In modern organizational and academic environments, effective task management and role-based coordination are essential for improving productivity and accountability. This paper presents the design and implementation of Crewmate, a webbased task management system developed using React.js and Vite. The proposed system provides a structured platform that enables administrators to create, assign, and monitor tasks, while employees can view and update task statuses through a userfriendly interface. The application follows a modular and component-based architecture, ensuring maintainability, scalability, and efficient state management. Vite is utilized as the build tool to enhance development speed and optimize application performance. The system demonstrates key functionalities such as role-based dashboards, task categorization, and real-time interface updates, making it suitable for small organizations and academic use. The results indicate that the proposed solution offers a lightweight, responsive, and effective approach to task management using modern frontend web technologies.

This paper presents the design and implementation of a web-based Hotel Booking System. The system provides a comprehensive solution for both hotel administrators and cus-tomers, enabling efficient room management, booking operations, payment processing, and administrative oversight. The system is developed using modern web technologies including React for the frontend, Spring Boot for the backend, and MySQL for data storage. The application implements a role-based access control mechanism, distinguishing between administrative and customer functionalities. Administrators can manage room inventory, view booking statistics, process payments, and generate reports. Customers can search for available rooms, make reservations, process payments, and manage their bookings. The system incorporates various modules including authenti-cation, room management, availability search, booking management, payment gateway simulation, invoice generation, refund processing, and coupon management. The implementation follows RESTful API architecture principles, ensuring scalability and maintainability. The project demonstrates the application of software en-gineering principles in developing a real-world solution. The system has been tested for functionality, usability, and reliability, meeting the specified requirements. The implementation provides a foundation for future enhancements such as real payment gateway integration, advanced analytics, and mobile application support.

Agriculture is a key contributor to economic growth and food security, yet farmers continue to face problems such as climate uncertainty, crop diseases, inefficient irrigation, and limited access to expert advice. Recent developments in Artificial Intelligence (AI) and web technologies make it possible to support farmers through intelligent digital platforms. This review paper presents SmartAgro, an AI‑based web application that provides crop recommendations, disease identification support, irrigation guidance, and weather‑based advisories. The proposed system focuses on a low‑cost, website‑only approach that avoids the use of expensive hardware. SmartAgro aims to enhance productivity, sustainability, and ease of access for farmers, particularly small and marginal farmers.

Abstract – Traditional study methods often struggle to maintain student engagement, particularly in an era characterized by digital distractions and reduced attention spans. This paper presents LearnIsle, a web-based AI-powered personalized learning platform designed to transform uploaded study materials, such as PDF documents, into structured and interactive learning resources including summarized notes, flashcards, podcast-style audio explanations, and AI-generated mini-games. The platform operates through a dual-environment model consisting of a Learning World and an Arcade World. In the Learning World, users engage in document reading, audio-based learning, and active recall activities, which generate limited “lives” based on sustained study effort. These lives are then utilized in the Arcade World, where learners reinforce their understanding through AI-driven educational mini-games such as multiple-choice challenges, memory matching, speed recall exercises, and fill-in-the-blank tasks. LearnIsle integrates intelligent content extraction, automated question generation, experience points, achievement-based progress tracking, and visual feedback mechanisms to promote consistent study habits. Developed using scalable modern web technologies, the system ensures accessibility, reliability, and cross-device compatibility. By combining structured learning with controlled, personalized and guided mascot-based support, LearnIsle enhances motivation, strengthens knowledge retention, and delivers a more engaging and effective study experience. Keywords - personalized learning, educational technology, intelligent document processing, interactive learning systems, adaptive study platforms, web-based applications.

