ASAI - Advanced Strategic AI Research Lab

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ASAI - Advanced Strategic AI Research Lab

The lab conducts publication-oriented research emphasizing methodological rigor, reproducibility, and real-world relevance.

Advanced Strategic AI Research Lab (ASAI) is an independent, multidisciplinary research lab focused on rigorous AI, machine learning, and deep learning research with real-world impact. Advanced Strategic AI Research Lab (ASAI) is an independent, multidisciplinary research laboratory dedicated to rigorous research in artificial intelligence, machine learning, and deep learning. ASAI’s research spans data-driven modeling, predictive analytics, optimization, decision intelligence, explainable and trustworthy AI, and intelligent systems engineering, with applications across healthcare, business analytics, sustainability, energy systems, infrastructure, and electrical engineering.

24/01/2026

𝗦𝘁𝗿𝗼𝗻𝗴 𝗿𝗲𝘀𝗲𝗮𝗿𝗰𝗵 𝗶𝗱𝗲𝗮𝘀 𝗮𝗿𝗲 𝗯𝗼𝗿𝗻 𝘄𝗵𝗲𝗿𝗲 𝗽𝗮𝘀𝘀𝗶𝗼𝗻, 𝗰𝘂𝗿𝗶𝗼𝘀𝗶𝘁𝘆, 𝗮𝗻𝗱 𝗿𝗲𝗮𝗹-𝘄𝗼𝗿𝗹𝗱 𝗶𝗺𝗽𝗮𝗰𝘁 𝗺𝗲𝗲𝘁.

22/01/2026

We are pleased to announce that our research paper titled

𝗔𝗜-𝗘𝗻𝗵𝗮𝗻𝗰𝗲𝗱 𝗣𝗿𝗲𝗱𝗶𝗰𝘁𝗶𝗼𝗻 𝗼𝗳 𝗥𝗲𝘀𝗽𝗶𝗿𝗮𝘁𝗼𝗿𝘆 𝗜𝗿𝗿𝗶𝘁𝗮𝘁𝗶𝗼𝗻 𝗳𝗿𝗼𝗺 𝗕𝗶𝗼𝗺𝗮𝘀𝘀 𝗖𝗼𝗺𝗯𝘂𝘀𝘁𝗶𝗼𝗻 𝗕𝘆𝗽𝗿𝗼𝗱𝘂𝗰𝘁𝘀: 𝗔𝗻 𝗜𝗻𝘁𝗲𝗴𝗿𝗮𝘁𝗶𝘃𝗲 𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗙𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸 𝗳𝗼𝗿 𝗦𝘂𝘀𝘁𝗮𝗶𝗻𝗮𝗯𝗹𝗲 𝗕𝗶𝗼𝗲𝗻𝗲𝗿𝗴𝘆 𝗦𝘆𝘀𝘁𝗲𝗺𝘀,

has been accepted and presented at the IEEE-supported ICCCES 2026, held from 21–23 January 2026 in Coimbatore, India.
Conference link: https://icoces.com/

The study was led by Shehabul Alam (𝗖𝗮𝗹𝗶𝗳𝗼𝗿𝗻𝗶𝗮, 𝗨𝗦𝗔). Notably, this paper has been accepted by two international conferences.

Our team also presented 𝘁𝘄𝗼 𝗮𝗱𝗱𝗶𝘁𝗶𝗼𝗻𝗮𝗹 𝗽𝗮𝗽𝗲𝗿𝘀 at the event.

Please keep us in your prayers and good wishes for future publications.

