Embracing Machines and Humanity Through Cognitive Computing and IoT

1. Front Matter

   Pages i-x

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2. Publishing Temperature and Humidity Sensor Data to ThingSpeak

   • T. Sai Surya Teja, G. Venkata Hari Prasad, I. Meghana, T. Manikanta

   Pages 1-9

3. Design of Hybrid Logic Full Subtractor Using 10 T XOR – XNOR Cell

   • P. Pardhasaradhi, G. V. Ganesh, V. V. S. Krishna, M. Naga Sai Kiran, K. Manikanta Sai

   Pages 11-21

4. Stuck-At Fault Detection in Ripple Carry Adders with FPGA

   • P. Pardhasaradhi, K. V. K. V. L. Pavan Kumar, Raghava Yathiraju, Yadalapalli Sri Sai Sumanth, Surisetty Nishith, Tiyyagura Vishnu Vardhan Reddy

   Pages 23-31

5. Prediction of COVID-19 Spreaders

   • M. Siva Ganga Prasad, Rohitha Mikkilineni, N. Sampath, K. Yashwanth, G. V. Ganesh

   Pages 33-39

6. Smart Agricultural Solutions Through Machine Learning

   • K. V. Daya Sagar, Jasti Lalitha Sai, Shaik Sadiq, Malladi Krishna Prasad

   Pages 41-55

7. Low-Cost ECG-Based Heart Monitoring System with Ubidots Platform

   • A. Vamseekrishna, M. Siva Ganga Prasad, P. Gopi Krishna, P. Bhargavi, S. Rohit, B. Tanmayi

   Pages 57-66

8. Automatic Attendance Management System Using AI and Deep Convolutional Neural Network

   • J. RajaSekhar, Sridevi Sakhamuri, A. Dhruva Teja, T. Siva Sai Bhargav

   Pages 67-76

9. Automatic Vehicle Alert and Accident Detection System Based on Cloud Using IoT

   • Pachipala Yellamma, P. G. Sandeep, R. Revanth Sai, S. Rohith Reddy, D. Mahesh

   Pages 77-85

10. AEFA-ANN: Artificial Electric Field Algorithm-Based Artificial Neural Networks for Forecasting Crude Oil Prices

    • Sarat Chandra Nayak, Subhranginee Das, Biswajit Sahoo, B. Satyanarayana

    Pages 87-94

11. A Critical Survey on Machine Learning Paradigms to Forecast Software Defects by Using Testing Parameters

    • Y. Prasanth, T. Satya Sai Vinuthna, P. Komali, K. Kavya, N. Aneera

    Pages 95-103

12. Low-Power Comparator-Triggered Method of Multiplication for Deep Neural Networks

    • K. Mariya Priyadarshini, C. Santosh, G. U. S. Aiswarya Likitha, I. B. V. Sai Srikar, Peram Ramya

    Pages 105-115

13. Assembly Line Implementation for IOT Applications

    • N. Siddaiah, P. Pardhasaradhi, M. Phanigopi, Y. Vasanthi, Y. Deepika

    Pages 117-122

14. Dementia Disease Detection from Psychiatric Disorders Based on Automatic Speech Analysis

    • Merugu Suresh, Abdul Subhani Shaik, Manir Ahmed

    Pages 123-130

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 information—making the shop floor more cognitive through effective processing, analysis, and operational optimization. Now we are seeing the first-hand potential of cognitive computing—its 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 information—making 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. 

• Cognitive abilities
• Human Computer Interaction (HCI)
• Human Tempered Machine Action
• Human aware devices
• Watson assistant
• Cognitive fusion
• Artificial Intelligence (AI)
• Cognitive IoT
• Cognitive analytic data
• Pattern visualization
• Industry 4.0

• King Khalid University, Abha, Saudi Arabia

  Mohammed Usman

• School of Computing, University of Eastern Finland, Kuopio, Finland

  Xiao-Zhi Gao

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.   

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