International Journal of Electrical, Electronics and Computer Systems
International Journal of Electrical, Electronics and Computer Systems E-ISSN: 2347-2820 Frequency: Biannual Publisher Website: https://mriindia.com/ Editor in Chief: Dr. Parikshit Mahalle Article Processing Charges: 50 USD Indexing In: Google Scholar, SCILIT, BASE, Semantic Scholar, Scribd, ResearchGate
EduQuest-Educational Question generation and Test Automations Using AI
This paper presents EduQuest, an AI-driven framework designed for automated question generation and test management. The system leverages advanced Natural Language Processing (NLP) models and Large Language Models (LLMs) to generate high-quality, context-aware questions from diverse learning materials such as textbooks, lecture notes, syllabi, videos, and digital resources. By incorporating multiple question formats, including multiple-choice, fill-in-the-blank, short-answer, and coding questions, EduQuest supports adaptive testing aligned with curriculum objectives. Furthermore, the platform automates test assembly, delivery, grading, and feedback, reducing the workload on educators while ensuring fairness, consistency, and scalability.
Result Paper On Gramnagar live - Humanity in service, Unity in future
Gramnagar Live is an integrated digital platform designed to enhance the efficiency and Connect, Serve, Unite! The project proposes a comprehensive community development platform that bridges the gap between rural and urban are as through digital technology. By lever aging web technologies, mobile applications, and data analytics, Gramnagar Live aims to create a unified ecosystem that promotes social welfare, community engagement, and sustainable development. The proposed system involves developing an integrated platform that connects local authorities, service providers, and community members to address various social challenges including healthcare access, education, employment opportunities, and disaster management. This project not only addresses the need for digital transformation in rural communities but also contributes to the fields of social computing and community-driven development.
A Result Paper On Tranzo: A Smart Commercial Vehicle Platform
The project, Tranzo: A Smart Commercial Vehicle Platform, presents an AI- and ML-enabled logistics solution designed to protect cargo, improve driver safety, and strengthen fleet management in the transportation sector. The system integrates GPS-based vehicle tracking with real-time weather forecasting APIs to monitor rainfall, humidity, temperature, and wind speed along the active route of a commercial vehicle. A cargo-specific rule engine analyzes these environmental condi-tions and generates timely preventive alerts according to the sensitivity of the goods in transit. For example, the system can warn drivers carrying grains to secure the load during expected rainfall, while vehicles transporting frozen or temperature-sensitive goods receive alerts when rising temperatures threaten cargo quality. The platform delivers alerts through multiple channels, in-cluding mobile notifications, SMS, and IVR voice calls, with multilingual communication support for practical field use across different regions. A fleet dashboard provides live vehicle monitor-ing, weather-triggered alert logs, and summary reports to support better operational decisions. By combining predictive intelligence, cloud-based accessibility, and user-oriented interfaces, Tranzo reduces cargo loss, delays, and manual error while improving transparency and reliability in logistics operations. Overall, the system demonstrates how Artificial Intelligence, Machine Learning, and real-time data integration can modernize commercial transportation into a more predictive, efficient, and sustainable logistics framework.
A Result Paper on ALFRED-X: The WayneCore Assistant
Alfred X – Wayne Core Assistant is an intelligent personal assistant system designed to simplify human-computer interaction through voice-based communication and automation. It integrates ad-vanced technologies such as speech recognition, natural language processing, and OpenAI’s GPT models to provide human-like conversational abilities. Developed in Python, the system offers a modular architecture that ensures flexibility, scalability, and efficient task handling. Core features include smart reminders, media control, weather and news updates, emotion-based responses, and QR-code security authentication. It also supports multilingual communication, enhancing accessi-bility and user reach. The assistant can manage local files, automate tasks, and provide intelligent insights based on user queries. Integration with APIs and IoT devices allows Alfred X to act as a smart home and productivity companion. A secure password management and AI chat system further enhance its usability and safety. The project employs SQLite for lightweight data storage and Flask for backend service management. Through this project, the potential of artificial intelli-gence in everyday digital environments is explored. Alfred X demonstrates how automation and AI can merge to create a personalized, efficient, and secure digital ecosystem. Overall, it represents a step toward developing a human-like AI assistant capable of adaptive learning and intelligent task execution.
