Volume 8 Issue 4
Author's Name: Saurabh S. Hher, Dhainje V. S
Abstract— In India large population is living all over the country and electricity supply demand need for this population makes recruitment of a large transmission and distribution system. Formulation of Transmission tower is proffered in assessment of confronting high voltage transmitting conductors and insulators to stance in need of altitude from ground level. Transmission Line Towers comprise of about 28 to 45 percent of the absolute cost of the Transmission Lines. The purpose of a transmission line tower is to support conductors booming electrical power and one or two ground wires at appropriate distances above the ground level and from each other. The observations from both structural and electrical fields are analysis in designing transmission line towers. There are various factors which influence the stability and performance of transmission tower are viewed in this paper. This Paper discussed and review the Methodology adopted for Analysis and Design of transmission towers. This paper can also be used for study of different parameters used in Analyzing and Designing of transmission towers.
Author's Name: Mangal Kalyani, Gupta Raju
Abstract— In this paper we are merely want to explore digital image processing which is a motivating field because it provides the higher image information for human understanding and process the image for storage, transmission, and illustration for machine perception. Image process could be a technique to reinforce raw pictures received from cameras/sensors placed on satellites, aircrafts or photos taken in traditional everyday life for varied applications. Pre-processing is the initial process applied on images which are at lowest level of abstraction. Also Shape analysis methods play an important role in systems for object recognition, matching, registration, analysis, accurate detection of image, Research in shape analysis has been motivated in part, for fast recognition of image from large data base. Accessing the desired and relevant image from large data base in an efficient manner is another motive for research. Various techniques of shape detection are based on shape boundary or interior.
Author's Name: Raju Patel, Rishil Mayank
Abstract— The use of different types of pavement block has been increased in India a decade ago. The specific requirements of paver block has been increased in the areas of footpaths and parking areas. Port land Cement Concrete and Asphalt Concrete are the most common roadway and highway construction material used. It is very necessary to use the materials which is durable and have the high compressive strength and can absorb the water to greater extent to have the greater water absorption value. In this project we have given greater stress on about the use of materials and machinery involved for the manufacture of different types of paver block in construction Purposes. For design of paver block the tests is to be performed is used to find the compressive strength and water absorption. The blocks having greater compressive strength and of less cost and can be used for much more time have been studied out in this project.
Author's Name: Gaurav Paniwal, Shruti Gupta
Abstract— This work explores the tools and technologies used in smart agriculture. Artificial Intelligence and Machine Learning techniques, including basic block models that are used to do smart agriculture. How can we use fuzzy logic and Artificial Neural Network, is also covered in this paper. We have explored some of the IOT based irrigation systems including crop prediction systems. The necessary hardware, software and sensors that can be used to make precision agriculture are also included. The main motto of this paper is to get a detailed literature review that is required for smart agriculture.
Author's Name: Aaditya Sharma, Aman Sharma, Punit Sharma, Punit Ekka
Abstract— Physically challenged people or the people with disabilities are those section of the society in which the society embarks no decision or gives any judgement about their physical abilities of the working body parts and the other requirements of them according to their physical fitness as well, the present generation has the awareness regarding keeping themselves fit and sportingly active but on the other hand the people who are physically challenged are unable to work on the same. The project hereby tries to implement the solution for the same by giving the various physical exercises that could be done by the physically challenges people according to their working body parts and according to their ability accordingly.
Author's Name: Santhosh, Sai Laxmi
Abstract— Identifying of the plant diseases is essential in prevention of yield and volume losses in agriculture Product. Studies of plant diseases mean studies of visually observable patterns on the plant. Health surveillance and detecting diseases in plants is essential for sustainable development agriculture. It is very difficult to monitor plant diseases manually. It requires a lot of experiences in work, expertise in these field plant diseases and also requires excessive processing time. Therefore; image processing is used to detect plant diseases. Disease detection includes steps such as acquisition, image Pre-processing, image segmentation, feature extraction and Classification. We describe these methods for the detection of plant diseases on the basis of their leaf images; automatic detection of plant disease is done by the image processing and machine learning. The different leaf images of plant disease are collected and feature extracted of the various machine learning methods
Author's Name: Shubham Kumar, Kanchan Bhatt
Abstract— Weather Forecasting is the attempt to predict the weather conditions based on parameters such as temperature, wind, humidity and rainfall. These parameters will be considered for experimental analysis to give the desired results. Data used in this project has been collected from various government institution sites. The algorithm used to predict weather includes Neural Networks(NN), Random Forest, Classification and Regression tree (C &RT), Support Vector Machine, KNN Nearest neighbor. The correlation analysis of the parameters will help in predicting the future values. This web based application we will have its own chat bot where user can directly communicate about their query related to Weather Forecast and can have experience of two-way communication.
