Journal of Advance Research in Mobile Computing
Journal of Advance Research in Mobile Computing is presenting wide-ranging coverage of speedy moving field, this journal provides the research and development communities working in telecommunication and networking industries with the sharing of research and ideas. This journal reflects the novel development of standards like development, emerging technologies. Focus and Scope Wireless transmission basics Cellular networks Mobile business apps using the sybase unwired platform Networking GSM, HSCSD, GPRS Mobile internet protocols MAC protocols, routing, transport, ad-hoc Security, scalability, and reliability issues Mobile ad hoc and sensor networks Satellite and broadcast systems Wearable computing and networking Power management and control algorithms Channel allocation, multiple access, location management , handoffs
This paper presents the design and implementation of a Distributed Database System aimed at efficient data storage, processing, and retrieval across multiple nodes. The system uses a client–coordinator– worker architecture, where queries are received by the coordinator and routed to appropriate worker nodes using hash-based partitioning. It supports operations such as data insertion, selection, and distributed query execution. Replication is implemented to ensure data availability, while a fault tolerance mechanism allows the system to continue operation even if a node fails. The coordinator aggregates results from multiple workers, removes duplicates, and returns a consistent output to the client. Experimental results show that the system performs efficiently with improved scalability, reliability, and performance, making it suitable for distributed data management applications.
With the increasing need for smart and automated systems, attendance management using face recognition has become an effective alternative to traditional methods. Manual attendance processes are often time-consuming, inaccurate, and vulnerable to proxy entries. This paper presents a real-time Face Recognition Attendance Management System developed using Artificial Intelligence and Computer Vision techniques. The system captures facial images through a webcam, detects faces using Haar Cascade classifiers, and identifies individuals using the LBPH face recognition algorithm. Once verified, attendance is automatically recorded with timestamps and stored in CSV and Excel files for easy management and reporting. The proposed system minimizes human intervention, improves attendance accuracy, and reduces administrative workload while operating efficiently on standard computing devices, making it suitable for educational institutions and workplaces.
Public Health Chat Bot for Disease Awareness Disease Awareness
The contemporary healthcare environment is confronted with two major issues: the exponential growth of health-related information and the ongoing spread of misinformation. In numerous regions, people continue to depend on unverified digital platforms, resulting in misconceptions and unnecessary anxiety about various diseases. With advancements in conversational artificial intelligence, chatbots have emerged as interactive, efficient, and affordable tools for accurate health communication. These systems support two-way interactions, allowing users to pose questions in natural language and obtain responses grounded in verified medical knowledge. This research focuses on the conceptualization and development of a public health chatbot aimed at disseminating credible information on diseases, preventive healthcare strategies, and wellness practices.
A Survey Paper on Gamified Environmental Education Platform for Schools and Colleges
This survey offers a thorough analysis of gamified environmental education resources for educational institutions. Technological enablers, and practical applications of these platforms, this paper synthesizes literature from IEEE and other academic sources. Along with highlighting new trends like AI- driven personalization and IoT-enabled environmental monitoring, we also identify important challenges like scalability, data privacy, and integration with current educational systems. Future directions and open research problems are discussed in the survey's conclusion. These platforms have drawn a lot of attention due to their potential to improve student engagement, encourage pro- environmental behaviors, and support curriculum- aligned learning as sustainability and digital learning become more and more important. In order to analyze the architectural frameworks, gamification mechanics.
The fast rise of artificial intelligence, along with computer vision tools, is changing how we monitor exams in places like schools. Old-school ways of watching students during tests usually come with mistakes, personal bias, or gaps in attention - especially when hundreds take the test at once. To fix this, our idea is a smart exam watcher using AI that spots odd actions, keeps tabs on what students do, flags forbidden items, while making sure rules are followed as things happen. It uses advanced neural networks to recognize faces, follow where someone’s eyes move, detect objects, check body positions - all helping make oversight more solid and quicker. This setup combines a smooth front-end made with up-to-date design tools plus a back-end running ML workflows and protected data storage. Live camera feeds get analyzed instantly to catch warning signs: sudden head turns, constant glancing off-screen, phone use, or multiple people showing up unexpectedly. Notifications pop up on their own for supervisors, so they spend less time checking things by hand while tests stay fairer. This smart exam helper works without help from people, runs smooth whether it’s used online or offline, scales easily when needed, keeps data safe, shows what's happening clearly, avoids favoritism, spots odd behavior fast, cuts down errors, saves effort across schools and boards alike.
AI Powered Habit/Behaviour Visualizer among Students
Students often struggle to maintain consistent habits due to limited feedback, lack of timely guidance, and difficulty visualizing their own progress. To address these challenges, this paper presents the rationale for an AI-driven, web-based student habit tracker designed to provide continuous monitoring, intelligent insights, and personalized recommendations. By integrating modern web technologies with AI-powered analytics, the system transforms raw habit data into meaningful patterns—such as completion trends, streaks, and time-based performance summaries—that help students make informed adjustments to their routines. The platform delivers real-time nudges, contextual suggestions, and adaptive goal-setting support, ultimately fostering self-regulation, accountability, and long-term behavioral improvement. This rationale establishes the need for such a tool in academic environments and highlights its potential to enhance student productivity, motivation, and sustained habit formation.