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Artificial Intelligence Theory, Models, and Applications [Hardback]

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  • Formāts: Hardback, 480 pages, height x width: 234x156 mm, weight: 825 g, 206 Line drawings, black and white; 206 Illustrations, black and white
  • Izdošanas datums: 22-Oct-2021
  • Izdevniecība: Auerbach
  • ISBN-10: 1032008091
  • ISBN-13: 9781032008097
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  • Hardback
  • Cena: 145,75 €
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  • Formāts: Hardback, 480 pages, height x width: 234x156 mm, weight: 825 g, 206 Line drawings, black and white; 206 Illustrations, black and white
  • Izdošanas datums: 22-Oct-2021
  • Izdevniecība: Auerbach
  • ISBN-10: 1032008091
  • ISBN-13: 9781032008097
Citas grāmatas par šo tēmu:

Artificial Intelligence (AI) is a pivotal tool transforming industry and everyday life through intelligent computational systems and applications. AI automates repetitive learning and discovery through data. AI systems can perform high-volume tasks reliably and without fatigue. For this type of automation, human inquiry is still essential to set up the system and ask the right questions. AI adds intelligence to existing products. Automation, conversational platforms, bots and smart machines can be combined with large amounts of data to improve many technologies. AI is everywhere and it plays a significant role in various aspects of life. AI is a critical component of robotic automation, self-driving cars, smart healthcare, defense, and many other technologies and applications.

Artificial Intelligence: Theory, Models, and Applications

examines the fundamentals and technologies of AI and describes tools, challenges, and issues. It also explains relevant theory as well as industrial applications in various domains such as healthcare, economics, education, product development, agriculture, human resource management. environment management, and marketing.

Highlights of the book include:

  • Gender disparity in the enterprises involved in the development of artificial intelligence-based software development as well as solutions to eradicate such gender bias in the AI world
  • A general framework for AI in environmental management, smart farming, e-waste management, and smart energy optimization
  • The potential and applications of using AI in medical imaging as well as the challenges of AI in precision medicine
  • AI’s role in the diagnosis of various diseases such as cancer and diabetes
  • The role of machine learning models in product development and statistically monitoring product quality
  • Machine learning to make robust and effective economic policy decisions
  • Machine learning and data mining approaches to provide better video indexing mechanisms resulting in better searchable results.


This book covers advances that have emerged over the last two decades of AI research. Examining both theoretical and practical aspects, it presents applications of AI in such various domains as policy management, medical imaging, agriculture, technology education, business, and healthcare   It also covers machine learning techniques.
Preface. Acknowledgments. Editors. Contributors.
Chapter 1 Artificial
Intelligence: A Complete Insight.
Chapter 2 Artificial Intelligence and
Gender.
Chapter 3 Artificial Intelligence in Environmental Management.
Chapter 4 Artificial Intelligence in Medical Imaging.
Chapter 5 Artificial
Intelligence (AI) Improving Customer Experience (CX).
Chapter 6 Artificial
Intelligence in Radiotherapy.
Chapter 7 Artificial Intelligence in Systems
Biology: Opportunities in Agriculture, Biomedicine, and Healthcare.
Chapter 8
Artificial Intelligence Applications in Genetic Disease/Syndrome Diagnosis.
Chapter 9 Artificial Intelligence in Disease Diagnosis via Smartphone
Applications.
Chapter 10 Artificial Intelligence in Agriculture.
Chapter 11
Artificial Intelligence-Based Ubiquitous Smart Learning Educational
Environments.
Chapter 12 Artificial Intelligence in Assessment and Evaluation
of Programme Outcomes/Programme Specific Outcomes.
Chapter 13 Artificial
Intelligence-Based Assistive Technology.
Chapter 14 Machine Learning.
Chapter
15 Machine Learning in Human Resource Management.
Chapter 16 Machine Learning
Models in Product Development and its Statistical Evaluation.
Chapter 17
Influence of Artificial Intelligence in Clinical and Genomic Diagnostics.
Chapter 18 Applications of Machine Learning in Economic Data Analysis and
Policy Management.
Chapter 19 Industry 4.0: Machine Learning in Video
Indexing.
Chapter 20 A Risk-Based Ensemble Classifier for Breast Cancer
Diagnosis.
Chapter 21 Linear Algebra for Machine Learning.
Chapter 22
Identification of Lichen Plants and Butterflies Using Image Processing and
Neural Networks in Cloud Computing.
Chapter 23 Artificial Neural Network for
Decision Making. Index.
Prof. P. Kaliraj is the Vice-Chancellor of Bharathiar University, Coimbatore, India.



Prof. T. Devi is the dean of faculty of research at the Department of Computer Applications, Bharathiar University, Coimbatore, India.