Tabla de Contenidos: “…Cover -- Title Page -- Copyright Page -- Dedication Page -- Contents -- Preface -- Acknowledgement -- Chapter 1 Band Reduction of HSI Segmentation Using FCM -- 1.1 Introduction -- 1.2 Existing Method -- 1.2.1 K-Means Clustering Method -- 1.2.2 Fuzzy C-Means -- 1.2.3 Davies Bouldin Index -- 1.2.4 Data Set Description of HSI -- 1.3 Proposed Method -- 1.3.1 Hyperspectral Image Segmentation Using Enhanced Estimation of Centroid -- 1.3.2 Band Reduction Using K-Means Algorithm -- 1.3.3 Band Reduction Using Fuzzy C-Means -- 1.4 Experimental Results -- 1.4.1 DB Index Graph -- 1.4.2 K-Means-Based PSC (EEOC) -- 1.4.3 Fuzzy C-Means-Based PSC (EEOC) -- 1.5 Analysis of Results -- 1.6 Conclusions -- References -- Chapter 2 A Fuzzy Approach to Face Mask Detection -- 2.1 Introduction -- 2.2 Existing Work -- 2.3 The Proposed Framework -- 2.4 Set-Up and Libraries Used -- 2.5 Implementation -- 2.6 Results and Analysis -- 2.7 Conclusion and Future Work -- References -- Chapter 3 Application of Fuzzy Logic to the Healthcare Industry -- 3.1 Introduction -- 3.2 Background -- 3.3 Fuzzy Logic -- 3.4 Fuzzy Logic in Healthcare -- 3.5 Conclusions -- References -- Chapter 4 A Bibliometric Approach and Systematic Exploration of Global Research Activity on Fuzzy Logic in Scopus Database -- 4.1 Introduction -- 4.2 Data Extraction and Interpretation -- 4.3 Results and Discussion -- 4.3.1 Per Year Publication and Citation Count -- 4.3.2 Prominent Affiliations Contributing Toward Fuzzy Logic -- 4.3.3 Top Journals Emerging in Fuzzy Logic in Major Subject Areas -- 4.3.4 Major Contributing Countries Toward Fuzzy Research Articles -- 4.3.5 Prominent Authors Contribution Toward the Fuzzy Logic Analysis -- 4.3.6 Coauthorship of Authors -- 4.3.7 Cocitation Analysis of Cited Authors -- 4.3.8 Cooccurrence of Author Keywords…”

Tabla de Contenidos: “…Conférence multipartite organisée dans le cadre de l'initiative pour la transparence dans l'industrie extractive -- Document 2. Table ronde informelle sur la responsabilité des entreprises dans l'économie mondiale - Synthèse -- I. …”

Tabla de Contenidos: “…-- Acknowledgments -- 1 Introduction -- 1.1 AI, Machine learning, and Data Science -- 1.2 What is Data Science? -- 1.2.1 Extracting Meaningful Patterns -- 1.2.2 Building Representative Models -- 1.2.3 Combination of Statistics, Machine Learning, and Computing -- 1.2.4 Learning Algorithms -- 1.2.5 Associated Fields -- 1.3 Case for Data Science -- 1.3.1 Volume -- 1.3.2 Dimensions -- 1.3.3 Complex Questions -- 1.4 Data Science Classification -- 1.5 Data Science Algorithms -- 1.6 Roadmap for This Book -- 1.6.1 Getting Started With Data Science -- 1.6.2 Practice using RapidMiner -- 1.6.3 Core Algorithms -- References -- 2 Data Science Process -- 2.1 Prior Knowledge -- 2.1.1 Objective -- 2.1.2 Subject Area -- 2.1.3 Data -- 2.1.4 Causation Versus Correlation -- 2.2 Data Preparation -- 2.2.1 Data Exploration -- 2.2.2 Data Quality -- 2.2.3 Missing Values -- 2.2.4 Data Types and Conversion -- 2.2.5 Transformation -- 2.2.6 Outliers -- 2.2.7 Feature Selection -- 2.2.8 Data Sampling -- 2.3 Modeling -- 2.3.1 Training and Testing Datasets -- 2.3.2 Learning Algorithms -- 2.3.3 Evaluation of the Model -- 2.3.4 Ensemble Modeling -- 2.4 Application -- 2.4.1 Production Readiness -- 2.4.2 Technical Integration -- 2.4.3 Response Time -- 2.4.4 Model Refresh -- 2.4.5 Assimilation -- 2.5 Knowledge -- References -- 3 Data Exploration -- 3.1 Objectives of Data Exploration -- 3.2 Datasets -- 3.2.1 Types of Data -- Numeric or Continuous -- Categorical or Nominal -- 3.3 Descriptive Statistics -- 3.3.1 Univariate Exploration -- Measure of Central Tendency -- Measure of Spread -- 3.3.2 Multivariate Exploration -- Central Data Point -- Correlation -- 3.4 Data Visualization -- 3.4.1 Univariate Visualization -- Histogram…”

