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gill s applications ai interdisciplinary research 2025

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Textbook in PDF format 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 fuelling these changes, which can be seen in fields as disparate as healthcare and emerging Internet of Things (IoT) applications. Machine learning techniques, and AI more broadly, are expected to play an ever-increasing 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. 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 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. • This 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, IoT, the telecom sector, federated learning, and network performance optimisation. Each chapter focuses on the corresponding subject outline to offer readers a thorough grasp of the concepts and technologies connected to AI and data analytics, and their emerging applications. Preface Part I Healthcare Machine Learning-Based Prediction of Thyroid Disease HeartGuard: A Deep Learning Approach for Cardiovascular Risk Assessment Using Biomedical Indicators Using Cloud Computing Deep Convolutional Neural Networks-Based Skin Lesion Classification for Cancer Prediction Explainable AI for Cancer Prediction: A Model Analysis Machine Learning-Based Web Application for Breast Cancer Prediction Part II Natural Language Programming (NLP) Machine Learning-Based Opinion Mining and Visualization of News RSS Feeds for Efficient Information Gain Part III Economics and Finance Advanced Machine Learning Models for Real Estate Price Prediction Stock Market Price Prediction: A Hybrid LSTM and Sequential Self-Attention-Based Approach Federated Learning for the Predicting Household Financial Expenditure Part IV Computing and Business Deep Neural Network-Based Prediction of Breast Cancer Using Cloud Computing Performance Analysis of Machine Learning Models for Data Visualisation in SME: Google Cloud vs. AWS Cloud Part V Security and Edge/Cloud Computing Enhancing Data Security for Cloud Service Providers Using AI 1 Centralised and Decentralised Fraud Detection Approaches in Federated Learning: A Performance Analysis AI-Based Edge Node Protection for Optimizing Security in Edge Computing Part VI Telecom Sector and Network Predictive Analytics for Optical Interconnection Network Performance Optimisation in Telecom Sector Part VII Emotional Intelligence Machine Learning-Based Emotional State Inference Using Mobile Sensing Part VIII Internet of Things (IoT) and Mobile Applications Social Event Tracking System with Real-Time Data Using Machine Learning

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gill s applications ai interdisciplinary research 2025

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