Note: Front Cover -- Praise -- Title Page -- Rights Page -- Contents -- Preface -- Chapter 1: Where is the Vision? -- Chapter 2: Other Worlds are Possible -- Chapter 3: Way Beyond Recycling - Off-the-Scale Alternatives to Stuff -- Chapter 4: After Transportation - Whole-System Redesign in the City -- Ahapter 5: Adaptation with Sass - Embracing Climate Change -- Chapter 6: A More-Than-Human World - Redesign for Connection -- Chapter 7: Fellowship with Animals - The Great Second Chance -- Chapter 8: The World's Great Liturgies - Toward a Celebratory Environmentalism -- Chapter 9: To the Stars - From Earthlings to Spacelings -- Chapter Notes -- Return of Thanks -- Index -- About the Author -- Books to Build a New Society.

Note: Intro -- Advance Praise -- Title Page -- Rights Page -- Dedication -- Contents -- Acknowledgments -- Preface -- Introduction -- Part One: Compass for Change -- Chapter 1: Introducing and Using the Natural Step Framework -- Chapter 2: Sustainability: The Trouble We Have Talking About It -- Chapter 3: The Natural Step Approach: Why Is It Useful? -- Part Two: Practices that Changed -- Chapter 4: The Eco-municipalities of Sweden: A Little Background -- Chapter 5: Changing to Renewable Energy Sources -- Chapter 6: Getting Away from Fossil-fueled Vehicles: Transportation and Mobility -- Chapter 7: Ecological Housing -- Chapter 8: Green Businesses -- Green Buildings -- Chapter 9: Journeys to Self-sufficiency: Community Eco-economic Development -- Chapter 10: Ecological Schools -- Ecological Education -- Chapter 11: Sustainable Agriculture: Growing Healthy -- Growing Locally -- Chapter 12: Dealing with Waste -- Chapter 13: Natural Resources: Protecting Biodiversity -- Chapter 14: Sustainable Land Use and Planning -- Part Three: How Communities Can Change -- Chapter 15: What Gets in the Way of Change? -- Chapter 16: Three Change Processes That Work -- Chapter 17: Steps to Change -- Chapter 18: Inside the Head of a Process Leader -- Epi logue -- Appendix A: Location Map -- Appendix B: Guide to Swedish Name Pronunciation -- Appendix C: National Association of Swedish Ecomunicipalities (SeKom) Members in 2002 -- References and Sources -- Endnotes -- Index -- About the Authors.

Note: Intro -- Foreword -- Further Reading -- Preface -- Contents -- 1 Introduction to Advances in Machine Learning/Deep Learning-Based Technologies -- 1.1 Editorial Note -- 1.2 Book Summary and Future Volumes -- References -- Part I Machine Learning/Deep Learning in Socializing and Entertainment -- 2 Semi-supervised Feature Selection Method for Fuzzy Clustering of Emotional States from Social Streams Messages -- 2.1 Introduction -- 2.2 The FS-EFCM Algorithm -- 2.2.1 EFCM Execution: Main Steps -- 2.2.2 Initial Parameter Setting -- 2.3 Experimental Results -- 2.3.1 Dataset -- 2.3.2 Feature Selection -- 2.3.3 FS-EFCM at Work -- 2.4 Conclusion -- References -- 3 AI in (and for) Games -- 3.1 Introduction -- 3.2 Game Content and Databases -- 3.3 Intelligent Game Content Generation and Selection -- 3.3.1 Generating Content for a Language Education Game -- 3.4 Conclusions -- References -- Part II Machine Learning/Deep Learning in Education -- 4 Computer-Human Mutual Training in a Virtual Laboratory Environment -- 4.1 Introduction -- 4.1.1 Purpose and Development of the Virtual Lab -- 4.1.2 Different Playing Modes -- 4.1.3 Evaluation -- 4.2 Background and Related Work -- 4.3 Architecture of the Virtual Laboratory -- 4.3.1 Conceptual Design -- 4.3.2 State-Transition Diagrams -- 4.3.3 High Level Design -- 4.3.4 State Machine -- 4.3.5 Individual Scores -- 4.3.6 Quantization -- 4.3.7 Normalization -- 4.3.8 Composite Evaluation -- 4.3.9 Success Rate -- 4.3.10 Weighted Average -- 4.3.11 Artificial Neural Network -- 4.3.12 Penalty Points -- 4.3.13 Aggregate Score -- 4.4 Machine Learning Algorithms -- 4.4.1 Genetic Algorithm for the Weighted Average -- 4.4.2 Training the Artificial Neural Network with Back-Propagation -- 4.5 Implementation -- 4.5.1 Instruction Mode -- 4.5.2 Evaluation Mode -- 4.5.3 Computer Training Mode -- 4.5.4 Training Data Collection Sub-mode. , 4.5.5 Machine Learning Sub-mode -- 4.6 Training-Testing Process and Results -- 4.6.1 Training Data -- 4.6.2 Training and Testing on Various Data Set Groups -- 4.6.3 Genetic Algorithm Results -- 4.6.4 Artificial Neural Network Training Results -- 4.7 Conclusions -- References -- 5 Exploiting Semi-supervised Learning in the Education Field: A Critical Survey -- 5.1 Introduction -- 5.2 Semi-supervised Learning -- 5.3 Literature Review -- 5.3.1 Performance Prediction -- 5.3.2 Dropout Prediction -- 5.3.3 Grade Level Prediction -- 5.3.4 Grade Point Value Prediction -- 5.3.5 Other Studies -- 5.3.6 Discussion -- 5.4 The Potential of SSL in the Education Field -- 5.5 Conclusions -- References -- Part III Machine Learning/Deep Learning in Security -- 6 Survey of Machine Learning Approaches in Radiation Data Analytics Pertained to Nuclear Security -- 6.1 Introduction -- 6.2 Machine Learning Methodologies in Nuclear Security -- 6.2.1 Nuclear Signature Identification -- 6.2.2 Background Radiation Estimation -- 6.2.3 Radiation Sensor Placement -- 6.2.4 Source Localization -- 6.2.5 Anomaly Detection -- 6.3 Conclusion -- References -- 7 AI for Cybersecurity: ML-Based Techniques for Intrusion Detection Systems -- 7.1 Introduction -- 7.1.1 Why Does AI Pose Great Importance for Cybersecurity? -- 7.1.2 Contribution -- 7.2 ML-Based Models for Cybersecurity -- 7.2.1 K-Means -- 7.2.2 Autoencoder (AE) -- 7.2.3 Generative Adversarial Network (GAN) -- 7.2.4 Self Organizing Map -- 7.2.5 K-Nearest Neighbors (k-NN) -- 7.2.6 Bayesian Network -- 7.2.7 Decision Tree -- 7.2.8 Fuzzy Logic (Fuzzy Set Theory) -- 7.2.9 Multilayer Perceptron (MLP) -- 7.2.10 Support Vector Machine (SVM) -- 7.2.11 Ensemble Methods -- 7.2.12 Evolutionary Algorithms -- 7.2.13 Convolutional Neural Networks (CNN) -- 7.2.14 Recurrent Neural Network (RNN) -- 7.2.15 Long Short Term Memory (LSTM). , 7.2.16 Restricted Boltzmann Machine (RBM) -- 7.2.17 Deep Belief Network (DBN) -- 7.2.18 Reinforcement Learning (RL) -- 7.3 Open Topics and Potential Directions -- 7.3.1 Novel Feature Representations -- 7.3.2 Unsupervised Learning Based Detection Systems -- References -- Part IV Machine Learning/Deep Learning in Time Series Forecasting -- 8 A Comparison of Contemporary Methods on Univariate Time Series Forecasting -- 8.1 Introduction -- 8.2 Related Work -- 8.3 Theoretical Background -- 8.3.1 ARIMA -- 8.3.2 Prophet -- 8.3.3 The Holt-Winters Seasonal Models -- 8.3.4 N-BEATS: Neural Basis Expansion Analysis -- 8.3.5 DeepAR -- 8.3.6 Trigonometric BATS -- 8.4 Experiments and Results -- 8.4.1 Datasets -- 8.4.2 Algorithms -- 8.4.3 Evaluation -- 8.4.4 Results -- 8.5 Conclusions -- References -- 9 Application of Deep Learning in Recurrence Plots for Multivariate Nonlinear Time Series Forecasting -- 9.1 Introduction -- 9.2 Related Work -- 9.2.1 Background on Recurrence Plots -- 9.2.2 Time Series Imaging and Convolutional Neural Networks -- 9.3 Time Series Nonlinearity -- 9.4 Time Series Imaging -- 9.4.1 Dimensionality Reduction -- 9.4.2 Optimal Parameters -- 9.5 Convolutional Neural Networks -- 9.6 Model Pipeline and Architecture -- 9.6.1 Architecture -- 9.7 Experimental Setup -- 9.8 Results -- 9.9 Conclusion -- References -- Part V Machine Learning in Video Coding and Information Extraction -- 10 A Formal and Statistical AI Tool for Complex Human Activity Recognition -- 10.1 Introduction -- 10.2 The Hybrid Framework-Formal Languages -- 10.3 Formal Tool and Statistical Pipeline Architecture -- 10.4 DATA Pipeline -- 10.5 Tools for Implementation -- 10.6 Experimentation with Datasets to Identify the Ideal Model -- 10.6.1 KINISIS-Single Human Activity Recognition Modeling -- 10.6.2 DRASIS-Change of Human Activity Recognition Modeling -- 10.7 Conclusions. , References -- 11 A CU Depth Prediction Model Based on Pre-trained Convolutional Neural Network for HEVC Intra Encoding Complexity Reduction -- 11.1 Introduction -- 11.2 H.265 High Efficiency Video Coding -- 11.2.1 Coding Tree Unit Partition -- 11.2.2 Rate Distortion Optimization -- 11.2.3 CU Partition and Image Texture Features -- 11.3 Proposed Methodology -- 11.3.1 The Hierarchical Classifier -- 11.3.2 The Methodology of Transfer Learning -- 11.3.3 Structure of Convolutional Neural Network -- 11.3.4 Dataset Construction -- 11.4 Experiments and Results -- 11.5 Conclusion -- References.

