{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,15]],"date-time":"2026-04-15T17:53:18Z","timestamp":1776275598085,"version":"3.50.1"},"reference-count":70,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2025,1,24]],"date-time":"2025-01-24T00:00:00Z","timestamp":1737676800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"U.S. Department of Energy\u2019s Omni Technology Alliance Internship Program","award":["DE-AC05-00OR22725"],"award-info":[{"award-number":["DE-AC05-00OR22725"]}]},{"name":"U.S. Department of Energy\u2019s Omni Technology Alliance Internship Program","award":["2244396"],"award-info":[{"award-number":["2244396"]}]},{"DOI":"10.13039\/100006229","name":"US Department of Energy (DOE)","doi-asserted-by":"publisher","award":["DE-AC05-00OR22725"],"award-info":[{"award-number":["DE-AC05-00OR22725"]}],"id":[{"id":"10.13039\/100006229","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100006229","name":"US Department of Energy (DOE)","doi-asserted-by":"publisher","award":["2244396"],"award-info":[{"award-number":["2244396"]}],"id":[{"id":"10.13039\/100006229","id-type":"DOI","asserted-by":"publisher"}]},{"name":"NSF","award":["DE-AC05-00OR22725"],"award-info":[{"award-number":["DE-AC05-00OR22725"]}]},{"name":"NSF","award":["2244396"],"award-info":[{"award-number":["2244396"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Imaging"],"abstract":"<jats:p>This research study utilized artificial intelligence (AI) to detect natural disasters from aerial images. Flooding and desertification were two natural disasters taken into consideration. The Climate Change Dataset was created by compiling various open-access data sources. This dataset contains 6334 aerial images from UAV (unmanned aerial vehicles) images and satellite images. The Climate Change Dataset was then used to train Deep Learning (DL) models to identify natural disasters. Four different Machine Learning (ML) models were used: convolutional neural network (CNN), DenseNet201, VGG16, and ResNet50. These ML models were trained on our Climate Change Dataset so that their performance could be compared. DenseNet201 was chosen for optimization. All four ML models performed well. DenseNet201 and ResNet50 achieved the highest testing accuracies of 99.37% and 99.21%, respectively. This research project demonstrates the potential of AI to address environmental challenges, such as climate change-related natural disasters. This study\u2019s approach is novel by creating a new dataset, optimizing an ML model, cross-validating, and presenting desertification as one of our natural disasters for DL detection. Three categories were used (Flooded, Desert, Neither). Our study relates to AI for Climate Change and Environmental Sustainability. Drone emergency response would be a practical application for our research project.<\/jats:p>","DOI":"10.3390\/jimaging11020032","type":"journal-article","created":{"date-parts":[[2025,1,24]],"date-time":"2025-01-24T05:24:31Z","timestamp":1737696271000},"page":"32","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Optimizing Deep Learning Models for Climate-Related Natural Disaster Detection from UAV Images and Remote Sensing Data"],"prefix":"10.3390","volume":"11","author":[{"given":"Kim","family":"VanExel","sequence":"first","affiliation":[{"name":"Bioenvironmental Sciences Department, Morgan State University, Baltimore, MD 21251, USA"}]},{"given":"Samendra","family":"Sherchan","sequence":"additional","affiliation":[{"name":"Center for Climate Change & Health, Morgan State University, Baltimore, MD 21251, USA"}]},{"given":"Siyan","family":"Liu","sequence":"additional","affiliation":[{"name":"Oak Ridge National Laboratory, Oak Ridge, TN 37830, USA"}]}],"member":"1968","published-online":{"date-parts":[[2025,1,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"012001","DOI":"10.1088\/2752-5295\/ac6e7d","article-title":"Extreme weather impacts of climate change: An attribution perspective","volume":"1","author":"Clarke","year":"2022","journal-title":"Environ. Res. Clim."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Intergovernmental_Panel_On_Climate_Change(IPCC) (2023). Climate Change 2021\u2014The Physical Science Basis: Working Group I Contribution to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, Cambridge University Press.","DOI":"10.1017\/9781009157896"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"eaaw1838","DOI":"10.1126\/sciadv.aaw1838","article-title":"The emergence of heat and humidity too severe for human tolerance","volume":"6","author":"Raymond","year":"2020","journal-title":"Sci. Adv."