Tabla de Contenidos: “…Front Cover -- INDUSTRIAL APPLICATIONS OF RENEWABLE PLASTICS -- Series Page -- INDUSTRIAL APPLICATIONS OF RENEWABLE PLASTICS: ENVIRONMENTAL, TECHNOLOGICAL,AND ECONOMIC ADVANCES -- Copyright -- Disclaimer -- Contents -- Preface -- Chapter 1 -- Chapter 2 -- Chapter 3 -- Chapter 4 -- Chapter 5 -- Chapter 6 -- Chapter 7 -- Chapter 8 -- Chapter 9 -- Chapter 10 -- Acronyms and Abbreviations -- 1 - Outline of the Actual Situation of Plastics Compared to Conventional Materials -- 1.1 Polymers: The Industrial and Economic Reality Compared to Traditional Materials -- 1.1.1 Plastic and Metal Consumption -- 1.1.2 Mechanical Properties -- 1.1.2.1 Intrinsic Mechanical Properties -- 1.1.2.2 Specific Mechanical Properties -- 1.1.3 Thermal and Electrical Properties -- 1.1.4 Durability -- 1.1.5 Material Costs -- 1.1.5.1 Cost per Weight of Various Materials -- 1.1.5.2 Cost per Volume of Various Materials -- 1.1.5.3 Performance/Cost per Liter Ratios of Various Materials -- 1.2 What Are Thermoplastics, Thermoplastic Elastomer, Thermosets, Composites, and Hybrids? …”

Tabla de Contenidos: “…3.6.6.1 Transmission lines tour inspection system characteristics -- 3.6.6.2 The advantages of tour inspection system -- References -- 4 Transmission lines detection technology -- 4.1 Faulty Insulator Detection -- 4.1.1 The Characteristics of Faulty Insulators -- 4.1.2 Detection Methods of Faulty Insulators -- 4.1.2.1 Electrical quantity detection methods -- 4.1.2.2 Nonelectric quantity detection method -- 4.2 Voltage Detection in Operation -- 4.2.1 Requirements for Voltage Detector -- 4.2.1.1 Functions and technical requirements of voltage detectors -- 4.2.1.2 Electrical insulation requirements for voltage detectors -- 4.2.2 Methods of Voltage Detection Working -- 4.2.2.1 Direct voltage detection and indirect voltage detection -- 4.2.2.2 Methods and devices of voltage detection -- 4.3 Detection of Grounding Devices -- 4.3.1 Requirements for Grounding Type and Grounding Resistance -- 4.3.1.1 Grounding type -- 4.3.1.2 The main defects of grounding devices -- 4.3.1.3 Requirements for power frequency ground resistance in poles and towers -- 4.3.2 Measurement Methods of Power Frequency of Tower Ground Resistance -- 4.3.2.1 Three-electrode method -- 4.3.2.2 Four-electrode method -- 4.3.2.3 Clamp meter measuring method -- 4.3.3 Notes for Measuring Grounding Resistance and the Operating Maintenance of the Grounding Device -- 4.3.3.1 Notes for measuring grounding resistance -- 4.3.3.2 Operation maintenance for grounding device -- 4.4 Detection of Conductors and Ground Wires and Splicing Fittings -- 4.4.1 Performance Requirements and Heating Reasons for Conductors and Ground Wires and Splicing Fittings -- 4.4.1.1 Performance requirements -- 4.4.1.2 Reasons for temperature increase -- 4.4.1.3 Measures taken -- 4.4.2 Detection Methods -- 4.4.2.1 Ocular estimate method -- 4.4.2.2 Infrared thermal imaging detection method…”