Note: BOOK COVER -- TITLE -- COPYRIGHT -- CONTENTS.

Note: Intro -- Table of Contents -- About the Author -- About the Technical Reviewer -- Introduction -- Part I: Machine Learning for Forecasting -- Chapter 1: Models for Forecasting -- Reading Guide for This Book -- Machine Learning Landscape -- Univariate Time Series Models -- A Quick Example of the Time Series Approach -- Supervised Machine Learning Models -- A Quick Example of the Supervised Machine Learning Approach -- Correlation Coefficient -- Other Distinctions in Machine Learning Models -- Supervised vs. Unsupervised Models -- Classification vs. Regression Models -- Univariate vs. Multivariate Models -- Key Takeaways -- Chapter 2: Model Evaluation for Forecasting -- Evaluation with an Example Forecast -- Model Quality Metrics -- Metric 1: MSE -- Metric 2: RMSE -- Metric 3: MAE -- Metric 4: MAPE -- Metric 5: R2 -- Model Evaluation Strategies -- Overfit and the Out-of-Sample Error -- Strategy 1: Train-Test Split -- Strategy 2: Train-Validation-Test Split -- Strategy 3: Cross-Validation for Forecasting -- K-Fold Cross-Validation -- Time Series Cross-Validation -- Rolling Time Series Cross-Validation -- Backtesting -- Which Strategy to Use for Safe Forecasts? -- Final Considerations on Model Evaluation -- Key Takeaways -- Part II: Univariate Time Series Models -- Chapter 3: The AR Model -- Autocorrelation: The Past Influences the Present -- Compute Autocorrelation in Earthquake Counts -- Positive and Negative Autocorrelation -- Stationarity and the ADF Test -- Differencing a Time Series -- Lags in Autocorrelation -- Partial Autocorrelation -- How Many Lags to Include? -- AR Model Definition -- Estimating the AR Using Yule-Walker Equations -- The Yule-Walker Method -- Train-Test Evaluation and Tuning -- Key Takeaways -- Chapter 4: The MA Model -- The Model Definition -- Fitting the MA Model -- Stationarity -- Choosing Between an AR and an MA Model. , Application of the MA Model -- Multistep Forecasting with Model Retraining -- Grid Search to Find the Best MA Order -- Key Takeaways -- Chapter 5: The ARMA Model -- The Idea Behind the ARMA Model -- The Mathematical Definition of the ARMA Model -- An Example: Predicting Sunspots Using ARMA -- Fitting an ARMA(1,1) Model -- More Model Evaluation KPIs -- Automated Hyperparameter Tuning -- Grid Search: Tuning for Predictive Performance -- Key Takeaways -- Chapter 6: The ARIMA Model -- ARIMA Model Definition -- Model Definition -- ARIMA on the CO2 Example -- Key Takeaways -- Chapter 7: The SARIMA Model -- Univariate Time Series Model Breakdown -- The SARIMA Model Definition -- Example: SARIMA on Walmart Sales -- Key Takeaways -- Part III: Multivariate Time Series Models -- Chapter 8: The SARIMAX Model -- Time Series Building Blocks -- Model Definition -- Supervised Models vs. SARIMAX -- Example of SARIMAX on the Walmart Dataset -- Key Takeaways -- Chapter 9: The VAR Model -- The Model Definition -- Order: Only One Hyperparameter -- Stationarity -- Estimation of the VAR Coefficients -- One Multivariate Model vs. Multiple Univariate Models -- An Example: VAR for Forecasting Walmart Sales -- Key Takeaways -- Chapter 10: The VARMAX Model -- Model Definition -- Multiple Time Series with Exogenous Variables -- Key Takeaways -- Part IV: Supervised Machine Learning Models -- Chapter 11: The Linear Regression -- The Idea Behind Linear Regression -- Model Definition -- Example: Linear Model to Forecast CO2 Levels -- Key Takeaways -- Chapter 12: The Decision Tree Model -- Mathematics -- Splitting -- Pruning and Reducing Complexity -- Example -- Key Takeaways -- Chapter 13: The kNN Model -- Intuitive Explanation -- Mathematical Definition of Nearest Neighbors -- Combining k Neighbors into One Forecast -- Deciding on the Number of Neighbors k. , Predicting Traffic Using kNN -- Grid Search on kNN -- Random Search: An Alternative to Grid Search -- Key Takeaways -- Chapter 14: The Random Forest -- Intuitive Idea Behind Random Forests -- Random Forest Concept 1: Ensemble Learning -- Bagging Concept 1: Bootstrap -- Bagging Concept 2: Aggregation -- Random Forest Concept 2: Variable Subsets -- Predicting Sunspots Using a Random Forest -- Grid Search on the Two Main Hyperparameters of the Random Forest -- Random Search CV Using Distributions -- Distribution for max_features -- Distribution for n_estimators -- Fitting the RandomizedSearchCV -- Interpretation of Random Forests: Feature Importance -- Key Takeaways -- Chapter 15: Gradient Boosting with XGBoost and LightGBM -- Boosting: A Different Way of Ensemble Learning -- The Gradient in Gradient Boosting -- Gradient Boosting Algorithms -- The Difference Between XGBoost and LightGBM -- Forecasting Traffic Volume with XGBoost -- Forecasting Traffic Volume with LightGBM -- Hyperparameter Tuning Using Bayesian Optimization -- The Theory of Bayesian Optimization -- Bayesian Optimization Using scikit-optimize -- Conclusion -- Key Takeaways -- Part V: Advanced Machine and Deep Learning Models -- Chapter 16: Neural Networks -- Fully Connected Neural Networks -- Activation Functions -- The Weights: Backpropagation -- Optimizers -- Learning Rate of the Optimizer -- Hyperparameters at Play in Developing a NN -- Introducing the Example Data -- Specific Data Prep Needs for a NN -- Scaling and Standardization -- Principal Component Analysis (PCA) -- The Neural Network Using Keras -- Conclusion -- Key Takeaways -- Chapter 17: RNNs Using SimpleRNN and GRU -- What Are RNNs: Architecture -- Inside the SimpleRNN Unit -- The Example -- Predicting a Sequence Rather Than a Value -- Univariate Model Rather Than Multivariable -- Preparing the Data -- A Simple SimpleRNN. , SimpleRNN with Hidden Layers -- Simple GRU -- GRU with Hidden Layers -- Key Takeaways -- Chapter 18: LSTM RNNs -- What Is LSTM -- The LSTM Cell -- Example -- LSTM with One Layer of 8 -- LSTM with Three Layers of 64 -- Conclusion -- Key Takeaways -- Chapter 19: The Prophet Model -- The Example -- The Prophet Data Format -- The Basic Prophet Model -- Adding Monthly Seasonality to Prophet -- Adding Holiday Data to Basic Prophet -- Adding an Extra Regressor to Prophet -- Tuning Hyperparameters Using Grid Search -- Key Takeaways -- Chapter 20: The DeepAR Model -- About DeepAR -- Model Training with DeepAR -- Predictions with DeepAR -- Probability Predictions with DeepAR -- Adding Extra Regressors to DeepAR -- Hyperparameters of the DeepAR -- Benchmark and Conclusion -- Key Takeaways -- Chapter 21: Model Selection -- Model Selection Based on Metrics -- Model Structure and Inputs -- One-Step Forecasts vs. Multistep Forecasts -- Model Complexity vs. Gain -- Model Complexity vs. Interpretability -- Model Stability and Variation -- Conclusion -- Key Takeaways -- Index.

