model. The emission model is based on empirical measurements which relate vehicle emission to the type, the instantaneous speed and acceleration of the vehicle. The traffic model captures the second-by-second speed and acceleration of individual vehicles travelling in a road network based on their individual driving style, the vehicle mechanics, and their interaction with other traffic and with traffic control in the network. The integrated model is applied to test a new technology to actively manage the driving speed of the vehicles in an urban network. Their impacts on vehicle emission in the network are assessed to give an indication of the relative effectiveness of the different technological designs and different levels of driver responses.

Nano Fluid Particles through a Rectangular Corrugated Channel

Authors- Mayank Dwivedi, Dr. Sanjay Kumar Singh

Abstract-In this study, the 3-D turbulent forced convective flow of six different nano fluids ZnO (ajeel et al. 2018), SiO2, TiO2, Fe2O3, Al2O3, and CuO with base fluid as water for trapezoidal corrugated channels for Re 10,000 was investigated. The channel was tested under constant heat flux of 10 KW and the volume fraction of nanofluid is taken as 0.08. The study reveals that ZnO-water nanofluid exhibits the highest heat transfer, while CuO-water has the highest heat transfer coefficient. TiO2-water nanofluid experiences the highest pressure drop, and Fe2O3-water shows the highest Nusselt number. These findings highlight the varying thermal performance and flow characteristics of different nanofluids for heat transfer applications.

Artificial Thunder Electricity Bomb Technology (ATEB)

Authors- Deepak Singh

Abstract-This paper explores the theoretical feasibility of creating a “thunder bomb” – a device capable of producing artificial thunder by replicating the physical and chemical mechanisms of natural thunder. The research focuses on the physics of shockwaves, rapid air expansion, and the role of high-energy inputs. Mathematical models are derived to quantify the energy requirements, heat transfer, and resulting acoustic shockwave. Practical challenges, potential applications, and technological constraints are also discussed. The concept of an Artificial Thunder Electricity Bomb (ATEB) introduces a novel approach to energy release and atmospheric manipulation, leveraging principles of electrical discharge and artificial lightning generation. The ATEB is designed to mimic the natural phenomenon of thunder and lightning through controlled, high- voltage electrical discharges that replicate the effects of thunder while releasing immense energy in a compact, targeted manner. This paper explores the underlying technology, mechanisms, and potential applications of the ATEB, including its military, environmental, and scientific implications. By examining the challenges of harnessing and directing high-voltage electrical energy, the study outlines the methods for improving energy efficiency, control precision, and safety. The feasibility of utilizing ATEB technology for controlled destruction, power generation, and atmospheric studies is discussed in detail, offering insights into its transformative potential across various fields. Through experimental analysis and simulation, the research identifies the limitations and opportunities of ATEB, providing a foundation for future advancements in artificial lightning and energy weaponry.

DOI: /10.61463/ijset.vol.13.issue1.101

Diagnosis of Acute Diseases in Villages and Smaller Towns Using AI

Authors- Kiran Kumar L N, Prajwal J, Rahul M, Subbireddy K, Suprith M, Assistant Professor Serin V Simpson

Abstract-Feasibility and Field Testing for Diagnosis of Acute Diseases in Villages’ and Smaller Towns Using AI is an empathetic intervention to use artificial intelligence (AI) to cover critical healthcare gaps in remote areas. Untreated forms of acute diseases will fly below the radar due to a lack of access to serious medical expertise and infrastructure in rural areas. This distinct project features a voice-activated interface and is multilingual, plying a new mobile-ai diagnostic platform in line with being accessible and inclusive. The technology includes NLP and ML to look at symptoms in real-time and identify infections, headaches, and flu and other diseases. The platform developed for low-literate people in the digital world ensures easy access even in resource poor environments.

DOI: /10.61463/ijset.vol.13.issue1.102

Health Buddy: Bridging Technology and Wellness for Holistic Health Management

Authors- T Srujan Bisneer, Vicky Raj V, Abhinav C S, Mohammed Farris, Assitant Professor Serin V Simpson

Abstract-Basically our app allows you to enter the food you ate and with the help of a trained model it’ll be able to predict the different types of vitamins and proteins and also calculate the calories consumed. After analyzing the data entered by the user for a couple of days, the app will tell you what vitamin you might be deficient of and the possible diseases you might get if you don’t include that in your diet. It also keeps a track of the number of glasses of water you’ve had while also reminding you to hydrate yourself regularly. It also keeps a track of your physical activities and calories burned Health Buddy is an innovative mobile application designed to empower users to take control of their overall health and well-being. By leveraging advanced machine learning models, the app analyzes users’ food intake to predict nutrient consumption, including vitamins, proteins, and calories, while identifying potential deficiencies. Over time, this data- driven approach enables users to proactively address dietary gaps and reduce the risk of health issues associated with nutrient deficiencies. Additionally, the app tracks hydration levels, sending regular reminders to encourage adequate water intake, and monitors physical activities to provide insights into calories burned, promoting a balanced lifestyle. With a user-friendly interface, Health Buddy integrates seamlessly into daily routines, offering personalized health insights, goal-setting features, and progress tracking to keep users motivated. The app also includes mental health resources, such as stress management tools, mindfulness exercises, and mood tracking, addressing both physical and emotional well-being. By combining educational content, data analytics, and community engagement, Health Buddy fosters a holistic approach to health management.

DOI: /10.61463/ijset.vol.13.issue1.103

The Double Burden of Malnutrition in Children: Tackling Under nutrition And Obesity in Emerging Economies

Authors- Dr. Venugopal Reddy Iragam Reddy

Abstract-The double burden of malnutrition (DBM), characterized by the coexistence of undernutrition and obesity, presents a significant public health challenge in emerging economies. Factors such as urbanization, dietary transitions, and socio-economic disparities contribute to this paradoxical phenomenon, exacerbating health risks among children. This review explores the epidemiology, drivers, and consequences of DBM in children and evaluates effective interventions, including policy reforms, community-based programs, and school-based nutrition initiatives. By synthesizing global evidence, this article highlights the urgent need for integrated strategies to address DBM and foster sustainable child health outcomes in emerging economies.

DOI: /10.61463/ijset.vol.13.issue1.104

Water Quality Monitoring System Using TDS

Authors- M Likith Mahendra, H Lithik Raj, Maddela ManiSaketh

Abstract-This project presents a water quality monitoring system using an Arduino Uno and a Total Dissolved Solids (TDS) sensor. The system measures dissolved solid levels in water to assess its quality, providing real-time data via an LCD display or IoT-enabled devices for remote monitoring. The Arduino Uno processes sensor data and triggers alerts when TDS levels exceed safety thresholds. Designed as a low-cost, efficient, and user-friendly solution, this system is ideal for households, rural areas, and small-scale industries lacking advanced water testing facilities. It ensures timely detection of contamination, promoting safe water usage and contributing to public health and sustainability.

DOI: /10.61463/ijset.vol.13.issue1.105

Analyzing Customer Adoption of Digital Channels in Commercial Banks, a Case Study of Postbank Uganda

Authors- Kaggwa Andrew, Anyama Moses

Abstract-This study aimed to analyze customer adoption of digital channels by commercial banks in Uganda, specifically focusing on Postbank Uganda (PBU). The research was guided by three specific objectives: examining the perceived ease of use of digital channels of commercial banks in Uganda, exploring the challenges associated with their adoption, and identifying effective strategies to enhance their utilization. The study was underpinned by two theories: Innovation Diffusion Theory (IDT) and the Technology Acceptance Model (TAM). A descriptive survey design was employed to gather, summarize, present, and interpret information, using convenience sampling to select a sample of 80 customers based on Morgan’s Tables (1970). Primary data was collected via a self-administered questionnaire, with a 97.5% response rate. The findings revealed that mobile banking is highly valued for its convenience, intelligent ATMs are effective but require improved user experience, internet banking needs enhancements for greater engagement, POS terminals are useful but not central to everyone’s experience, the Wendi App has low adoption, and agency banking, while vital for remote areas, faces accessibility issues. Key barriers to adoption include security concerns, communication errors, cybersecurity threats, inadequate infrastructure, network connectivity issues, the digital divide, and higher transaction costs. The study recommends implementing regular education and awareness campaigns, enhancing security measures, offering incentives, strengthening legal frameworks, ensuring system reliability, targeted marketing, improving usability, and revising pricing models. Intelligent ATMs should be upgraded for better functionality, and the Wendi App requires increased promotion and support to boost adoption. Agency banking would benefit from improved infrastructure and training programs to overcome accessibility challenges. Strengthening cybersecurity protocols and educating users on safe digital practices are essential. Collaboration with stakeholders to improve infrastructure and regulatory support, along with initiatives to bridge the digital divide, are also crucial.

DOI: /10.61463/ijset.vol.13.issue1.106

An Algorithmic Appraoch Applied on Big Data Strategies Using Mapping Techniques

Authors- Research Scholar Shilpa Sharma, Professor (Dr.) R.K. Bathla

Abstract-Big Data has enthralled a lot of attentiveness from academia, ndustry as well as government sectors. We are living n an era when a dangerous amount of data s being generated every day. Data s shaped through many sources like commerce processes, transactions, social networking sites, web servers, etc. and leftovers n well thought-out as well as shapeless form. Processing and extracting a vast amount s data s a demanding task. Big Data refers to technologies and nitiatives that grip data that s too diverse, fast- altering or enormous for conservative technologies, skills and nfrastructure to address efficiently. Big Data size s continually moving target currently ranging from few dozen terabytes to many petabytes n a single data sets. Accurateness n big data may escort to more positive managerial. And better decisions can mean greater functioning efficiency, cost reductions and reduced hazard. Big Data s the newest trend n business and T world right now. Big Data s derived from the datasets nvolved are so bulky that distinctive database systems are not able to store and analyse the datasets. Big Data s a well-liked term used to describe the exponential growth and availability of data, both structured and unstructured. Big Data s defined as the depiction of the progress of human being cognitive processes, regularly ncludes data set with sizes further than the capability of present technology, mode and hypothesis to capture, manage, and process the data within adequate elapsed time. The terms mply Big Data have following properties:

DOI: /10.61463/ijset.vol.13.issue1.107

Integrating Digital and Traditional Marketing Strategies: A Comparative Analysis of Brand-Consumer Interactions

Authors- Ms. Nikita Jain

Abstract-This research compares traditional and digital marketing tactics, focusing on their effects on brand-consumer interactions. Traditional marketing, such as print ads and television commercials, is compared to digital strategies leveraging online platforms, social media, and data analytics. Using case studies of firms like Coca-Cola and Amazon, this study demonstrates how combining both approaches can improve consumer engagement. The findings indicate that traditional marketing remains crucial for broad reach, while digital marketing fosters personalized interactions. Together, these tactics in multichannel campaigns enhance brand loyalty and consumer connection.

DOI: /10.61463/ijset.vol.13.issue1.108

Energy Projects and Environmental Sustainability among Selected Local Communities in Takoradi

Authors- Pamela Ami Seyram Biah, Maxwell Wahabu Manpaya

Abstract-The study examines the social and environmental impacts of energy projects among selected local communities in Takoradi, Ghana, focusing on how these projects influence environmental sustainability. The main objective of the study sought to determine the pragmatic measures taken to ensure that energy projects affect environmental sustainability among selected local communities in Takoradi. Using a mixed-methods research approach, the study combines quantitative surveys with qualitative interviews to gather insights from residents, community leaders, and industry stakeholders. Quantitative data analysis involved descriptive and inferential statistics, while thematic analysis was used for qualitative data. Findings reveal significant environmental challenges, including pollution and biodiversity loss, alongside socio-economic implications such as displacement and changes in livelihoods. The study identifies barriers to the adoption of renewable energy, such as regulatory inadequacies, limited community engagement, and economic constraints. Recommendations emphasize the need for strengthened policy frameworks, enhanced community participation, and a focus on sustainable practices to mitigate the negative impacts of energy projects while maximizing benefits. The findings contribute to understanding the dynamics between energy projects and environmental sustainability, offering practical strategies for policymakers, industry actors, and local communities. The study recommended that there is the need to build trust and foster positive relationships with local communities, energy companies should priotize transparent and open communication. This includes; providing clear accessible, and timely information about potential risks and impacts of energy projects.

DOI: /10.61463/ijset.vol.13.issue1.109

Anonymous Communication on Social Networks using AI-Powered Content Moderation: A Review

Authors- Siddhesh Mengade, Pranjali Chopade, Parth Tate, Shraddha Patil

Abstract-The Anonify application facilitates anonymous message exchange, offering a platform for users to receive feedback or questions without fear of judgment. Despite the prevalence of anonymous platforms, a significant gap exists in content moderation to ensure safe and constructive communication. This project aims to address this gap by incorporating AI-based content moderation and fraud detection mechanisms. Leveraging technologies such as Next.js, NLP, Netlify, Auth.js, and Tailwind CSS, the application employs machine learning to filter inappropriate content and prevent abuse. The findings highlight the importance of maintaining a secure, anonymous feedback system, enhancing interactions in professional and social settings.

DOI: /10.61463/ijset.vol.13.issue1.110

Intelligent Parking System Using IoT and Cloud

Authors- Associate Professor Rajesh L, Assistant Professor Lokabhiram Dwarakanath

Abstract-In the smart cities demand many smart services that are provided with the advent of technology collaborations and have changed the people lifestyle for better achievements in various fields attainable because of the progress of the Internet of Things. Continuously analyzing the IoT, improves the efficiency and reliability of city infrastructure. Finding an empty parking space in the lot is the goal of smart parking which inturn reduce fuel consumption and time loss while avoiding disrupting others. There is difficulty in finding the vacant places in the big cities. This paper highlights on problems faced in the customary parking systems. It also contains the glitches and drawbacks that result from the inefficiency of traditional parking lots. We addressed parking issue by IR sensors and openCV to capture and provide slot and generate parking bill to make the system more easier and user friendly.

Comprehensive review on VLSI-Based Power-Efficient Booth Multiplier for IoT Applications in FPGA Systems

Authors- M.Tech. Scholar Ms. Mansi Yadav, Assistant Professor Mrs. Deepali Sahu

Abstract-The rapid expansion of the Internet of Things (IoT) has heightened the need for energy-efficient and high-performance hardware, especially in power-constrained embedded systems. Booth multipliers, widely used for multiplication in IoT applications, are efficient but often consume substantial power. This review paper explores advancements in VLSI-based power-efficient Booth multipliers optimized for FPGA systems in IoT. Focusing on techniques like reduced switching activity, optimized data paths, and minimized signal transitions, the paper highlights innovative designs that achieve lower power consumption while maintaining speed and accuracy. A comparative analysis of power, area, and speed performance metrics demonstrates the effectiveness of these approaches over conventional multipliers. This review provides insights into state-of-the-art power-efficient Booth multipliers, supporting the development of sustainable and high-performance IoT hardware.

DOI: /10.61463/ijset.vol.13.issue1.111

VLSI-Based Power-Efficient Booth Multiplier for IoT Applications in FPGA Systems

Authors- M.Tech. Scholar Ms. Mansi Yadav, Assistant Professor Mrs. Deepali Sahu

Abstract-As the Internet of Things (IoT) continues to expand, the need for energy-efficient and high-performance hardware becomes critical, particularly for embedded systems where power and resource constraints are paramount. Multiplication is a fundamental operation in many IoT applications, and traditional Booth multipliers, while efficient in reducing the number of partial products, often consume significant power. This research addresses this challenge by proposing a VLSI-based power-efficient Booth multiplier specifically optimized for FPGA systems utilized in IoT applications. The proposed design implements advanced techniques to minimize power consumption without sacrificing computational speed or accuracy. Key optimizations include reducing switching activity, improving data path efficiency, and minimizing unnecessary signal transitions, resulting in a significant reduction in overall energy usage. The architecture is synthesized and implemented on FPGA platforms, ensuring flexibility, scalability, and compatibility with IoT hardware constraints. Performance metrics such as power consumption, area utilization, and operating speed are analyzed and compared to conventional Booth multipliers and other low-power designs. The results demonstrate that the proposed design achieves a significant reduction in power consumption while maintaining high throughput, making it a suitable solution for power-sensitive IoT environments where energy efficiency is critical. This research contributes to the development of more sustainable and high-performance hardware architectures for the next generation of IoT devices.

DOI: /10.61463/ijset.vol.13.issue1.112

PAPR Reduction and Improvement of OFDM System Performance Using Artificial Intelligence Machine Learning Algorithm

Authors- M.Tech Scholars Rahul Mishra, Assistant Professor Vijay Bisen

Abstract-Orthogonal frequency division multiplexing (OFDM) is extensively applied in the downlink of narrowband Internet of Things (IoT). However, the high peak-to-average power ratio (PAPR) of OFDM systems leads to a decrease in transmitter efficiency. Therefore, the researchers proposed the artificial neural network (ANN) based PAPR reduction schemes. However, these schemes have the disadvantages of high complexity or cannot overcome the defects of traditional schemes. In this Synopsis, a novel PAPR reduction scheme based on neural networks (NNs) is proposed for OFDM systems. This scheme establishes a PAPR reduction module based on NN, which is trained using the low PAPR data obtained by the simplified clipping and filtering (SCF) method. To overcome the defect of poor BER performance of the SCF scheme, a recovery module is introduced at the receiver, to recover the distorted signal. To realize the improvement of BER performance and the reduction of PAPR simultaneously, the two modules are jointly trained based on multi objective optimization.

Study on the Impact of Take-off Weight on Aircraft Range and Flight Time for Iak-130 Aircraft

Authors- Van Tuyen Nguyen, Trong Son Phan, Huu Dung Nguyen, Duc Anh Hoang, Van Rang Cao

Abstract-Range and flight time are critical parameters that determine the efficiency and safety of each flight. Therefore, studying these parameters and the factors influencing them plays a key role in the field of aviation. This paper presents a method for calculating range and flight time, while also analyzing the impact of weight on these parameters for the IAK-130 aircraft.

DOI: /10.61463/ijset.vol.13.issue1.113

Comparative Study of Language Teachers’ Attitude in Using Information & Communication Technology for Secondary School Students

Authors- Research Scholar Mrs Beena Purohit, Associate Professor Dr. Raju Talreja

Abstract-The usage of technology changes the role of the instructor from one of a traditional knowledge provider to one of a facilitator, guiding the students’ learning processes and working together to solve problems.In this study, language instructors from different institutions are asked a series of questions about how they use technology to teach their respective languages. The answers are then compared to conclude the study’s findings. The current study focuses on the state board secondary teachers’ digital literacy. The goals are to investigate and contrast the attitudes of State Board secondary teacher sabout digital literacy. The study’s conclusions also showed that there was no discernible variation in teacher’s attitudes regarding their sex and stream. (Attitude of Teachers about the Use of Information and Communication Technology (ICTs) in Teaching-Learning Process January 2020 Conference: Education for Social Inclusion, Sustainable Development and Empowerment at School of Education, Ravenshaw University, Cuttack, Odisha Enhancing knowledge and abilities through the use of information technology in the classroom helps students gain the necessary expertise to thrive in today’s ever changing world. People are exposed to new advances through technology which piques their interest in avariety of fields that call for creativity and innovation.

