LEADER 06686nam 22006135 450 001 9910298356303321 005 20200704024027.0 010 $a3-319-93251-9 024 7 $a10.1007/978-3-319-93251-4 035 $a(CKB)4100000006674957 035 $a(MiAaPQ)EBC5520940 035 $a(DE-He213)978-3-319-93251-4 035 $a(EXLCZ)994100000006674957 100 $a20180920d2018 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aParent-Child Interaction Therapy with Toddlers $eImproving Attachment and Emotion Regulation /$fby Emma I. Girard, Nancy M. Wallace, Jane R. Kohlhoff, Susan S. J. Morgan, Cheryl B. McNeil 205 $a1st ed. 2018. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2018. 215 $a1 online resource (367 pages) 311 $a3-319-93250-0 327 $aSection 1. Parent-Child Interaction Therapy-Toddler (PCIT-T): Theoretical Underpinnings, Empirical Background and Program Description -- Chapter 1. An Introduction to PCIT-T: Integrating Attachment and Behavioral Principles -- Chapter 2. Core Elements of PCIT-T and Treatment Goals -- Chapter 3. Setting the Empirical Stage: An Overview of Standard PCIT -- Chapter 4. The Application of PCIT to the Toddler Age Group -- Chapter 5. Conceptualizing PCIT-T as an Emotion Regulation Treatment for Toddlers -- Chapter 6. Behavioral Assessment in PCIT-T -- Chapter 7. Room Set-Up, Toy Selection, and Special Considerations -- Chapter 8. Child-Directed Interaction ? Toddler -- Chapter 9. Parent Directed Interaction - Toddler -- Chapter 10. Conclusions -- Section 2. Parent-Child Interaction Therapy with Toddlers Clinical Manual: A Session-by-Session Guide -- Chapter 11. Treatment Overview and Implementation of the Current Protocol -- Chapter 12. Pre-Treatment Interview and Assessment Session -- Chapter 13. Child Directed Interaction - Toddler Teach Session -- Chapter 14. Child Directed Interaction - Toddler Coach Session -- Chapter 15. Parent Directed Interaction - Toddler Teach Session -- Chapter 16. Parent Directed Interaction - Toddler Coach Session -- Chapter 17. Life Enhancement Coach Sessions -- Chapter 18. Graduation Session -- Appendix A: Coaching Child Directed Interaction Excerpt -- Appendix B: Understanding Your Child's Behavior: Reading Your Child's Cues from Birth to Age 2 -- Appendix C: Sleep Needs Guide for Infants 0 to 3 Years Old -- Appendix D: Teaching Your Child to Become Independent with Daily Routines -- Appendix E: Responding to Your Child?s Bite -- Appendix F: Making the Most of Playtime -- Appendix G: Toddler Book Suggestions -- Appendix H: Developmental Tip of the Day Cards -- Appendix I: Additional Resources. . 330 $aThis book presents an early treatment model for toddlers. It describes the early life span development, trajectory, and future potential of toddlers and how it may be powerfully influenced by the protection and guidance of caregivers to meet toddlers? physical and mental health needs. It offers an in-depth guide to Parent-Child Interaction Therapy with Toddlers (PCIT-T), an evidence-based program for addressing and preventing behavior problems affecting young children?s development. The book details the innovative intervention design and how it guides clinicians in providing treatment for 12-month old to 24-month old toddlers with disruptive behaviors in addition to being used as a prevention model for caregivers experiencing stress of child rearing. PCIT-T focuses on core areas of social and emotional development, including behavior management and language skills, and can be used in dealing with difficulties as diverse as tantrums, language issues, autistic