Titlebook: Machine Learning Paradigms; Advances in Deep Lea George A. Tsihrintzis,Lakhmi C. Jain Book 2020 The Editor(s) (if applicable) and The Autho

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Deep Learning Approaches in Food Recognitionracy in the past, whereas deep learning approaches enabled the identification of food types and their ingredients. The contents of food dishes are typically deformable objects, usually including complex semantics, which makes the task of defining their structure very difficult. Deep learning methods

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Deep Learning for Twitter Sentiment Analysis: The Effect of Pre-trained Word Embeddingresulted in an enormous source of unstructured data, Big Data. Such a Big Data can be analyzed by companies or organizations with the purpose of extracting customer perspective about their products or services and monitoring marketing trends. Understanding automatically the opinions behind user-gene

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A Good Defense Is a Strong DNN: Defending the IoT with Deep Neural Networksn particular, it discusses a comprehensive solution to enhancing IoT defense in the form of a new protocol for IoT security. The need for IoT security solutions was revisited after the recent attacks on 120 million devices. In the current work, deep learning is studied for critical security applicat

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The Implementation and the Design of a Hybriddigital PI Control Strategy Based on MISO Adaptive Neur’ 2018 International Conference on single-input single-output autoregressive moving average with exogenous input models in terms of accuracy, simplicity and real time fast implementation. The improvement is performed by developing accurate multi-inputs single-output adaptive neuro-fuzzy inference sy

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