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It helps to train the image and apply the classification techniques to categorise the turbid images for the selected features from the Benchmark Turbid Image Dataset. The proposed system was trained with several underwater images based on CNN models, which are independent to each sort of underwater image formation. Experimental results show that DUICM provides better classification accuracy against turbid underwater images. The proposed neural network model is validated using turbid images with different characteristics to prove the generalization capabilities.<\/p>","DOI":"10.4018\/ijghpc.2020070106","type":"journal-article","created":{"date-parts":[[2020,6,23]],"date-time":"2020-06-23T14:10:56Z","timestamp":1592921456000},"page":"88-100","source":"Crossref","is-referenced-by-count":15,"title":["DUICM Deep Underwater Image Classification Mobdel using Convolutional Neural Networks"],"prefix":"10.4018","volume":"12","author":[{"given":"Manimaran","family":"Aridoss","sequence":"first","affiliation":[{"name":"Department of Computer Applications, Madanapalle Institute of Technology and Science, India"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5279-950X","authenticated-orcid":true,"given":"Chandramohan","family":"Dhasarathan","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Madanapalle Institute of Technology and Science, India"}]},{"given":"Ankur","family":"Dumka","sequence":"additional","affiliation":[{"name":"Graphic Era (Deemed to be University), Dehradun, India"}]},{"given":"Jayakumar","family":"Loganathan","sequence":"additional","affiliation":[{"name":"Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, India"}]}],"member":"2432","reference":[{"key":"IJGHPC.2020070106-0","doi-asserted-by":"publisher","DOI":"10.1016\/j.future.2018.01.001"},{"key":"IJGHPC.2020070106-1","doi-asserted-by":"crossref","unstructured":"Li, J., Skinner, K. 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