Madhusudhan H AI Cloud Computing Appl-Biomedical Engineering 2026 torrent

Textbook in PDF format

Biomedical engineering is undergoing a transformation because of Artificial Intelligence (AI), which is allowing creative solutions that enhance patient outcomes, diagnosis, treatment planning, and healthcare delivery. Artificial Intelligence and Cloud Computing Applications in Biomedical Engineering examines the salient characteristics of AI in biomedical engineering, highlighting its practical applications and new directions. Highlights of the book include:
Genome sequence and visualization
The role of AI and cloud in detection of diseases
Nature-inspired algorithms for disease detection
Frameworks for disease classification
With a focus on designing AI techniques for disease detection, the book explores the role of AI in biomedical engineering. It discusses how Machine Learning (ML) and Deep Learning (DL) are at the heart of AI applications in biomedical engineering. ML algorithms, particularly those based on neural networks, enable computers to learn from large datasets, identify patterns, and make predictions or decisions without explicit programming, and implementing ML algorithms is a focus of the book. Another focus is on DL, a subset of ML, and how it uses multi-layered neural networks to achieve high accuracy in such complex tasks as image and speech recognition. Biomedical engineering generates massive amounts of data from medical imaging, genomic sequencing, wearable devices, electronic health records (EHR), and other sources. This book also discusses AI-driven big data analytics, which allows researchers and clinicians to derive from data meaningful insights, aiding in early disease detection, personalized treatment plans, and patient monitoring.
Preface
Artificial Intelligence and Computational Biology in Drug Discovery
Techniques of AI/ML for Genomics Visualization in Plants
Computing Architectures on the Cloud to Address Current Problems in Structural Bioinformatics
Application of AI for Disease Detection and Prevention
AI for Diseases Detection and Prevention
Machine Learning Techniques for Detecting Lung Cancer
A Review on AI Approaches for the Detection of Diabetic Retinopathy
Intelligent Applications for Medical Image Analysis
Machine Learning Integration with Biomedical Problems
Intelligent Tools and Techniques for Real-Life Diseases
Free Space Detection in Medical Image Analysis for Visually Impaired Using Histogram Equalization and Adaptive Region Growing