Research and Applications of Web Development and Design
This is blind peer-reviewed journal that provides a platform which aspires to cover all the aspects of Web Development and Design. Focus and Scope Integration with Legacy Systems, Migration of Legacy Systems to Web Environments Managing System Evolution/Maintenance; Performance Monitoring, Evaluation Metrics Internet of Things Development and Business Models for Internet of Things-based Applications Service Oriented Computing and Privacy Protection/Security issues in the Internet of Things Mobile Web Services, Service-Based Approaches, E-Commerce and E-Business Web Engineering for Wireless Platforms User Interface Design Issues, Customisation/Personalisation Web Standards, Usability, Universal Accessibility, Semantic Web Big Data and Knowledge Management/Innovation Big Data Theoretical Development and Business Models of Big Data Applications Social Networks and Network Security
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A Research Paper on Crowdsourced Civic Issue Reporting and Resolution System
Reporting road and citizen issues was no longer an easy process for citizens. Reporting problems based on road damage, street cleaning, dents, trash can overflow, street light post damage, or anything under the supervision of a municipality requires long steps and procedures. This is still not a given. The appropriate authority will handle complaints that are reported. If a thousand complaints are filed per day, then because the company is too big to handle minor complaints from a single person, the majority of problems go ignored, unanswered, and unresolved. In order to address this complaint, we will develop a smart Android application that residents can use to notify the appropriate authorities about infrastructure problems in their city. Therefore, with this online Android platform, users may communicate, discuss, and address issues with relevant authorities anytime they come across urban infrastructure, transit, environmental cleanliness, or disturbances in their everyday life. The mobile application is used to register complaints. Higher severity concerns are addressed first because a hybrid CNN-RNN image processing technique and the SVM-NLP model identify the severity of registered proble ms. The problem zone can be precisely located using the GPS (Global Positioning System) sensor on smart mobile devices, and the problem area can be captured on camera as visual proof. The system then generates a form containing all the data and location and visual evidence entered by the user and sends it to a central server. The central server notifies the relevant agencies.
VisualSeek AI is an intelligent fashion discovery platform that helps users find similar clothing items and personalized recommendations from large product catalogs. It combines computer vision, metadata understanding, and interactive search to translate broad style intent into precise matches. Users can upload an image or browse categories and quickly receive suggestions aligned with color, texture, shape, and use context. Unlike traditional keyword search, the system supports exploratory shopping behavior and improves relevance for visually driven decisions. Its modular architecture, practical deployment design, and user friendly interface make VisualSeek AI suitable for both experimentation and production scale retail recommendation experiences worldwide. At the core of VisualSeek AI is a multimodal embedding pipeline that maps products and user queries into a shared semantic space for high quality similarity retrieval. Deep visual encoders capture subtle style cues, while structured attributes such as category, gender, and occasion enrich ranking logic. Embeddings are stored in a vector database to enable efficient nearest neighbor search at scale. The platform also includes a fashion knowledge graph to model relationships among products, materials, and style concepts, enabling explainable recommendations. This hybrid approach balances content based matching with contextual intelligence and performs even when history is sparse or unavailable VisualSeek AI is built for usability, transparency, and continuous extension as product needs evolve over time. The web application supports authentication, image upload, recommendation presentation, and interactive exploration of fashion relationships through a knowledge graph view. This visual layer helps users and developers understand why specific items are suggested, strengthening trust in model behavior. The codebase separates model components, recommendation services, and frontend assets, which simplifies maintenance, testing, and future upgrades. Overall, VisualSeek AI demonstrates how modern AI can make fashion search more engaging, explainable, and decision oriented, creating better discovery experiences for shoppers and intelligence tools for commerce teams.
