Journal of Android and IOS Applications and Testing

DopaSense: A Native Android Application for On-Device Usage Analytics and Heuristic Behavioral Scoring

2026-08-07T11:09:44+00:00

The rapid growth of smartphones has significantly increased digital connectivity worldwide, but it has also raised concerns about excessive screen usage and digital addiction. This paper presents DopaSense, a native Android application designed to analyze smartphone usage behavior and provide real-time intervention for digital addiction management. Unlike traditional systems that rely on offline datasets or survey-based analysis, the proposed system performs continuous on-device monitoring using Android system services such as Usage Stats Manager and Accessibility Service. A heuristic behavioral scoring model is introduced to compute a Dopamine Score based on application usage duration and category weights. Additionally, an Addiction Risk Index is derived from total screen time using a normalized scaling approach. The system incorporates a self-healing background architecture using Work Manager and boot-time recovery mechanisms, ensuring uninterrupted operation even under aggressive OS constraints. Experimental results demonstrate that the system effectively captures real-world usage patterns and provides meaningful behavioral insights, enabling users to develop healthier digital habits.

Focus-Buddy: A Design Thinking Approach to Combating Digital Distraction Through Gamified Focus Management

2026-06-16T11:11:09+00:00

In the modern highly connected digital era, a sustainable attention is one of the very acute issues facing both students and working professionals. The development of smartphones, social media, and instant messaging apps has essentially changed how individuals interact with their duties such that it generally breaks focus and decreases overall productivity. The continuous digital disruptions over time not only reduce the level of cognitive output but also cause mental fatigue and anxiety as well as overall reduction in the quality of work output. Although productivity tools and time-management apps are so common, the retention rates among users are significantly low, and the primary reason is that the majority of the currently existing tools do not cover the psychological motivation elements, which promote regular use. Focus-Buddy is a new productivity management application that is presented in this paper and has been designed to integrate the systematic discipline of the Pomodoro Technique with the inspirational force of gamification to establish a powerful and engaging focus management experience. Created as a product of an extensive five-stage Design Thinking experience, which is Empathize, Define, Ideate, Prototype, and Test, Focus-Buddy is set to appeal to the target users on a personal level by filling the gap between cognitive and behavioral obstacles to sustained attention. The system has built-in distracting features tracking, a virtual incentive system with points, levels, and achievement badges, adjustable work-break interval indicators, and a social leaderboard that promotes healthy competition among peers. To test the suitability of the offered solution, the user research with a heterogeneous sample of thirty undergraduate students was carried out, and the obtained results show significant growth in the mean focus session, the rate of completion of tasks, and the reported degree of motivation, with a statistically significant value. The results of this research show that well-considered gamified focus management tools, created with a real understanding of user requirements, can have a significant impact on decreasing digital distraction and developing long-run productive behaviours. The next generation of human-centred productivity software is a promising direction which Focus-Buddy will take.

AI General Physician Agent: A Multi-Base Intelligent System for Healthcare Assistant

2026-08-10T04:47:24+00:00

Recent advancements in artificial intelligence have significantly improved the ability of systems to understand and respond to human language. In the healthcare domain, conversational assistants are increasingly used to provide quick guidance and improve access to basic medical information. However, many existing solutions rely on a single processing unit, which often leads to generalized responses and limited understanding of user needs across different health-related areas. This paper presents an intelligent multi-agent system designed to deliver personalized support for health and wellness. The proposed system divides responsibilities among specialized agents that focus on medical assistance, fitness guidance, and mental well-being. A central decision mechanism analyzes user input and directs each query to the most suitable agent based on its context. The system is developed using Python and integrates modern frameworks for managing agent workflows and natural language processing. It also includes a memory component that captures user information, such as preferences and health goals, to improve response relevance over time. Experimental evaluation indicates that the multi-agent approach enhances accuracy, adaptability, and user-specific recommendations compared to traditional single-agent systems. The study demonstrates the potential of intelligent agent-based systems in building accessible and reliable digital healthcare assistants. The AI General Physician Agent is a Multi-Base Intelligent System for healthcare assistants. It is an innovative collaborative framework that aims to improve diagnostic precision, operational efficiency, and tailored patient care. Instead of one chatbot, this solution uses a Multi-Agent System (MAS) concept of a team of specialist AI agents working together to handle patient journeys from triage to follow-up. Less Physician Burnout. Automated repetitive administrative activities like paperwork, coding, and prior authorization save physicians significant time. Improved Accuracy in a Multi-agent collaboration boosts diagnostic accuracy by eliminating errors related to a lack of information. Agents’ Personalized Care Planning examines genetic profiles, medical histories, and therapy reactions to prescribe targeted medicines.

