{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,10]],"date-time":"2026-04-10T05:31:48Z","timestamp":1775799108870,"version":"3.50.1"},"reference-count":25,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2022,12,27]],"date-time":"2022-12-27T00:00:00Z","timestamp":1672099200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,12,27]],"date-time":"2022-12-27T00:00:00Z","timestamp":1672099200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Innovations Syst Softw Eng"],"published-print":{"date-parts":[[2025,3]]},"DOI":"10.1007\/s11334-022-00521-y","type":"journal-article","created":{"date-parts":[[2022,12,27]],"date-time":"2022-12-27T04:20:57Z","timestamp":1672114857000},"page":"247-256","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Comparative exploration of CNN model and transfer learning on fire image dataset"],"prefix":"10.1007","volume":"21","author":[{"given":"Sudip","family":"Suklabaidya","sequence":"first","affiliation":[]},{"given":"Indrani","family":"Das","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,12,27]]},"reference":[{"key":"521_CR1","doi-asserted-by":"crossref","unstructured":"Suklabaidya S, Das I (2021) Fire Detection and real time monitoring systems through IoT sensors. In: Innovations in electrical and electronic engineering.","DOI":"10.1007\/978-981-16-0749-3_67"},{"key":"521_CR2","unstructured":"Jadon A, Omama M, Varshney A, Ansari MS, Sharma R (2019) FireNet: A specialized lightweight fire & smoke detection model for real-time IoT applications. Computer Vision and Pattern Recognition."},{"key":"521_CR3","doi-asserted-by":"crossref","unstructured":"Valova I, Harris C, Mai T, Gueorguieva N (2020) Optimization of convolutional neural networks for imbalanced set classification, Procedia Computer Science 176 (2020) 660\u2013669, 24th International Conference on Knowledge-Based and Intelligent Information & Engineering Systems.","DOI":"10.1016\/j.procs.2020.09.038"},{"issue":"11","key":"521_CR4","doi-asserted-by":"publisher","first-page":"1241","DOI":"10.3390\/atmos11111241","volume":"11","author":"Y Valikhujaev","year":"2020","unstructured":"Valikhujaev Y, Abdusalomov A, Cho YI (2020) Automatic fire and smoke detection method for surveillance systems based on dilated CNNs. Atmosphere 11(11):1241","journal-title":"Atmosphere"},{"key":"521_CR5","doi-asserted-by":"crossref","unstructured":"Fan Z, Jamil M, Sadiq, MT, Huang X, Yu X (2020) Exploiting multiple optimizers with transfer learning techniques for the identification of COVID-19 patients, Hindawi Journal of Healthcare Engineering.","DOI":"10.1155\/2020\/8889412"},{"key":"521_CR6","doi-asserted-by":"crossref","unstructured":"Ayala A, Fernandes B, Cruz F, Mac\u02c6edo D, Oliveira AL, Zanchettin C (2020) KutralNet: A portable deep learning model for fire recognition. In: IEEE international joint conference on neural networks (IJCNN).","DOI":"10.1109\/IJCNN48605.2020.9207202"},{"key":"521_CR7","doi-asserted-by":"publisher","first-page":"18174","DOI":"10.1109\/ACCESS.2018.2812835","volume":"6","author":"K Muhammad","year":"2018","unstructured":"Muhammad K, Ahmad J, Mehmood I, Rho S, Baik SW (2018) Convolutional neural networks based fire detection in surveillance videos. IEEE Access 6:18174\u201318183","journal-title":"IEEE Access"},{"issue":"04","key":"521_CR8","first-page":"2277","volume":"9","author":"N Prabhu Ram","year":"2020","unstructured":"Prabhu Ram N, Gokul Kannan R, Gowdham V, Arul Vignesh R (2020) Fire detection using CNN approach. Int J Sci Technol Res 9(04):2277\u20138616","journal-title":"Int J Sci Technol Res"},{"key":"521_CR9","doi-asserted-by":"crossref","unstructured":"Suklabaidya S, Das I (2021) Framing fire detection system of higher efficacy using supervised machine learning techniques. In: Advances in Applications of Data-Driven Computing.","DOI":"10.1007\/978-981-33-6919-1_9"},{"issue":"4","key":"521_CR10","doi-asserted-by":"publisher","first-page":"121","DOI":"10.4316\/AECE.2018.04015","volume":"18","author":"A Namozov","year":"2018","unstructured":"Namozov A, Im Cho Y (2018) An efficient deep learning algorithm for fire and smoke detection with limited data. Adv Electr Comput Eng 18(4):121\u2013128","journal-title":"Adv Electr Comput Eng"},{"issue":"7553","key":"521_CR11","doi-asserted-by":"publisher","first-page":"436","DOI":"10.1038\/nature14539","volume":"521","author":"Y LeCun","year":"2015","unstructured":"LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521(7553):436\u2013444","journal-title":"Nature"},{"issue":"3","key":"521_CR12","doi-asserted-by":"publisher","first-page":"911","DOI":"10.3390\/s20030911","volume":"20","author":"S Li","year":"2020","unstructured":"Li S, Zuo X, Li Z, Wang H (2020) Applying deep learning to continuous bridge deflection detected by fiber optic gyroscope for damage detection. Sensors 20(3):911","journal-title":"Sensors"},{"issue":"4","key":"521_CR13","doi-asserted-by":"publisher","first-page":"478","DOI":"10.1139\/er-2020-0019","volume":"28","author":"P