DOI: /10.61463/ijset.vol.13.issue1.114

Unlocking Students Potential: A Predictive Model to Enhance Educational Performance Prediction

Authors- Assistant Professor Dr.Pandya Vishal Kishorchandra, Dr. Pandya Rajnikant

Abstract-This study highlights important developments in the use of machine learning in educational technology by presenting the creation and implementation of a predictive model for student exam scores. With the help of the Random Forest method, the model was able to handle complex, nonlinear interactions with efficacy and resilience against overfitting. It also demonstrated great accuracy and reliability. The model’s performance was largely attributed to extensive data preparation and feature engineering, highlighting the significance of using domain expertise to convert unstructured data into useful features. With its user-friendly interface and dynamic form generation, the web application created for this project prioritizes ease of use and maintenance. We believe that usability testing, user feedback, multilingual support, and accessibility features will all be beneficial for future improvements. To optimize the model’s impact, a number of important topics were identified for further research. These include broadening the dataset to include a range of demographics and extracurricular activities, stress levels, and other variables, and refining the application’s usability in response to feedback from teachers and students. Other machine learning algorithms that are being investigated include support vector machines and deep learning models. To ensure continued accuracy and usefulness, real-world validation via partnerships with academic institutions will be essential for assessing the application’s efficacy in classroom environments. By going in these directions, the project will become a comprehensive instrument that will advance educational technology and improve educational outcomes dramatically.

DOI: /10.61463/ijset.vol.13.issue1.115

Polyglot Assistant: Multilingual AI Companion

Authors- Harsh Pandey, Kavya M, Kiran S B, Lohith Kumar K, Professor Dr. R. Amutha

Abstract-This project is dedicated to the generation of voice-activated virtual assistant that seamlessly integrates cutting-edge technologies such as machine learning, speech recognition, and natural language processing. The objective is to redefine user interaction along with digital systems by offering a highly personalized, responsive, and efficient experience. This assistant goes beyond conventional functionalities like setting reminders or managing schedules; it also incorporates advanced capabilities such as making calls, sending messages, automating repetitive tasks, and managing smart home devices. Additionally, the assistant provides real-time updates on dynamic topics such as weather forecasts, breaking news, sports scores, and general information. A standout feature of the project is its multilingual support, enabling interaction across diverse languages while maintaining contextual awareness from previous conversations. This contextual retention ensures that user interactions are fluid and coherent, enhancing the overall usability. Through continuous learning from user engagements, the assistant improves its efficiency and adapts to individual preferences. Ultimately, the project aims to deliver a hands-free, intelligent assistant that significantly enhances task management, accessibility, and user satisfaction.

DOI: /10.61463/ijset.vol.13.issue1.116

Images-based Plant Pest Detection Using Deep Learning Model

Authors- Gaurav Samdani, Rishipal Bansode, Michael Braham

Abstract-The use of artificial intelligence (AI) and machine learning (ML) in agricultural applications has gained significant attention in recent years due to its potential to revolutionize farming practices and enhance food security. One such area of research is the development of AI-powered systems for the early detection and diagnosis of crop diseases. Detecting diseases in crops at an early stage is crucial for farmers to implement timely interventions and prevent significant yield losses.According to the Food and Agriculture Organization (FAO) of the United Nations, plant diseases are responsible for substantial crop losses worldwide, with estimates suggesting that up to 40% of global crop production is lost annually due to pests and diseases (FAO, 2019). Traditional methods of disease detection often rely on visual inspection by farmers, which can be time-consuming, subjective, and prone to errors. In contrast, AI-based approaches offer the potential for automated, accurate, and rapid identification of crop diseases using image analysis techniques

DOI: /10.61463/ijset.vol.13.issue1.117

Impact of Explosive Strength Training on Performance and Injury Prevention in Cricket Fast Bowlers

Authors- Dr.B.Prabakar

Abstract-This study investigates the efficacy of explosive strength training (EST) in improving the performance and reducing injury risks among cricket fast bowlers. The unique physical demands of fast bowling require high levels of explosive strength, power, and endurance. Despite the importance of these attributes, traditional training often neglects the specific needs of fast bowlers, resulting in suboptimal performance and a heightened risk of injury. To address this gap, the study recruited 40 male fast bowlers aged 18-25 and divided them into experimental and control groups. Over 12 weeks, the experimental group underwent a targeted EST program featuring plyometric exercises, Olympic lifts, and sprint drills, while the control group engaged in traditional resistance training. Performance metrics, including bowling speed, vertical jump height, and sprint times, were assessed, alongside biomechanical analyses and functional movement screens to evaluate injury risk. Results showed that the experimental group achieved significant improvements in bowling speed (+6.8%), vertical jump height (+9.2%), and sprint performance (−5.6% sprint time) compared to the control group. Additionally, biomechanical assessments indicated reduced injury risk factors such as lumbar spine load and knee misalignment. These findings suggest that EST is a critical intervention for enhancing performance and promoting long-term athletic health in cricket fast bowlers.

DOI: /10.61463/ijset.vol.13.issue1.118

Application of Machine Learning to Next-Generation Communication Protocols for IoT and 5G Network

Authors- Abayomi I. O. Yussuff, Oluwafemi S. Adeleke, Kehinde G. Adekusibe

Abstract-The rapid expansion of the Internet of Things (IoT) and the deployment of 5G network call for advanced communication protocols capable of meeting the stringent demands of diverse applications. 5G network slicing— categorized into enhanced Mobile Broadband (eMBB), Ultra-Reliable Low- Latency Communication (URLLC), and massive Machine-Type Communication (mMTC) enables customized network services for different uses. However, existing protocols struggle to address the complex requirements of these slices, suchas low latency, high throughput, reliability, and scalability. This research proposed next- generation communication protocols for IoT and 5G network slicing, leveraging Machine Learning (ML) to enhance adaptability and performance. ML techniques are applied for predictive traffic management, resource allocation, dynamic protocol optimization, and slice orchestration in real-time. By anticipating network behavior and adjusting protocol parameters proactively, the proposed approach ensures optimal Quality of Service (QoS) for eMBB, URLLC, and mMTC slices. The study evaluates the ML- augmented protocols through simulations, demonstrating improvements in latency, reliability, and network efficiency across heterogeneous IoT environments. This work laid a foundation for intelligent, adaptive communication frameworks that address the evolving needs of future IoT and 5G ecosystems.

DOI: /10.61463/ijset.vol.13.issue1.119

Combating Drone Intrusions: A Survey of Detection Methods & Future Research

Authors- Kabir Kohli

Abstract-The Indian drone market is expected to grow from about $1 billion in 2022 to over $5 billion by 2025, driven by government initiatives and supportive regulations. Globally, the drone market, valued at $30 billion in 2022, is projected to reach $100 billion by 2030. Drones, or Unmanned Aerial Vehicles (UAVs), have become crucial in military operations, transforming how forces conduct surveillance, reconnaissance, and combat missions. Their remote and autonomous capabilities offer key advantages, such as reducing human risk, gathering real-time intelligence, and executing precision strikes in hostile environments. While autonomous aerial monitoring and identification mechanisms (AAS) provide flexibility for military applications, the rise in civilian drone use brings individual confidentiality and safeguarding measures concerns. To safeguard critical infrastructure and protect individual individual confidentiality from unauthorized drone use, effective drone monitoring and identification monitoring and identification mechanisms are necessary. Such monitoring and identification mechanisms must be efficient, accurate, robust, scalable, and cost-effective. Although various monitoring and identification methods have been developed, they often fall short due to limitations in the underlying technology, leading to trade-offs between factors like accuracy, monitoring and identification range, and resilience to environmental conditions. This highlights the need for a detailed evaluation of existing approaches. This paper reviews current drone monitoring and identification techniques and provides key insights for improving future monitoring and identification monitoring and identification mechanisms.

DOI: /10.61463/ijset.vol.13.issue1.120

A Review on Pest Detection Device in Agriculture

Authors- Nikitha K S, Assistant Professor Sharmeela R

Abstract-The primary cause of the economic loss in agriculture is insects and pests. Pest attacks are one of the world’s significant issues. The main challenge farmers confront during insect attacks is that the infection cannot be diagnosed early on when done manually. As a result, illness spreads quickly, causing farmers to suffer significant losses. This massive loss will remain uncontrollable until pests are attacked physically. Agriculturists apply pesticides to manage weeds, pests, and plant illnesses. Overuse of pesticides poses dangers not only to the ecosystem but also to human health and the country’s economic stability. This approach tries to detect pest attacks at an early stage. In the current research, we present a pest control system that Utilizing IoT and image processing technologies to manage pests while reducing pesticide usage. This review presents an automated approach for identifying insect attacks and informing farmers about the illness. When a camera image does not match a healthy leaf, real-time values are matched to a database to identify the afflicted leaf and notify the farmer via CNN algorithm. The suggested system detects the presence of insects and pest using image captures by the camera. Taking pictures of pests and verifying their existence in the field are done using image processing. Once an insect’s presence has been confirmed by image processing and soil nutrient content, pesticides will be sprayed based on the pest’s level to keep it out of the agricultural field. The suggested method helps farmers increase agricultural management and productivity without harming the environment.

DOI: /10.61463/ijset.vol.13.issue1.121

Review On: Chiller and Freezing Device

Authors- Shrinidhi M R, Assistant Professor Mrs. Janani B

Abstract-Refrigeration is essential for preserving perishable goods, but in developing countries like Nigeria, unreliable electricity hampers the efficiency of conventional refrigeration systems. This has led to an increased demand for alternative cooling solutions, such as ice, particularly in areas with frequent power outages and tropical climates. Traditional ice-making machines, while critical in various industries, are often bulky, expensive, and labor-intensive. To address these challenges, portable, efficient, and eco-friendly chilling and freezing devices based on the Peltier Effect have been proposed. There are two different ways that the cold chain operates. The goal of procedures like primary and secondary cooling or freezing is to alter the food’s average temperature. The fundamental goal of others, such transit, retail display, and chilled or frozen storage, is to keep the food at a consistent temperature. Although it is a challenging, time-consuming, and energy-intensive process, removing the necessary amount of heat from food is essential to the cold chain’s functioning. It gets harder to regulate and maintain a food’s temperature as it travels up the cold chain. This is due to the fact that single consumer packs in vast storerooms are far more susceptible to little heat inputs than bulk packs of chilled food. These devices utilize thermoelectric cooling, offering several advantages, including portability, energy efficiency, silent operation, and environmental sustainability. By eliminating harmful refrigerants and moving parts, they provide a reliable, cost-effective solution for rapid cooling or freezing of liquids across industries like food and beverage, pharmaceuticals, and chemical processing. The development of these devices ensures precise temperature control, reduces waste, and improves overall operational efficiency, especially in regions with unreliable power supply. This paper explores the need, design, and advantages of Peltier-based portable cooling devices, emphasizing their potential to meet the growing demand for efficient and eco-friendly refrigeration in various applications.

DOI: /10.61463/ijset.vol.13.issue1.122

A Review on Value Addition of Products Using Red Banana Powder

Authors- Jyothika.V, Lovelin Jerald.A

Abstract-Red banana is reviewed as a promising ingredient to develop new and healthy food products in this review, which also shows where food manufacturers can go to open up innovation and market-tailored products. Last but not least food manufacturers can take advantage of the increase in demand for functional as well as healthier products by dealing with the hurdles and limitations arising due to the use of red banana powder. The Red Banana Review provides different food products for red bananas which include baked goods, snacks, beverages, and desserts. Physicochemical, Sensory, and Nutritional Properties of the Products are Evaluated for Influence by Red Banana Powder Effects The potential health advantages of red banana powder-fortified foods, as well as improved digestive health and antioxidant status are also discussed in this context. Red banana, rich in prebiotic fibers, has potential benefits for gut health. However, its powdery form poses challenges in terms of stability and bioavailability. A combination of red banana powder, sweet potato, and Xanthan gum was used to develop a gut-friendly formulation. RSM was employed to optimize the formulation, investigating the effects of ingredient ratios, temperature, and pH on the physical and functional properties of the encapsulated powder. The optimized formulation showed improved stability, solubility, and bioavailability, with enhanced prebiotic activity. Xanthan gum demonstrated excellent binding properties, enhancing the powder’s flowability and compressibility. The results highlight the potential of red banana powder as a gut health supplement, with RSM-optimized formulation and Xanthan gum-based encapsulation enhancing its efficacy. This review provides valuable insights for food manufacturers and researchers seeking to develop gut-friendly products.

DOI: /10.61463/ijset.vol.13.issue1.123

Design Optimization and Implementation of 5g Microstrips Patch Antenna for C Band Application

Authors- Mr. Amrish Kumar Singh, Professor Dr. Sanjeev Kumar Gupta

Abstract-Fifth-generation wireless communication systems have been deployed or are soon to be deployed in many countries. However, with an explosion of wireless mobile devices and services, there are still some challenges that cannot be accommodated even by 4G, such as the spectrum crises and high energy consumption. Wireless system designers have been facing the continuously increasing demand for high data rates and mobility required by new wireless applications and therefore has started research on fifth generation (5G) wireless systems that are expected to be deployed beyond 2022. The main purpose of 5G is planned to design the best wireless world that is free from limitations and hindrance of the previous generations. 5G is going to change the way most high bandwidth users access their mobile radio communication. This dissertation presents 2×2 microstrip patch array antenna for 5G C-band access point application. The simulation results are presented and discussed. Structure of proposed antenna is simple and compact in size of approx. 32×32.2×1.6 mm3. The compact size of designed antenna makes it easy to be incorporated in small devices. Results show that the frequency bandwidth covers C band (4-8) GHz, at center frequencies 6.9 GHz for VSWR less than 2, and S11 less than -10 dB. The final results satisfy all the parameters of an efficient antenna. The designed antenna works efficiently under all conditions with low return loss and proper impedance matching. Proposed antenna achieves -18.34dB return loss while previously shows -16.899 dB. Bandwidth optimized 903.73MHz while previous bandwidth is 150 MHz. Therefore, proposed design shows that significant improvement achieved in this research work.

Growth and Characterization of NI-Catalyzed Branched Carbon Nanotubes by RF-PECVD

Authors- Professor Dr. B. Purna Chandra Rao, R. Hari Babu, B .V. Ramam Rao, Professor G.Ramesh Babu

Abstract-The present work reports the growth of Y-and T-branched Carbon Nanotubes (CNTs) with and without SiO2 oxide layer by Nickel catalyst using Radio Frequency Plasma Enhanced Chemical Vapor Deposition (RF-PECVD). The branched CNTs are grown at very less temperature (700o C). Field Emission Scanning Electron Microscopy (FE-SEM) images show the growth of many Y-branched CNTs with some main stems have branches with long lengths and some of them have less lengths, the measured lengths of main stems are roughly about 1.5 um. It is also reported that the bents and kinks at the junctions are formed due to the changes in the direction of movements of the catalyst particles. We report the oriental growth of branched CNTs with oxide layer with grown lengths roughly about 2um. Selected Area Electron Diffraction Pattern (SAED) shows the presence of (0 0 2) graphitic plane, and we also explored the in-depth mechanism of branched CNTs.

DOI: /10.61463/ijset.vol.13.issue1.124

Phytochemical Studies with Comparative Anti-Oxidant Analysis and Method Development Using Analytical Techniques for Adiantum Lunulatum

Authors- Mrs. Anandi Rebello, Ms. Nichitha Narsaiah Nandagiri, Ms. Sonali Sanjay Shingare, Ms. Aarti Amardeep Ghatge

Abstract-Due to the increasing demand for medicinal plants in both developed and developing countries, they depend on traditional plants because of their non-toxic, and lack of side effects. As compared to the other plant genera ferns which are one of the primitive plant groups are less studied. “Adiantum Lunulatum” also known as hamsapadi an Indian medicinal plant with a small leafy green. It is used to treat throat infections, febrile conditions etc. First Phytochemical Analysis was done which shows the presence of most of the secondary Metabolites like phenol, saponins, tannins, flavonoids, steroids, and anthocyanin. This plant is also reported to have high concentrations of antioxidants, possibly contributing to disease prevention following human consumption. We are checking the concentration of antioxidants in different parts of the plant ( Leaves & Midrib ). Following analysis of Adiantum lunulatum leaves and midrib, we report the antioxidant content potential of these species using two comparable techniques assessing the consistency between the assays – the ferric reducing antioxidant power (FRAP) assay and the Phosphomolybdate (PMA) assay. We conclude that there is variation in Adiantum lunulatum antioxidant potential and that ferric reducing antioxidant power (FRAP) assay and Phosphomolybdate (PMA) assay are useful techniques for measuring antioxidants in this plant. The extracts showed a considerable antioxidant effect in Ethanol & Acetone and Methanol & Acetone from two different assays respectively. Thin-layer chromatography reveals the presence of different metabolites of all plant extracts.

DOI: /10.61463/ijset.vol.13.issue1.125

Time History Analysis of Building in various Earthquake Zones: A Review

Authors- Ms. Janhavi Vinod Jaisingpure, Assistant Professor Dr Swati Ambadkar

Abstract-In accordance with Indian Standard standards, this research intends to use STAAD-PRO software to do a time history analysis of structures situated in Earthquake Zones II, III, IV, and V. In order to provide insights that can guide earthquake-resistant design methods, the main objective is to assess the structural resilience and seismic performance of buildings across a range of seismic intensities. Buildings exposed to both artificial and actual earthquake ground movements are modeled for each seismic zone as part of the investigation. To comprehend the dynamic behavior of structures under seismic loads, key structural performance indicators are evaluated, including displacements, base shear, and natural frequency fluctuations. Furthermore, this study investigates how well particular design changes might improve structural resilience in the various zones.

A Review on Gluten Free Rice Bread

Authors- Gayathri P V, Assistant Professor Dr.S.Ramani

Abstract-As more people look to eat gluten-free products, it is becoming increasingly common for the general public to use gluten-free rice bread as an alternative for individuals with celiac disease or intolerance to gluten. The purpose of this review is to investigate ingredients and formulation, the quality characteristics, and the nutritional value of typical wheat-free rice bread. An extensive literature review has indicated that there are specific parameters that have to be considered while formulating rice bread in order for the ingredients (rice flour, starches, and hydrocolloids) to be optimal for texture and structure. This review will deal with the positive influence of additives as individuals such as gums stabilizers and enzymes on bread quality and shelf-life. Bread characteristics and processing conditions (mixing, fermentation, baking) effects Also highlight the positive/negative aspects of gluten-free rice bread nutritional wise, specifically GI and protein & mineral profile. This review will help food industries, researchers, and healthcare experts with the perspectives to make improved high-quality gluten-free rice bread products. The review also points to the need for further research and development to overcome the bottlenecks and hurdles in the production of gluten-free rice bread. Also, it underlines the sensory evaluation/consumer acceptability must be performed to validate gluten-free rice bread as a real diet option. So this review gives an extensive overview of the present market scenario, products in gluten free rice bread and narrows down certain areas of research (and development) реж. Solutions to overcoming the obstacles and boundaries of gluten-free rice bread can guide manufacturers in producing better quality goods that address the demand of gluten intolerant, celiac people.

DOI: /10.61463/ijset.vol.13.issue1.126

Edible Cutlery: A Passing Trend or a Sustainable Innovation

Authors- Abhirami VG, Jenni S, Sowmiyadevi S, Assistant Professor Reni A

Abstract-As awareness of environmental issues has increased, edible cutlery has become a popular eco-friendly substitute for disposable plastic utensils. This review examines whether it is merely a fleeting trend or a sustainable long-term option. The research investigates the origins and advancements of edible cutlery. Additionally, it analyzes applications across various industries to assess if edible cutlery is just a temporary trend driven by environmental concerns or a lasting innovative solution capable of effectively replacing throwaway utensils. Finally, the conclusion considers how edible cutlery could contribute to a more sustainable future and suggests directions for further research and development.

