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B. Suresh Kumar is an Associate Professor in the Department of EEE at Chaitanya Bharathi Institute of Technology, India. His contribution focuses on Big Data Analytics, Artificial Intelligence, cloud computing, and scalable data processing.
The swift production of data from social media, Internet of Things (IoT) devices, corporate applications, sensors, mobile devices, healthcare systems, financial platforms, and industrial infrastructure has engendered a substantial need for scalable data-processing technology. Conventional data-processing systems frequently encounter difficulties with the magnitude, speed, diversity, and intricacy of contemporary datasets. Big Data Analytics tackles these issues by offering distributed methodologies for the collection, storage, processing, analysis, and visualisation of extensive datasets. The amalgamation of Artificial Intelligence (AI) with cloud computing significantly augments Big Data Analytics by facilitating intelligent forecasting, categorisation, anomaly identification, automated decision processes, and instantaneous analytics. Cloud platforms offer scalable computing, decentralised storage, virtualisation, and resources available on demand, whereas AI algorithms convert extensive datasets into practical insights. This chapter examines the principles of Big Data, cloud architectures, AI-enhanced analytics, data processing systems, machine learning, deep learning, real-time data analysis, data security, applications, problems, performance indicators, and prospective research avenues.
Dermatological disorders provide a considerable worldwide medical dilemma due to their varied clinical presentations, overlapping visual traits, and differing degrees of severity. Prompt and precise recognition of skin disorders is crucial for prompt intervention, suitable referral, and enhanced patient results. Conventional diagnosis of skin disorders predominantly depends on clinical assessment, dermoscopic analysis, patient medical history, and, where warranted, histological study. This chapter offers an extensive summary of AI-based skin disease identification, encompassing picture collection, preprocessing, feature extraction, classification, interpretable AI, model assessment, and clinical decision assistance. The section additionally addresses issues related to dataset imbalance, discrepancies in skin tones, insufficient data diversity, diagnostic ambiguity, privacy concerns, and clinical validation. The suggested paradigm establishes a basis for creating dependable, comprehensible, and fair AI-enhanced dermatological screening systems. Such systems ought to augment, rather than supplant, proficient clinical evaluation.
Liver ailments constitute a significant worldwide health issue owing to their correlation with metabolic disorders, viral infections, alcohol intake, drug-induced toxicity, and various chronic health issues. This chapter examines the prediction of liver disease and patient risk assessment with AI, emphasising data preprocessing, feature selection, predictive modelling, risk stratification, model interpretability, and clinical decision support. The discourse encompasses machine learning algorithms including logistic regression, decision trees, random forests, support vector machines, gradient boosting, and artificial neural networks. A theoretical AI-driven framework is suggested to amalgamate patient information, assess liver disease risk, categorise risk tiers, and deliver comprehensible forecasts for healthcare practitioners. The section additionally explores issues related to data integrity, class disparity, confidentiality, equity, clinical verification, and implementation. The suggested methodology is to facilitate preliminary screening and evidence-based clinical evaluation, highlighting that AI forecasts need to augment, not supplant, expert medical discernment.