12/01/2026

We are pleased to share another research achievement from the 𝟮𝟬𝟮𝟲 𝗜𝗻𝘁𝗲𝗿𝗻𝗮𝘁𝗶𝗼𝗻𝗮𝗹 𝗖𝗼𝗻𝗳𝗲𝗿𝗲𝗻𝗰𝗲 𝗼𝗻 𝗦𝗺𝗮𝗿𝘁 𝗙𝘂𝘁𝘂𝗿𝗶𝘀𝘁𝗶𝗰 𝗧𝗲𝗰𝗵𝗻𝗼𝗹𝗼𝗴𝘆 (𝗜𝗖𝗦𝗙𝗧 𝟮𝟬𝟮𝟲), 𝗵𝗲𝗹𝗱 𝗼𝗻 𝟮𝗻𝗱–𝟯𝗿𝗱 𝗝𝗮𝗻𝘂𝗮𝗿𝘆 𝟮𝟬𝟮𝟲.

Md Maniruzzaman (Fremont, USA) served as the lead author for two papers, while K. M. Zubair (Fremont, USA) led one paper. They initiated the core research ideas and developed effective, well-structured plans that guided the studies from conception to completion.

Muhammad Shayan Nafees (Fremont, USA), Hasebul Hassan Chowdhury (Dhaka, Bangladesh), and Sahadat Hossen Sajib (Rajshahi, Bangladesh) served as sub-lead authors, contributing significantly to the methodology, analysis, implementation, and overall refinement of the research.

Presented Papers

𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴-𝗗𝗿𝗶𝘃𝗲𝗻 𝗘𝗻𝗲𝗿𝗴𝘆 𝗗𝗲𝗺𝗮𝗻𝗱 𝗙𝗼𝗿𝗲𝗰𝗮𝘀𝘁𝗶𝗻𝗴 𝗳𝗼𝗿 𝗦𝘂𝘀𝘁𝗮𝗶𝗻𝗮𝗯𝗹𝗲 𝗛𝗼𝘀𝗽𝗶𝘁𝗮𝗹 𝗜𝗻𝗳𝗿𝗮𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲: 𝗔 𝗣𝗿𝗲𝗱𝗶𝗰𝘁𝗶𝘃𝗲 𝗙𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸 𝗳𝗼𝗿 𝗢𝗽𝗲𝗿𝗮𝘁𝗶𝗼𝗻𝗮𝗹 𝗘𝗳𝗳𝗶𝗰𝗶𝗲𝗻𝗰𝘆 𝗮𝗻𝗱 𝗖𝗮𝗿𝗯𝗼𝗻 𝗙𝗼𝗼𝘁𝗽𝗿𝗶𝗻𝘁 𝗥𝗲𝗱𝘂𝗰𝘁𝗶𝗼𝗻

𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴-𝗕𝗮𝘀𝗲𝗱 𝗘𝗮𝗿𝗹𝘆 𝗥𝗶𝘀𝗸 𝗦𝘁𝗿𝗮𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗙𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸 𝗳𝗼𝗿 𝗖𝗵𝗿𝗼𝗻𝗶𝗰 𝗞𝗶𝗱𝗻𝗲𝘆 𝗗𝗶𝘀𝗲𝗮𝘀𝗲 𝗣𝗿𝗼𝗴𝗿𝗲𝘀𝘀𝗶𝗼𝗻 𝗨𝘀𝗶𝗻𝗴 𝗖𝗼𝗺𝗽𝗿𝗲𝗵𝗲𝗻𝘀𝗶𝘃𝗲 𝗠𝘂𝗹𝘁𝗶-𝗗𝗼𝗺𝗮𝗶𝗻 𝗖𝗹𝗶𝗻𝗶𝗰𝗮𝗹 𝗮𝗻𝗱 𝗕𝗶𝗼𝗰𝗵𝗲𝗺𝗶𝗰𝗮𝗹 𝗗𝗮𝘁𝗮

𝗔𝗱𝗮𝗽𝘁𝗶𝘃𝗲 𝗟𝗶𝗴𝗵𝘁𝘄𝗲𝗶𝗴𝗵𝘁 𝗘𝗻𝘀𝗲𝗺𝗯𝗹𝗲 𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗳𝗼𝗿 𝗥𝗲𝗮𝗹-𝗧𝗶𝗺𝗲 𝗜𝗼𝗧 𝗜𝗻𝘁𝗿𝘂𝘀𝗶𝗼𝗻 𝗗𝗲𝘁𝗲𝗰𝘁𝗶𝗼𝗻 𝗼𝗻 𝗘𝗱𝗴𝗲 𝗗𝗲𝘃𝗶𝗰𝗲𝘀

Conference Link: https://icsft.in/

We also sincerely acknowledge all co-authors for their valuable contributions, collaboration, and dedication to this research.