DOI: /10.61463/ijset.vol.13.issue1.127

A Comprehensive Framework for Blood Bank Management Systems: Enhancing Efficiency and Accessibility

Authors- Suraj Kumar Panda, Associate Professor Dr S R Raja

Abstract-Blood banks play a crucial role in saving lives by ensuring the availability and efficient distribution of blood and its components. However, traditional blood bank management systems often face challenges such as inefficiencies in inventory management, delayed communication, and limited accessibility for donors and recipients. This paper proposes a comprehensive framework for a modernized Blood Bank Management System (BBMS) to address these challenges. The suggested solution incorporates cutting-edge technology, such as blockchain, cloud computing, and machine learning, to increase accessibility, guarantee transparency, and boost operational efficiency. Some of the salient features are a secure donor-recipient matching system, predictive analytics for demand forecasting, and real-time inventory tracking. The framework also strongly emphasizes user-centric design, integrating mobile applications for both donors and recipients to promote accessibility and easy communication. A case study was conducted at the All India Institute of Medical Sciences (AIIMS), New Delhi. Demonstrates the effectiveness of the framework, showing a significant reduction in blood wastage, improved donor engagement, and faster processing times. This research highlights the potential of technology-driven solutions to revolutionize blood bank operations and sets the foundation for further innovation in healthcare management systems.

Age and Gender Identification Using Neural Image Processing

Authors- Tanmay Hadke

Abstract-The rapid advancement in the fields of computer vision and deep learning has enabled out- standing achievements in the recognition of facial attributes such as age and gender. This report is about developing a useful system that uses neural image processing methods for real-time age and gender classification. The proposed system uses two distinct methods: one by using OpenCV’s ‘resize‘ function to resize the image and the other using Pillow library’s ‘Image.resize‘ method. These preprocessing techniques are critical to preparing facial image data to feed into a chosen MobileNetV2-based neural network architecture, selected for its lightweight design and efficiency in computation. This research extensively evaluates the two different preprocessing methods to focus on their effect on accuracy, computation time, and resource usage. Optimized MobileNetV2 architecture is used to classify age and gender using facial datasets available in the public domain for training purposes. The models are then tested using the webcam for real-time input to analyze their usefulness in practice. This project presents a comparative analysis of two different feature extraction techniques to determine the best preprocessing method for neural network-driven age and gender detection systems. Relevant conclusions regarding the interplay between preprocessing methodologies and model efficacy can be obtained from the results and be put forward towards lightweight, accurate, and resource-conserving demographic analysis systems that can be success- fully applied in different real-life scenarios. Keywords: Age and Gender Classification, Neural Image Processing, MobileNetV2, OpenCV, Pillow Library, Feature Extraction, Deep Learning, Computer Vision.

DOI: /10.61463/ijset.vol.13.issue1.128

Graphene-Based FET Prepared by Mechanical Exfoliation Technique for High-Performance

Authors- Laith M. Al Taan, Nawfal Y. Jamil, Duha H. Al Refaei

Abstract-This study aims to prepare high-quality graphene using mechanical exfoliation and to fabricate a Field-Effect Transistor (FET) on a silicon substrate. The graphene’s structural and electronic properties were evaluated through Raman spectroscopy, showing a prominent G Band (~1580 cm⁻¹) and 2D Band (~2700 cm⁻¹), indicating high-quality, monolayer graphene with low defect density. The transfer I-V characteristics reveal effective current modulation with high electron mobility, leading to faster switching speeds and lower power consumption. The Id-Vg characteristics demonstrate a distinct threshold voltage (0.75V) and highlight the importance of minimizing hysteresis for reliable device performance. The results confirm the efficacy of the exfoliation method in producing graphene suitable for high-performance FET applications.

DOI: /10.61463/ijset.vol.13.issue1.129

Securing Digital Assets in the Quantum Era: A Comprehensive Approach to Blockchain Wallet Security

Authors- Amratanshu

Abstract-This research addresses the growing need for enhanced security in blockchain wallets, focusing on emerging threats like quantum computing. It proposes a framework to future-proof wallets by integrating post-quantum cryptography (PQC), advanced biometric authentication, and decentralized security. With traditional cryptographic algorithms like RSA and ECC vulnerable to quantum decryption, PQC is essential for securing digital assets long-term. Additionally, biometric techniques, such as fingerprint and facial recognition, offer a robust defense against unauthorized access. The study also explores decentralized security by combining PQC with blockchain’s immutable ledger to resist quantum threats. Innovative solutions, such as zero-knowledge proofs (ZKPs) for privacy-preserving biometric authentication and advanced techniques like multi-signature and behavioral biometrics, are examined to strengthen wallet security. A decentralized biometric sharding approach is proposed to protect encrypted biometric data, while context-aware dynamic authentication adjusts security policies based on user behavior. A decentralized recovery system using threshold cryptography is introduced to tackle key recovery. Looking ahead, the paper explores the potential of emerging interfaces like holographic displays and brain-machine interface (BMI) authentication to improve wallet usability and security. This research outlines a comprehensive roadmap for the next generation of blockchain wallets, combining advanced security technologies, energy efficiency, and future-ready features for a seamless, secure digital asset management experience.

DOI: /10.61463/ijset.vol.13.issue1.130

Smart Power Source Management Automatic Selection of Optimal Energy Source (Solar, Grid, Inverter) for Sustainable Efficiency

Authors- Himanshu Ramanlal Patel

Abstract-This paper presents a smart power source management system that automatically selects the optimal energy source among solar power, grid supply, and an inverter to ensure uninterrupted power delivery with sustainable efficiency. The system is designed primarily for industrial applications where both three-phase and single-phase power are required. Using logic gates (NAND, AND, NOT), the system prioritizes power sources based on availability. Solar power is given the highest priority, followed by grid power, and finally the inverter in case of failure of the other sources. The circuit ensures seamless switching between sources using relays and includes voltage and frequency protection features to maintain stable output. This system effectively minimizes power interruptions, reduces energy costs, and enhances overall efficiency, making it suitable for both commercial and domestic applications. Future advancements may incorporate renewable sources such as wind power for further sustainability.

DOI: /10.61463/ijset.vol.13.issue1.131

Digital Twin Technology -An Overview in Healthcare

Authors- Assistant Professor C.K. Krithika, Assistant Professor B. Rajeswari, Assistant Professor S. Gayathri

Abstract-Digital Twin(DT) technology is transforming healthcare by constructing virtual clones of physical items, such as organs, systems, or even entire people, in order to improve diagnosis, treatments, and operational efficiency. DT technology promises a paradigm shift in healthcare, transitioning from reactive to proactive, tailored, and data-driven treatment. Its significance will only increase as health care systems face greater demands for effectiveness, quality, and innovation. This study explores the application of digital twin technology in several healthcare management applications.

DOI: /10.61463/ijset.vol.13.issue1.132

Development of One App and One Village for Connecting Rural Community in a Single Platform

Authors- Associate Professor Dr.T.Sengolrajan, T.Abisheik, P.Anandan, N.Gowtham

Abstract-This paper focuses on development of “One App, One Village” a unified digital platform designed to connect rural communities with essential services, fostering convenience, inclusivity and economic empowerment. This app provides a comprehensive solution by integrating daily needs such as groceries, vegetable vendors and household services alongside specialized services like event management (e.g., catering, photography and decorations). In addition to addressing everyday needs, the platform includes educational resources, local market updates and entertainment options tailored specifically for rural areas. By promoting local service providers, the app boosts economic opportunities, reduces reliance on manual interactions and supports time and cost efficiency. The app features a user-friendly interface, regional language support, and real-time notifications, ensuring accessibility for first-time users and underserved communities. It empowers local businesses, enhances community engagement, and fosters digital inclusion, creating a sustainable and self-reliant rural ecosystem. This initiative not only bridges the digital divide but also contributes significantly to rural development and sustainable growth.

DOI: /10.61463/ijset.vol.13.issue1.133

Multi-Level Intrusion Detection and Log Management System in Cloud Computer

Authors- Okoni E. Bennett, Ernest O. Amadi

Abstract-This journal addresses critical security challenges in cloud computing, such as management complexities, data falsification, unauthorized access, and advanced evasion techniques, proposing a Multi-Level Intrusion Detection and Log Management System (ML-IDS) leveraging Behavioral Traffic Analysis Techniques to enhance security, scalability, and performance. The system integrates a Cloud Management Platform (CMP) for efficient resource allocation and monitoring, ensuring optimal performance while maintaining security, and employs Deep Packet Inspection (DPI) for real-time traffic analysis, enabling the identification of malicious activities at a granular level. Additionally, it incorporates a Multi-Tier Temporal Traffic Analysis (MTTTA) module to detect anomalies by analyzing patterns across different timeframes, improving detection accuracy and reducing false alarms. The robust log management component provides secure storage, correlation, and analysis of event logs, offering actionable insights into potential security breaches. Experimental results demonstrate the system’s effectiveness, achieving a high detection rate of 90.00%, a low false-positive rate of 5.00%, and faster response times compared to traditional approaches. The ML-IDS exhibits exceptional scalability and adaptability, making it suitable for real-time protection of dynamic and diverse cloud infrastructures. This innovative solution not only bridges critical security gaps in cloud environments but also enhances the overall reliability and resilience of cloud ecosystems by providing a multi-layered approach to intrusion detection and log management. The results highlight the potential of the proposed system to redefine security benchmarks and establish a robust framework for mitigating emerging threats in cloud-based infrastructures.

DOI: /10.61463/ijset.vol.13.issue1.134

Observation and Identification of Carbon Footprints of Highway Construction Materials in India

Authors- Rohan Verma, Professor Jitendra Chouhan

Abstract-Globalization and liberalization policies of the government of India have increased the number of roads and vehicles playing on them. These vehicles mainly consume non- renewable fossil fuels, and are a major contributor of greenhouse gases, particularly CO2 emission. The intensification of Carbon emissions of road construction sector has strived transportation agencies involved in the construction and maintenance of transportation infrastructure, to make their practices and policies greener and more sustainable. Accordingly, environmental consciousness is on rise and has motivated transportation agencies involved in the construction to investigate strategies that reduce the life cycle greenhouse gas (GHG) emissions associated with the construction and rehabilitation of highway infrastructure. The present paper reviews concept of carbon foot printing and assess carbon dioxide emissions and energy consumption for the production of road pavements by means of a literature review.

Soil Stabilization Water Resistance Soil Bases and Dust Control Method

Authors- Nirmal Kumar Porwal, Assistant Professor Vinay Deulkar

Abstract-Water resistance soil bases and dust control are common climatic phenomena in arid and semi-arid regions. In Iran, one of the environmental concerns is increasing dust storms. There are several ways to control this phenomenon, each of which has its limitations. Conventional methods for reducing dust storms (especially in arid and semi-arid areas) have been the stabilization of the dust generating center using chemical polymers and petroleum products, which in the current situation, due to the high cost and disagreement about the effects on their environment is not cost-effective. Therefore, due to the problems of this type of soil cover, the use of biopolymers, bio-mulch, and organisms to stabilize dust in recent years has been recommended as a suitable alternative. Water resistance soil bases and dust control form a continuous or partially structured structure with each other by forming granulation soils, bonding fine particles together, and forming larger particles.

Meta-Heuristic Ant Colony Optimization Approach for Leukemia Cancer Prediction with Deep Learning Method

Authors- Bamidele, Samson A., Asaju-Gbolagade Aishat W., Gbolagade, Kazeem A.

Abstract-Reliable sources such as the World Health Organization (WHO) stated that cancer kills millions of people annually and is one of the world’s top causes of death. Machine learning (ML) is essential for the early detection of cancer. It uses powerful techniques to examine intricate data and spot subtle trends that might point to the existence of cancer. Early cancer identification is essential for improving patient outcomes because it opens the door to quicker intervention and therapy, which may result in improved prognoses and greater survival rates. This work suggests a deep learning-based model that uses the Histogram of Oriented Gradients (HOG) for feature extraction after image pre-processing to choose features. Residue Number System (RNS) forward conversion and dimensionality reduction achieved by Ant Colony Optimization (ACO). After that, classification is done using Convolutional Neural Networks (CNN). To prevent overfitting, 30% of different cancer microarray datasets are utilized to evaluate the method. The empirical study shows a 98.36% accuracy rate on the cancer dataset. The findings also show that less training time and a lower mistake rate were attained.

Exploring the Role and Impact of Digital Twin Technology in Industry 4.0: A Comprehensive Review

Authors- Amil Mustafa Abdulhamid Rashid

Abstract-Digital Twin (DT) technology has emerged as a critical innovation in the era of Industry 4.0, enabling real-time monitoring, simulation, and optimization of physical systems through virtual models. This paper provides a comprehensive review of Digital Twin technology, exploring its key concepts, enabling technologies, applications, benefits, and challenges. The study highlights the role of DT in smart manufacturing, predictive maintenance, and industrial automation by integrating technologies such as the Internet of Things (IoT), Artificial Intelligence (AI), and Big Data analytics. Additionally, the paper discusses the implementation of DT in various industries, including healthcare, aerospace, automotive, and smart cities. Despite its potential advantages, DT adoption faces challenges such as high implementation costs, data security concerns, and integration complexities. Finally, the review identifies emerging trends and future research directions, emphasizing the need for standardization, enhanced AI-driven models, and expanded applications in Industry 4.0. The findings of this study contribute to a better understanding of Digital Twin technology and its transformative impact on modern industries.

Hydrological Harmony: Unlocking the Potential of Water Reuse and Recycling in Urban Ecosystems

Authors- Mfon O. Ukut, Nnamso D. Ibuotenang, Harmony K. Sunny, Emmanuel I. Uwah, Uduak F. Udotong, Etiowo G. Ukpong, Solomon E. Shaibu

Abstract-Water scarcity and deteriorating water quality are critical challenges exacerbated by rapid urbanization, population growth, and climate change. In urban environments, water reuse and recycling offer sustainable solutions by reducing reliance on freshwater resources, minimizing untreated wastewater discharge, and enhancing water security. This study examines the benefits, challenges, and best practices associated with water reuse and recycling, focusing on their potential to address urban water management complexities. Key benefits include environmental conservation, economic efficiency, and social improvements, such as reduced waterborne diseases and enhanced resource availability. However, barriers such as inadequate infrastructure, regulatory gaps, and public resistance hinder their widespread adoption. By exploring successful case studies and innovative approaches, this study provides actionable recommendations for integrating water reuse and recycling into urban water management systems. These strategies aim to mitigate water scarcity, reduce environmental pollution, and promote resilience in urban communities, contributing to global sustainability efforts and the achievement of Sustainable Development Goal 6.

DOI: /10.61463/ijset.vol.13.issue1.135

A Review on Development of Automated Cutter with Multiple Blades for Baked Products

Authors- Navaneetha M, Sathyaruba D, Vidarshana T, Dr.A.Lovelin Jerald

Abstract-In the modern era of industrialization, the automated machines become an essential phase of human life. These machines assist in decreasing the time to do a precise task. Nowadays, human lifestyles will become extra aggressive and quicker than the previous. The implementation of technology in automation has significantly reduced the amount of human effort and time required for tasks. Slicing and cutting baked foods are a volatile and time-consuming mission in our busy lifestyles. For an effective cutting operation in baked goods, the force that the tool requires to incise the reference body is the Degree of Sharpness (DoS). A food cutter is used to cut or incise the food particles. The primary focus of this paper is to integrate a multi-blade system that is adjustable and adaptable for different shapes and sizes of baked goods to incise products such as cakes, sandwiches, jam rolls, coconut bun, pizzas, etc. The main disadvantage of ordinary food cutters is that the food products get deformed while cutting. It also affects the structure of the next product during cutting with the same blade containing some residual food material attached to it. Additionally, slicing presents the option to create very thin slices of meals. Food cutter that operates automatically cuts down on human error, operation time and participation.

DOI: /10.61463/ijset.vol.13.issue1.136

Cow Dung Cakes as a Sustainable Energy Source: A Comprehensive Review of Environmental Benefits, and Entrepreneurial Opportunities

Authors- Professor Gajanan R. Bharsakle

Abstract-Cow dung, a traditional energy source in India, has gained renewed attention as a sustainable and renewable energy resource. This review paper explores the potential of cow dung cakes as an alternative energy source, focusing on their environmental benefits, technological advancements, and entrepreneurial opportunities in India. The paper highlights the role of cow dung in addressing India’s energy needs, reducing greenhouse gas emissions, and promoting rural development. It also examines government policies supporting cow dung utilization, economic models for startups, and the advantages and limitations of cow dung-based energy systems. The present research work is going on at Gadgebaba Gaurakshan Charitable Trust,Mahuli (Dhande) Tq Daryapur Dist – Amravati under the guidance and supervision of Prof. Gajanan Bharsakle, and the proposed technology is under the patent procedures. The study concludes that cow dung cakes offer a viable solution for sustainable energy production, waste management, and rural entrepreneurship, aligning with India’s goals of energy security and environmental sustainability.

DOI: /10.61463/ijset.vol.13.issue1.137

Exploring the Influence of Temperature, Pressure, and Pore Fluid on Rock Mechanical Properties: A Comprehensive Review

Authors- (Dr) Ekeinde, Evelyn Bose, Professor Adewale Dosunmu

Abstract-Evaluating and identifying the rock mechanical parameters are significant in geological and engineering applications, specifically in exploring and exploiting the Earth’s energy reserves, preserving the earth’s biotope, and utilizing the subterranean space. It is the hope of this review to discuss the effects of temperature, pressure and pore fluid on rock mechanical properties, to assess the current state of knowledge in the field and to point to future research prospects. Therefore, from reviewed literature, we ascertained and analyzed the influential factors of rock mechanical properties. Three main profiles were defined to present the role of temperature, pressure and pore fluid for rock mechanics properties. The evaluation focuses on temperature and pressure as well as pore fluid; and on their influence on the strengths, deformability, and failure mechanisms of the rock mass. The outcomes also highlight the significance of performing a comprehensive evaluation of the rock mechanical characteristics corresponding to these factors. Through this review, the most recent interpretation of temperature, pressure and pore fluid on rock mechanical properties has been described. These considerations give us insights into how they may influence the selection of the rock mechanical properties and this should inform us that the aspect needs more research.

DOI: /10.61463/ijset.vol.13.issue1.138

Automated Hot Water Treatment System for Banana Suckers Using IoT

Authors- Assistant Professor J.Karthikeyan, A.Dhanasekaran, B.Srinivasan, E.Suthiksan

Abstract-The Soil waste is a common problem in banana agriculture, which results in low yields and high mortality rates. In order to ensure healthy plant growth, this study presents an automated hot water treatment system for banana suckers. It is intended to eradicate pests and diseases prior to planting. The suckers are effectively disinfected by the system’s regulated hot water treatment procedure, which keeps the temperature at 52°C for 30 minutes. The risk of over- or under-treatment is decreased by automated elements that precisely manage the water temperature, such as a temperature sensor and relay module. By lowering the need for chemical pesticides, this technique encourages sustainable farming and makes banana agriculture more environmentally friendly. In addition to improving plant health, the technique increases the likelihood that suckers will successfully establish themselves in the soil, resulting in stronger plants and increased crop yields. The method ensures ideal growing conditions for banana suckers while promoting ecologically conscious practices by doing away with the usage of hazardous chemicals. In the end, healthier crops and increased agricultural productivity result from the development of this automated system, which provides a practical and efficient answer to the difficulties associated with banana farming.

