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This book is a review of recent Artificial Intelligence (AI) approaches, initiatives and applications in engineering and science fields. It features contributions that highlight the use of techniques such as Machine Learning (ML), mining engineering, modeling and simulation, and fuzzy logic methods in the fields of communication, networking and information engineering.
This book aims to discuss computational intelligence approaches, initiatives, and applications in engineering and science fields (including Machine Intelligence, Mining Engineering, Modeling and Simulation, Computer, Communication, Networking and Information Engineering, Systems Engineering, Innovative Computing Systems, Adaptive Technologies for Sustainable Growth, and Theoretical and Applied Sciences). This collection should inspire various scholars to contribute research on intelligence principles and approaches in their respective research communities while enriching the body of research on Computational Intelligence (CI).
Soft computing has arisen as a standard presuming perspective fuzzy ideas have advanced as imperative in the field of processing. The present chapter would expect to motivate and support the reader by giving all the necessary flavors empowering him to seek after and exude creative thoughts in the field of the fuzzy framework. The chapter presents the fundamental fuzzy ideas, including the operations performed on uncertainty sets. Fuzzy ideas and operations are contrasted with crispy sets for the better cognizance of the pursuers, specifically the naive. How the probabilistic and fuzzy frameworks (level of truthness) vary would be underscored with sufficient situations. Uncertainty, vagueness in the data, and historical crispy data when utilized later, would connect some vulnerability with it an embodiment of such vulnerability and its need to consider in the processing is managed in detail. Different fuzzy membership functions commonly used are explored here.
Recommendation systems are widely used today by online stores and various other leading sites, like , Instagram and LinkedIn, for providing suggestions to the users. The recommendation process helps the users to find the items that they may be interested in. Also, it is beneficial for the company to improve its overall profit. Recommendation engines use collaborative filtering technique or content-based approach to acquaint the users with such items. Various algorithms are used for making such recommendations. As these engines are so beneficial for users as well as for the trading websites, they have already been applied to a large number of fields, such as medical, education, tourism, finance, marketing and business however, some areas are yet left unexplored. In this paper, we are presenting one such area where if recommendation engines are used, they can help a huge number of researchers around the globe.
Understanding the behavior of humans is a very important concern for social communication. Especially in real-time, predicting human activity and behavior has become the most vigorous research area in digital image processing and computer vision. To enhance the security in public and private domains in the field of human-computer interaction and intelligent video surveillance, human behavior analysis is an important challenge in various applications. There are many basic approaches to analyze human activity, but recently, Deep Learning (DL) approaches have been shown that yield very interesting results in different domains. Human actions and behavior can be observed in the open as well as in sensitive areas, such as airports, banks, bus and train station, colleges, parking areas, etc., and prevent terrorism, theft, accidents, fighting, as well as other abnormal and suspicious activities through visual surveillance. This chapter thus seeks to reflect on methods of human activity recognition