10/01/2026

𝗖𝗼𝗻𝗴𝗿𝗮𝘁𝘂𝗹𝗮𝘁𝗶𝗼𝗻𝘀 𝘁𝗼 MD Mehedi Hassan Melon (𝗞𝗲𝗻𝘁𝘂𝗰𝗸𝘆, 𝗨𝗦𝗔) 𝗳𝗼𝗿 𝗹𝗲𝗮𝗱𝗶𝗻𝗴 𝗮𝗻𝗱 𝘀𝘂𝗰𝗰𝗲𝘀𝘀𝗳𝘂𝗹𝗹𝘆 𝗽𝗿𝗲𝘀𝗲𝗻𝘁𝗶𝗻𝗴 𝘁𝗵𝗲𝗶𝗿 𝗿𝗲𝘀𝗲𝗮𝗿𝗰𝗵 𝘄𝗼𝗿𝗸 𝗮𝘁 𝗜𝗖𝗦𝗙𝗧 𝟮𝟬𝟮𝟲.

𝗠𝗲𝗹𝗼𝗻 served as the 𝗽𝗿𝗶𝗺𝗮𝗿𝘆 𝗹𝗲𝗮𝗱 𝗺𝗲𝗺𝗯𝗲𝗿 of the research project. His leadership and coordination were instrumental in the successful completion and presentation of this study.

The paper,
𝗘𝘅𝗽𝗹𝗮𝗶𝗻𝗮𝗯𝗹𝗲 𝗘𝗻𝘀𝗲𝗺𝗯𝗹𝗲 𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗙𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸 𝗳𝗼𝗿 𝗘𝗺𝗽𝗹𝗼𝘆𝗲𝗲 𝗔𝘁𝘁𝗿𝗶𝘁𝗶𝗼𝗻 𝗙𝗼𝗿𝗲𝗰𝗮𝘀𝘁𝗶𝗻𝗴: 𝗔𝗻 𝗜𝗻𝘁𝗲𝗴𝗿𝗮𝘁𝗲𝗱 𝗦𝗛𝗔𝗣-𝗕𝗮𝘀𝗲𝗱 𝗔𝗽𝗽𝗿𝗼𝗮𝗰𝗵 𝘁𝗼 𝗛𝗥 𝗗𝗲𝗰𝗶𝘀𝗶𝗼𝗻 𝗜𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝗰𝗲,

𝘄𝗮𝘀 𝗽𝗿𝗲𝘀𝗲𝗻𝘁𝗲𝗱 𝗮𝘁 𝘁𝗵𝗲 𝟮𝟬𝟮𝟲 𝗜𝗻𝘁𝗲𝗿𝗻𝗮𝘁𝗶𝗼𝗻𝗮𝗹 𝗖𝗼𝗻𝗳𝗲𝗿𝗲𝗻𝗰𝗲 𝗼𝗻 𝗦𝗺𝗮𝗿𝘁 𝗙𝘂𝘁𝘂𝗿𝗶𝘀𝘁𝗶𝗰 𝗧𝗲𝗰𝗵𝗻𝗼𝗹𝗼𝗴𝘆 (𝗜𝗖𝗦𝗙𝗧), 𝗵𝗲𝗹𝗱 𝗼𝗻 𝗝𝗮𝗻𝘂𝗮𝗿𝘆 𝟮–𝟯, 𝟮𝟬𝟮𝟲, 𝗼𝗿𝗴𝗮𝗻𝗶𝘇𝗲𝗱 𝘂𝗻𝗱𝗲𝗿 𝘁𝗵𝗲 𝗜𝗘𝗘𝗘 𝗕𝗮𝗻𝗴𝗮𝗹𝗼𝗿𝗲 𝗦𝗲𝗰𝘁𝗶𝗼𝗻.

