Embracing Machines and Humanity Through Cognitive Computing and IoT

This book sheds light on systems that learn extensively, with purpose and naturally interact with humans. Improving operations and increasing competitive differentiation among manufacturing organizations by harnessing the power of cognitive abilities, IoT can help build and influence the flow of informationmaking the shop floor more cognitive through effective processing, analysis, and operational optimization. Now we are seeing the first-hand potential of cognitive computingits ability to transform businesses, governments, and society. The real potential of the cognitive age can be realized by combining data analysis and statistical reasoning of machines with uniquely human qualities, such as self-directed goals, common sense, and moral values, improving operations and increasing competitive differentiation among manufacturing organizations. By harnessing the power of cognitive abilities, IoT can help build and influence the flow of informationmaking the shop floor more cognitive through effective processing, analysis, and operational optimization. Cognitive initiatives come in all shapes and sizes, from change to strategy and everything in between. What most successful projects have in common, no matter how ambitious, is they start with a clear view of what technology can do. Therefore, the first job of a cognitive scientists is to gain a firm understanding of cognitive abilities, as presented in this book.

Highlights the ability of cognitive computing in transforming businesses, governments, and society Presents systems that can learn extensively, with purpose, and interact with humans Discusses how the combination of data analysis of machines can exploit cognitive understanding

Auteur

Mohammed Usman received his Ph.D. from University of Strathclyde, UK. He has more than a decade of experience in academics and academic administration. He is a senior member of IEEE and IEEE Communications Society. He has been the TPC chair and the organizing chair for IEEE conferences and actively involved in IEEE activities. He is currently working as an assistant professor in the department of Electrical Engineering at King Khalid University, Saudi Arabia. He received the academic excellence award from the College of Engineering at King Khalid University and also received the award for best project in 2016. A senior design project under his supervision received the talent and innovation award in 2018. His research is focused on technologies for next-generation wireless networks, signal processing for biomedical application, probabilistic modelling, and channel coding.

Xiao-Zhi Gao received his B.Sc. and M.Sc. degrees from the Harbin Institute of Technology, China, in 1993 and 1996, respectively. He obtained his D.Sc. (Tech.) degree from the Helsinki University of Technology (now Aalto University), Finland, in 1999. He has been working as a professor at the University of Eastern Finland, Finland, since 2018. He is also a guest professor at the Harbin Institute of Technology, Beijing Normal University, and Shanghai Maritime University, China. Prof. Gao has published more than 400 technical papers in refereed journals and international conferences. His research interests are nature-inspired computing methods with their applications in optimization, data mining, machine learning, control, signal processing, and industrial electronics.

Contenu
Publishing Temperature and Humidity Sensor Data to ThingSpeak.- Design of Hybrid Logic Full Subtractor Using 10T XOR-XNOR Cell.- Stuck at Fault Detection in Ripple Carry Adders with FPGA.- Prediction of Covid-19 Spreaders.- SMART AGRICULTURAL SOLUTIONS THROUGH MACHINE LEARNING.- LOW-COST ECG BASED HEART MONITORING SYSTEM WITH UBIDOTS PLATFORM.- AUTOMATIC ATTENDANCE MANAGEMENT SYSTEM USING AI & DEEP CONVOLUTIONAL NEURAL NETWORK.- Automatic Vehicle Alert and Accident Detection System based on CloudUsing IoT.- AEFA-ANN: artificial electric field algorithm-based artificial neural networks for forecasting crude oil prices.- A CRITICAL SURVEY ON MACHINE LEARNING PARADIGMS TO FORECAST SOFTWARE DEFECTS BY USING TESTING PARAMETERS.- Low Power Comparator-Triggered Method of Multiplication for Deep Neural Networks.- Assembly line implementation for IOT applications.- Dementia disease detection from psychiatric disorders based on automatic speech analysis.