Journal of Web Development and Web Designing

Development and Implementation of a Web-Based Leave Management System for Educational Institutions

2026-03-07T06:13:08+00:00

Leave Management System (LMS) is a web-based application that aims to provide an effective and efficient means of managing and automating leave application processes in educational institutions. The system supports leave application, approval, tracking, and management processes for staff members, departmental heads, and administrators. The system overcomes the limitations of the conventional manual processing system, which is time-consuming, paperwork-intensive, prone to errors, and hard to maintain. LMS completely automates the entire process of leave management, from leave application to approval or rejection by authorized personnel, cancellation of approved leaves, and periodic leave crediting as per institutional policies. The system also supports automated email notifications to notify users of leave status updates. Other features include automatic approval options, real-time leave balance tracking, report generation, and centralized database management. The system adopts a three-tier software architecture model to promote scalability, enhanced system performance, and system reliability. The system is developed using web-based technologies like PHP, MySQL, HTML, CSS, and JavaScript. The centralized database provides a secure means of storing data and rapid retrieval of data records. Performance assessment carried out on 150 employees showed significant improvements. The system resulted in an average processing time reduction of 70% and ensured a 90% reduction in manual errors. User satisfaction analysis indicated that 94% of users found the system easy to use, 95% found it convenient, and 98% were satisfied with its efficient and on-time delivery of services.

RashtraRakshak: An AI-Driven Advisory and Mentorship Platform for Farmers, Soldiers, and Citizens

2026-02-18T05:56:34+00:00

Rashtra Rakshak is an AI-powered web-based platform developed to support farmers, soldiers, and citizens through a unified digital system. The platform provides farmers with AI-based crop advisory services and plant identification using a CNN-based image analysis module. Soldiers contribute structured mentorship content by uploading training videos related to survival skills, disaster preparedness, and safety awareness, which are made accessible to citizens for learning and awareness. The proposed platform addresses these challenges by offering role-based dashboards supported by artificial intelligence. Farmers receive AI-based crop advisory and plant identification using a convolutional neural network (CNN) image analysis module. Soldiers contribute mentorship and training videos related to survival skills, disaster preparedness, and safety awareness, which are made accessible to citizens. A multilingual chatbot assists users with agricultural queries, safety guidance, and system navigation in both English and local languages. An emergency SOS module enables location-based alert reporting for enhanced public safety. The system also integrates a multilingual chatbot that assists users with agricultural guidance, safety-related queries, and platform navigation in simple local languages. RashtraRakshak is implemented as a working Minimum Viable Product (MVP) using Django, Python, and web technologies, with a focus on ease of use, accessibility, and real-world applicability. By combining advisory services, visual analysis, mentorship learning, and conversational assistance into a single platform, the project demonstrates how AI can be effectively used for community support without reliance on complex hardware or external sensor systems. RashtraRakshak is implemented as a working Minimum Viable Product (MVP) using Django, Python, and web technologies, with a strong focus on usability, accessibility, and real-world deployment. The platform demonstrates how AI-based advisory systems, computer vision, and digital mentorship can be combined effectively without relying on IoT devices or complex hardware infrastructure.

Detecting Spam Using Machine Learning Techniques on Social Media Platforms

2026-01-30T11:47:25+00:00

The rapid growth of social media platforms like Twitter has revolutionized information sharing but also paved the way for malicious activities such as spam reviews and fraudulent content. Spam on Twitter not only degrades user experience but also threatens the reliability of online opinions and marketing efforts. With an emphasis on detecting false or misleading reviews and promotional content, this review paper thoroughly examines the many machine learning approaches used for spam detection on Twitter. It is examined different learning models and detection frameworks by analyzing aspects such as feature selection (including user behavior, tweet content, hashtags, URLs, and follower-following patterns), data preprocessing, handling of imbalanced datasets, and performance evaluation criteria. The paper also highlights commonly used datasets for Twitter spam detection and identifies ongoing challenges such as evolving spam tactics, scarcity of labeled data, and evasion strategies used by spammers. The study outlines key areas for future research, including the development of real-time and adaptive detection systems, the use of semantic and contextual understanding, and the integration of cross-platform detection mechanisms to enhance spam filtering on social media platforms.

Smart Crop Diagnostics: An AI-Driven Web Platform for Crop Disease Detection and Management

2026-02-20T11:43:54+00:00

Plant diseases greatly affect how much food is produced, the security of food supplies, and the income of farmers, especially in poorer countries where it's hard to get help from experts or use lab facilities. New developments in deep learning and computer vision have made it possible to create systems that can automatically detect plant diseases through images, helping farmers make quick and correct decisions. This paper introduces Smart Crop Diagnostics, an online smart system that helps identify leaf diseases in crops and suggests tailored treatments and ways to prevent them. The system uses Convolutional Neural Networks (CNNs) to classify diseases based on images and combines advanced Application Programming Interfaces (APIs) to improve accuracy and provide better, situation-aware advice. Farmers can upload leaf pictures, get immediate disease diagnoses along with confidence levels, and find organic and chemical solutions based on data. Studies show that the system is fast and dependable, lessens the need for expert help, and helps in farming in a more sustainable way. The framework shows great potential for growing and connecting with environmental and IoT-based data sources, making it a good choice for precision agriculture.

YourMarkNet: A Role-Based Digital Marketplace Platform for Buyer–Seller–Investor Ecosystems

2026-02-11T05:53:11+00:00

Digital marketplaces have become essential platforms for enabling business transactions and collaboration among diverse stakeholders. However, most existing systems primarily focus on buyer–seller interactions, often neglecting the role of investors in early-stage business ecosystems. This paper presents YourMarkNet, a role-based digital marketplace platform designed to integrate buyers, sellers, and investors within a single, unified environment. The proposed system offers distinct dashboards and functionalities for each user role, enabling sellers to showcase products or services, buyers to explore offerings and post requirements, and investors to identify potential business opportunities. The platform is developed using Python (Flask) for backend processing, HTML, CSS, and Jinja2 for frontend rendering, and SQLite/MySQL for data storage. Secure authentication and role-based access control mechanisms are incorporated to ensure data integrity and user privacy. The system architecture emphasizes simplicity, modularity, and ease of deployment, making it suitable for small-scale enterprises and startups. Experimental evaluation and functional testing demonstrate that the platform effectively supports multi-role interaction, improves information visibility, and enhances user experience. The proposed solution highlights how lightweight web technologies can be leveraged to build scalable and inclusive digital business ecosystems.