Result Paper on Acadlinker - Skill-Based Student Network with a Touch of AI
AcadLinker is an AI-powered student networking platform that connects students based on their skills, interests, and goals. It helps bridge the gap between academic learning and practical application by enabling users to create skill-based profiles and collaborate on real-world projects. Using AI, the platform provides personalized recommendations for peers, projects, and learning paths, improving engagement and collaboration. It also includes tools for communication, project management, skill validation, and performance tracking. Additionally, AcadLinker offers career guidance by aligning student skills with industry needs, helping them become more job-ready and competitive.
Result Paper On Cyberfence: Intelligent Defence Against Phishing Links
Phishing continues to be one of the most prevalent and damaging forms of cybercrime, exploiting social engineering techniques to deceive users into divulging sensitive information such as login credentials, banking details, and personal data. Traditional defenses such as spam filters, antivirus software, and employee awareness campaigns have proven insufficient against the growing sophistication of phishing attacks. This research proposes a machine learning–based phishing domain detection system that can identify malicious URLs before users interact with them, thereby minimizing the risks of compromise. The system analyzes domain-level and lexical features—such as URL length, numerical patterns, IP address usage, and entropy—to classify URLs as benign or phishing.
Real-Time AI-Based Border Surveillance System for Military Threat Detection
This paper presents an AI-powered military border surveillance system that integrates face recognition and vehicle detection for enhanced security. The system uses Haar Cascade and LBPH algorithms to identify authorized personnel and YOLOv8 for real-time military vehicle detection. It continuously monitors live video feeds and generates instant alerts for unauthorized access. The proposed system improves accuracy, reduces human dependency, and enables faster response, making it effective for modern border surveillance applications.
TeachSmart: A Real-Time, OCR-Integrated Cloud-Based Teaching Analytics Dashboard
TeachSmart (also referred to as Teachora) is a cloud- based teaching analytics dashboard designed to digitize and streamline academic and administrative activities in educational institutions. Traditional classroom processes rely heavily on man- ual record-keeping, leading to inefficiencies, data redundancy, and lack of real-time visibility. The proposed system integrates Optical Character Recognition (OCR) to convert handwritten and printed academic documents, such as attendance records and assignments, into structured digital data. This significantly reduces manual effort and improves data accuracy. The plat- form provides a centralized dashboard that enables real-time monitoring of attendance, academic performance, and classroom activities. Teachora is developed using a scalable Software-as- a-Service (SaaS) architecture, supporting role-based access for administrators, teachers, and students. The system leverages modern technologies including Expo for cross-platform appli- cation development and Appwrite for backend services such as authentication, database management, and storage. The proposed solution enhances operational efficiency, ensures data consistency, and enables data-driven decision-making through intuitive vi- sualizations. Its modular and scalable design makes it suitable for deployment in modern educational environments, supporting both web and mobile platforms.
Growora: A Peer-to-Peer Learning Platform for Collaborative Skill Development
The rapid advancement of digital education platforms has significantly transformed how individuals access knowledge and develop skills. Despite this progress, many existing e-learning systems primarily rely on pre-recorded content and subscription-based models, which limit accessibility, reduce interaction, and hinder personalized learning experiences. To address these challenges, this paper proposes Growora, a peer-to-peer learning platform designed to foster collaborative and interactive learning environments. The platform enables users to both teach and learn by connecting them with peers based on shared skills, interests, and availability through an intelligent matching system. Growora incorporates real-time communication features such as live video sessions, instant messaging, and discussion forums to facilitate seamless knowledge exchange. Additionally, gamification elements including badges, progress tracking, and achievement milestones are integrated to enhance user motivation and engagement. The system is built using scalable architecture and modern web technologies, ensuring reliability, flexibility, and efficient performance even with increasing user demand. By promoting community-driven learning and removing financial barriers, Growora aims to create an inclusive and accessible educational ecosystem. The platform not only enhances user engagement but also supports continuous skill development, knowledge sharing, and lifelong learning in a dynamic and interactive manner.
