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All Journal Control Systems and Optimization Letters
Islam, Asm Mohaimenul
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Aerospace Engineering Automotive Engineering Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering

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AI-Driven Microgrid Solutions for Enhancing Energy Access and Reliability in Rural and Remote Areas: A Comprehensive Review Ahmed, Faisal; Uzzaman, Asif; Adam, Md Ibrahim; Islam, Monirul; Rahman, Md Moklesur; Islam, Asm Mohaimenul
Control Systems and Optimization Letters Vol 3, No 1 (2025)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/csol.v3i1.183

Abstract

As localized energy systems, microgrids provide a viable way to solve problems with energy dependability and access in rural and isolated locations. These regions often have inadequate and unstable grid infrastructure, which restricts their access to energy. Artificial Intelligence (AI) improves the overall performance, flexibility, and efficiency of microgrid systems. AI ensures a steady and dependable power supply by enabling predictive maintenance, optimal load forecasting, energy storage management, and renewable energy resource optimization. AI may help microgrids anticipate system faults, better control energy consumption, and prolong the life of vital parts. Additionally, AI ensures the sustainability of microgrids in resource-constrained places by optimizing the usage of renewable energy sources like solar and wind. Successful case studies from places like the US, India, and Africa have shown the promise of AI-enhanced microgrids in raising the standard of living for marginalized areas, despite obstacles like data infrastructure and upfront installation costs. Microgrids have a bright future thanks to developments in artificial intelligence (AI), which might increase electricity availability and promote economic growth in rural and isolated regions of the world.
The Intersection of Remote Sensing and Biomedical Imaging: A Review of Techniques for Cancer Detection Islam, Asm Mohaimenul; Drishty, Shohely Muntaha; Alam, Md. Adnanul
Control Systems and Optimization Letters Vol 3, No 2 (2025)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/csol.v3i2.207

Abstract

Early and accurate cancer detection remains a major challenge in modern healthcare. This study explores the emerging intersection between biomedical imaging and remote sensing technologies, focusing on the adaptation of microwave radar, thermal imaging, multispectral imaging, and hyperspectral imaging (HSI) for non-invasive cancer diagnosis. Originally developed for environmental and aerospace applications, these technologies are now being repurposed to detect subtle biochemical, morphological, and metabolic changes in human tissues that signal the presence of cancer. When integrated with artificial intelligence and machine learning, these imaging modalities enable real-time classification, high-throughput analysis, and enhanced surgical guidance. We highlight their advantages over traditional imaging methods and examine their application in detecting malignancies of the brain, breast, skin, oral cavity, and gastrointestinal tract. This review also discusses critical clinical and technical challenges, including the lack of standardized datasets, issues with device mobility, data complexity, and regulatory hurdles. Finally, we outline promising future directions such as edge-based data processing, explainable AI systems, multimodal imaging fusion, and ethical considerations for deployment in resource-constrained settings. This multidisciplinary approach has the potential to revolutionize cancer diagnostics, making them faster, safer, and more accessible, particularly for underserved populations.
Energy Management Strategies for Electric Vehicle Charging in Microgrids: A Case Study of Optimization Techniques Akash, Khairul Bashar; Akter, Mst Sumi; Emon, Md Afrad Hasan; Kazmi, Muhammad Meisam; Islam, Asm Mohaimenul
Control Systems and Optimization Letters Vol 3, No 2 (2025)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/csol.v3i2.202

Abstract

The integration of Electric Vehicles (EVs) into microgrids presents both significant opportunities and complex challenges in energy management. As the adoption of EVs increases, efficient charging strategies become essential for maintaining grid stability, reducing energy costs, and maximizing the utilization of renewable energy sources. This review explores various optimization techniques applied to energy management in EV charging within microgrids, including deterministic approaches, stochastic programming, Model Predictive Control (MPC), game theory, machine learning, and heuristic/metaheuristic methods. Each technique is evaluated based on its strengths, weaknesses, and applicability to different system requirements, such as real-time responsiveness, adaptability to uncertainties, and scalability. Moreover, the paper identifies emerging trends and key research areas, such as hybrid optimization frameworks, decentralized energy markets, Vehicle-to-Grid (V2G) technology, and the integration of explainable AI for enhanced decision-making transparency. Additionally, challenges related to cybersecurity, resilience to system faults, and the integration of large-scale EV infrastructure are discussed. The paper concludes by highlighting the need for multi-objective optimization approaches that balance cost efficiency, user satisfaction, and grid reliability. With rapid advancements in EV technology and microgrid systems, research must focus on developing scalable and secure energy management solutions. While AI-driven methods show strong potential, real-world adoption faces challenges such as high costs, technical complexity, and integration issues. Practical applications highlight feasibility, but broader implementation demands further refinement.
Co-Authors Adam, Md Ibrahim Ahmed, Faisal Akash, Khairul Bashar Akter, Mst Sumi Alam, Md. Adnanul Drishty, Shohely Muntaha Emon, Md Afrad Hasan Islam, Monirul Kazmi, Muhammad Meisam Rahman, Md Moklesur Uzzaman, Asif