The book intends to cover various problematic aspects of emerging smart computing and self-adapting technologies comprising of machine learning, artificial intelligence, deep learning, robotics, cloud computing, fog computing, data mining algorithms, including emerging intelligent and smart applications related to these research areas. Further coverage includes implementation of self-adaptation architecture for smart devices, self-adaptive models for smart cities and self-driven cars, decentralized self-adaptive computing at the edge networks, energy-aware AI-based systems, M2M networks, sensors, data analytics, algorithms and tools for engineering self-adaptive systems, and so forth. Acts as guide to Self-healing and Self-adaptation based fully automatic future technologies Discusses about Smart Computational abilities and self-adaptive systems Illustrates tools and techniques for data management and explains the need to apply, and data integration for improving efficiency of big data Exclusive chapter on the future of self-stabilizing and self-adaptive systems of systems Covers fields such as automation, robotics, medical sciences, biomedical and agricultural sciences, healthcare and so forth This book is aimed researchers and graduate students in machine learning, information technology, and artificial intelligence.

This book is useful to readers who are just beginning in the field of drug design and development. It starts with an introduction and some basic information about drugs. Pharmacokinetic and pharmacodynamic aspects are covered in the initial chapters. Systemic descriptions of pharmacokinetic parameters such as absorption, distribution, metabolism, and excretion are explained in simple and easy language. This book demonstrates detailed concepts of lead molecules and their sources. In addition, drug target classification, identification and validation strategies, as well as the design and development of combinatorial libraries for new lead generation are also covered. This book contains various methodologies, such as structure-based drug designing, quantitative structure activity relationship, pharmacophore modelling, and docking studies for screening and designing the drug candidates. This book will help in the identification and design of drugs that could be potentially useful in deriving the candidate drugs, which may have efficacy in animal models or cell cultures. Thus, the most effective compounds could be employed based on the above results, and will then be moved from preclinical studies to clinical trials. The last couple of chapters describe dosage drug regulatory affairs and recent advancements in healthcare. The aim of this book is to provide comprehensive ideas about overall drug design and development.

The book covers application of various machine learning methodologies such as Support vector machine, Artificial neural network and Bayesian classifier on biological data. Individual prediction models and their comparison in terms of accuracy are also presented. The biological data sets were collected from diverse databases to develop unbiased models. This book presents complete research overview on application of machine learning using biological data.