Recurrent Neural Networks: Concepts and Applications
- Length: 396 pages
- Edition: 1
- Language: English
- Publisher: CRC Pr I Llc
- Publication Date: 2022-08-08
- ISBN-10: 1032081643
- ISBN-13: 9781032081649
- Sales Rank: #0 (See Top 100 Books)
The text discusses recurrent neural networks for prediction and offers new insight into the learning algorithms, architectures, and stability of recurrent neural networks.
It discusses important topics including recurrent and folding networks, long short-term memory 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.
The book-
- 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, healthcare, multimedia (Audio/ Video), transportation, stock market, and logistics.
The text is primarily written for senior undergraduate, graduate students, researchers, and professionals in the fields of electrical, electronics and communication, and computer engineering.
The text examines a comparative study on the problem of real-world’s applications’ forecast, by using different classes of state-of-the-art recurrent neural networks. It provides an overview of the most important architectures and defines guidelines for configuring recurrent networks to predict real-valued time series. It will be a valuable resource for senior undergraduate, graduate students, researchers, and professionals in the fields of electrical, electronics and communication, and computer engineering.
Cover Page Half Title page Title Page Copyright Page Contents Preface Editors Contributors Section I Introduction 1 A Road Map to Artificial Neural Network 1.1 Introduction 1.2 Biological Inspiration of Artificial Neural Network 1.3 The Architecture of Artificial Neural Network 1.4 Activation Functions for Artificial Neural Network 1.5 Types of Artificial Neural Network 1.6 Training Algorithms for Artificial Neural Network 1.7 Applications of Artificial Neural Network 1.8 Conclusion References 2 Applications of Recurrent Neural Network Overview and Case Studies 2.1 Introduction 2.1.1 EEG Signal Analysis on Seizure Detection 2.1.1.1 Types of Recurrent Neural Networks 2.1.1.2 EEG Dataset 2.1.1.3 Methodology Implemented 2.1.1.4 Result 2.1.2 Recognizing Textual Entailment 2.1.2.1 Architecture 2.1.2.1.1 Decomposable Attention Model 2.1.2.1.2 Asymmetric Attention Model 2.1.2.2 LSTM 2.1.2.3 GRU 2.1.2.4 Evaluation 2.1.3 Dataset 2.1.4 Evaluation of Models 2.1.5 Analysis of Sentences 2.1.5.1 General Sentences 2.1.5.2 Active-Passive Sentences 2.1.6 Inference 2.2 Conclusion 2.2.1 EEG Analysis 2.2.2 RTE Analysis References 3 Image to Text Processing Using Convolution Neural Networks 3.1 Introduction 3.1.1 Convolutional Neural Networks 3.2 Literature Survey 3.3 Methodology 3.3.1 Recurrent Neural Networks 3.4 Implementation 3.5 Results and Discussion 3.6 Conclusion References 4 Fuzzy Orienteering Problem Using Genetic Search 4.1 Introduction 4.2 Chance-Constrained Programming 4.3 The Orienteering Problem 4.3.1 Deterministic Model 4.3.2 Fuzzy Model 4.4 The Proposed Method 4.4.1 Encoding Scheme 4.4.2 Fitness Function 4.4.3 Selection 4.4.4 Crossover 4.4.5 Mutation 4.4.6 Probability of Crossover and Mutation 4.4.7 The Proposed Genetic Algorithm 4.5 Result and Discussion 4.5.1 Data 4.5.2 Parameters of Proposed Algorithm 4.5.3 Results 4.6 Conclusion and Future Scope References 5 A Comparative Analysis of Stock Value Prediction Using Machine Learning Technique 5.1 