Algorithm and data structure visualization tools are vital educational resources bridging abstract theory and practical understanding. These foundational yet challenging topics remain difficult to learn due to their dynamic and mathematically intensive nature. This literature review systematically examines ten recent research papers (2008–2025) on interactive visualization systems for algorithmic education, evaluating their visualization techniques, interaction methods, technological implementations, and pedagogical frameworks. Key findings reveal a fragmented field where systems effectively visualize basic algorithms like sorting and searching but offer limited coverage of complex data structures and advanced topics. While contemporary tools use modern web technologies (React, GSAP, D3.js), they emphasize technical features over pedagogical value. Significant gaps remain in adaptive learning support, rigorous educational evaluation, mobile accessibility, and personalized pathways, with most tools operating as standalone demonstrations rather than integrated learning environments. The review highlights the need for holistic, pedagogically informed, and technologically robust visualization systems. Future tools should integrate comprehensive algorithmic coverage with adaptive features, multimodal interaction, and evidence-based instructional design to transcend simple demonstration and foster deeper learner engagement, conceptual application, and personalized educational journeys.

Abstract The rapid growth of digital technologies has significantly transformed traditional business operations, especially in the retail sector. Small and medium-scale retail shops often rely on manual or semi-automated methods for inventory management and billing, which leads to inefficiency, data inconsistency, operational delays, and financial losses. Maintaining accurate stock records, preparing bills manually, and generating reports using traditional methods are time-consuming and highly prone to human errors. This paper presents the design and development of a Smart Inventory and Billing System for Shops using modern web technologies. The proposed system is developed using HTML, CSS, Bootstrap, and JavaScript for the frontend interface, PHP for backend processing, and MySQL as the database management system. The system provides an integrated and centralized platform for managing product information, monitoring stock levels in real time, generating bills automatically, and maintaining secure transaction records. The application enables administrators to efficiently control inventory operations, track product movement, and analyze sales performance through detailed reports. A low-stock alert mechanism is incorporated to support proactive inventory planning and timely restocking of products. Automated billing ensures accurate price calculation, reduces customer waiting time, and improves service quality. In addition, data security and user authentication mechanisms are implemented to protect sensitive business information. The system reduces human intervention, minimizes operational errors, and enhances overall business productivity. Experimental evaluation and practical implementation demonstrate that the developed application is reliable, scalable, user-friendly, and cost- effective. The proposed system is suitable for small and medium-scale retail shops and also serves as an effective academic project model for students in computer applications and information technology. Keywords Inventory Management, Billing System, Web Application, PHP, MySQL, Automation, Retail Management, Database System

Digital transformation and digital humanism are reshaping knowledge and interactions across diverse fields and industries. To adapt and fit within the modern digital economy, professional patterns and career pathways require critical and vital capabilities to herald new opportunities and prospects. In light of these dynamics, the research explores the implications of applied artificial intelligence and realities to bridge career prospects in the field of information science, while focusing on digital transformations to foster the development of novel applications. It evaluates essential emerging programs and applications to bridge and advance career projects in the field through the lens of digital transformation; demonstrates how to facilitate effective integration and adoption of applied artificial intelligence technologies and applications; explores factors that hinder these emerging trends and dynamic changes; and formulates a strategic framework to leverage applied artificial intelligence and digital transformation for future opportunities. Research applied content analysis and knowledge from diverse electronic journals, books, online databases, the Internet and the World Wide Web. Research publications and articles were identified and searched using a statistical approach of preferred reporting items for systematic reviews and meta-analyses (PRISMA) strategy and scoping review methodologies. A mixed method research design incorporating quantitative and qualitative approaches was applied to collect and analyze, with concurrent and sequential triangulation used to enhance the validity of the findings. First insights indicate that integration of emerging programs, such as artificial intelligence, machine learning, deep learning, generative artificial intelligence and large language models – reflects transformative technical expertise and strategic innovation in information science, which positions digital transformation as the defining framework to foster interdisciplinary competencies, enhance employability and advance sustainable technological and socio-economic development in the global knowledge economy. Second insights demonstrate that adoption of applied artificial intelligence depends on technological, human and ethical pillars, with digital infrastructure and cloud readiness emerging as the most influential. Third insights highlight multiple interrelated factors that hinder these emerging trends and dynamic changes - inadequate preparedness and training, limited institutional support and resources, resistance to change and lack of awareness of practical AI tools. Fourth insights determine a strategic framework to leverage artificial intelligence and digital transformation for future opportunities from curriculum coordination to technical instruction, as well as AI mentorship and leadership to effectively enhance market competitions and industrial portfolios.