Author's Name: P. Kavitha, S. Ramadevi
Abstract— Sentimental analysis is also called "opinion mining" analyses attitudes and classifies text views. It relates to the use of natural language processing, text, and linguistic processing. A huge amount of data is created with the rapid growth of web technologies. Social networking sites are now popular and normal places where feelings can be shared by short messages. These sentiments involve happiness, sadness, anxiety, fear, etc. The analysis of short texts tends to recognize the crowd's sentiment. Sentiment Analysis on IMDb moviereviews describes a reviewer's general feeling or impression of a movie. Since the perceptions of humans improve the effectiveness of products & since a movie'ssuccess or failure depending on its review, costs are rising, and a good sentiment analysis model needs to be developed, that classifies moviereviews. Machine learning methods use ML algorithms to carry out sentiment analysis as a standard classification problem using syntactic and language characteristics. There are some methods of machine learning used for sentiment analysis in this paper. Most of the sentiment analysis is performed using SVM, RF, ANN, and NB, Algorithms of DT, BN, & KNN.
Author's Name: Khaily Mohammed Shah, Sathyam Pandey
Abstract— A human brain responds at a much faster rate to images and the information it contains. An image is considered as proof of past events that have occurred, but in today's world where editing tools are made available so easily tampering of images and hiding the original content has become too mainstream. The identification of these tampered images is very important as images are considered as vital sources of information in crime investigation and in various other fields. The image forgery detection techniques check the credibility of the image. Various research has been carried out in dealing with image forgery and tampering detection techniques, this paper highlights various the type of forgery and how they can be detected using various techniques. The fusion of various algorithms so that a complete reliable type of algorithm can be developed to deal mainly with copy-move and image splicing forgery. The copy-move and image splicing method are main focus of this paper.
Author's Name: Tulshiram Bansod , Monali Shivram
Abstract— With the quick rise in living standards, people's shopping passion grew, and their desire for clothing grew as well. A growing number of people are interested in fashion these days. However, when confronted with a large number of garments, consumers are forced to try them on multiple times, which takes time and energy. As a result of the suggested Fashion Recommendation System, a variety of online fashion businesses and web applications allow buyers to view collages of stylish items that look nice together. Clients and sellers benefit from such recommendations. On the one hand, customers can make smarter shopping decisions and discover new articles of clothes that complement one other. Complex outfit recommendations, on the other hand, assist vendors in selling more products, which has an impact on their business. FashionNet is made up of two parts: a feature network for extracting features and a matching network for calculating compatibility. A deep convolutional network is used to achieve the former. For the latter, a multi-layer completely connected network topology is used. For FashionNet, you must create and compare three different architectures. To achieve individualised recommendations, a two-stage training technique was created.
Author's Name:Dipayan Kumar Ghosh,Tanmay Sharma,Rajeev Khatri,Rajat Sharma,Ramandeep Singh
Abstract— This Personality classification is a field of research that focuses on categorizing individuals based on their behavioral patterns, preferences, and traits. Leveraging advancements in psychology, machine learning, and natural language processing, personality classification enables deeper insights into human behavior. This study explores methodologies for personality classification using various models, including the Big Five personality traits (Openness, Conscientiousness, Extraversion, Agreeableness, and Neuroticism). Data is analyzed from text, speech, or social media interactions to identify patterns that reflect individual personality types. The goal is to improve applications in recruitment, personalized marketing, mental health assessments, and social network analysis. Emphasis is placed on ethical considerations, including data privacy and the potential for bias. Findings demonstrate how integrating computational tools with psychological theories can enhance the accuracy and reliability of personality classification systems. Personality classification involves categorizing individuals based on traits like openness, extraversion, and agreeableness, often using models like the Big Five. By analyzing text, speech, or behavior through machine learning, it supports applications in recruitment, marketing, and mental health. This approach combines psychology and technology, emphasizing accuracy, ethical considerations, and data privacy.