Tabla de Contenidos: “…Exclusion criteria -- 3.2.3. Data Extraction -- 3.2.4. Data Items -- 3.2.5. Quality Assessment of Selected Papers -- 3.3. …”

Tabla de Contenidos: “…JavaScript analysis -- Specialized web technology fingerprinting libraries -- Proactive web security measures with Python -- Input validation and data sanitization -- Secure authentication and authorization -- Secure session management -- Secure coding practices -- Implementing security headers -- Summary -- Chapter 4: Exploiting Web Vulnerabilities Using Python -- Web application vulnerabilities - an overview -- SQL injection -- XSS -- IDOR -- A case study concerning web application security -- SQL injection attacks and Python exploitation -- Features of SQLMap -- How SQLMap works -- Basic usage of SQLMap -- Intercepting with MITMProxy -- XSS exploitation with Python -- Understanding how XSS works -- Reflected XSS (non-persistent) -- Stored XSS (persistent) -- Python for data breaches and privacy exploitation -- XPath -- CSS Selectors -- Summary -- Chapter 5: Cloud Espionage - Python for Cloud Offensive Security -- Cloud security fundamentals -- Shared Responsibility Model -- Cloud deployment models and security implications -- Encryption, access controls, and IdM -- Security measures offered by major cloud providers -- Access control in cloud environments -- Impact of malicious activities -- Python-based cloud data extraction and analysis -- Risks of hardcoded sensitive data and detecting hardcoded access keys -- Enumerating EC2 instances using Python (boto3) -- Exploiting misconfigurations in cloud environments -- Types of misconfigurations -- Identifying misconfigurations -- Exploring Prowler's functionality -- Enhancing security, Python in serverless, and infrastructure as code (IaC) -- Introducing serverless computing -- Introduction to IaC -- Summary -- Part 3: Python Automation for Advanced Security Tasks -- Chapter 6: Building Automated Security Pipelines with Python Using Third-Party Tools…”

Tabla de Contenidos: “…Sources and Target Data Warehouse -- 2.2. Extract, Transform, and Load -- 2.3. Front-End Applications -- 3. …”

Tabla de Contenidos: “…11.6 The Tribology of Joint Replacements: Impact on Joint Lifetime -- 11.7 Point to the Future -- 11.8 Thought Exercise: Short-Term Surgical Viability Versus Long-Term Survival -- 11.9 Other Schemes to Reduce the Wear on Sterilized Surfaces -- 11.10 Conclusions -- 11.11 Problems -- References -- 12 Neural Interventions -- 12.1 Introduction -- 12.2 Aneurysm and Cerebrovascular Modulation -- 12.2.1 Clips -- 12.2.2 Coils -- 12.2.3 Embolic Fluids -- 12.2.3.1 Dispersion-based Embolics -- 12.2.3.2 Reactive Liquid Embolics -- 12.2.4 Filling of Other Defects -- 12.3 Neural Probes and Stimulators -- 12.4 Conclusion -- 12.5 Problems -- References -- 13 Cardiovascular Interventions -- 13.1 Introduction -- 13.2 Valvular Repairs: Rationale for Intervention: Murmurs, Regurgitation, Congestive Heart Failure -- 13.2.1 Sutures to Address Leaflet Tears -- 13.2.2 Annulolasty Rings -- 13.3 Prosthetic and Bioprosthetic Replacement Valves -- 13.4 Outcomes -- 13.5 Interchamber Defects -- 13.6 Vascular Grafts -- 13.6.1 Dacron Grafts -- 13.6.2 Expanded Polytetrafluoroethylene (ePTFE) -- 13.7 Stents -- 13.8 Drug Eluting Stents -- 13.9 Added Constraints: Pediatric Cardiac Interventions -- 13.10 Pacemakers, Defibrillators, and Associated Hardware -- 13.11 Conclusions -- 13.12 Pointing to the Future -- 13.13 Problems -- References -- 14 Artificial Organs -- 14.1 Kidney: Dialysis -- 14.1.1 Dialysis Options -- 14.1.2 Peritoneal Dialysis -- 14.1.3 Hemodialysis -- 14.1.4 Continuous Metabolite Extraction -- 14.2 Artificial Pancreas -- 14.3 Artificial Bladders -- 14.4 Pivoting to the future -- 14.5 Problems -- References -- 15 Special Topics: Assays Applied to Both Health and Sports -- 15.1 Introduction and Historical Basis -- 15.2 What Can be Learned From Urinalysis? …”