Jain","year":"2020","unstructured":"Jain P, Coogan SC, Subramanian SG, Crowley M, Taylor S, Flannigan MD (2020) A review of machine learning applications in wildfire science and management. Environ Rev 28(4):478\u2013505","journal-title":"Environ Rev"},{"issue":"8","key":"521_CR14","doi-asserted-by":"publisher","first-page":"1619","DOI":"10.1007\/s11760-018-1319-4","volume":"12","author":"Z Zhong","year":"2018","unstructured":"Zhong Z, Wang M, Shi Y, Gao W (2018) A convolutional neural network-based flame detection method in video sequence. Signal, Image Video Process 12(8):1619\u20131627","journal-title":"Signal, Image Video Process"},{"issue":"11","key":"521_CR15","doi-asserted-by":"publisher","first-page":"139","DOI":"10.1145\/3422622","volume":"63","author":"I Goodfellow","year":"2020","unstructured":"Goodfellow I, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, Courville A, Bengio Y (2020) Generative adversarial networks. Commun ACM 63(11):139\u2013144","journal-title":"Commun ACM"},{"key":"521_CR16","doi-asserted-by":"crossref","unstructured":"Frizzi S, Kaabi R, Bouchouicha M, Ginoux JM, Moreau E, Fnaiech F (2016) Convolutional neural network for video fire and smoke detection. In: Proceedings of the IECON 2016, 42nd annual conference of the IEEE industrial electronics society, Florence, Italy; IEEE: Piscataway.","DOI":"10.1109\/IECON.2016.7793196"},{"key":"521_CR17","first-page":"30","volume":"288","author":"K Muhammad","year":"2018","unstructured":"Muhammad K, Ahmad J, Baik SW (2018) Early fire detection using convolutional neural networks during surveillance for effective disaster management. Neuro Comput 288:30\u201342","journal-title":"Neuro Comput"},{"key":"521_CR18","unstructured":"Andola FN, Han S, Moskewicz MW, Ashraf K, Dally WJ, Keutzer K (2016) SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5 MB model size. arXiv."},{"key":"521_CR19","doi-asserted-by":"crossref","unstructured":"Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D, Erhan D, Vanhoucke V, Rabinovich A (2015) Going deeper with convolutions. In: Proceedings of the IEEE computer society conference on computer vision and pattern recognition, Boston, MA, USA","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"521_CR20","doi-asserted-by":"crossref","unstructured":"Sandler M, Howard A, Zhu M, Zhmoginov A, Chen L-C (2018) MobileNetV2: Inverted residuals and linear bottlenecks. In: Proceedings of the 2018 IEEE\/CVF conference on computer vision and pattern recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00474"},{"issue":"9","key":"521_CR21","doi-asserted-by":"publisher","first-page":"1545","DOI":"10.1109\/TCSVT.2015.2392531","volume":"25","author":"P Foggia","year":"2015","unstructured":"Foggia P, Saggese A, Vento M (2015) Real-time fire detection for video-surveillance applications using a combination of experts based on color, shape, and motion. IEEE Trans Circ Syst Video Technol 25(9):1545\u20131556","journal-title":"IEEE Trans Circ Syst Video Technol"},{"issue":"14","key":"521_CR22","doi-asserted-by":"publisher","first-page":"1702","DOI":"10.3390\/rs11141702","volume":"11","author":"R Ba","year":"2019","unstructured":"Ba R, Chen C, Yuan J, Song W, Lo S (2019) SmokeNet: Satellite smoke scene detection using convolutional neural network with spatial and channel-wise attention. Remote Sens 11(14):1702","journal-title":"Remote Sens"},{"key":"521_CR23","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"521_CR24","unstructured":"Nair V, Hinton GE (2010) Rectified linear units improve restricted boltzmann machines. In: Proceedings of the international conference on machine learning, Haifa, Israel"},{"key":"521_CR25","unstructured":"Srivastava N, Hinton G, Krizhevsky A, Sutskever I, Salakhutdinov R (2014) Dropout: A simple way to prevent neural networks from overfitting. JMLR."}],"container-title":["Innovations in Systems and Software Engineering"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11334-022-00521-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11334-022-00521-y\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11334-022-00521-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,3,8]],"date-time":"2025-03-08T02:41:18Z","timestamp":1741401678000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11334-022-00521-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,12,27]]},"references-count":25,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2025,3]]}},"alternative-id":["521"],"URL":"https:\/\/doi.org\/10.1007\/s11334-022-00521-y","relation":{},"ISSN":["1614-5046","1614-5054"],"issn-type":[{"value":"1614-5046","type":"print"},{"value":"1614-5054","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,12,27]]},"assertion":[{"value":"31 December 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"9 December 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 December 2022","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}