DOI: /10.61463/ijset.vol.13.issue1.139

Enhancing the Efficiency of Solar Dryer using Thermal Imaging Technique

Authors- Assistant Professor P.Tamilnesan, P.Aadhikesavan, R.Dharun, P. Siva

Abstract-The efficiency of solar dryers can be significantly improved through the integration of thermal imaging technology. This paper explores the application of thermal image cameras to monitor and analyse the temperature distribution within solar dryers, providing valuable insights into their operational performance. By identifying hotspots and areas of inadequate heating, adjustments can be made to optimize airflow and improve heat retention. This proposed system demonstrates that utilizing thermal imaging not only enhances the drying efficiency of solar dryers but also reduces drying time and energy consumption. The findings indicate that the implementation of thermal imaging in solar drying systems can lead to more effective and sustainable drying processes, ultimately benefiting agricultural practices and food preservation efforts. This innovative approach paves the way for further advancements in solar drying technology, promoting its adoption in various industries.

DOI: /10.61463/ijset.vol.13.issue1.140

A Review on Sustainable Solar Air Dryer Solution

Authors- Mr. Ankush S. Ambarkar, Dr. D. P. Sakarkar

Abstract-One of the sustainable and renewable energy sources that has drawn a sizable international research community is solar. Our earliest food preservation technique is drying. Dates, figs, apricots, grapes, herbs, potatoes, corn, milk, meat, and fish have all been dried for preservation for thousands of years. There is a need for traditional energy sources because of rising pollution and population increase. Successful combination of traditional energy sources with renewable energy sources will be required for solving this problem. One of the many energy sources that are available on Earth is solar energy. Food and agricultural products are dried using solar dryers, which also assist in reducing down on waste. In the majority of tropical and subtropical nations, sun drying remains the most popular technique for preserving agricultural products. However, items may be severely damaged to the point that they occasionally become inedible due to exposure to rain, wind-borne dirt and dust, insects, rodents, and other animals, which results in a loss of food quality in the dried Both domestic and foreign markets may be negatively impacted by products. A solar dryer, which consists of a collector, a drying chamber, and occasionally a chimney, can help with some of the issues related to outside sun drying. As a result, solar-powered drying equipment for agricultural and marine products became more effective and affordable, enhancing both the items’ quality and people’s quality of life. Utilizing solar dryers to dry agricultural products can drastically cut down on or completely eradicate food illness and product waste, while occasionally increasing farmer production to increase profits. A review of the solar dryer is provided in this work. The various designs of solar dryers that have been recorded in the literature until this time are presented.

Time-Series Analysis of Behavioral Patterns from Wearable Device Data for Depression Detection

Authors- Dr. Pankaj Malik

Abstract-Depression is a prevalent and debilitating mental health disorder that significantly affects an individual’s well-being and daily functioning. Traditional diagnostic methods rely on self-reported symptoms and clinical interviews, which can lead to delayed or inaccurate diagnoses. Wearable devices, such as smartwatches and fitness trackers, offer a novel approach for continuous, real-time monitoring of physiological and behavioral patterns, enabling early detection of depressive symptoms. This study explores the application of time-series analysis and deep learning models to detect depression using data collected from wearable sensors, including step count, heart rate variability (HRV), sleep patterns, and activity levels. We propose a predictive framework leveraging Long Short-Term Memory (LSTM) networks, Transformer models, and statistical time-series methods to analyze sequential behavioral data and classify depression risk. Our experiments, conducted on real-world datasets, demonstrate that deep learning-based models outperform traditional machine learning approaches in detecting depressive states, achieving high accuracy and robustness. The findings suggest that wearable-based mental health monitoring can provide objective, continuous, and non-invasive screening for depression, potentially improving early intervention strategies. Future work will focus on enhancing personalization, integrating federated learning for privacy-preserving predictions, and expanding datasets to diverse populations for greater generalizability.

DOI: /10.61463/ijset.vol.13.issue1.141

AI-ML Powered Smart Crop Monitoring System for Preventing Peacock Movements in Agriculture

Authors- S. Ragul, N.Dharanidharan, K.Mouleeswaran, A.Santhosh Kumar

Abstract-Agriculture faces significant challenges from wildlife intrusion, particularly from peacocks, which can cause extensive damage to crops. Traditional methods of managing such movements are often labor-intensive, inefficient, or disruptive to the ecosystem. This project presents an AI-ML powered smart crop monitoring system designed to detect and prevent peacock landings on crops using sound-based deterrent system. The system triggers pre-recorded fear- inducing sounds specifically designed to deter peacocks, preventing them from landing on the crops without causing harm. This solution provides an environmentally friendly way to safeguard crops while ensuring the safety of wildlife.

DOI: /10.61463/ijset.vol.13.issue1.142

Intergrated Health Care Solution for Real Time Monitoring and AI-Assist Diagnosis for Senior Citizens

Authors- Dr.R.Shankar, Anushiya T, Keerthana Shivani C, Srinitha K

Abstract-The Proposed system focuses on utilizing IoT sensors and ESP32 microcontroller for tracking the vital health parameters such as heart rate and body temperature in real time. This data is transmitted securely to the cloud, where advanced machine learning algorithms analyze it to identify early symptoms of common diseases like heart attacks and fever. Abnormal readings are detected, the system sends alerts to caregivers and healthcare providers for timely intervention. Additionally, the system features an AI-powered ChatBot designed to assist patients and caregivers by answering health- related questions, suggesting possible diseases based on symptoms and recommending relevant doctors for consultation. It also stores both medico-legal and medical records, ensuring that patient’s health histories, treatment plans and legal documents are accessible in a secure, cloud-based platform. By integrating real-time health monitoring with predictive analytics, AI-powered assistance and comprehensive record-keeping, this system offers an innovative solution to enhance healthcare management for senior citizens.

DOI: /10.61463/ijset.vol.13.issue1.143

Design of Smart Agriculture Wearable for Enhanced Productivity

Authors- S.Revathi, R.Brundha, M.Priyadharshini, V.Vijika

Abstract-The design of smart agricultural wearable for enhanced productivity aims to improve the health and productivity of farmers working in extreme heat conditions. Exposure to high temperatures can lead to heat stress, dehydration and heat-related illnesses, which not only jeopardize farmers’ well-being but also reduce their efficiency and crop yield. This paper proposes the development of a wearable cooling suit designed to regulate body temperature, enhance comfort and protect farmers from heat-related health risks. The suit integrates advanced cooling technologies, such as evaporative cooling fabric, phase-change materials and thermoregulated components, to provide consistent cooling through evaporation and heat dissipation. Additionally, the suit is designed for durability, breathability and ease of use, ensuring that farmers can wear it for extended periods without discomfort. The cooling suit represents a sustainable innovation that supports agricultural workers in overcoming the challenges of working in extreme heat.

DOI: /10.61463/ijset.vol.13.issue1.144

An Examination of Contemporary Techniques for Assessing Microbial Load in Milk: A Comprehensive Review

Authors- Professor Dr. Lovelin Jerald A, Ms.Farha Jahan S, Ms.Nandhini S, Ms.Srilakshmi V

Abstract-Milk is a very diverse source of nutrition for people of all age groups. Milk is one of the most commonly consumed food items across the globe but it is highly prone to microbial contamination and spoilage due to its nature and composition. Hence, it becomes crucial to assess the microbiological safety of milk. Conventional techniques like culture and MBRT even though they have been used for a long time, rather require too much of work and time. Modern dairy technology made it possible to enhance the safety of milk through the use of techniques that are more accurate, faster, and reliable as compared to the traditional methods. This paper explores technologies like molecular photocopying (PCR), biosensors and rapid detection techniques including immunoassays and ATP bioluminescence. To determine the effectiveness, cost, and viability of different technologies in ensuring milk quality and safety on modern dairy farms, this paper thus compares the various technologies. This study also identifies the colorimetric analysis as a potential answer to the problem. The device which includes ESP 32( microcontroller) and RGB sensor-TCS 3200(color detector) can identify even the slightest change in the color of the milk due to microbial activity, process the data collected, and display the actual and precise results at the same time. Furthermore, the application of AI/ML learning models in the device enhances the accuracy of the results through a comparison between the acquired.

DOI: /10.61463/ijset.vol.13.issue1.145

Some Investigtion Stratgies for Detection, Deterrence and Mitigation of Animalia in Agricultural Fields

Authors- Aiysvarya B, Inika R G, Nandhanaa K S, A.Lovelin Jerald, Janani B

Abstract-The bird and animal Mitigation System designed to protect agricultural fields and other sensitive areas from intrusion and damage by birds and animals. The system employs a combination of unique sound signals and a smell transmitter to deter wildlife without causing harm. The sound signals are specifically tailored to target specific species, leveraging frequencies and patterns that effectively repel birds and animals. Additionally, the smell transmitter releases non-toxic, species-specific odors to enhance deterrence. The system is powered by a solar panel, ensuring continuous operation and making it environmentally sustainable. Mostly electric fences are used which causes loss of life.

DOI: /10.61463/ijset.vol.13.issue1.146

Performance Analysis of Rigid Pavements Under Varying Traffic Loads

Authors- Assistant Professor K Blanagaiah, P.V Sridevi, J Narmada, T Surya Narayana, P Padmakar, G Jayachandra

Abstract-Rigid pavements are essential for modern road infrastructure, offering high durability and load-bearing capacity. However, their performance is significantly affected by varying traffic loads, leading to structural deterioration over time.1This study investigates the impact of different traffic load conditions on rigid pavement performance through field data analysis, laboratory testing, and numerical simulations. Key factors such as axle loads, traffic volume, pavement thickness, and subgrade conditions are examined to understand their influence on pavement distress and longevity. The results indicate that increased traffic loads accelerate fatigue cracking, joint faulting, and surface roughness, particularly in pavements with inadequate structural design. Proper load transfer mechanisms and optimized material selection are found to enhance pavement durability. This research provides valuable insights for engineers and policymakers in designing more resilient rigid pavements, ensuring long-term performance and cost-effectiveness. Further studies are recommended to explore advanced materials and innovative construction techniques for improved pavement sustainability.

DOI: /10.61463/ijset.vol.13.issue1.179

Hydroelectricity: The Future of Renewable Energy

Authors- Professor Dr.B. Raghunath Reddy, P.Lahari, S.Mohammad, S.M.Akram, Vikram

Abstract-Hydroelectricity is a renewable energy source that generates electricity by harnessing the power of moving or falling water. It is one of the most widely used forms of clean energy, contributing significantly to global electricity production. 1The process involves directing water through turbines, which drive generators to produce electricity. Hydropower plants can be classified into dam-based, run-of-the-river, pumped storage, and tidal systems.Hydroelectric power offers numerous advantages, including sustainability, low greenhouse gas emissions, reliability, and energy storage capabilities. However, it also poses challenges such as high initial costs, environmental disruption, displacement of communities, and dependency on water availability. Despite these challenges, hydroelectricity remains a crucial part of the renewable energy mix, with ongoing advancements aimed at minimizing its ecological impact. As countries seek sustainable energy solutions, hydropower continues to play a key role in meeting global energy demands.

DOI: /10.61463/ijset.vol.13.issue1.178

Advanced Sewage Infrastructure Planning

Authors- Associate Professor C.Chinna Suresh Babu, C.Guru Sandeep, D.Amrutha, J.Guru Vardhan, K.Ram Charan

Abstract-Sewage systems are a fundamental component of modern sanitation infrastructure, designed to collect, transport, treat, and dispose of wastewater from residential, commercial, and industrial sources. Proper sewage management is essential for maintaining public health, preventing waterborne diseases, and protecting the environment from pollution.This paper explores the different types of sewage systems, including sanitary, stormwater, combined, and on-site sewage systems. It also outlines the wastewater treatment process, which involves primary, secondary, and tertiary treatment stages to remove contaminants before discharge or reuse. Despite their importance, sewage systems face several challenges, such as aging infrastructure, pollution, climate change impacts, and the need for sustainable wastewater management. Innovations in sewage treatment, including advanced filtration, water recycling, and energy recovery, are helping to create more efficient and eco-friendly solutions.By understanding the complexities of sewage systems and their role in environmental and public health protection, we can work toward more sustainable and resilient wastewater management practices.

DOI: /10.61463/ijset.vol.13.issue1.177

Trust-Based Secure Route Discovery Method for Enhancing Security in Mobile Ad-Hoc Networks

Authors- Dr. J. Viji Gripsy, V. Selva Swathi

Abstract-In Mobile Ad-Hoc Networks (MANETs), secure and efficient route discovery is essential due to the vulnerability of these networks to various security threats, such as black hole, Sybil, and wormhole attacks. This paper proposes a trust-based secure route discovery mechanism designed to enhance the reliability and security of communication in MANETs. By evaluating node behavior and assigning trust values based on packet forwarding success, cooperation, and reliability, the proposed mechanism identifies and isolates malicious nodes. The performance of the proposed method is evaluated and compared with traditional routing protocols such as AODV, DSR, and SAODV. Simulation results demonstrate that the proposed method significantly improves key metrics such as Packet Delivery Ratio (PDR), Routing Overhead, Throughput, and Energy Consumption, thereby ensuring more secure, efficient, and scalable communication. This work highlights the potential of trust-based mechanisms in enhancing the robustness and security of MANETs, with applications in dynamic and large-scale environments.

DOI: /10.61463/ijset.vol.13.issue1.147

Review of Applications of Phase Change Materials for Refrigeration and Air Conditioning

Authors- Professor Dr. M. Rajagopal

Abstract-Phase Change Materials (PCMs) have emerged as an innovative solution for enhancing the energy efficiency and performance of refrigeration and air conditioning systems. PCMs store and release thermal energy during phase transitions, typically from solid to liquid and vice versa, offering a sustainable means of temperature regulation. This review examines the diverse applications of PCMs in refrigeration and air conditioning, including their role in thermal energy storage, integration with refrigeration cycles, building cooling systems, and cold chain logistics. Additionally, it highlights their potential in reducing peak load, improving system efficiency, and extending operational life spans. Despite their advantages, challenges such as low thermal conductivity, high costs, and material stability issues remain. The review also discusses strategies to overcome these limitations, including enhanced materials and system integration techniques. Overall, PCMs present a promising path toward reducing energy consumption, improving comfort, and supporting environmental sustainability in cooling systems, with ongoing research likely to address existing challenges and drive broader adoption in both residential and commercial applications.

DOI: /10.61463/ijset.vol.13.issue1.148

Tools of Artificial Intelligence for Improving Interpersonal Skills of Higher Education Learners

Authors- Shashank R, Professor Dr. M. Rajagopal

Abstract-This study explores how higher education learners utilize artificial intelligence (AI) tools and large language models (LLMs), focusing on their use for both general and academic purposes. From a general perspective, learners primarily adopt AI and LLMs for convenience, time-saving, and often due to a lack of curiosity or interest in traditional problem-solving methods. Academically, their use of these technologies is driven by factors such as limited knowledge, insufficient foundational skills, low confidence, a desire for high grades, and an interest in exploring diverse perspectives. To enhance soft skill development and mitigate potential negative impacts of AI reliance, the study advocates for the strategic integration of AI tools into teaching practices. This includes aligning AI use with curriculum goals, designing interactive lessons that incorporate AI platforms, and leveraging these technologies for feedback and reciprocal engagement. The study also provides specific recommendations for various disciplines. In communication courses, educators should foster supportive environments, encourage debates, and offer opportunities for public speaking, oral communication practice, and multimedia integration. In business writing courses, teachers should emphasize effective communication, teamwork, role-playing, and the use of project management tools, while also promoting professional etiquette and networking. In composition courses, the focus should be on critical evaluation of online sources, fostering creativity, and conducting writing workshops that integrate technology to encourage deeper thinking.

DOI: /10.61463/ijset.vol.13.issue1.149

Machine Learning Algorithm for Optimising Comfort Cooling in Buildings

Authors- Shashank R, Professor Dr. M. Rajagopal

Abstract-Optimizing comfort cooling in commercial buildings is essential for reducing energy consumption while ensuring occupant comfort. Traditional HVAC systems often operate on fixed schedules and static parameters, leading to inefficiencies. This paper presents a machine learning (ML)-based approach to dynamically optimize HVAC cooling operations. The proposed system integrates predictive algorithms for cooling load forecasting and reinforcement learning (RL) for real-time HVAC control optimization. Real-time sensor data (temperature, humidity, occupancy) and external inputs (weather forecasts, time-of-day) are used to predict cooling demand. A regression model forecasts cooling loads, while RL algorithms optimize HVAC actions, such as adjusting compressor and fan speeds and air distribution and, to minimize energy use while maintaining thermal comfort. The RL model’s reward function penalizes energy overuse and deviations from comfort thresholds (e.g., 22-25°C temperature, 40-60% humidity). The results indicate that the ML-based system significantly lowers energy consumption compared to traditional control methods, without sacrificing comfort. The model adapts to real-time changes in building usage and external conditions, offering a scalable, flexible solution for smart building management. This method improves energy efficiency while also prolonging the lifespan of HVAC components by reducing unnecessary wear. The proposed method presents a promising framework for integrating advanced ML techniques, such as predictive analytics and real-time optimization, into building management systems, contributing to sustainability through reduced carbon emissions and dynamic adaptation to occupant preferences.

DOI: /10.61463/ijset.vol.13.issue1.150

Review on Tutti Frutti Production Using Vegetable Rind

Authors- Dhivya.S, Indhu Mughi.M, Sruthi Subin, B.Janani

Abstract-Tutti Frutti, a popular confectionery product made from candied fruits, is traditionally produced using raw papaya. However, this study investigates the creative use of bottle gourd rind—a often wasted by product from vegetable processing—as an alternative base material for Tutti Frutti manufacture, in response to the growing demand for sustainable and environmentally friendly food products. In addition, the natural colour taken from the peel of the jamun (Syzygium cumini) fruit is used in place of artificial dyes to improve the product’s nutritional profile and visual appeal. The project majorly focuses on use the rind of bottle gourds as an affordable, nutrient-rich base for making Tutti Frutti, and use a natural, anthocyanin-rich pigment produced from the peel of jamun fruit to substitute artificial colouring, which also have health advantages. The jamun peel is processed to extract anthocyanins, which provide a deep purple hue to the Tutti Frutti. This method ensures that the colour is entirely natural, avoiding synthetic additives that are commonly found in similar products.

DOI: /10.61463/ijset.vol.13.issue1.151

Formulation and Development of Biriyani Seasonings in the form of Pellets – A Review

Authors- Mundluru Deeksha, Rakshitha P, Tharshina D, Assistant Professor Sharmeela R

Abstract-Biriyani is a culinary dish cherished by millions around the globe. Time constraints, limited cooking facilities and the unavailability of authentic ingredients are just a few of the obstacles that can stand in the way of consuming a delicious and nutritious home-cooked meal. The current work intended to create solid dispersion-based pellets of biriyani spices. The growing demand for accessible and authentic culinary experiences encouraged innovation in the creation of these pellets. The development of biryani seasoning pellets represents a step forward in modernizing traditional cooking practices while honouring the heritage of biryani. This study focuses on the formulation and development of biryani seasoning pellets as a unique alternative for improving the convenience of biryani cooking while preserving its traditional flavour. In addition to their convenience, seasoning pellets provide health benefits due to their natural spice composition such as anti-inflammatory effects, improved digestion, increased metabolism and immune system support. These biryani seasoning pellets are a ready-to-use solution that entirely dissolves in hot rice, resulting in a tasty biryani experience with minimal preparation time. This study combines innovation and tradition addressing consumer needs for convenience and quality.

