Paperback. This book is based on the best papers accepted for presentation during the International Conference on Current Problems of Applied Mathematics and Computer Systems (APAMCS-2023). The book includes research materials on mathematical problems and solutions in the field of scientific computing, artificial intelligence, data analysis and modular computing. The scope of numerical methods in scientific computing presents original research, including mathematical models and software implementations, related to the following topics: numerical methods in scientific computing; solving optimization problems; methods for approximating functions, etc. The studies in data analysis and modular computing include contributions in the field of deep learning, neural networks, mathematical statistics, machine learning methods, residue number system and artificial intelligence. In addition, some articles focus on mathematical modeling of nonlinear physical phenomena. Finally, the book gives insights into the fundamental problems in mathematics education. The book intends for readership specializing in the field of scientific computing, parallel computing, computer technology, machine learning, information security and mathematical education The scope of numerical methods in scientific computing presents original research, including mathematical models and software implementations, related to the following topics: numerical methods in scientific computing; Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

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D�tails bibliographiques

Titre: Current Problems of Applied Mathematics and Computer Systems (Paperback)
�diteur: Springer International Publishing AG, Cham
Date de parution: 2024
Langue: anglais
ISBN � 10 chiffres: 3031640098
ISBN � 13 chiffres: 9783031640094
Reliure: Paperback
�tat de l'article: new

A propos de ce titre

Synopsis

This book is based on the best papers accepted for presentation during the International Conference on Current Problems of Applied Mathematics and Computer Systems (APAMCS-2023). The book includes research materials on mathematical problems and solutions in the field of scientific computing, artificial intelligence, data analysis and modular computing. The scope of numerical methods in scientific computing presents original research, including mathematical models and software implementations, related to the following topics: numerical methods in scientific computing; solving optimization problems; methods for approximating functions, etc. The studies in data analysis and modular computing include contributions in the field of deep learning, neural networks, mathematical statistics, machine learning methods, residue number system and artificial intelligence. In addition, some articles focus on mathematical modeling of nonlinear physical phenomena. Finally, the book gives insights into the fundamental problems in mathematics education. The book intends for readership specializing in the field of scientific computing, parallel computing, computer technology, machine learning, information security and mathematical education

� propos de l?auteur

Anatoly Alikhanov has obtained his Ph.D. in Physical and Mathematical Sciences. He is Vice-Rector for Scientific and Research work of the North Caucasus Federal University. He is the Head of the Regional Scientific and Educational Mathematical Center "North Caucasian Center for Mathematical Research." Earlier, he worked as Visiting Member of the dissertation council at Southeast University, Department of Mathematics, Nanjing, China. In 2016, he was invited for an internship in the field of numerical methods for solving fractional differential equations at the Nanjing University of China. А. Alikhanov is Member of the Editor Board of the Fractional Calculus and Applied Analysis Journal and Reviewer of over 30 reputable scientific journals in computational mathematics.

Andrei Tchernykh, Full Professor in computer science at CICESE Research Center (Centro de Investigaci�n Cient�fica y de Educaci�n Superior de Ensenada), Ensenada, Baja California, Mexico and Adjunct Professor at ISP RAS - Institute for System Programming of the Russian Academy of Sciences, Russia. He is chairing "Parallel Computing Laboratory" at CICESE, Mexico and "International Laboratory of Problem-Oriented Cloud Computing" at South Ural State University, Russia. He gained industrial experience as supercomputer design team leader in Advance Technical Products Corp, and Supercomputer Design Department of Electro-Mechanical Enterprise. He received Ph.D. degree in Computer Science from the IPMCE in 1986. He is a founding member of the Mexican Supercomputer Society (RedMexSu), a Regional President of Global Association for Academic Supervision (GAAS) Latin America, and member of the Mexican National Researchers System SNI level II. He has led a number of research projects and grants in different countries funded by CONACYT, NSF, ANII, Ochoa, INRIA, FNR, UC MEXUS, DAAD, LAFMI, AMEXCID, ANII, etc. He is awarded Global Scholars Fellow at Tsinghua University (China), German Academic Exchange Service fellowship at University of G�ttingen, Dortmund University, Technische Universit�t Clausthal (Germany), and Severo Ochoa fellowship at Barcelona Supercomputing Center (Spain). He is an editorial board member of several journals and served as a guest editor for special issues including Mobile Networks & Applications (MONET) Springer, International Journal of Approximate Reasoning, Elsevier.

Mikhail Babenko is the Head of the Department of Computational Mathematics and Cybernetics North-Caucasus Federal University. He is a member of the scientific school named after Professor Chervyakov "Neuromathematics, modular neurocomputers and high-performance computing". He received PhD in Mathematical modeling, numerical methods, and software. Mikhail took internships in CICESE Research Center in M�xico; Le Quy Don Technical University, Hanoi, Vietnam in 2015; University of Lorraine, Nancy, France in 2017. Research area: Security, Cryptography, Residue Number System, Big Data, Internet of Things, Fog-Edge-Cloud Computing, Trust, Uncertainty, Scalable and Reliable Systems, Elliptic Curve.

Irina Samoilenko is PhD, Associate Professor at Informational Systems Department, Stavropol State Agrarian University, Russia. She received M.S. degree in Applied Mathematics and Informatics, PhD in System Analysis, Control and Processing of Information in North-Caucasus Federal University. Irina is a researcher in North-Caucasus Centre for Mathematical Research, North-Caucasus Federal University. She is a member of Association of Scientific Editors and Publishers, Russia. She participated in international conferences and internships in Italy, Turkey, Romania and Czech Republic. Her research interests include wireless sensor networks, IoT, optimization tasks and mathematical modeling.

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