From Theory to Practice: Implementing Intelligent Systems in Engineering Applications

Abstract

This research focuses on identifying the feasibility of applying intelligent systems in engineering disciplines to achieve veritable paradigms from academic ideas into real-world deploymen Analyzing and discussing theoretical and empirical antecedents, as well as performing field trials with AI and machine learning applications, the research identifies potential directions of development and possible issues in the use of AI and ML in various fields. That the advancement of AI and Machine Learning algorithms and implementation is promising for various industries and applications, such as, manufacturing, transportation, energy systems, health-care, and communication systems by providing high accuracy and performance. On a general note, neural networks have higher accuracy than support vector machines in manufacturing with an accuracy of 92 per cent in predictive maintenance, as compared to support vector machines, which have an accuracy of 85 per cent in traffic management in the context of transportation. Decision tree based energy consumption optimization has mean squared error holds 0 and it has a confident level of efficiency as compared to other methods. 005. However, there are still problems in healthcare applications, it is a fact that, the evolutionary computation came across many issues in privacy-preserving patient monitoring. In conclusion, this study provides an understanding of the intelligent systems on the innovation process that happens in the frame of engineering and sharpen on the methodical, ethical, and interdisciplinary approaches to achieve the optimal outcomes.