Author's Name: Aditya Swami, shish Kumar
Abstract— By this research, we want to display the key factors of how effective blogging as technology is. We collected information curated by several researchers on the same for understanding their point of view as well. By doing this we found out what makes blogging the best way of sharing information online. We also analyzed the technological aspect of modern-day blogging which includes modern technologies being used, privacy rules and control as well as data analytics. By these approaches, we showed how blogging is not only the best medium for sharing information but can also help in other fields as well like marketing, education, data analysis, community development. With this research, we also wanted to highlight the downsides of using blogging or microblogging.
Author's Name:J. R. Arun Kumar , Aastha Sharma, Chirag Gupta, Karan Chawla, Garvit Kirodiwal
Abstract— This Personality classification is a field of research that focuses on categorizing individuals based on their behavioral patterns, preferences, and traits. Leveraging advancements in psychology, machine learning, and natural language processing, personality classification enables deeper insights into human behavior. This study explores methodologies for personality classification using various models, including the Big Five personality traits (Openness, Conscientiousness, Extraversion, Agreeableness, and Neuroticism). Data is analyzed from text, speech, or social media interactions to identify patterns that reflect individual personality types. The goal is to improve applications in recruitment, personalized marketing, mental health assessments, and social network analysis. Emphasis is placed on ethical considerations, including data privacy and the potential for bias. Findings demonstrate how integrating computational tools with psychological theories can enhance the accuracy and reliability of personality classification systems. Personality classification involves categorizing individuals based on traits like openness, extraversion, and agreeableness, often using models like the Big Five. By analyzing text, speech, or behavior through machine learning, it supports applications in recruitment, marketing, and mental health. This approach combines psychology and technology, emphasizing accuracy, ethical considerations, and data privacy.
Author's Name: Shubham Patil, Saurav Daware, Ameya Bhagat
Abstract— This paper is aimed at developing an Online College Management System that is of importance to the educational institute or college. This system is named College ERP using MERN stack. This system may be used to monitor college students and their various activities. This application is being developed for an engineering college to maintain and facilitate ease of access to information. For this the users must be registered with the system. College ERP is an Internet based application that aims at providing information to all levels of management within an organization. This system is used as an information management system for the college. For a given student and staff (technical and nontechnical) can access the system to either upload and access some information from the database.
Author's Name: Aditi Shedge , Shaily Shah
Abstract— Air pollution has become a major issue in large cities because increasing traffic, industrialization and it becomes more difficult to manage due to its hazardous effects on the human health and many air pollution-triggering factors. This paper puts forth a machine learning approach to evaluate the accuracy and potential of such mobile generated information for prediction of air pollution. Temperature, wind, humidity play a vital role in influencing the pollution dispersion and accumulation, majorly influencing the prediction of pollution levels. Thus, this paper includes the atmospheric condition information registered throughout the study period in order to understand the influence of these factors on air pollution monitoring. Data driven modelling is an efficient way of extracting valuable information from generated data sets, however it is less efficient when the data is incomplete or contains inaccuracies. This modelling approach has true potential for real time operations because it can detect non-linear spatial relationships between sensing units and could aggregate results for regional investigation. Neural networks comparatively showed good capability in air quality prediction than support vector regression.
Author's Name:Arun Kumar, Vanshita Jain, Kritika Kumari, Sarthak Vashishtha, Nikhil Kirodiwal, Shruti,
Abstract— This paper there are number of people who are relocating from their native place to some other place for their jobs. Their daily life balance gets disturbed. No proper access to home like foods, which can lead to their health deterioration. They unwantedly depend on the hotel and restaurant for their survival. So to overcome this problem The Food Station is a perfect platform for getting homely foods. The platform connects tenants, who seek affordable and healthy food options, with housewives and home cooks, who can offer their culinary services from the comfort of their own kitchens. Built using the MERN (MongoDB, Express.js, React.js, Node.js) stack, the website provides a seamless user experience, enabling tenants to browse, order, and rate meals, while housewives can manage their menus, set prices, and track orders. The platform emphasizes community-driven food services, fostering local connections and promoting homebased entrepreneurship. The ML system offers personalized meal recommendations to users based on their preferences, past orders, and ratings, improving customer satisfaction and helping home cooks optimize their offerings.
