Mathematical Methods and Modelling in Applied Sciences (eBook, PDF)

Redaktion: Zeki Sarikaya, Mehmet; Srivastava, Hari M.; Ocak Akdemir, Ahmet; Dutta, Hemen

• Format: PDF

Produktbeschreibung

This book presents a collection of original research papers from the 2nd International Conference on Mathematical and Related Sciences, held in Antalya, Turkey, on 27 - 30 April 2019 and sponsored/supported by Düzce University, Turkey; the University of Jordan; and the Institute of Applied Mathematics, Baku State University, Azerbaijan. The book focuses on various types of mathematical methods and models in applied sciences; new mathematical tools, techniques and algorithms related to various branches of applied sciences; and important aspects of applied mathematical analysis. It covers mathematical models and modelling methods related to areas such as networks, intelligent systems, population dynamics, medical science and engineering, as well as a wide variety of analytical and numerical methods.

The conference aimed to foster cooperation among students, researchers and experts from diverse areas of mathematics and related sciences and to promote fruitful exchanges on crucial research in the field.

This book is a valuable resource for graduate students, researchers and educators interested in applied mathematics and interactions of mathematics with other branches of science to provide insights into analysing, modelling and solving various scientific problems in applied sciences.

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Produktdetails

• Verlag: Springer Nature Switzerland
• Seitenzahl: 259
• Erscheinungstermin: 2. März 2020
• Englisch
• ISBN-13: 9783030430023
• Artikelnr.: 58751051

Inhaltsangabe

Fuzzy Kernel-Based Clustering and Support Vector Machine Algorithm in Analyzing Cerebral Infarction Dataset.- A predator-prey model with fear factor, Allee e ect and periodic harvesting.- Mathematical Modeling of Rock Massif Dynamics under Explosive Sources of Disturbances.- Residual Power Series Approach for Solving Linear Fractional Swift-Hohenberg Problems.- Kernel-based Fuzzy Clustering for Sinusitis Dataset.