Tabla de Contenidos: “…8.2 Methodology -- 8.3 AI-Based Predictive Modeling -- 8.3.1 Linear Regression -- 8.3.2 Random Forests -- 8.3.3 XGBoost -- 8.3.4 SVM -- 8.4 Performance Indices -- 8.4.1 Root Mean Squared Error (RMSE) -- 8.4.2 Mean Squared Error (MSE) -- 8.4.3 R2 (R-Squared) -- 8.5 Results and Discussion -- 8.5.1 Key Performance Metrics (KPIs) During the Model Training Phase -- 8.5.2 Key Performance Index Metrics (KPIs) During the Model Testing Phase -- 8.5.3 K Cross Fold Validation -- 8.6 Conclusions -- References -- Chapter 9 Performance Comparison of Differential Evolutionary Algorithm-Based Contour Detection to Monocular Depth Estimation for Elevation Classification in 2D Drone-Based Imagery -- 9.1 Introduction -- 9.2 Literature Survey -- 9.3 Research Methodology -- 9.3.1 Dataset and Metrics -- 9.4 Result and Discussion -- 9.5 Conclusion -- References -- Chapter 10 Bioinspired MOPSO-Based Power Allocation for Energy Efficiency and Spectral Efficiency Trade-Off in Downlink NOMA -- 10.1 Introduction -- 10.2 System Model -- 10.3 User Clustering -- 10.4 Optimal Power Allocation for EE-SE Tradeoff -- 10.4.1 Multiobjective Optimization Problem -- 10.4.2 Multiobjective PSO -- 10.4.3 MOPSO Algorithm for EE-SE Trade-Off in Downlink NOMA -- 10.5 Numerical Results -- 10.6 Conclusion -- References -- Chapter 11 Performances of Machine Learning Models and Featurization Techniques on Amazon Fine Food Reviews -- 11.1 Introduction -- 11.1.1 Related Work -- 11.2 Materials and Methods -- 11.2.1 Data Cleaning and Pre-Processing -- 11.2.2 Feature Extraction -- 11.2.3 Classifiers -- 11.3 Results and Experiments -- 11.4 Conclusion -- References -- Chapter 12 Optimization of Cutting Parameters for Turning by Using Genetic Algorithm -- 12.1 Introduction -- 12.2 Genetic Algorithm GA: An Evolutionary Computational Technique -- 12.3 Design of Multiobjective Optimization Problem…”