A Result Paper on Deforestation Monitoring and Cause Detection in India Using Satellite Imagery
Forests in India are disappearing rapidly. This paper presents an automated system that monitors deforestation using satellite imagery and AI. Users enter any location’s coordinates. The system pulls images from Sentinel-2 via Google Earth Engine and analyzes changes over multiple years. Ten vegetation indices (NDVI, NDWI, EVI, MSAVI2, NDMI, NBR, BSI, NDBI, SAVI, GNDVI) are calculated to track plant health, soil, and water conditions. A large language model interprets the data and identifies likely causes of forest loss. A web interface built with FastAPI makes the tool accessible to non-experts. Testing on Anamalai Hills, Kerala successfully detected a severe drought event in September 2025 and tracked the forest’s rapid recovery. The system shows that combining satellite data with AI enables scalable, automated forest monitoring.
Local Selfie: A Web-Based Application for Digital Footprint Visualization and Privacy Awareness
The rapid growth of digital systems has increased concerns regarding user privacy, security, and transparency. Local Selfie is a web-based application designed to monitor and visualize a user’s local digital footprint in real time. It tracks system parameters such as CPU usage, memory consumption, processes, file activities, and network connections to provide comprehensive insights into system behavior. The system integrates machine learning using the Isolation Forest algorithm to detect anomalies, along with behavioral analysis to identify deviations from normal patterns. AI-generated explanations enhance user understanding of potential threats. All data is stored locally using SQLite, ensuring privacy. The platform delivers real-time analytics and intuitive dashboards, enabling users to effectively monitor and secure their computing environment.
A Comprehensive Result Paper on NeoBord: AI Based Futuristic Learning Software –Black Board
NeoBoard: AI-Based Futuristic Learning Software – Blackboard is an intelligent digital learn-ing platform designed to transform traditional education methods through advanced artificial intelligence and inter-active technologies. The system integrates speech recog-nition, gesture control, computer vision, and AI-based tutoring to provide a seamless and engaging learning expe-rience. Developed using Python and Streamlit, NeoBoard enables users to interact with a digital blackboard using voice, text, and hand gestures. The platform leverages Natural Language Processing (NLP) and multimodal AI models to deliver accurate, step-by-step explanations for academic queries. It supports real-time voice-to-text conversion, gesture-based drawing, and AI-powered analysis of images and documents. The system also includes secure authentication and role-based access for teachers and students. NeoBoard uses MongoDB for data storage and allows users to save, manage, and share notes efficiently. Its modular architecture ensures scalability and flexibility for future enhancements. Overall, the project demonstrates how AI and interactive technologies can be combined to create a smart, efficient, and futuristic educational environment.
Second-Hand Marketplace Platform – ShareMate
With the growing demand for affordable and sustainable consumption, users are increasingly turning to peer-to-peer rental and resale platforms. However, existing marketplaces like OLX and Quikr often face challenges related to trust, verification, and data transparency, which discourage users from engaging confidently. Low user trust, fraudulent listings, and inefficient transaction handling are examples of hidden issues that can compromise user safety and platform reliability. Existing systems do not have an integrated, community-focused approach; instead, they rely solely on basic user ratings and fragmented listings. This paper presents ShareMate, a community-driven second-hand rental and resale platform designed to ensure secure, verified, and transparent exchanges among users. The platform uses AI-driven recommendations and multi-layered verification to scan listings for hidden risks, and generates complete trust and sustainability reports. Furthermore, the system integrates smart deposit handling and sustainability tracking, allowing users to better understand the positive environmental impact of their resource reuse. By integrating multi-content threat detection with consequence visualization, this work proposes a unified solution to improve cybersecurity awareness and promote safe online practices. The rapid growth of online rental and resale platforms underscores the need for trustworthy, transparent, and sustainable systems to serve both urban and semi-urban communities. Existing second-hand marketplaces, such as OLX and Quikr, are often fragmented, unregulated, and struggle to establish user confidence due to a lack of structured frameworks and effective verification mechanisms.
