دانلود کتاب Augmented Intelligence: Deep Learning, Machine Learning, Cognitive Computing, Educational Data Mining

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Cover Title Copyright End User License Agreement Contents Preface List of Contributors Integrating Educational Data Mining in Augmented Reality Virtual Learning Environment Carlos Ankora1 and D. Aju1,* 1.1. INTRODUCTION 1.2. VIRTUAL LEARNING ENVIRONMENTS (VLE) 1.3. AUGMENTED REALITY (AR) 1.3.1. Elements of AR in a VLE 1.3.2. Target Markers used in AR Applications 1.3.3. Hardware Platforms/Peripherals 1.3.4. AR Applications in Education 1.4. DEVELOPMENT OF METHODOLOGY FOR AR IN VLE 1.4.1. Agile Methodology 1.4.2. Developing AR-VLE using Agile Scrum 1.5. AR SYSTEM DEVELOPMENT FOR VLE 1.5.1. Designing the 3D Models BLENDER 1.5.2. Developing the AR Modules UNITY 3D 1.6. EDUCATIONAL DATA MINING IN AR-VLE CONCLUSION CONSENT FOR PUBLICATION CONFLICT OF INTEREST ACKNOWLEDGEMENTS REFERENCES Brain and Computer Interface Kuldeep Singh Kaswan1,* and Jagjit Singh Dhatterwal2 2.1. INTRODUCTION 2.2. WHAT IS BCI? 2.3. BCI SENSOR WORLD OVERVIEW 2.4. HISTORY OF IMPLANTABLE ELECTRODES 2.5. TYPES OF MICROELECTRODES 2.5.1. Mass-Fabricated Microelectrodes 2.5.2. Silicon-Based Microelectrodes 2.5.3. Ceramic-Based Microelectrodes 2.5.4. Polyimide Microelectrode 2.5.5. Microelectrodes Connectors 2.5.6. ECoG Strip Electrodes 2.6. BCI EEG SENSORS 2.7. MODELING AND SIGNAL PROCESSING OF BMI/BCI TECHNIQUES OF MULTI MICRO ELECTRODE ARRAY 2.7.1. Binned Conceptual Data Models 2.7.2. EEG/ECoG Recordings 2.8. HARDWARE IMPLEMENTATION 2.8.1. Paralysis Patients Restoring Movement 2.8.2. EEG-Based Brain-Computer Interfaces 2.8.3. Direct Brain-Computer Interfaces 2.8.4. Recording, Extracting, and Decoding Neural Motor Commands 2.8.5. Predict Limb Movement Kinematics use of Multivariate Regression Analysis (MRA) 2.8.6. Monkey Brain Motor Commands of Extraction 2.8.7. Biofeedback Changes Coding of Robot Arm Movement 2.9. BRAIN CONTROL OF MULTIPLE-OUTPUT FUNCTIONS 2.10. BIOMIMETIC ROBOT RESEARCH 2.10.1. The Rationale for Biomimetic Hand Prostheses 2.10.2. Research Approach to Bio Mechatronics at SSSA 2.10.3. Using Direct BCIs to Control Biomimetic Robotic Prostheses CONCLUSION CONSENT FOR PUBLICATION CONFLICT OF INTEREST ACKNOWLEDGEMENTS REFERENCES Potential Use of Tree-based Tools for Chemometric Analysis of Infrared Spectra Lucas A.C. Minho1,*, Bárbara E.A. de Magalhães2 and Alexandre G.M. de Freitas3 3.1. INTRODUCTION 3.2. DECISION TREES (DT) 3.3. RANDOM FOREST (RF) 3.4. EXPERIMENTS 3.5. DIMENSIONALITY REDUCTION IN RAW SPECTROSCOPIC SPACE 3.5.1. Importance Measurements and Feature Ranking with Random Forest 3.5.1.1. Boruta Wrapper Algorithm 3.5.1.2. Feature Subset Selection with Boruta 3.6. ROBUSTNESS OF TREE-BASED ALGORITHMS TO NOISE 3.7. TREE-BASED ALGORITHMS IN DISCRIMINANT ANALYSIS CONCLUSION NOTES CONSENT FOR PUBLICATION CONFLICT OF INTEREST ACKNOWLEDGEMENTS REFERENCES Applications of Deep Learning in Medical Engineering Sumit Kumar Jindal1,*, Sayak Banerjee1, Ritayan Patra1 and Arin Paul1 4.1. HISTORICAL OVERVIEW OF DEEP LEARNING 4.1.1. Machine Learning 4.1.2. Neural Networks 4.1.3. Deep Learning 4.2. ACTIVATION FUNCTIONS 4.2.1. Binary Activation Function 4.2.2. Sigmoid and SoftMax Activation Function 4.2.3. TanH Activation Function 4.2.4. ReLU and Leaky ReLU Activation Function 4.3. OPTIMIZERS AND LOSS 4.3.1. Optimizers 4.3.1.1. Adagrad 4.3.1.2. RMSProp 4.3.1.3. Adam Optimizer 4.3.2. Loss Functions 4.3.2.1. Mean Squared Error Loss 4.3.2.2. Cross – Entropy Loss 4.4. IMAGE RECOGNITION AND CLASSIFICATION 4.4.1. Convolution Layer 4.4.2. Pooling Layer 4.4.3. FULL CONNECTION 4.5. AUDIO SIGNAL PROCESSING 4.6. DEEP LEARNING IN DETECTION OF SLEEP APNEA 4.6.1. System Design 4.6.2. Detection of Apnea or Hypopnea Event 4.6.3. Deep Learning Model 