Author's Name: Rarishma Kaushik, Rahesh Parmar
Abstract— Air pollution and its harm to human health has become a serious problem in many cities around the world. In recent years, research interests in measuring and predicting the quality of air around people has spiked. Since the Internet of things has been widely used in different domains to improve the quality for people by connecting multiple sensors. In this work an IOT based air pollution monitoring with prediction system is proposed. The internet of Things is a action interrelated computing devices that are given unique identifiers and the capability of exchange information over a system without anticipating that human to human or human to machine communication. The deep learning algorithm approach is to evaluate the accuracy for the prediction of air pollution. The main objective of the project is used to predict the air Quality. The large dataset works with LSTM for better air quality prediction. The prediction accuracy of air quality with LSTM, the evaluation indicator Root means square error is chosen to measure performance.
Author's Name:Dipayan Kumar Ghosh, Priyansh Agarwal, Tushar Dixit, Ritik Saini, Rishi Gupta,Vansh Gill
Abstract— This paper This Most people tend to live a long and healthy life, but people are busy in their day-to-day life and it is not possible for everyone to visit doctors for minor symptoms of a disease. Many people do not know about medicines and to visit a doctor and consult for minor symptoms for medicines is a time consuming process. AI and machine learning like emerging technology can help us to create a recommended system that will prescribe medicine and this system can accurately predict a medicine to use. In this paper proposes the medicine recommendation system which will predict disease and medicine according to symptoms entered by patients/users. This paper presents a novel medical recommendation system utilizing deep learning techniques. The system employs a recurrent neural network (RNN) architecture to analyze longitudinal patient data, including medical history, vital signs, and medication records. The RNN model learns complex temporal patterns to predict future health states and recommend proactive interventions. Evaluation on a real-world dataset demonstrates the system's ability to accurately forecast health events and provide timely, personalized recommendations.
Author's Name: Dipayan Kumar Ghosh, Rahul Soni, Shivani, Yana Sharma, Kunal Tiwari, Riya Khandelwal
Abstract— This Students today often struggle with productivity due to the scattered nature of digital tools required for managing study schedules, tracking tasks, taking notes, and summarizing learning content. EduMate is an AI-powered all-in-one study companion designed to address these challenges by integrating essential learning tools into a unified platform. The system includes a Pomodoro Timer for focused study sessions, a Smart To-Do List to manage tasks efficiently, and a Note-Taking application for organized information storage. Additionally, EduMate integrates AI-based features such as a PDF Summarizer, Chat with PDF, and a Quiz Generator to help students interact with study materials in smarter ways. These AI tools accept user-uploaded PDFs or text inputs and provide summarized content or automatically generated quizzes for enhanced understanding. Developed using Python, Flask, and modern web technologies, EduMate creates a seamless and user-friendly environment for students to improve their learning productivity. This journal presents the system architecture, development process, implementation details, and key results from the working prototype of EduMate.
