Название: Artificial Intelligence and Machine Learning: An Intelligent Perspective of Emerging Technologies
Автор: Rohit Tanwar, Surbhi Bhatia Khan, Varun Sapra
Издательство: CRC Press
Год: 2024
Страниц: 155
Язык: английский
Формат: pdf (true)
Размер: 10.1 MB

This book focuses on Artificial Intelligence (AI) and Machine Learning (ML) technologies and how they are progressively being incorporated into a wide range of products, including consumer gadgets, "smart" personal assistants, cutting-edge medical diagnostic systems, and quantum computing systems. This concise reference book offers a broad overview of the most important trends and discusses how these trends and technologies are being created and employed in the applications in which they are being used.

Artificial Intelligence and Machine Learning: An Intelligent Perspective of Emerging Technologies offers a broad package involving the incubation of AI and ML with various emerging technologies such as Internet of Things (IoT), healthcare, smart cities, robotics, and more. The book discusses various data collection and data transformation techniques and also maps the legal and ethical issues of data-driven e-healthcare systems while covering possible ways to resolve them. The book explores different techniques on how AI can be used to create better virtual reality experiences and deals with the techniques and possible ways to merge the power of AI and IoT to create smart home appliances.

In the public’s field of vision, Deep Learning (DL) has grown since its emergence as a subfield of Machine Learning (ML) in 2006. It is a process control technique that makes use of multiple computational levels made up of numerous linear models or many layers of complex structures. In the fields of informatics, ML, speech synthesis, computational linguistics, and audio identification, supervised learning has recently achieved strides. Because of the vast potential for its use in data analysis, since 2013, one of the top 10 breakthroughs has been named as training data. Tchapga et al. described the connecting layers with artificial neurons, and the arti- ficial neural network (ANN), one of the ML algorithms, was created. It was inspired by the functioning of the human brain system. However, overfitting and vanishing gradient issues have plagued ANN for training deep networks due to the low computing power and lack of learnable data. When it comes to computer vision and speech recognition tasks, deep neural networks (NNs) outperform human or other ML capabilities due to the increase of computing power with graphics processing units and the availability of Big Data acquisition.

With contributions from experts in the field, this reference book is useful to healthcare professionals, researchers, and students of industrial engineering, systems engineering, biomedical, Computer Science, electronics, and communications engineering.




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