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.

Software effort estimation is the process of estimating the amount of time, effort, and money required to develop and manage software. It has an important role in software project planning and management. It impacts activities such as bidding, budgeting, and resource allocation. However, it is not easy to find a model that fits a project to help in its success. Over the past five decades, a wide range of models and techniques have been proposed. However, not all of them were effective enough. The constant development of these models and techniques show how these are important for accurate prediction. Our survey provides a comprehensive overview of some major software estimation approaches. These include parametric models, expert-based techniques, regression-based methods, analogy approaches, fuzzy logic, neural networks, and machine learning-based estimators. We reviewed research papers from 1970 to 2025 to get information of older models. But we mainly focused on recent papers from 2019 to 2025. We traced the progression and popularity trends of these models and discovered the increasing adoption of methods such as COCOMO, regression, and neural networks. Our study aims to help researchers and practitioners in understanding the landscape of software effort estimation models and techniques according to different generations with their pros and cons. It will also help in selecting or developing models ideal for some specific systems. Additionally, it highlights that no one model remains the best option for every software project.