International Journal of Science, Engineering and TechnologyAlast Circle: A Centralized Student And Alumni Interaction System
Authors: Harsh B Sheth
Abstract: Alast circle is a centralized school and university community platform designed to maintain continuous interaction between administrators, staff members, students, and alumni. In many institutions, communication with students becomes limited after graduation, and important announcements or events are often shared through scattered channels. The proposed system addresses this issue by providing a single web-based platform where current students and former students can remain connected with their institution. Alast circle enables users to receive announcements, participate in events, and communicate with one another within an organized environment. The platform also allows administrators and staff to manage updates, publish event details, and maintain institutional engagement. By integrating communication, event participation, and user interaction into one system, Alast circle strengthens long-term relationships within the academic community and improves information accessibility for all users.
DOI: https://doi.org/10.5281/zenodo.19664046
Iot-Enabled Advanced Water Quality Monitoring System and Analysis of Water Consumption Using Flow Sensor
Authors: Mr. Satheesh Kumar D, Mr. Kishore Kumar S, Mr. Mohammed Abdul Jaleel J, Mr. Praveen Kumar S, Mr. Perumal S
Abstract: Water is one of the most vital resources for human survival, and maintaining quality and proper usage is a growing concern in modern urban environments. Corporation water supplied to residential and commercial buildings often experiences contamination due to pipeline corrosion or improper treatment. To ensure safe and efficient water usage, this project proposes an IoT-based water quality and usage monitoring system. The system integrates multiple sensors pH sensor, a TDS sensor, a temperature sensor, and a retrofit meter to continuously monitor the chemical and physical characteristics of water. The data collected from these sensors is processed by an Arduino microcontroller and transmitted over Wi-Fi to a cloud server or dashboard for real-time visualization and analysis. This helps detect contamination, water wastage, or abnormal consumption patterns early. The proposed system provides a low-cost, scalable, and automated solution suitable for smart city infrastructure, corporate facilities, and residential water distribution networks. By enabling continuous monitoring and data-driven decision-making, the system ensures water safety, supports conservation efforts, and promotes sustainable water management.
DOI: https://doi.org/10.5281/zenodo.19669642
Design And Implementation Of Secure Wireless Network Deployment With SRX300
Authors: Mr. Satheeshkumar S, Mrs.Thendral K, Madhan Kumar S, Madhan R, Mukesh Pandian A, Ravivarma R
Abstract: Wireless networking has become a fundamental component of modern organizations and educational institutions, enabling users to access the internet and communicate efficiently. However, these networks are often vulnerable to several security challenges, including unauthorized device access and the presence of rogue access points. This work focuses on the design and deployment of a secure wireless network using Juniper networking technologies such as the SRX300 firewall, EX series switches, and Mist AP32/AP63 wireless access points. Network segmentation is implemented using Virtual Local Area Networks (VLANs) to separate traffic and enhance overall network security and management. A dedicated guest wireless network is also configured through the Juniper Mist Cloud platform using a captive portal authentication mechanism, which provides controlled access for external users. The SRX300 firewall is used to enforce security policies that regulate network traffic and restrict unauthorized activities. Furthermore, advanced security features including rogue access point detection is integrated to identify and analyze potential wireless threats. The developed system demonstrates an effective solution for establishing a secure, scalable, and well-managed enterprise wireless network environment.
DOI: https://doi.org/10.5281/zenodo.19670690
Cloud Computing: Architectural Frameworks, Security Considerations, And Advanced Technologies
Authors: Mrs Shwetambari Sukhadeo Dhupe
Abstract: Cloud computing has emerged as a dominant computing paradigm by enabling flexible, scalable, and cost-effective access to shared computing resources over the internet. This research paper presents a comprehensive examination of cloud computing architectures, service and deployment models, security and privacy mechanisms, performance enhancement strategies, and emerging technological trends. The study highlights key challenges such as data protection, interoperability, and regulatory compliance, while also exploring future research directions including edge computing, serverless models, and sustainable cloud infrastructures.
Outdoor Wifi Coverage & Environmental Optimization With Juniper AP63
Authors: Dr.Thirumala Lakshmi K, Mathan S, Kavin S, Praveen Kumar P, Rishi S, Mrs. Thendral K
Abstract: In the era of smart campuses, seamless outdoor wireless connectivity is essential for enabling real-time applications, IoT integration, and uninterrupted user experiences. This project focuses on extending and optimizing outdoor Wi Fi coverage using Juniper AP63 access points, supported by Mist AI for intelligent performance monitoring and environmental adaptation. The deployment aims to eliminate dead zones and ensure robust signal transitions between outdoor AP63 and indoor AP32 zones through seamless device handoff. To address the unique challenges of outdoor environments such as signal degradation from terrain, foliage, and weather this study integrates insights from advanced literature, including clustering-based AP placement, energy-efficient AP activation, and RF propagation modeling. The project also explores environmental optimization strategies to enhance coverage reliability and reduce energy consumption. Performance validation is conducted through field testing and simulation, ensuring that the proposed solution meets the demands of a dynamic, high-density campus environment. This work contributes to the development of scalable, AI-driven outdoor wireless networks that are resilient, energy-aware, and capable of supporting future smart campus innovations.
DOI: https://doi.org/10.5281/zenodo.19671607
Implementation of Vlan, Ospf and Inter-Vlan Routing for Seamless Connectivity
Authors: Ms.Thendral K, Kaviya Sri K, Padmapriya B, Parameshwari P, Rubashri M
Abstract: In modern enterprise and campus networks, maintaining seamless connectivity, scalability, and performance is a major challenge when multiple departments share the same network segment. Such configurations often lead to excessive broadcast traffic, reduced efficiency, and security vulnerabilities. Additionally, static routing becomes inefficient and prone to configuration errors during network changes. This project focuses on implementing Virtual Local Area Networks (VLANs) and OSPF-based Inter-VLAN Routing on Juniper SRX300 devices to achieve optimized network segmentation and dynamic routing. VLANs are configured to logically separate departmental traffic, thereby reducing broadcast domains and enhancing network security. Inter-VLAN routing enables communication between different VLANs, ensuring seamless connectivity across departments. The Open Shortest Path First (OSPF) protocol is used to provide scalable and adaptive routing, allowing the network to automatically adjust to topology changes. The iPerf tool is utilized to analyze network performance parameters such as throughput, latency, and bandwidth utilization, validating the effectiveness of the proposed setup. Experimental results demonstrate improved network performance, reduced broadcast traffic, and enhanced routing efficiency, ensuring a secure, reliable, and high-performance network infrastructure.