4.6.4. Evaluation of the Model 4.7. DEEP LEARNING IN CARDIAC ARRHYTHMIA DETECTION 4.7.1. System Design 4.7.2. Deep Learning Model 4.7.3. Evaluation of the Model 4.8. DEEP LEARNING IN DETECTION OF BRAIN TUMOURS 4.8.1. System Design 4.8.2. Deep Learning Model 4.8.3. Training the Model 4.8.4. Result DISCUSSION AND FUTURE WORKS CONSENT FOR PUBLICATION CONFLICT OF INTEREST ACKNOWLEDGEMENTS REFERENCES Bankruptcy Prediction Model Using an Enhanced Boosting Classifier based on Sequential Backward Selector Technique Makram Soui1,*, Nada Namani Zitouni2, Salima Smiti3, Kailash Kumar1 and Ahmad Aljabr1 5.1. INTRODUCTION 5.2. RELATED WORK 5.2.1. Statistical Techniques 5.2.2. Artificial Intelligent Techniques 5.2.2.1. Machine Learning Techniques 5.2.2.2. Deep Learning Techniques 5.3. BACKGROUND 5.3.1. Feature Selection (FS) Algorithms 5.3.1.1. Sequential Feature Selection (SFS) 5.3.1.2. Particle Swarm Optimization (PSO) 5.3.1.3. Random Subset Feature Selection (RSFS) 5.3.2. Rule-Based Classifiers 5.3.2.1. Decision Tree Classifier: CART 5.3.2.2. Decision Tree Classifier: J48 (C4.5) 5.3.2.3. OneR Classifier 5.3.2.4. PART Classifier 5.3.3. Ensemble Methods 5.3.3.1. Random Forest Classifier 5.3.3.2. Boosting Techniques 5.4.2.3. Proposed Method 5.4.1. Feature Selection Phase 5.4.2. Classification Phase 5.4.3. Testing Sub-Phase 5.5. Validation 5.5.1. Research Questions 5.5.2. Description of the Experimental Database 5.5.3. Evaluation Criteria 5.6. RESULTS AND DISCUSSION 5.6.1. Parameter Settings 5.6.2. Results for Research Question 1 5.6.3. Results for Research Question 2 CONCLUSION CONSENT FOR PUBLICATION CONFLICT OF INTEREST ACKNOWLEDGEMENTS REFERENCES Detecting Ballot Stuff Collusion Attack in Reputation System for Mobile Agents Security Priyanka Mishra1,* 6.1. INTRODUCTION 6.2. TRUST BASED REPUTATION SYSTEM 6.3. RELATED WORKS 6.4. MREP MODEL 6.5. DETECTION METHODOLOGY 6.6. Simulation Results CONCLUSION CONSENT FOR PUBLICATION CONFLICT OF INTEREST ACKNOWLEDGEMENTS REFERENCES Crow Search Algorithm: A Systematic Review Ali Aloss1, Barnali Sahu1,* and Om Prakash Jena2 7.1. INTRODUCTION 7.2. CROW SEARCH OPTIMIZATION 7.2.1. Overview of Crow Search Optimization 7.2.2. Features of Crow Search Algorithm 7.2.3. Algorithm structure of CSO 7.2.4. Pseudocode of CSA 7.3. CSA STUDIES 7.3.1. Modifications of CSA 7.3.1.1. Chaotic CSA(CCSA) 7.3.1.2. Fuzzy CSA(FCSA) 7.3.1.3. Other updates of CSA 7.3.2. Hybridization 7.3.3. Multi-objective and Binary Optimization. 7.3.4. Other applications: To date, CSA has been used in many other applications in varied academic and industrial fields. Table 5 shows the other applications of CSA. 7.4. APPLICATION OF CSA IN MEDICAL DOMAIN 7.5. DISCUSSION AND CRITICAL ANALYSIS CONCLUSION CONSENT FOR PUBLICATION CONFLICT OF INTEREST ACKNOWLEDGEMENTS REFERENCES The Quantitative and Qualitative Assessment of Re-Search Conducted Using Computational Intelligence for the Diagnosis or Treatment of COVID-19 Mallikarjun Kappi1,*, Madhu S.2, Balabhim Sankrappa Biradar3 and B.U. Kannappanavar4 8.1. INTRODUCTION, BACKGROUND, AND OVERVIEW 8.2. RELATED STUDIES 8.3. OVERVIEW OF COMPUTATIONAL INTELLIGENCE 8.3.1. What is Computational Intelligence (CI) 8.3.2. Types of Computational Intelligence 8.3.2.1. Fuzzy (Logic) Sets 8.3.2.2. Artificial Neural Network 8.3.2.3. Evolutionary Computing (EC) 8.3.2.4. Swarm Intelligence (SI) 8.3.2.5. Artificial Immune Systems (AIS) 8.4. COVID-19 DIAGNOSIS TOOLS 8.5. DATA SOURCE AND METHODOLOGY 8.6. RESULTS ANALYSIS AND DISCUSSION 8.6.1. Most Productive Countries 8.6.2. Most Preferred Sources 8.6.3. Highly Prolific Institutions 8.6.4. Highly Prolific Authors 8.6.5. Most Global Cited Papers 8.6.6. Most Frequent Author Keywords 8.7. CONCLUSION AND FORTHCOMING OUTLOOK CONSENT FOR PUBLICATION CONFLICT OF INTEREST ACKNOWLEDGEMENTS REFERENCES Subject Index Back Cover