behaviors, and separation anxiety. Play therapy and compliance training in child-directed as well as parent-directed sessions are also examined. Initial chapters provide an overview of attachment and behavioral theory components that are foundational to the treatment model. Subsequent chapters provide a session-by-session guide and clinical manual for implementation of PCIT-T as well as the clinician tools needed to monitor treatment integrity and fidelity to the model. Topics featured in this book include: Core elements and treatment goals of PCIT-T. A range of behavioral assessments used in PCIT-T. Instructions for room set-up, toy selection, and special considerations when providing PCIT-T treatment. Preparation guides for the pretreatment interview, assessment sessions, and weekly coaching sessions. The importance of child-directed interaction toddler (CDI-T) and parent-directed interaction toddler (PDI-T) in teaching children the necessary skills to regulate their emotions and develop self-control. Parent-Child Interaction Therapy with Toddlers is a must-have resource for clinicians and related professionals, researchers and professors, and graduate students in the fields of clinical child and school psychology, social work, pediatrics, infancy and early childhood development, child and adolescent psychiatry, primary care medicine, and related disciplines. 606 $aChild psychology 606 $aSchool psychology 606 $aSocial service 606 $aPediatrics 606 $aInfant psychology 606 $aChild and School Psychology$3https://scigraph.springernature.com/ontologies/product-market-codes/Y12040 606 $aSocial Work$3https://scigraph.springernature.com/ontologies/product-market-codes/X21000 606 $aPediatrics$3https://scigraph.springernature.com/ontologies/product-market-codes/H49006 606 $aInfancy and Early Childhood Development$3https://scigraph.springernature.com/ontologies/product-market-codes/Y12050 615 0$aChild psychology. 615 0$aSchool psychology. 615 0$aSocial service. 615 0$aPediatrics. 615 0$aInfant psychology. 615 14$aChild and School Psychology. 615 24$aSocial Work. 615 24$aPediatrics. 615 24$aInfancy and Early Childhood Development. 676 $a618.928914 700 $aGirard$b Emma I$4aut$4http://id.loc.gov/vocabulary/relators/aut$0880293 702 $aWallace$b Nancy M$4aut$4http://id.loc.gov/vocabulary/relators/aut 702 $aKohlhoff$b Jane R$4aut$4http://id.loc.gov/vocabulary/relators/aut 702 $aMorgan$b Susan S. J$4aut$4http://id.loc.gov/vocabulary/relators/aut 702 $aMcNeil$b Cheryl B$4aut$4http://id.loc.gov/vocabulary/relators/aut 906 $aBOOK 912 $a9910298356303321 996 $aParent-Child Interaction Therapy with Toddlers$91965600 997 $aUNINA LEADER 06374nam 22008653u 450 001 9911019143203321 005 20170809172635.0 010 $a1-283-17783-8 010 $a9786613177834 010 $a1-119-97401-1 010 $a1-119-97400-3 035 $a(CKB)2550000000041193 035 $a(EBL)697607 035 $a(OCoLC)747411905 035 $a(SSID)ssj0000539756 035 $a(PQKBManifestationID)11327619 035 $a(PQKBTitleCode)TC0000539756 035 $a(PQKBWorkID)10580065 035 $a(PQKB)10298968 035 $a(PPN)262117177 035 $a(EXLCZ)992550000000041193 100 $a20130418d2011|||| u|| | 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aOptimal Design of Experiments $eA Case Study Approach 210 $aChicester $cWiley$d2011 215 $a1 online resource (305 p.) 300 $aDescription based upon print version of record. 