Abstract The increasing demand for online retail services has accelerated the need for scalable, responsive, and secure e-commerce platforms. This paper presents Bloomora, a full-stack flower e-commerce web application developed to streamline the process of browsing, purchasing, and delivering floral products through a digital platform. The system leverages modern web technologies to ensure high performance, reliability, and an enhanced user experience. The front-end is built using React with TypeScript to provide a dynamic, component-based architecture with improved maintainability and type safety. The back-end is implemented using the Django framework to manage RESTful APIs, authentication, and server-side logic. MongoDB is employed as a NoSQL database to efficiently handle flexible and scalable product, user, and order data. Bloomora incorporates key functionalities including user registration and authentication, product catalog management, shopping cart operations, secure payment integration, and order tracking. The modular architecture supports scalability and future feature expansion while ensuring data security and faster response times. Experimental evaluation shows improved performance and usability compared to traditional monolithic systems. The proposed solution demonstrates the effectiveness of integrating modern full-stack technologies to build a robust and scalable e-commerce platform.

PSLite Online is a modern, web-based application designed to facilitate the analysis of top-down mass spectrometry fragmentation data, particularly for targeted proteoform studies. As a successor to the Windows-only ProSight Lite, PSLite Online retains and expands upon its core functionalities while offering platform independence and enhanced usability through a responsive, browser-based interface. Users can load or manually define proteoforms, apply a wide range of modifications, and visualize fragmentation coverage in real time. Key new features include ProForma integration, correction for common mass deconvolution errors, SVG export, and cloud-based session sharing via persistent URLs. The application leverages modern web technologies and component libraries to deliver a lightweight, installable progressive web app (PWA) experience. Here, we show that PSLite Online can be used for the characterization of antibody subunits with glycan modifications. This solution broadens accessibility for proteomics researchers by eliminating installation barriers and enabling seamless collaboration across devices and platforms. PSLite Online is freely available at https://pslite.proteinaceous.net.

Abstract* Background School attendance monitoring is an essential component of educational management in public schools, especially at secondary schools. In Peruvian rural regions such as Imperial-Cañete, attendance is registered manually, generating inefficiencies, human error, and lack of immediate traceability. This situation is amplified by digital divide: only 18.5% of rural houses have stable internet access, limiting the implementation platforms that depend on constant connectivity. Methods The study was realized using agile methodology (Scrum) with five phases to develop a web-based attendance system. Phase one involved analyzing requirements for the attendance system based on needs of school employees and parents. Phase two involved prioritizing requirements and defining tasks with deadlines. Implementation used modern web technologies (Next.js, Node.js, WebSockets) and hybrid data architecture (PostgreSQL, MongoDB, Redis, Google Drive) to ensure efficient and reliable operation. Finally, after completing previous phases, developed software was implemented and additional testing to ensure correct functionality. Results After implementation of the web platform, we identified significant improvements: complete automation of attendance registration using QR codes, which drastically reduced registration times and errors associated with manual methods; successful implementation of real-time parental monitoring, allowing parents access to their children’s daily and monthly attendance registers; integration of automatic email notifications to tutors and administrators in cases of consecutive absences or tardiness; and differentiated role-based access for school employees (assistants, teachers, and directors). The use of information technology ensured operational consistency even under poor connectivity conditions through local caching (IndexedDB) and synchronization. Conclusions This research focuses specifically on optimizing attendance management in rural secondary schools using information technology. The study is characterized by its personalized web platform designed to support the specific needs of educational institutions with limited connections. The results demonstrated the efficiency of this robust approach and its positive impact on school attendance management.

