Journal of Web Development and Web Designing

PMS: A Smart Campus Placement Management System for Engineering Colleges

2026-07-30T12:11:18+00:00

Campus placement is one of the most important transitions in an engineering student’s academic life. In most colleges, this process is still managed manually through spreadsheets, emails, and phone calls, which leads to frequent miscommunication, missed deadlines, and a heavy administrative burden on Training and Placement Officers (TPOs). PMS (Placement Management System) is a full-stack, role-based web application designed to digitize and streamline the entire campus recruitment process on a single, unified platform. The system supports five distinct roles: Student, TPO, Company, College Admin, and Super Admin, each with a dedicated dashboard and role-specific features. Students can browse approved job openings, apply directly, track their application status through multi-round selection pipelines, and receive AI-powered career guidance and resume feedback. TPOs can create and manage placement drives with custom selection rounds, approve company job postings, and monitor college-wide placement analytics. Companies can register, partner with colleges, and post targeted job opportunities. College Admins manage student records, TPO accounts, and academic departments. The AI features, powered by the Anthropic Claude API, deliver personalized career suggestions and intelligent resume review inside the student portal. Evaluation across six weeks with real users confirmed that PMS significantly reduces administrative effort, eliminates communication gaps, and brings full transparency to the placement process.

Verdant: A Design Thinking Approach to Sustainable E-Commerce and Real-Time Carbon Impact Tracking

2026-05-29T09:02:23+00:00

Online shopping has grown enormously over the past two decades, and that growth has come with a real environmental cost. Global e-commerce supply chains, energy-hungry data centers, and wasteful packaging together generate millions of tons of greenhouse gas emissions every year. Most shoppers care about this—but existing platforms give them almost no way to act on that concern. There is no carbon label at checkout, no easy comparison between a sustainable product and its conventional alternative. This paper introduces Verdant, a web application built specifically to close that gap by showing users the real-time environmental impact of what they are buying. The goal is simple: make sustainable shopping the easiest and most natural choice, not a niche one. Verdant was built using the five-stage Design Thinking process: Empathize, Define, Ideate, Prototype, and Test. This approach kept real user needs at the centre of every design decision. Early interviews showed that people want to shop sustainably but find the research exhausting. In response, the design team focused on turning complex emissions data into simple, at-a-glance visuals—like a live carbon meter, eco-badges, and an interactive sustainability dashboard. On the technical side, Verdant is built with semantic HTML5, CSS custom properties for theming, and responsive design patterns to work well on any device. The architecture draws on React-style component thinking and Node.js patterns for fast, efficient data handling. This lets the carbon calculator update in real time as shoppers add or remove items from their cart. Early usability testing showed strong results. Beta users chose low-carbon products 47% more often when the carbon data was visible, and overall eco-awareness scores improved significantly. Users also responded well to the gamified dashboard—seeing their carbon savings add up over time gave them genuine motivation to keep making better choices. These findings suggest that Verdant is more than a useful tool: it is a practical model for what sustainable e-commerce can look like, showing that doing right by the environment and building an engaging shopping experience are not competing goals.

Web Application Security Attacks: A Comprehensive Study on SQL Injection Vulnerabilities

2026-08-03T11:10:22+00:00

Web applications have become an essential component of modern digital infrastructure, supporting a wide range of services, including online communication, financial transactions, e-commerce, education systems, healthcare platforms, government services, and enterprise management solutions. However, the rapid expansion and increasing complexity of web applications have also increased exposure to cybersecurity threats and vulnerabilities. Among the various security risks affecting web applications, SQL Injection (SQLi) remains one of the most critical and widely exploited vulnerabilities. SQL Injection occurs when attackers manipulate backend database queries by injecting malicious SQL statements through vulnerable user input fields, potentially allowing unauthorized access to sensitive information, authentication bypass, data modification, data deletion, and, in severe cases, complete system compromise. This paper presents a comprehensive study of SQL Injection attacks, focusing on their fundamental concepts, major classifications, common exploitation techniques, and potential impacts on web application security. Furthermore, the study examines various approaches for detecting and identifying SQL Injection vulnerabilities, including signature-based methods, static and dynamic analysis, and machine learning-based techniques. The paper also discusses effective prevention and mitigation mechanisms, such as input validation, parameterized queries, prepared statements, secure database configuration, and proper access control. The study emphasizes the importance of secure software development practices and continuous security assessment in reducing SQL Injection risks and strengthening the overall security of modern web applications.

Machine Learning-based Fair Product Exposure and Recommendation System for Online Marketplaces

2026-06-30T12:01:40+00:00

Online marketplaces such as Amazon, Flipkart, and eBay have transformed digital commerce by connecting buyers and sellers globally. However, one major challenge faced in these platforms is the lack of fair exposure among sellers. Popular sellers with high sales history dominate search results, while new or small-scale sellers struggle to gain visibility. This imbalance reduces competition and affects marketplace diversity. This project presents a Fair Market Exposure System (FairCart) for Online Marketplaces that aims to provide equal visibility to all sellers using a data-driven ranking approach. The system analyzes multiple factors such as seller rating, product quality, pricing, and customer reviews instead of relying only on popularity metrics. A fairness-based scoring model is developed to reduce bias and ensure balanced product ranking. The proposed system integrates a ranking algorithm with a dynamic exposure mechanism that adjusts product visibility in real time. It ensures that new sellers are given opportunities while maintaining relevance for users. A web-based dashboard is also developed to monitor seller performance, exposure levels, and ranking decisions. Experimental analysis shows that the system improves fairness in product visibility while maintaining user satisfaction. This solution provides a scalable and practical approach for enhancing transparency and fairness in online marketplaces.