311 $a0-470-74461-8 327 $aOptimal Design of Experiments : A Case Study Approach; Contents; Preface; Acknowledgments; 1 A simple comparative experiment; 1.1 Key concepts; 1.2 The setup of a comparative experiment; 1.3 Summary; 2 An optimal screening experiment; 2.1 Key concepts; 2.2 Case: an extraction experiment; 2.2.1 Problem and design; 2.2.2 Data analysis; 2.3 Peek into the black box; 2.3.1 Main-effects models; 2.3.2 Models with two-factor interaction effects; 2.3.3 Factor scaling; 2.3.4 Ordinary least squares estimation; 2.3.5 Significance tests and statistical power calculations; 2.3.6 Variance inflation 327 $a2.3.7 Aliasing2.3.8 Optimal design; 2.3.9 Generating optimal experimental designs; 2.3.10 The extraction experiment revisited; 2.3.11 Principles of successful screening: sparsity, hierarchy, and heredity; 2.4 Background reading; 2.4.1 Screening; 2.4.2 Algorithms for finding optimal designs; 2.5 Summary; 3 Adding runs to a screening experiment; 3.1 Key concepts; 3.2 Case: an augmented extraction experiment; 3.2.1 Problem and design; 3.2.2 Data analysis; 3.3 Peek into the black box; 3.3.1 Optimal selection of a follow-up design; 3.3.2 Design construction algorithm; 3.3.3 Foldover designs 327 $a3.4 Background reading3.5 Summary; 4 A response surface design with a categorical factor; 4.1 Key concepts; 4.2 Case: a robust and optimal process experiment; 4.2.1 Problem and design; 4.2.2 Data analysis; 4.3 Peek into the black box; 4.3.1 Quadratic effects; 4.3.2 Dummy variables for multilevel categorical factors; 4.3.3 Computing D-efficiencies; 4.3.4 Constructing Fraction of Design Space plots; 4.3.5 Calculating the average relative variance of prediction; 4.3.6 Computing I-efficiencies; 4.3.7 Ensuring the validity of inference based on ordinary least squares; 4.3.8 Design regions 327 $a4.4 Background reading4.5 Summary; 5 A response surface design in an irregularly shaped design region; 5.1 Key concepts; 5.2 Case: the yield maximization experiment; 5.2.1 Problem and design; 5.2.2 Data analysis; 5.3 Peek into the black box; 5.3.1 Cubic factor effects; 5.3.2 Lack-of-fit test; 5.3.3 Incorporating factor constraints in the design construction algorithm; 5.4 Background reading; 5.5 Summary; 6 A "mixture" experiment with process variables; 6.1 Key concepts; 6.2 Case: the rolling mill experiment; 6.2.1 Problem and design; 6.2.2 Data analysis; 6.3 Peek into the black box 327 $a6.3.1 The mixture constraint6.3.2 The effect of the mixture constraint on the model; 6.3.3 Commonly used models for data from mixture experiments; 6.3.4 Optimal designs for mixture experiments; 6.3.5 Design construction algorithms for mixture experiments; 6.4 Background reading; 6.5 Summary; 7 A response surface design in blocks; 7.1 Key concepts; 7.2 Case: the pastry dough experiment; 7.2.1 Problem and design; 7.2.2 Data analysis; 7.3 Peek into the black box; 7.3.1 Model; 7.3.2 Generalized least squares estimation; 7.3.3 Estimation of variance components; 7.3.4 Significance tests 327 $a7.3.5 Optimal design of blocked experiments 330 $a""This is an engaging and informative book on the modern practice of experimental design. The authors' writing style is entertaining, the consulting dialogs are extremely enjoyable, and the technical material is presented brilliantly but not overwhelmingly. The book is a joy to read. Everyone who practices or teaches DOE should read this book."" - Douglas C. Montgomery, Regents Professor, Department of Industrial Engineering, Arizona State University ""It's been said: 'Design for the experiment, don't experiment for the design.' This book ably demonstrates this notion 606 $aExperimental design - Data processing 606 $aExperimental design -- Data processing 606 $aIndustrial engineering 606 $aIndustrial engineering -- Case studies 606 $aIndustrial engineering - Experiments - Computer-aided design 606 $aIndustrial engineering -- Experiments -- Computer-aided design 606 $aSCIENCE / Experiments & Projects 606 $aIndustrial engineering$xExperiments$xComputer-aided design$vCase studies 606 $aExperimental design$xData processing 606 $aIndustrial engineering 606 $aEngineering & Applied Sciences$2HILCC 606 $aApplied Mathematics$2HILCC 615 4$aExperimental design - Data processing. 615 4$aExperimental design -- Data processing. 615 4$aIndustrial engineering. 615 4$aIndustrial engineering -- Case studies. 615 4$aIndustrial engineering - Experiments - Computer-aided design. 615 4$aIndustrial engineering -- Experiments -- Computer-aided design. 615 4$aSCIENCE / Experiments & Projects. 615 0$aIndustrial engineering$xExperiments$xComputer-aided design 615 0$aExperimental design$xData processing. 615 0$aIndustrial engineering. 615 7$aEngineering & Applied Sciences 615 7$aApplied Mathematics 676 $a500 676 $a620.00420285 686 $aSCI028000$2bisacsh 700 $aGoos$b Peter$01598894 701 $aJones$b Bradley$042705 801 0$bAU-PeEL 801 1$bAU-PeEL 801 2$bAU-PeEL 906 $aBOOK 912 $a9911019143203321 996 $aOptimal Design of Experiments$94045089 997 $aUNINA LEADER 13062nam 22005653 450 001 9911019472703321 005 20250817110028.0 010 $a1-394-27242-1 010 $a1-394-27241-3 035 $a(MiAaPQ)EBC32226739 035 $a(Au-PeEL)EBL32226739 035 $a(CKB)39672128500041 035 $a(OCoLC)1528398487 035 $a(CaSebORM)9781394272396 035 $a(OCoLC)1528627619 035 $a(OCoLC-P)1528627619 035 $a(EXLCZ)9939672128500041 100 $a20250722d2025 uy 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aAutomated Machine Learning and Industrial Applications 205 $a1st ed. 210 1$aNewark :$cJohn Wiley & Sons, Incorporated,$d2025. 210 4$dİ2025. 215 $a1 online resource (351 pages) 311 08$a1-394-27239-1 327 $aCover -- Series Page -- Title Page -- Copyright Page -- Contents -- Preface -- Chapter 1 Design and Architecture of AutoML for Data Science in Next-Generation Industries -- 1.1 Introduction -- 1.2 Modular Design -- 1.3 Data Handling -- 1.4 Model Training and Selection -- Conclusion -- References -- Chapter 2 Automated Machine Learning Model in Secure Data Transmission in Sustainable Healthcare Sensor Network Using Quantum Blockchain Architecture -- 2.1 Introduction -- 2.2 Related Works -- 2.3 Proposed Model -- 2.4 Results and Discussion -- 2.5 Conclusion -- References -- Chapter 3 Automated Machine Learning in the Biological and Medical Healthcare Industries: Analysis Interpretation and Evaluation -- 3.1 Introduction -- 3.1.1 Rise of AutoML -- 3.1.2 Significance of AutoML in Biological and Medical Healthcare -- 3.2 Methodology for Effective Data Management -- 3.3 Foundations of Automated Machine Learning -- 3.3.1 Understanding Automated Machine Learning -- 3.3.2 Components and Workflow -- 3.3.3 Pros of AutoML Implementation -- 3.3.4 Cons of AutoML Implementation -- 3.4 Applications in Healthcare -- 3.4.1 Disease Diagnosis -- 3.4.2 Drug Discovery and Development -- 3.4.3 Personalized Medicine -- 3.4.4 Predictive Analytics in Healthcare -- 3.5 Case Studies and Success Stories -- 3.5.1 Noteworthy Implementations -- 3.5.2 Impact on Patient Outcomes -- 3.5.3 Challenges Encountered and Overcome -- 3.6 Ethical Implications -- 3.6.1 Data Privacy and Security -- 3.6.2 Fairness and Bias Considerations -- 3.7 Practical Implementation: From Concept to Application -- 3.7.1 Problem Formulation and Data Preparation -- 3.7.2 Tool Selection -- 3.7.3 Training and Evaluation -- 3.7.4 Explainability and Interpretability -- 3.7.5 Deployment and Monitoring -- 3.8 Future Directions and Trends -- 3.8.1 Integration with Emerging Technologies. 