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Delforge, D., Wathelet, V., Below, R., Sofia, C.L., Tonnelier, M., van Loenhout, J., and Speybroeck, N. (2023). EM-DAT: The Emergency Events Database. Res. Sq., preprint.","DOI":"10.21203\/rs.3.rs-3807553\/v1"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"797","DOI":"10.1007\/s00382-015-2613-2","article-title":"Anthropogenic Footprint of Climate Change in the June 2013 Northern India Flood","volume":"46","author":"Cho","year":"2015","journal-title":"Clim. Dyn."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.wace.2017.03.004","article-title":"Diagnosing conditional anthropogenic contributions to heavy Colorado rainfall in September 2013","volume":"17","author":"Pall","year":"2017","journal-title":"Weather. Clim. Extrem."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"897","DOI":"10.5194\/hess-21-897-2017","article-title":"Rapid attribution of the August 2016 flood-inducing extreme precipitation in south Louisiana to climate change","volume":"21","author":"Kapnick","year":"2017","journal-title":"Hydrol. Earth Syst. Sci."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1881","DOI":"10.1175\/JHM-D-18-0074.1","article-title":"Validation of a rapid attribution of the May\/June 2016 flood-inducing precipiation in France to climate change","volume":"19","author":"Philip","year":"2018","journal-title":"J. Hydrometerorol."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"4193","DOI":"10.1007\/s00382-018-4375-0","article-title":"Investigation of the mechanisms leading to the 2017 Montreal flood","volume":"52","author":"Teufel","year":"2018","journal-title":"Clim. Dyn."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1380","DOI":"10.1002\/ldr.3556","article-title":"Global desertification vulnerability to climate change and human activities","volume":"31","author":"Huang","year":"2020","journal-title":"Land Degrad. Dev."},{"key":"ref_11","unstructured":"UNCCD (2024, October 01). United Nations Convention to Combat Desertification in Countries Experiencing Serous Drought and\/or Desertification, Paticularly in Africa. Paris. Available online: https:\/\/www.researchgate.net\/profile\/Salah-Tahoun\/publication\/2870529_Scientific_aspects_of_the_United_Nations_convention_to_combat_desertification\/links\/558008b908aeea18b77a835d\/Scientific-aspects-of-the-United-Nations-convention-to-combat-desertification.pdf."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"815","DOI":"10.1175\/1520-0477(1998)079<0815:DDASVA>2.0.CO;2","article-title":"Desertification, Drought, and Surface Vegetation: An Example from the West African Sahel","volume":"79","author":"Nicholson","year":"1998","journal-title":"Bull. Am. Meteorol. Soc."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"143","DOI":"10.1016\/j.agrformet.2006.03.025","article-title":"Interactions between climate and desertification","volume":"142","author":"Sivakumar","year":"2007","journal-title":"Agric. For. Meteorol."},{"key":"ref_14","unstructured":"Millennium Ecosystem Assessment (MEA) (2005). Ecosystems and Human Well-Being: Desertification Synthesis, World Resources Institute."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"7286","DOI":"10.1109\/JIOT.2021.3098379","article-title":"AI-Enabled Autonomous Drones for Fast Climate Change Crisis Assessment","volume":"9","author":"Hernandez","year":"2021","journal-title":"IEEE Internet Things J."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Alsumayt, A., El-Haggar, N., Amouri, L., Alfawaer, Z.M., and Aljameel, S.S. (2023). Smart Flood Detection with AI and Blockchain Integration in Saudi Arabia Using Drones. Sensors, 23.","DOI":"10.3390\/s23115148"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"109768","DOI":"10.1016\/j.dib.2023.109768","article-title":"Dataset for flood area recognition with semantic segmentation","volume":"51","author":"Intizhami","year":"2023","journal-title":"Data Brief"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Alrayes, F.S., Alotaibi, S.S., Alissa, K.A., Maashi, M., Alhogail, A., Alotaibi, N., Mohsen, H., and Motwakel, A. (2022). Artificial Intelligence-Based Secure Communication and Classification for Drone-Enabled Emergency Monitoring Systems. Drones, 6.","DOI":"10.3390\/drones6090222"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"109164","DOI":"10.1016\/j.dib.2023.109164","article-title":"FloodIMG: Flood image DataBase system","volume":"48","author":"Karanjit","year":"2023","journal-title":"Data Brief"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Malik, I., Ahmed, M., Gulzar, Y., Baba, S.H., Mir, M.S., Soomro, A.B., Sultan, A., and