Decentralized Evidence Management and AI-Assisted Forensic Sketch Generation System
The increasing reliance on digital evidence in criminal investigations necessitates secure and transparent management systems. Traditional forensic systems suffer from vulnerabilities such as data tampering, lack of traceability, and inefficient suspect identification. This paper proposes a decentralized system, ForensicChain, which integrates blockchain technology, InterPlanetary File System (IPFS), and artificial intelligence for secure evidence management and forensic sketch generation. Blockchain ensures immutability and transparent chain of custody, while IPFS provides decentralized storage.
Travel Together: A Real-Time Buddy Finder System for Travelers
Many people love to travel but often face challenges when doing so alone. Solo travelers frequently experience loneliness, safety concerns, and higher costs compared to those who travel in groups. For example, they miss out on opportunities to share transportation or accommodation expenses, which can make trips more expensive. Additionally,traveling alone, especially in unfamiliar regions or foreign countries, can pose safe- tyrisks—particularly for women or first-time travelers. Another common issue is the difficulty in finding companions with similar interests, languages, or travel goals, which canlimit the quality of the travel experience. Many also struggle with planning due to limited local knowledge, which a companion could help bridge. To solve these issues, a Travel Together Buddy Finder System can be introduced. This system would help travelers connect with others who have similar travel plans, preferences, and interests. Each traveler would create a basic profile that includes their travel destinations, dates, budget range, travel style (such as adventure, culture, or relaxation), interests, and languages spoken. Based on these inputs, the system would suggest suitable travel companions by matching overlapping plans and shared preferences. Once matched, travelers can communicate to plan trips together, discuss shared expenses, and align on travel goals. They can either travel one-on-one or join small travel groups with similar interests. Such a system would make traveling safer, more enjoyable, and cost-effective. It would also encourage social connections and cultural exchange among travelers from different backgrounds. By making it easier for like- minded individuals to find each other, this system would reduce,the common challenges of solo travel and make the overall experience more fulfilling.
Result Paper: Automating Team Building with Smart Team Builder
Hackathons serve as vital platforms for rapid innovation in engineering education, yet the efficacy of these events is frequently undermined by suboptimal team formation. This paper presents the final implementation of the Smart Team Builder, an AI-driven system designed to replace manual and self-selection methods. By leveraging machine learning, optimization algorithms, and social network analysis, the platform automates the creation of balanced teams based on a multi-dimensional Skill-Experience Matrix. Results indicate that the system significantly improves skill diversity, ensures fairness across gender and academic backgrounds, and reduces administrative overhead through real-time adaptability.
Result Paper on Mouse Dynamics Based Bot Detection System
With the increasing number of automated attacks on web applications, traditional security mechanisms are becoming insufficient. Bots can bypass CAPTCHA and mimic human-like interactions, making detection challenging. This project proposes a Mouse Dynamics Based Bot Detection System to distinguish human users from automated scripts. The system captures real-time mouse movement data such as position, velocity, and acceleration. Behavioral features are extracted and analyzed using machine learning techniques. The system evaluates patterns like movement randomness, speed variation, and interaction duration. A web-based interface integrates data collection and real-time prediction. The system provides immediate classification results with confidence scores. It enhances security by identifying abnormal interaction patterns. The approach is lightweight and does not interrupt user experience. Overall, it improves authentication systems by adding behavioral biometrics.
Result Paper on Disk Space Renting System
The Disk Space Renting System using Blockchain is a decentralized platform where users can rent out unused disk space and earn income, while others can rent affordable storage. Providers list available space with price and duration, and renters choose and pay via smart contracts. Blockchain ensures transparency, records transactions securely, and prevents tampering. Smart contracts automatically handle rental terms and payments. Firebase manages authentication, profiles, and real-time data. Security is ensured as renter’s files are encrypted before storage, preventing providers from accessing them. This project reduces dependency on centralized providers, ensures data security, and creates a cost-effective, user-friendly storage marketplace.