Note: Intro -- Table of Contents -- About the Author -- About the Technical Reviewer -- Acknowledgments -- Introduction -- Chapter 1: Rise of the Quantum Machines: Fundamentals -- How This Book Is Organized -- The Essentials of Quantum Computing -- The Qubit -- State of a Qubit -- The Bloch Sphere -- Observables and Operators -- The Hilbert Space -- Measurements -- Superposition -- Entanglement -- Quantum Operators and Gates -- Identity Operators -- Unitary Operators -- The Pauli Group of Matrices and Gates -- Phase Gates -- Cartesian Rotation Gates -- Hadamard Gate -- CNOT Gate -- SWAP Gate -- Density Operator -- Hamiltonian -- Time Evolution of a System -- No-Cloning Theorem -- Fidelity -- Complexity -- Grover's Algorithm -- Shor's Algorithm -- Heisenberg's Uncertainty Principle -- Learning from Data: AI, ML, and Deep Learning -- Quantum Machine Learning -- Setting up the Software Environment -- Option 1: Native Python -- Option 2: Anaconda Python -- Installing the Required Packages and Libraries -- Quantum Computing Cloud Access -- Summary -- Chapter 2: Machine Learning -- Algorithms and Models -- Bias -- Variance -- Bias vs. Variance Trade-off -- Overfitting of Data -- Underfitting of Data -- Ideal Fit of Data -- Model Accuracy and Quality -- Bayesian Learning -- Applied ML Workflow -- Validation of Models -- The Hold-Out Method -- The Cross-Validation Method -- Regression -- Linear Regression -- Least Squares Technique -- Gradient Descent -- Nonlinear and Polynomial Regression -- Classification -- Data-Driven Prediction -- Complexity -- Confusion Matrix -- Supervised Learning -- From Data to Prediction -- Support-Vector Machines (SVM) -- SVM Example with Iris Dataset -- k-Nearest Neighbors -- Error and Loss Functions -- Error and Loss Function for Regression -- Error and Loss Function for Classification -- Unsupervised Learning. , k-Means Clustering -- Reinforcement Learning -- Summary -- Chapter 3: Neural Networks -- Perceptron -- Activation Functions -- Hidden Layers -- Backpropagation -- Hands-on Lab: NN with TensorFlow Playground -- Linear Regression: -- Neural Network Architecture -- Convolutional Neural Network (CNN) -- Feedforward Neural Network -- Hands-on Lab: Image Analysis Using MNIST Dataset -- Hands-on Lab: Deep NN Classifier with Iris Dataset -- Summary -- Chapter 4: Quantum Information Science -- Quantum Information -- Quantum Circuits and Bloch Sphere -- Superposition on Bloch Sphere -- Quantum Circuits with Qiskit -- Bell States with Qiskit -- Quantum Circuits with Cirq -- Bell State Measurement with Cirq -- Entropy: Classical vs. Quantum -- Shannon Entropy -- Von Neumann Entropy -- Evolution of States -- GHZ State -- No-Cloning Theorem Revisited -- Quantum Teleportation -- Gate Scheduling -- Quantum Parallelism and Function Evaluation -- Deutsch's Algorithm -- Deutsch's Algorithm with Cirq -- Quantum Computing Systems -- Summary -- Chapter 5: QML Algorithms I -- Quantum Complexity -- Quantum Feature Maps -- Quantum Embedding -- Information Encoding -- Basis Encoding -- Amplitude Encoding -- Tensor Product Encoding -- Hamiltonian Encoding -- Deutsch-Jozsa Algorithm -- Deutsch-Jozsa with Cirq -- Quantum Phase Estimation -- Quantum Programming with Rigetti Forest -- Installing the QVM -- Installing the QVM and Compiler on Linux -- Measurement and Mixed States -- Mixed States -- Open and Closed Quantum Systems -- Quantum Principal Component Analysis -- Summary -- Chapter 6: QML Algorithms II -- Schmidt Decomposition -- Quantum Metrology -- Entanglement Measurement -- Linear Models -- Generalized Linear Models -- Swap Test -- Kernel Methods -- Kernel Method with Qiskit -- State Preparation -- Interference as a Kernel -- Kernel Method with Rigetti Forest. , State Preparation -- Quantum k-Means Clustering -- Quantum k-Medians Algorithm -- Summary -- Chapter 7: QML Techniques -- HHL Algorithm (Matrix Inversion) -- QUBO -- Ising Model -- QUBO from the Ising Model -- Variational Quantum Circuits -- Variational Quantum Eigensolver (VQE) -- VQE with Qiskit -- Computing the Expectation Value of the Operators -- Ansatz for the VQE -- VQE with Google Cirq -- VQE with Rigetti Forest -- VQE for Molecular Systems -- QAOA -- Hands-on QAOA with Rigetti Forest -- QAOA Solution for a QUBO -- Supervised Learning: Quantum Support-Vector Machines -- Quantum Computing with D-Wave -- Programming the D-Wave Quantum Annealing System -- Adiabatic Quantum Computing -- Quantum Annealing -- Solving NP-Hard Problems -- Unsupervised Learning and Optimization -- Max-Cut with Annealing (D-Wave) -- Max-Cut with QAOA (pyQuil) -- Summary -- Chapter 8: Deep Quantum Learning -- Optimized Learning by D-Wave -- Traveling Salesperson Problem (qbsolve) -- Running the Problem -- Quantum Deep Neural Networks -- Quantum Learning with Xanadu -- PennyLane for Neural Networks -- Cosine Function Fitting with QNN -- Binary Classifier with PennyLane -- QNN with TensorFlow Quantum -- Quantum Convolutional Neural Networks -- Summary -- Chapter 9: QML: Way Forward -- Quantum Computing for Chemistry -- OpenFermion -- Quantum Walks -- Coding Quantum Walk -- Polynomial Time Hamiltonian Simulation -- Ensembles and QBoost -- Ensembles -- QBoost -- Quantum Image Processing (QIMP) -- Tensor Networks -- Quantum Finance -- Quantum Communication -- Summary -- Appendix A: Mathematical Review -- Preliminaries -- Tensor Product -- Eigenvalues and Eigenvectors -- The Fourier Transform (also known as Discrete Fourier Transform) -- Appendix B: Buzzwords in Quantum Tech -- References -- Index.

