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Machine Learning (ML) is a subfield of Artificial Intelligence (AI) that uses soft computing and algorithms to enable computers to learn on their own and identify patterns in observed data, build models that explain the world, and predict things without having explicit pre-programmed rules and models. This book discusses various applications of ML in engineering fields and the use of ML algorithms in solving challenging engineering problems ranging from biomedical, transport, supply chain and logistics, to manufacturing and industrial. Through numerous case studies, it will assist researchers and practitioners in selecting the correct options and strategies for managing organizational tasks.
This book is organized into 18 chapters. A brief description of each chapter is presented below:
Chapter 1 reports successful ML models in smart health care through clinical applications such as medical diagnostics and precision or monitoring health, their potential, and limitations. With the increasing power of supercomputers, ML developments would have the proper environment for managing health such as detecting diseases precisely and early, improving diagnosis, and better therapies.
Chapter 2 proposes predictive ML models for assisting exact foreseeing models in recognizing and mapping flood hazard territories. The calculations obtained through ML techniques lead to hazard exhaustion, arrangement suggestion, alleviating loss of helpful lives, and decreasing flood- related property harm.
The main purpose of Chapter 3 is to identify the flaws in current air quality and to recommend measures or policies to improve it so that it is safe to consume.
Chapter 4 implements an expert system model for solving the problem associated with the precise prediction of the dynamic trajectory of an autonomous vehicle. This was accomplished by deriving a new equation for determining the lateral tire forces and adjusting some of the vehicle parameters under road test conductions. A universal approach to performing the reverse engineering of electric power steering (EPS) for external control is also presented in this chapter.
Chapter 5 proposes a speech recognition model that compiles contemporary expertise on the detection of gestures and their associated mannerisms while proposing modifications for inclusivity of a wider audience, with a prominent emphasis on people with communication disabilities. Furthermore, the work done in this chapter may culminate in the development of real- world applications such as determining the attentivity of students in real time.
Chapter 6 covers the fundamental phenomenon of brain–computer interface as an emerging technology that may help to become possible to communicate dreamers into their real dream through modern technologies. This chapter contains only the essential concepts and theories in integration for developing a deep learning-based model for mapping dream contents