https://milestoneresearch.in/JOURNALS/index.php/IJCLI/issue/feed 2026-05-06T16:07:28+00:00 Dr. Syed Muzamil Basha, Editor-in-Chief muzamilbasha.s@reva.edu.in Open Journal Systems <p>International Journal of Computational Learning &amp; Intelligence is a peer reviewed journal published under Milestone Research Foundation (MRF). It publishes original research work/reviews/editorials on all futuristic aspects of computational learning and intelligence. The targeted papers should demonstrate the use and need of traditional techniques in computational learning and intelligence with impactful social relevance.</p> https://milestoneresearch.in/JOURNALS/index.php/IJCLI/article/view/302 2026-04-30T13:05:45+00:00 Nikhil Kumar nikk4645@gmail.com Vinay Kumar vinaykumarbharwal@gmail.com Sourav souravdanyal04@gmail.com Mayank Chopra mayankchopra@hpcu.ac.in Parveen Sadotra sadotramca2k6@gmail.com Pradeep Chouksey dr.pchouksey@hpcu.ac.in

<p><strong>Forest fires have severe impacts on ecosystems and property, but current detection methods continue to suffer from detection latency, coverage and environmental limitations. In this paper, we review the state of the art in integrated forest fire detection systems that leverage deep learning, Internet of Things (IoT) sensor networks, unmanned aerial vehicle (UAV) monitoring, and satellite remote sensing. Over two dozen recent works are reviewed to discuss object detection algorithms, hybrid deep learning models, sensor fusion techniques, satellite remote sensing, and land use/land cover (LULC) change analyses for predictive fire risk mapping. Key research challenges are identified in the areas of integration, data, environmental adaptability, efficiency, and the use of contextual information. Popular benchmark datasets, performance metrics and system characteristics are also presented. Based on this review, a visionary research proposal is provided detailing the design and approach for developing a holistic multi-platform detection system to achieve detection within 5 minutes with an accuracy of more than 95% and false alarm rate of less than 5% in different ecosystems. </strong></p>

2026-05-06T00:00:00+00:00 Copyright (c) 2026 Nikhil Kumar, Vinay Kumar, Sourav, Mayank Chopra, Parveen Sadotra, Pradeep Chouksey