The Personal Expense Tracker with Currency Converter is a web-based application designed to assist users in efficiently managing and monitoring their daily financial activities. The system enables users to record, categorize, and analyse income and expenses through a simple and user-friendly interface while maintaining a digital log of all transactions. It provides analytical insights using summaries and visual representations to help users understand their spending behaviour. A significant feature of the proposed system is the integrated currency converter, which allows real-time conversion of expenses into multiple currencies using updated exchange rates, making it particularly useful for users handling international transactions or travel-related expenses. The primary objective of this project is to enhance personal financial management, promote savings awareness, and simplify expense tracking through automation. The application is implemented using web technologies such as HTML, CSS, JavaScript, and a backend language like Python for data handling and processing, thereby offering an efficient and practical solution for modern personal finance management.

ABSTRACT:In recent years, unpredictable climate variations and irregular rainfall patterns have created significant challenges in agriculture, water resource management, and disaster preparedness. Traditional rainfall forecasting methods often rely on limited statistical models and manual interpretation, which may lead to inaccurate predictions at local levels. To address this issue, we propose a Machine Learning Based Rainfall Prediction System that integrates historical weather data analysis with a web-based intelligent prediction platform. The system analyzes atmospheric parameters such as temperature, humidity, wind speed, and pressure using a Logistic Regression model trained on historical weather datasets. The model classifies rainfall occurrence as “Rain” or “No Rain” for selected Indian states and districts. A user-friendly web interface built using HTML, CSS, JavaScript, and Flask allows users to enter weather parameters and receive real-time predictions. The platform ensures accurate data preprocessing, feature scaling, and consistent model evaluation before deployment. By combining machine learning with web technologies, the system delivers fast, reliable, and location-specific rainfall forecasts. This project demonstrates how data-driven techniques can enhance environmental prediction systems and support informed decision-making in agriculture and public planning. KEYWORDS: Rainfall Prediction, Machine Learning, Logistic Regression, Weather Forecasting, Flask Framework, Climate Analysis, Data Preprocessing, Environmental Monitoring.

The growing complexity of educational demands and the expansion of digital learning environments have underscored the need for intelligent automation in teaching and assessment processes. Traditional educational platforms often lack adaptability, resulting in limited personalization and increased workload for educators. The integration of artificial intelligence (AI) into learning systems presents a promising avenue for addressing these challenges by enhancing scalability, efficiency, and individualized instruction. This study aimed to improve the quality and efficiency of the educational process by developing and implementing an AI-powered automated system for generating, verifying, and analyzing educational control tasks. The system was designed to support personalized learning, streamline assessment, and reduce the burden of routine academic activities. The proposed solution is built on a modular architecture using contemporary web technologies (React, Next.js, Firebase) in combination with the GPT model API. The system includes modules for test generation, automated answer checking, a conversational AI assistant, performance analytics, and real-time feedback. Document processing capabilities (DOCX, PDF) and seamless integration with Google Forms are also incorporated. The system’s performance was evaluated based on assessment accuracy, time savings, and usability. Implementation results indicate high effectiveness of the system in real educational scenarios. The automated evaluation module achieved an accuracy rate of 80–96 %, closely aligning with manual grading benchmarks. Additionally, the time required to prepare instructional content and assessments was reduced by 60–80 %. The user interface enabled intuitive access to system functionalities, and the adaptive features provided a personalized experience for students of varying proficiency levels. The developed system demonstrates significant potential for transforming educational practices through AI integration. It enhances personalization, reduces educator workload, and improves the consistency and objectivity of assessments. Future research will focus on expanding system functionality, including support for multimodal learning and large-scale institutional deployment.