327 $a3.8.2 AutoML in Clinical Trials and Research -- 3.9 Conclusion -- References -- Chapter 4 Advancements in AI and AutoML for Plant Leaf Disease Identification in Sustainable Agriculture -- 4.1 Introduction -- 4.2 Literature Survey -- 4.3 Preliminary Analysis for Agricultural Diseases -- 4.3.1 Datasets and Descriptions -- 4.3.2 Normalization and Scaling -- 4.3.3 Feature Extraction and Classification -- 4.3.4 Spectral Image Analysis -- 4.4 Proposed Methods -- 4.4.1 Leaf Disease Identification Using ResNet -- 4.4.2 Pixel-Based Information Extraction and Ant Colony Optimization -- 4.4.3 Image Enhancement and Segmentation -- 4.5 Conclusion -- References -- Chapter 5 Predictive Maintenance in Industrial Settings: Video Analytics at the Edge with AutoML -- 5.1 Introduction -- 5.2 Literature Review -- 5.3 Proposed Design of an Efficient Model for Enhancing Predictive Maintenance in Industrial Settings -- 5.4 Result Evaluation and Comparative Analysis -- 5.5 Conclusion and Future Scope -- Future Scope -- References -- Chapter 6 AutoCRM-An Automated Customer Relationship Management Learning System with Random Search Hyper-Parameter Optimization -- 6.1 Introduction -- 6.1.1 Opinion Mining or Sentiment Analysis -- 6.1.2 Machine Learning Approaches -- 6.1.3 Machine Learning Pipeline (ML) -- 6.1.4 Automated Machine Learning (AutoML) -- 6.1.4.1 AutoML Core Goals -- 6.1.4.2 AutoML Tools -- 6.1.5 Objectives of this Research -- 6.1.6 Outline -- 6.2 Literature Review -- 6.3 Methodology -- 6.3.1 Data Preparation -- 6.3.1.1 Data Collection -- 6.3.1.2 Data Cleaning and Labeling -- 6.3.1.3 Data Visualization -- 6.3.1.4 Feature Engineering -- 6.3.2 AutoKeras -- 6.3.2.1 Neural Architecture Search and Hyper-Parameter Tuning -- 6.3.3 Model Selection -- 6.4 Results and Discussions -- 6.4.1 Comparative Analysis: AutoML vs ML -- 6.5 Conclusion -- References. 327 $aChapter 7 The Competence of Customer Support Team for Sentiment Analysis in Chatbots Using AutoML -- 7.1 Introduction -- 7.1.1 Background -- 7.1.2 Problem Definition -- 7.1.3 Scope -- 7.1.4 Technical Highlights -- 7.1.5 Objectives -- 7.1.6 Common Chatbot Use Cases -- 7.1.7 The Basics of Sentiment Analysis -- 7.1.8 Levels of Sentiment Analysis -- 7.2 Literature Survey -- 7.3 Methodology for Chatbot Sentiment Analysis -- 7.3.1 AutoML-Based Exploratory Data Analysis and Subjectivity Detection -- 7.3.2 Trilateral Modifier Utilization -- 7.3.3 Sentiment Polarity Detection -- 7.3.4 Workflow of Customer Service Inquiry-Chatbot Response -- 7.3.5 Scoring -- 7.4 Experimentation and Results -- 7.4.1 Performance Metrics -- 7.4.2 Data Collection -- 7.4.3 Evaluation -- 7.5 Conclusion -- References -- Chapter 8 Financial Risk Prediction with Banking Monitoring for Cyber Security Analysis Using Automated Machine Learning -- 8.1 Introduction -- 8.2 Related Works -- 8.3 System Model -- 8.3.1 Cyber Security Detection Using Gaussian Encoder Belief Network -- 8.4 Results and Discussion -- 8.5 Conclusion -- References -- Chapter 9 AutoML Ecosystem and Open-Source Platforms: Challenges and Limitations -- 9.1 Introduction -- 9.2 Related Study -- 9.3 Ecosystem of AutoML -- 