バイオメディカルインフォマティクスにおける群知能とその応用 Swarm Intelligence and its Applications in Biomedical Informatics

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バイオメディカルインフォマティクスにおける群知能とその応用<br>Swarm Intelligence and its Applications in Biomedical Informatics

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バイオメディカルインフォマティクスにおける群知能とその応用
Swarm Intelligence and its Applications in Biomedical Informatics

著者名：Abdullah, A. Sheik
価格 ¥12,105 （本体¥11,005）
CRC Press（2023/12/14発売）
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ポイント 3,300pt　（実際に付与されるポイントはご注文内容確認画面でご確認下さい）

言語：ENG
ISBN：9781032356495
eISBN：9781003816713

ファイル： /

Description

Swarm Intelligence and Its Applications in Biomedical Informatics discusses Artificial Intelligence (AI) applications in medicine and biology, as well as challenges and opportunities presented in these arenas. It covers healthcare big data analytics, mobile health, personalized medicine, and clinical trial data management. This book shows how AI can be used for early disease diagnosis, prediction, and prognosis, and it offers healthcare case studies that demonstrate the application of AI and Machine Learning.

Key Features:

• Covers all major topics of swarm intelligence research and development such as novel-based search methods and novel optimization algorithm: applications of swarm intelligence to management problems and swarm intelligence for real-world application.
• Provides a unique insight into the complex problems of bioinformatics and the innovative solutions which make up ‘intelligent bioinformatics’.
• Covers a wide range of topics on the role of AI, Machine Learning, and Big Data for healthcare applications and deals with the ethical issues and concerns associated with it.
• Explores applications in different areas of healthcare and highlights the current research.

This book is designed as a reference text, and it aims primarily at advanced undergraduates and postgraduate students studying computer science and bioinformatics. Researchers and professionals will find this book useful.

1. Introduction. 2. Statistical Methods and Swarm Intelligence Techniques in Clinical Data Prediction. 3. Data Classification by Decision Trees - An Illustration . 4. Predictive Analytics Using Ant Colony Optimization with Decision Trees for Medical Data. 5. Predictive Analytics Using Bee-Based Harmony Search with Decision Trees for Medical Data. 6. Predictive Analytics Using Particle Swarm Optimization with Decision Trees for Type II Diabetes. 7. Case-Based Analysis in Medical Informatics. 8. Intelligent Optimization Unit. 9. Conclusion.