Result Paper On: Emphasizing Preparation and Development Using AI
This project proposes an Emphasizing Preparation & Development Using AI designed to simulate real world interviews while assessing both technical and soft skills. The system incorporates Natural Language Processing (NLP) for answer analysis, Convolutional Neural Networks (CNN) for facial expression recognition, and Media Pipe for body posture detection. Additionally, video sentiment analysis is employed to evaluate confidence and emotional state. The platform generates dynamic, job- specific technical questions and provides instant feedback on communication, body language, and emotional intelligence. This integrated approach enables candidates to improve their strengths, identify weaknesses, and prepare comprehensively for real job interviews.
Result Paper on Animal Disease Prediction System Using Machine Learning (CNN and SVM)
Animal disease prediction is a critical challenge in veterinary science and agricultural management. Early and accurate identification of animal diseases can significantly reduce mortality, economic losses, and prevent the spread of zoonotic infections. Traditional diagnostic methods are time-consuming, require expert veterinarians, and are not always accessible in rural or remote areas. This project presents a desktop-based application for animal disease prediction using two machine learning techniques — Convolutional Neural Networks (CNN) and Support Vector Machine (SVM). The CNN model is employed for image-based feature extraction and pattern recognition from animal symptoms and affected body regions, while the SVM classifier provides robust classification of disease categories based on extracted features. The system allows users to upload animal images, which are then preprocessed and passed through the CNN+SVM pipeline to predict the most probable disease condition along with execution time details. Results indicate that the CNN+SVM hybrid approach achieves high prediction accuracy. The paper also discusses future directions including web-based integration, real-time monitoring, and explainable AI for transparent veterinary decision-making.
Deep Fake detection Using Deep Learning for Images, Videos & Audios
Deepfake detection has emerged as a significant challenge in the field of artificial intelligence due to the rapid advancement of generative models such as Generative Adversarial Networks (GANs). These techniques enable the creation of highly realistic manipulated media, including images, videos, and audio, which can be misused for misinformation, identity theft, and cybercrime. This project presents a deep learning-based system for detecting deepfake content using Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM). The system is designed to process multi-modal inputs such as images, videos, and audio, and performs both spatial and temporal analysis to identify inconsistencies in manipulated media. The system provides a user-friendly interface for uploading media files, analyzing authenticity, and generating results with high accuracy. The proposed solution improves detection reliability, reduces manual effort, and enhances digital media security. The paper concludes with experimental evaluation and discusses future improvements such as real-time detection and enhanced robustness against advanced deepfake techniques.
Video to Text Summarizer with Highlights and Quiz Generator
The increasing use of video-based learning has created a need for efficient content understanding and extraction. This paper presents Video to Text Summarizer with Highlights and Quiz Generator, an AI-powered system that converts video content into structured learning materials such as transcripts, summaries, highlights, quizzes, and timelines. The system uses offline speech recognition (Vosk) and Google Gemini AI to process and analyze video data. It reduces the time required to understand long videos and enhances user engagement through interactive outputs. The proposed solution provides a scalable and efficient approach for improving digital learning and content accessibility.
Geopolymer Concrete Crack Prediction System Using Machine Learning and Image Processing
Geopolymer concrete is a sustainable alternative to conventional cement concrete due to re-duced environmental impact and improved durability. However, crack formation remains a major challenge affecting structural safety and long-term service life. This paper presents a machine learning based crack detection and prediction system using im-age processing techniques. Convolutional Neural Network (CNN), Support Vector Machine (SVM), and Random Forest algorithms are used for crack classification and severity analysis. Experimental results achieved an accuracy of 94.5%, proving the effectiveness and reliability of the proposed intelligent monitoring system.
IoT Based Smart Energy Solution for Hostels
The increasing demand for efficient energy uti-lization in residential facilities such as hostels has led to the need for intelligent monitoring systems. This paper presents an IoT-based smart energy solution designed to monitor, analyze, and control electricity consumption in hostel environments. The system utilizes a PZEM sensor for real-time measurement of electrical parameters such as voltage, current, power, and energy consumption, integrated with a NodeMCU (ESP8266) microcontroller for data transmission. The collected data is stored in Firebase Realtime Database and processed using a Flask-based backend API. The system also incorporates an alert mechanism using Twilio to notify users when energy consumption exceeds predefined thresholds. A web-based dashboard provides real-time visual-ization, historical analysis, and room-wise monitoring of energy usage. The proposed solution enhances energy efficiency, reduces wastage, and enables data-driven decision-making for hostel management. The modular and scalable architecture ensures adaptability for larger deployments and smart building appli-cations.