Hispanic youth in the United States have the highest rates of pediatric obesity and do not often meet national guidelines for physical activity and dietary intake. Family-based interventions can improve health outcomes in both youth and their parents and are highly relevant to Hispanics due to the cultural value of familismo (familism). However, few existing family-based obesity prevention interventions for Hispanics target adolescents and their parents, and those that do are not designed to facilitate widespread reach. This study describes the development of Healthy Juntos (Healthy Together), a family-based intervention for Hispanic adolescents and their parents that leverages the web and smartphone technology to prevent the onset of adolescent obesity by promoting healthy lifestyle behaviors (physical activity and diet). We used an iterative co-design process guided by the Integrate, Design, Assess, and Share (IDEAS) framework, which outlines 10 phases for developing digital interventions. Hispanic adolescents at risk for obesity and their parents (n=90; 45 dyads) participated across different phases of the intervention development process. We conducted qualitative interviews to understand their needs and preferences and to gather feedback on a series of intervention prototypes (conceptual, paper and minimally functional, and fully functional). Participants reported using technology for their health in limited ways (eg, to search for medical symptoms and recipes). They described the importance of having interactive and social features as part of a family-based digital health intervention. Their suggestions related to content, functionality, and aesthetics resulted in a fully functional prototype of a digital lifestyle intervention for Hispanic adolescents and their parents. The iterative co-design process was crucial for refining the Healthy Juntos intervention. Our next steps are to evaluate its feasibility, acceptability, and preliminary effects through a pilot randomized controlled trial.

ABSTRACT Retail investors increasingly rely on digital tools for investment planning; however, most available platforms either provide static advisory insights or depend heavily on expensive proprietary data and complex predictive models. This research presents the design and development of an AI-enabled, web-based investment decision support system focused on practical usability, transparency, and zero-cost deployment. The system extends traditional investment simulations by integrating advanced rule-based strategies such as Value Averaging Pro Rata and Constant Share, along with a linear regression–based forecasting module to assist users in understanding potential future trends. A secure administrative module enables structured management of equity and mutual fund datasets, version control, and automated report generation in spreadsheet format. The platform evaluates investment outcomes using financial performance metrics including Return on Investment (ROI), Compound Annual Growth Rate (CAGR), and Extended Internal Rate of Return (XIRR). Unlike research-oriented predictive studies, the emphasis of this work lies in transforming analytical outputs into actionable insights through dashboards and downloadable reports. The findings demonstrate that the integration of adaptive strategy models with forecasting and reporting features enhances decision clarity for retail investors without introducing computational or financial complexity. The proposed system highlights how AI-inspired logic, when combined with full-stack web technologies, can serve as an effective decision support mechanism in real-world financial planning scenarios. Keywords: Keywords: Investment Decision Support, Rule-Based AI, Value Averaging Pro Rata, Linear Regression Forecasting, Web-Based Financial System

Cybersecurity teams rely on signature-based scanners such as Loki, a command-line tool for scanning malware, to identify Indicators of Compromise (IOCs), malicious artifacts, and YARA-rule matches. However, the raw Loki log output delivered as CSV or plaintext is challenging to interpret without additional visualization and correlation tools. Therefore, this research discusses the creation of a web-based dashboard that displays results from the Loki scanner. The project focuses on processing and displaying information collected from Loki’s scans, which are available in log files or CSV format. DIGITRACKER was developed as a proof-of-concept (PoC) to process this data and present it in a user-friendly, visually appealing way, enabling system administrators and cybersecurity teams to monitor potential threats and vulnerabilities effectively. By leveraging modern web technologies and dynamic data visualization, the tool enhances the user experience, transforming raw scan results into a well-organized, interactive dashboard. This approach simplifies the often-complicated task of manual log analysis, making it easier to interpret output data and to support low-budget or resource-constrained cybersecurity teams by transforming raw logs into actionable insights. The project demonstrates the dashboard’s effectiveness in identifying and addressing threats, providing valuable tools for cybersecurity system administrators. Moreover, our evaluation shows that DIGITRACKER can process scan logs containing hundreds of IOC alerts within seconds and supports multiple concurrent users with minimal latency overhead. In test scenarios, the integrated Loki scans were achieved, and the end-to-end pipeline from the end of the scan to the initiation of dashboard visualization incurred an average latency of under 20 s. These results demonstrate improved threat visibility, support structured triage workflows, and enhance analysts’ task management. Overall, the system provides a practical, extensible PoC that bridges the gap between command-line scanners and operational security dashboards, with new scan results displayed on the dashboard faster than manual log analysis. By streamlining analysis and enabling near-real-time monitoring, the PoC tool DIGITRACKER empowers cyber defense initiatives and enhances overall system security.

