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Computational Analysis and Understanding of Natural Languages:
Principles, Methods and Applications, Volume 38, the latest release
in this monograph that provides a cohesive and integrated
exposition of these advances and associated applications, includes
new chapters on Linguistics: Core Concepts and Principles,
Grammars, Open-Source Libraries, Application Frameworks, Workflow
Systems, Mathematical Essentials, Probability, Inference and
Prediction Methods, Random Processes, Bayesian Methods, Machine
Learning, Artificial Neural Networks for Natural Language
Processing, Information Retrieval, Language Core Tasks, Language
Understanding Applications, and more. The synergistic confluence of
linguistics, statistics, big data, and high-performance computing
is the underlying force for the recent and dramatic advances in
analyzing and understanding natural languages, hence making this
series all the more important.
We have witnessed an explosion of research activity around
nature-inspired computing and bio-inspired optimization techniques,
which can provide powerful tools for solving learning problems and
data analysis in very large data sets. To design and implement
optimization algorithms, several methods are used that bring
superior performance. However, in some applications, the search
space increases exponentially with the problem size. To overcome
these limitations and to solve efficiently large scale
combinatorial and highly nonlinear optimization problems, more
flexible and adaptable algorithms are necessary. Nature-inspired
computing is oriented towards the application of outstanding
information-processing aptitudes of the natural realm to the
computational domain. The discipline of nature-inspired
optimization algorithms is a major field of computational
intelligence, soft computing and optimization. Metaheuristic search
algorithms with population-based frameworks are capable of handling
optimization in high-dimensional real-world problems for several
domains including imaging, IoT, smart manufacturing, and
healthcare. The integration of intelligence with smart technology
enhances accuracy and efficiency. Smart devices and systems are
revolutionizing the world by linking innovative thinking with
innovative action and innovative implementation. The aim of this
edited book is to review the intertwining disciplines of
nature-inspired computing and bio-inspired soft-computing (BISC)
and their applications to real world challenges. The contributors
cover the interaction between metaheuristics, such as evolutionary
algorithms and swarm intelligence, with complex systems. They
explain how to better handle different kinds of uncertainties in
real-life problems using state-of-art of machine learning
algorithms. They also explore future research perspectives to
bridge the gap between theory and real-life day-to-day challenges
for diverse domains of engineering. The book will offer valuable
insights to researchers and scientists from academia and industry
in ICTs, IT and computer science, data science, AI and machine
learning, swarm intelligence and complex systems. It is also a
useful resource for professionals in related fields, and for
advanced students with an interest in optimization and IoT
applications.
Cognitive Computing: Theory and Applications, written by
internationally renowned experts, focuses on cognitive computing
and its theory and applications, including the use of cognitive
computing to manage renewable energy, the environment, and other
scarce resources, machine learning models and algorithms,
biometrics, Kernel Based Models for transductive learning, neural
networks, graph analytics in cyber security, neural networks, data
driven speech recognition, and analytical platforms to study the
brain-computer interface.
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