9.3.1 Data Preprocessing -- 9.3.2 Model Selection -- 9.3.3 Hyperparameter Tuning -- 9.3.4 Model Evaluation and Deployment -- 9.4 AutoML Frameworks -- 9.4.1 Google AutoML -- 9.4.2 IBM Watson AutoAI -- 9.4.3 Microsoft Azure AutoML -- 9.4.4 H2O.ai -- 9.4.5 Data Robot -- 9.4.6 Databricks AutoML -- 9.4.7 Tune -- 9.4.8 AutoKeras -- 9.4.9 H2O Driverless AI -- 9.4.10 RapidMiner -- 9.4.11 Google Cloud AutoML Tables -- 9.4.12 H2O Sparkling Water -- 9.4.13 Turi Create -- 9.4.14 Big ML -- 9.4.15 Hail -- 9.5 Open-Source AutoML Libraries -- 9.5.1 Auto-Sklearn -- 9.5.2 TPOT (Tree-Based Pipeline Optimization Tool). 327 $a9.5.3 AutoKeras -- 9.5.4 MLBox -- 9.5.5 AutoGluon -- 9.5.6 H2O AutoML -- 9.5.7 Auto-WEKA -- 9.5.8 AutoGluon Tabular -- 9.5.9 FLAML -- 9.5.10 Ludwig -- 9.6 Types of AutoML Approaches -- 9.6.1 Fully Automated -- 9.6.2 Human-in-the-Loop -- 9.6.3 Model Assisted -- 9.7 Benefits of AutoML -- 9.8 Challenges and Limitations -- 9.9 Conclusion -- References -- Chapter 10 Plant Disease Identification Using Extended-EfficientNet Deep Learning Model in Smart Farming -- 10.1 Introduction -- 10.1.1 Obstacles in the Agricultural Sector -- 10.1.1.1 Soil Erosion -- 10.1.1.2 Absence of High-Quality Seeds -- 10.1.1.3 Lack of Contemporary Farming Machinery -- 10.1.2 Challenges of AI in Agriculture -- 10.1.3 Existing Plant Disease Identification Methods -- 10.2 Literature Review -- 10.3 Materials and Methods -- 10.3.1 Dataset -- 10.3.2 Existing CNN Models -- 10.3.2.1 AlexNet -- 10.3.2.2 VGG16 -- 10.3.2.3 ResNet50 -- 10.3.2.4 Inception V3 -- 10.4 Methodology-E-ENet -- 10.4.1 Localization of the Leaf -- 10.4.2 Segmentation of Leaf Image -- 10.4.3 The Diseased Leaf Identification -- 10.5 Experimental Analysis -- 10.5.1 The Acquisition of Data -- 10.5.2 The Parameter Setup -- 10.5.2.1 The Configuration of Parameters for Leaf Localization -- 10.5.2.2 The Configuration of Parameters for Leaf Segmentation -- 10.5.2.3 The Configuration of Parameters for Leaf Retrieval -- 10.6 Results -- 10.6.1 The Leaf Localization Outcome -- 10.6.2 The Outcomes of Leaf Segmentation -- 10.6.3 The Result of Disease Identification -- 10.7 Comparative Test -- 10.8 Summary -- References -- Chapter 11 AutoML-Driven Deep Learning for Fake Currency Recognition -- 11.1 Introduction -- 11.2 Literature Review -- 11.2.1 Scope -- 11.2.2 Objectives -- 11.3 Proposed System -- 11.4 Methodology -- 11.5 Convolutional Neural Network -- 11.6 Analysis Modeling -- 11.6.1 Behavioral Modeling -- 11.7 Software Testing. 327 $a11.7.1 Types of Testing -- 11.7.2 Test Cases -- 11.8 Results and Discussions -- 11.9 Conclusion -- References -- Chapter 12 Blockchain and Automated Machine Learning-Based Advancements for Banking and Financial Sectors -- 12.1 Introduction -- 12.2 Understanding Blockchain and AutoML -- 12.3 Need of Blockchain -- 12.4 Synergies Between Blockchain and AutoML -- 12.5 Applications in Banking and Finance -- 12.6 Applications of AutoML in Industries -- 12.7 Case Studies and Real-World Applications -- 12.8 Blockchain in Finance -- 12.9 Real-World Examples and Case Studies -- 12.10 Benefits and Challenges -- 12.11 Discussion -- 12.12 Limitations -- 12.13 Recommendations for Implementation -- 12.14 Ethical Considerations and Responsible AI -- 12.15 Future Directions and Emerging Trends -- 12.16 Future Scope -- 12.17 Conclusion -- References -- Chapter 13 Advances in Automated Machine Learning for Precision Healthcare and Biomedical Discoveries -- 13.1 Introduction -- 13.1.1 