{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,11,19]],"date-time":"2024-11-19T18:49:59Z","timestamp":1732042199331},"reference-count":39,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2022,12,6]],"date-time":"2022-12-06T00:00:00Z","timestamp":1670284800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["MAKE"],"abstract":"Climate change is expected to increase fire events and activity with multiple impacts on human lives. Large grids of forest and city monitoring devices can assist in incident detection, accelerating human intervention in extinguishing fires before they get out of control. Artificial Intelligence promises to automate the detection of fire-related incidents. This study enrols 53,585 fire\/smoke and normal images and benchmarks seventeen state-of-the-art Convolutional Neural Networks for distinguishing between the two classes. The Xception network proves to be superior to the rest of the CNNs, obtaining very high accuracy. Grad-CAM++ and LIME algorithms improve the post hoc explainability of Xception and verify that it is learning features found in the critical locations of the image. Both methods agree on the suggested locations, strengthening the abovementioned outcome.<\/jats:p>","DOI":"10.3390\/make4040057","type":"journal-article","created":{"date-parts":[[2022,12,7]],"date-time":"2022-12-07T11:22:18Z","timestamp":1670412138000},"page":"1124-1135","source":"Crossref","is-referenced-by-count":9,"title":["An Explainable Deep Learning Framework for Detecting and Localising Smoke and Fire Incidents: Evaluation of Grad-CAM++ and LIME"],"prefix":"10.3390","volume":"4","author":[{"ORCID":"http:\/\/orcid.org\/0000-0001-6439-9282","authenticated-orcid":false,"given":"Ioannis D.","family":"Apostolopoulos","sequence":"first","affiliation":[{"name":"Department of Medical Physics, School of Medicine, University of Patras, 26504 Rio, Greece"}]},{"given":"Ifigeneia","family":"Athanasoula","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Technology Engineering, University of Patras, 26504 Rio, Greece"}]},{"given":"Mpesi","family":"Tzani","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Technology Engineering, University of Patras, 26504 Rio, Greece"}]},{"given":"Peter P.","family":"Groumpos","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Technology Engineering, University of Patras, 26504 Rio, Greece"}]}],"member":"1968","published-online":{"date-parts":[[2022,12,6]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1016\/j.gloenvcha.2010.09.012","article-title":"Are We Adapting to Climate Change?","volume":"21","author":"Ford","year":"2011","journal-title":"Glob. 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