Artificial Intelligence

The Artificial Intelligence,Volume 49 in the Handbook of Statistics series, highlights new advances in the field, with this new volume presenting interesting chapters on a variety of timely topics.

Format: Hardback
Length: 258 pages
Publication date: 25 August 2023
Publisher: Elsevier Science Publishing Co Inc

Artificial Intelligence,Volume 49 in the Handbook of Statistics series, presents a comprehensive exploration of the latest advancements in this dynamic field. This latest volume offers a diverse range of captivating chapters on timely topics, ensuring a thorough examination of the latest developments. Among the noteworthy chapters in this release are:

AI Teacher-Student based Adaptive Structural Deep Learning Model and Its Estimating Uncertainty of Image Data: This chapter delves into the development of an adaptive structural deep learning model, designed to facilitate effective teaching and learning processes. It explores the use of machine-derived intelligence to estimate the uncertainty of image data, aiding in the classification and analysis of medical images.

Machine-derived Intelligence: Computations Beyond the Null Hypothesis: This chapter examines the realm of machine-derived intelligence, focusing on computations that go beyond the traditional null hypothesis approach. It explores the potential applications of machine learning in various fields, including finance, healthcare, and natural language processing.

Object-oriented Basis of Artificial Intelligence Methodologies I in Judicial Systems in India: This chapter explores the object-oriented basis of artificial intelligence methodologies in judicial systems in India. It discusses the role of AI in enhancing the efficiency and effectiveness of legal proceedings, as well as the challenges and opportunities associated with its implementation.

Artificial Intelligence in Systems Biology: This chapter examines the application of artificial intelligence in systems biology. It explores the use of AI to understand complex biological systems, analyze data, and develop new therapies and treatments.

Machine-Learning in Geometry and Physics: This chapter explores the use of machine learning in geometry and physics. It discusses the development of algorithms for solving complex geometric problems and analyzing physical phenomena, with applications in fields such as computer graphics, robotics, and astrophysics.

Innovation and Machine Learning: Crowdsourcing Open-Source Natural Language Processing (NLP) Algorithms to Advance Public Health Surveillance: This chapter explores the use of crowdsourcing and machine learning to advance public health surveillance. It discusses the development of open-source NLP algorithms that can be used to analyze large amounts of medical data and identify potential health threats.

Other chapters in this volume cover topics such as learning and identity testing of Markov chains, data privacy for machine learning and statistics, and the interface between AI and mathematics.

This comprehensive volume provides a valuable resource for researchers, practitioners, and students interested in the field of artificial intelligence. It showcases the latest advancements, research findings, and applications, fostering a deeper understanding of the potential of AI to transform various industries and improve our lives.

Dimension: 229 x 152 (mm)
ISBN-13: 9780443137631