Digital Learning Platform for Rural Area: An Android-based AI-assisted Educational Ecosystem with Role-based Access Control and Firebase Push Notifications

2026-07-29T12:14:46+00:00

This paper presents the design, implementation, and functional evaluation of the Digital Learning Platform for Rural Areas, an Android-based educational application developed to bridge the persistent digital divide between rural students and quality academic resources. The platform adopts a dual-portal, role-based architecture providing distinct interfaces for teachers and students. Teachers manage the distribution of multi-format educational content — text notes, PDF documents, and video lecture metadata — targeted to specific departments and academic years, while students access distributed materials and engage with two integrated artificial intelligence tools: an AI-powered document summarizer and a real-time academic doubt-solving chatbot, both powered by the Groq language model via a PHP middleware layer. Student access to platform resources is gated behind a teacher-controlled approval mechanism, ensuring institutional security and content integrity. Firebase Cloud Messaging delivers targeted push notifications to student groups using a department-and-year topic naming convention, confirmed effective on Android 13+ during testing. The Android application is developed in Java using an activity-based architecture, with Volley handling standard REST API communication and OkHttp3 managing streaming connections for the AI chatbot. A PHP-MySQL backend hosted at tsm.ecssofttech.com provides user management, content metadata storage, and AI request routing. In-app PDF rendering is delivered through Android-PDF-Viewer, and text extraction for AI summarization is performed using PDFBox-Android. Cleartext traffic support and legacy storage configurations ensure compatibility with low-end Android devices commonly used in rural communities. System testing confirmed correct operation of all primary functional flows, including role-based authentication, content distribution, AI summarization, chatbot interaction, notification delivery, and student approval management.

An Experimental Evaluation of Lazy Loading and Code Splitting for React.js Performance Optimization

2026-05-25T09:24:26+00:00

React.js-based single-page applications often suffer from large initial bundle sizes, leading to degraded performance, particularly on mobile and low-bandwidth networks. As application complexity grows, large JavaScript bundles must be fully downloaded and parsed before any content is rendered, resulting in slower startup times and a poor user experience. Although lazy loading and code splitting are widely adopted in practice, controlled experimental validation within React.js environments remains limited. This study implements these techniques using React.lazy(), Suspense boundaries, and Webpack’s SplitChunksPlugin, and evaluates their combined impact under three simulated network conditions (Fast 4G, Slow 4G, and 3G) using Google Lighthouse in mobile simulation mode. A controlled prototype application comprising multiple functional pages and realistic third-party dependencies was developed in both baseline and optimized configurations. Experimental results demonstrate a 47.3% reduction in initial JavaScript transfer size, a 39.8% improvement in First Contentful Paint (FCP), and a 52.1% reduction in Total Blocking Time (TBT). Performance benefits are most pronounced under constrained 3G conditions, where absolute FCP gains exceed 2,200 ms. Statistical analysis using paired t-tests confirms that these improvements are significant (p < 0.01). Despite a limited sample size (n = 5), the controlled experimental design ensures consistency and reproducibility. The findings provide empirical evidence supporting lazy loading and code splitting as effective, production-ready frontend performance optimization strategies for modern React.js applications.