Neuware -In this book we have shown application of Intelligent Systems and Optimization techniques in the field of interdisciplinary engineering. In the 1st chapter, we have described the use of IS namely S-system for the inference of Gene Regulatory Network (GRN) from time series microarray data in the field of computational biology and bioinformatics. In the 2nd chapter, we have used a recently proposed optimization technique namely Elephant Swarm Water Search Algorithm (ESWSA) for the efficient Modeling for prediction and optimizing the parameters of grinding processes which is a typical problem of mechanical engineering. In the 3rd chapter, we have used a hybrid AI technique with the help of Decision Tree & parametric optimization by Genetic Algorithm in Diabetes prediction in the field of biomedical field by proper analysis of small & large disjuncts present in medical datasets. In chapter 4, we have used a hybrid cuckoo search optimization (CSO) & particle swarm optimization (PSO) technique in the field of renewable energy. In chapter 5, a hybrid optimization technique used by two evolutionary algorithm: Genetic algorithm & Particle swarm optimization in the field of Mechatronics.Books on Demand GmbH, �berseering 33, 22297 Hamburg 124 pp. Englisch.

Bestandsnummer des Verk�ufers 9786204201665

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Titel: Application of Optimization and Intelligent System on Inter-disciplinary Engineering
Verlag: LAP LAMBERT Academic Publishing Sep 2021
Ver�ffentlichungsdatum: 2021
Sprache: Englisch
ISBN-10: 6204201662
ISBN-13: 9786204201665
Bindung: Taschenbuch
Zustand: Neu

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In this book we have shown application of Intelligent Systems and Optimization techniques in the field of interdisciplinary engineering. In the 1st chapter, we have described the use of IS namely S-system for the inference of Gene Regulatory Network (GRN) from time series microarray data in the field of computational biology and bioinformatics. In the 2nd chapter, we have used a recently proposed optimization technique namely Elephant Swarm Water Search Algorithm (ESWSA) for the efficient Modeling for prediction and optimizing the parameters of grinding processes which is a typical problem of mechanical engineering. In the 3rd chapter, we have used a hybrid AI technique with the help of Decision Tree & parametric optimization by Genetic Algorithm in Diabetes prediction in the field of biomedical field by proper analysis of small & large disjuncts present in medical datasets. In chapter 4, we have used a hybrid cuckoo search optimization (CSO) & particle swarm optimization (PSO) technique in the field of renewable energy. In chapter 5, a hybrid optimization technique used by two evolutionary algorithm: Genetic algorithm & Particle swarm optimization in the field of Mechatronics.

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