Tabla de Contenidos: “…Cover -- Title Page -- Copyright -- Contents -- Biographies -- Foreword by Professor Sun -- Foreword by Professor Ouyang -- Series Preface -- Preface -- Chapter 1 Introduction -- 1.1 Background -- 1.2 Electric Vehicle Fundamentals -- 1.3 Requirements for Battery Systems in Electric Vehicles -- 1.3.1 Range Per Charge -- 1.3.2 Acceleration Rate -- 1.3.3 Maximum Speed -- 1.4 Battery Systems -- 1.4.1 Introduction to Electrochemistry of Battery Cells -- 1.4.1.1 Ohmic Overvoltage Drop -- 1.4.1.2 Activation Overvoltage -- 1.4.1.3 Concentration Overvoltage -- 1.4.2 Lead-Acid Batteries -- 1.4.3 NiCd and NiMH Batteries -- 1.4.3.1 NiCd Batteries -- 1.4.3.2 NiMH Batteries -- 1.4.4 Lithium‐Ion Batteries -- 1.4.5 Battery Performance Comparison -- 1.4.5.1 Nominal Voltage -- 1.4.5.2 Specific Energy and Energy Density -- 1.4.5.3 Capacity Efficiency and Energy Efficiency -- 1.4.5.4 Specific Power and Power Density -- 1.4.5.5 Self‐discharge -- 1.4.5.6 Cycle Life -- 1.4.5.7 Temperature Operation Range -- 1.5 Key Battery Management Technologies -- 1.5.1 Battery Modeling -- 1.5.2 Battery States Estimation -- 1.5.3 Battery Charging -- 1.5.4 Battery Balancing -- 1.6 Battery Management Systems -- 1.6.1 Hardware of BMS -- 1.6.2 Software of BMS -- 1.6.3 Centralized BMS -- 1.6.4 Distributed BMS -- 1.7 Summary -- References -- Chapter 2 Battery Modeling -- 2.1 Background -- 2.2 Electrochemical Models -- 2.3 Black Box Models -- 2.4 Equivalent Circuit Models -- 2.4.1 General n‐RC Model -- 2.4.2 Models with Different Numbers of RC Networks -- 2.4.2.1 Rint Model -- 2.4.2.2 Thevenin Model -- 2.4.2.3 Dual Polarization Model -- 2.4.2.4 n‐RC Model -- 2.4.3 Open Circuit Voltage -- 2.4.4 Polarization Characteristics -- 2.5 Experiments -- 2.6 Parameter Identification Methods -- 2.6.1 Offline Parameter Identification Method -- 2.6.2 Online Parameter Identification Method…”

Tabla de Contenidos: “…New tool for measuring code performance -- PART 2: Client-Side JavaScript -- Introduction to Part 2.…”

Tabla de Contenidos: “…-- 7.3.8 Whether Comparative Analysis of Multi-Objective Algorithms for Test Case Prioritization in Object-Oriented Testing Has Been Performed? And What are The Results? -- 7.4 Research Gaps -- References -- Chapter 8 A Systematic Review of the Tools and Techniques in Distributed Agile Software Development -- 8.1 Introduction -- 8.1.1 Why Agile? …”

Tabla de Contenidos: “…Ressources mises en œuvre et performance des services publics généraux…”

Tabla de Contenidos: “…. -- La recherche d'efficience s'imposera aussi dans l'enseignement -- Références -- 1 L'importance du capital humain pour la performance économique -- Introduction -- Le capital humain compte parmi les principales priorités de politique structurelle identifiées par l'OCDE -- Dans ses examens économiques des pays membres et non membres de l'OCDE, l'Organisation ne cesse de pointer l'éducation et le développement des compétences comme une priorité essentielle des politiques structurelles -- Cette importance soulignée de l'éducation et du développement des compétences fait écho à ce que nous dit la théorie économique du capital humain, vu comme un moteur essentiel de la productivité. -- Le capital humain en tant que moteur de la performance économique : définition et cadre conceptuel -- La théorie économique a longtemps envisagé le capital humain comme un stock limité de connaissances et de compétences, perçu comme un facteur de production ayant un impact limité sur le PIB par habitant -- Mais les modèles de croissance endogène ont renforcé l'intérêt pour le capital humain, en faisant valoir qu'il contribue également à la croissance à long terme de manière indirecte, par la diffusion de l'innovation et par son impact sur la PGF…”