DOI: https://doi.org/10.5281/zenodo.19675180
AI Based Travel Itinerary System
Authors: Amol Pramod Parab, Yash Kishor Nadkar, Vighnesh Sanjay Sathe, Prof. Sanjay N. Jadhav
Abstract: The AI-Based Travel Itinerary System is an intel- ligent web application designed to simplify trip planning by automatically generating personalized travel itineraries based on user preferences. The system uses Artificial Intelligence to analyze inputs such as destination, travel duration, budget, and interests. It integrates APIs like Google Maps and AI models to provide real-time recommendations including routes, attractions, and accommodations. The system reduces manual effort and enhances travel planning efficiency by generating optimized schedules and cost estimations.
DOI: https://doi.org/10.5281/zenodo.19675227
Smart Vision Care System Using Ai Based Eye Activity Recognition Air Blower Control
Authors: Mrs. A.K. Nivedha M.E., S.Pappathi, J. Priyadharshini, K.Sivapriya, B. Subasini
Abstract: The rapid increase in digital device usage has led to significant eye-related problems such as eye strain, dryness, and reduced blinking rate. Continuous exposure to screens without proper eye care can negatively impact human vision and overall health. To address this issue, this project proposes an AI-based Smart Vision Care System that monitors eye activity in real time and provides automatic relief mechanisms. The proposed system utilizes a camera to continuously capture the user's eye movements and applies image processing techniques using artificial intelligence to detect eye states such as blinking frequency and eye closure duration. By analyzing these parameters, the system can identify signs of eye fatigue or strain. When abnormal eye activity is detected, the system automatically activates an air blower to provide cooling and relaxation to the eyes without requiring any manual intervention. The system is developed using software tools such as Python and OpenCV for eye detection and recognition, along with hardware components including a microcontroller, camera module, and air blower. This integration of AI and embedded systems ensures accurate monitoring and timely response. The main objective of this project is to reduce eye strain, improve user comfort, and promote healthy screen usage habits. The proposed system is cost-effective, efficient, and suitable for students and professionals who spend long hours in front of digital devices. In the future, the system can be enhanced by integrating alert notifications and mobile applications for better user interaction and monitoring.
Loan Approval Prediction System
Authors: S.Rohith Reddy, P.Sri Nanda Kishore, Dr.R.sivaramakrishnan
Abstract: The Loan Approval Prediction System is a machine learning-based application developed to automate the process of evaluating loan applications. It analyzes applicant data such as income, credit history, employment status, and loan amount to predict whether a loan should be approved or not. By using historical data and applying data preprocessing techniques, the system improves the accuracy and reliability of predictions. The model is built using classification algorithms such as Logistic Regression, Decision Tree, or Random Forest, and the best-performing model is selected for deployment. This system helps financial institutions reduce manual effort, minimize risk, and make faster, data-driven decisions. It also enhances efficiency and ensures a more consistent and unbiased loan approval process. Overall, The Loan Approval Prediction System is a machine learning-based application that predicts whether a loan will be approved based on user details like income and credit history. It uses models such as Logistic Regression, Random Forest, and XGBoost for accurate decision-making. Built with Python and Flask, it provides quick, data-driven loan predictions through a simple web interface.
Smart Flood Water Evacuation System In Urban Areas
Authors: Abinaya S, Jayasoorya S, Lathika K, Dr. B. Veerasamy, Dr. G. Gandhimathi
Abstract: Urban flooding has become a recurring and critical challenge due to rapid urbanization, inadequate drainage infrastructure, and unpredictable climate conditions. This paper presents an enhanced Smart Flood Water Evacuation System based on an ESP32 (NodeMCU-32) IoT platform integrated with analog float sensors and a three-tank sequential water routing mechanism. The proposed system continuously monitors water levels in three independent storage tanks using calibrated analog sensors with a 15-sample averaging filter for noise-free readings. An automatic relay-based pump control activates or deactivates each motor based on predefined threshold levels (safe: 30%, warning: 60%, critical: forced ON). Floodwater is intelligently routed through Tank 1, Tank 2, and Tank 3 sequentially before being diverted to the outlet, enabling water conservation and reuse. Real-time sensor data and system status are published to a HiveMQ cloud-based MQTT broker over a secure TLS connection, enabling remote monitoring and manual override via any MQTT-compatible dashboard. An integrated battery management module automatically controls the charging relay based on voltage thresholds, ensuring sustainable solar-powered operation. Experimental results demonstrate high detection accuracy, sub-second MQTT latency, and reliable autonomous operation. The proposed system is low-cost, scalable, and practically deployable in urban flood-prone areas.
DOI: https://doi.org/10.5281/zenodo.19675764
Neural Net And Deep Learning Based Brain Lump Categorization & Intensity Gradient Platform
Authors: Prashant Yadav, Mohd Danish, Md Zishan Ansari
Abstract: Early and reliable detection of Brain lumps including mening and gliome, remains a significant concern in clinical practice. Conventional Magnetic Resonance Imaging (MRI) analysis relies heavily on expert interpretation, which may introduce variability and delay diagnosis. This study describes an integrated deep learning-based system for automated brain tumor detection. The developed system, termed the Automated Neuro-Diagnostic Assistant (ANDA), is designed using a customized Convolutional Neural Network (CNN) trained on preprocessed MRI datasets. A key feature of the system is its deployment as an interactive real-time web application using Flask, incorporating modules such as a confidence score visualizer and an automated report generator. Experimental results indicate a classification accuracy of 96% along with an F1-score of 94%, demonstrating reliable prediction capability. The proposed evaluation metric (Fscore) provides a unified assessment of system performance by combining accuracy, interpretability, and usability factors. Fscore = ⌊0.35A + 0.25C + 0.15V + 0.10(HI + SI + UI)⌉ Where A represents classification accuracy, C denotes confidence reliability, V indicates the effectiveness of visual interpretation, HI refers to healthcare insight generation, SI represents stroke identification capability, and UI denotes overall system usability. Further evaluation on diverse MRI samples shows strong agreement (r = 0.89) with expected diagnostic patterns and consistent performance across tumor categories. The system improves interpretability and user interaction efficiency, enabling faster and more structured medical image analysis. Overall, the system provides stable processing, real-time prediction capability, and efficient handling of medical data. By integrating automated detection with supportive visual outputs, it enhances accessibility to AI-assisted diagnostic tools and supports preliminary medical assessment.
Automatic Multi-Functional Agricultural Robot For Smart Crop
Authors: Aiswarya Sreejith, M.Radha, G.Subaparameshwari, Mrs.S.Revathi
Abstract: Agriculture is rapidly transforming with the integration of automation and intelligent systems. However, small and marginal farmers still rely heavily on manual labour for irrigation, pesticide spraying and crop monitoring. This work presents the design and development of an Automatic Multi-Functional Agricultural Robot intended for smart crop. The robot is capable of monitoring soil moisture, controlling irrigation automatically, spraying pesticides and navigating fields autonomously. By integrating sensors, a microcontroller and IoT-based monitoring, the system reduces human effort, optimizes water usage and enhances crop productivity. The system is affordable, energy-efficient and suitable for real-time agricultural applications.
