Название: Artificial Intelligence: Applications and InnovationsАвтор: Rashmi Priyadarshini, R.M. Mehra, Amit Sehgal, Prabhu Jyot Singh
Издательство: CRC Press
Год: 2023
Страниц: 301
Язык: английский
Формат: pdf (true)
Размер: 22.6 MB
Artificial Intelligence: Applications and Innovations is a book about the science of Artificial Intelligence (AI). AI is the study of the design of intelligent computational agents. This book provides a valuable resource for researchers, scientists, professionals, academicians and students dealing with the new challenges and advances in the areas of AI and innovations. This book also covers a wide range of applications of Machine Learning such as fire detection, structural health and pollution monitoring and control.
In the last few years, with the improvement of algorithms in general and the lethal development of computing power, the availability of large amounts of information has led to the increased interest and opportunities for applications of Machine Learning (ML). The most common deployments of ML algorithms are for classification, regression, and clustering. Another domain of application is based on quantitative reduction of large sets of data with high- dimensionality. In fact, it has been proven that ML has superhuman abilities in a number of areas, such as autonomous vehicles, classification, photography, and so on. ML has also become an integral part of various daily activities through techniques such as image recognition, voice recognition, web-search, and fraud detection, as well as in e-mail/spam filters, credit scores, and many others with ML algorithms.
Also, as always, when someone enters a different field of science, classification should be used correctly, for example, the term “Deep Learning” (DL), which is responsible for most of the past successes of ML methods (e.g., in pattern recognition and Natural Language Processing). Of course, it’s tempting to describe this work as DL. However, choosing neural networks with one or two fully connected layers and hidden learning depths may not suffice the purpose of DL algorithms. The success of DL is rooted in the ability of a deep neural network to teach a data format, with variouslevels of abstraction, without the need for human intervention.
Key Features:
Provides insight into prospective research and application areas related to industry and technology
Discusses industry- based inputs on success stories of technology adoption
Discusses technology applications from a research perspective in the field of AI
Provides a hands- on approach and case studies for readers of the book to practice and assimilate learning
This book is primarily aimed at graduates and post-graduates in Computer Science, information technology, civil engineering, electronics and electrical engineering and management.
Contents:
1. Introduction to Artificial Intelligence
2. Machine Learning - Principles and Algorithms
3. Applications of Machine Learning and Deep Learning
4. Environmental Monitoring in WSN Using AI
5. Applications of Machine Learning - Fire detection
6. Structural Health Monitoring
7. Application of Machine learning in Agriculture with some examples
8. Deep learning in Smart Agriculture Applications
9. Applications of Deep learning – in aerial robotics
10. The Memristor and Its Implementation in Deep Neural Network Designing: A Review
11. Machine learning applications to recognize autism and Alzheimer’s Disease
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