Tabla de Contenidos: “…1.15.5 Superconductors -- 1.15.6 3D printing -- 1.15.7 Autonomous vehicle -- 1.16 Conclusion -- References -- Chapter 2: Advances of deep learning and related applications -- 2.1 Introduction -- 2.2 Deep learning techniques -- 2.3 Multilayer perceptron -- 2.4 Convolutional neural network -- 2.5 Recurrent neural network -- 2.6 Long-term short-term memory -- 2.7 GRU -- 2.8 Autoencoders -- 2.9 Attention mechanism -- 2.10 Deep generative models -- 2.11 Restricted Boltzmann machine -- 2.12 Deep belief network -- 2.13 Modern deep learning platforms -- 2.13.1 PyTorch -- 2.13.2 TensorFlow -- 2.13.3 Keras -- 2.13.4 Caffe (Convolutional architecture for fast feature embedding) and Caffe2 -- 2.13.5 Deeplearning4j -- 2.13.6 Theano -- 2.13.7 Microsoft cognitive toolkit (CNTK) -- 2.14 Challenges of deep learning -- 2.15 Applications of deep learning -- 2.16 Conclusion -- References -- Chapter 3: Machine learning for big data and neural networks -- 3.1 Introduction -- 3.2 Machine learning and fundamentals -- 3.2.1 Supervised learning -- 3.2.2 Decision trees -- 3.2.3 Linear regression -- 3.2.4 Naive Bayes -- 3.2.5 Logistic regression -- 3.3 Unsupervised learning -- 3.3.1 K-Means algorithm -- 3.3.2 Principal component analysis -- 3.4 Reinforcement learning -- 3.5 Machine learning in large-scale data -- 3.6 Data analysis in big data -- 3.7 Predictive modelling -- 3.7.1 Understanding customer behavior and preferences -- 3.7.2 The role of supply chain and performance management in organizational success -- 3.7.3 Management of quality and enhancement -- 3.7.4 Risk mitigation and detection of fraud -- 3.8 Neural networks -- 3.8.1 Artificial neural network -- 3.8.2 RNN -- 3.8.3 CNN -- 3.8.4 Deep learning using convolutional neural networks to find building defects -- 3.9 Data generation and manipulation -- 3.9.1 Generative Adversarial Networks…”

Tabla de Contenidos: “…8.1.4 Servlet engine thread pool size -- 8.1.5 Data source connection pool size -- 8.1.6 Statement cache size -- 8.2 Database server tuning -- 8.3 Web server tuning tips -- 8.3.1 ThreadsPerChild -- 8.3.2 KeepAliveTimeout -- 8.3.3 MaxKeepAliveRequests -- 8.4 Security filters (LDAP filters) -- 8.5 WebSphere Portal service properties -- 8.6 Startup performance tuning -- Chapter 9. Maintenance -- 9.1 Introduction to Integration Assistant for iSeries -- 9.1.1 What Integration Assistant provides -- 9.1.2 What Integration Assistant does not provide -- 9.1.3 Assisted products -- 9.1.4 Installing IA1 -- 9.1.5 What is installed with IA1 base -- 9.1.6 The IA1 assisted product option 1: WebSphere Portal V5.0.2 -- 9.2 Choosing a fix strategy -- 9.2.1 WebSphere Portal Update Installer -- 9.2.2 Pros and cons of using Integration Assistant PTFs and WebSphere Portal Update Installer -- 9.3 Maintaining log files -- 9.3.1 Installation logs -- 9.3.2 Configuration logs -- 9.3.3 Runtime logs -- 9.4 WebSphere Portal runtime code -- Chapter 10. …”

Tabla de Contenidos: “…Scaling Transparency -- 1.3.5.8. Performance Transparency -- 1.3.5.9. Distribution Transparency -- 1.3.5.10. …”

Tabla de Contenidos: “…Introduction -- 1.1 Job scheduling -- 1.2 Introduction to end-to-end scheduling -- 1.3 Introduction to Tivoli Workload Scheduler for z/OS -- 1.3.1 Overview of Tivoli Workload Scheduler for z/OS -- 1.3.2 Tivoli Workload Scheduler for z/OS architecture -- 1.4 Introduction to Tivoli Workload Scheduler -- 1.4.1 Overview of IBM Tivoli Workload Scheduler -- 1.4.2 IBM Tivoli Workload Scheduler architecture -- 1.5 Benefits of integrating Tivoli Workload Scheduler for z/OS and Tivoli Workload Scheduler -- 1.6 Summary of enhancements in V8.2 related to end-to-end scheduling -- 1.6.1 New functions related with performance and scalability -- 1.6.2 General enhancements -- 1.6.3 Security enhancements -- 1.7 The terminology used in this book -- Chapter 2. …”

Tabla de Contenidos: “…Evaluating performance metrics for training and test data…”

Tabla de Contenidos: “…Determining lower bounds on model performance -- Summary -- Chapter 2. Starting with R and data -- 2.1. …”

Tabla de Contenidos: “…Implementing a business object handler -- 16.1 Extending the business-object-handler base class -- 16.2 Implementing the doVerbFor() method -- 16.2.1 Obtaining the active verb -- 16.2.2 Verifying the connection before processing the verb -- 16.2.3 Branching on the active verb -- 16.3 Performing the verb operation -- 16.3.1 Accessing the business object -- 16.3.2 Implementing our verb operation -- Chapter 17. …”

