A. V. Senthil Kumar – författare

This volume demonstrates the diverse state-of-the-art applications that combine artificial intelligence with soft computing, which has great potential for creating smart personalized healthcare services. The book showcases the myriad uses of AI and computer techniques in healthcare that employ deep learning, robotics, machine learning, blockchain, emerging cloud, edge computing, Practical Byzantine Fault Tolerance consensus, CNN architecture, Splunk, genetic algorithms (GA), DurBhashan, and many more. These technologies can be used in healthcare for enhanced data sharing, remote health monitoring, tele-rehabilitation, connecting rural populations with healthcare services, identifying diseases and health issues, automated medical diagnosis, analyzing information in surgical videos, ensuring timely communication and transportation during health disasters and emergencies, for optimizing expenditures, and more.

This volume demonstrates the diverse state-of-the-art applications that combine artificial intelligence with soft computing, which has great potential for creating smart personalized healthcare services. The book showcases the myriad uses of AI and computer techniques in healthcare that employ deep learning, robotics, machine learning, blockchain, emerging cloud, edge computing, Practical Byzantine Fault Tolerance consensus, CNN architecture, Splunk, genetic algorithms (GA), DurBhashan, and many more. These technologies can be used in healthcare for enhanced data sharing, remote health monitoring, tele-rehabilitation, connecting rural populations with healthcare services, identifying diseases and health issues, automated medical diagnosis, analyzing information in surgical videos, ensuring timely communication and transportation during health disasters and emergencies, for optimizing expenditures, and more.

This new book takes an in-depth look at the emerging and prospective field of computational biology and bioinformatics, which possesses the ability to analyze large accumulated biological data collected from sequence analysis of proteins and genes and cell population with an aim to make new predictions pertaining to drug discovery and new biology. The book explains the basic methodology associated with a bioinformatics and computational approach in drug designing. It then goes on to cover the implementation of computational programming, bioinformatics, pharmacophore modeling, biotechnological techniques, and pharmaceutical chemistry in designing drugs. The major advantage of intervention of computer language or programming is to cut down the number of steps and costs in the field of drug designing, reducing the repeating steps and saving time in screening the potent component for drug or vaccine designing.

The book describes algorithms used for drug designing and the use of machine learning and AI in drug delivery and disease diagnosis, which are valuable in clinical decision-making. The implementation of robotics in different diseases like stroke, cancer, COVID-19, etc. is also addressed. Topics include machine learning, AI, databases in drug design, molecular docking, bioinformatics tools, target-based drug design, and immunoinformatics, chemoinformatics, and nanoinformatics in drug design. Drug repurposing in drug design in general as well as for specific diseases, including cancer, Alzheimer’s disease, tuberculosis, COVID-19, etc., is also addressed in depth.

This new book takes an in-depth look at the emerging and prospective field of computational biology and bioinformatics, which possesses the ability to analyze large accumulated biological data collected from sequence analysis of proteins and genes and cell population with an aim to make new predictions pertaining to drug discovery and new biology. The book explains the basic methodology associated with a bioinformatics and computational approach in drug designing. It then goes on to cover the implementation of computational programming, bioinformatics, pharmacophore modeling, biotechnological techniques, and pharmaceutical chemistry in designing drugs. The major advantage of intervention of computer language or programming is to cut down the number of steps and costs in the field of drug designing, reducing the repeating steps and saving time in screening the potent component for drug or vaccine designing.

The book describes algorithms used for drug designing and the use of machine learning and AI in drug delivery and disease diagnosis, which are valuable in clinical decision-making. The implementation of robotics in different diseases like stroke, cancer, COVID-19, etc. is also addressed. Topics include machine learning, AI, databases in drug design, molecular docking, bioinformatics tools, target-based drug design, and immunoinformatics, chemoinformatics, and nanoinformatics in drug design. Drug repurposing in drug design in general as well as for specific diseases, including cancer, Alzheimer’s disease, tuberculosis, COVID-19, etc., is also addressed in depth.

The new book highlights the application of artificial intelligence, machine learning, and deep learning techniques to diagnose medical problems. General predictive analysis methods are explained in disease detection applications along with machine learning techniques for the implementation of predictive analysis methods. The book covers object detection approaches for diseases such as pneumonia, application of monitoring and tracking the coronavirus disease, various endodontic applications for handling diagnostic abnormalities using deep learning methods, applications of healthcare for human activity recognition, and more.

The new book highlights the application of artificial intelligence, machine learning, and deep learning techniques to diagnose medical problems. General predictive analysis methods are explained in disease detection applications along with machine learning techniques for the implementation of predictive analysis methods. The book covers object detection approaches for diseases such as pneumonia, application of monitoring and tracking the coronavirus disease, various endodontic applications for handling diagnostic abnormalities using deep learning methods, applications of healthcare for human activity recognition, and more.