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All Journal Journal of Computer Science Application and Engineering
Anson, Adriel Moses
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering

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Beyond the Classroom: MOOCs and the Evolution of Lifelong Learning Anson, Adriel Moses
Journal of Computer Science Application and Engineering (JOSAPEN) Vol. 2 No. 1 (2024): JOSAPEN - January
Publisher : PT. Lentera Ilmu Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70356/josapen.v2i1.22

Abstract

This study explores the transformative impact of Massive Open Online Courses (MOOCs) on lifelong learning, challenging traditional education paradigms and offering accessible, flexible, and global learning opportunities. Focusing on the dynamics, challenges, and potentials associated with MOOCs, the research emphasizes their role in shaping education beyond conventional classroom settings. The study employs a multidimensional analysis, combining a comprehensive literature review, empirical research methods, and a discussion of findings to provide nuanced insights. Key findings reveal that MOOCs, characterized by massive scalability and interactive features, reach a global audience and engage learners dynamically. However, challenges such as completion rates, learner engagement, quality assurance, access, monetization, and integration persist, necessitating strategic interventions. The study identifies the potential of MOOCs to revolutionize education by offering global accessibility, supporting lifelong learning, accommodating diverse learning styles, integrating innovative technologies, bridging industry-relevant skills gaps, and fostering collaborative learning communities. In conclusion, MOOCs stand at the forefront of the educational landscape, poised to play a pivotal role in the future of education. The article recommends practical strategies for stakeholders to enhance MOOC accessibility, improve learner engagement, address certification concerns, and integrate these platforms into formal education systems. Embracing the transformative power of MOOCs is crucial for building a global learning community that thrives on accessibility, innovation, and continuous learning in the evolving digital era.
Enhanced Dynamic Programming Approaches for Efficient Solutions to the Traveling Salesman Problem Anson, Adriel Moses
Journal of Computer Science Application and Engineering (JOSAPEN) Vol. 2 No. 2 (2024): JOSAPEN - July
Publisher : PT. Lentera Ilmu Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70356/josapen.v2i2.32

Abstract

This study aims to enhance dynamic programming techniques for efficiently solving the Traveling Salesman Problem, a fundamental combinatorial optimization challenge. Given its NP-hard classification, traditional exact algorithms become computationally infeasible as the problem size increases. The research revisits foundational dynamic programming principles, notably the Held-Karp algorithm, and identifies existing limitations. The study begins with a comprehensive literature review, followed by an analysis of the dynamic programming challenges specific to TSP. Novel algorithms are then developed, implemented, and rigorously tested against benchmark instances. Performance evaluation is conducted using metrics such as execution time, memory usage, and solution optimality across different problem sizes. Results demonstrate significant improvements in efficiency and scalability, with enhanced algorithms achieving optimal solutions in reduced time and computational resource usage. However, the exponential growth in complexity remains a challenge for larger instances. The study concludes with recommendations for future research, focusing on further algorithmic refinements and exploring hybrid approaches to address large-scale TSPs.
Machine Learning-Based Route Optimization for Smart Urban Transportation Systems Anson, Adriel Moses; Amirah
Journal of Computer Science Application and Engineering (JOSAPEN) Vol. 3 No. 2 (2025): JOSAPEN - July
Publisher : PT. Lentera Ilmu Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70356/josapen.v3i2.65

Abstract

Urban transportation systems face increasing challenges due to rapid population growth, traffic congestion, and unpredictable road conditions. Traditional routing algorithms like Dijkstra and A* are limited in their ability to respond to real-time events such as accidents, roadwork, or weather disruptions. This study aims to develop a smarter, more adaptive route optimization system using machine learning techniques. The goal is to enhance travel time accuracy, reduce congestion, and improve commuter satisfaction through intelligent, data-driven decision-making. The proposed method integrates supervised learning for travel time prediction and reinforcement learning for real-time route selection, using data from GPS trajectories, traffic flow, weather reports, and user behaviors. A grid-based environment is used for reinforcement learning simulations, while OpenStreetMap data supports city-level route optimization. Results show that the machine learning-enhanced model significantly outperforms traditional algorithms in terms of adaptability, responsiveness, and reliability. In particular, reinforcement learning proved effective in dynamic environments, learning optimal routes over time and adjusting to disruptions. This research contributes to the development of intelligent transportation systems and supports the broader vision of smart cities, where mobility is safer, faster, and more efficient through the power of AI and real-time data integration.
Co-Authors Amirah