Tabla de Contenidos: “…References -- 4 Artificial Intelligence and Machine Learning: Discovering New Ways of Doing Banking Business -- Structure of the Chapter -- 4.1 Introduction -- 4.2 AI in the Banking Sector: Where It Works and What For -- 4.2.1 AI and Customer Service -- 4.2.1.1 Chatbots -- 4.2.1.2 AI and Personalized Banking -- 4.2.1.3 Smart Wallets -- 4.2.1.4 Voice Assisted Banking -- 4.2.1.5 Robo Advice -- 4.2.1.6 AI Backed Blockchain for Expedite Payments -- 4.2.2 AI and Magnifying Efficiency of Banks -- 4.2.2.1 Determining Credit Scoring and Lending Decisions -- 4.2.2.2 AI and CRM -- 4.2.3 Magnifying Security and Risk Control -- 4.2.3.1 Detection and Prevention of Financial Fraud -- 4.2.3.2 Reducing Money Laundering -- 4.2.3.3 Cybersecurity -- 4.2.3.4 AI: Managing and Controlling Risk -- 4.3 AI Applications in Indian Banks: Some Selected Examples -- 4.3.1 State Bank of India -- 4.3.2 HDFC Bank -- 4.3.3 Axis Bank -- 4.3.4 Punjab National Bank -- 4.4 AI and its Impact on Banks' KPIs -- 4.4.1 Impact of AI on Profitability -- 4.4.2 Impact of AI on Productivity and Efficiency of Banks -- 4.4.3 Impact of AI on Improved Customer Satisfaction -- 4.4.4 AI Helps in Offering Innovative and Tailor-Made Services -- 4.4.5 AI Helps in Reducing Customer Attrition -- 4.4.6 Impact of AI on Overall Performance -- 4.5 Conclusion and Future of AI -- References -- 5 Analysis and Comparison of Credit Card Fraud Detection Using Machine Learning -- 5.1 Introduction -- 5.2 Related Work -- 5.3 Proposed Method -- 5.4 Results -- 5.5 Conclusion and Future Scope -- References -- 6 Artificial Intelligence for All: Machine Learning and Healthcare: Challenges and Perspectives in India -- 6.1 Introduction -- 6.2 Healthcare in India: Challenges -- 6.3 Frameworks in Health must consider Missingness -- 6.3.1 Wellsprings of Missingness Must Be Painstakingly Comprehended…”

Tabla de Contenidos: “…Cover -- Title Page -- Copyright Page -- Contents -- Preface -- Chapter 1 Introduction of AI in Innovative Engineering -- 1.1 Introduction to Innovation Engineering -- 1.2 Flow for Innovation Engineering -- 1.3 Guiding Principles for Innovation Engineering -- 1.4 Introduction to Artificial Intelligence -- 1.4.1 History of Artificial Intelligence -- 1.4.2 Need for Artificial Intelligence -- 1.4.3 Applications of AI -- 1.4.4 Comprised Elements of Intelligence -- 1.4.5 AI Tools -- 1.4.6 AI Future in 2035 -- 1.4.7 Humanoid Robot and AI -- 1.4.8 The Explosive Growth of AI -- 1.5 Types of Learning -- 1.6 Categories of AI -- 1.7 Branches of Artificial Intelligence -- 1.8 Conclusion -- Bibliography -- Chapter 2 An Analytical Review of Deep Learning Algorithms for Stress Prediction in Teaching Professionals -- 2.1 Introduction -- 2.2 Literature Review -- 2.3 Dataset and Pre-Processing -- 2.4 Machine Learning Techniques Used -- 2.5 Performance Parameter -- 2.6 Proposed Methodology -- 2.7 Result and Experiment -- 2.8 Comparison of Six Different Approaches For Stress Detection -- 2.9 Conclusions -- 2.10 Future Scope -- References -- Chapter 3 Deep Learning: Tools and Models -- 3.1 Introduction -- 3.1.1 Definition -- 3.1.2 Elements of Neural Networks -- 3.1.3 Tool: Keras -- 3.2 Deep Learning Models -- 3.2.1 Deep Belief Network [DBN] -- 3.2.1.1 Fundamental Architecture of DBN -- 3.2.1.2 Implementing DBN Using MNIST Dataset -- 3.2.2 Recurrent Neural Network [RNN] -- 3.2.2.1 Fundamental Architecture of RNN -- 3.2.2.2 Implementing RNN Using MNIST Dataset -- 3.2.3 Convolutional Neural Network [CNN] -- 3.2.3.1 Fundamental Architecture of CNN -- 3.2.3.2 Implementing CNN Using MNIST Dataset -- 3.2.4 Gradient Adversarial Network [GAN] -- 3.2.4.1 Fundamental Architecture of GAN -- 3.2.4.2 Implementing GAN Using MNIST Dataset -- 3.3 Research Perspective of Deep Learning…”