DOI: /10.61463/ijset.vol.13.issue1.152

Smart Sensor System for Real-Time Meat Freshness Monitoring – A Review

Authors- Ms.Divyadharshini N, Ms.Karthika S , Ms.Unni Mishma Roy, Assistant Professor Ms.Sharmeela.R

Abstract-The public’s growing health consciousness has led to a sharp rise in the significance of meat quality. Therefore, it’s critical to monitor every step of the procedure to get an accurate assessment of the meat’s freshness. Before distribution, every meat product must undergo quality evaluation based on atleast one parameters. A variety of characteristics of raw meat are referred to as meat quality. Two often recurring indications among several factors are surface texture and colour. One of the most perishable types of fleshy foods is known to be meat. It frequently exhibits microbial development, which causes spoiling. Meats with a high bacterial count have a higher pH content. Meat microbiological growth can be ascertained after up to 48 hours of incubation. It is well recognized that biosensors are important tools for evaluating the freshness, safety, microbial proliferation, and other aspects of raw meat quality. The primary topic of this review is the monitoring of meat freshness utilizing quality characteristics like pH, temperature, color, etc. This system will be small in size, which facilitates portability. Other benefits of the system are its low initial cost, ease of use, efficacy, and reduced time commitment.

DOI: /10.61463/ijset.vol.13.issue1.153

A Review on Performance of Domestic Bulb and Tuber Vegetables Peeling Machines

Authors- Dr.S.Ramani, Kabini V, Maryamul Asiya M

Abstract-Root vegetable peeling is a necessary step before processing them further. A batch loading peeling machine was designed, built, and tested for potato, onion, shallot and ginger in this study. Based on the idea of surface scratching, the machine was made to run between 100 and 500 rpm between 5 and 12 minutes. The machine’s performance was evaluated in terms of flesh loss, peel weight percentage, and peeling efficiency. The peeling efficiency using abrasive material with various motor speed control systems are categorized for various root vegetables include shallots, onion, ginger and garlic. The vegetable peeling mainly removes the skin in which the manual power is used also time consuming, so the Abrasive peeling machines are capable of processing large quantities of roots and tubers quickly, which is essential for industrial-scale food production. For instance, in food processing plants, potatoes, onions, and other tubers need to be peeled efficiently and at high speed, which manual peeling cannot match. It is also Labour-intensive & Time-consuming. One man can peel more skin and other may leave too much skin on the produce. Manually peeling the roots and tubers often results in uneven skin removal that leads to products often not in uniform size and shape. These machines can be integrated into automated production lines, reducing the need for manual power, speed up the peeling mechanism and also to minimise the human error.

DOI: /10.61463/ijset.vol.13.issue1.154

Deep Learning Infused Secure Retinal IRIS Identification Using Orca Predators Algorithm

Authors- Assistant Professor M.Blessy Queen Mary, Associate Professor S.G.Sam Stanley

Abstract-Biometric authentication leveraging retinal and iris images is a highly refined and secure method in the field of biometrics. The retina and iris possess distinctive and unchanging characteristics that can be effectively used for identity verification. Recognition systems employing these features are well-known for their superior accuracy and security, making them challenging to forge or duplicate. Enhancing biometric recognition with deep learning (DL) models has ushered in a new era of highly efficient identity authentication. DL frameworks, such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs), facilitate feature extraction and matching, capturing intricate and unique patterns in retinal and iris images with remarkable precision. This approach significantly enhances security and reliability across various domains, including access control, border security, healthcare, and mobile authentication, while addressing challenges such as varying lighting conditions and accommodating individuals with eye-related conditions. This paper introduces a robust biometric retinal-iris identification system employing the Orca Predators Algorithm integrated with deep learning (OPADL). The core objective of this system is to enhance biometric security using retinal and iris images. Initially, the OPADL framework employs the Wiener filtering (WF) technique to eliminate noise present in input iris images. Furthermore, the EfficientNet model is utilized for feature vector extraction, with hyperparameter optimization conducted using the Orca Predators Algorithm (OPA). The final biometric identification process is executed using a convolutional autoencoder (CAE). To validate the efficacy of the OPADL method, extensive testing is conducted using biometric iris datasets, demonstrating superior performance compared to other existing models.

Training US Workforce for Generative AI Models and Prompt Engineering: ChatGPT, Copilot, and Gemini

Authors- Satyadhar Joshi

Abstract-A structured literature review categorizes existing research into five key areas: comparative studies, tutorials, expert opinions, editorials, and performance applications. We analyze the types of instruction, duration, costs, providers, and intended audience for training programs involving these tools. A comparative table synthesizes findings from the literature to highlight key differences. We explore their functionalities, strengths, weaknesses, and applications across education, software development, and various industries. The study examines how these tools enhance skills through structured training programs, covering curriculum design, prompt engineering techniques, and ethical considerations.

DOI: /10.61463/ijset.vol.13.issue1.155

Detecting Text Similarity: A Machine Learning Approach to Plagiarism Checking

Authors- Femenca Noronha, Kaif Khan

Abstract-This research introduces Plagiarism Checker, an advanced plagiarism detection system that utilises machine learning and NLP algorithms to efficiently detect textual similarities. Using TF-IDF for feature extraction and cosine similarity, the system ensures high accuracy in identifying plagiarism within documents. Correlating with this, Jaccard similarity and n-gram analysis can highlight common word patterns in documents as well as identify paraphrased text. Built with Flask, the web interface allows for the seamless upload of documents as well as analysis. To improve these accuracy findings, understanding the text pre-processing techniques such as tokenization, stopword removal, and lemmatization is important. The error of processing AI-generated text where obfuscation techniques are used is addressed within the research. Future updates will see the incorporation of deep learning models such as LSTM and transformers to enhance the detection capabilities of Plagiarism checkers. The contribution of the research to the advancement of automated plagiarism detection is to ensure originality as well as academic integrity.

DOI: /10.61463/ijset.vol.13.issue1.156

Advancing University Educators: A Review of Continuing Professional Development

Authors- Farida Farj Emhemed Elshtiwi

Abstract-Continuous Professional Development (CPD) is essential in enhancing university educators’ skills, knowledge, and effectiveness. As higher education landscapes evolve with technological advancements, shifting pedagogical approaches, and increasing research demands, CPD remains essential for maintaining teaching quality, fostering innovation, and promoting academic leadership. This review explores the significance of CPD for university educators, examining key areas such as pedagogical enhancement, technological integration, research development, and leadership skills. It discusses various CPD models, including formal institutional programs, informal learning opportunities, and self-directed professional growth. Additionally, the review identifies common barriers to effective CPD, such as time constraints, institutional limitations, and access to resources. Best practices and strategic recommendations are provided to optimize CPD initiatives, ensuring sustained professional growth and improved student learning experiences. The study highlights the need for universities to adopt comprehensive, flexible, and evidence-based CPD frameworks to support educators in an ever-changing academic environment.

AI-Driven Risk Platform Automating Data Aggregation and Risk Insight Generation

Authors- Sanjay Moolchandani

Abstract-In the ever-evolving landscape of business and technology, effective risk management has become a crucial aspect of organizational success. The emergence of AI-driven risk platforms has revolutionized how organizations aggregate data, identify risks, and generate insights. These platforms leverage machine learning (ML), natural language processing (NLP), and advanced data aggregation techniques to automate traditionally manual processes, enhancing decision-making, accuracy, and efficiency. This article explores the significance of AI in automating data aggregation and risk insight generation, the role of AI in improving risk management practices, and the various challenges and benefits associated with these innovations. Furthermore, it investigates real-world applications and future advancements in AI-driven risk platforms, shedding light on the potential for continued growth and refinement in this field.

User Behavior Prediction of Social Hotspots Based on Multi-Message Interaction and Neural Networks

Authors- Anil Mothe, Satyam Kumar, Sampath Kumar

Abstract-Introduces a prediction model for user participation behavior in the context of public-opinion analysis on social hot topics. It emphasizes the importance of message diversity in shaping user behavior and addresses the complexity of interactions among multiple messages. The proposed model leverages a multi- message interaction influence-driving mechanism to enhance the accuracy of user participation behavior predictions. To accommodate the intricate behaviors of users in multi-message hotspots and the limitations of simple backpropagation neural networks, the study combines this mechanism with a backpropagation neural network (BPNN) to create a user participation behavior prediction model. Recognizing that multi- message interaction can lead to overfitting in the BPNN, the article employs a simulated annealing algorithm to optimize the network, further improving prediction accuracy. The model not only forecasts user participation behavior in real-world situations involving multiple message interactions but also quantifies the relationships between different messages on hot topics.

DOI: /10.61463/ijset.vol.13.issue1.157

Eco-Eye: Object Detection System for Blind People

Authors- Assistant Professor Deepali Mane, Rahul Kolhe, Mohit Patil, Vaishnavi Bharambe, Aarya Raghuvanshi

Abstract-This paper presents a research study on a smart walking stick designed to enhance mobility for visually impaired individuals. The system uses a Raspberry Pi with a camera module to process live video via the YOLOv3 object detection algorithm trained on the COCO dataset. It detects obstacles and hazards, providing real-time audio feedback through a speaker. The study details the hardware and software design, surveys related assistive technologies, and explores advancements in computer vision and deep learning. Edge detection, sensor fusion, and embedded optimizations ensure portability, affordability, and reliability. Experimental results show improved navigation safety, positioning this as a viable low-cost assistive solution. Future enhancements include multi-sensor integration, voice interaction, and cloud- based processing for next-generation assistive technology. Index Terms – Smart Stick, Object Detection, YOLOv3, Raspberry Pi, Assistive Technology, Blind Assistance, COCO Dataset, Edge Detection, Real-Time Processing.

DOI: /10.61463/ijset.vol.13.issue1.158

The Role of Physics in Forest Conservation and Management in India

Authors- Surendra Kumar Pandey

Abstract-Forest conservation is vital for biodiversity, climate regulation, and ecological sustainability. In India, where forests cover approximately 21.71% of the land, efficient forest management is essential. Physics plays a crucial role in conservation efforts through remote sensing, climate modelling, hydrological studies, and fire prevention. This paper explores the applications of physics in forest management, emphasizing satellite monitoring, LiDAR technology, fluid dynamics in soil and water conservation, and thermodynamics of wildfire control. These approaches contribute to effective conservation strategies, ensuring the sustainable management of India’s forests. Encouraging interdisciplinary research and investing in physics–based environmental technologies will be key to preserve India’s rich and diverse forest ecosystems. Future research should focus on the development of advanced physics–driven technologies to further strengthen conservation strategies.

DOI: /10.61463/ijset.vol.13.issue1.159

Defect Control in Product Design and Development Using Particle Swarm Optimization

Authors- I.I. Okachi, O.E. Isaac, O.T. Briggs, G.A. Sibete

Abstract-This study focuses on improving product quality and reducing defects in the production of Polyethylene and Polypropylene by combining Six Sigma methodologies with Particle Swarm Optimization (PSO). The main goal was to understand how Six Sigma can control defects and enhance quality, while PSO helps optimize production parameters such as the quality control data, production records, process parameters, cost of quality data and statistical control records to minimize errors and improve consistency. The results were impressive, showing clear improvements in both quality and process efficiency. Initially, defect rates were as high as 4.8%, but after applying Six Sigma, the rate dropped to 2.5%, showing a significant reduction in defects. At the same time, the Sigma level, a key measure of process quality, improved from 3.2σ to 4.7σ, which indicates better control over production processes. PSO was crucial in fine-tuning key production variables such as temperature and pressure, which helped reduce defect rates further. For example, adjusting the temperature range from 230°C to 245°C led to a defect rate as low as 2.3%, highlighting how optimization can directly improve product quality. In terms of process capability, Six Sigma and PSO together improved the Cp index from 1.1 to 1.83 and the Cpk index from 0.87 to 1.72, reflecting a more stable and capable production process. Furthermore, the combination of Six Sigma and PSO brought significant cost savings, with external failure costs reduced by 73%, demonstrating the financial benefits of improving quality through preventive measures. The findings show how using a developed practical framework for integrating Six Sigma and PSO can lead to better product quality, fewer defects, and overall process improvements in manufacturing.

Gastritis: Etiology, Pathophysiology, Diagnosis, and Management

Authors- Professor Dr. Saleem Ahmed Abdul Rasheed, Assistant Professor Dr. Ansari Tahzeeb Afroz, Professor Dr. Ansari Shadiya Tahseen Sajjad, Associate Professor Dr Siddiqui Rashid Rafeeque Ahmad

Abstract-Gastritis, an inflammation of the gastric mucosa, is a prevalent gastrointestinal disorder with acute and chronic forms. It is often caused by factors such as Helicobacter pylori infection, prolonged nonsteroidal anti- inflammatory drug (NSAID) use, and lifestyle choices. This paper explores the etiology, pathophysiology, clinical manifestations, diagnostic approaches, and management strategies for gastritis. Advances in endoscopy, histology, and targeted therapies have improved outcomes, but challenges remain in addressing complications such as peptic ulcers and gastric cancer.

Pawsome Community: A MERN Stack-Based Pet Adoption, Support, and Donation Platform

Authors- Devanshi Parimal Padia, Shivbhadrasinh Vijaykumar Sankhat, Assistant Professor Mrs.Sujaya Bhattacharjee

Abstract-This paper presents “Pawsome Community,” a web application designed using the MERN (MongoDB, Express.js, React.js, Node.js) stack. The platform addresses the challenges in pet adoption, veterinary support, and community-driven donations for animal welfare. As a college project, “Pawsome Community” aims to simplify the pet adoption process, provide reliable access to veterinary professionals, and create a seamless donation channel to support animal welfare organizations. The platform aspires to foster a community-driven approach to pet welfare by integrating modern web technologies. Although the platform is yet to be deployed on a live server, its development reflects the potential of technology in enhancing animal welfare and community engagement. This paper explores the technical architecture, features, and societal implications of the project, emphasizing its future scalability and potential impact on the animal welfare ecosystem.

DOI: /10.61463/ijset.vol.13.issue1.160

Analyzing the Importance of Functional Interior Design in Public Healthcare Sector in India

Authors- Vedanti Bhat

Abstract-The purpose of this paper is to review the literature on the significance of interior design in the built environment and healthcare design, and propose a conceptual framework for usability to achieve quality service. This paper will focus on three key factors: efficiency, functionality, and users’ satisfaction. This overview will help future researchers investigate the relationship between spatial design and functionality from the users’ experience and expectations in the outpatient area. This approach to usability helps improve service outcomes in outpatient areas, which is more valuable to the end-users.

DOI: /10.61463/ijset.vol.13.issue1.161

A Multitask Learning Model for Traffic Flow and Speed Forecasting

Authors- Sravanthi Pateru, Jahnavi Mekala, Suchith Kumar

Abstract-Improve Activity Stream and Speed determining exactness, we proposed a profound learning-based multitask learning Gated Repetitive Units (MTL-GRU) with remaining mappings.To improve the execution of the MTL-GRU, including building is presented to choose the foremost enlightening highlights for the estimating.At that point, based on real-world datasets, numerical comes about, and utilizing MTL-GRU can well gauge activity stream and speed at the same time, and perform way better than other techniques. Tests appear that the profound learning-based MTL-GRU show can overwhelm the bottleneck caused by extending preparing datasets and proceeding to pick up benefits. Although a number of models have been developed, many of them leverage conventional methods that may be unsatisfying to penetrate the deep correlation hidden in large datasets consequently forecasting accuracy cannot profit from sharply increasing traffic data. Therefore, new techniques are eagerly demanded to handle the abundant traffic data at a deep level.

DOI: /10.61463/ijset.vol.13.issue1.162

Implementation of Charging Station for Electric Vehicle Using Solar Panel with IOT

Authors- Sanika kadam, Shweta kadam and Rameez Shamalik

Abstract-Electric vehicles (EVs) have been gaining popularity through the time, which has increased demand for more efficient and eco-friendly charging solutions. With IOT connectivity, Solar charging stations have been identified as an emerging technology to cater this requirement. A Survey Paper on Solar Charging stations for EVs with IOT available in this paper gives a detailed description about the current state of solar charging stations utilizing IOT. Architecture, major components and protocols in use are all discussed For solar charging stations IOT enabled: think benefits… then challenges & the way out The combination of electric vehicles (EV), solar energy and IOT connectivity has given us a ground breaking technology solar charging station for electric vehicles (EV) with IOT. In this survey paper, we will review the architecture, essential sub-systems and communication protocols of this emerging field, which helps to understand this technology. These systems in particular have great potential to disrupt the transportation sector if we could only tap into the power of solar and IOT connectivity for sustainable, efficient and connected mobility.

DOI: /10.61463/ijset.vol.13.issue1.163

Mumps: A Comprehensive Review on Epidemiology, Clinical Manifestations, Prevention, and Future Perspectives

Authors- Associate Professor Dr Parveen Akhtar Shaukat Ali, Dr Irshad Ahmad, Dr. Salim Khan Yunus Khan, Professor Dr. Mudassar Nazar Abdul Nabi

Abstract-Mumps is a contagious viral disease caused by the mumps virus, primarily affecting the salivary glands and leading to painful swelling. Despite the widespread use of the MMR vaccine, outbreaks still occur due to factors like waning immunity and vaccine hesitancy. The virus spreads through respiratory droplets and direct contact, with complications including meningitis, encephalitis, orchitis, pancreatitis, and hearing loss. Diagnosis is based on clinical symptoms, serological tests, and PCR detection. There is no specific antiviral treatment, and management focuses on symptomatic relief. Vaccination remains the most effective prevention method, though research suggests booster doses may be necessary for long-term immunity. Ongoing studies aim to improve vaccine formulations, diagnostic techniques, and public health strategies to prevent future outbreaks.

Context-Aware Music Embedding in Silent Videos Leveraging Transformer Architectures: A Review

Authors- Research Scholar Badhe Om Ghanshyambhai

Abstract-This paper gives an excellent assessment of context-conscious track embedding in silent films using Transformer architectures. The study addresses the critical undertaking of dynamically integrating suit- able musical accompaniment on video content by way of using advanced deep studying techniques. We discover the evolution from traditional strategies the use of RNNs and CNNs to fashionable Trans-former-primarily based solutions, focusing on actual-time processing and emotional coherence. The studies examine diverse methodologies, together with the Vision Transformer algorithm for video analysis and context know-how, in conjunction with sophisticated tune era strategies. Our proposed frame- work consists of a three-phase approach: video evaluation, song technology, and integration, with unique emphasis on keeping temporal align- ment and emotional consistency. The assessment framework encompasses a couple of parameters, consisting of Detection Ac-curacy Rate , Emotional Coherence Score , and Synchronization Accuracy , providing a sturdy evaluation technique. The evaluation additionally identifies modern-day boundaries in existing systems and proposes future guidelines for studies, which includes multi-modal enhancement and personalization features. This work contributes to the growing subject of AI-driven multimedia processing with the aid of imparting an established technique to context-conscious music embedding, capacity reaping re- wards each instructional researchers and industry practitioners in developing more state-of-the-art audio-visual content generation systems.