Introduction 5.1.1 Stock Market 5.1.2 Deep Learning 5.1.3 Objective 5.2 Literature Review 5.3 Methodology and Analysis 5.3.1 Deep Neural Network 5.3.1.1 Input Layer 5.3.1.2 Hidden Layers 5.3.1.3 Activation Function 5.3.1.4 Neuron Weights 5.3.1.5 Output Layer 5.3.2 Recurrent Neural Network (RNN) 5.3.2.1 Back-Propagation through Time 5.3.2.2 Problems in RNN 5.3.3 Long Short-Term Memory Models (LSTM) 5.3.4 Activation Functions 5.3.4.1 Rectified Linear Unit Function 5.4 Experimentation and Results 5.4.1 Data Collection 5.4.2 Data Preprocessing 5.4.3 Analysis of Various Models on Stock Data 5.4.3.1 DNN Model 5.4.3.2 RNN Model 5.4.3.3 LSTM Model 5.5 Conclusion References Section II Process and Methods 6 Developing Hybrid Machine Learning Techniques to Forecast the Water Quality Index (DWM-Bat & DMARS) 6.1 Introduction 6.2 Literature Survey 6.3 Building IM12CP-WQI 6.3.1 Description of Dataset 6.3.2 Results of IM12CP-WQI 6.4 Conclusions and Recommendation for Future Works References 7 Analysis of RNNs and Different ML and DL Classifiers on Speech-Based Emotion Recognition System Using Linear and Nonlinear Features 7.1 Introduction 7.2 Methodology 7.2.1 Workflow 7.2.2 Preprocessing 7.2.2.1 Silence Removal 7.2.2.2 Zero Crossing Rate 7.2.2.3 Short Time Energy 7.2.2.4 Pre-emphasis 7.2.2.5 Framing 7.2.2.6 Windowing 7.2.3 Feature Extraction 7.2.4 Audio Features 7.2.4.1 Chromagram 7.2.4.2 Mel-Frequency Cepstrum (MFC) 7.2.4.3 Mel-Frequency Cepstrum Coefficients (MFCC) 7.3 Classification Models 7.3.1 MLP Classifier 7.3.2 SVC 7.3.3 Random Forest Classifier 7.3.4 Gradient-Boosting Classifier 7.3.5 K-Neighbors Classifier 7.3.6 Recurrent Neural Networks 7.3.7 Bagging Classifier 7.4 Experimentation 7.4.1 Dataset Description 7.4.1.1 EMODB 7.4.1.2 RAVDESS 7.4.2 Training Process 7.5 Results 7.5.1 Recurrent Neural Network (RNN) 7.6 Conclusion 7.7 Future Directions References 8 Web Service User Diagnostics with Deep Learning Architectures 8.1 Introduction 8.2 Convolution Neural Networks 8.3 Recurrent Neural Networks 8.4 Importance of Deep Learning versus Machine Learning 8.5 Feature Extraction and Feature Engineering 8.6 Model Representation and Generation 8.7 Related Work 8.7.1 Deep Learning Architectures 8.8 Deep Learning and Web Services 8.9 Deep Learning Performance Evaluation 8.10 CNN and Web Service Diagnostics 8.11 Convolution Neural Network 8.12 Recurrent Neural Network and Web Services 8.12.1 Recurrent Neural Network 8.13 Long Short-Term Memory and Web Service State Diagnostics 8.13.1 Long Short-Term Memory 8.14 Gated Recurrent Units and Web Service State Diagnostics 8.15 Dataset 8.16 Results and Discussion 8.16.1 Convolution Neural Network 8.16.2 Recurrent Neural Network 8.16.3 Comparison of CNN, LSTM, GRU, RNN 8.17 Summary References 9 D-SegNet A Modified Encoder-Decoder Approach for Pixel-Wise Classification of Brain Tumor from MRI Images 9.1 Introduction 9.2 Literature Review 9.3 System Model 9.3.1 Database 9.3.2 Data Preprocessing 9.3.3 Patch Extraction 9.3.4 Encoder 9.3.5 Feature Fusion 9.3.6 Decoder 9.3.7 Training 9.3.8 Evaluation 9.4 Results and Discussion 9.5 Conclusion References 10 Data Analytics for Intrusion Detection System Based on Recurrent Neural Network and Supervised Machine Learning Methods 10.1 Introduction 10.2 Related Work 10.3 Proposed System 10.3.1 Methodology 10.3.2 Description of the Dataset 10.3.3 Particle Swarm Optimization 10.3.4 Recurrent