Author's Name: Bahubali A. Kaipale, V.R. Naik, Santosh
Abstract— In centrifugal pump, priming function plays a vital role. Priming means to remove the air from pump and its piping systems & to create vacuum so that pumping liquid will flood inside the pump. When water level is positive means above the center line of pump then priming is not required but in case of negative water level means water level is below the pump center line, priming is required. There are some conventional methods exist like use of foot valve, use of priming pots etc. which requires more time & human efforts during every start of pump which is not possible. To avoid the more initial time & efforts, some self-priming systems & mechanism
Author's Name:Nigamh Kumar Tiwari, Akhilesh Pandey
Abstract— Our economy is extremely relying on agriculture production. One of the recent research topics is recognition, classification and detection of diseases from the leaf image of a plant. Plants are affected by various kinds of diseases, which causes great harm to the agricultural plant production. Today artificial intelligence and machine learning techniques is capable enough to detect and identify the plant disease. One of the biggest advantages of using AI and ML in agriculture is to reduce the hectic work of constant monitoring of crops field spread over hundreds of acres. Machine learning algorithms detect diseases in early stages as soon as they emerge on the plant surface. The aim of this research is to develop a software system that mechanically finds and classify diseases. The techniques like “image acquisition, pre-processing, segmentation, extraction and classification” are used in it. The leaves picture is captured to detect the plant disease. Thus, use of image processing techniques to detect and classify diseases in agricultural application is useful
Author's Name: J.R.Arun Kumar, Mohit Gupta, Paramjeet Singh, Diksha Gupta, Harshita Chauhan, Shivam Gupta
Abstract— This paper presents an AI-powered job portal integrated with a dynamic resume builder and an intelligent job matching system. Built using the MERN stack, the platform leverages NLP-based algorithms to optimize resume creation, streamline candidate-job matching, and enhance the recruitment process. The system benefits both job seekers and recruiters by providing real-time recommendations, ATS-optimized resumes, and actionable analytics, delivering an intuitive, secure, and scalable hiring platform
Author's Name: KUMARA VISHWAKARMA, R.M.SAVITA
Abstract— Social networked sites are not only to speak or interact with peoples globally, but also one ineffectual way for big business encouragement. A this paper, we examine &+ study the cyber threats in community networked web-sites. We submit yourself to the gathering times gone by of online social web-sites, pigeonhole their types & also talk about the computer-generated threats suggest the anti-threats strategies & see in your mind's eye the longerest terms trend of such hoppy well-liked Web-sites. The maxi-mum number of users are not aware of the high risks & share their in sequence unintentionally & their lack of knowledge make them defenseless to cyberattacks. So cyber security is the main apprehension in today’s world of compute.
Author's Name: R.Anusuya, Abhishek Sain
Abstract— Code Wave: Empowering Students to Learn is an interactive e-learning platform designed to enhance programming education by combining structured tutorials, realtime coding practice, and collaborative learning. Addressing the challenges of fragmented resources and lack of hands-on experience, the platform leverages technologies like React.js, Node.js, and MongoDB to provide an intuitive, scalable, and secure environment where students can write, execute, and debug code instantly. With features such as adaptive learning paths, gamification, and AI-powered feedback, Code Wave personalizes education for diverse skill levels while fostering peer interaction through community-driven projects. By bridging the gap between theory and practical application, the platform equips learners with industry relevant skills, preparing them for the evolving demands of the tech world. Future expansions include mobile accessibility and career-focused modules to further democratize coding education.
Author's Name: Shavana, Sameer Fathima,
Abstract— The primary aim of the system is to implement a low cost and secure Android based Classroom Automation System using Natural Language Processing (NLP). The system is voice based as we use voice to ON/OFF appliances of classroom. The system can be made secure by sharing the android application only accessible to authorized user. When the user sends a voice command to the mobile device, which interprets the message and sends that proper command to the specific appliances. The mobile device acts as a central console as it determines what operation must be completed by which appliance to full the user’s request. The Raspberry pi will be used as a interface for the appliances connected to the relay board and programmed in a manner that they respond to mobile inputs.