Note: Intro -- Editor's Preface -- Contents -- Late Fusion of Convolutional Neural Network with Wavelet-Based Ensemble Classifier for Acoustic Scene Classification -- 1 Introduction -- 2 Proposed Methodology -- 2.1 Pre-processing and Feature Extraction -- 2.2 Convolutional Neural Network -- 2.3 Wavelet Scattering -- 2.4 Ensemble Classifiers -- 2.5 Fusion of CNN and Classifiers -- 3 Results and Discussion -- 4 Conclusion -- References -- Deep Learning and Social Media for Managing Disaster: Survey -- 1 Introduction -- 2 Background and Related Works -- 2.1 Recent Surveys -- 2.2 Disaster -- 2.3 Disaster Management -- 3 Disaster Management Models -- 3.1 Discussion About Disaster Management Models -- 4 Social Media -- 5 Retrieving Relevant Information from Social Media -- 5.1 Classification Algorithms -- 5.2 Machine Learning (ML) -- 5.3 Deep Learning (DL) -- 6 Conclusion and Future Works -- References -- A Framework for Adaptive Mobile Ecological Momentary Assessments Using Reinforcement Learning -- 1 Introduction -- 2 Related Work -- 3 Adaptive Mobile EMA -- 3.1 An Unbiased Formulation for Mobile EMA -- 3.2 Using Reinforcement Learning Framework for Adaptive Mobile EMA -- 4 A Two-Level User State Model -- 5 K-Routine Mining Algorithm -- 5.1 Mining K-Routines -- 5.2 Merging K-Routines -- 5.3 Mapping K-Routines -- 6 Designing Adaptive mEMA Method Using RL -- 6.1 RL Algorithm -- 6.2 State Space for Adaptive mEMA -- 6.3 Action Space for Adaptive mEMA -- 6.4 Reward Signal for Adaptive mEMA -- 6.5 Experience Replay for Sample Efficiency Using Dyna-Q -- 6.6 Performance Evaluation -- 7 Experiments -- 7.1 Data -- 7.2 Baseline Methods -- 7.3 Experimental Settings and Research Questions -- 8 Results -- 8.1 Comparisons Within RL Strategies -- 8.2 Comparisons Between RL Strategies and Baseline Methods -- 8.3 Performance by Data Segments -- 9 Discussion -- 10 Conclusion. , References -- Reputation Analysis Based on Weakly-Supervised Bi-LSTM-Attention Network -- 1 Introduction -- 2 Related Work -- 2.1 Machine Learning for Sentiment Analysis -- 2.2 Deep Learning for Sentiment Analysis -- 3 Weakly-Supervised Deep Embedding -- 3.1 The Classic WDE Network Architecture -- 3.2 Model Enhancement - WDE-BiLSTM-Attention -- 4 Experiments -- 4.1 Oversampling -- 4.2 Baselines and Comparison -- 4.3 Sentiment Classification -- 4.4 Topic Mining Based on T-LDA -- 5 Conclusion -- 5.1 Deficiency and Future Work -- References -- Multi-GPU-based Convolutional Neural Networks Training for Text Classification -- 1 Introduction -- 2 Related Work -- 2.1 Data Parallelism Approaches -- 2.2 Communications in Distributed Environment -- 3 Distributed CNN for Text Categorization -- 3.1 Motivation and Objective -- 3.2 Baseline Model -- 3.3 A Parallel CNN Algorithm for Text Classification -- 4 Experimental Results -- 4.1 Experimental Protocol -- 4.2 Experiment 1: Sequential CNN Training -- 4.3 Experiment 2: Sequential vs Distributed Training -- 4.4 Experiment 3: Varying the Number of GPUs -- 5 Conclusion -- References -- Performance Analysis of Data-Driven Techniques for Solving Inverse Kinematics Problems -- 1 Introduction -- 2 Testing Model -- 3 Forward Kinematics -- 4 Analytical Approach -- 4.1 Results of Analytical Techniques -- 4.2 Limitation and Critical Analysis of Analytical Techniques -- 5 Neural Network Approach -- 5.1 Preparation of Data Set -- 5.2 The Neural Network Architecture -- 6 Experimental Results and Validation -- 7 Conclusion and Future Work -- References -- Machine Learning Based H2 Norm Minimization for Maglev Vibration Isolation Platform -- 1 Introduction -- 2 Vibration Isolator Modelling -- 2.1 Derivation of the Balancing Levitation Force -- 2.2 Isolator Dynamics -- 2.3 State-Space Framework of Single Axis Levitation. , 2.4 Four Pole Electromagnet Configuration -- 3 Experimental Setup -- 3.1 Hardware -- 3.2 General Structure -- 4 FSF Controller Syntheses -- 4.1 H2 SF Controller Structure -- 5 Deep Reinforcement Learning Algorithm -- 6 Experimental Results -- 7 Conclusions -- References -- A Vision Based Deep Reinforcement Learning Algorithm for UAV Obstacle Avoidance -- 1 Introduction -- 2 Related Work -- 2.1 Reinforcement Learning for Obstacle Avoidance -- 2.2 Exploration -- 3 Methodology: Towards Improving Exploration -- 3.1 Training Setup -- 3.2 Convergence Exploration -- 3.3 Guidance Exploration -- 4 Results and Discussion -- 5 Conclusion -- References -- Detecting and Fixing Nonidiomatic Snippets in Python Source Code with Deep Learning -- 1 Introduction -- 2 Related Work -- 3 Method -- 3.1 Formal Approach -- 3.2 Neural Architectures -- 4 Dataset Generation -- 4.1 Template Generation -- 4.2 Augmentation of Templates -- 5 Evaluation -- 5.1 Automated and Manual Evaluation -- 5.2 Precision -- 5.3 Recall -- 5.4 Precision of Subsystems -- 6 Conclusion -- A Appendix -- References -- BreakingBED: Breaking Binary and Efficient Deep Neural Networks by Adversarial Attacks -- 1 Introduction -- 2 Compression of Deep Neural Networks -- 2.1 Knowledge Distillation -- 2.2 Pruning -- 2.3 Binarization -- 3 Adversarial Attacks -- 3.1 White-Box Attacks -- 3.2 Black-Box Attacks -- 4 Breaking Binary and Efficient DNNs -- 4.1 CNN Compressed Variants -- 4.2 Evaluation of Robustness -- 4.3 Class Activation Mapping on Attacked CNNs -- 4.4 Robustness Evaluation on ImageNet Dataset -- 4.5 Discussion -- 5 Conclusion -- References -- Parallel Dilated CNN for Detecting and Classifying Defects in Surface Steel Strips in Real-Time -- 1 Introduction -- 2 Related Work -- 3 Dataset and Augmentation -- 4 Proposed DSTEELNet Architecture -- 5 Experiments -- 5.1 Experiment Metrics -- 5.2 Setup. , 5.3 Results -- 5.4 Computational Time -- 6 Conclusion -- References -- Selective Information Control and Network Compression in Multi-layered Neural Networks -- 1 Introduction -- 2 Theory and Computational Methods -- 2.1 Network Compression -- 2.2 Controlling Selective Information -- 2.3 Selective Information-Driven Learning -- 3 Results and Discussion -- 3.1 Experimental Outline -- 3.2 Selective Information Control -- 3.3 Generalization Performance -- 3.4 Interpreting Compressed Weights -- 4 Conclusion -- References -- DAC-Deep Autoencoder-Based Clustering: A General Deep Learning Framework of Representation Learning -- 1 Introduction -- 2 Overview of