DOI: https://doi.org/10.5281/zenodo.19675985
Smart Multimodal Neonatal Screening System Using Salivary Biomarkers
Authors: A. Naveen, N. Shahul Hameed, R. Thilagar, S. Vishwabharathi, Dr. S. Sutha
Abstract: Neonatal healthcare is a critical domain in modern medicine, focusing on the survival, growth, and development of newborn infants during the first few weeks of life. Early detection of neonatal disorders such as jaundice, dehydration, and neurological abnormalities plays a vital role in preventing long-term complications and reducing infant mortality rates. However, conventional diagnostic techniques primarily rely on blood-based tests, which are invasive, time-consuming, and often require well-equipped laboratory facilities and trained personnel. These limitations make such methods less suitable for continuous monitoring and challenging to implement in rural or low-resource settings. In recent years, there has been growing interest in the development of non-invasive diagnostic approaches that can provide accurate and real-time health assessment without causing discomfort to the patient. Among various biological fluids, saliva has emerged as a promising diagnostic medium due to its ease of collection, safety, and ability to reflect physiological and biochemical conditions of the body. Saliva contains a wide range of biomarkers, including enzymes, proteins, hormones, and metabolites, which can be used to detect various health conditions. The non-invasive nature of saliva collection is particularly beneficial for neonatal applications, where minimizing pain and stress is of utmost importance. The proposed system introduces a smart multimodal neonatal screening approach that utilizes salivary biomarkers for disease detection. By integrating microfluidic technology, optical sensing, and artificial intelligence, the system aims to provide a comprehensive and efficient diagnostic solution. Microfluidic chips enable precise handling of small sample volumes and facilitate controlled biochemical reactions, while optical sensors detect colour changes corresponding to biomarker concentrations. These signals are processed using embedded systems and analysed using machine learning algorithms to classify neonatal conditions accurately. Furthermore, the incorporation of smartphone-based imaging and mobile applications enhances the accessibility and usability of the system. The ability to store and transmit data through cloud platforms enables remote monitoring and telemedicine applications, making the system highly suitable for deployment in rural and underserved areas. Overall, the proposed system represents a significant advancement in neonatal healthcare by combining non-invasive diagnostics with modern technological innovations.
Design And Development Of A Low-Power Embedded Edge-Computing Framework For Real-Time Wildlife Monitoring and Deterrence
Authors: Mr.Loganathan S, Mr.Mohan R, Mr.Nitheesh K M, , Mr.Ramesh C, Mr.Saleem Ulla Khan S
Abstract: In modern agriculture, protecting crops from animal intrusions is a major challenge. This project presents a real-time wildlife monitoring and deterrence system using the YOLO V8 object detection algorithm. The system employs AI-based image processing with OpenCV for pre processing and integrates automatic notification and control mechanisms for enhanced farm security. A camera continuously captures images, and YOLO V8 detects and classifies animals in real time. Detected images are uploaded to a remote server for analysis and then deleted to save storage. Pre-processing steps like noise reduction, resizing, and normalization improve detection accuracy, while compression and feature extraction ensure real-time performance. When an animal is detected, the system sends an email alert with the timestamp and type of animal, activates a buzzer, and displays details on an LCD screen. LED floodlights turn on in low light to increase visibility and deter nocturnal animals. The YOLO V8 model is continuously refined for accuracy and adaptability, offering a practical, efficient solution for smart farm wildlife monitoring and deterrence.
DOI: https://doi.org/10.5281/zenodo.19676429
VidyaRaksha AI: A Quality Assurance Approach For Intelligent Online Assessment Systems
Authors: Diya Parmar, Dr. Chetan Soni
Abstract: With the growing shift toward digital education, the need for efficient and reliable online assessment systems has become increasingly important. Many existing platforms face challenges such as inaccurate evaluation, delayed results, and system reliability issues. This paper presents VidyaRaksha AI, an intelligent web-based assessment system designed to automate the examination process using Artificial Intelligence. The system supports key functionalities such as test creation, user participation, auto-submission, and instant result generation, which help reduce manual effort and improve overall efficiency. From a Quality Assurance (QA) perspective, the system was tested using techniques such as functional testing and regression testing to ensure correct system behavior and stability. Various test cases, including both positive and negative scenarios, were executed to identify issues like auto- submission errors, missing UI elements, and functional inconsistencies. These defects were reported and resolved to enhance system performance. The results indicate that the system provides accurate evaluation, faster result processing, and improved user experience. Overall, VidyaRaksha AI demonstrates a reliable and efficient approach to modernizing online assessment systems through the integration of AI and effective QA practices.
DOI: https://doi.org/10.5281/zenodo.19676606
Intelliquiz — A Gamified Adaptive Quiz Generator Using Llms with Real-Time Analytics
Authors: Avvari Pavithra, Y Jeevan Nagendra Kumar, Yenna Gnaneshwar Reddy, Ponnamanda Eswar, Madupathi Nithin Kumar
Abstract: Honestly, I've seen that most schools are still based on blunt tests and even though they make the exams are the same whether it is a person who is answering it but at his own pace, it's a bad thing when people learn at different pace. One tool taking the game far further is IntelliQuiz which automatically generates entirely new quizzes using AI and sprinkles in some gamifying fun as well to keep things as interactive as possible. Instead of offering one format, it will take material from PDFs, videos and even just plain text, to construct its questions that shift in real time during class, based on how each student is doing. Harder questions pop up after a correct answer the easier ones pop up if someone's struggling. Behind the curtain, the system has a combination of Spring Boot to manage server-related operations, React with Tailwind for frontend styling and storing everything in a PostgreSQL database. Access control divides the users into three groups namely Admin, Teacher and Student as per which they are provided only the permission as secured tokens. The engine really stands out because it fits large language models with retrieval methods to bring facts in into the room before generating the questions so that any prompt will be linked back to specific learning objectives and kept on point. The challenge scales over the levels of beginners, intermediaries and experts automatically without additional effort from teachers. It even tweaks questions on the fly based on how fast a learner is answering, the success rate and how his summers are without having to work on the testing questions instead of fixed testing questions, round by round helps to form itself around what a user actually knows. Points, badges and leaderboards pop-up as the progress is made encouraging that little quiet competition that keeps the attention wired. Live charts are updated as the activity comes through illustrating the gaps, the strengths and patterns. One benefit of this is that teachers may recognize trends early and students may realize where they want to expend their energy. The modular aspect to this means it can scale up very easily and shift very easily from one device to another including network condition. Every answer generated by AI gets screened first: it gets double checked before making its way to a student meanwhile the logic behind each score is traceable with difficulty weighed and usefulness measured. Regardless of whether the solution is online or offline, on the school servers or in a University data center, the solution remains lightweight. The time which until used to manually build quizzes now gets freed up via smart automation. Grading occurs plainly and obviously, comments are specific, and monitoring is close always using gripping levels of tracking. As validation is built in along the way, the trust is intact as the system becomes faster. Learning isn't simply tracked it changes itself in real-time according to personal rhythm, pace, depth and direction. It's not some type of magic or noise, it's a constant consistency between effort and growth.