Author's Name: Dipayan Kumar Ghosh, Khushi Jangid, Paridhi Vijay, Anjali Sharma
Abstract— This In an era of overwhelming digital content, choosing the right movie has become increasingly difficult for users. Traditional recommendation systems, primarily based on collaborative filtering or content-based filtering, often fail to capture the emotional nuance and real-time trends reflected in user-generated reviews. This project proposes a Sentiment AnalysisBased Movie Recommendation System that enhances the accuracy and personalization of movie suggestions by analysing public sentiment from user reviews. The system leverages Natural Language Processing (NLP) techniques to process and classify user sentiments into positive, negative, or neutral categories. Using a dataset containing movie details and corresponding user reviews, the system performs text pre-processing (tokenization, stop word removal, stemming/lemmatization) followed by sentiment scoring using machine learning or deep learning models like Logistic Regression, Naive Bayes, or LSTM-based neural networks. Once sentiment scores are computed, movies are ranked and recommended to users based on aggregated sentiment analysis and personal preferences such as genre, language, and release year. This hybrid approach allows the recommendation system to not only understand what users like based on ratings but also why they like it, offering a more human-centered and emotion-aware movie discovery experience. The final product is a web-based application with a clean and intuitive user interface, allowing users to input preferences, browse movie suggestions, and see why a movie is recommended through sentiment-based visual insights. The system demonstrates how integrating sentiment analysis with recommender systems can significantly improve user satisfaction, engagement, and trust
Author's Name: R.Anusuya, Tathya Dixit, Shubham Jangid, Sarthak Saxena, Kartik Bharti,
Abstract— This The Student Management System is a web-based application built with Django that streamlines the management of student records, grades, and academic performance. Designed primarily for teachers and administrators, this system allows educators to efficiently monitor students' marks, track their progress, and manage student data. Teachers can input, update, and review individual student scores across multiple subjects, giving them a comprehensive view of each student's academic performance. The SMS will be developed using a combination of appropriate technologies, including a robust database system, a user-friendly web interface, and a secure backend infrastructure. The system will be designed with scalability and maintainability in mind to accommodate future growth and evolving requirements. By implementing this SMS, the educational institution will be able to significantly improve its administrative efficiency, reduce paperwork, enhance data accuracy, and provide a better experience for both students and staff. In the era of digital transformation, managing educational institutions through traditional methods has become increasingly inefficient and prone to human error. This journal presents the design, development, and implementation of a Student Management System (SMS) — a comprehensive web-based application tailored for educational institutions. Built using Django and modern web technologies, the system addresses key administrative challenges, streamlines operations, and enhances communication among stakeholders including students, faculty, and administrators
Author's Name: Saumya Agarwal, Sandeep Kumar, Vimal Kumar
Abstract— Water pollution is one of the biggest threats for the green globalization. Water pollution affects human health by causing waterborne diseases. To prevent the water pollution, necessary steps are to be taken. First step is to estimate the water parameters like pH, turbidity, conductivity etc., as the variations in the values of these parameters point towards the presence of pollutants. In the present scenario, water parameters are detected by chemical tester laboratory tests, where the testing equipment are stationary and samples are provided to testing equipment. Thus, it is a manual system with a tedious process and is very time consuming. In order to minimize the time and to make the system automated, the testing equipment can be placed in the river water and detection of pollution can be made remotely. To ensure the safe supply of drinking water, the quality should be monitored in real time for that purpose Arduino based water quality monitoring has been proposed. In this report, the design of an Arduino based water quality monitoring system that monitors the quality of water in real time is presented.
Author's Name: : R.Anusuya, Arnav Sharma, Anshita Saini, Aman Yadav, Harendra Saini, Prince Baghel
Abstract— This Our project involves developing an AI-based web application using React and MongoDB, leveraging Machine Learning and Deep Learning technologies to predict pulley failures and locate cracks on conveyor belts in industrial settings. This solution addresses the issue of unpredictable poly pulley failures in cable belt conveyor systems by integrating predictive maintenance sensors and quick change pulley mechanisms. Users interact with the system by uploading audio, video, or image data, enabling the application to analyze features like cracks and corrosion using Convolutional Neural Networks (CNNs) with Auto Encoders. The application predicts the operational status and health of pulleys, utilizing a dataset comprising over 7,000 entries related to past pulley failures, material types, and standards. With its focus on minimizing downtime, enhancing safety, and improving reliability, the system is designed to cater to professionals and organizations managing conveyor systems. The project has reached an advanced Technology Readiness Level (TRL), with its core functionalities, user interface, and database integration fully operational, ensuring readiness for real-world deployment. Secured by Intellectual Property (IP), the solution represents a unique approach to mitigating unpredictable pulley failures, providing a robust and scalable predictive maintenance tool for industrial applications.
Author's Name: J.RArun Kumar, Khushi Sharma, Shirsty Singhal, Sameer Kumar, Vinod Kumar Yadav, Devankit Sahu,
Abstract— This Next-word prediction is a pivotal task in natural language processing (NLP) with applications in predictive text, conversational agents, and intelligent writing systems. This project leverages deep learning techniques to develop a next-word prediction model that generates contextually relevant suggestions based on user input. Using a Long Short-Term Memory (LSTM) network, the model captures sequential dependencies and context from textual data, enabling accurate predictions in diverse linguistic settings. The dataset is preprocessed to include tokenization, padding, and vocabulary creation to prepare it for training. The LSTM based architecture is designed to learn and generalize patterns in the data, optimizing for loss functions suited to sequence prediction. User interaction is incorporated through a dynamic interface, enabling real-time input and response generation. This project demonstrates the integration of deep learning with NLP for enhanced user experiences, achieving robust and context-aware predictive capabilities, and opens avenues for further enhancements in language modeling tasks.
