Shikha Agarwal – författare

Imidazole-Based Drug Discovery covers all categories of imidazole and its derivatives, synthesis, pharmacological applications and drug-based studies. Imidazole scaffolds act as a channel between organic synthesis and medicinal chemistry and compel researchers to explore new drug candidates. This book provides detailed coverage of several greener synthetic protocols and pharmacological applications of imidazole derivatives that are useful to researchers working on designing more promising clinical lead compounds with this scaffold. It also includes information on past decades of research on the synthesis and biological applications of imidazole derivatives.

This is an ideal resource for researchers in organic chemistry both in academic and industrial settings, as well as postgraduates in chemistry and medicinal chemistry.

Reviews the most current developments and future perspectives of imidazole on different disease therapies to achieve the ultimate goal of disease eradication Discusses the role of imidazole in contemporary science, technological innovation, drug development, critical challenges and future research directions Covers emerging trends on different eco-benign pathways to synthesize imidazole derivatives for the development of simpler synthetic protocols

Today the healthcare sector is facing challenges such as detecting the cause of ailments, disease prevention, high operating costs, availability of skilled technicians and infrastructure bottlenecks. Intelligent healthcare management technologies are needed to manage these challenges. Healthcare organizations also need to continuously discover useful and actionable knowledge to gain insight from tons of data being generated for saving lives, reducing medical errors, enhancing efficiency, reducing costs and making the whole world a healthy place.The book introduces techniques that developed using machine learning along with swarm intelligence in healthcare informatics. It also discusses one of the major applications of artificial intelligence: using machine learning to extract useful information from multimodal data optimally by using swarm intelligence. It reviews optimization methods that help to minimize the error in developing patterns and classifications, which further helps improve prediction and decision-making. The objective of this book is to use swarm intelligence and machine learning techniques for various medical issues such as diagnosing cancer, brain tumor, diabetic retinopathy, heart diseases as well as drug design and development. The book will act as one-stop reference to think and explore swarm intelligence and machine learning algorithms seriously for real-time patient diagnosis.

Today the healthcare sector is facing challenges such as detecting the cause of ailments, disease prevention, high operating costs, availability of skilled technicians and infrastructure bottlenecks. Intelligent healthcare management technologies are needed to manage these challenges. Healthcare organizations also need to continuously discover useful and actionable knowledge to gain insight from tons of data being generated for saving lives, reducing medical errors, enhancing efficiency, reducing costs and making the whole world a healthy place.The book introduces techniques that developed using machine learning along with swarm intelligence in healthcare informatics. It also discusses one of the major applications of artificial intelligence: using machine learning to extract useful information from multimodal data optimally by using swarm intelligence. It reviews optimization methods that help to minimize the error in developing patterns and classifications, which further helps improve prediction and decision-making. The objective of this book is to use swarm intelligence and machine learning techniques for various medical issues such as diagnosing cancer, brain tumor, diabetic retinopathy, heart diseases as well as drug design and development. The book will act as one-stop reference to think and explore swarm intelligence and machine learning algorithms seriously for real-time patient diagnosis.