AI Based Interview Preparation Platform
Authors: K.Balaji, T.Toneswarreddy, S.Ramesh, P.Nihkil, N.Sannajaji
Abstract: The AI-Based Interview Preparation Platform is also created to facilitate a holistic, interactive and safe medium to candidates undergoing a job interview preparation. The site uses AI-powered simulation to create realistic interview experiences, providing real-time feedback and custom suggestions to users. The chatbot feature will allow users to train on a large set of interview questions, as well as to receive live and artificial intelligence-based answers, improving their training process. Also, the site uses a resume analysis which ultimately examines resumes according to Applicant Tracking System (ATS) standards by applying Natural Language Processing (NLP). This feature creates an ATS score and offers a lot of feedback so users can maximize their resumes and enhance their probability of attention by employers. The users are able to post their resumes, provide an understanding of the points that they need to improve, and adjust their content to the needs of a particular industry. The system also monitors user progress and suggests personalized interview questions according to their profile and the job specifications. It allows candidates to perfect their speaking and improve the answers and overall performance during simulated interviews as AI-derived feedback on the response assists them in honing their skills and improving their response. Moreover, the platform provides secure and confidential data to users, as well as uncomplicated navigation and scheduling of mock interviews. Combining NLP-driven resume analysis and AI-driven chatbot functionality, the platform provides an interactive and data-driven experience, helping job seekers to prepare for interviews better and shine in a competitive job market.
A Comparative Evaluation Of Machine Learning Algorithms For Early Detection Of Type 2 Diabetes
Authors: Seema Ahirwar, Rupali Chaure
Abstract: This paper presents a comparative evaluation of seven supervised ML classifiers Logistic Regression, Naive Bayes, k-Nearest Neighbors, Decision Tree, Random Forest, XGBoost, and SVM for early Type 2 diabetes (T2DM) prediction. Using the Pima Indians Diabetes Database (PIDD, n=768) and Frankfurt Hospital Diabetes Dataset (FHDD, n=2000), we apply standardized preprocessing with SMOTE-based class imbalance correction and stratified 10-fold cross-validation[1]. Models are evaluated on Accuracy, Precision, Recall, F1-Score, ROC-AUC, and MCC. Results show XGBoost consistently outperforms all classifiers (AUC: 0.901 PIDD, 0.951 FHDD), while Logistic Regression retains interpretability advantages for clinical deployment. Feature importance analysis identifies fasting plasma glucose, BMI, and HbA1c as top predictors, aligning with clinical guidelines.
DOI: https://doi.org/10.5281/zenodo.19678064
A Comprehensive Review Of Artificial Intelligence Techniques In Computer Science
Authors: Vanita Ganesh Bagal
Abstract: Artificial Intelligence (AI) has emerged as a foundational pillar of modern computer science, profoundly influencing a wide range of domains including automation, data analytics, natural language understanding, intelligent systems, and human–computer interaction. Rapid advancements in computational power, availability of large-scale datasets, and algorithmic innovations have accelerated the development and adoption of AI-based techniques across academic research and real-world applications. This review paper presents a comprehensive analysis of contemporary Artificial Intelligence techniques used in computer science, with particular emphasis on machine learning, deep learning, natural language processing, computer vision, reinforcement learning, generative models, and explainable artificial intelligence. The study systematically examines the computational foundations of these techniques, their underlying algorithms, and their practical applications in areas such as intelligent decision-making, pattern recognition, autonomous systems, and data-driven modeling. In addition, the paper highlights the advantages of AI techniques in improving efficiency, accuracy, and scalability of computing systems, while also discussing key limitations including model interpretability, data dependency, computational complexity, ethical concerns, and security challenges. Recent developments such as transformer-based architectures, large language models, and explainable AI frameworks are reviewed to illustrate current research trends and emerging directions. By synthesizing findings from recent surveys, scholarly articles, and systematic reviews, this paper provides an up-to-date overview of the state-of-the-art in Artificial Intelligence research within computer science. The review aims to serve as a valuable reference for researchers, academicians, and practitioners by identifying research gaps, outlining future opportunities, and emphasizing the need for responsible, transparent, and efficient AI systems in next-generation computing environments.
AI Interview Preparation System
Authors: Guntipalli Dhana Lakshmi, Kotha Harika, Kankipati John, Padala Srinivas
Abstract: The AI interview preparation system is a tool that uses automated evaluation and customized feedback to assist users in preparing for interviews. The system uses speech processing, keyword- based analysis, and LLM Modules to mimic interviewers. It creates questionnaires according to the domain and degree of difficulty chosen by the user. Similarity matching and scoring methods were used to assess the responses. Comments on accuracy, assurance, and clarity to assess communication effectiveness and speech input were also examined. It reduces the need for conventional interviewing techniques and gives users the opportunity to improve their performance through tracking and feedback mechanisms.
Enhancing Personal Finance Management Using AIBased Predictive Analytics And Intelligent Chatbot System
Authors: Anurag Mall, Nitin, Kirti Rastogi, Arvind Kumar
Abstract: The rapid growth of digital financial transactions, including UPI, online payments, and subscription-based services, has significantly increased the complexity of personal finance management. Despite the availability of various financial tracking applications, most systems lack intelligent analytics, predictive capabilities, and personalized guidance. This paper presents an AI-driven platform for enhancing Optimizing personal financial management using predictive analytics and an intelligent chatbot system. The proposed system integrates deterministic data handled using evaluation methods based on large language models to produce a multi-dimensional financial insight score. User financial data is processed through a structured pipeline, where expenses are categorized using machine learning algorithms, and spending behaviour is analysed using time-series predictive models. The system leverages a Natural Language Processing (NLP)-based chatbot that Allows users to query financial insights in instantly. A composite financial health score is computed using a weighted model: 𝑭𝒔𝒄𝒐𝒓𝒆 = ⌊𝟎. 𝟑𝟎𝑬 + 𝟎. 𝟐𝟓𝑷 + 𝟎. 𝟏𝟓𝑩 + 𝟎. 𝟏𝟎(𝑺𝑨 + 𝑭𝑨 + 𝑹𝑨)⌉ where 𝑬represents expense categorization accuracy, 𝑷denotes predictive accuracy, 𝑩indicates budgeting efficiency, 𝑺𝑨is savings analysis, 𝑭𝑨is financial awareness, and 𝑹𝑨is risk assessment. Experimental evaluation conducted on 100+ user datasets demonstrates a strong correlation (𝒓 = 𝟎. 𝟗𝟏) with manual financial assessments and an average improvement of 27.5% in user savings behavior after applying AI-generated recommendations. The system maintains secure data handling, real-time insights, and scalability. The proposed platform democratizes intelligent financial planning tools, making them accessible to a wider population.