Author's Name: Mohit Sharma, Ankit Kumar Singh, Aakash Garg, Himanshu Gupta, Mantuja Khan, Piyush Saini, Ashwani Yadav
Abstract— This In the age of digital education, students require a centralized platform that seamlessly integrates learning materials, quizzes, and previous year questions (PYQs) with realtime access and personalized feedback. "Knowledge Hub" addresses this need by offering an interactive, web-based educational ecosystem. The platform leverages React.js for the frontend and Firebase for cloud-based storage, authentication, and real-time updates. Knowledge Hub supports adaptive quizzes, note-sharing, and progress tracking while enabling educators to upload content dynamically. This paper explores the system design, features, and potential of Knowledge Hub as a modern solution for organized and efficient learning.
Author's Name: R.Anusuya, Himanshu Kumar, Khushi Jain, Diya Jain, Aanya Jain, Divyanshi Singh
Abstract— This A Library Management System (LMS) is a software application designed to automate and streamline the day-to-day operations of a library. This system facilitates efficient management of books, users, borrowing and returning transactions, and cataloging. The primary objective of an LMS is to enhance user experience while reducing the manual workload librarians and staff. Key features include book tracking, member registration, due date reminders inventory control, and report generation. By integrating database management and user-friendly interfaces, the system ensures accurate recordkeeping, faster information retrieval, and improved access to library resources. The implementation of an LMS significantly contributes to the digital transformation of traditional libraries, making them more adaptive to modern educational and research needs. A Library Management System (LMS) is a software application designed to automate and streamline the operations of a library. This system provides a centralized platform for managing books, patrons, lending transactions, and administrative tasks efficiently. Key functionalities include book cataloging, user registration, book issuing and returning, fine calculation, and real-time inventory tracking. By digitizing traditional library processes, the LMS reduces manual effort, minimizes errors, enhances accessibility, and improves user experience The system can be implemented using various technologies and is adaptable for schools universities, and public libraries, aiming to promote reading habits and knowledge sharing through improved resource management.
Author's Name: Piyush Bhargava*, Srijan Yadav, K Vedamurthy
Abstract— Heart disease and diabetes are two of the main sources of death everywhere throughout the world for a rs. There have been a few machine learning methods utilized for the conclusion of heart disease and diaeviously. Neural Network, Logistic Regression Naïve Bayes etcetera are a portion of a couple of machine ng strategies utilized in the analysis of these diseases giving some measure of achievement. We explore ous algorithms, for example, Neural Networks, K - Nearest Neighbours, Naive Bayes, Decision tree algoSupport vector classifiers and Logistic Regression alongside cross breed procedures including the aboveed algorithms for the finding of heart disease and diabetes. The framework hasi been implementedi in Python mi and prepared to utilize benchmari k dataset from the UCIi machine learningi repository. The framework is e perhaps expandable for the new datasets.
Author's Name: Saumya Agarwal, Sandeep Kumar, Vimal Kumar
Abstract— Water pollution is one of the biggest threats for the green globalization. Water pollution affects human health by causing waterborne diseases. To prevent the water pollution, necessary steps are to be taken. First step is to estimate the water parameters like pH, turbidity, conductivity etc., as the variations in the values of these parameters point towards the presence of pollutants. In the present scenario, water parameters are detected by chemical tester laboratory tests, where the testing equipment are stationary and samples are provided to testing equipment. Thus, it is a manual system with a tedious process and is very time consuming. In order to minimize the time and to make the system automated, the testing equipment can be placed in the river water and detection of pollution can be made remotely. To ensure the safe supply of drinking water, the quality should be monitored in real time for that purpose Arduino based water quality monitoring has been proposed. In this report, the design of an Arduino based water quality monitoring system that monitors the quality of water in real time is presented.