Elwasila, O. (2023). Estimation of the Extent of the Vulnerability of Agriculture to Climate Change Using Analytical and Deep-Learning Methods: A Case Study in Jammu, Kashmir, and Ladakh. Sustainability, 15.","DOI":"10.3390\/su151411465"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Seelwal, P., Dhiman, P., Gulzar, Y., Kaur, A., Wadhwa, S., and Onn, C.W. (2024). A systematic review of deep learning applications for rice disease diagnosis: Current trends and future directions. Front. Comput. Sci., 6.","DOI":"10.3389\/fcomp.2024.1452961"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Alkanan, M., and Gulzar, Y. (2024). Enhanced corn seed disease classification: Leveraging MobileNetV2 with feature augmentation and transfer learning. Front. Appl. Math. Stat., 9.","DOI":"10.3389\/fams.2023.1320177"},{"key":"ref_23","first-page":"753","article-title":"The Impact of Global Warming on Agriculture: A Ricardian Analysis","volume":"84","author":"Nordhaus","year":"1994","journal-title":"Am. Econ. Rev."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"100555","DOI":"10.1016\/j.crm.2023.100555","article-title":"Satellite monitoring for coastal dynamic adaptation policy pathways","volume":"42","author":"Hamlington","year":"2023","journal-title":"Clim. Risk Manag."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Dash, J.P., Pearse, G.D., and Watt, M.S. (2018). UAV Multispectral Imagery Can Complement Satellite Data for Monitoring Forest Health. Remote. Sens., 10.","DOI":"10.3390\/rs10081216"},{"key":"ref_26","first-page":"193","article-title":"AI-driven maize yield forecasting using unmanned aerial vehicle-based hyperspectral and lidar data fusion","volume":"V-3-2022","author":"Dilmurat","year":"2022","journal-title":"ISPRS Ann. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Raniga, D., Amarasingam, N., Sandino, J., Doshi, A., Barthelemy, J., Randall, K., Robinson, S.A., Gonzalez, F., and Bollard, B. (2024). Monitoring of Antarctica\u2019s Fragile Vegetation Using Drone-Based Remote Sensing, Multispectral Imagery and AI. Sensors, 24.","DOI":"10.3390\/s24041063"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Santangeli, A., Chen, Y., Kluen, E., Chirumamilla, R., Tiainen, J., and Loehr, J. (2020). Integrating drone-borne thermal imaging with artificial intelligence to locate bird nests on agricultural land. Sci. Rep., 10.","DOI":"10.1038\/s41598-020-67898-3"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"106079","DOI":"10.1016\/j.compag.2021.106079","article-title":"A study on the use of UAV images to improve the separation accuracy of agricultural land areas","volume":"184","author":"Malamiri","year":"2021","journal-title":"Comput. Electron. Agric."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"100019","DOI":"10.1016\/j.srs.2021.100019","article-title":"UAV & satellite synergies for optical remote sensing applications: A literature review","volume":"3","author":"Corpetti","year":"2021","journal-title":"Sci. Remote Sens."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"2331","DOI":"10.1080\/01431161.2017.1280637","article-title":"UAV data for multi-temporal Landsat analysis of historic reforestation: A case study in Costa Rica","volume":"38","author":"Marx","year":"2017","journal-title":"Int. J. Remote. Sens."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Hassan, N., Miah, A.S.M., and Shin, J. (2024). Residual-Based Multi-Stage Deep Learning Framework for Computer-Aided Alzheimer\u2019s Disease Detection. J. Imaging, 10.","DOI":"10.3390\/jimaging10060141"},{"key":"ref_33","first-page":"67","article-title":"Skin Cancer Classification Using Transfer Learning by VGG16 Architecture (Case Study on Kaggle Dataset)","volume":"15","author":"Ibrahim","year":"2023","journal-title":"J. Intell. Learn. Syst. Appl."},{"key":"ref_34","first-page":"1","article-title":"Predictive Modeling of Breast Cancer Diagnosis Using Neural Networks:A Kaggle Dataset Analysis","volume":"7","year":"2023","journal-title":"Int. J. Acad. Eng. Res."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"587","DOI":"10.1016\/j.ijforecast.2020.07.007","article-title":"Kaggle forecasting competitions: An overlooked learning opportunity","volume":"37","author":"Bojer","year":"2020","journal-title":"Int. J. Forecast."