Neural Network 10.3.5 Extra Tree 10.3.6 Cat Boost 10.3.7 Random Forest 10.3.8 Gradient Boosting 10.4 Results and Discussion 10.4.1 Experimental Findings of PSO-RNN 10.4.2 Experimental Findings of PSO-Extra Tree, PSO-Cat Boost, PSO-RF, PSO-GB 10.4.3 Comparison with Existing Studies Reported in the Literature 10.5 Conclusion and Future Work References Section III Applications 11 Triple Steps for Verifying Chemical Reaction Based on Deep Whale Optimization Algorithm (VCR-WOA) 11.1 Introduction 11.2 Main Concepts Related to Problem 11.2.1 Tokenization Process 11.2.2 Coding 11.2.3 Selection Algorithms 11.2.4 Optimization 11.3 Building VCR-WOA 11.3.1 The VCR-WOA Design Stages 11.3.1.1 Preprocessing Stage 11.3.1.2 Building VCR-WOA Predictor 11.3.1.3 Evaluation Stage 11.4 Implementation and Results of VCR-WOA 11.4.1 Description of Database 11.4.2 Coding Elements of Periodic Table 11.4.3 Tokenization 11.4.4 Applying Whale Optimization Steps 11.4.5 Evaluation the Results 11.5 Conclusion and Future Works References 12 Structural Health Monitoring of Existing Building Structures for Creating Green Smart Cities Using Deep Learning 12.1 Introduction 12.2 Fundamental Objectives of Monitoring of Civil Structures 12.2.1 Cost-Effectiveness 12.2.2 Detecting Early Risk 12.2.3 Improved Public Safety 12.2.4 Increased Life Span of Structure 12.3 Artificial Intelligence 12.3.1 Machine Learning 12.3.2 Deep Learning 12.4 Artificial Intelligence in Civil Engineering 12.4.1 Artificial Intelligence in SHM 12.4.1.1 Image Binarization (IB) 12.4.1.2 Threshold Values 12.4.1.3 Concerns in Crack-Sensing Images 12.5 Case Study of an Unoccupied Building at CSIR-CBRI Campus 12.5.1 Results of Cracks Identification from the Proposed Images in MATLAB 12.6 Conclusion Abbreviation Used References 13 Artificial Intelligence–Based Mobile Bill Payment System Using Biometric Fingerprint 13.1 Overview of Payment Transactions 13.2 Literature Review 13.2.1 Current Scenario 13.2.1.1 Disadvantages of Current Payment Methods 13.2.2 Mobile Banking 13.2.3 e-Wallets 13.2.3.1 NFC Chips 13.2.3.2 Face Recognition 13.2.4 Biometrics Method 13.2.4.1 QR Code Method 13.2.4.2 IoT Method 13.2.4.3 Fingerprint Method 13.2.4.4 E-Cash Transaction 13.2.4.5 Merits and Demerits of Biometric 13.3 Bill Payment System Using Biometric Fingerprint 13.3.1 Architecture Description 13.3.2 Minutiae Extraction and Comparison Algorithm 13.3.2.1 Fingerprint Acquisition 13.3.2.2 Fingerprint Preprocessing 13.3.2.3 Fingerprint Enhancement 13.3.2.4 Feature Extraction 13.3.2.5 Minutiae Matching 13.4 Implementation 13.5 Experimental Results and Discussion 13.6 Conclusion and Future Scope References 14 An Efficient Transfer Learning–Based CNN Multi-Label Classification and ResUNET Based Segmentation of Brain Tumor in MRI 14.1 Introduction 14.2 Review of Related Work 14.3 Dataset and Preprocessing 14.4 Methods Used 14.4.1 VGG16 Model 14.4.2 Convolutional Neural Network 14.5 Transfer Learning (TL) 14.5.1 ResUNET Model 14.6 Implementation 14.6.1 Classification Model 14.6.2 Classifier Metrics and Evaluation 14.6.3 Comparison with Related Classification Models 14.6.4 Segmentation Model 14.6.5 Segmentation Metrics and Evaluation 14.6.6 Comparison with Some Exiting Segmentation Models 14.7 Results 14.8 Conclusion References 15 Deep Learning–Based Financial Forecasting of NSE Using Sentiment Analysis 15.1 Introduction 15.2 Related Work 15.3 