DOI: /10.61463/ijset.vol.13.issue1.164

Piles and Lifestyle: Analyzing Risk Factors, Treatment Approaches, and Preventive Measures

Authors- Associate Professor Dr. Shaikh Mohd Naeem Rafiuddin, Dr. Sharique Zohaib, Dr. Shamshad Aalam Waheeduzzama, Assistant Professor Dr. Ansari Tahzeeb Afroz

Abstract-Piles (hemorrhoids) are a common anorectal disorder that affects a significant portion of the population, causing discomfort, bleeding, and pain. This study aims to analyze the prevalence, risk factors, and treatment efficacy for piles using data from 50 patients. The study examines demographics, lifestyle habits, and treatment outcomes. Statistical analysis is performed to evaluate the impact of dietary habits, obesity, and sedentary lifestyles on the severity of the disease. The findings suggest that dietary fiber intake and physical activity play a crucial role in the management and prevention of piles.

Plant Disease Detection Using Deep Learning

Authors- Kiran Sai Tapa, Rithika Gundlapalli, Vaishnavi Allenki

Abstract-When plants and crops are affected by pests it affects the agricultural production of the country. Usually, farmers or experts observe the plants with naked eye for the detection and identification of disease. However, this method can be time- consuming, expensive, and inaccurate. Automatic detection using image processing techniques provides fast and accurate results. This is concerned with a new approach to the development of a plant disease recognition model, based on leaf image classification, by the use of deep convolutional networks. Advances in computer vision present an opportunity to expand and enhance the practice of precise plant protection and extend the market of computer vision applications in the field of precision agriculture. Novel ways of training and the methodology used to facilitate a quick and easy system implementation in practice. All essential steps required for implementing this disease recognition model are fully described, starting from gathering images in order to create a database, assessed by agricultural experts, and a deep learning framework to perform the deep CNN training. This method is a new approach in detecting plant diseases using the deep convolutional neural network trained and fine-tuned to fit accurately to the database of a plant’s leaves that was gathered independently for diverse plant diseases. The advance and novelty of the developed model lie in its simplicity; healthy leaves and background images are in line with other classes, enabling the model to distinguish between diseased leaves and healthy ones or from the environment by using deep CNN.

Detection of Amoebiasis by Using ELISA Technique among Patients in Duhok City

Authors- Muslim Abbas Allu, Hassan Mohsen Hassan

Abstract-In the present study, spanning from July 1, 2022, to December 31, 2022, fecal samples were procured from a cohort of 166 adult patients with symptoms of diarrhea Azadi Teaching Hospital of duhok city, Iraq Samples that exhibited positive results under microscopy were subjected to additional analysis using ELISA technique. The current data revealed that out of 166 stool samples examined with microscopy and iodine preparation, 53(31.92%) specimens were positive for E. histolytica trophozoites and cysts, while the remaining 113(68.08%) were negative for any amoebic stages. male was more infected than women. The percentage of specimens that were tested with E. histolytica ELISA Out of 166 stool specimens, 89(53.61%) were was from male, while the remaining specimens was from female 77(46.39%). The total of positive samples was 33 samples, while negative samples were 56 in males, while in females, the positive samples was 20 samples, while negative samples were 57 samples. The DRG stool ELISA revealed sensitivity and specificity (81.13%and 93.33%) respectively and predictive value of (97.87%).

DOI: /10.61463/ijset.vol.13.issue1.165

Peiyue Care and Postpartum Depression: Narrative Review

Authors- Research Scholar Shivangi R Kiran, Assistant Professor Ritu K Sureka

Abstract-Background: A number of original studies have indicated a potential link between cultural practices, such as “pieyue” care, and postpartum experiences, including postpartum depression. While this association isn’t universal, it warrants further investigation. This review systematically examines the existing evidence to assess the correlation between peiyue care and postpartum depression, aiming to inform the development of culturally appropriate healthcare interventions and policies to improve maternal and child health outcomes. Methods: A comprehensive literature search was conducted using PubMed, PsycNet, and Google Scholar to identify relevant studies published between January 2013 and 2023. A rigorous selection process, based on specific keywords and criteria, was employed to ensure the inclusion of high-quality studies. Results: The findings of the reviewed studies underscore the need for culturally sensitive healthcare interventions and policies to address the unique needs of postpartum women. Conclusion: In India, the prevalence of postpartum depression is influenced by a complex interplay of sociocultural factors, including traditional practices like postpartum confinement, dietary restrictions, and family support. While these practices can provide a supportive environment for some women, rigid adherence to them, coupled with societal stigma surrounding mental health, can contribute to feelings of isolation and stress, increasing the risk of postpartum depression.

A Study on knowledge Related to Hospital Infection Control According to NABH Accreditation Standards

Authors- Dr.Bharat Patil, Dharman Patel

Abstract-Hospital Infection control is very essential for the safety and wellbeing of patients, hospital staffs and visitors of the hospital. It affects various Departments of the hospital and it also involves problems of quality risk management, clinical governance of health and safety. Hospital infection control program with stable structure should be present in all institutions that provide health care in order to create a managed environment. The study aims to assess and compare the infection control practices and policies among the healthcare providers. The study will serve as a source of finding the aspects of existing infection control practices and thus will be helpful in bridging the gap between the current infection control practices. It also integrates the process with the organization to maintain and improve the. Providers. The hospital staff be made aware of NABH through induction program & Employee Training Modules that, the board is structured to cater to much desired needs of the consumers and to set benchmarks for progress of health industry. The board is functionally autonomous in its operation. There should be more information flow from Leadership, regarding the NABH as it gives a brand image for quality services of the hospital for the image building process. More and more awareness should be brought even in the society so that, people should know what NABH accreditation stands for quality service of the hospital.scope of health services, and more importantly, it grossly abuses the components of inclusive and integrative views of the general public, especially consumers’ current state of health knowledge. We think that the utilizing users’ experiences as the basis for integrated planning and management of health services has given healthcare planning a solid grounding. However, the Indian healthcare system rarely discusses the experiences of its patients. It is crucial to talk about such a scenario from the users’ point of view. Regmi’s study on the efficiency of healthcare services shed light on the impediments and enablers of efficient healthcare. Despite studies in other developing nations like Nepal, there haven’t been many studies on the perceptions of effective health services in India. The goal of this study is to examine users’ perceptions of successful health services and look into any obstacles or difficulties that may be preventing the management of health services effectively. The results of this study could offer valuable information for planning and managing health services to increase their efficiency.

The Evolution of Smart Cities: Integrating Technology for Urban Efficiency

Authors- Omkar Deshpande

Abstract-The concept of smart cities represents a transformative approach to urban development, harnessing advanced technologies to improve efficiency, sustainability, and quality of life in metropolitan areas. This paper investigates the evolution of smart cities, from their inception during early urbanization to the incorporation of modern technologies such as the Internet of Things (IoT), big data analytics, artificial intelligence (AI), and cloud computing. The research explores how these innovations are being applied to optimize urban infrastructure, enhance public services, and foster environmental sustainability. Through case studies of prominent smart cities globally, the paper identifies the challenges and opportunities in implementing smart city strategies. The study emphasizes the need for a comprehensive urban planning approach, where technology is fully integrated into city operations to achieve enhanced efficiency, resilience, and inclusivity. Ultimately, the paper contends that the continued evolution of smart cities hinges on collaboration between governments, private sector stakeholders, and citizens to develop urban spaces that are both technologically sophisticated and socially responsive.

DOI: /10.61463/ijset.vol.13.issue1.167

Movie Recommendation System Using Sentiment Analysis

Authors- Roshan Joel Sandri, Sabavath Prakash, Bandaru Varun Kumar, Durgam Dayakar

Abstract-This paper introduces a movie recommendation system that leverages cosine similarity to recommend those movies that are similar to one another based on user selection. In more detail, the movie recommendation system transforms the movie text into vectors using Count Vectorizer, and cosine similarity helps to find the nearest movies (vectors) to recommend. To improve the user experience, the recommendation system tries to analyze the sentiment of the movie review by applying the Naïve Bayes algorithm to let the user understand whether the movie is worth watching.

AI-Driven Multimodal Framework for Transparent and Secure Faceless Registrations

Authors- Dr. K. Baskar, R. Sathyaraj, P.Annumalika, K. Archana, M. Jayasree

Abstract-Faceless registration is increasingly utilized in digital identity verification, enabling seamless authentication without requiring physical presence. However, traditional unimodal authentication methods, such as facial recognition or voice-based verification, often exhibit security vulnerabilities, limited robustness, and susceptibility to spoofing attacks. To address these challenges, we propose a Multimodal Sentiment Analysis (MSA) framework based on a Multichannel Cross- modal Fusion Network (MCFNet), which integrates text, audio, and video modalities to enhance sentiment-driven user verification. The MCFNet architecture employs cross-modal attention mechanisms to capture interdependencies between different modalities, redundancy reduction techniques to minimize irrelevant features, and a Text-Guided Information Interactive Module (TIIM) to prioritize meaningful textual data. The system enhances security and reliability by incorporating deep learning-based feature extraction, nonverbal information refinement (NIRM) for multimodal synchronization, and an optimized decision making model for accurate sentiment analysis. Our experimental evaluation, conducted on benchmark multimodal biometric datasets, demonstrates that the proposed approach significantly improves authentication accuracy while reducing false acceptance and rejection rates. The results highlight the potential of multimodal sentiment analysis in ensuring secure, transparent, and robust faceless registration systems, making it a viable solution for next-generation digital identity management.

DOI: /10.61463/ijset.vol.13.issue1.168

Voice to Sign-Language Translator Using NLP

Authors- Dr. K. Baskar, Mr.S.Azarudeen, S.Mythili, K.Reshma, A.Shalini

Abstract-The Voice-To-Sign-Language Translator is an innovative web-based application developed to address the communication barriers faced by individuals with hearing impairments. This project enables real-time translation of spoken or text inputs into corresponding Indian Sign Language (ISL) animations, leveraging advanced web technologies and natural language processing techniques. The system uses the JavaScript Web Speech API to capture and transcribe speech into text, which is then pre-processed using the Natural Language Toolkit (NLTK) to conform to ISL grammar. A pre-defined database of ISL gestures is utilized to map the processed text to 3D animations crafted in Blender, and these animations are rendered in real-time through a user-friendly interface designed with HTML, CSS, and JavaScript. By eliminating the dependency on human sign language interpreters, this project offers a scalable and efficient solution for inclusive communication, particularly in public events and government functions. The application exemplifies the transformative potential of AI and animation technologies in enhancing accessibility and fostering social empowerment for individuals with hearing and speech impairments.

DOI: /10.61463/ijset.vol.13.issue1.169

Resources Endowment and Total Factor Productivity Growth in the Sub-Saharan African Economies

Authors- Adeleye Ebenezer Oloniluyi, Julius Oyebanji Ibitoye, Akindele John Ogunsola

Abstract-The study examined the contribution of resources to the total factor productivity of the Sub-Saharan African countries with data from 48 African countries. We employed System (GMM) estimation techniques. The empirical results show that there is significant evidence that natural resource endowment in Sub-Saharan African countries is positively associated with TFP growth, also the larger the distance of host countries to the technology frontier countries in exploring the endowed resource the lower the productivity growth rate. In addition, the Arellano Bond estimation test at (0.0165) indicates first-order autocorrelation and no second-order autocorrelation in the residuals at (0.1823). Furthermore, Sargan p-value (0.96) confirmed the validity of the instruments with the non-correlation with residuals. The Sub-Saharan African countries especially the resourcefully endowed countries should continue to direct their policies towards improving the quality of human capital through improving the quality of education and research and development to improve the level of productivity in the countries.

DOI: /10.61463/ijset.vol.13.issue1.170

A Secure De-Centralized Protocol for Voting System

Authors- Pothuganti Abhiram, Dhane Rithika, Kondi Varun Kumar Reddy, Dr.Kumar Gurrampally

Abstract-In the ever-evolving landscape of digital elections, this paper introduces a pioneering solution – a Secure Decentralized Protocol for Voting Systems utilizing blockchain technology. Traditional voting systems face challenges like fraud, coercion, and transparency issues, prompting the need for innovative, secure, and transparent alternatives. Leveraging blockchain’s decentralized architecture, the protocol establishes a tamper- resistant and transparent framework, mitigating vulnerabilities associated with centralized databases. The objectives encompass developing a secure election mechanism with a focus on verifiability, immutability, and anonymity while addressing common pitfalls of traditional systems. Key components include unique private keys for voters, smart contracts written in Solidity, and cryptographic techniques for privacy. Implemented on the Ethereum blockchain, the protocol utilizes smart contracts to automate secure and transparent voting logic. The decentralized infrastructure ensures immutability and tamper resistance, providing a resilient foundation. The advantages include transparency, elimination of central points of failure, verifiability, and security through cryptography. Challenges, such as scalability and user accessibility, are addressed through ongoing research and testing. Future directions include real-world pilots, interoperability exploration, educational initiatives, and continuous research for scalability and privacy. The protocol stands as a transformative step towards secure, transparent, and inclusive digital elections, redefining the democratic process in the digital era.

Human-Centric AI Development: Navigating Ethical Pitfalls of Unregulated AI Development

Authors- Joel Frenette

Abstract-Artificial Intelligence (AI) holds transformative potential but is fraught with significant ethical and existential risks. Unregulated AI development could lead to catastrophic scenarios, including autonomous weapons targeting humans, biased AI systems perpetuating inequalities, and self-developing AI systems evading human control. This paper explores the need for human- centric AI governance, focusing on embedding ethical principles into AI design, addressing bias, and implementing global governance frameworks. Through case studies and critical analysis, it argues for urgent multi- stakeholder action to ensure AI systems serve humanity’s long-term interests while avoiding societal harm.

Innovative Perspectives on Fractional Calculus and Integral Transforms in Advanced Mathematical Analysis

Authors- Research Scholar Mrs. U. Naga Rekha Rani

Abstract-This study explores the intricate connections between fractional calculus and integral transformations, emphasizing their applications in resolving complex mathematical problems. The research delves into fractional derivatives such as the Riemann-Liouville and Caputo derivatives and their interplay with classical integral transforms, including Laplace, Fourier, and Mellin transforms. Through an in-depth examination of mathematical frameworks and practical implementations, this paper highlights the significance of these methodologies in modeling real-world phenomena and solving fractional differential equations.

Patient Experience and Healthcare Quality: A Comprehensive Analysis of Tertiary Hospitals in Sagar District, Madhya Pradesh

Authors- Assistant Professor Mrs Ritu Jain

Abstract-This research examines the determinants of patient experience and satisfaction across tertiary healthcare facilities in Sagar District, Madhya Pradesh, with particular emphasis on urban-rural disparities. The study employs a mixed-methods approach to identify key factors influencing patient perceptions of healthcare quality, including service delivery, provider communication, waiting times, infrastructure adequacy, and cost considerations. Data collected from 450 patients across 8 hospitals reveals significant variations in satisfaction levels based on socioeconomic factors, geographical location, and facility type. The findings suggest that while technical competence remains important, interpersonal aspects of care significantly influence overall patient satisfaction. This research contributes to the growing body of literature on patient-centered care in developing regions and offers evidence-based recommendations for healthcare administrators and policymakers to enhance service quality and patient outcomes in Sagar District’s evolving healthcare landscape.

Social Media Sentiment Analysis and Voting Patterns: A Machine Learning Framework for Electoral Behavior in Madhya Pradesh

Authors- Research Scholar Pavan Kumar Goyal, Dr. Prashant Sen, Dr. Anil Pimplapure

Abstract-The rapid proliferation of social media has transformed political discourse, influencing voter behavior and election outcomes. This study explores the relationship between social media sentiment and voting patterns in Madhya Pradesh, India. Using a machine learning framework, we analyze sentiment trends from social media platforms and correlate them with historical electoral data. Our findings suggest that sentiment analysis can serve as a predictive tool for electoral behavior, highlighting the growing role of digital interactions in democratic processes. The study employs multiple machine learning algorithms, including deep learning approaches, to capture the nuanced relationship between online sentiment and electoral outcomes. Results indicate significant correlations between digital discourse and voting behavior, with important implications for political campaigns, election monitoring, and democratic participation in the digital age.

Fracture Detection and Classification Using Deep Learning Methods

Authors- Endla Ganesh Varma, Syed Arfath, Kandhanuri Chandrashekar Goud, Mohd Muneer

Abstract-In today’s fast-paced and dynamic healthcare environment, time is of the essence, and every moment wasted can have significant repercussions for both patients and medical professionals. By automating the entire process of obtaining an X-ray, analyzing it for fractures, and determining the appropriate course of action, our system aims to streamline patient care, minimize unnecessary visits to the doctor, and maximize the efficient allocation of medical resources. Through the implementation of advanced artificial intelligence technologies, our project not only addresses the immediate need for improved fracture detection but also sets the stage for a transformative shift in healthcare delivery. By leveraging the capabilities of AI to navigate the complexities of fracture diagnosis with speed and precision, we empower healthcare providers to deliver timely and effective care while optimizing their workflows and resources. Furthermore, by reducing the burden of manual tasks and administrative duties associated with fracture diagnosis, our system frees up valuable time for medical professionals to focus on more critical patient cases, ultimately enhancing overall productivity and patient outcomes In essence, our project represents a bold step towards a future where AI-driven automation plays a central rothe le in revolutionizing healthcare delivery, ensuring that patients receive the timely and accurate care they need while maximizing efficiency and resource utilization within the healthcare system.

Predicting Voter Decisions: A Machine Learning Approach to Quantifying Social Media Influence in Madhya Pradesh Elections

Authors- Research Scholar Pavan Kumar Goyal, Dr. Prashant Sen, Dr. Anil Pimplapure

Abstract-The rise of social media has revolutionized political engagement, influencing voter perceptions and decision-making processes across the democratic landscape. This study explores the relationship between social media influence and voter decisions in Madhya Pradesh elections through a comprehensive machine learning framework. By analyzing sentiment trends, engagement patterns, and political discourse on social platforms, we develop predictive models to quantify social media’s impact on electoral behavior. Utilizing a diverse array of machine learning algorithms, including deep learning approaches, we demonstrate the predictive potential of digital metrics in forecasting electoral outcomes. The findings highlight the increasing role of digital interactions in shaping voter choices, reveal the varying impact across demographic segments, and provide insights into future political strategies. Results indicate significant correlations between online engagement and offline voting behavior, with important implications for campaign management, electoral analysis, and democratic processes in the digital age.

Real Time Object Detection Using Deep Learning

Authors- Akula Sagar, Cherlagudem Tejaswi, Bhaskari Prashanth Reddy, Dr.S.Ravi Kumar Raju

Abstract-Real-time object detection is a vast, vibrant and complex area of computer vision. If there is a single object to be detected in an image, it is known as Image Localization, and if there are multiple objects in an image, then it is Object Detection. This detects the semantic objects of a class in digital images and videos. The applications of real time object detection include tracking objects, video surveillance, pedestrian detection, people counting, self-driving cars, face detection, ball tracking in sports and many more. Convolution Neural Networks is a representative tool of Deep learning to detect objects using Open CV (Open source Computer Vision), which is a library of programming functions mainly aimed at real-time computer vision. One of the crucial components regarding this is vision, apart from other types of intelligences such as learning and cognitive thinking. A robot cannot be too intelligent if it cannot see and adapt to a dynamic environment. The searching or recognition process in real time scenario is very difficult. So far, no effective solution has been found for this problem. Despite a lot of research in this area, the methods developed so far are not efficient, require long training time, are not suitable for real-time application, and are not scalable to large number of classes. Object detection is relatively simpler if the machine is looking for one particular object. However, recognizing all the objects inherently requires the skill to differentiate one object from the other, though they may be of same type.