A Result Paper on Smart Patinet Helthcare System Using AI/ML Based Web Portal
The rapid advancement of Artificial Intelligence (AI) and Machine Learning (ML) has created new opportunities to improve healthcare accessibility and efficiency. This project presents a Smart Patient Healthcare System implemented through an AI/ML-based web portal designed to assist patients in identifying the appropriate medical specialist based on their symptoms. The system aims to bridge the information gap between patients and healthcare providers by providing intelligent guidance during the initial stage of healthcare consultation. The proposed platform allows patients to enter their symptoms through a user-friendly interface, after which a machine learning model analyzes the input and recommends the most suitable medical specialization.
A Result Paper on Next-Gen Financial Planning System
This study introduces a Next-Generation Financial Planning System that leverages modern web technologies, predictive analytics, and AI-based recommendations to simplify personal financial management [4][5].The platform offers automated tracking of income and expenditures, adaptive budgeting, and forward-looking financial forecasting supported by real-time visual insights[10]. Unlike existing solutions that are limited in scope or fragmented in design, the proposed framework integrates diverse financial data sources into a single, secure, and intelligent dashboard. By combining scalability, security, and personalized guidance, the system is intended to strengthen financial awareness, promote better savings strategies, and assist users in achieving long-term financial goals.
Result Paper on AI-Driven Conflict-Free Academic Timetabling System
Academic timetabling is the problem of assigning courses, instructors, rooms, and student groups to limited time slots while respecting hard constraints (no clashes, room capacity, teacher availability) and optimizing soft preferences (time spreads, load balancing). Manual methods are tedious, error-prone, and slow to react to change. This project implements a web-based, role-secured timetable system that automates schedule generation through an Al solver service. Core capabilities include authenticated data entry and governance, building a solver input from authoritative master data, computing feasible schedules, presenting conflicts for review, and publishing official outputs (including PDF exports). The system emphasizes accuracy, scalability, security, and operational transparency through auditability and clear state transitions. The paper concludes by discussing future research directions, including real-time rescheduling, user in-the-loop systems, and explainable AI for transparent decision-making. By consolidating recent advancements and open challenges, this survey provides a strong foundation for future studies and assists educational institutions in adopting intelligent, conflict-free, and scalable timetabling solutions.
A Result Paper On Organ Tissue Transplant Prediction
This project focuses on developing an intelligent prediction model for organ tissue transplant compatibility using advanced machine learning and data-driven decision support techniques. The system integrates patient medical records, genetic information such as Human Leukocyte Antigen (HLA) typing, blood group, and biochemical parameters to predict the donor–recipient matching probability. By analysing historical transplant data and learning complex relationships between genetic markers and immune responses, the proposed model aims to minimize the risk of graft rejection and improve clinical decision-making efficiency. The model employs supervised learning algorithms like Random Forest, Support Vector Machine (SVM), and Neural Networks to classify and predict compatibility levels. A feature selection mechanism ensures that only the most influential medical parameters are considered, enhancing accuracy and reducing computational complexity. Additionally, the system may use optimization techniques to prioritize the best donor-recipient pairs when multiple candidates are available.
