H
Hamido Fujita
Researcher at University of Granada
Publications - 423
Citations - 18313
Hamido Fujita is an academic researcher from University of Granada. The author has contributed to research in topics: Computer science & Rough set. The author has an hindex of 58, co-authored 366 publications receiving 11420 citations. Previous affiliations of Hamido Fujita include Ho Chi Minh City University of Technology & Iwate Prefectural University.
Papers
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Journal ArticleDOI
Application of deep convolutional neural network for automated detection of myocardial infarction using ECG signals
U. Rajendra Acharya,U. Rajendra Acharya,U. Rajendra Acharya,Hamido Fujita,Shu Lih Oh,Yuki Hagiwara,Jen Hong Tan,Muhammad Adam +7 more
TL;DR: A convolutional neural network algorithm is implemented for the automated detection of a normal and MI ECG beats (with noise and without noise) and can accurately detect the unknown ECG signals even with noise.
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Automated detection of arrhythmias using different intervals of tachycardia ECG segments with convolutional neural network
TL;DR: A convolutional neural network (CNN) technique to automatically detect the different ECG segments and can serve as an adjunct tool to assist clinicians in confirming their diagnosis is presented.
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An efficient binary Salp Swarm Algorithm with crossover scheme for feature selection problems
Hossam Faris,Majdi Mafarja,Ali Asghar Heidari,Ibrahim Aljarah,Ala' M. Al-Zoubi,Seyedali Mirjalili,Hamido Fujita +6 more
TL;DR: Two new wrapper FS approaches that use SSA as the search strategy are proposed and it is observed that the proposed approach significantly outperforms others on around 90% of the datasets.
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Consensus Reaching in Social Network Group Decision Making: Research Paradigms and Challenges
Yucheng Dong,Quanbo Zha,Hengjie Zhang,Gang Kou,Hamido Fujita,Francisco Chiclana,Enrique Herrera-Viedma,Enrique Herrera-Viedma +7 more
TL;DR: A review of CRPs in SNGDM is provided, and as a result it classifies them into two paradigms: (i) the CRP paradigm based on trust relationships, and (ii) theCRP paradigmbased on opinion evolution.
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Deep convolution neural network for accurate diagnosis of glaucoma using digital fundus images
U. Raghavendra,Hamido Fujita,Sulatha V. Bhandary,Anjan Gudigar,Jen Hong Tan,U. Rajendra Acharya +5 more
TL;DR: An eighteen layer CNN framework is proposed for glaucoma diagnosis with the highest accuracy of 98.13% using 1426 fundus images, which demonstrates the robustness of the system, which can be used as a supplementary tool for the clinicians to validate their decisions.