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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