This project delivers a unified, secure Mern- based Smart Finance Management System that consolidates income, expenses, savings, and investments into one intuitive web application. Organizes data by customizable categories. Interactive dashboards (pie, bar, and line charts) transform raw records into clear insights, helping users spot spending patterns and understand cash flow at a glance. Beyond tracking, the system enables monthly and category budgets with instant overspend alerts, recurring transactions for routine entries (salary, rent, EMIs), and savings goals with visual progress. Investment tracking provides a consolidated view of portfolios, while AI-driven spend prediction forecasts upcoming expenses and highlights the optimization opportunities. Powerful search, filters, and exports to PDF/CSV/Excel support audits, reporting, and tax preparation. Security and collaboration are first-class concerns: authentication, encryption, a protect sensitive data, while role-based access and audit trails ensure accountability. A responsive UI/UX supports mobile and desktop use, and modular APIs make the system extensible for future capabilities (e.g., bank integrations or advanced analytics). Overall, the application promotes financial literacy, reduces missed payments through reminders, and empowers users to make informed, long-term decisions.
AI-Powered Github Assistant Saas Platform
A web-based AI SaaS application for GitHub repository analysis provides an efficient solution for developers to understand complex codebases quickly and effectively. This system allows users to securely log in and access a personalized dashboard where repositories are analyzed using advanced AI techniques to generate code explanations and project summaries. The primary objective of this application is to minimize manual code analysis, reduce development time, and improve overall productivity. Users can interact with the system by asking questions related to files, functions, or project structure and receive accurate, context-aware responses. Additionally, the application supports subscription-based access for premium features, secure authentication, and scalable deployment. This approach enhances collaboration, ensures efficient data handling, and provides a seamless experience for developers while simplifying project understanding and improving decision-making processes.
Web -Based Leave Approval System With AI-Assistance
Managing leave requests in organizations and educational institutions is often a time-consuming process due to manual handling and lack of structured systems. Traditional approaches rely on paper-based applications or informal communication methods, which can result in delays, miscommunication, and lack of transparency. This project presents a Web-Based Leave Approval System with AI-Based Email Assistance, developed using Flask, HTML, and CSS. The system allows users to apply for leave through an online portal, while administrators or staff can review, approve, or reject requests efficiently. A key feature of this system is the integration of an AI-based email generation module, which helps users create professional leave request emails automatically based on their input. The system evaluates factors such as leave type, duration, and reason, ensuring proper validation and structured processing. By automating the leave workflow, the system reduces manual effort, improves response time, and enhances communication. It is scalable, user-friendly, and suitable for real-time applications.
Academia is a web-based Student Progress and Portfolio Management System designed to centralize and organize academic data efficiently. It replaces traditional record-keeping methods such as spreadsheets and manual documentation. The system enables students to upload and manage their academic achievements, including projects, certifications, and course completions, ensuring structured storage and data consistency. Faculty members are provided authorized access to view and analyze student profiles in real time. Integrated search and filtering features simplify data retrieval and support effective academic evaluation and decision-making. The backend is designed to handle dynamic queries for efficient data processing, while secure authentication ensures data protection. The platform reduces redundancy, minimizes data loss risks, and offers a user-friendly interface for both students and faculty. It promotes transparency through continuous updates and supports scalability for institutional use. Overall, Academia enhances efficiency, accessibility, and accuracy in academic management, contributing to improved institutional productivity.
This paper presents a comprehensive and scalable Student Career Enhancement Analyzer that focuses on transforming traditional academic evaluation into a data-driven intelligent system. Unlike conventional systems that merely store marks and attendance, the proposed system performs deep analysis on multiple dimensions such as academic performance, skill sets, interests, and behavioral trends. The system integrates multiple modules including data collection, preprocessing, statistical analysis, and recommendation generation. By applying structured analytical methods, it identifies patterns that are not visible in traditional systems. These insights help in understanding student potential more effectively. The system provides personalized recommendations that include skill enhancement strategies, subject focus areas, and suitable career paths. This approach ensures that students receive guidance tailored to their unique profile, thereby improving their confidence and decision-making ability. Overall, the system bridges the gap between academic learning and industry requirements, enhances employability, and supports institutions in delivering better educational outcomes.