Noteverse: Intelligent Note Management for Organizational Hub
In today’s digital era, managing and organizing information efficiently has become es-sential for both academic and professional environments. Traditional note-taking appli-cations often suffer from limitations such as scattered data, lack of centralized storage, limited collaboration features, and absence of intelligent organization mechanisms. These challenges reduce productivity, hinder knowledge sharing, and make information retrieval inefficient in dynamic organizational settings. This paper presents NoteVerse: Intelli-gent Note Management for Organizational Hub, a modern web-based platform de-signed to streamline note creation, storage, and collaboration through a unified and scal-able digital ecosystem. The system is developed using the MERN stack (MongoDB, Ex-press.js, React.js, Node.js) along with Next.js to deliver a responsive, high-performance, and user-friendly interface. It provides secure authentication, role-based access control, and cloud-based storage to ensure data integrity, privacy, and accessibility across multiple devices. NoteVerse enables structured note, advanced search functionality, and real-time synchronization, allowing users to efficiently manage and retrieve information. The plat-form also supports collaborative features such as shared workspaces and multi-user in-teraction, enhancing teamwork and organizational efficiency. Its intuitive design ensures ease of use while maintaining powerful backend capabilities for handling large-scale data. Furthermore, the system architecture emphasizes scalability, modularity, and security, making it adaptable for future enhancements such as offline access, analytics dashboards, and personalized workflow. These capabilities aim to transform traditional note-taking into an intelligent knowledge management system. The proposed solution significantly improves productivity, knowledge retention, and collaboration within organizations. Its cost-effective and scalable design makes it suitable for educational institutions, corporate environments, and personal productivity use cases, contributing to the development of smart and efficient digital workspaces.
Result Paper on Jarvis 2.0: AI-Powered Conversational Assistant with Voice and Chatbot Interaction and Dashboard Management
Artificial Intelligence (AI) virtual assistants have rapidly evolved, becoming integral to modern user interaction across various domains, including customer service, personal productivity, and smart environments. This literature survey investigates the develop and implementation of AI-powered voice and chatbot assistants, with a specific focus on virtual voice assistants. The study reviews key technologies such as automatic speech recognition (ASR), natural language processing (NLP), and text-to-speech (TTS), which enable seamless human-AI interaction. It also explores the use of the MERN stack (MongoDB, Express.js, React.js, Node.js) for building scalable, real-time, and interactive assistant platforms. Particular emphasis is placed on dashboard management systems that enhance transparency, allow customization of assistant behaviour, and provide in sights into user interactions. Through a comprehensive analysis of existing systems and methodologies, this paper aims to provide a foundation for the design and development of Jarvis 2.0, an AI virtual assistant that supports both voice and text-based communication with intuitive dashboard control.
Fake Product Identification by QR Code Using Blockchain
Counterfeit products present a serious threat to industries, consumers, and economies, resulting in financial losses, brand damage, and safety risks. Traditional product authentication methods are often centralized and prone to tampering, making them unreliable and lacking transparency. To overcome these limitations, the proposed system integrates Blockchain technology with QR codes to create a secure and transparent authentication mechanism. Each product is assigned a unique QR code during manufacturing, and its details are stored on a decentralized blockchain ledger. Consumers can easily verify the authenticity of a product in real time by scanning the QR code using a mobile or web application, ensuring trust and reliability. The system enhances supply chain visibility by recording every transaction—from manufacturing to distribution and retail—as immutable blocks on the blockchain. This creates a complete and traceable history of the product, enabling stakeholders to track its journey and quickly identify any point of tampering or fraud. The use of smart contracts can further automate verification and validation processes, ensuring that only genuine products move through the supply chain. Additionally, the decentralized nature of blockchain eliminates dependence on a single authority, reducing the risk of data manipulation and increasing overall system security. Moreover, the proposed solution is scalable, user-friendly, and applicable across multiple industries such as pharmaceuticals, electronics, luxury goods, and food. It can be further enhanced by integrating technologies like IoT for real-time monitoring and data analytics for improved decision-making and demand forecasting. By combining transparency, security, and ease of use, this system not only combats counterfeiting but also strengthens consumer confidence and brand reputation. Ultimately, it provides a future-ready, reliable approach to ensuring product authenticity in a globalized market.
The increasing demand for reliable and efficient electrical power distribution has em- phasized the necessity for intelligent monitoring and protection systems. Conventional systems often suffer from limitations such as delayed fault detection, lack of real-time monitoring, minimal automation, and inability to detect unauthorized power usage, which can lead to equipment damage, energy losses, and reduced system reliability. This paper presents an IoT-based three-phase monitoring and protection system designed to con- tinuously observe electrical parameters and ensure safe operation of power distribution networks. The proposed system utilizes voltage and current sensors to measure electrical parameters across all three phases (R, Y, B) and employs an ESP32 microcontroller for real-time data acquisition and processing. The system is capable of identifying various abnormal conditions such as phase failure, overvoltage, undervoltage, short circuit. Upon detection of any fault condition, the system automatically disconnects the load using a relay mechanism to prevent damage to connected equipment. Simultaneously, real-time notifications are transmitted to users through IoT platforms, enabling remote monitoring and quick response. Additionally, the system can be extended to detect irregularities in power consumption, helping to identify potential electricity theft. The proposed solution enhances operational efficiency, improves system reliability, reduces maintenance time, and ensures safety in electrical systems. Its cost-effective and scalable design makes it suitable for industrial, commercial, and residential applications, contributing to the de- velopment of smart and secure power distribution networks.