Author's Name: Neeraj Jain, Ravi Kumar, Rohan Jonwal, Vishal, Punit Choudhary, Neeraj Saini
Abstract— This Communication is an essential element of human life, but for individuals with hearing impairments, everyday interactions can present significant challenges due to the absence of accessible, real-time translation tools. Although technologies such as speech-to-text converters and closed captioning systems have improved accessibility to some extent, they often fall short when it comes to catering to users who primarily rely on sign language for communication. This paper presents the design and development of the English-Audio-Speech-to-Sign-Language-Converter, an intelligent system that enables real-time translation of spoken or typed input into animated Indian Sign Language (ISL) gestures. The system aims to bridge the communication divide by functioning as a digital interpreter capable of translating both audio and text inputs into visual sign animations that are clear, accurate, and contextually meaningful. The project incorporates a multi-layered methodology involving the integration of Google Speech-to-Text API for accurate speech recognition, Natural Language Processing (NLP) techniques for textual analysis, and a locally stored animation database for rendering sign gestures. It features a user-friendly interface that supports both speech and text input, providing greater accessibility and usability across different scenarios. The modular architecture allows the system to process input in real time, analyze sentence structure, extract relevant keywords, and map them to corresponding animations, which are then rendered seamlessly through the frontend. This system not only demonstrates the practical application of AI and NLP in the field of accessibility technology but also lays the groundwork for future enhancements such as multilingual support, mobile platform integration, and dynamic sign gesture generation. Through this work, the project aims to promote digital inclusivity and empower hearing-impaired individuals by offering a tool that is both innovative and impactful. The proposed system, though still in development for broader application, presents a promising step forward in making technology more inclusive and accessible for all segments of society.
Author's Name: Neeraj Jain, Saurabh Kumar, Shubhendra Singh Rathore, Vinay Saini, Ankit Yadav
Abstract— This paper presents a real-time object detection system using YOLOv8, a state-of-the-art deep learning model. The system achieves high speed and accuracy, enabling it to detect and classify multiple objects simultaneously in dynamic environments like surveillance, traffic management, and autonomous systems. YOLO's unified approach to object detection makes it significantly faster than traditional algorithms by processing the entire image in one pass.
Author's Name: Mohit Sharma, Tarun Kataria, Nitin
Abstract— This In the era of remote work and virtual hiring, technical interviews require platforms that combine real-time coding collaboration with face-to-face communication. This paper introduces an Online Coding Environment designed to address this gap. The platform enables multiple users — including interviewers and candidates — to collaboratively write and execute code in real-time, while communicating through integrated video calls. It supports multiple programming languages (JavaScript, Java, Python, and C) and uses modern web technologies like React.js, Node.js, Socket.IO, WebRTC, and MongoDB. The system enhances the assessment process by unifying communication and problem-solving in one seamless environment.
Author's Name: Hussien Razz, Kaniyashu, sultan bairu, Ramnath sharma
Abstract— This process requires large storage space and extensive manual effort. The conventional technique of visual analysis to inspect the ECG signals by doctors or physicians are not effective and time consuming. Therefore, an automatic system which involves digital signal integration and analysis is required. In this study a MATLAB-based tool is being designed to convert electrocardiography (ECG) information from paper charts into digital ECG signals. Here we develop a method that involves processing of ECG paper records by an efficient and iterative set of digital image processing techniques for the conversion of ECG paper image data to time series digitized signal form, resulting in convenient storage and retrieval of ECG information. In addition, this tool can be used to potentially integrate digitized ECG information with digital ECG analysis programs and with the patient’s electronic medical record
Author's Name: J.R.Arun Kumar, Nancy Sharma, Naman Jain, Sachin Kr. Garg
Abstract— This project, titled "Hospital Management System (HMS)", is designed to enhance healthcare delivery by providing dedicated panels for administrators, doctors, and patients. The Admin Panel manages users, doctors, enquiries, and ambulance services. The Doctor Panel helps healthcare providers manage their profiles, appointments, and consultations. The User Panel allows patients to book appointments, request ambulance services, and access a deep learning-based disease prediction tool. The system also includes a visual component that shows how various health parameters impact disease prediction using easy-to-understand graphs. Built using the MERN stack and Python, the HMS integrates machine learning for predictive analytics and offers an efficient, data-driven approach to healthcare management.