Web-Based Health Score and Health Calculator System
Authors: Shewale Isha Avinash, Vinod Patidar
Abstract: Remaining healthy becomes increasingly difficult for people due to the changing lifestyle and health- related threats they face. Mostly, all these individuals fail to determine their health condition until they are ill. Health Score is an online solution that analyses a patient’s body mass index, lifestyle, and health records to calculate their overall health level. Information will be gathered from users through the framework's easy-to-use interface and later processed using the defined health scoring logic. The initial phase involves analyzing the data, followed by normalization to determine the user's first score. The second phase will involve the use of predictive techniques to classify the users according to their health conditions.
DOI: https://doi.org/10.5281/zenodo.19684377
Crop Yield Prediction And Smart Agricultural Advisory System
Authors: Siddharth Nagesh Gaikwad, Sanket Sadashiv Adling, Danesh Balkrishn Sutar, Mrs. A. G. Chendke
Abstract: Agriculture plays a vital role in the economy of India, acting as the primary source of livelihood for nearly 58% of the country’s population. However, the sector faces significant challenges, including unpredictable weather conditions, pest attacks, and improper fertilizer usage, which often lead to reduced crop yields and economic instability for farmers. This paper presents a comprehensive Crop Yield Prediction and Agricultural Advisory System utilizing advanced machine learning techniques. The proposed system predicts crop yield based on critical environmental factors such as rainfall, temperature, and pesticide usage. Additionally, it integrates specialized modules for disease risk assessment, crop recommendation, and fertilizer recommendation, creating a holistic decision-support platform. The models are trained on large-scale historical datasets. Specifically, the study employs Random Forest for yield and disease prediction, Multinomial Logistic Regression for crop recommendation, and a rule-based system for fertilizer dosage. The system is implemented using the Flask web framework, providing an interactive and user-friendly interface for stakeholders. The experimental results demonstrate that the system can assist farmers in making data-driven decisions, optimizing resource allocation, and significantly improving agricultural productivity and sustainability.
DOI: https://doi.org/10.5281/zenodo.19685934
Iot Based Wearable Monitoring System For Alzheimer’s Patients
Authors: Dr S. Sutha, M. Gowsalya, B. Karthiga, C. Kohila
Abstract: This project focuses on the Alzheimer’s disease is one of the most prevalent neurodegenerative disorders, affecting mil-lions of individuals worldwide and imposing a significant burden on caregivers and healthcare systems. Patients suffering from Alzheimer’s often experience cognitive decline, memory loss, disorientation, and impaired motor function, making them vulnerable to hazards such as falls, wandering, and sudden health deterioration. Traditional monitoring methods rely heavily on manual observation and closed-circuit television (CCTV) systems, which are limited in scope, lack automation, and cannot provide real-time health data.
Smart Farming with Sensors and Automated Pest Control
Authors: Usha S, Kanishkar S K, Nishanth K, Ramakrishnan R, Sakthi S
Abstract: The integration of sensor technologies and automated pest control systems is revolutionizing modern agriculture, ushering in a new era of smart farming. This approach leverages Internet of Things (IoT) devices, such as soil moisture sensors, temperature and humidity monitors, and multispectral imaging, to collect real-time data on crop health and environmental conditions. These insights enable precision agriculture practices, optimizing irrigation, fertilization, and pest management strategies.
DOI: https://doi.org/10.5281/zenodo.19686273
Idea2Team: A Smart Web Platform For Freelancer Hiring And Team Building
Authors: Patel Meet, Patel Smit
Abstract: Finding the right team members and managing collaboration efficiently are major challenges for founders and freelancers. Many users depend on multiple platforms for recruitment, communication, task management, and file sharing, which creates inefficiency. Idea2Team is developed as an integrated web-based platform that simplifies team formation and project collaboration. The platform allows founders to create projects, define required skills, and find suitable candidates through a smart matching system. Freelancers can create profiles, explore projects, and apply based on their skills and experience. Once a team is formed, the system automatically creates a workspace where members can collaborate using real-time chat, task management, and file sharing. The system is developed using React.js, Node.js, Express.js, and MySQL. Real-time communication is enabled using Socket.io. Overall, Idea2Team provides an efficient solution for startup team building and collaborative project execution.
Smart Electric Vehicle Charging Station Locator With Route Planning & Slot Booking
Authors: Gudala Priyanka Sai ramya, Bontha Tarun Kumar, Akumuri George, Mr. A Kabir Das
Abstract: Electric Vehicle Charging Station Locator with Route Planning and Slot Booking using Blockchain is an online platform designed to improve the access, transparency, and reliability of the electric vehicles (EV) charging network. The customers can search the charging stations in a specified city or place, check the slot's real-time availability, plan the most efficient route, and even book a slot in advance securely to avoid waiting. The blockchain technology ensures the integrity and transparency of booking records as it logs all transactions through cryptographic hashing, thereby creating unalterable, permanent records. This decentralized approach is a win-win situation for both parties, users and administrators, as it not only gives them access to secure and verifiable documentary evidence of booking histories but also helps them to build trust. The admin module includes a centralized dashboard, thereby providing the city, location, station, slot, and user account manager the ability to supervise and control everything, thus, securing data handling and ensuring accurate slot utilization. The system comprises a user interface built in ReactJS, a server coded in Spring Boot (Java), and MySQL for database management. So in conclusion, the system not only provides higher convenience for EV charging but also reduces the waiting period and supports the green electric transport initiative.
DOI: https://doi.org/10.5281/zenodo.19689326
Hire Hub Job Portal System: A Web-Based Recruitment Platform
Authors: Ritik Patel, Raj patel, Dr.Pooja Sapra , Jagruti Parmar
Abstract: The increasing demand for efficient recruitment systems, combined with the limitations of traditional hiring processes, highlights the need for a digital transformation in the recruitment industry. Conventional hiring methods such as manual resume screening, physical interviews, and offline job postings suffer from inefficiency, lack of transparency, and delayed communication. The Hire Hub Job Portal System introduces a modern web-based solution that connects job seekers and employers through a unified platform. The system provides features such as job search, automated application tracking, resume management, real-time communication, and intelligent job recommendations. The platform ensures improved accessibility, faster hiring processes, and enhanced user experience. By integrating modern web technologies, the system creates a scalable, secure, and efficient recruitment ecosystem for both candidates and organizations.