Smart Campus Surveillance and Guidance System
The rapid growth of educational campuses has increased the need for intelligent systems to ensure security, automate attendance, and assist navigation. This paper presents a Smart Campus Surveillance and Guidance System that integrates Artificial Intelligence, Computer Vision, and Machine Learning techniques to enhance campus management. The system utilizes CCTV video streams to perform face recognition-based attendance, real-time anomaly detection, and indoor navigation assistance. The proposed solution uses deep learning models such as FaceNet for facial recognition and YOLO for object detection. Experimental results show improved accuracy in attendance marking and effective detection of suspicious activities. The system is scalable, cost-effective, and improves overall campus safety and efficiency.
A Result Paper on Medical Shop Stock Management System
The Medical Shop Stock Management System is a secure, user-friendly, and offline-first mobile application developed to simplify and automate the daily operations of medical shops and pharmacies. In many small and medium medical stores, stock management and billing are still performed manually, which often leads to issues such as incorrect stock records, difficulty in tracking expiry dates, medicine wastage, and billing errors. The proposed system provides a complete digital solution by integrating features such as batch-wise inventory management, automated billing, barcode-based medicine entry, low-stock alerts, and near-expiry notifications. One of the key features of the system is the implementation of the FEFO (First Expire First Out) algorithm, which ensures that medicines with the nearest expiry dates are sold first, thereby reducing wastage and preventing the sale of expired medicines. The application is developed using the Flutter framework for cross-platform compatibility and uses SQLCipher-encrypted SQLite database to ensure data security and privacy. It also includes patient refill reminder functionality to improve medication adherence for long-term treatments. The system follows an offline-first approach, allowing it to function without continuous internet connectivity, making it suitable for rural and low-network areas. Overall, the system improves efficiency, reduces manual errors, enhances data security, and provides a reliable solution for modern pharmacy management.
AI Smart Keyboard System: Real-Time Grammar Correction and Smart Prediction
The rapid growth of digital communication has significantly increased the demand for intelligent typing systems that enhance accuracy, efficiency, and user experience. Traditional keyboard systems primarily provide basic word suggestions and lack the ability to understand context, perform real-time grammar correction, or support multilingual communication effectively. This paper presents an AI Smart Keyboard System that integrates artificial intelligence and natural language processing techniques for real-time grammar correction and smart next-word prediction. The proposed system utilizes advanced AI models along with machine learning approaches such as TF-IDF with Logistic Regression for grammar classification and a Trigram Language Model for next-word prediction. The methodology includes text input processing, language detection, contextual analysis, and generation of accurate suggestions. The system supports multiple languages including English, Hindi, and Marathi, improving accessibility for diverse users. This intelligent approach provides significant advantages over traditional keyboards by enhancing typing speed, reducing grammatical errors, and improving communication clarity. The technical implementation focuses on developing a robust and scalable web-based system using modern technologies such as React.js, Next.js, and MongoDB. The system integrates Google Gemini AI for advanced language understanding and uses the LanguageTool API as a fallback mechanism to ensure reliability. It addresses challenges such as real-time processing, context-aware prediction, multilingual input handling, and system performance optimization. Performance evaluation is conducted using metrics such as accuracy, response time, and prediction efficiency. The proposed system demonstrates high accuracy in grammar correction and effective next-word prediction while maintaining low latency suitable for real-time applications. This AI-powered smart keyboard system has the potential to significantly improve typing efficiency, reduce user effort, and enhance overall digital communication experience across various platforms.