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"9375","DOI":"10.1109\/ACCESS.2017.2788044","article-title":"Deep Learning Applications in Medical Image Analysis","volume":"6","author":"Ker","year":"2017","journal-title":"IEEE Access"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Ghnemat, R., Alodibat, S., and Abu Al-Haija, Q. (2023). Explainable Artificial Intelligence (XAI) for Deep Learning Based Medical Imaging Classification. J. Imaging, 9.","DOI":"10.3390\/jimaging9090177"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Kwenda, C., Gwetu, M., and Fonou-Dombeu, J.V. (2024). Hybridizing Deep Neural Networks and Machine Learning Models for Aerial Satellite Forest Image Segmentation. J. Imaging, 10.","DOI":"10.3390\/jimaging10060132"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Boston, T., Van Dijk, A., and Thackway, R. (2024). U-Net Convolutional Neural Network for Mapping Natural Vegetation and Forest Types from Landsat Imagery in Southeastern Australia. J. Imaging, 10.","DOI":"10.2139\/ssrn.4727252"},{"key":"ref_40","first-page":"1","article-title":"Sentiment Analysis Using Cuckoo Search for Optimized Feature Selection on Kaggle Tweets","volume":"9","author":"Kumar","year":"2019","journal-title":"Int. J. Inf. Retr. Res."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Ahmed, A.A., Shaahid, A., Alnasser, F., Alfaddagh, S., Binagag, S., and Alqahtani, D. (2023). Android Ransomware Detection Using Supervised Machine Learning Techniques Based on Traffic Analysis. Sensors, 24.","DOI":"10.3390\/s24010189"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"382","DOI":"10.1016\/j.ijforecast.2013.07.005","article-title":"A gradient boosting approach to the Kaggle load forecasting competition","volume":"30","author":"Taieb","year":"2014","journal-title":"Int. J. Forecast."},{"key":"ref_43","unstructured":"(2024, October 01). Kaggle. Available online: https:\/\/www.kaggle.com\/."},{"key":"ref_44","unstructured":"RahulTP (2024, October 01). Louisiana Flood 2016. Kaggle. Available online: www.kaggle.com\/datasets\/rahultp97\/louisiana-flood-2016."},{"key":"ref_45","unstructured":"Wang, M. (2024, October 01). FDL_UAV_flooded Areas. Kaggle. Available online: www.kaggle.com\/datasets\/a1996tomousyang\/fdl-uav-flooded-areas."},{"key":"ref_46","unstructured":"Rupak, R. (2024, October 01). Cyclone, Wildfire, Flood, Earthquake Database. Kaggle. Available online: www.kaggle.com\/datasets\/rupakroy\/cyclone-wildfire-flood-earthquake-database."},{"key":"ref_47","unstructured":"Reda, M. (2024, October 01). Satellite Image Classification. Kaggle. Available online: www.kaggle.com\/datasets\/mahmoudreda55\/satellite-image-classification."},{"key":"ref_48","unstructured":"Mystriotis, G. (2024, October 01). Disasters Dataset. Kaggle. Available online: https:\/\/www.kaggle.com\/datasets\/georgemystriotis\/disasters-dataset."},{"key":"ref_49","unstructured":"Bhardwaj, A., and Tuteja, Y. (2024, October 01). Aerial Landscape Images. Kaggle. Available online: https:\/\/www.kaggle.com\/datasets\/ankit1743\/skyview-an-aerial-landscape-dataset."},{"key":"ref_50","unstructured":"Tuteja, Y., and Bhardwaj, A. (2024, October 01). Aerial Images of Cities. Kaggle. Available online: https:\/\/www.kaggle.com\/datasets\/yessicatuteja\/skycity-the-city-landscape-dataset."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Demir, I., Koperski, K., Lindenbaum, D., Pang, G., Huang, J., Basu, S., Hughes, F., Tuia, D., and Raskar, R. (2018, January 18\u201322). DeepGlobe 2018: A Challenge to Parse the Earth through Satellite Images. Proceedings of the 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Salt Lake City, UT, USA.","DOI":"10.1109\/CVPRW.2018.00031"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"3965","DOI":"10.1109\/TGRS.2017.2685945","article-title":"AID: A Benchmark Dataset for Performance Evaluation of Aerial Scene Classification","volume":"55","author":"Xia","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"1865","DOI":"10.1109\/JPROC.2017.2675998","article-title":"Remote Sensing Image Classification: Benchmark and State of the Art","volume":"105","author":"Cheng","year":"2017","journal-title":"Proc. IEEE"},{"key":"ref_54","unstructured":"Simonyan, K., and Zisserman, A. (2015). Very Deep Convolutional Networks for Large-Scale Image Recognition. arXiv."},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., and Fei-Fei, L. (2009, January 20\u201325). ImageNet: A large-scale hierarchical image database. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Miami, FL, USA.","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Zan, X., Zhang, X., Xing, Z., Liu, W., Zhang, X., Su, W., Liu, Z., Zhao, Y., and Li, S. (2020). Automatic Detection of Maize Tassels from UAV Images by Combining Random Forest Classifier and VGG16. Remote Sens., 12.","DOI":"10.3390\/rs12183049"},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Huang, G., Liu, Z., van der Maaten, L., and Weinberger, K.Q. (2018). Densely Connected Convolutional Networks. arXiv.","DOI":"10.1109\/CVPR.2017.243"},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"100258","DOI":"10.1016\/j.array.2022.100258","article-title":"Data augmentation: A comprehensive survey of modern approaches","volume":"16","author":"Mumumi","year":"2022","journal-title":"Array"},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"112086","DOI":"10.1016\/j.matdes.2023.112086","article-title":"Robustness of machine learning to color, size change, normalization, and image enhancement on micrograph datasets with large sample differences","volume":"232","author":"Pei","year":"2023","journal-title":"Mater. Des."},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Habib, G., and Qureshi, S. (2022). GAPCNN with HyPar: Global Average Pooling convolutional neural network with novel NNLU activation function and HYBRID parallelism. Front. Comput. Neurosci., 16.","DOI":"10.3389\/fncom.2022.1004988"},{"key":"ref_61","first-page":"1929","article-title":"Dropout: A Simple Way to Prevent Neural Networks from Overfitting","volume":"15","author":"Srivastava","year":"2014","journal-title":"J. Mach. Learn. Res."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"112","DOI":"10.1016\/j.neucom.2019.10.008","article-title":"Impact of fully connected layers on performance of convolutional neural networks for image classification","volume":"378","author":"Basha","year":"2020","journal-title":"Neurocomputing"},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2015). Deep Residual Learning for Image Recognition. arXiv.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"5008854","DOI":"10.1155\/2022\/5008854","article-title":"Automatic Building Extraction on Satellite Images Using Unet and ResNet50","volume":"2022","author":"Alsabhan","year":"2022","journal-title":"Comput. Intell. Neurosci."},{"key":"ref_65","doi-asserted-by":"crossref","unstructured":"Abu, M., Zahri, N.A.H., Amir, A., Ismail, M.I., Yaakub, A., Anwar, S.A., and Ahmad, M.I. (2022). A Comprehensive Performance Analysis of Transfer Learning Optimization in Visual Field Defect Classification. Diagnostics, 12.","DOI":"10.3390\/diagnostics12051258"},{"key":"ref_66","doi-asserted-by":"crossref","unstructured":"Yang, Y., Zhang, L., Du, M., Bo, J., Liu, H., Ren, L., Li, X., and Deen, M.J. (2021). A comparative analysis of eleven neural networks architectures for small datasets of lung images of COVID-19 patients toward improved clinical decisions. Comput. Biol. Med., 139.","DOI":"10.1016\/j.compbiomed.2021.104887"},{"key":"ref_67","doi-asserted-by":"crossref","unstructured":"Zafar, A., Aamir, M., Nawi, N.M., Arshad, A., Riaz, S., Alruban, A., Dutta, A.K., and Almotairi, S. (2022). A Comparison of Pooling Methods for Convolutional Neural Networks. Appl. Sci., 12.","DOI":"10.3390\/app12178643"},{"key":"ref_68","doi-asserted-by":"crossref","unstructured":"Devnath, L., Luo, S., Summons, P., Wang, D., Shaukat, K., Hameed, I.A., and Alrayes, F.S. (2022). Deep Ensemble Learning for the Automatic Detection of Pneumoconiosis in Coal Worker\u2019s Chest X-ray Radiography. J. Clin. Med., 11.","DOI":"10.3390\/jcm11185342"},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"100128","DOI":"10.1016\/j.atech.2022.100128","article-title":"Effect of varying training epochs of a Faster Region-Based Convolutional Neural Network on the Accuracy of an Automatic Weed Classification Scheme","volume":"3","author":"Ajayi","year":"2022","journal-title":"Smart Agric. Technol."},{"key":"ref_70","first-page":"821","article-title":"Identification of COVID-19 samples from chest X-Ray images using deep learning: A comparison of transfer learning approaches","volume":"28","author":"Rahaman","year":"2020","journal-title":"J. X-Ray Sci. Technol."}],"container-title":["Journal of Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2313-433X\/11\/2\/32\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,8]],"date-time":"2025-10-08T10:35:19Z","timestamp":1759919719000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2313-433X\/11\/2\/32"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,1,24]]},"references-count":70,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2025,2]]}},"alternative-id":["jimaging11020032"],"URL":"https:\/\/doi.org\/10.3390\/jimaging11020032","relation":{},"ISSN":["2313-433X"],"issn-type":[{"value":"2313-433X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,1,24]]}}}