Deep Autoencoder-Based Clustering -- 3 Deep Autoencoder for Representation Learning -- 3.1 Encoder -- 3.2 Decoder -- 3.3 Objective Function -- 4 Experimental Results -- 4.1 Data Set -- 4.2 Measurement Metrics -- 4.3 Experiment Setup -- 4.4 Results on MNIST -- 4.5 Results on Other Datasets -- 5 Limitation -- 6 Conclusion -- References -- Enhancing LSTM Models with Self-attention and Stateful Training -- 1 Introduction -- 2 Background -- 2.1 Feed-Forward Networks, Recurrent Neural Networks, Back Propagation Through Time -- 2.2 Long Short-Term Memory and Truncated BPTT -- 2.3 Self-attention -- 2.4 Experimental Rationale -- 3 Methodology -- 3.1 Statefulness -- 3.2 LSTM and Attention -- 4 Data -- 4.1 Data Characteristics -- 4.2 Data Sets -- 5 Models -- 5.1 Architectures -- 5.2 Hyperparameters -- 6 Experiments and Results -- 6.1 Model-to-Model and Model-to-Study Comparisons -- 7 Discussion: Training Behavior -- 8 Conclusions -- References -- Domain Generalization Using Ensemble Learning -- 1 Introduction -- 2 Related Work -- 2.1 Ensemble Learning -- 2.2 Transfer Learning -- 2.3 Domain Generalization -- 3 Methods -- 3.1 Data Preparation -- 3.2 Experiments -- 3.3 Hyperparameter Tuning -- 4 Results. , 5 Conclusion -- References -- Research on Text Classification Modeling Strategy Based on Pre-trained Language Model -- 1 Introduction -- 2 Related Work -- 3 Model Architecture -- 3.1 Model Input -- 3.2 Transformer -- 3.3 Capsule Networks -- 3.4 Model Framework -- 4 Experiment Design and Analysis -- 4.1 Experiment Corpus -- 4.2 Evaluation Metrics -- 4.3 Experimental Setup -- 4.4 Comparative Experiment -- 4.5 Ablation Experiment -- 4.6 Experiment Analysis -- 5 Conclusion and Future Work -- References -- Discovering Nonlinear Dynamics Through Scientific Machine Learning -- 1 Introduction -- 2 Scientific Machine Learning Models -- 2.1 Physics-Informed Neural Networks -- 2.2 Universal Differential Equations -- 2.3 Hamiltonian Neural Networks -- 2.4 Neural Ordinary Differential Equations (Neural ODE) -- 3 Physical Experiments -- 3.1 Quadruple Spring Mass System -- 3.2 Pendulum -- 3.3 Simulated Pendulum -- 3.4 Simulation of Wind Forced Pendulum -- 3.5 Physical Experimental Pendulum -- 4 Learning the Nonlinear Dynamics with Scientific Machine Learning -- 4.1 What Do These SciML Models Learn? -- 4.2 Can SciML Predict the Future? -- 4.3 Can HNN Solve Complex Dynamic Problems? -- 5 Conclusion -- References -- Tensor Data Scattering and the Impossibility of Slicing Theorem -- 1 Introduction -- 2 Tensor -- 3 Pick and Slice -- 4 Tensor Variator and Its Provision Tensor -- 5 Nondeterministic of Applying Variator -- 6 Scattering -- 6.1 Scatter APIs in Two Popular Deep Learning Frameworks -- 6.2 Defining Scattering -- 6.3 Sliceable Scattering -- 7 Sparse Tensor with X-Sparse Representation -- 7.1 The Limitations in Current Scattering APIs -- 7.2 X-Sparse Tensor -- 7.3 Counting Sparsity and Analyzing Performance -- 7.4 Mocking Current Scattering APIs -- 8 Conclusion -- References -- Scope and Sense of Explainability for AI-Systems -- 1 Introduction. , 2 Superhuman Abilities of AI.

Note: Intro -- GANs in Action: Deep learning with Generative Adversarial Networks -- Jakub Langr and Vladimir Bok -- Copyright -- Dedication -- Brief Table of Contents -- Table of Contents -- front matter -- Preface -- Acknowledgments -- Jakub Langr -- Vladimir Bok -- About this book -- Who should read this book -- About the code -- liveBook discussion forum -- Other online resources -- How this book is organized: a roadmap -- About the authors -- About the cover illustration -- Saint-Sauveur -- Part 1. Introduction to GANs and generative modeling -- Chapter 1. Introduction to GANs -- 1.1. What are Generative Adversarial Networks? -- 1.2. How do GANs work? -- 1.3. GANs in action -- 1.3.1. GAN training -- 1.3.2. Reaching equilibrium -- 1.4. Why study GANs? -- Summary -- Chapter 2. Intro to generative modeling with autoencoders -- 2.1. Introduction to generative modeling -- 2.2. How do autoencoders function on a high level? -- 2.3. What are autoencoders to GANs? -- 2.4. What is an autoencoder made of? -- 2.5. Usage of autoencoders -- 2.6. Unsupervised learning -- 2.6.1. New take on an old idea -- 2.6.2. Generation using an autoencoder -- 2.6.3. Variational autoencoder -- 2.7. Code is life -- 2.8. Why did we try aGAN? -- Summary -- Chapter 3. Your first GAN: Generating handwritten digits -- 3.1. Foundations of GANs: Adversarial training -- 3.1.1. Cost functions -- 3.1.2. Training process -- 3.2. The Generator and the Discriminator -- 3.2.1. Conflicting objectives -- 3.2.2. Confusion matrix -- 3.3. GAN training algorithm -- 3.4. Tutorial: Generating handwritten digits -- 3.4.1. Importing modules and specifying model input dimensions -- 3.4.2. Implementing the Generator -- 3.4.3. Implementing the Discriminator -- 3.4.4. Building the model -- 3.4.5. Training -- 3.4.6. Outputting sample images -- 3.4.7. Running the model -- 3.4.8. Inspecting the results. , 3.5. Conclusion -- Summary -- Chapter 4. Deep Convolutional GAN -- 4.1. Convolutional neural networks -- 4.1.1. Convolutional filters -- 4.1.2. Parameter sharing -- 4.1.3. ConvNets visualized -- 4.2. Brief history of the DCGAN -- 4.3. Batch normalization -- 4.3.1. Understanding normalization -- 4.3.2. Computing batch normalization -- 4.4. Tutorial: Generating handwritten digits with DCGAN -- 4.4.1. Importing modules and specifying model input dimensions -- 4.4.2. Implementing the Generator -- 4.4.3. Implementing the Discriminator -- 4.4.4. Building and running the DCGAN -- 4.4.5. Model output -- 4.5. Conclusion -- Summary -- Part 2. Advanced topics in GANs -- Chapter 5. Training and common challenges: GANing for success -- 5.1. Evaluation -- 5.1.1. Evaluation framework -- 5.1.2. Inception score -- 5.1.3. Fréchet inception distance -- 5.2. Training challenges -- 5.2.1. Adding network depth -- 5.2.2. Game setups -- 5.2.3. Min-Max GAN -- 5.2.4. Non-Saturating GAN -- 5.2.5. When to stop training -- 5.2.6. Wasserstein GAN -- 5.3. Summary of game setups -- 5.4. Training hacks -- 5.4.1. Normalizations of inputs -- 5.4.2. Batch normalization -- 5.4.3. Gradient penalties -- 5.4.4. Train the Discriminator more -- 5.4.5. Avoid sparse gradients -- 5.4.6. Soft and noisy labels -- Summary -- Chapter 6. Progressing