J Joshua Thomas

J Joshua ThomasJ. Joshua Thomas is a senior lecturer at KDU Penang University College, Malaysia since 2008. He obtained his PhD (Intelligent Systems Techniques) in 2015 from University Sains Malaysia, Penang, and Master's degree in 1999 from Madurai Kamaraj Universty, India. From July to September 2005, he worked as a research assistant at the Artificial Intelligence Lab in University Sains Malaysia. From March 2008 to March 2010, he worked as a research associate at the same University. Currently, he is working with Machine Learning, Big Data, Data Analytics, Deep Learning, specially targeting on Convolutional Neural Networks (CNN) and Bi-directional Recurrent Neural Networks (RNN) for image tagging with embedded natural language processing, End to end steering learning systems and GAN. His work involves experimental research with software prototypes and mathematical modelling and design He is an editorial board member for the Journal of Energy Optimization and Engineering (IJEOE), and invited guest editor for Journal of Visual Languages Communication (JVLC-Elsevier). He has published more than 30 papers in leading international conference proceedings and peer reviewed journals. Read More Read Less

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Scalable and Distributed Machine Learning and Deep Learning Patterns50 %
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Creasy and Resnik's Maternal-Fetal Medicine: Principles and Practice2 % NR
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Data Fabric Architectures14 % NR
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Intelligent Computing and Optimization37 %
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Intelligent Computing and Optimization37 %
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Intelligent Computing and Optimization37 %
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Intelligent Computing and Optimization37 %
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Intelligent Computing and Optimization37 %
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Intelligent Computing and Optimization37 %
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Intelligent Computing and Optimization37 %
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Intelligent Computing and Optimization37 %
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Smart Green Energy Production33 %
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Intelligent Computing & Optimization37 %
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Artificial Intelligence in Industry 4.0 and 5g Technology
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Going Alt-Ac35 %
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Orthopaedic Basic Science: Foundations of Clinical PracticeNR
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Intelligent Computing and OptimizationNR
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Scalable and Distributed Machine Learning and Deep Learning Patterns43 % NR
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