IoT-enabled Convolutional Neural Networks: Techniques and Applications (eBook, ePUB)

Redaktion: Naved, Mohd; Elngar, Ahmed A.; Gaur, Loveleen; Devi, V. Ajantha

• Format: ePub

Produktbeschreibung

Convolutional neural networks (CNNs), a type of deep neural network that has become dominant in a variety of computer vision tasks, in recent years, CNNs have attracted interest across a variety of domains due to their high efficiency at extracting meaningful information from visual imagery. CNNs excel at a wide range of machine learning and deep learning tasks. As sensor-enabled internet of things (IoT) devices pervade every aspect of modern life, it is becoming increasingly critical to run CNN inference, a computationally intensive application, on resource-constrained devices.

Through this edited volume, we aim to provide a structured presentation of CNN-enabled IoT applications in vision, speech, and natural language processing. This book discusses a variety of CNN techniques and applications, including but not limited to, IoT enabled CNN for speech denoising, a smart app for visually impaired people, disease detection, ECG signal analysis, weather monitoring, texture analysis, etc.

Unlike other books on the market, this book covers the tools, techniques, and challenges associated with the implementation of CNN algorithms, computation time, and the complexity associated with reasoning and modelling various types of data. We have included CNNs' current research trends and future directions.

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Produktdetails

• Verlag: Taylor & Francis eBooks
• Seitenzahl: 408
• Erscheinungstermin: 8. Mai 2023
• Englisch
• ISBN-13: 9781000879711
• Artikelnr.: 67647389

Autorenporträt

Dr. Mohd Naved is a machine learning consultant and researcher, currently teaching in Amity International Business School (AIBS), Amity University for various degree and research programs in data science, analytics and machine learning. He is actively engaged in academic research on various topics in management as well as on 21st century technologies. He has published 40+ research articles in reputed journals (SCI/Scopus/ABDC indexed). He has 17 patents in AI/ML and is actively engaged in the commercialization of innovative products developed at university level. Interviews with him have been published in various national and international magazines. A former data scientist, he is an alumnus of Delhi University. He holds a PhD from Noida International University. Dr. V. Ajantha Devi is working as Research Head in AP3 Solutions, Chennai, Tamil Nadu, India. She received her Ph.D. from University of Madras in 2015. She has worked as Project Fellow under a UGC Major Research Project. She is a Senior Member of IEEE. She has been certified as a Microsoft Certified Application Developer (MCAD) and Microsoft Certified Technical Specialist (MCTS) from Microsoft Corp. She has more than 35 papers in international journals and conference proceedings to her credit. She has written, co-authored, and edited a number of books in the field of computer science with international and national publishers such as Elsevier, Springer, etc. She has been a member of the Program Committee/Technical Committee/Chair/Review Board for a variety of international conferences. She has five Australian Patents and one Indian Patent to her credit in the areas of artificial intelligence, image processing and medical imaging. Her work in image processing, signal processing, pattern matching, and natural language processing is based on artificial intelligence, machine learning, and deep learning techniques. She has won many Best paper presentation awards as well as a few research-oriented international awards. Prof. Loveleen Gaur is Professor and Program Director of Artificial Intelligence, Business Intelligence and Data Analytics at the Amity International Business School, Amity University, Noida, India. Her research areas cover interdisciplinary fields including but not limited to artificial intelligence, machine learning and IoT. She is an established author and researcher and has filed five patents and two copyrights in AI/IoT. She is a senior IEEE member and series editor with CRC. Dr. Ahmed A. Elngar is Assistant Professor of Computer Science at the Faculty of Computers and Artificial Intelligence, Beni-Suef University, Egypt. Dr. Elngar is the Founder and Head of the Scientific Innovation Research Group (SIRG). He is a Director of the Technological and Informatics Studies Center (TISC), Faculty of Computers and Artificial Intelligence, Beni-Suef University. He has more than 55 scientific research papers published in prestigious international journals and over 25 books covering such diverse topics as data mining, intelligent systems, social networks and smart environment. Dr. Elngar is a collaborative researcher and is a member of the Egyptian Mathematical Society (EMS) and International Rough Set Society (IRSS). His other research areas include internet of things (IoT), network security, intrusion detection, machine learning, data mining, artificial intelligence, big data, authentication, cryptology, healthcare systems, and automation systems. He is an editor and reviewer of many international journals around the world. Dr. Elngar has won several awards including the Young Researcher in Computer Science Engineering at the Global Outreach Education Summit and Awards 2019, January 2019, Delhi, India. Also, he was awarded Best Young Researcher Award at the Global Education and Corporate Leadership Awards (GECL-2018).

Inhaltsangabe

1. Convolutional Neural Networks in Internet of Things: A Bibliometric
Study 2. Internet of Things Enabled Convolutional Neural Networks:
Applications, Techniques, Challenges, and Future Prospects 3. Convolutional
Neural Network-Based Models for Speech Denoising and Dereverberation:
Algorithms and Applications 4. Edge Computing and Controller Area Network
(CAN) for IoT Data Classification using Convolutional Neural Network 5.
Assistive Smart Cane for Visually Impaired People Based on Convolutional
Neural Networks (CNN) 6. Application of IoT-Enabled CNN for Natural
Language Processing 7. Classification of Myocardial Infarction in ECG
Signals Using Enhanced Deep Neural Network Technique 8. Automation
Algorithm for Labeling of Oil Spill Images using Pre-trained Deep Learning
Model 9. Environmental Weather Monitoring and Predictions System Using
Internet of Things (IoT) Using Convolutional Neural Network 10. E-Learning
Modeling Technique and Convolution Neural Networks in Online Education 11.
Quantitative Texture Analysis with Convolutional Neural Networks 12.
Internet of Things Based Enabled Convolutional Neural Networks in
Healthcare