with GANs -- 6.1. Latent space interpolation -- 6.2. They grow up so fast -- 6.2.1. Progressive growing and smoothing of higher-resolution layers -- 6.2.2. Example implementation -- 6.2.3. Mini-batch standard deviation -- 6.2.4. Equalized learning rate -- 6.2.5. Pixel-wise feature normalization in the generator -- 6.3. Summary of key innovations -- 6.4. TensorFlow Hub and hands-on -- 6.5. Practical applications -- Summary -- Chapter 7. Semi-Supervised GAN -- 7.1. Introducing the Semi-Supervised GAN -- 7.1.1. What is a Semi-Supervised GAN?. , 7.1.2. Architecture -- 7.1.3. Training process -- 7.1.4. Training objective -- 7.2. Tutorial: Implementing a Semi-Supervised GAN -- 7.2.1. Architecture diagram -- 7.2.2. Implementation -- 7.2.3. Setup -- 7.2.4. The dataset -- 7.2.5. The Generator -- 7.2.6. The Discriminator -- 7.2.7. Building the model -- 7.2.8. Training -- 7.3. Comparison to a fully supervised classifier -- 7.4. Conclusion -- Summary -- Chapter 8. Conditional GAN -- 8.1. Motivation -- 8.2. What is Conditional GAN? -- 8.2.1. CGAN Generator -- 8.2.2. CGAN Discriminator -- 8.2.3. Summary table -- 8.2.4. Architecture diagram -- 8.3. Tutorial: Implementing a Conditional GAN -- 8.3.1. Implementation -- 8.3.2. Setup -- 8.3.3. CGAN Generator -- 8.3.4. CGAN Discriminator -- 8.3.5. Building the model -- 8.3.6. Training -- CGAN training algorithm -- 8.3.7. Outputting sample images -- 8.3.8. Training the model -- 8.3.9. Inspecting the output: Targeted data generation -- 8.4. Conclusion -- Summary -- Chapter 9. CycleGAN -- 9.1. Image-to-image translation -- 9.2. Cycle-consistency loss: There and back aGAN -- 9.3. Adversarial loss -- 9.4. Identity loss -- 9.5. Architecture -- 9.5.1. CycleGAN architecture: building the network -- 9.5.2. Generator architecture -- 9.5.3. Discriminator architecture -- 9.6. Object-oriented design of GANs -- 9.7. Tutorial: CycleGAN -- 9.7.1. Building the network -- 9.7.2. Building the Generator -- 9.7.3. Building the Discriminator -- 9.7.4. Training the CycleGAN -- CycleGAN training algorithm -- 9.7.5. Running CycleGAN -- 9.8. Expansions, augmentations, and applications -- 9.8.1. Augmented CycleGAN -- 9.8.2. Applications -- Summary -- Part 3. Where to go from here -- Chapter 10. Adversarial examples -- 10.1. Context of adversarial examples -- 10.2. Lies, damned lies, and distributions -- 10.3. Use and abuse of training -- 10.4. Signal and the noise. , 10.5. Not all hope is lost -- 10.6. Adversaries to GANs -- 10.7. Conclusion -- Summary -- Chapter 11. Practical applications of GANs -- 11.1. GANs in medicine -- 11.1.1. Using GANs to improve diagnostic accuracy -- 11.1.2. Methodology -- 11.1.3. Results -- 11.2. GANs in fashion -- 11.2.1. Using GANs to design fashion -- 11.2.2. Methodology -- 11.2.3. Creating new items matching individual preferences -- 11.2.4. Adjusting existing items to better match individual preferences -- 11.3. Conclusion -- Summary -- Chapter 12. Looking ahead -- 12.1. Ethics -- 12.2. GAN innovations -- 12.2.1. Relativistic GAN -- 12.2.2. Self-Attention GAN -- 12.2.3. BigGAN -- 12.3. Further reading -- 12.4. Looking back and closing thoughts -- Summary -- Training Generative Adversarial Networks (GANs) -- Index -- List of Figures -- List of Tables -- List of Listings.

Note: Intro -- Preface -- Introduction -- Contents -- Cross Entropy Based Thresholding Segmentation of Magnetic Resonance Prostatic Images Using Metaheuristic Algorithms -- 1 Introduction -- 1.1 Applied Metaheuristic Algorithms -- 2 Moth-Flame Optimizer Algorithm -- 3 Sine Cosine Optimization Algorithm -- 4 Sunflower Optimization Algorithm -- 5 Image Segmentation Using Minimum Cross Entropy -- 6 Results of the Experiments -- 6.1 Experimental Setup -- 6.2 Metrics and Experimental Results -- 7 Conclusions -- Appendix: MRIs Detailed Series Header Information -- References -- Hyperparameter Optimization in a Convolutional Neural Network Using Metaheuristic Algorithms -- 1 Introduction -- 2 Convolutional Neural Networks -- 2.1 Artificial Neuron -- 2.2 Artificial Neural Network -- 2.3 Training -- 2.4 Convolutional Neural Network Architecture -- 3 Hyperparameters -- 4 Metaheuristic Algorithms -- 4.1 Ant Lion Optimization (ALO) -- 4.2 Artificial Bee Colony (ABC) -- 4.3 Bat Algorithm (BA) -- 4.4 Particle Swarm Optimization -- 5 The General Procedure for Hyperparameter Optimization in a Convolutional Neural Network Using Metaheuristic Algorithms -- 6 Experimental Result -- 7 Conclusion -- References -- Diagnosis of Collateral Effects in Climate Change Through the Identification of Leaf Damage Using a Novel Heuristics and Machine Learning Framework -- 1 Introduction -- 2 Materials and Methods -- 2.1 Data Acquisition -- 2.2 Frequency Histogram -- 2.3 Brightness Reduction -- 2.4 Contrast Enhancement -- 2.5 Segmentation -- 2.6 Otsu Method Thresholding -- 2.7 Database Creation -- 2.8 Classification -- 3 Experiments and Results -- 4 Analysis and Discussion -- 5 Conclusions and Future Research -- References -- Feature Engineering for Machine Learning and Deep Learning Assisted Wireless Communication -- 1 Introduction. , 2 Feature Engineering for Machine Learning and Deep Learning Assisted Wireless Communication System -- 3 Feature Transformation -- 3.1 Feature Construction -- 3.2 Feature Extraction -- 3.3 Feature Transformation Application for Automatic Modulation Classification -- 4 Features Selection -- 4.1 Feature Analysis and Evaluation -- 4.2 Feature Engineering Application for Path-Loss Prediction in Wireless Communication -- 4.3 Feature Engineering-Based Path-Loss Prediction -- 5 Solutions and Recommendations -- 6 Conclusion -- References -- Genetic Operators and Their Impact on the Training of Deep Neural Networks -- 1 Introduction -- 2 Preliminary Concepts -- 2.1 Genetic Algorithm -- 2.2 Artificial Neural Networks -- 2.3 Direct and Indirect Encoding -- 3 Problem Definition -- 3.1 The Maze Problem -- 3.2 Neural Network Architecture -- 3.3 Genetic Algorithm -- 4 Experimental Results -- 4.1 GA Variation 1 Analysis -- 4.2 GA Variation 2 Analysis -- 4.3 GA Variation 3 Analysis -- 4.4 GA Variation 4 Analysis -- 4.5 Statistical Validation -- 5 Conclusions -- References -- Implementation of Metaheuristics with Extreme Learning Machines -- 1 Introduction -- 2 Extreme Learning Machines (ELM) -- 