Tabla de Contenidos: “…DEPENDING ON‌‌ -- 9.3 Vorbemerkung zu internen Unterprogrammen -- 9.4 PERFORM-Anweisung‌‌‌‌ -- 9.5 EXIT‌‌-Anweisung -- 9.6 EXIT PERFORM‌‌‌-Anweisung -- 9.7 EXIT SECTION‌‌‌-Anweisung -- Kapitel 10: Die Anweisungen IF und EVALUATE -- 10.1 IF‌‌-Anweisung -- 10.1.1 Vorzeichenbedingung‌ -- 10.1.2 Klassenbedingung‌ -- 10.1.3 Bedingungsnamen-Bedingung‌ -- 10.1.4 Abfragen eines Pointers -- 10.1.5 Zusammengesetzte Bedingungen‌‌ -- 10.2 CONTINUE‌‌-Anweisung -- 10.3 EVALUATE‌‌-Anweisung -- Kapitel 11: Quellcode wiederverwenden mit COPY -- 11.1 COPY‌-Anweisung -- 11.2 COPY-Bibliotheken‌ -- 11.2.1 REPLACING‌-Zusatz -- 11.2.2 SUPPRESS‌-Angabe -- 11.3 REPLACE‌-Anweisung -- Kapitel 12: Externe Unterprogramme‌ -- 12.1 Sprachelemente für Unterprogramm-Technik -- 12.2 Die Programmverbindung‌ -- 12.3 CALL‌‌-Anweisung -- 12.4 LINKAGE SECTION‌‌ -- 12.5 USING‌‌-Zusatz der PROCEDURE DIVISION -- 12.5.1 BY REFERENCE‌‌ -- 12.5.2 BY REFERENCE ADDRESS OF‌‌ -- 12.5.3 BY CONTENT‌‌ -- 12.5.4 BY CONTENT LENGTH OF‌‌ -- 12.5.5 BY VALUE‌‌ -- 12.6 EXIT PROGRAM-Anweisung‌‌ -- 12.7 Rekursive COBOL-Programme‌‌‌ -- 12.8 CANCEL‌‌-Anweisung -- 12.9 GOBACK‌‌-Anweisung -- 12.10 Weitere Angaben zur Programmkommunikation -- 12.11 EXTERNAL‌‌-Klausel -- 12.12 Schachtelung von Programmen‌‌ -- 12.12.1 Die Schachtelungsebene eines Unterprogramms -- 12.12.2 Der Aufruf eines geschachtelten Unterprogramms -- 12.13 GLOBAL‌‌-Klausel -- 12.14 INITIAL‌‌-Klausel -- Kapitel 13: Tabellenverarbeitung -- 13.1 OCCURS-Klausel‌‌‌ -- 13.1.1 Definition einer eindimensionalen Tabelle‌‌ -- 13.1.2 Adressierung‌‌‌ von Elementen einer Tabelle…”

Tabla de Contenidos: “…3.3 Snowflake Cloud Data Warehouse -- 3.3.1 High-Level Architecture of Snowflake Cloud Data Warehouse -- 3.3.2 Features of Snowflake Cloud Data Warehouse -- 3.3.3 Snowflake Cloud Data Warehouse Pricing -- 3.4 Google BigQuery Cloud Data Warehouse -- 3.4.1 High-Level Architecture of Google BigQuery Cloud Data Warehouse -- 3.4.2 Features of Google BigQuery Cloud Data Warehouse -- 3.4.3 Google BigQuery Cloud Data Warehouse Pricing -- 3.5 Microsoft Azure Synapse Cloud Data Warehouse -- 3.5.1 Microsoft Azure Synapse Cloud Data Warehouse Architecture -- 3.5.2 Features of Microsoft Azure Synapse Cloud Data Warehouse -- 3.5.3 Pricing of Microsoft Azure Synapse Cloud Data Warehouse -- 3.6 Informatica Intelligent Cloud Services (IICS) -- 3.6.1 Informatica Intelligent Cloud Services Architecture -- 3.6.2 Salient Features of Informatica Intelligent Cloud Services -- 3.6.3 Informatica Intelligent Cloud Services Pricing Model -- 3.7 Conclusion -- Acknowledgements -- References -- Chapter 4 Data Mining with Cluster Analysis Through Partitioning Approach of Huge Transaction Data -- 4.1 Introduction -- 4.2 Methodology Used in Proposed Cluster Analysis System -- 4.2.1 Design of Algorithms -- 4.3 Literature Survey on Existing Systems -- 4.3.1 Experimental Results -- 4.4 Conclusion -- References -- Chapter 5 Application of Data Science in Macromodeling of Nonlinear Dynamical Systems -- 5.1 Introduction -- 5.2 Nonlinear Autonomous Dynamical System -- 5.3 Nonlinear System - MOR -- 5.3.1 Proper Orthogonal Decomposition -- 5.4 Data Science Life Cycle -- 5.4.1 Problem Identification -- 5.4.2 Identifying Available Data Sources and Data Collection -- 5.4.3 Data Processing -- 5.4.4 Data Exploration -- 5.4.5 Feature Extraction -- 5.4.6 Modeling -- 5.4.7 Model Performance Evaluation -- 5.5 Artificial Neural Network in Modeling -- 5.5.1 Machine Learning…”

