Loading...
P. Santhosh is an Associate Professor in the Department of Electronics and Communication Engineering at Hyderabad Institute of Technology and Management, India. His research interests include Big Data Analytics, IoT, and intelligent communication systems.
The swift expansion of digital banking, e-commerce, cloud computing, cryptocurrencies, and Internet of Things (IoT) technologies has led to an unparalleled surge in financial transaction data. Although these technological innovations have enhanced efficiency and accessibility, they have simultaneously created new avenues for financial misconduct, including fraud, money laundering, cyber-enabled scams, identity theft, insider trading, and terrorist financing. Conventional investigative methods frequently encounter difficulties in managing vast amounts of diverse data and detecting concealed crime trends instantaneously. Big Data Analytics (BDA) has surfaced as a formidable method for investigating financial crimes through extensive data processing, predictive modelling, behavioural assessment, network analysis, and anomaly identification. This chapter elucidates the principles, frameworks, methodologies, applications, obstacles, and prospective trajectories of Big Data Analytics in the realm of financial crime investigation within contemporary digital environments.
Deep learning has profoundly revolutionised medical image analysis through the facilitation of automated feature extraction, picture classification, segmentation, object detection, illness recognition, and clinical decision assistance. The growing accessibility of medical imaging techniques like X-ray, Magnetic Resonance Imaging (MRI), Computed Tomography (CT), ultrasound, and histopathological images has opened avenues for intelligent systems to aid healthcare practitioners in identifying anomalies and facilitating diagnostic choices. This chapter elucidates the essential principles, frameworks, and utilisations of deep learning in the realm of medical image analysis and clinical decision-making support.A comprehensive framework for AI-enhanced medical picture analysis is introduced, accompanied by performance assessment techniques, implementation obstacles, ethical implications, and prospective research avenues. The section emphasises the capability of deep learning to facilitate precise, effective, transparent, and human-centric healthcare decision-making.