IntelliResume AI Power Screening System
Authors: Posipoyina Jaya Sree, Guna Tirumala Satish, Naimisa Bamala, Gokarakonda Leela Bhavani
Abstract: Organizations now find the hiring process to be inef- fective and time-consuming due to the sharp increase in job appli- cations. Conventional resume screening techniques mostly depend on human labor, keyword matching, and rudimentary Applicant Tracking Systems (ATS), which frequently fall short of capturing individuals’ actual potential and may introduce prejudice. This project suggests an Intelligent Resume AI-Powered Screening System that uses cutting-edge methods from Natural Language Processing and Artificial Intelligence to improve and automate the applicant screening process in order to address these issues. The suggested system uses NLP-based parsing algorithms to extract, evaluate, and interpret data from resumes. It finds important characteristics and transforms them into structured data, including abilities, education, experience, certificates, and projects. The necessary competences are extracted from job descriptions concurrently. The system uses vector representations and semantic analysis to compare resumes with job criteria using embedding techniques and similarity metrics like cosine similar- ity. In order to rank applicants according to their suitability for the position, the system also incorporates machine learning algorithms. Deep learning and transformer-based architectures are examples of advanced models that may be used to enhance contextual comprehension and lessen reliance on precise keyword matches. Additionally, the system has capabilities like suggestion creation, candidate classification (e.g., extremely appropriate, somewhat suitable, and not suitable), and automated scoring. By anonymizing sensitive information like name, gender, and location throughout the screening process, this initiative aims to reduce bias in hiring. This encourages impartial and equitable hiring practices. The system is also scalable and effective at managing high resume quantities, which makes it appropriate for real-world hiring situations. The suggested approach seeks to greatly shorten the time needed for recruiting, increase ap- plicant selection accuracy, and boost overall hiring effectiveness. Additionally, it lays the groundwork for upcoming improvements including integration with chatbot-based hiring platforms, real- time analytics, and adaptive learning models. Thus, in the age of digital transformation, the Intelligent Resume AI-Powered Screening System is a contemporary, effective, and equitable method of hiring.
Review Of Architecture, Challenges, And Prospective Directions For 5G Technology
Authors: Tanisha Jethwa, Smit Chauhan, Param Mehta, Aryan Ramesh Chandran, Dr. Asha Durafe
Abstract: 5G represents the most recent mobile communica- tion technology which aims to improve speed, reduce latency, and offer more reliable connections than past versions. This document provides an insight into 5G network architecture and describes the main network components such as the core network and radio access network. Additionally, the document covers the technologies used in the implementation of 5G communications and different uses cases such as IoT, smart cities, self-driving cars, and telemedicine. The document demonstrates the contribution of 5G technology towards modern digital services. In addition to the benefits of increased throughput, latency reduction, and device density capabilities, several significant problems remain unsolved – from the issue of mmWave propagation restrictions and the energy consumption associated with the densification of networks to compatibility with legacy systems and fragmentation in global standardization. The comparative study demonstrates the advantage of AI/ML-based solutions for autonomous network management. Finally, the document outlines critical research questions and suggests prospective directions for further re- search.
Career Catalyst Ai-Based Online Job Portal
Authors: M.P.P.S.Sai Durga, G.J.Anjana Santhi, K.John Mark, M.Bharath, Shaik M Unnisha Begum
Abstract: Job portal system based on the ideas of Blockchain and Gemini AI to revolutionize the recruitment and employment search process. The program is programmed and consists of three major modules ( Admin, Company and User (Candidate) and is based on the MERN stack (MongoDB, Express.js, React, Node.js).Admin module offers the simplified management of the users, companies, and mock interviewing time, which allows managing the platform efficiently. The Company module enables job advertisements, media of application and shortlisting of candidates, which creates an easy and smooth user experience to employers? The system will offer the candidate (User) a powerful platform, in which he or she could post resumes are subjected to ATS (Applicant Tracking System). It also provides job suggestions on the analysis of skill gap that helps the candidates to match their profiles in accordance with the market demands. One of the most innovative features of the system is the combination of Gemini AI that performs sophisticated analysis in the context of mock interviews, deserves to be mentioned. In addition, the information of the user (and interview) (including resumes and feedback) is securely stored on the Blockchain, connotes integrity, privacy, and transparency of data. Such an incorporation of Blockchain technology ensures that the information of users cannot be tampered and kept confidential.
A Study of Inventory Control Techniques to Optimise Revenue in Small Retail Stores
Authors: Dr Ravendra Kumar
Abstract: According to an estimation, there were around 12.65 million retail grocery stores in India in 2020. These Stores typically covered 100-200 square feet and kept stock of around 500-1000 items. These small retail shops generate yearly average revenue of 18-25 lac, of this the profit margins are around 3-4% i.e., Rs 54,000-72,000 yearly. From discussions with shop owners running small to medium retail stores from two-three generations, the challenge seems to be either overstocking leading to wastage, or stock-out leading to customer diversion. Small retail stores usually are managed using the primitive hit and trial method when it comes to inventory management, that only becomes moderately efficient in case of extensive experience of the manager. The following paper presents a study of three inventory control techniques in order to optimize revenue by controlling the inventory efficiently for small retail stores in India. The purpose of this study is to figure out a pattern connecting inventory control with optimal revenue which may further be converted into an algorithm for software that may help small retail store owners all over India.
DOI: https://doi.org/10.5281/zenodo.19691144
A Machine Learning-Based Framework for Network Traffic Anomaly Detection Using Isolation Forest
Authors: Arjun Raju Polenwar, M. Adarsh Ram, Kembasaram Harini, Dr. G. Venkanna
Abstract: The exponential growth in network traffic volume, velocity, and heterogeneity has rendered manual threat monitoring operationally infeasible for modern enterprise, cloud, and hybrid infrastructures. Conventional signature-based intrusion detection systems (IDS) perform reliably for catalogued attack fingerprints but exhibit critical detection gaps when confronted with polymorphic, obfuscated, or zero-day attack behaviors that have no prior signature representation. This paper presents an anomaly-driven intrusion detection framework grounded in Isolation Forest — an unsupervised machine learning algorithm that isolates statistically rare patterns in high-dimensional data without requiring labeled attack samples during training. The proposed system integrates data ingestion, feature preprocessing, model training, anomaly scoring, quantitative evaluation, and dashboard-based visual reporting into a single reproducible end-to-end pipeline. Validation on an NSL-KDD based benchmark dataset comprising 22,543 records and 42 attributes yields Accuracy = 0.730, Precision = 0.799, Recall = 0.702, and F1-score = 0.747 under label-aware evaluation. These results confirm that a computationally lightweight unsupervised model can serve as an effective first-stage network threat detector while preserving operational interpretability and deployment feasibility.
