High-Performance Automation Methods for Computational Intelligent Systems : Challenges, Opportunities, and Applications - Vijay Kumar Sharma

High-Performance Automation Methods for Computational Intelligent Systems

Challenges, Opportunities, and Applications

By: Vijay Kumar Sharma (Editor), Sachin Kumar Gupta (Editor)

Hardcover | 3 October 2025 | Edition Number 1

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Intelligent computation is the need of the current society. This field attracts significant attention due to its application-oriented systems. It is essential for implementing various society-related solutions. Automation methods help improve the quality of life and support the development of efficient systems. The available theories and concepts are implemented for the benefit of soci-ety, drastically changing the world. The advancement of the existing ideas is simulated in the scenario of future intelligent automation systems. The com-plicated problems can be easily solved using the mentioned approaches. The decisive nature of the algorithms builds the system more intelligent. This book proposal targets current trends in the relevant field and outlines a path for the development of future automated systems. The intelligence behaviour of the systems is the prime requirement for the age development. The gaps in the current development scenarios need to be identified and bridged by incorporating highly efficient algorithms, ideas, and working prototypes.

This book:

⢠Presents a comprehensive survey, performance analysis, and time complexity comparison of machine learning techniques in network security.

⢠Discusses energy energyâ'efficient hybrid algorithms for reconfiguration in radial distribution systems.

⢠Covers a detailed examination of automation approaches for the study and detection of disparate leaf diseases using computational intelligent systems.

⢠Illustrates applications of quantum cellular energyâ'efficient computing methods for automation.

⢠Explain cybersecurity strategies for advanced computational intelligence and customer segmentation through machine learning techniques.

It is primarily written for senior undergraduates, graduate students, and academic researchers in the fields of electrical engineering, electronics and communications engineering, computer science and engineering, and information technology.

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