1.

Record Nr.

UNINA9910962422903321

Autore

Kurtz Lisa A

Titolo

Understanding controversial therapies for children with autism, attention deficit disorder, and other learning disabilities : a guide to complementary and alternative medicine / / Lisa A. Kurtz

Pubbl/distr/stampa

London ; ; Philadelphia, : Jessica Kingsley Publishers, 2008

ISBN

9786611781958

9781281781956

1281781959

9781846427619

1846427614

Edizione

[First edition.]

Descrizione fisica

1 online resource (212 pages)

Collana

JKP essentials

Classificazione

DT 2000

Disciplina

618.92/85889

Soggetti

Learning disabilities - Alternative treatment

Autism in children - Alternative treatment

Attention-deficit hyperactivity disorder - Alternative treatment

Lingua di pubblicazione

Inglese

Formato

Materiale a stampa

Livello bibliografico

Monografia

Note generali

Description based upon print version of record.

Nota di bibliografia

Includes bibliographical references and index.

Nota di contenuto

front cover; Understanding Controversial Therapies for Children with Autism, Attention Deficit Disorder, and Other Learning Disabilities: A Guide to Complementary and Alternative Medicine; CONTENTS; Part 1: Introduction; 1. Introduction; 2. Thinking Out of the Box: An Overview of Complementary and Alternative Medicine Approaches; Part 2: Selected Interventions; 3. Alternative Medical Systems; 4. Mind-body Interventions; 5. Biologically-based Interventions; 6. manipulative and Body-based Methods; 7. Energy Therapies

Part 3: Resources for Children with Autism, Attention Deficit Disorders, and Other Learning Disabilities8. Recommended Reading about Complementary and Alternative Medicine; 9. Agencies, Organizations and Websites; SUBJECT INDEX; AUTHOR INDEX; back cover;

Sommario/riassunto

Offering a balanced overview of complementary and alternative therapies, this book will be useful for parents of children with autism, ADD or other learning disabilities. The book covers a wide variety of mind-body interventions and manipulative techniques, as well as



energy therapies, biologically based methods, and alternative medical systems. For each approach, the author provides a detailed description of what the treatment involves, which professionals will be working with the child, and an explanation of the rationale behind the therapy. She also offers advice on who to approach for trea

2.

Record Nr.

UNINA9910983346303321

Autore

Pham Hoang

Titolo

Analytics Modeling in Reliability and Machine Learning and Its Applications / / edited by Hoang Pham

Pubbl/distr/stampa

Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2025

ISBN

9783031726361

9783031726354

Edizione

[1st ed. 2025.]

Descrizione fisica

1 online resource (480 pages)

Collana

Springer Series in Reliability Engineering, , 2196-999X

Disciplina

006.31

Soggetti

Machine learning

Computers

Medical care

Industrial engineering

Production engineering

Mathematical optimization

Aerospace engineering

Astronautics

Machine Learning

Hardware Performance and Reliability

Health Care

Industrial and Production Engineering

Optimization

Aerospace Technology and Astronautics

Aprenentatge automàtic

Ordinadors

Assistència sanitària

Enginyeria industrial

Optimització matemàtica

Astronàutica

Llibres electrònics



Lingua di pubblicazione

Inglese

Formato

Materiale a stampa

Livello bibliografico

Monografia

Nota di contenuto

Preface -- 1. Reliability Analysis For Inventory Management For Repair Parts Based on Imperfect Data.-2. Improved Industrial Risk Analysis via a Human Factor-driven Bayesian Network Approach -- 3. Unsupervised Representation Learning Approach for Intrusion Detection in the Industrial Internet of Things Network Environment -- 4. Aero-engine Life Prediction Based on ARIMA and LSTM with Multi-Head Attention Mechanism -- 5. Human-Machine Integration to Strengthen Risk Management in the Winemaking Industry -- 6. One-Class Classification for Credit Card Fraud Detection: A Detailed Study with Comparative Insights from Binary Classification -- 7. Performance Analysis of Big Transfer Models on Biomedical Image Classification -- 8. Machine Learning Approach for Testing the Efficiency of Software Reliability Estimators of Weibull Class Models -- 9. Holistic Perishable Pharmaceutical Inventory Management System -- 10. Optimum Switch Self-Check Interval for Safety-Critical Device Mission Reliability -- 11. Accurate Estimation of Cargo Power Using Machine Learning Algorithms -- 12. Digital Transformation in Software Quality Assurance -- 13. Stress Studies: A Review -- 14. Higher Order Dynamic Mode Decomposition-based Timeseries Forecasting for Covid-19 -- 15. System Trustability: New Concept and Applications -- 16. Digital Twin Implementation in Small and Medium Size Enterprises: A Case Study -- 17. Software Reliability Modeling: A Review.

Sommario/riassunto

This book presents novel research and application chapters on topics in reliability, statistics, and machine learning. It has an emphasis on analytical models and techniques and practical applications in reliability engineering, data science, manufacturing, health care, and industry using machine learning, AI, optimization, and other computational methods. Today, billions of people are connected to each other through their mobile devices. Data is being collected and analysed more than ever before. The era of big data through machine learning algorithms, statistical inference, and reliability computing in almost all applications has resulted in a dramatic shift in the past two decades. Data analytics in business, finance, and industry is vital. It helps organizations and business to achieve better results and fact-based decision-making in all aspects of life. The book offers a broad picture of current research on the analytics modeling and techniques and its applications in industry. Topics include: l Reliability modeling and methods. l Software reliability engineering. l Maintenance modeling and policies. l Statistical feature selection. l Big data modeling. l Machine learning: models and algorithms. l Data-driven models and decision-making methods. l Applications and case studies in business, health care, and industrial systems. Postgraduates, researchers, professors, scientists, engineers, and practitioners in reliability engineering and management, machine learning engineering, data science, operations research, industrial and systems engineering, statistics, computer science and engineering, mechanical engineering, and business analytics will find in this book state-of-the-art analytics, modeling and methods in reliability and machine learning.