Design of an Induction Motor for Electric Two-Wheeler Applications
Authors: Assistant Professor N. Santosh Kumar, Rohith Patel, CH. Rahul Karthik, R. Nithai Charan
Abstract: University support systems are under increasing pressure to handle high volumes of student queries accurately and at scale. Traditional rule-based chatbots are rigid and brittle, while large language model (LLM)-based systems, though fluent, are prone to hallucination. This paper presents UNI ASSISTAI, a Retrieval- Augmented Generation (RAG)-based intelligent university assistant that grounds every generated response in verified institutional knowledge. The system integrates a semantic vector retrieval pipeline with a GPT-based generative model, and extends it with multimodal input capabilities—supporting text, voice (via ASR), and image (via OCR) queries. The backend is served through a FastAPI interface, and the frontend is implemented in React with TypeScript and Tailwind CSS. Experimental evaluation on a curated university FAQ and policy corpus yields a Precision of 0.87, Recall of 0.84, and F1-score of 0.85, outperforming both rule-based and vanilla LLM baselines. This work demonstrates that domain-specific RAG architectures offer a scalable, reliable path to academic AI assistants.
DOI: http://doi.org/
Design And Evaluation Of An AI Based Adaptive Mock Interview System Using NLP And Real-Time Feedback Analysis
Authors: Himanshu Kumar, Rohit Kumar, Aditya, Prof. Shivangi Patel, Prof. Nitin Pal
Abstract: Preparing for interviews is often inconsistent, as many candidates depend on repeated question lists and general advice that does not clearly show how to improve. This work presents an adaptive mock interview system that creates a more practical way to prepare by combining interaction, evaluation, and guidance in one place. The system is designed to behave like an interviewer by asking questions, examining answers, and giving feedback during the same session. To achieve this, the system processes user responses using language understanding methods and supports both typed and spoken input. Each answer is reviewed from multiple perspectives, including how well it addresses the question, how clearly it is expressed, and the overall tone of the response. Based on these observations, the system assigns a score and provides suggestions that users can apply immediately. A key feature of the system is its ability to adjust question difficulty during the session. When a user performs well, the system gradually increases the level of challenge, while weaker performance leads to simpler or more guided questions. This adjustment helps maintain balance and keeps the user engaged without making the session too easy or too difficult. The system was tested under controlled conditions to observe its behaviour across different types of responses. The results show stable performance, with timely feedback and consistent evaluation. Repeated interaction also leads to noticeable improvement, indicating that the system supports gradual learning and skill development. Overall, the proposed approach offers a structured and flexible way to practice interviews, reducing dependence on manual guidance while helping users build confidence through continuous feedback and adaptation.
A Review Of IoT-Based Smart Home Automation Systems: Insights From Recent Indian Research
Authors: Dipak Sanjay Rothe
Abstract: The accelerating convergence of wireless communication technologies and miniaturized sensor hardware has substantially broadened the scope of intelligent residential systems. This paper presents a systematic review of five peer-reviewed Indian research publications, each examining a distinct dimension of Internet of Things (IoT)-enabled home automation. The studies collectively address control architectures, embedded hardware selection, energy management strategies, real-time hazard detection, and multi-modal user interaction mechanisms. Through structured comparative analysis, recurring themes, prevailing technical constraints, and prospective research trajectories are identified. The review affirms that IoT- driven smart home systems have demonstrated consistent utility in advancing residential convenience, curtailing energy expenditure, and facilitating remote appliance governance, while also highlighting that cybersecurity robustness and large-scale deployment scalability remain underexplored frontiers requiring sustained investigative attention.
DOI: https://doi.org/10.5281/zenodo.19693627
Tradesphere : Multi-Dimensional Trust Platform
Authors: Jashn Patel, 2Bhavesh Parmar, Jugal Khambhata, Raj Howladar, Dr Ritesh Singh Malik
Abstract: Due to the fast-paced emergence of online e-commerce platforms, the landscape of the virtual marketplace has been completely revolutionized by facilitating efficient communication between the buyers and sellers. However, the current systems suffer from a number of trust-related issues such as fake reviews, bias in product ratings, lack of seller responsibility, and lack of means to assess buyer behavior. Rating systems employed in the literature are mainly concerned about the ratings provided by the customers, which leads to an incomplete and often times inaccurate assessment of transaction-based trust. In this research, a Multi-Dimensional Trust Based Rating System (MDTRS) is proposed, where product rating, seller reputation, and buyer credibility are taken into account together in order to form a complete rating mechanism. The proposed rating mechanism differentiates itself from other rating algorithms since it evaluates all the parties involved in the transaction system and hence ensures complete responsibility. In the proposed system, a trust score calculator is developed using dynamic weights according to the credibility of the input and interaction history between the user and the service provider. The implementation of the system is performed through a scalable full-stack architecture along with buyer, seller, and administrator interfaces.
Meachine Learning Approach For House Price Prediction
Authors: Dr. S. W. Mohod, Ms. Falguni N. Mawale
Abstract: House Price Prediction focuses on the development of methods that use machine learning algorithms to accurately predict house prices. Random Forest and Gradient Boosting algorithms have lower mean square error (MSE) and are chosen as the best algorithms for predicting house price. Random forest algorithms handle relationships and provide reliable predictions. Gradient boosting algorithm is used to process large amounts of data to make accurate predictions. Ensemble combines all these individual predictions to produce a final and more accurate prediction. The house information in the dataset also helps improve the estimated house price. This system will help people in the real estate market to make more informed decisions when buying or selling a house.
Comparative Evaluation Of FP32 And INT8 Quantization For Edge Device Real-Time Indian Sign Language Recognition
Authors: Siddharth Roy, Ravish Kumar
Abstract: The high memory and computational requirements of standard floating-point architectures make it difficult to deploy Deep Learning models on edge devices with limited resources. The Post-Training Quantization (PTQ) methods used with a MobileNetV2 architecture for real-time Indian Sign Language (ISL) recognition are compared in this paper. We show that the baseline 32-bit floating-point (FP32) model can be converted to an 8-bit integer (INT8) format, which significantly reduces the model footprint and improves inference latency without significantly degrading accuracy. The INT8 quantized model achieves a 72.4% reduction in memory size (from 9.52 MB to 2.63 MB) and a 31.7% increase in inference speed (from 14.40 ms to 9.83 ms per frame), according to experimental results on a 27-class ISL dataset. Importantly, compared to the FP32 baseline, the quantized model maintains a strong validation accuracy of 98.60%, with an accuracy decline of less than 1%. These results confirm that INT8 quantization is effective in enabling offline, high-framerate computer vision applications on low-power edge hardware.