System Design 15.4 Data Collection and Processing 15.4.1 Stock Price Prediction Using Historical Data 15.4.2 Textual Data Collection for Sentiment Analysis 15.5 Evaluation Methodology 15.5.1 Accuracy 15.5.2 F1 Score 15.5.3 Mean Absolute Error 15.5.4 Root Mean Square Error 15.6 Experimental Setup 15.6.1 Stock Price Prediction 15.6.1.1 Decision Tree Regressor 15.6.1.2 Random Forest Regressor 15.6.1.3 Gradient Boosting Regressor 15.6.1.4 Long Short-Term Memory 15.6.2 Sentiment Analysis 15.6.2.1 Logistic Regression 15.6.2.2 Support Vector Machine 15.6.3 Hyperparameter Optimization 15.7 Result and Analysis 15.7.1 Feature Selection 15.7.2 Historical Data Analysis 15.7.2.1 Historical Data Analysis Using LSTM 15.8 Conclusion and Future Scope References 16 An Efficient Convolutional Neural Network with Image Augmentation for Cassava Leaf Disease Detection 16.1 Introduction 16.2 Related Works 16.3 Materials and Methods 16.3.1 Data Set Description 16.3.2 Methodology 16.3.2.1 Preprocessing and Augmentation 16.3.2.2 CNN Feature Extractors 16.3.2.3 EfficientB4 Transfer Learning Architecture 16.3.2.4 GPU Training 16.3.2.5 TPU Training 16.3.2.6 Parameters on GPU and TPU 16.3.2.7 Loss Functions Used 16.3.2.8 Optimizer 16.3.2.9 Learning Rate Function 16.4 Experiment Results and Discussion 16.4.1 Performance Analysis 16.5 Conclusion References Section IV Post–COVID-19 Futuristic Scenarios–Based Applications: Issues and Challenges 17 AI-Based Classification and Detection of COVID-19 on Medical Images Using Deep Learning 17.1 Introduction 17.2 Literature Survey 17.2.1 Methodology 17.3 Proposed Model 17.3.1 Dataset Description 17.4 Result and Discussion 17.5 Conclusion References 18 An Innovative Electronic Sterilization System (S-Vehicle, NaOCI.5H2O and CeO2NP) 18.1 Introduction 18.2 Related Works 18.3 Main Tools and Materials 18.3.1 Platform Waspmote [11] 18.3.2 LoRa Modem 18.3.3 Autonomous Vehicles (AVs) [12,13] 18.3.4 GPS Module [14] 18.3.5 Control System [17] 18.3.6 Arduino UNO [13] 18.3.7 Arduino Uno components 18.4 Designed System 18.5 Applications 18.6 Conclusions Declarations References 19 Comparative Forecasts of Confirmed COVID-19 Cases in Botswana Using Box-Jenkin’s ARIMA and Exponential Smoothing State-Space Models 19.1 Introduction 19.2 Literature Review 19.3 Methodology 19.3.1 Dataset Description 19.3.2 Time Series Analysis 19.3.3 ARIMA Algorithm 19.3.4 Exponential Smoothing Algorithm 19.3.5 Testing for Goodness of Fit 19.3.6 Models Prediction Accuracy Measurement 19.3.7 Models Forecast Accuracy Measurement 19.4 Results and Discussions 19.4.1 Visualization of Time Series 19.4.2 ARIMA Modeling 19.4.2.1 Forecasting with the Best ARIMA Model 19.4.3 ETS Modeling 19.4.3.1 Forecasting with the Best ETS Model 19.4.4 Comparison of ARIMA and ETS Models 19.4.5 Summary on the Best-Performing Model 19.5 Conclusion and Future Works References 20 Recent Advancement in Deep Learning Open Issues, Challenges, and a Way Forward 20.1 Introduction 20.2 Evolution 20.3 Motivation 20.4 Popular Applications Using Natural Language Processing (NLP) 20.4.1 Deep Learning Libraries and Framework 20.4.1.1 Tensor Flow 20.4.1.2 PyTorch 20.4.1.3 Keras 20.4.1.4 Sonnet 20.4.1.5 MXnet 20.4.1.6 DL4j (Deep Learning for Java) 20.5 Benefits and Pitfalls of Existing Algorithms 20.6 Related Work 20.7 Conclusion Appendix A References Index
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