𝗖𝗼𝗻𝗳𝗲𝗿𝗲𝗻𝗰𝗲 𝗟𝗶𝗻𝗸:
🔗 https://icsft.in/

Sincere appreciation is also extended to 𝗮𝗹𝗹 𝗰𝗼-𝗮𝘂𝘁𝗵𝗼𝗿𝘀 for their valuable contributions, collaboration, and dedication throughout the research process.

Best wishes to the entire research team for continued success in future research endeavors.

09/01/2026

We are pleased to announce that our research paper titled
𝗔𝗜-𝗘𝗻𝗵𝗮𝗻𝗰𝗲𝗱 𝗣𝗿𝗲𝗱𝗶𝗰𝘁𝗶𝗼𝗻 𝗼𝗳 𝗥𝗲𝘀𝗽𝗶𝗿𝗮𝘁𝗼𝗿𝘆 𝗜𝗿𝗿𝗶𝘁𝗮𝘁𝗶𝗼𝗻 𝗳𝗿𝗼𝗺 𝗕𝗶𝗼𝗺𝗮𝘀𝘀 𝗖𝗼𝗺𝗯𝘂𝘀𝘁𝗶𝗼𝗻 𝗕𝘆𝗽𝗿𝗼𝗱𝘂𝗰𝘁𝘀: 𝗔𝗻 𝗜𝗻𝘁𝗲𝗴𝗿𝗮𝘁𝗶𝘃𝗲 𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗙𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸 𝗳𝗼𝗿 𝗦𝘂𝘀𝘁𝗮𝗶𝗻𝗮𝗯𝗹𝗲 𝗕𝗶𝗼𝗲𝗻𝗲𝗿𝗴𝘆 𝗦𝘆𝘀𝘁𝗲𝗺𝘀

This paper has been accepted at the 𝗜𝗘𝗘𝗘-𝘀𝘂𝗽𝗽𝗼𝗿𝘁𝗲𝗱 𝟱𝘁𝗵 𝗜𝗻𝘁𝗲𝗿𝗻𝗮𝘁𝗶𝗼𝗻𝗮𝗹 𝗖𝗼𝗻𝗳𝗲𝗿𝗲𝗻𝗰𝗲 𝗼𝗻 𝗖𝗼𝗺𝗺𝘂𝗻𝗶𝗰𝗮𝘁𝗶𝗼𝗻, 𝗖𝗼𝗺𝗽𝘂𝘁𝗶𝗻𝗴 𝗮𝗻𝗱 𝗘𝗹𝗲𝗰𝘁𝗿𝗼𝗻𝗶𝗰𝘀 𝗦𝘆𝘀𝘁𝗲𝗺𝘀 (𝗜𝗖𝗖𝗖𝗘𝗦 𝟮𝟬𝟮𝟲),
𝘁𝗼 𝗯𝗲 𝗵𝗲𝗹𝗱 𝗼𝗻 𝟮𝟭–𝟮𝟯 𝗝𝗮𝗻𝘂𝗮𝗿𝘆 𝟮𝟬𝟮𝟲 𝗮𝘁 𝗣𝗣𝗚 𝗜𝗻𝘀𝘁𝗶𝘁𝘂𝘁𝗲 𝗼𝗳 𝗧𝗲𝗰𝗵𝗻𝗼𝗹𝗼𝗴𝘆, 𝗖𝗼𝗶𝗺𝗯𝗮𝘁𝗼𝗿𝗲, 𝗜𝗻𝗱𝗶𝗮.