Federated Time-Series Learning For Cross-Platform Rug Pull Detection
Authors: Dr. Pankaj Malik, Mohit Kapoor, Akshat Gupta, Aman Singhai, Akul Laad
Abstract: The rapid expansion of decentralized finance (DeFi) platforms has been accompanied by a surge in rug pull scams, where malicious actors exploit liquidity pools and abandon projects, causing substantial investor losses. Existing detection approaches are largely centralized and platform-specific, limiting their effectiveness due to privacy constraints, fragmented data sources, and the dynamic behavior of blockchain ecosystems. This paper proposes a novel Federated Time-Series Learning (FTSL) framework for cross-platform rug pull detection that enables collaborative model training without sharing raw transaction data. The proposed system integrates federated learning with advanced time-series modeling to capture temporal patterns in token price volatility, liquidity changes, transaction frequency, and smart contract activities. A hybrid deep learning architecture combining Long Short-Term Memory (LSTM) networks with an attention mechanism is employed to effectively learn sequential dependencies and identify early indicators of fraudulent behavior. The federated setup ensures privacy preservation while enabling knowledge sharing across multiple decentralized platforms. Experimental results on multi-chain DeFi datasets demonstrate that the proposed FTSL model achieves 96.3% detection accuracy, outperforming traditional centralized models (91.2%) and single-platform approaches (88.7%). The model also improves precision (95.1%), recall (94.6%), and F1-score (94.8%), indicating robust and balanced performance. Furthermore, the system is capable of detecting rug pull events 6–12 hours earlier than baseline methods, providing critical early warning signals. Communication overhead is reduced by approximately 28% through optimized federated aggregation, while maintaining scalability across heterogeneous platforms. These findings highlight that Federated Time-Series Learning offers a scalable, privacy-preserving, and highly effective solution for real-time rug pull detection, contributing to enhanced security, transparency, and trust in decentralized financial ecosystems.
DOI: https://doi.org/10.5281/zenodo.19694831
Edge-Aided Acoustic Analysis For Early Detection Of Stem-Borer Infestations: A Multidisciplinary Approach
Authors: Kabisree P, Sandhiya T, Kamalini K
Abstract: Much of the damage caused by stem-boring larvae, specifically the “red palm weevil (Rhynchophorus ferrugineus)”, could be mitigated through “early detection and treatment” of infestations. “Acoustic technology” has the potential to enable this early detection by identifying the short, high-frequency sound impulses produced by larvae as they feed and move within palm tree trunks. However, distinguishing these signals from background noise and wind-induced tapping remains a significant challenge. This paper explores a multidisciplinary approach combining “entomological behavioural analysis”, “advanced signal processing”, and “edge-aided automated machine learning” to provide reliable detection in agricultural environments. By processing signals at the source, these systems can overcome traditional barriers like instrumentation costs and training needs.
DOI: https://doi.org/10.5281/zenodo.19695069
Self-Healing AIoT Systems Using Autonomous Fault Detection And Recovery Mechanisms
Authors: Nafisa S, Dr. Balaji. K
Abstract: The intersection of artificial intelligence (AI) and Internet of Things (IoT) has led to the emergence of massive distributed systems prone to faults due to hardware, network, or software issues. In this paper, we design a self-healing system for AIoT systems that employs automated fault diagnosis, root cause identification, and dynamic healing mechanisms. Our self-healing system uses an efficient transformer-based anomaly detection model coupled with graph neural networks for fault localization and reinforcement learning (RL) agents for recovery policy decision making. The performance of our self-healing framework is evaluated using simulations of a smart factory with 1,200 sensor and actuator nodes, where the framework achieves an average accuracy of 94.2% and latency of 2.3 seconds. When compared to reactive approaches, the framework reduces system downtime by 76.4%, increases task success rate by 15.8 percentage points, and outperforms adaptive recovery approaches by a wide margin.
DOI: https://doi.org/10.5281/zenodo.19697807
Multi-Agent Reinforcement Learning In AIoT For Dynamic Resource Allocation And Optimization
Authors: Nafisa S, Dr. Balaji. K, Shruthi N
Abstract: Due to the rapid rise in the use of artificial intelligence in things (AIoT), the dynamic resource allocation problem is now more complex than ever. In this research paper, a new dynamic resource allocation framework based on multi-agent reinforcement learning (MARL) for AIoT systems is described. A CTDE architecture based on improved MAPPO (IMAPPO) is utilized, which optimizes AoI for action spaces with both discrete and continuous variables. In simulation tests, the framework has achieved 99% successful task completion with 18 MEC servers available, resulting in a minimal probability of failure of 0.01% at 30 dBm. Furthermore, comparative studies show that MARL has better performance than conventional deep Q network (DQN) and proximal policy optimization (PPO) algorithms by 22.01% and 8.26%, respectively. Moreover, a maximum cumulative reward value of 62,306.58 and 99.98% accuracy were obtained, marking a 6.9% improvement over other MARL models.
DOI: https://doi.org/10.5281/zenodo.19698681
Mathematics in the 21st Century: A Review of Key Developments
Authors: Assistant Professor Chavan Akshata Jaydeep, Assistant Professor Dr. G. V. Khandekar
Abstract: Mathematics education in the 21st century is experiencing significant transformation due to rapid technological advancements and evolving pedagogical practices. This review paper examines key developments in mathematics education, focusing on the shift from traditional teaching methods to student-centered approaches that promote critical thinking, creativity, collaboration, and problem-solving skills. The study highlights the growing role of STEM integration and the use of digital technologies, including artificial intelligence, gamification, and online learning platforms, in enhancing student engagement and personalized learning experiences. Furthermore, it discusses major challenges such as teacher preparedness, unequal access to technological resources, and the limitations of traditional assessment systems in evaluating higher-order thinking skills. The review also emphasizes the need for continuous professional development and inclusive educational strategies to ensure effective learning outcomes.
DOI: http://doi.org/
Smart Expense Management Using AI: “Financial Storytelling with Google Sheets Integration
Authors: Yatin Yadav, Prashant Pal, Prince Kumar, Amit Kumar, Mr. Abhishek Chaudhary
Abstract: Personal financial management remains a critical challenge for individuals seeking to track expenses, analyze spending patterns, and achieve savings goals. This paper presents the design, implementation, and evaluation of an AI-powered expense tracking system that combines a user-friendly web interface with intelligent financial insights. The system leverages Streamlit for frontend visualization, Google Sheets as a cloud- based backend, and integrates AI-driven storytelling to provide users with actionable spending analyses. Unlike SMS-based tracking solutions, this system prioritizes privacy, supports cash transactions, and enables rich categorical data entry. Experimental results demonstrate that the system effectively visualizes weekly, monthly, and yearly financial trends while generating meaningful health scores and savings recommendations. The proposed solution offers a secure, scalable, and insightful approach to personal expense management.
DOI: http://doi.org/