Tabla de Contenidos: “…. -- 1.7 Applications -- 1.8 A Simple Example of Digital Twin Application -- 1.9 Digital Twin Technology and the Metaverse -- 1.10 Challenges -- 1.10.1 Careful Handling of Different Factors Involved in Digital Twin -- 1.10.2 Expertise Required -- 1.10.3 Data Security and Privacy -- 1.11 Conclusion -- References -- Chapter 2 Introduction, History, and Concept of Digital Twin -- 2.1 Introduction -- 2.2 History of Digital Twin -- 2.3 Concept of Digital Twin -- 2.3.1 DTP -- 2.3.2 DTI -- 2.3.3 DTE -- 2.3.4 Conceptualization -- 2.3.5 Comparison -- 2.3.6 Collaboration -- 2.4 Working Principle -- 2.5 Characteristics of Digital Twin -- 2.5.1 Homogenization -- 2.5.2 Digital Trail -- 2.5.3 Connectivity -- 2.6 Advantages -- 2.6.1 Companies Can Benefit From Digital Twin by Tracking Performance-Related Data -- 2.6.2 Different Sector's Progress Can Be Accelerated -- 2.6.3 Digital Twins Can Be Used for Various Application -- 2.6.4 Digital Twin Can Help Decide Future Course of Work…”

Tabla de Contenidos: “…2.3 Deep learning applications in brain cancer -- 2.3.1 Tumor grading -- 2.3.2 Survival analysis -- 2.3.3 Radiogenomics -- 2.3.3.1 1p/19q -- 2.3.3.2 Isocitrate dehydrogenase -- 2.3.3.3 6-methylguanine-DNA methyltransferase -- 2.3.4 Pseudoprogression -- 2.4 Deep learning applications in breast cancer -- 2.4.1 Increasing accuracy in breast cancer risk assessment -- 2.4.2 Reproducible breast density assessment for improved breast cancer risk prediction -- 2.4.3 Improving performance in breast cancer diagnosis -- 2.4.4 Enhancing efficacy in breast cancer clinical practice -- 2.5 Conclusion -- Acknowledgments -- References -- 3 Deep neural networks and advanced computer vision algorithms in the early diagnosis of skin diseases -- 3.1 Introduction and motivation for the early diagnosis of melanoma -- 3.2 Artificial intelligence and computer vision in melanoma diagnosis -- 3.3 Medical diagnostic procedures for screening of skin diseases -- 3.4 State-of-the-art survey on skin mole segmentation methods -- 3.4.1 Comparison of the state of the art -- 3.4.2 Summary -- 3.5 Improved local and global patterns detection algorithms by deep learning algorithms -- 3.6 Early classification of skin melanomas in dermoscopy -- 3.6.1 Diagnostic algorithms -- 3.6.2 Approaches to detect the diagnostic criteria -- 3.6.3 Approaches to directly classify skin conditions -- 3.6.3.1 Classifiers utilizing the convolutional neural networks as a feature extractor -- 3.6.3.2 Classifiers using end-to-end learning convolutional neural networks model training with transfer learning -- 3.6.3.3 Convolutional neural networks model training from scratch -- 3.6.3.4 Ensembles of convolutional neural networks models -- 3.7 Conclusions -- 3.8 How to speed up the classification process with field-programmable gate arrays? …”