Augmented Reality and Machine Learning based Product Identification in Retail Using Vuforia and MobileNets

Authors- Supriya Bammidi, Gayatri Paidi, Mohd Kaif, Mohd Munner

Abstract-Interactive shopping using augmented reality (AR) is a rapidly growing trend in the retail industry. This technology enables customers to view products in a more immersive and interactive way, enhancing their shopping experience. In this paper, we present an abstract for the development of an AR-based shopping platform that allows customers to virtually try on clothes, accessories, and makeup productive, and interact with them in real-time. The platform will be designed to be user-friendly and intuitive, making it accessible to customers of all ages and technical abilities. The proposed system will use advanced computer vision and machine learning algorithms to recognize the customer’s body shape, size, and skin tone to provide personalized recommendations. The platform’s potential benefits include increased customer engagement, improved conversion rates, and enhanced customer satisfaction. This abstract provides an overview of the proposed system’s features and functionally and outlines the overlays digital information onto the real world, creating an interactive and immersive experience. In recent years, retailers have started using AR to enhance the shopping experience for or their customers. By using AR customers can visualize products in 3D, see how they would look in different colors or sizes, and even interact with them in real-time. This technology for items has the clothing accessories, and makeup, where seeing how they look on the body is essential.

Optimistic Crop Management Using Deep Learning and Machine Learning for Sustainable Agriculture

Authors- Golla Jensy Sandilya, Alluri Harshitha, Dama Greeshman, Adari Phanendra, Assistant Professor Kaki Leela Prasad

Abstract-Agriculture is the backbone of the Indian economy, providing a living for more than half of the country’s people. India is an agrarian country whose economy is mostly focused on crop productivity. Sustainable agriculture faces numerous challenges, including pest infestations, unpredictable crop yields, and plant diseases, which can significantly impact food security and economic stability. Pest infestations can devastate crops, leading to significant yield losses and necessitating the use of harmful pesticides, which can have adverse environmental effects. Unpredictable crop yields make it difficult for farmers to plan and allocate resources effectively. Additionally, plant diseases, often detected too late, can spread rapidly and cause widespread damage. Traditional methods of crop management are typically reactive, labor-intensive, and inefficient, exacerbating these issues. These problems collectively undermine agricultural sustainability, highlighting the urgent need for innovative solutions to improve crop management practices. This research aims to address these issues by leveraging deep learning and machine learning technologies to create an advanced, optimistic crop management system. The proposed solution involves the integration of various machine learning models to monitor and predict crop health, yield, and potential threats. Key areas of investigation include predictive crop yield modeling and disease detection. The project will develop sophisticated algorithms that analyze a combination of historical and real-time data. These algorithms aim to predict crop yields with high accuracy, enabling farmers to make informed decisions regarding resource allocation and planning. Implementing deep learning techniques, the system will detect early signs of plant diseases through analysis of leaf images. Early disease detection allows for prompt treatment, preventing the spread of infections and minimizing crop losses.

DOI: /10.61463/ijset.vol.13.issue1.171

Impacts of Heavy Metal Toxicity on Plant Growth and Development: Insights into Physiological and Biochemical Responses

Authors- B. Gyani Priyanka Patnaik, J. Anuradha, and R. Sanjeevi

Abstract-Heavy metal contamination poses a significant threat to plant growth and development, with detrimental effects on agricultural productivity, ecosystem stability, and human health. In this study, we examine how plants respond physiologically, biochemically, and morphologically to heavy metal toxicity to provide insight into the mechanisms underlying interactions between plants and metals as well as possible mitigating strategies. Heavy metals (HMs) such as chromium, cadmium, nickel, mercury, lead, and arsenic can enter plants through several different processes. Once inside, these metals interfere with vital physiological functions including photosynthesis, food intake, and water interactions. Heavy metals cause oxidative stress at the biochemical level, which results in the production of ROS and lipid peroxidation. These processes harm cellular constituents and interfere with metabolic processes. Furthermore, the morphology of plants is altered by heavy metal poisoning, which has an impact on root growth, leaf shape, and reproductive development. Plants have developed several defines mechanisms against heavy metal stress, despite the metals’ harmful effects. These defines mechanisms include metal sequestration, chelation, and detoxification by complication with PC’s and MET’S. It is essential to create sustainable solutions that include these plant responses to reduce the toxicity of heavy metals to plants and the environment. This paper highlights the need for interdisciplinary research efforts to address this pressing environmental issue by summarizing the state of the art upon the impacts of toxic heavy metals on the development and growth of plants.

DOI: /10.61463/ijset.vol.13.issue1.172

Driver Drowsiness Detection Using OpenCV

Authors- Onaladevi Akhila, Bandi Sanjana, Md Kashif Ali Tabrez, Regulapati Akhila

Abstract-The security system proposed in this project leverages machine learning and artificial intelligence to enhance passenger safety. While airbags are designed to mitigate injuries after an accident, they do not prevent accidents from occurring in the first place. A significant number of accidents are caused by drowsy and fatigued driving, posing a serious risk to lives. To address this issue, our project utilizes the OpenCV library for image processing. The system captures live video input of the user and analyzes it using trained data to detect signs of drowsiness or fatigue. If the system identifies that the driver is closing their eyes or exhibiting symptoms of drowsiness, it verifies the input against the trained model. Upon detection, the application triggers an alarm to alert the driver, potentially preventing accidents and saving lives.

Study on Patient Satisfaction in the Hospital with Special Reference to Sagar District, Madhya Pradesh

Authors- Assistant Professor Mrs Ritu Jain

Abstract-This study examines patient satisfaction in hospitals within Sagar District, Madhya Pradesh, emphasizing factors influencing healthcare experiences. The research investigates demographic disparities, hospital characteristics, and key patient interaction points affecting overall satisfaction. Utilizing both quantitative and qualitative methods, the study evaluates communication, waiting times, facility cleanliness, and technology integration. Findings highlight significant variations in satisfaction levels across demographics and hospital types. The results provide insights for healthcare administrators to enhance service quality and patient trust, ensuring better health outcomes. Recommendations focus on patient-centered care improvements, hospital infrastructure enhancement, and policy reforms.

A Survey on Intrusion Detection System Based on Deep Learning

Authors- Saddam Hussain, Professor Santosh Nagar, Professor Anurag Shrivastava

Abstract-Intrusion Detection Systems (IDS) play a critical role in safeguarding computer networks against malicious activities and security breaches. With the advent of deep learning techniques, IDS have achieved remarkable advancements in accuracy, adaptability, and real-time detection. This survey explores the state-of-the-art deep learning-based IDS, categorizing them by architectures such as Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), and hybrid models. Key challenges, including handling imbalanced datasets, scalability, and adversarial attacks, are critically analyzed. Emerging trends, such as transfer learning and explainable AI, are also discussed to highlight future research directions. The survey aims to provide researchers and practitioners with a comprehensive understanding of leveraging deep learning techniques for building robust and efficient IDS in dynamic network environments.

DOI: /10.61463/ijset.vol.13.issue1.173

A Novel Approach for Blood Group Identification through Fingerprint Image Analysis

Authors- Akula Venkata Sai Vandana, Adduri Anunay, Daneti Chandana, Anaparthi Harika, Assistant Professor Kaki Leela Prasad

Abstract-The advancements in biometric technology have paved the way for innovative methods of personal identification and data analysis. This project introduces a novel approach for detecting blood groups using fingerprint images, utilizing a comprehensive feature extraction process and advanced classification techniques. Features are extracted from fingerprint images, including geometric and texture-based attributes, and organized into a dataset. These features are analyzed using machine learning techniques, specifically leveraging neural networks, to classify the input fingerprint into its corresponding blood group. The implementation aims to achieve high accuracy and reliability in blood group detection by focusing on feature quality and classifier efficiency. This solution is envisioned as a step forward in integrating biometric data into medical diagnostics and personal identification systems.

DOI: /10.61463/ijset.vol.13.issue1.174

Solar-Powered Agricultural Crop Protection from Wild Animals

Authors- Piyush Pakade, Sharayu Wankhade, Adarsh Vaidya, Yash Maliye, Dr. A. M. Agrawal

Abstract-Agricultural crop protection is a critical challenge faced by farmers, particularly in regions prone to damage caused by wildlife animals. This paper presents an innovative, sustainable solution through a Solar-Powered Agricultural Crop Protection from Wild Animals System. The proposed system integrates a metallic structure equipped with a 360° rotating light and speakers, powered entirely by solar panels. The rotating light disorients animals by shining into their eyes, while the speakers emit sounds to deter them, ensuring minimal harm to the environment and animals. The system leverages renewable energy, reducing dependency on conventional power sources and making it cost-effective for rural applications. This paper discusses the design methodology, technical specifications, and testing results, highlighting the system’s effectiveness in deterring wildlife and its long-term economic feasibility for farmers. The implementation of such systems can significantly reduce crop losses, promote sustainable farming practices, and ensure food security.

DOI: /10.61463/ijset.vol.13.issue1.175

Predicting Agriculture Yields Based on Machine Learning Using Regression and Deep Learning

Authors- Doddikindi Navya, Logaom Ajay, Nagulapally Nithish Reddy, Dr. A.Ramesh Babu

Abstract-Agriculture plays a crucial role in India’s economy, serving as the backbone of the nation’s livelihood and food security. However, rapid population growth has significantly increased the demand for food, creating pressure on agricultural production. To meet these rising demands, farmers must enhance crop yields without expanding cultivable land. Crop yield prediction serves as a valuable decision-support tool, leveraging advanced computational techniques to analyze factors such as rainfall, meteorological conditions, soil quality, cultivated area, production trends, and yield history. By utilizing machine learning and deep learning models, farmers and policymakers can make informed decisions regarding crop selection, resource allocation, and farming practices, thereby improving agricultural sustainability and reducing yield losses due to environmental uncertainties. This study aims to develop an effective crop yield prediction model using machine learning algorithms such as Decision Tree, Random Forest, and XGBoost regression, alongside deep learning approaches, including Convolutional Neural Networks (CNN) and Long Short- Mean Squared Error (MSE), standard deviation, and loss functions Comparative analysis reveals that the Random Forest algorithm outperforms other machine learning methods, achieving a maximum accuracy of 98.96%, a Mean Absolute Error of 1.97, an RMSE of 2.45, and a standard deviation of 1.23. These results indicate that both Random Forest and CNN are highly effective in predicting agricultural yield, offering robust insights for farmers and stakeholders. The findings of this research contribute to the ongoing efforts to revolutionize agriculture through data-driven methodologies, ensuring a more sustainable and efficient food production system. By leveraging these innovative technologies, India can take significant strides toward achieving long-term agricultural resilience and food security.

Energy Consumption and Optimization

Authors- Yenna Manohar Reddy, Boggula Saketh, Muthyala Ashish Shiva, Dr. G.Kumar

Abstract-The project aims to develop Machine Learning (ML) algorithms to optimize energy consumption. The project seeks to intelligently manage energy usage monitoring , data analysis and predictive modeling . In the complex working environment of the Internet of things (IoT) , there are many differences in the work between many devices , as well as complex associations and energy constraints. The IoT devices will collect energy consumption data and environmental variables , fed into the ML model to create personalized energy optimization strategies for users. The ultimate goal of this project to contribute to a greener and more sustainable future by empowering individuals and businesses to make informed decisions and reduce their carbon footprint. This research paper delves into the fusion of Internet of things (IoT) devices and Machine learning (ML) algorithms to address this pressing challenge By synthesizing IoT data streams with ML models this research define novel strategies for dynamic energy management, load forecasting, anomaly detection ,and adaptive control . Through an extensive review of existing literature and empirical analysis , this study investigates the efficacy of leveraging IoT sensors ML techniques to optimize usage across diverse domains. In the future intelligent machines will replace or enhance human capabilities in many areas. Artificial Intelligence is the intelligence exhibited by machines or software. ”John McCarthy” who coined the tem in 1955 defines it as the“ science and engineering of making intelligent machines”. As we are aware of the fact that energy consumption has grown tremendously over a few decades in all over the world which is environmentally unfriendly. It is essential at this stage of development to pause and critically examine the state of affairs via the application of AI in energy conservation and environmental system engineering. In this paper we describe in a general way on how the existing applications of AI techniques provide intelligent solution to optimize the energy conservation now and in the future. Also, how Wireless Sensor and Actuator Networks are used to remotely monitor and control the environment according to the decisions made by the centralized reasoner and EEMS(Energy Efficiency Management System) provides effective energy saving measures and high quality energy conservation services Energy efficiency has nowadays become one of the most challenging task for both academic and commercial organizations and this has boosted research on novel fields.

Detection and Removal Haze, Fog, Rain for Accident Prevention

Authors- Professor Dr. G Kumar, T Akhil Reddy, A Anand Paul, U Raghavendar

Abstract-Modern research efforts are highly focused on autonomous vehicles both for private and public sectors. Such robust and efficient sensor systems will have to be developed in order to minimize.This project mitigates one particular failure mode: reduced visibility caused by rainy weather. In this project we worked on three types of images-Rain,Haze and Fog,and improved the image resolution of these images with the help of DCP and YOLO. We apply the defogging techniques by using DCP(Dark Channel Prior) and further improve the image resolution by means of the Discrete Wavelet Transform (DWT) approach. Object detection is performed via the YOLO framework,. The demonstration of these capabilities improves visibility while detecting objects for safer autonomous navigation under adverse weather conditions. Using the DCP algorithm we have achieved 93% accuracy. This way, vehicles can navigate safely, even in adverse weather conditions, reducing the risks associated with low visibility.

CNN Based Tracking System for Virtually Impaired People

Authors- Nadipelli Ruchitha, P.Shiva Kumar, Marugalla Sridhar, Agadam Jyotsna

Abstract-As object recognition technology has developed recently, various technologies have been applied to autonomous vehicles, robots, and industrial facilities. However, the benefits of these technologies are not reaching the visually impaired, who need it the most. In this paper, we proposed an object detection system for the blind using deep learning technologies. We use voice recognition technology in order to know what objects a blind person wants, and then to find the objects via object recognition. Furthermore, a voice guidance technique is used to inform sight impaired persons as to the location of objects. The object recognition deep learning model utilizes Deep neural network architecture, and voice recognition is designed through speech-to-text (STT) technology. In addition, a voice announcement is synthesized using text-to-speech (TTS) to make it easier for the blind to get information about objects. The system is built using python OpenCV tool. As a result, we implement an efficient object-detection system that helps the blind find objects in a specific space without help from others, and the system is analyzed through experiments to verify performance.

Cyber Hacking Breaches Prediction & Detection Using Machine Learning

Authors- Nidhi Thakur, Aeta Nehal, G.Vishnu Vardhan Reddy, Regulapati Akhila

Abstract-Predicting cyber-hacking breaches through machine learning (ML), specifically using the Random Forest classifier, is one of the latest advancements. This approach utilizes computer algorithms to identify and anticipate breaches, which has been a challenging task. The primary focus is on making malware detection more rapid, scalable, and efficient than traditional systems that require human input. Websites that could launch cyberattacks can provide the necessary information. Data breaches may result in identity theft, fraud, and other damages, affecting around 70% of companies according to data. The analysis demonstrates the likelihood of a data breach, emphasizing the increasing threat due to the growing use of computer applications and security vulnerabilities. The proposed system integrates automated data preprocessing, real-time monitoring, and adaptive learning to detect cyber threats efficiently. Unlike traditional methods, which rely on signature-based detection, this model continuously learns from new attack patterns, improving detection rates for zero-day vulnerabilities. The system utilizes a Flask-based web interface for user interaction, providing an intuitive and accessible cybersecurity tool. Compared to existing anomaly detection models like Autoencoders, Isolation Forests, and Support Vector Machines (SVM), our approach enhances accuracy, reduces false positives, and scales effectively for large datasets. The proposed model ensures scalability, adaptability, and seamless integration with existing cybersecurity frameworks. By implementing real-time alerts and automated threat mitigation strategies, organizations can proactively defend against cyber threats rather than reacting post-breach.This research demonstrates how ML-powered cybersecurity solutions can strengthen digital defenses, minimize risks, and improve overall security resilience. Future enhancements will focus on expanding datasets, refining model performance, and integrating deep learning techniques for even more robust threat detection capabilities.

The Reform of Logics and the Calculation Principles in G. W. Leibniz

Authors- Rosanna Festa

Abstract-The systems are mathematical; so on we have complex systems; convex-complex systems; notable systems; analytical systems; affines systems; hyperbolic systems and algebraic systems. So making a distinction of the mathematical entities G. W. Leibniz and its metaphysics evaluates the concept of a priori.

DOI: /10.61463/ijset.vol.13.issue1.176

AI Language Translation Using Open AI

Authors- Sirusan Vaishnavi, Doddy Vijay Kumar, Shaik Nizam Uddin, V. Krishna Reddy

Abstract-Language barriers present a significant challenge in global communication, making efficient translation tools essential. This project aims to develop a web-based language translation system using Flask, Google Translate API, and Google Text-to-Speech (gTTS) to provide accurate and accessible translations. The system allows users to input text in one language and receive an immediate translation in their desired language. Additionally, the translated text is converted into speech to enhance accessibility for users who prefer auditory learning or have reading difficulties. The backend, developed using Flask, handles user input, processes translation requests via the Google Trans library, and generates speech output using gTTS. The user-friendly web interface ensures ease of use, allowing selection of input and output languages. The application can be particularly useful for travelers, students, and professionals who frequently engage with multilingual content. Future enhancements may include offline translation capabilities, support for additional speech synthesis engines, and improved translation accuracy through machine learning models. This project demonstrates the potential of AI- powered language processing tools in bridging communication gaps effectively.

Advance Network Intrusion Detection System Using Deep Learning Techniques

Authors- K.Chandrashekar, K.Sai Teja Reddy,Y.Sai Giresh Kumar, Dr.S.Ravi Kumar Raju

Abstract-With the increasing prevalence of cyber threats, traditional Intrusion Detection Systems (IDS) struggle to combat sophisticated attacks. This project focuses on developing an Advanced Network Intrusion Detection System (NIDS) utilizing Deep Learning techniques to efficiently detect and classify network intrusions. The system processes real-time network traffic and employs deep learning models to categorize it as either normal or malicious. To enhance accuracy, the dataset undergoes preprocessing with feature engineering techniques such as One-Hot Encoding and Min-Max Scaling. The trained model is then integrated into a Flask-based web application, which continuously monitors network activity and alerts administrators to potential threats. Unlike conventional signature-based IDS, this system leverages learned patterns from past intrusions to identify zero-day attacks. By evaluating multiple deep learning architectures, our objective is to achieve high accuracy, precision, and recall in intrusion detection. This proposed system strengthens network security, assisting organizations in mitigating unauthorized access and preventing data breaches effectively.

