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Recurrent Neural Networks

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advanced neural sequence modeling applications
ARIMA Model
biomedical data modeling
Category=UYQN
chaos attractor discovery
CNN Architecture
CNN Model
Computational Analysis
computational neuroscience
Convolution Layer
Convolutional Layers
Deep Learning
DNN
DNN Model
EEG Dataset
eq_bestseller
eq_computing
eq_isMigrated=1
eq_isMigrated=2
eq_nobargain
eq_non-fiction
Execution Time
Feature Extraction
Feed-forward Network
Language Modeling
LSTM
Mars Algorithm
Max Pooling Layer
Mri Image
Neural Network
Neural Network Model
Neural Turing Machines
Op
Random Forest
Recurrent and Folding Networks
Recurrent Neural Networks
sentiment analysis methods
Sgd
Simple RNN
Smart Phone
speech signal analysis
Stock Price Prediction
SVM
time series forecasting
TL

Product details

  • ISBN 9781032081649
  • Weight: 920g
  • Dimensions: 156 x 234mm
  • Publication Date: 08 Aug 2022
  • Publisher: Taylor & Francis Ltd
  • Publication City/Country: GB
  • Product Form: Hardback
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Description

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 logistics

The 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.
About

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.