Titlebook: Hybrid ADCs, Smart Sensors for the IoT, and Sub-1V & Advanced Node Analog Circuit Design; Advances in Analog C Pieter Harpe,Kofi A. A. Maki

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nections between data elements that must be probabilistically inferred .Big Data Imperatives. explains ‘what big data can do‘. It can batch process millions and billions of records both unstructured and structured much faster and cheaper. Big data analytics provide a platform to merge all analysis w

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Kostas Dorisnections between data elements that must be probabilistically inferred .Big Data Imperatives. explains ‘what big data can do‘. It can batch process millions and billions of records both unstructured and structured much faster and cheaper. Big data analytics provide a platform to merge all analysis w

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Alessandro Venca,Nicola Ghittori,Alessandro Bosi,Claudio Nanid convolutional neural network (CNN) have been utilized. In the empirical analysis, different subsets of Twitter messages, ranging from 5000 to 50.000 are taken into consideration. The prediction results obtained by deep-learning based schemes have been compared to conventional classifiers (such as,

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Yun-Shiang Shu,Liang-Ting Kuo,Tien-Yu Lor than 25 GB, which consists of accelerometer and gyroscope sensor data from 21 distinct devices is utilized. We employ different classification methods on extracted 40 features based on various time windows from mobile sensors. Namely, we use random forest, gradient boosting machine, and generalize

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Burak Gönen,Fabio Sebastiano,Robert van Veldhoven,Kofi A. A. Makinwad convolutional neural network (CNN) have been utilized. In the empirical analysis, different subsets of Twitter messages, ranging from 5000 to 50.000 are taken into consideration. The prediction results obtained by deep-learning based schemes have been compared to conventional classifiers (such as,