Some of the Recent Publications and their Findings -- 13.2 Current Day Usage of AI -- 13.2.1 Deep Learning and Neural Networks -- 13.2.2 Natural Language Processing (NLP) -- 13.2.2.1 Clinical Documentation -- 13.2.2.2 Disease Prediction -- 13.2.2.3 Chatbots and Virtual Assistants -- 13.2.2.4 Report Analysis -- 13.2.3 Automation -- 13.2.3.1 Appointment Scheduling -- 13.2.3.2 Medication Dispensary -- 13.2.3.3 Robotic Surgeries -- 13.2.3.4 Inventory Management -- 13.3 Data Management and Security in Healthcare AI -- 13.3.1 Data Acquisition and Storage -- 13.3.2 Data Processing and Analysis -- 13.3.3 Data Protection and Privacy -- 13.3.4 Balancing Technological Advancements with Data Governance -- 13.3.5 The Evolving Role of AI in Data Security -- 13.3.6 Continuous Education in Data Management and AI -- 13.3.7 Preparing for the Future of Healthcare AI and Data Management. 327 $a13.4 Challenges in Integrating AI into Healthcare Systems. 330 $aThe book provides a comprehensive understanding of Automated Machine Learning's transformative potential across various industries, empowering users to seamlessly implement advanced machine learning solutions without needing extensive expertise. Automated Machine Learning (AutoML) is a process to automate the responsibilities of machine learning concepts for real-world problems. The AutoML process is comprised of all steps, beginning with a raw dataset and concluding with the construction of a machine learning model for deployment. The purpose of AutoML is to allow non-experts to work with machine learning models and techniques without requiring much knowledge in machine learning. This advancement enables data scientists to produce the easiest solutions and most accurate results within a short timeframe, allowing them to outperform normal machine learning models. Meta-learning, neural network architecture, and hyperparameter optimization, are applied based on AutoML. Automated Machine Learning and Industrial Applications offers an overview of the basic architecture, evolution, and applications of AutoML. Potential applications in healthcare, banking, agriculture, aerospace, and security are discussed in terms of their frameworks, implementation, and evaluation. This book also explores the AutoML ecosystem, its integration with blockchain, and various open-source tools available on the AutoML platform. It serves as a practical guide for engineers and data scientists, offering valuable insights for decision-makers looking to integrate machine learning into their workflows. Readers will find the book: Aims to explore current trends such as augmented reality, virtual reality, blockchain, open-source platforms, and Industry 4.0; Serves as an effective guide for professionals, researchers, industrialists, data scientists, and application developers; Explores technologies such as IoT, blockchain, artificial intelligence, and robotics, serving as a core guide for undergraduate and postgraduate students. Audience Data and computer scientists, research scholars, professionals, and industrialists interested in technology for Industry 4.0 applications. 606 $aMachine learning 615 0$aMachine learning. 676 $a006.3/1 700 $aGangadevi$b E$01839872 701 $aShri$b M. Lawanya$01839873 701 $aBalusamy$b Balamurugan$01340583 701 $aDhanaraj$b Rajesh Kumar$01380450 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9911019472703321 996 $aAutomated Machine Learning and Industrial Applications$94420566 997 $aUNINA