Conference link: https://icoces.com/

This work was led by Shehabul Alam from 𝗖𝗮𝗹𝗶𝗳𝗼𝗿𝗻𝗶𝗮, 𝗨𝗦𝗔, who initiated the research idea, led the study, and assumed primary responsibility for the project.

Notably, this paper has been accepted by 𝘁𝘄𝗼 𝗶𝗻𝘁𝗲𝗿𝗻𝗮𝘁𝗶𝗼𝗻𝗮𝗹 𝗰𝗼𝗻𝗳𝗲𝗿𝗲𝗻𝗰𝗲𝘀, reflecting its research quality and impact.

08/01/2026

𝐑𝐞𝐬𝐞𝐚𝐫𝐜𝐡 𝐅𝐨𝐜𝐮𝐬 𝐚𝐧𝐝 𝐌𝐮𝐥𝐭𝐢𝐝𝐢𝐬𝐜𝐢𝐩𝐥𝐢𝐧𝐚𝐫𝐲 𝐒𝐜𝐨𝐩𝐞 | 𝐀𝐒𝐀𝐈

The 𝐀𝐝𝐯𝐚𝐧𝐜𝐞𝐝 𝐒𝐭𝐫𝐚𝐭𝐞𝐠𝐢𝐜 𝐀𝐈 𝐑𝐞𝐬𝐞𝐚𝐫𝐜𝐡 𝐋𝐚𝐛 (𝐀𝐒𝐀𝐈) conducts rigorous, publication-oriented research in Artificial Intelligence, Machine Learning, Deep Learning, and Decision Intelligence, treating AI as a unifying scientific tool across disciplines.

𝐎𝐮𝐫 𝐫𝐞𝐬𝐞𝐚𝐫𝐜𝐡 𝐬𝐜𝐨𝐩𝐞 𝐢𝐧𝐜𝐥𝐮𝐝𝐞𝐬:
• 𝐂𝐨𝐦𝐩𝐮𝐭𝐞𝐫 𝐒𝐜𝐢𝐞𝐧𝐜𝐞 & 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫𝐢𝐧𝐠 (AI, ML, data-driven systems)
• 𝐁𝐮𝐬𝐢𝐧𝐞𝐬𝐬, 𝐄𝐜𝐨𝐧𝐨𝐦𝐢𝐜𝐬, 𝐅𝐢𝐧𝐚𝐧𝐜𝐞 & 𝐏𝐨𝐥𝐢𝐜𝐲 𝐀𝐧𝐚𝐥𝐲𝐭𝐢𝐜𝐬
• 𝐄𝐥𝐞𝐜𝐭𝐫𝐢𝐜𝐚𝐥 & 𝐄𝐥𝐞𝐜𝐭𝐫𝐨𝐧𝐢𝐜 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫𝐢𝐧𝐠 (intelligent systems, energy, power)
• 𝐌𝐞𝐜𝐡𝐚𝐧𝐢𝐜𝐚𝐥, 𝐂𝐢𝐯𝐢𝐥, 𝐈𝐧𝐝𝐮𝐬𝐭𝐫𝐢𝐚𝐥 & 𝐏𝐫𝐨𝐝𝐮𝐜𝐭𝐢𝐨𝐧 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫𝐢𝐧𝐠
• 𝐀𝐫𝐜𝐡𝐢𝐭𝐞𝐜𝐭𝐮𝐫𝐞 & 𝐔𝐫𝐛𝐚𝐧 𝐏𝐥𝐚𝐧𝐧𝐢𝐧𝐠 (data-driven and smart systems)
• 𝐁𝐢𝐨𝐦𝐞𝐝𝐢𝐜𝐚𝐥 & 𝐇𝐞𝐚𝐥𝐭𝐡𝐜𝐚𝐫𝐞 𝐒𝐜𝐢𝐞𝐧𝐜𝐞𝐬 (medical AI, decision support)
• 𝐄𝐧𝐯𝐢𝐫𝐨𝐧𝐦𝐞𝐧𝐭𝐚𝐥 𝐒𝐜𝐢𝐞𝐧𝐜𝐞, 𝐒𝐮𝐬𝐭𝐚𝐢𝐧𝐚𝐛𝐢𝐥𝐢𝐭𝐲 & 𝐑𝐞𝐧𝐞𝐰𝐚𝐛𝐥𝐞 𝐄𝐧𝐞𝐫𝐠𝐲
• 𝐈𝐧𝐭𝐞𝐫𝐝𝐢𝐬𝐜𝐢𝐩𝐥𝐢𝐧𝐚𝐫𝐲 𝐝𝐨𝐦𝐚𝐢𝐧𝐬 𝐰𝐡𝐞𝐫𝐞 𝐀𝐈-𝐝𝐫𝐢𝐯𝐞𝐧 𝐢𝐧𝐭𝐞𝐥𝐥𝐢𝐠𝐞𝐧𝐜𝐞 𝐜𝐚𝐧 𝐛𝐞 𝐚𝐩𝐥𝐢𝐞𝐝

