Advances In Data Science And Optimization Of Complex Systems
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Advances in Data Science and Optimization of Complex Systems
This proceeding set contains 81 selected full papers presented at the International Conference on Applied Mathematics and Computer Science (ICAMCS 2024), which was held on December 20-21, 2024 in Hanoi, Vietnam, in honor of Professors Pham Dinh Tao and Le Thi Hoai An for the 40th birthday of DC (Difference of Convex functions) programming and DCA (DC Algorithm). The book covers theoretical and algorithmic as well as practical issues connected with several domains of Applied Mathematics and Computer Science, especially Optimization and Data Science. The present part I of the 2-volume set includes articles devoted to Mathematical programming and optimization, DC Programming & DCA, Operations research and decision making, Economics & Finance, Engineering Systems, Autonomous systems, Information theory and Information security. Researchers and practitioners in related areas will find a wealth of inspiring ideas and useful tools and techniques for their own work.
Modelling, Simulation and Applications of Complex Systems
This book discusses the latest progresses and developments on complex systems research and intends to give an exposure to prospective readers about the theoretical and practical aspects of mathematical modelling, numerical simulation and agent-based modelling frameworks. The main purpose of this book is to emphasize a unified approach to complex systems analysis, which goes beyond to examine complicated phenomena of numerous real-life systems; this is done by investigating a huge number of components that interact with each other at different (microscopic and macroscopic) scales; new insights and emergent collective behaviours can evolve from the interactions between individual components and also with their environments. These tools and concepts permit us to better understand the patterns of various real-life systems and help us to comprehend the mechanisms behind which distinct factors shaping some complex systems phenomena being influenced. This book is published in conjunction with the International Workshop on Complex Systems Modelling & Simulation 2019 (CoSMoS 2019): IoT & Big Data Integration. This international event was held at the Universiti Sains Malaysia Main Campus, Penang, Malaysia, from 8 to 11 April 2019. This book appeals to readers interested in complex systems research and other related areas such as mathematical modelling, numerical simulation and agent-based modelling frameworks.
Machine Learning, Predictive Analytics, and Optimization in Complex Systems
Author: John Joseph, Ferdin Joe
language: en
Publisher: IGI Global
Release Date: 2025-06-27
The integration of machine learning, predictive analytics, and optimization techniques revolutionizes the understanding and management of complex systems. From supply chains and energy grids to healthcare and financial markets, these systems are characterized by dynamic interactions, uncertainty, and large data amounts. Machine learning enables insights into data patterns, analytics predict future behaviors, and optimization methods guide decision-making. When combined, these tools offer solutions for enhancing system performance, resilience, and adaptability. As complexity grows, their collaboration becomes vital for creating intelligent, responsive, and sustainable systems. Machine Learning, Predictive Analytics, and Optimization in Complex Systems examines the integration of intelligent technologies into system design and management, and data analysis. It explores strategies for data-informed decisions, intelligent technology utilization, and security optimization. This book covers topics such as computer engineering, smart ecosystems, and system design, and is a useful resource for computer engineers, data analysts, academicians, researchers, and scientists.