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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.
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