Our research emphasizes 𝐦𝐞𝐭𝐡𝐨𝐝𝐨𝐥𝐨𝐠𝐢𝐜𝐚𝐥 𝐫𝐢𝐠𝐨𝐫, 𝐫𝐞𝐩𝐫𝐨𝐝𝐮𝐜𝐢𝐛𝐢𝐥𝐢𝐭𝐲, 𝐚𝐧𝐝 𝐫𝐞𝐚𝐥-𝐰𝐨𝐫𝐥𝐝 𝐫𝐞𝐥𝐞𝐯𝐚𝐧𝐜𝐞, with strong focus on 𝐩𝐞𝐞𝐫-𝐫𝐞𝐯𝐢𝐞𝐰𝐞𝐝 𝐣𝐨𝐮𝐫𝐧𝐚𝐥𝐬 𝐚𝐧𝐝 𝐢𝐧𝐭𝐞𝐫𝐧𝐚𝐭𝐢𝐨𝐧𝐚𝐥 𝐜𝐨𝐧𝐟𝐞𝐫𝐞𝐧𝐜𝐞𝐬.

This page serves as a 𝐩𝐮𝐛𝐥𝐢𝐜 𝐚𝐜𝐚𝐝𝐞𝐦𝐢𝐜 𝐩𝐥𝐚𝐭𝐟𝐨𝐫𝐦 for sharing research outputs and fostering 𝐢𝐧𝐭𝐞𝐫𝐝𝐢𝐬𝐜𝐢𝐩𝐥𝐢𝐧𝐚𝐫𝐲 𝐫𝐞𝐬𝐞𝐚𝐫𝐜𝐡 𝐝𝐢𝐚𝐥𝐨𝐠𝐮𝐞.

07/01/2026

𝐀𝐝𝐯𝐚𝐧𝐜𝐞𝐝 𝐒𝐭𝐫𝐚𝐭𝐞𝐠𝐢𝐜 𝐀𝐈 𝐑𝐞𝐬𝐞𝐚𝐫𝐜𝐡 𝐋𝐚𝐛 (𝐀𝐒𝐀𝐈) is an independent, multidisciplinary research laboratory dedicated to rigorous research in Artificial Intelligence, Machine Learning, and Deep Learning. Our work emphasizes high-quality, publication-oriented research with applications across healthcare, business analytics, sustainability, energy systems, infrastructure, and electrical engineering.

This page serves as a public academic platform to share research outputs, scholarly insights, and ongoing academic work. We welcome research dialogue, collaboration, and engagement grounded in scientific rigor and evidence-based inquiry.

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Dhaka

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https://www.linkedin.com/company/asai-research-lab/

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