Recurrent Neural Networks (Paperback)

Ajith Abraham

ISBN 10: 1032310561 / ISBN 13: 9781032310565

Verlag: Taylor & Francis Ltd, London, 2024

Beschreibung

Paperback. The text discusses recurrent neural networks for prediction and offers new insights into the learning algorithms, architectures, and stability of recurrent neural networks. It discusses important topics including recurrent and folding networks, long short-term memory (LSTM) networks, gated recurrent unit neural networks, language modeling, neural network model, activation function, feed-forward network, learning algorithm, neural turning machines, and approximation ability. The text discusses diverse applications in areas including air pollutant modeling and prediction, attractor discovery and chaos, ECG signal processing, and speech processing. Case studies are interspersed throughout the book for better understanding.FEATURES Covers computational analysis and understanding of natural languages Discusses applications of recurrent neural network in e-Healthcare Provides case studies in every chapter with respect to real-world scenarios Examines open issues with natural language, health care, multimedia (Audio/Video), transportation, stock market, and logisticsThe text is primarily written for undergraduate and graduate students, researchers, and industry professionals in the fields of electrical, electronics and communication, and computer engineering/information technology. This book comprehensively covers concepts of recurrent neural networks and discusses practical issues such as predictability and nonlinearity detecting. It will an ideal text for senior undergraduate, graduate students, researchers, and professionals in the fields of electrical, electronics and communication, and computer engineering. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

Bestandsnummer des Verkäufers 9781032310565

Bibliografische Details

• Titel: Recurrent Neural Networks (Paperback)
• Verlag: Taylor & Francis Ltd, London
• Erscheinungsjahr: 2024
• Sprache: Englisch
• ISBN-10: 1032310561
• ISBN-13: 9781032310565
• Einband: Paperback
• Zustand: new

Über diesen Titel

Inhaltsangabe

This book comprehensively covers concepts of recurrent neural networks and discusses practical issues such as predictability and nonlinearity detecting. It will an ideal text for senior undergraduate, graduate students, researchers, and professionals in the fields of electrical, electronics and communication, and computer engineering.

Über die Autorin bzw. den Autor

Amit Kumar Tyagi is Assistant Professor (Senior Grade), and Senior Researcher at Vellore Institute of Technology (VIT), Chennai Campus, India. His current research focuses on Machine Learning with Big data, Blockchain Technology, Data Science, Cyber Physical Systems, Smart & Secure Computing and Privacy. He has contributed to several projects such as "AARIN" and "P3-Block" to address some of the open issues related to the privacy breaches in Vehicular Applications (such as Parking) and Medical Cyber Physical Systems. He received his Ph.D. Degree from Pondicherry Central University, India. He is a member of the IEEE

Ajith Abraham is the Director of Machine Intelligence Research Labs (MIR Labs), a Not-for-Profit Scientific Network for Innovation and Research Excellence connecting Industry and Academia. As an Investigator and Co-Investigator, he has won research grants worth over 100+ Million US$ from Australia, USA, EU, Italy, Czech Republic, France, Malaysia and China. His research focuses on real world problems in the fields of machine intelligence, cyber-physical systems, Internet of things, network security, sensor networks, Web intelligence, Web services, and data mining. He is the Chair of the IEEE Systems Man and Cybernetics Society Technical Committee on Soft Computing. He is the editor-in-chief of Engineering Applications of Artificial Intelligence (EAAI) and serves/served on the editorial board of several International Journals. He received his Ph.D. Degree in Computer Science from Monash University, Melbourne, Australia.