Electronic commerce (e-commerce) has grown significantly since the first online shops appeared. Such a growth is quantifiable not only in terms of users and sales but also in complexity: assets, supply chains, shipment modalities and payment methods, auctions, digital negotiations and blockchains are examples of how e-commerce is evolving in the WEB 3.0 era. As consequence of the growth and spread, the need of realizing trustworthy marketplaces, especially when decentralized technologies are involved, came forward. Semantic Web technologies may play a crucial role in this mission as their adoption by the major online marketplaces evidenced. The GoodRelations ontology is a milestone in this context. However, many limitations prevent GoodRelations from addressing the challenges of the incoming releases of the WEB: indeed, intricate and intertwined relationships among the digital commerce stakeholders are outside the expressive power of GoodRelations. Improvements to ontological representation in the e-commerce realm derive from the Theory of Agents. Among the available models, the behavioristic approach pursued by the Ontology for Agents, Systems, and Integration of Services ( OASIS ) is well suited to addressing emerging challenges, including those posed by Web 3.0. In this contribution, we present an extension of OASIS for the e-commerce domain, aimed at supporting next-generation digital commerce by incorporating features such as supply chain modeling, distributed ledgers, negotiation mechanisms, and auction processes.

An open-source JavaScript clinical neurophysiology library for education and clinical research.

The Web of Things (WoT) is a set of standards established by the World Wide Web Consortium (W3C) to enable interoperability across various Internet of Things (IoT) platforms. These standards facilitate seamless device-to-device interactions and application-to-application communication across heterogeneous environments. To identify and utilize resources, whether data or services, offered by Web-connected devices and applications, these resources must be described using an open, shared, and dynamic knowledge representation capable of supporting both syntactic and semantic interoperability. In this paper, we present WoR+, a Web of Resources ontology based on a modular and unified vocabulary for describing Web resources (Web services and Web data). WoR+ offers several advantages: (a) it supports the discovery, selection, and composition of data and services provided by Web-connected devices and applications; (b) it provides reasoning capabilities for inferring new knowledge; and (c) it supports extensibility and adaptability to emerging domain requirements. Experimental evaluation shows that WoR+ ontology achieves high effectiveness, strong performance, and good clarity and consistency.

Graph Neural Networks (GNNs) have achieved significant success across various applications. However, their complex structures and inner workings can be challenging for non-AI experts to understand. To address this issue, this study presents GNN101, an educational visualization tool for interactive learning of GNNs. GNN101 introduces a set of animated visualizations that seamlessly integrates mathematical formulas with visualizations via multiple levels of abstraction, including a model overview, layer operations, and detailed calculations. Users can easily switch between two complementary views: a node-link view that offers an intuitive understanding of the graph data, and a matrix view that provides a space-efficient and comprehensive overview of all features and their transformations across layers. GNN101 was designed and developed based on close collaboration with four GNN experts and deployment in three GNN-related courses. We demonstrated the usability and effectiveness of GNN101 via use cases and user studies with both GNN teaching assistants and students. To ensure broad educational access, GNN101 is developed through modern web technologies and available directly in web browsers without requiring any installations.