Tabla de Contenidos: “…4.2.4 Cloud Computing -- 4.2.5 Blockchain -- 4.2.6 5G -- 4.3 Challenges in Transforming Digital Technology -- 4.3.1 Increasing Digitalization -- 4.3.2 Work From Home Culture -- 4.3.3 Workplace Monitoring and Techno Stress -- 4.3.4 Online Fraud -- 4.3.5 Accessing Internet -- 4.3.6 Internet Shutdowns -- 4.3.7 Digital Payments -- 4.3.8 Privacy and Surveillance -- 4.4 Implications for Research -- 4.5 Conclusion -- References -- Part II: Plant Pathology -- Chapter 5 Plant Pathology Detection Using Deep Learning -- 5.1 Introduction -- 5.2 Plant Leaf Disease -- 5.3 Background Knowledge -- 5.4 Architecture of ResNet 512 V2 -- 5.4.1 Working of Residual Network -- 5.5 Methodology -- 5.5.1 Image Resizing -- 5.5.2 Data Augmentation -- 5.5.2.1 Types of Data Augmentation -- 5.5.3 Data Normalization -- 5.5.4 Data Splitting -- 5.6 Result Analysis -- 5.6.1 Data Collection -- 5.6.2 Feature Extractions -- 5.6.3 Plant Leaf Disease Detection -- 5.7 Conclusion -- References -- Chapter 6 Smart Irrigation and Cultivation Recommendation System for Precision Agriculture Driven by IoT -- 6.1 Introduction -- 6.1.1 Background of the Problem -- 6.1.1.1 Need of Water Management -- 6.1.1.2 Importance of Precision Agriculture -- 6.1.1.3 Internet of Things -- 6.1.1.4 Application of IoT in Machine Learning and Deep Learning -- 6.2 Related Works -- 6.3 Challenges of IoT in Smart Irrigation -- 6.4 Farmers' Challenges in the Current Situation -- 6.5 Data Collection in Precision Agriculture -- 6.5.1 Algorithm -- 6.5.1.1 Environmental Consideration on Stage Production of Crop -- 6.5.2 Implementation Measures -- 6.5.2.1 Analysis of Relevant Vectors -- 6.5.2.2 Mean Square Error -- 6.5.2.3 Potential of IoT in Precision Agriculture -- 6.5.3 Architecture of the Proposed Model -- 6.6 Conclusion -- References -- Chapter 7 Machine Learning-Based Hybrid Model for Wheat Yield Prediction…”

Tabla de Contenidos: “…11.3.2.3 Application of 2D Nanomaterials in Photodynamic Therapy -- 11.3.3 2D Nanomaterials‐Cancer Detection/Diagnosis/Theragnostic -- 11.4 Tissue Engineering -- 11.5 Conclusion -- Acknowledgment -- References -- Chapter 12 Synthesis of Nanostructured Materials Via Green and Sol-Gel Methods: A Review -- 12.1 Introduction -- 12.2 Methods Used in Nanostructured Synthesis -- 12.2.1 Green Method of Nanoparticles Synthesis -- 12.2.2 Sol-Gel Method of Nanoparticles Synthesis -- 12.2.3 Green Method of Nanocomposites Synthesis -- 12.2.4 Sol-Gel Method of Nanocomposites -- 12.3 Discussion -- 12.4 Conclusion -- References -- Chapter 13 Study of Antimicrobial Activity of ZnO Nanoparticles Using Leaves Extract of Ficus auriculata Based on Green Chemistry Principles -- 13.1 Introduction -- 13.2 Materials and Methods -- 13.2.1 Chemicals -- 13.2.2 Methodology -- 13.2.3 Antimicrobial Activity -- 13.3 Results and Discussion -- 13.3.1 Characterization of Synthesized Zinc‐Oxide Nanoparticles (ZnONPs) -- 13.3.1.1 XRD Analysis -- 13.3.1.2 FT‐IR Analysis -- 13.3.1.3 SEM Analysis -- 13.3.1.4 TEM Analysis -- 13.3.2 Antibacterial Activity -- 13.4 Conclusion -- Acknowledgments -- References -- Chapter 14 Piezoelectric Properties of Na1−xKxNbO3 near x &amp -- equals -- 0.475, Morphotropic Phase Region -- 14.1 Introduction -- 14.2 Experimental Procedure -- 14.3 Results and Discussion -- References -- Chapter 15 Synthesis and Characterization of SDC Nano‐Powder for IT‐SOFC Applications -- 15.1 Introduction -- 15.1.1 Solid Oxide Fuel Cells (SOFCs) -- 15.1.2 Intermediate Temperature Solid Oxide Fuel Cells (IT‐SOFCs) -- 15.1.3 Why Samarium‐Doped Ceria (SDC) Material? …”

