Applications of AI for Interdisciplinary Research

Applying Artificial Intelligence (AI) to new fields has made AI and data science indispensable to researchers in a wide range of fields. The proliferation and successful deployment of AI algorithms are fueling these changes, which can be seen in fields as disparate as healthcare and emerging IoT applications. Machine learning techniques, and AI more broadly, are expected to play an ever-larger role in the modelling, simulation, and analysis of data from a wide range of fields by the interdisciplinary research community. Ideas and techniques from multidisciplinary research are being utilised to enhance AI; hence, the connection between the two fields is a two-way street at a crossroads. Algorithms for inference, sampling, and optimisation, as well as investigations into the efficacy of deep learning, frequently make use of methods and concepts from other fields of study. Cloud computing platforms may be used to develop and deploy several AI models with high computational power. The intersection between multiple fields, including math, science, and healthcare, is where the most significant theoretical and methodological problems of AI may be found. In order to gather, integrate, and synthesise the many results and viewpoints in the connected domains, refer to it as interdisciplinary research. In light of this, the theory, techniques, and applications of machine learning and AI, as well as how they are utilised across disciplinary boundaries, are the main areas of this research topic. This book apprises the readers about the important and cutting-edge aspects of AI applications for interdisciplinary research and guides them to apply their acquaintance in the best possible manner. This book is being formulated with the intent of uncovering the stakes and possibilities involved in using AI through efficient interdisciplinary applications. The main objective of this book is to provide scientific and engineering research on technologies in the fields of AI and data science and how they can be related through interdisciplinary applications and similar technologies. The book covers various important domains, such as healthcare, the stock market, Natural Language Processing (NLP), real estate, data security, cloud computing, edge computing, data visualisation using cloud platforms, event management systems, Internet of Things (IoT), smart driving, the telecom sector, federated learning and network performance optimization. Each chapter focus on the corresponding subject outline in order to offer readers a thorough grasp of the concepts and technologies connected to artificial intelligence, data analytics, and their emerging applications.