3 Swarm Intelligence Metaheuristics -- 3.1 Particle Swarm Optimization (PSO) -- 3.2 Grey Wolf Optimization -- 3.3 Artificial Bee Colony (ABC) -- 4 Metaheuristics in the Extreme Learning Machine Method -- 5 Results -- 6 Conclusions -- Appendix -- References -- Architecture Optimization of Convolutional Neural Networks by Micro Genetic Algorithms -- 1 Deep Learning and Neuroevolution -- 2 Deep CNN, GA and Neuroevolution Approaches -- 2.1 Deep Convolutional Neural Networks -- 2.2 Genetic Algorithms -- 2.3 Neuroevolution -- 3 The Micro Genetic Algorithm CNN Framework Proposal -- 4 Experiments and Results -- 5 Conclusion -- References. , Optimising Connection Weights in Neural Networks Using a Memetic Algorithm Incorporating Chaos Theory -- 1 Introduction -- 2 Feed-Forward Neural Networks -- 3 Swarm Intelligence -- 4 Memetic Algorithms -- 5 Colonial Competitive Algorithm -- 6 Proposed Algorithm -- 6.1 Representation -- 6.2 Cost Function -- 6.3 Back-Propagation Algorithm as a Local Search Operator -- 6.4 Chaos-Enhanced Memetic Algorithm -- 7 Experimental Results -- 7.1 Sigmoid Function -- 7.2 Cosine Function -- 7.3 Sine1 Function -- 7.4 Sine2 Function -- 8 Conclusions -- References -- A Review of Metaheuristic Optimization Algorithms in Wireless Sensor Networks -- 1 Introduction -- 2 Metaheuristic Intelligence Optimization and Evolutionary Algorithms -- 2.1 Evolutionary Algorithms -- 2.2 Swarm Intelligence-Based Algorithms -- 2.3 Bio-Inspired Algorithms -- 2.4 Physics and Chemistry-Based Algorithms -- 2.5 Other Algorithms -- 3 Wireless Sensor Networks -- 4 Application of Metaheuristic Algorithms in Wireless Sensor Networks -- 4.1 Optimization Algorithms -- 4.2 Deployment in Wireless Sensor Networks -- 4.3 Localization in Wireless Sensor Networks -- 4.4 Sink Node Placement and Energy Consumption in Wireless Sensor Networks -- 5 Conclusion -- References -- A Metaheuristic Algorithm for Classification of White Blood Cells in Healthcare Informatics -- 1 Introduction -- 2 Cognitive Computing Concept -- 3 Neural Networks Concepts -- 3.1 Convolutional Neural Network -- 4 Metaheuristic Algorithm Proposal -- 5 Results and Discussion -- 6 Future Research Directions -- 7 Conclusions -- References -- Multi-level Thresholding Image Segmentation Based on Nature-Inspired Optimization Algorithms: A Comprehensive Review -- 1 Introduction -- 2 Nature-Inspired Optimization Algorithms -- 2.1 Evolutionary-Based Techniques -- 2.2 Bio-Inspired Based Algorithms -- 2.3 Physics and Chemistry Based Algorithms. , 2.4 Other Algorithms -- 3 Segmentation Techniques -- 3.1 Threshold-Based Methods -- 3.2 Region-Based Methods -- 3.3 Edge Detection Methods -- 3.4 Clustering Methods -- 3.5 Neural Network Based Methods -- 3.6 Partial Differential Equation Methods -- 4 Segmentation Quality Assessment Parameters -- 4.1 Peak Signal to Noise Ratio -- 4.2 Structural Similarity Index Measure -- 4.3 Feature Similarity Index Measure -- 4.4 Mean Square Error -- 4.5 Quality Index Based on Local Variance -- 5 Nature-Inspired Optimization Algorithms in Image Multi-thresholding -- 6 Open Problems and Challenges -- 7 Conclusion and Future Research Issues -- References -- Hybrid Harris Hawks Optimization with Differential Evolution for Data Clustering -- 1 Introduction -- 2 Related Works -- 3 Preliminaries -- 3.1 Cluster Analysis -- 3.2 External Measure -- 4 H-HHO: Hybrid Harris Hawks Optimization with Differential Evolution -- 4.1 Harris Hawks Optimization Algorithm -- 5 Experiments Results and Discussions -- 5.1 Experimental Setup -- 5.2 Comparisons for Data Clustering -- 6 Conclusion and Future Work -- References -- Variable Mesh Optimization for Continuous Optimization and Multimodal Problems -- 1 Introduction -- 2 Variable Mesh Optimization for Continuous Optimization Problems -- 3 VMO with Niching Strategies for Multimodal Problems -- 3.1 Niching VMO -- 3.2 Generic Niching Framework for VMO -- 4 Empirical Assessment -- 5 Discussion and Conclusions -- References -- Traffic Control Using Image Processing and Deep Learning Techniques -- 1 Introduction -- 2 Background -- 2.1 Methodologies for Object Identification and Image Processing -- 2.2 Methodologies for Urban Mobility Systems -- 3 Proposal -- 3.1 Prerequisites -- 3.2 Green Light On-Minimum Time Analysis -- 3.3 Equations -- 4 Experiments -- 4.1 Simulation Using Random Data -- 4.2 Real Situation -- 5 Results. , 5.1 Results Using Random Data -- 5.2 Real Situation -- 6 Conclusion -- References -- Drug Design and Discovery: Theory, Applications, Open Issues and Challenges -- 1 Introduction -- 2 Background -- 3 Different Techniques Overview Used in Drug Design and Discovery -- 4 Drug Design and Discovery Overview -- 5 Applications of Drug Design and Discovery -- 6 Open Issues and Challenges -- 7 Conclusions -- References -- Thresholding Algorithm Applied to Chest X-Ray Images with Pneumonia -- 1 Introduction -- 2 The Whale Optimization Algorithm -- 2.1 Exploitation Phase -- 2.2 Exploration Phase -- 3 Image Multilevel Thresholding -- 3.1 Otsu's Method Based in Between-Class Variance -- 3.2 Kapur's Method Based in Entropy -- 4 Automatic Detection of Thresholds Values Using WOA with Kapur and Otsu as Objective Functions -- 4.1 Dataset Description -- 4.2 Experiments Details -- 4.3 Metrics -- 5 Experimental Results -- 5.1 Results of Otsu's Objective Function -- 5.2 Results of Kapur's Objective Function -- 6 Conclusions -- References -- Artificial Neural Networks for Stock Market Prediction: A Comprehensive Review -- 1 Introduction -- 2 Artificial Neural Networks and Stock Price Prediction -- 2.1 Artificial Neural Networks -- 2.2 Training the ANNs -- 3 Stock Market Prediction: Description and Need -- 3.1 Stock Market Prediction Techniques: A Survey -- 4 Discussion -- 4.1 Publication Years -- 4.2 Prediction Techniques -- 4.3 Data Sets -- 4.4 Performance Evaluation -- 4.5 Prediction Target -- 5 Conclusion -- References -- Image Classification with Convolutional Neural Networks -- 1 Introduction -- 2 Image Classification -- 3 Classification Metrics -- 3.1 Binary Classifier Metrics -- 3.2 Multi-class Classifier Metrics -- 4 Neural Nets -- 4.1 Nodes -- 4.2 Tensors and Data Batches -- 4.3 Activation Function -- 4.4 Layers -- 4.5 Supervised Learning -- 4.6 Learning Rate. , 5 Convolutional Neural Networks.

