Artificial intelligence (AI) has become a powerful force in the ever-changing industrial automation field. The subject of this research paper focuses on the diverse applications related to artificial intelligence (AI) for enhancing performance in modern industrial settings. This paper starts by examining the historical background and basic principles of AI. Afterwards, fundamental techniques and algorithms based on machine and deep learning are discussed. The review classifies and analyzes practical implementations in which AI has played a crucial role in improving efficiency, accuracy, and flexibility in industrial operations. The report examines case examples to emphasize successful implementations, providing insights into the real advantages and knowledge gained from these efforts. Moreover, it tackles the inherent difficulties, like the complexities of integration, concerns about data privacy, and ethical considerations, that come with the use of AI in industrial operations. This article offers a thorough examination of the latest and next developments of artificial intelligence (AI) in industrial automation, as enterprises strive to meet the increasing need for improved efficiency. The review seeks to provide guidance to researchers, practitioners, and policymakers in navigating the dynamic convergence of artificial intelligence and industrial optimization by assessing the possible advancements that lie ahead. In essence, this highlights the crucial significance of AI in determining the future of industrial automation, providing unmatched prospects for attaining optimal performance and operational superiority.

The past few decades have witnessed an enormous research growth in the field of natural language processing. In this regard, numerous machine learning (ML) algorithms have been applied in different sub-domains of NLP such as speech recognition, text classification, sentiment analysis, etc. Furthermore, their performances have been evaluated using diverse performance metrics. However, a comparative analysis of various ML algorithms in the aforementioned field is a feasible research area to explore. This may efficiently guide future research to precisely focus on the improvement of those particular algorithms that have been found to be more effective based on previous research. Thus, this article provides a comparative analysis regarding the application and effectiveness of different ML algorithms in the field of NLP. Additionally, it highlights the future research direction to be adopted for enhancing the ability of the natural language processing domain.