Alzheimer's disease (AD) poses a significant global health challenge, with an estimated 50 million people affected worldwide and no known cure. Traditional methods of diagnosis and prediction often rely on subjective assessments. They are limited in detecting the disease early, leading to delayed intervention and poorer patient outcomes. Additionally, the complexity of AD, with its multifactorial etiology and diverse clinical manifestations, requires a multidisciplinary approach for effect...

The convergence of quantum technologies and biomedical intelligence is a frontier of boundless potential. The quantum advancements revolutionize disease detection, personalized medicine, and health monitoring frameworks while confronting the pressing challenge of accountability in machine learning systems within the biomedical domain. How do quantum innovations at the nexus of biomedical intelligence redefine biomedical research and healthcare, addressing critical inquiries such as the tra...

Why are cutting-edge data science techniques such as bioinformatics, few-shot learning, and zero-shot learning underutilized in the world of biological sciences?. In a rapidly advancing field, the failure to harness the full potential of these disciplines limits scientists’ ability to unlock critical insights into biological systems, personalized medicine, and biomarker identification. This untapped potential hinders progress and limits our capacity to tackle complex biological challenges....

Water, our planet's life force, faces multiple challenges in the 21st century, including surging global demand, shifting climate patterns, and the urgent need for sustainable management. Guidance, knowledge, and hope is sharply needed in academia and technology industries, and Innovations in Machine Learning and IoT for Water Management is a formidable resource to provide these necessities. This book delves into the dynamic synergy of Artificial Intelligence (AI), Machine Learning (ML), an...

The proceedings of the International Conference on Hybrid and Advanced Technologies (ICHAT 2024) present a rich repository of cutting-edge research on the various applications of machine learning, deep learning and AI in cybersecurity, healthcare, agriculture and communication systems.It highlights the revolutionary potential of data science in transforming traditional practices, improving efficiency and accuracy across diverse domains and addressing complex real-world challenges. ...

This reference text helps us understand how the concepts of explainable artificial intelligence (XAI) are used in the medical and healthcare sectors. The text discusses medical robotic systems using XAI and physical devices having autonomous behaviors for medical operations. It explores the usage of XAI for analyzing different types of unique data sets for medical image analysis, medical image registration, medical data synthesis, and information discovery. It covers important topics inclu...

With the present emerging artificial intelligence (AI) technologies, computational "machine learning" techniques for training and generalisation from data, and cutting-edge statistical techniques, will play a significant role in analysing multidimensional datasets generated by the new technologies of systems medicine. This progressively but rapidly leads to a "new taxonomy," generating new approaches for disease diagnosis, therapy, and clinical decisions, promising more individualised trea...

Artificial intelligence (AI) and machine learning (ML) transform drug research and personalized medicine by enabling faster, more precise, and data-driven approaches to healthcare. By analyzing complex datasets, AI-driven models can identify drug targets, predict interactions, and optimize drug development timelines. These technologies pave the way for personalized medicine, allowing treatments to be tailored to individuals’ health. Together, AI and machine learning revolutionize how medic...

In the dynamic environment of healthcare, the fusion of Computational Intelligence and Healthcare Industry 4.0 has enabled remarkable advancements in disease detection and analysis. However, a critical challenge persists – the limitations of current computational intelligence approaches in dealing with small sample sizes. This setback hampers the performance of these innovative models, hindering their potential impact on medical applications. As we stand at the crossroads of technological ...

Deep Learning in Genetics and Genomics vol. 1, Foundations and Applications, the intersection of deep learning and genetics opens up new avenues for advancing our understanding of the genetic code, gene regulation, and the broader genomics landscape. The book not only covers the most up-to-date advancements in the field of deep learning in genetics and genomics, but also a wide spectrum of (sub) topics including medical and clinical genetics, predictive medicine, transcriptomic, and gene e...

Improved pharmaceuticals using intelligent technologies marks a fundamental shift in how life-changing therapies are conceived and brought to patients. By using predictive modeling and generative algorithms, researchers can identify promising compounds and optimize drug candidates with unprecedented speed and precision. This data analysis approach accelerates early discovery and reduces costly trial-and-error, while opening the door to more targeted, personalized treatments. As AI reshapes...

Advancements in artificial intelligence and machine learning are reshaping healthcare by enabling highly personalized wellness strategies tailored to individual needs. By analyzing vast datasets, including genetic, biometric, and lifestyle information, these technologies can predict disease risks, optimize treatment plans, and recommend proactive health interventions. Precision wellness moves beyond traditional healthcare models, offering dynamic, adaptive solutions that evolve with new sc...