The Impact of Transportation Planning on Transportation Infrastructure Development

Authors- Govind Kumar, Shashikant B. Dhobale

Abstract-The transport infrastructure can be defined as a factor that guarantees the growth and economic development of the region, due to the functions of traversing space in terms of the movement of people and the exchange of goods. The effects of the impact of transport infrastructure on the economy of the region largely depend on how the society uses the services offered by infrastructure facilities and devices. The study examines the impact of transport infrastructure on the sustainable socio-economic development. To conduct the analysis, a questionnaire addressed to entrepreneurs from this region was used. In the second part of the research, the indicators of sustainable development at the regional level were applied: the level of transport infrastructure and the level of socio-economic development of the studied area. The study is an attempt to fill the cognitive gap for areas outside the country’s main transport corridors. The existing differentiation in both the development of infrastructure and the economic attractiveness of urban and rural areas was shown. Factors influencing the effectiveness of implementing the concept of sustainable rural development were indicated.

IRIS Flower Species Prediction Using Machine Learning and Web Based Interactive Tool for Non Technical Users

Authors- Assistant Professor Mrs I.Sravani, R.S.MD.Sahil, Y Jaya Krishna, G Murari, M Murali, P Raghu

Abstract-The Iris flower species prediction tool is a web-based application that leverages machine learning to classify Iris flowers into one of three species (Setosa, Versicolor, or Virginica) based on their physical measurements: sepal length, sepal width, petal length, and petal width. Using a classification model trained on the famous Iris dataset, the tool predicts the species of a flower given these inputs. The system is designed to be user-friendly and accessible for non-technical users. A simple web interface, built with Flask, allows users to input flower measurements and receive predictions in real-time. This web tool integrates the power of machine learning with an intuitive user experience, making it easy for anyone, regardless of their technical background, to interact with and benefit from the model1. The system also includes validation features and visualizations to further enhance user engagement and understanding. By deploying the model on cloud platforms like Heroku, this tool can be accessed globally, serving educational purposes or assisting botanists and enthusiasts in identifying Iris species based on simple measurements.

DOI: /10.61463/ijset.vol.13.issue1.180

Prediction of Cardiovascular Diseases with Retinal Images Using Deep Learning

Authors- Professor Dr. G.Ramasubba Reddy, Yakasi Vasanthi, Panga Rupa, Shaik Basid Ahammad, Shaik Mulkisabgari Mahaboob Basha

Abstract-Cardiovascular diseases (CVDs) are among the leading causes of death worldwide, and early detection plays a crucial role in improving patient outcomes. Recent advancements in medical imaging, particularly retinal imaging, have opened new possibilities for identifying cardiovascular risk factors. The retina, with its direct connection to the central nervous system and vascular network, reflects the condition of systemic blood vessels, making retinal images a valuable tool for assessing cardiovascular health. This study explores the use of deep learning techniques, particularly Convolutional Neural Networks (CNNs), to predict cardiovascular diseases from retinal images. The approach involves preprocessing retinal images through normalization, data augmentation, and segmentation, followed by the application of deep learning models for classification. The models are trained to identify key features such as blood vessel abnormalities, microaneurysms, and optic disc changes that correlate with CVD risk. Transfer learning and multimodal approaches, combining retinal images with clinical data, are also explored to enhance prediction accuracy. The results demonstrate that deep learning models, with their ability to automatically extract complex patterns from retinal images, offer significant potential for non-invasive, early detection of cardiovascular diseases. Challenges such as data imbalance, model interpretability, and the need for large annotated datasets are discussed. Overall, this study highlights the promising role of deep learning in revolutionizing cardiovascular disease prediction through retinal imaging, offering a novel approach for preventive healthcare.

DOI: /10.61463/ijset.vol.13.issue1.181

Smart Wardrobe System Using Artificial Intelligence

Authors- Assistant Professor Dipali Mane, Anupama Yadav, Sanket Tawari, Nikita Nalawade

Abstract-The Smart Wardrobe aims to revolutionize the way users manage their clothing collections and make outfit decisions. In today’s fast-paced fashion environment, individuals often struggle with organizing their wardrobes effectively and selecting appropriate outfits for various occasions. This web application leverages modern web technologies and the Internet of Things (IoT) to provide users with a seamless, interactive platform for managing their clothing items and creating personalized outfit combinations. The application features a user-friendly interface that allows users to upload details of their clothing items, including categories, sizes, colors, and images. By utilizing advanced filtering and sorting options, users can quickly access their wardrobe inventory and discover new outfit combinations based on their preferences and upcoming events. Additionally, the Smart Wardrobe Web App incorporates a recommendation system that suggests outfits tailored to users’ tastes, weather conditions, and social occasions. This project not only promotes efficient wardrobe management but also encourages sustainable fashion practices by helping users make the most of their existing clothing items.

Reviewing the Enhancement of Consolidation Behaviour of Black Cotton Soil

Authors- Assistant Professor Manjushree V. Gaikwad, Pankaja S. Dhere, Vaishnavi S. Ghadage, Neha S. Misal, Rutuja K. Shinde

Abstract-This review paper the enhancement of consolidation properties of black cotton soil (BC soil) present significant challenges in civil engineering due to its high compressibility and instability. This review examines a novel approach to improving BC soil by incorporating construction demolition waste (CDW) and geo-polymers. Recent studies show that blending BC soil with treated CDW and various geo-polymer formulations enhances soil strength, reduces settlement, and improves overall stability. In addition to improving soil performance, this method promotes environmental sustainability by recycling CDW, offering an eco-friendly alternative to conventional soil stabilization techniques. The findings highlight the potential of using industrial by-products in geotechnical applications, supporting circular economy principles and reducing reliance on non-renewable resources. This review discusses the implications of these innovations in geotechnical engineering, focusing on their contribution to sustainable construction practices and environmental sustainability.

DOI: /10.61463/ijset.vol.13.issue1.182

AI-Powered and OCR-Based Identification System for Loan Waiver Verification

Authors- Mrs. V. Vidhya, Mrs. A. Sangeetha, T. Madhumitha, T. Priyadharshini, R. Shanthiya

Abstract-An automated image processing solution is proposed to streamline borrower verification for government loan waivers. The system uses Optical Character Recognition (OCR) and AI- powered algorithms to capture and validate Aadhar and smart card details, ensuring accuracy and reducing manual errors. The system employs advanced pre-processing techniques to enhance image quality, followed by OCR for text extraction and AI-driven algorithms for data validation against government databases. With features like multilingual support, fraud detection and scalability, the solution enhances efficiency, reduces time constraints and ensures reliable borrower identification. This approach minimizes administrative effort and ensures accurate verification, allowing only eligible borrowers to benefit from the loan waiver program.

DOI: /10.61463/ijset.vol.13.issue1.183

Role of Artificial Intelligence in Education: State-of-the-Art

Authors- Hitanshi Ahuja, Pragati Kumar, Ajay Shriram Kushwaha, Sharik Ahmad, Anurag Rai

Abstract-Artificial intelligence is a field that explores the creation o f c o m p u t e r machines, and other technologies that exhibit human-like intelligence. This includes cognitive abilities, learning, adaptability, and decision-making skills. The research found that AI has been widely adopted in education, especially by educational institutions, in various forms. This study sought to assess the influence of Artificial Intelligence (AI) on education, concentrating on its uses and effects in administration, teaching, and learning. Adopting a qualitative research approach, the study utilized a literature review as its main research design and methodology. It began with the use of computers and related technologies, then transitioned to web based and online intelligent education systems. Ultimately, it progressed to incorporate embedded computer systems, humanoid robots, and web- based chatbots, which now support instructors and students. By utilizing these platforms, instructors can carry out various administrative tasks, including reviewing and grading student assignments more effectively and efficiently, ultimately enhancing the quality of their teaching activities.

DOI: /10.61463/ijset.vol.13.issue1.184

A Strategic Approach to Mesh Term Selection for Systematic Reviews

Authors- Research Scholar S. Narmatha, Research Advisor Dr. V. Maniraj

Abstract-Systematic reviews in medicine require extensive literature searches to ensure the accuracy and reliability of the findings and recommendations. This process heavily depends on thorough searches for relevant medical literature, which typically involves collaboration between medical researchers and information professionals with expertise in both medicine and search techniques. Together, they develop detailed search queries, which often involve complex Boolean logic and include both free-text and standardized index terms like MeSH. Constructing these queries is a time-consuming and intricate task. Using MeSH terms has been shown to improve the quality of search results, but selecting the right MeSH terms can be difficult. Information specialists may not always be familiar with the MeSH database, leading to uncertainty about which terms are appropriate for a given query. As a result, the full potential of MeSH terminology is often not fully exploited. This study investigates methods for recommending MeSH terms based on an initial Boolean query that only includes free-text terms. These methods aim to automatically identify the most effective MeSH terms for inclusion in a systematic review query. The study empirically evaluates several strategies for suggesting MeSH terms, examining how these suggestions impact the effectiveness of Boolean queries through retrieval, ranking, and refinement.

DOI: /10.61463/ijset.vol.13.issue1.185

Beyond Screens: The Rise of Airborne Holographic Displays

Authors- Suraj Mhaiskar

Abstract-Researchers have continuously developed holographic display methods as a means to apply three- dimensional visualizations which overcome limitations of standard screens and physical projection areas. New research into optics and atmospheric control and light field management methods has enabled free-space image projection. Research demonstrates air-density-based holographic projection as one of the most promising advancements because it alters nearby air to develop a three-dimensional display medium. Research studies show the possibility of holographic projection safety through density-control methods applied to air which enforce either particulate-driven density changes or artificial fog production or light-generated air modification in localized areas. The paper examines important projection methods using air density modifications namely Heliodisplay and fog-based holography and advanced photon field manipulation techniques from Light Field Lab. The dissertation discusses light scattering behavior as well as changing refractive indices and volumetric display mechanics to determine their role in free-space hologram production. Several experimental approaches for influencing air density through micron-particle deployment and regulated water vapor distribution along with acoustic and electrostatically suspended particle implementations yield assessments regarding their ability to stabilize projected images.

AI Chatbot for College Mangament

Authors- Siri Sivani Tatineni, Dasarla Shiva Kumar, Rathod Vinod Naik, V.Krishna Reddy

Abstract-With the growing demand for instant information and seamless communication, chatbots have become an essential tool across various industries, including education. Many colleges and universities are now implementing chatbots to assist students and prospective applicants in quickly finding relevant information. This project focuses on developing a college enquiry chatbot capable of answering common queries about admissions, academic programs, campus life, financial aid, and student support services. Designed to offer a personalized and intuitive experience, the chatbot will enable users to interact with the college in a natural and user-friendly manner. To achieve this, the chatbot will feature a text-based or voice-based interface powered by natural language processing (NLP) to understand user input. It will have access to a comprehensive knowledge base of pre-written responses and will also generate dynamic replies as needed. By leveraging machine learning algorithms, the chatbot will improve over time, accurately interpreting user intent and providing more relevant responses. Seamlessly integrated with the college’s website and other digital platforms, the chatbot will be accessible from any device at any time. This ensures users can obtain information without navigating away from the college’s website or social media channels. Additionally, the chatbot will track user interactions and provide valuable insights into user behavior, enabling the institution to refine its chatbot strategy continuously. In summary, this college enquiry chatbot aims to enhance the user experience by delivering accurate, real-time information in a personalized and engaging way. By incorporating NLP and machine learning, the chatbot will not only provide reliable responses but also help colleges gain deeper insights into student needs and preferences. This version maintains clarity while improving readability and flow. Let me know if you’d like any further tweaks.

Vehicle Counting Detection Using OpenCV

Authors- Attapur Bhagyashree, Jayebaye Shivaprasad, Kasam Akash, Jagadam Jyotsna

Abstract-Moving vehicle detection, tracking, and counting are very critical for traffic flow monitoring, planning, and controlling. Video-based solution, comparing to other techniques, does not disturb traffic flow and is easily installed. By analyzing the traffic video sequence recorded from a video camera, this paper presents a video-based solution applied with adaptive subtracted background technology in combination with virtual detector and blob tracking technologies. Experimental results, implemented in python code with OpenCV development kits, indicate that the proposed method can detect, track, and count moving vehicles accurately. improve Activity Stream and Speed determining exactness, we proposed a profound learning-based multitask learning Gated Repetitive Units (MTL-GRU) with remaining mappings. To improve the execution of the MTL-GRU, including building is presented to choose the foremost enlightening highlights for the estimating. At that point, based on real-world datasets, numerical comes about, and utilizing MTL-GRU can well gauge activity stream and speed at the same time, and perform way better than other techniques. Tests appear that the profound learning-based MTL-GRU show can overwhelm the bottleneck caused by extending preparing datasets and proceeding to pick up benefits. Although a number of models have been developed, many of them leverage conventional methods that may be unsatisfying to penetrate the deep correlation hidden in large datasets consequently forecasting accuracy cannot profit from sharply increasing traffic data. Therefore, new techniques are eagerly demanded to handle the abundant traffic data at a deep level.

Unveiling the Nonlinear Optical Behavior of Schiff base N’-[(E)-(4-fluorophenyl) Methylidene] Biphenyl-4-Carbohydrazide Crystal: DFT Insights and Structure Property Relationship

Authors- Navneeta Kohli, Akansha Tyagi, Varsha Rani, Mukta Tripathi, Anuj Kumar

Abstract-Investigations on nonlinear Schiff base, namely N’-[(E)-(4-fluorophenyl) methylidene]biphenyl-4-carbohydrazide (FMBC)are reported.Intra-molecular interactions in crystal packing were examined by NBO analysisto understand the charge delocalization. Nonlinear optical (NLO) response, which depends upon the delocalization of the π-electrons and intra-molecular charge transfer (ICT), was evaluated in terms of dipole moment µ, the polarizability α, and first-order hyper polarizability β values. Hyper polarizability of FMBC molecule is assessed to be 3.44303x 10-30 esu which is 10 times greater than the value of urea. This establishes that FMBC molecule shows the potential for use in NLO applications. A comparison of NLO response of FMBC with similar Schiff base compounds further supports FMBC’s superiority as an NLO material and alsoenhancesunderstandingof the structure-property relationship.

DOI: /10.61463/ijset.vol.13.issue1.186

Investigation of Pavement Durability Under Heavy Traffic Loads and Aggressive Environmental Conditions

Authors- Ade Vikram, Assistant Professor Mudigonda Harish Kumar

Abstract-Pavement durability is a critical aspect of road infrastructure, as it directly influences the long-term performance and safety of road networks. This paper investigates the factors contributing to pavement deterioration under heavy traffic loads and aggressive environmental conditions. Emphasizing the behavior of both asphalt and concrete pavements, the research explores mechanisms of damage such as rutting, cracking, fatigue, and moisture infiltration, while examining methods to improve the resilience of pavements through advanced material selection, design innovations, and maintenance strategies. The study combines laboratory testing, field observations, and numerical modeling to assess the performance of pavement materials under various loading and environmental scenarios. The results provide valuable insights into optimizing pavement designs to enhance their service life in demanding conditions.

Non-Alcoholic Fatty Liver Disease Prediction Using Machine Learning and Deep Learning Techniques

Authors- Assistant Professor Mrs. Pyla Jyothi, Thummalagunti Vani, Pallantla Saicharan, Peethala Jaswanth

Abstract-The liver is a critical metabolic organ within the human body, performing many necessary biological functions, such as protein synthesis and the creation of many biochemicals required for digestion. It is crucial in the support of nearly all other organs. The most common chronic liver disease is Non-Alcoholic Fatty Liver Disease, estimated to affect nearly one-third of the world’s population. Early diagnosis of the disease is very important since it allows for prompt medical intervention and treatment. This research utilizes machine learning whereby data preprocessing is done by cleaning the data through imputation of missing values and feature and deep learning algorithm selection such as feed forward neural network with several dense layers constructed using TensorFlow and trained on the preprocessed data to predict disease status whose performance is measured using metrics.

DOI: /10.61463/ijset.vol.13.issue1.187

Safety and Efficacy of Detoxified Strychnos nux-vomica (Azaraqi) in Neurological and Musculoskeletal Disorders: A Traditional Unani Perspective

Authors- Dr. Sameeroddin Gayasoddin Shaikh, Dr. Gazala Shafeequerrahman, Dr. Saleem Ahmed Abdul Rasheed, Dr. Sharique Zohaib

Abstract-This study evaluates the safety and efficacy of detoxified Strychnos nux-vomica (Azaraqi) in treating musculoskeletal and neurological disorders in a cohort of 50 patients. Azaraqi, a key component of Unani medicine, is traditionally detoxified (Tadbeer) to eliminate toxic alkaloids such as strychnine and brucine while retaining its therapeutic benefits. Patients were treated with detoxified Azaraqi formulations for six weeks, assessing symptom improvement and adverse effects. The findings indicate that properly detoxified Azaraqi enhances mobility, reduces pain, and improves nervous system function with minimal side effects, supporting its potential integration into contemporary medical practices.

IoT Data Fusion: A Deep Learning and Data Mining Perspective

Authors- Professor Dr S Murali krishna

Abstract-The Internet of Things (IoT) generates vast amounts of heterogeneous data from a myriad of interconnected devices, presenting unique challenges and opportunities for effective data integration. This paper explores the role of data fusion in IoT systems, emphasizing deep learning and data mining techniques as pivotal tools for enhancing data analytics and decision-making processes. We provide a comprehensive overview of various data fusion methodologies, including multi-sensor data integration, which allows for improved accuracy and reliability of the information obtained. By employing deep learning algorithms, such as convolutional neural networks and recurrent neural networks, alongside advanced data mining strategies, we address the complexities inherent in IoT data, including its volume, velocity, and variety. The synergy between deep learning and data mining fosters enhanced capabilities for anomaly detection, predictive analytics, and real-time decision-making. This study highlights not only the theoretical advancements but also practical applications across various domains such as smart cities, healthcare, and industrial automation, underscoring the transformative potential of IoT data fusion in creating intelligent and responsive systems.

A Review Paper on Modelling and Analysis of Bridge Using Staad Pro

Authors- Shivani Korpe

Abstract-STAAD Pro is the present-day leading design software in the market. Many design companies use this software for their project design purposes. So, the project mainly deals with the comparative analysis of the results obtained from the design of a bridge when designed using STAAD.Pro and EXCEL design. In this project the RCC BOX CULVERT bridge is to be analysis and design on the STAAD.Pro software as well as on the EXCEL for comparative study. Box culverts are very important part of a transportation network as they provide a cost-effective alternate to substantial bridges. A culvert is a structure that allows water to flow under a road ways, railways, or similar obstruction from one side to the other side. A culvert may be made from a pipe, reinforced concrete or other material. Culverts are commonly used both as cross drains for channel release and to pass water under a road at natural drainage and river crossings. A culvert may be a bridge-like structure designed to allow vehicle or pedestrian traffic to cross over the watercourse while permitting suitable opening for the water. Culverts can be of different shapes such as arch, slab and box. These can be constructed with different material such as masonry (brick, stone etc.) or reinforced cement concrete. This project looks on the work of analysis and design of bridge deck and beam on software the specific bridge model is taken of a particular span and carriageway width the bridge is subjected to different IRC loadings like IRC Class AA, IRC Class 70R tracked loading etc. in order to obtain maximum bending moment and shear force. From the analysis it is observed and understand the behaviour of bridge deck under different loading condition and comparing the result. The aim of this project is to analyse the box culvert using STAAD PRO software. The structural elements of box culvert are designed to withstand maximum bending moment and shear force.