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Computational Intelligence - Engineering of Hybrid Systems (Hardcover, 2005 ed.): Mircea Gh. Negoita, Daniel Neagu, Vasile... Computational Intelligence - Engineering of Hybrid Systems (Hardcover, 2005 ed.)
Mircea Gh. Negoita, Daniel Neagu, Vasile Palade
R2,775 Discovery Miles 27 750 Ships in 18 - 22 working days

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."

Computational Intelligence - Engineering of Hybrid Systems (Paperback, Softcover reprint of hardcover 1st ed. 2005): Mircea Gh.... Computational Intelligence - Engineering of Hybrid Systems (Paperback, Softcover reprint of hardcover 1st ed. 2005)
Mircea Gh. Negoita, Daniel Neagu, Vasile Palade
R2,637 Discovery Miles 26 370 Ships in 18 - 22 working days

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."

Big Data in Predictive Toxicology (Hardcover): Daniel Neagu, Andrea-Nicole Richarz Big Data in Predictive Toxicology (Hardcover)
Daniel Neagu, Andrea-Nicole Richarz
R5,912 Discovery Miles 59 120 Ships in 10 - 15 working days

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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