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Hybrid Intelligent Systems has become an important research
topic in computer science and a key application field in science
and engineering. This book offers a gentle introduction to the
engineering aspects of hybrid intelligent systems, also emphasizing
the interrelation with the main intelligent technologies such as
genetic algorithms evolutionary computation, neural networks, fuzzy
systems, evolvable hardware, DNA computing, artificial immune
systems. A unitary whole of theory and application, the book
provides readers with the fundamentals, background information, and
practical methods for building a hybrid intelligent system. It
treats a panoply of applications, including many in industry,
educational systems, forecasting, financial engineering, and
bioinformatics. This volume is useful to newcomers in the field
because it quickly familiarizes them with engineering elements of
developing hybrid intelligent systems and a wide range of real
applications, including non-industrial applications. Researchers,
developers and technically oriented managers can use the book for
developing both new hybrid intelligent systems approaches and new
applications requiring the hybridization of the typical tools and
concepts to computational intelligence."
Hybrid Intelligent Systems has become an important research
topic in computer science and a key application field in science
and engineering. This book offers a gentle introduction to the
engineering aspects of hybrid intelligent systems, also emphasizing
the interrelation with the main intelligent technologies such as
genetic algorithms evolutionary computation, neural networks, fuzzy
systems, evolvable hardware, DNA computing, artificial immune
systems. A unitary whole of theory and application, the book
provides readers with the fundamentals, background information, and
practical methods for building a hybrid intelligent system. It
treats a panoply of applications, including many in industry,
educational systems, forecasting, financial engineering, and
bioinformatics. This volume is useful to newcomers in the field
because it quickly familiarizes them with engineering elements of
developing hybrid intelligent systems and a wide range of real
applications, including non-industrial applications. Researchers,
developers and technically oriented managers can use the book for
developing both new hybrid intelligent systems approaches and new
applications requiring the hybridization of the typical tools and
concepts to computational intelligence."
The rate at which toxicological data is generated is continually
becoming more rapid and the volume of data generated is growing
dramatically. This is due in part to advances in software solutions
and cheminformatics approaches which increase the availability of
open data from chemical, biological and toxicological and high
throughput screening resources. However, the amplified pace and
capacity of data generation achieved by these novel techniques
presents challenges for organising and analysing data output. Big
Data in Predictive Toxicology discusses these challenges as well as
the opportunities of new techniques encountered in data science. It
addresses the nature of toxicological big data, their storage,
analysis and interpretation. It also details how these data can be
applied in toxicity prediction, modelling and risk assessment. This
title is of particular relevance to researchers and postgraduates
working and studying in the fields of computational methods,
applied and physical chemistry, cheminformatics, biological
sciences, predictive toxicology and safety and hazard assessment.
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