Technology has revolutionized various aspects of human life, and networking plays an important role in this revolution. Networks have gained significant importance with the evolution of technology. Traditional networks have served human life for decades but cannot provide future networking requirements. Software-defined networking (SDN) is a major paradigm shift in the networking field; it provides an efficient way of managing and controlling networks by separating the control layer from the data layer. SDN has gained significant attention from researchers and industry experts in recent years. However, to accept SDN as a replacement for traditional networking, several areas need to be improved, and security is one of them. This exhaustive literature survey presents a detailed analysis of security solutions proposed by researchers for SDN security. This literature review can be classified into three main categories of SDN security solutions: conventional network security solutions, machine learning (ML)-based, and blockchain-based security solutions. A detailed domain analysis has been performed, and the proposed strategies and their results have been evaluated. Moreover, the shortcomings and contributions of previous approaches toward scientific society have been analyzed to facilitate the new research. This research has completed a detailed and diversified literature review to have wide concrete knowledge regarding the future trends for designing and developing innovative SDN security solutions. An exhaustive literature review revealed that various ML-based SDN security approaches have been proposed, including convolutional neural network (CNN), support vector machine (SVM), and k-nearest neighbors (KNN). Among these ML algorithms, CNN and SVM were found to be more effective and state-of-the-art in securing the SDN. Parametric analysis of different researchers demonstrated that blockchain-based flow rules, blockchain-based packet parsers, and controller authentication outperformed while securing the SDN networks. The paper covered all the aspects of previous research that have been experimented with to determine the robust and concrete techniques for the security of SDN networks.

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.