Utku Kose

Utku KoseDr. Utku Kose is an Associate Professor at Süleyman Demirel University, Turkey. He received his PhD from Selcuk University, Turkey, in the field of computer engineering. He has more than 100 publications to his credit, including Deep Learning for Medcal Decision Support Systems, Springer; Artificial Intelligence Applications in Distance Education, IGI Global; Smart Applications with Advanced Machine Learning and Human-Centered Problem Design, Springer; Artificial Intelligence for Data-Driven Medical Diagnosis, DeGruyter; Computational Intelligence in Software Modeling, DeGruyter; Data Science for Covid-19, Volumes 1 and 2, Elsevier/Academic Press; and Deep Learning for Medical Applications with Unique Data, Elsevier/Academic Press, among others. Dr. Kose is a Series Editor of the Biomedical and Robotics Healthcare series from Taylor & Francis/CRC Press. His research interests include artificial intelligence, machine ethics, artificial intelligence safety, optimization, chaos theory, distance education, e-learning, computer education, and computer science. Read More Read Less

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IoT Technologies and Wearables for HealthCare37 %
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Innovative Methods in Computer Science and Computational Applications in the Era of Industry 5.037 %
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Innovative Methods in Computer Science and Computational Applications in the Era of Industry 5.037 %
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Deep Sciences for Computing and Communications37 %
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Deep Sciences for Computing and Communications37 %
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Computational Modeling Applications for Climate Crisis45 %
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Deep Learning in Biomedical Signal and Medical Imaging
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Interpretable Cognitive Internet of Things for Healthcare37 %
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Artificial Intelligence
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Artificial Intelligence and Digital Twin Technology37 %
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Explainable Machine Learning for Multimedia Based Healthcare Applications37 %
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Innovative Methods in Computer Science and Computational Applications in the Era of Industry 5.037 %
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Innovative Methods in Computer Science and Computational Applications in the Era of Industry 5.037 %
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Interpretable Cognitive Internet of Things for Healthcare37 %
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Deep Sciences for Computing and Communications37 %
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Explainable Machine Learning for Multimedia Based Healthcare Applications37 %
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Smart Applications with Advanced Machine Learning and Human-Centred Problem Design37 %
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Deep Learning for Medical Applications with Unique Data45 %
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