Tabla de Contenidos: “…8.3.1.2 Goals of Meetings with Senior Leadership -- 8.3.1.3 Meetings with Key Leaders -- 8.3.1.4 Execute the Communication Process -- 8.3.1.5 Conduct Interviews with Designated Leaders, Managers, and High-Priority Staff -- 8.3.1.6 Provide a Written Questionnaire for Remaining Designated Staff -- 8.3.1.7 Perform an Extensive Analysis of Findings -- 8.3.1.8 Creation of a Written Report with Detailed Study Findings -- 8.3.1.9 Present Findings to Leadership and Determine Next Steps -- 8.3.1.10 Presentation to the Organization -- 8.4 Redesigning an Organization to Execute with Data and Analytics -- 8.4.1 Business Knowledge -- 8.4.2 Data Knowledge -- 8.4.3 AI and Analytics Knowledge -- 8.4.4 Technology Stack-Architecture, Platforms, Systems -- 8.4.5 Culture -- 8.4.6 People -- 8.5 Conclusion -- References -- 9 Additional Stories on the Data Front -- 9.1 Managing the End-to-End Process of Patient Experience-Two Case Studies by Carol Maginn -- 9.1.1 Patient Case A -- 9.1.2 Patient Case B -- 9.1.3 Pricing Offers an Intuitively Simple Problem That Provides Use of the Most Complicated Mathematical Models -- 9.2 Textual Extraction, Transformation, and Learning -- 9.2.1 Some Sources of Textual Data -- 9.2.1.1 Voice Recordings -- 9.2.1.2 Printed Text -- 9.2.1.3 Internet and Social Media -- 9.2.1.4 Email -- 9.2.1.5 Electronic Texts as a Source -- 9.2.1.6 What Is a Taxonomy? …”

Tabla de Contenidos: “…5.1.3.1 Pilot testing -- 5.1.3.2 Eliciting DEs' indigenous IC criteria -- 5.1.3.3 Developing a DEs' indigenous IC criteria rating scale -- 5.1.3.4 Theoretically expanding the IC rating scale -- 5.2 Results and initial discussion of study two -- 5.2.1 Pilot test findings -- 5.2.2 Domain experts' indigenous IC criteria -- 5.2.2.1 Conflict management -- 5.2.2.2 Solidarity promotion -- 5.2.2.3 Reasoning skills -- 5.2.2.4 Personal qualities -- 5.2.2.5 Social relations -- 5.2.2.6 Linguistic choices -- 5.2.2.7 Prosodic features -- 5.2.2.8 The structure of talk -- 5.2.2.9 Strategies, cultural norms, and miscellaneous -- 5.2.3 An indigenous IC rating scale -- 5.2.3.1 Collapsing indigenous criteria into five rating categories -- 5.2.3.2 Identifying steps in the rating categories -- 5.2.3.3 Identifying sub rating categories and extracting descriptors -- 5.2.3.4 Indigenous rating category: Conflict management -- 5.2.3.5 Indigenous rating category: Solidarity promotion -- 5.2.3.6 Indigenous rating category: Personal qualities -- 5.2.3.7 Indigenous rating category: Reasoning skills -- 5.2.3.8 Indigenous rating category: Social relations -- 5.2.4 CA and MCA validation and the generation of exemplars -- 5.2.4.1 The rationale behind the CA and MCA validation of the scale -- 5.2.4.2 The sample test task and the pilot test test-takers selected -- 5.2.4.3 Theorizing conflict management and social relations -- 5.2.4.4 Theorizing solidarity promotion and reasoning skills -- 5.2.4.5 Theorizing personal qualities -- 5.2.4.6 Address terms in social role management -- 5.2.4.7 Categories and predicates -- 5.2.4.8 Beginner L2-speakers' category knowledge -- 5.2.4.9 The power of categorization -- 5.2.5 A theorized IC rating scale -- 5.2.5.1 Theorized rating category: Disaffiliation control -- 5.2.5.2 Theorized rating category: Affiliation promotion…”

Tabla de Contenidos: “…-- 6.0.2 Simple Analogy: The Recipe of Language -- 6.0.3 Natural Language Processing in Everyday Marketing -- 6.0.4 Let's Dive Deeper -- 6.1 Introduction to Natural Language Processing -- 6.1.1 Overview of Natural Language Processing -- 6.1.2 Importance of Natural Language Processing in Marketing -- 6.1.3 Components of Natural Language Processing: Syntax, Semantics, and Pragmatics -- 6.1.4 Challenges in Natural Language Processing -- 6.2 Text Preprocessing and Feature Extraction in Marketing Natural Language Processing -- 6.2.1 Tokenization and Stemming -- 6.2.2 Stop Word Removal -- 6.2.3 Vectorization: Bag of Words and TF-IDF -- 6.2.4 Word Embeddings: Word2Vec, GloVe -- 6.3 Key Natural Language Processing Techniques for Marketing -- 6.3.1 Text Analytics -- 6.3.2 Sentiment Analysis -- 6.3.3 Topic Modeling -- 6.3.4 Named Entity Recognition -- 6.3.5 Text Classification -- 6.4 Chatbots and Voice Assistants in Marketing…”