Note: Front Cover -- Sustainable Resource Management -- Sustainable Resource Management: Modern Approaches and Contexts -- Copyright -- Dedication -- Contents -- Contributors -- Editors' biography -- Preface -- 1 - Evolution of the concept of sustainability. From Brundtland Report to sustainable development goals -- 1. Introduction -- 2. The concept of sustainable development -- 3. The definition of sustainable development -- 4. The trend of sustainable development -- 5. The evolution of sustainable development concept -- 6. Indicator development -- 7. Environmental sustainability -- 7.1 Social sustainability -- 7.2 Economic sustainable development -- 7.3 Context of sustainable development goals -- 7.4 Key Millennium Development Goal achievements -- 8. Sustainable development goals -- 8.1 Goal 1. End poverty everywhere in all of its forms -- 8.2 Goal 2. End hunger, achieve food security, improve nutrition, and promote sustainable agriculture -- 8.3 Goal 3. Ensure healthy lives and promote welfare for all people -- 8.4 Goal 4. Ensure inclusive and equitable quality education and promote lifelong learning opportunities for all -- 8.5 Goal 5. Achieve gender equality and empower all women and girls -- 8.6 Goal 6. Ensure availability and sustainable management of water and sanitation for all -- 8.7 Goal 7. Ensure access to affordable, reliable, sustainable and modern energy for all -- 8.8 Goal 8. Promote sustained, inclusive, and sustainable economic growth, full and productive employment, and decent work for all -- 8.9 Goal 9. Build resilient infrastructure, promote inclusive and sustainable industrialization, and foster innovation -- 8.10 Goal 10. Reduce inequality within and among countries -- 8.11 Goal 11. Make cities and human settlements inclusive, safe, resilient, and sustainable -- 8.12 Goal 12. Ensure sustainable consumption and production patterns. , 8.13 Goal 13. Take urgent action to combat climate change and its impacts -- 8.14 Goal 14. Conserve and sustainably use oceans, seas, and marine resources for sustainable development -- 8.15 Goal 15. Protect, restore, and promote sustainable use of terrestrial ecosystems, sustainably manage forests, combat desert ... -- 8.16 Goal 16. Promote peaceful and inclusive societies for sustainable development, provide access to justice for all, and build ... -- 8.17 Goal 17. Strengthen the means of implementation and revitalize the global partnership for sustainable development -- 8.17.1 Finance -- 8.17.2 Technology -- 8.17.3 Capacity-building -- 8.17.4 Trade -- 8.17.5 Systemic issues -- 8.18 Sustainable development goals and targets 2030 -- 9. Conclusions -- References -- Further reading -- 2 - Areas of sustainability: environment, economy, and society -- 1. Areas of sustainability: environment, economy, and society -- 1.1 Pillars of social responsibility-social, economic, and environmental -- 1.1.1 Social pillar -- 1.1.2 Economic pillar -- 1.1.3 Environmental pillar -- 1.1.4 Corporate philanthropy -- 1.2 Interconnections between economic growth, energy consumption, social welfare, and sustainable quality of life -- 1.3 Limitations of stable economic growth -- 1.3.1 Growth and the rules of arithmetic -- 1.3.2 Stable growth in a confined space -- 1.3.3 Economic growth, increase in population, and energy resources consumption increase -- 1.3.4 Natural and environmental limitations of economic growth -- 1.3.4.1 European criteria for sustainable forest management -- 1.3.5 Change of climate conditions-global warming -- 2. Conclusion -- References -- Further reading -- 3 - Evolution and trends of sustainable approaches -- 1. Introduction -- 2. Emergence of sustainable development. , 3. Approaches to sustainability assessment: sustainability indicators and assessment based on life cycles -- 3.1 Sustainability assessment and indicators -- 3.2 Sustainability assessment approach based on life cycles (LCSA) -- 3.2.1 Life cycle assessment -- 3.2.2 Social life cycle assessment -- 3.2.3 Life cycle costing -- 4. Approaches to sustainable solutions -- 4.1 Products, services, and technology -- 4.2 Sustainable business models -- 4.2.1 Product-service system -- 4.2.2 Circular economy -- 4.2.3 Industrial symbiosis -- 5. Conclusions -- References -- 4 - Modern age of sustainability: supply chain resource management -- 1. Introduction -- 2. Sustainable supply chain management -- 3. Practices for a greener and more sustainable supply chain -- 3.1 Design for sustainability -- 3.2 Eco-efficiency -- 3.3 Eco-design -- 3.4 Green purchasing -- 3.5 Reverse logistics -- 4. Sustainability evaluation methods -- 4.1 Full LCA -- 4.2 Direct life cycle assessment -- 4.3 Streamlined life cycle assessment -- 4.4 Environmental labeling -- 5. Conclusions -- References -- Other references of interest -- 5 - Sustainable management of agricultural resources (agricultural crops and animals) -- 1. Introduction -- 2. Sustainable management of agrobiodiversity -- 2.1 Biodiversity -- 2.2 Agrobiodiversity -- 2.2.1 Genetic agrobiodiversity -- 2.2.2 Specific agrobiodiversity -- 2.2.3 Ecosystem agrobiodiversity -- 2.2.4 Landscape agrobiogeodiversity -- 2.2.5 Loss of agrobiodiversity -- 2.2.6 Conservation of agrobiodiversity -- 3. Sustainable management of agricultural species -- 3.1 Agricultural plants (Crops) -- 3.2 Agricultural animals -- 3.2.1 Livestock biodiversity -- 4. Sustainable management of agroecosystems -- 4.1 Agroecology -- 4.2 Agroecosystems services -- 4.2.1 Agricultural crop services -- 4.2.2 Agricultural animals services -- 4.3 Agroecosystem health. , 4.4 Agroecosystems conservation -- 5. Sustainable management of agricultural landscapes -- 5.1 Agricultural landscape ecology -- 5.2 Sustainable management of agricultural landscapes -- 5.2.1 Agrolandscape services -- 5.2.2 Sustainable landscape management -- 6. Sustainable agriculture -- 6.1 Sustainable management of agricultural resources (sustainable management of crop and animal production) -- 6.1.1 Alternative agriculture -- 6.1.2 Intensive agriculture -- 6.1.3 Biodynamic agriculture -- 6.1.4 Ecological agriculture -- 6.1.5 Conservation agriculture -- 6.1.6 Organic agriculture -- 6.1.7 Permanent agriculture (permaculture) -- 6.1.8 Regenerative agriculture -- 6.1.9 Climate-smart agriculture -- 7. Sustainable rural development -- 7.1 Sustainable rural systems -- 7.1.1 Guidelines for sustainable rural systems -- 7.1.2 Sustainable crop production -- 7.1.2.1 Assessment and implementation of sustainable crop production -- 7.1.2.2 Sustainable crop production intensification -- 7.1.2.3 Sustainable crop production techniques -- 7.1.2.4 Sustainable management of crop nutrients -- 7.1.3 Sustainable animal production -- 7.1.3.1 Global agenda for sustainable livestock -- 7.1.3.2 Integrated crop-livestock production -- 7.2 Sustainable intensification of rural systems -- 7.2.1 Sustainable intensification of crop and animal production -- 7.2.1.1 Farming practices for sustainable intensification -- 8. Conclusions -- 8.1 The State of Food and Agriculture -- 8.2 The State of Food Security and Nutrition in the World -- 8.3 Industrialized agriculture -- 8.4 Holistic agriculture -- 8.5 Agrobiodiversity -- 8.6 Agroecosystems and Agrolandscapes -- 8.7 Sustainable agriculture -- 8.8 Sustainable rural systems -- 8.9 Landscape approaches for climate-smart agriculture -- 9. Metascientific approach to sustainable management of agricultural resources. , 10. Covid-19 pandemic impacts on agriculture, food security and nutrition -- 10.1 COVID-19 pandemic -- 10.2 COVID-19 pandemic impact on food and agriculture -- 10.3 COVID-19 pandemic development and solutions -- The FAO COVID-19 Response and Recovery Programme -- Acknowledgment -- References -- Internet links -- 6 - Sustainable water resources -- 1. Introduction -- 2. Water resource management system -- 3. Water resource management: an integrated approach -- 3.1 Historical background -- 3.2 Review of water resource management frameworks -- 3.3 Key issues for sustainable water management -- 4. Interventions of modern computation techniques for sustainable water management -- 4.1 Decision-making model approach -- 4.2 Agent-Based Modeling -- 4.3 Machine learning approach -- 5. Concluding remarks -- References -- Further reading -- 7 - Minerals and metal Industry in the global scenario and environmental sustainability -- 1. Introduction -- 2. The vision of this study -- 3. The vast scientific doctrine of environmental sustainability -- 4. Sustainable resource management, integrated water resource management, and the vast vision for the future -- 5. Today's mineral and metal industry and the needs of environmental sustainability -- 6. Recent scientific advancements in the field of environmental sustainability -- 7. Recent scientific prowess and research endeavor in the field of environmental sustainability, wastewater treatment, and min ... -- 8. Industrial wastewater treatment and mineral and metal industry -- 9. Heavy metal and arsenic groundwater remediation and the future of mineral and metal industry -- 10. Future scientific recommendations and the future flow of scientific thoughts -- 11. Conclusion, summary, and scientific perspectives -- References -- 8 - Sustainable land use and management -- 1. Introduction -- 2. Land uses and changes. , 3. Impacts of land use changes.