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.
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.
Section I: Introduction
1. A Road Map to Artificial Neural Network
2.
Applications of Recurrent Neural Network: Overview and Case Studies
3. Image
to Text Processing Using Convolution Neural Networks
4. Fuzzy Orienteering
Problem Using Genetic Search
5. A Comparative Analysis of Stock Value
Prediction Using Machine Learning Technique Section II: Process and Methods
6. Developing Hybrid Machine Learning Techniques to Forecast the Water
Quality Index (DWM-Bat & DMARS)
7. Analysis of RNNs and Different ML and DL
Classifiers on Speech- Based Emotion Recognition System Using Linear and
Nonlinear Features
8. Web Service User Diagnostics with Deep Learning
Architectures
9. D-SegNet: A Modified Encoder-Decoder Approach for Pixel-Wise
Classification of Brain Tumor from MRI Images
10. Data Analytics for
Intrusion Detection System Based on Recurrent Neural Network and Supervised
Machine Learning Methods Section III: Applications
11. Triple Steps for
Verifying Chemical Reaction Based on Deep Whale Optimization Algorithm
(VCR-WOA)
12. Structural Health Monitoring of Existing Building Structures
for Creating Green Smart Cities Using Deep Learning 13 Artificial
Intelligence-Based Mobile Bill Payment System Using Biometric Fingerprint
14.
An Efficient Transfer LearningBased CNN Multi-Label Classification and
ResUNET Based Segmentation of Brain Tumor in MRI
15. Deep LearningBased
Financial Forecasting of NSE Using Sentiment Analysis
16. An Efficient
Convolutional Neural Network with Image Augmentation for Cassava Leaf Disease
Detection Section IV: PostCOVID-19 Futuristic Scenarios Based Applications:
Issues and Challenges
17. AI-Based Classification and Detection of COVID-19
on Medical Images Using Deep Learning
18. An Innovative Electronic
Sterilization System (S-Vehicle, NaOCI.5H2O and CeO2NP)
19. Comparative
Forecasts of Confirmed COVID-19 Cases in Botswana Using Box-Jenkins ARIMA
and Exponential Smoothing State-Space Models
20. Recent Advancement in Deep
Learning: Open Issues, Challenges, and a Way Forward
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.
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