Data-driven Modeling and Optimization: Applications to Social Computing
| Author | : Chao Gao |
| Publisher | : Frontiers Media SA |
| Total Pages | : 252 |
| Release | : 2022-09-14 |
| Genre | : Science |
| ISBN | : 2889769607 |
Data Driven Modeling And Optimization Applications To Social Computing
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Stochastic Modeling, Optimization and Data-driven Adaptive Control with Applications in Cloud Computing and Cyber Security
| Author | : Yue Tan |
| Publisher | : |
| Total Pages | : |
| Release | : 2015 |
| Genre | : |
| ISBN | : |
Big Data has flown into every sector of the global economy ranging from social networks to online business to finance to medicine. With the rapid growth of data in many applications in the society, operations research (OR) professionals must shift to a broader view of developing analytical solutions characterized by the integrated use of data, processes and systems. Classical stochastic modeling, although proved to be useful in many traditional application areas (e.g. call centers, manufacturing systems), few works have been done in new applications arising from big data. Existing methods are lack of integration between data and modeling, recent development in adaptive control fails to address these new applications. In this dissertation, we aim to fill these gaps by developing new stochastic modeling, optimization and data-driven adaptive control approaches for managerial problems such as the resource provisioning of cloud computing and password management in cyber security systems.
Data-Driven Evolutionary Modeling in Materials Technology
| Author | : Nirupam Chakraborti |
| Publisher | : CRC Press |
| Total Pages | : 507 |
| Release | : 2022-09-15 |
| Genre | : Technology & Engineering |
| ISBN | : 1000635864 |
Due to efficacy and optimization potential of genetic and evolutionary algorithms, they are used in learning and modeling especially with the advent of big data related problems. This book presents the algorithms and strategies specifically associated with pertinent issues in materials science domain. It discusses the procedures for evolutionary multi-objective optimization of objective functions created through these procedures and introduces available codes. Recent applications ranging from primary metal production to materials design are covered. It also describes hybrid modeling strategy, and other common modeling and simulation strategies like molecular dynamics, cellular automata etc. Features: Focuses on data-driven evolutionary modeling and optimization, including evolutionary deep learning. Include details on both algorithms and their applications in materials science and technology. Discusses hybrid data-driven modeling that couples evolutionary algorithms with generic computing strategies. Thoroughly discusses applications of pertinent strategies in metallurgy and materials. Provides overview of the major single and multi-objective evolutionary algorithms. This book aims at Researchers, Professionals, and Graduate students in Materials Science, Data-Driven Engineering, Metallurgical Engineering, Computational Materials Science, Structural Materials, and Functional Materials.
Optimization and Computing Using Intelligent Data-Driven Approaches for Decision-Making
| Author | : Asaju La'aro Bolaji |
| Publisher | : |
| Total Pages | : 0 |
| Release | : 2024-11-27 |
| Genre | : Computers |
| ISBN | : 9781032781112 |
This book comprehensively discusses nature-inspired algorithms, deep learning methods, applications of mathematical programming and artificial intelligence techniques. It will further cover important topic such as linking green supply chain management practices with competitiveness, industry 4.0 and social responsibility. This book: Addresses solving practical problems such as supply chain management, take-off, and healthcare analytics using intelligent computing. Presents a comparative analysis of machine learning algorithms for the power consumption prediction. Discusses machine learning-based multi-objective optimization technique for load balancing in an integrated fog cloud environment. Illustrates a data-driven optimization concept for modeling environmental and economic sustainability. Explains the use of heuristics and metaheuristics in supply chain networks and the use of fuzzy optimization in sustainable development goals. The text is primarily written for graduate students, and academic researchers in diverse fields including electrical engineering, electronics and communications engineering, mathematics and statistics, computer science and engineering.
Data Science: New Issues, Challenges and Applications
| Author | : Gintautas Dzemyda |
| Publisher | : Springer Nature |
| Total Pages | : 325 |
| Release | : 2020-02-13 |
| Genre | : Computers |
| ISBN | : 3030392503 |
This book contains 16 chapters by researchers working in various fields of data science. They focus on theory and applications in language technologies, optimization, computational thinking, intelligent decision support systems, decomposition of signals, model-driven development methodologies, interoperability of enterprise applications, anomaly detection in financial markets, 3D virtual reality, monitoring of environmental data, convolutional neural networks, knowledge storage, data stream classification, and security in social networking. The respective papers highlight a wealth of issues in, and applications of, data science. Modern technologies allow us to store and transfer large amounts of data quickly. They can be very diverse - images, numbers, streaming, related to human behavior and physiological parameters, etc. Whether the data is just raw numbers, crude images, or will help solve current problems and predict future developments, depends on whether we can effectively process and analyze it. Data science is evolving rapidly. However, it is still a very young field. In particular, data science is concerned with visualizations, statistics, pattern recognition, neurocomputing, image analysis, machine learning, artificial intelligence, databases and data processing, data mining, big data analytics, and knowledge discovery in databases. It also has many interfaces with optimization, block chaining, cyber-social and cyber-physical systems, Internet of Things (IoT), social computing, high-performance computing, in-memory key-value stores, cloud computing, social computing, data feeds, overlay networks, cognitive computing, crowdsource analysis, log analysis, container-based virtualization, and lifetime value modeling. Again, all of these areas are highly interrelated. In addition, data science is now expanding to new fields of application: chemical engineering, biotechnology, building energy management, materials microscopy, geographic research, learning analytics, radiology, metal design, ecosystem homeostasis investigation, and many others.
Computational Science — ICCS 2004
| Author | : Marian Bubak |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 1376 |
| Release | : 2004-05-26 |
| Genre | : Computers |
| ISBN | : 3540221166 |
The International Conference on Computational Science (ICCS 2004) held in Krak ́ ow, Poland, June 6–9, 2004, was a follow-up to the highly successful ICCS 2003 held at two locations, in Melbourne, Australia and St. Petersburg, Russia; ICCS 2002 in Amsterdam, The Netherlands; and ICCS 2001 in San Francisco, USA. As computational science is still evolving in its quest for subjects of inves- gation and e?cient methods, ICCS 2004 was devised as a forum for scientists from mathematics and computer science, as the basic computing disciplines and application areas, interested in advanced computational methods for physics, chemistry, life sciences, engineering, arts and humanities, as well as computer system vendors and software developers. The main objective of this conference was to discuss problems and solutions in all areas, to identify new issues, to shape future directions of research, and to help users apply various advanced computational techniques. The event harvested recent developments in com- tationalgridsandnextgenerationcomputingsystems,tools,advancednumerical methods, data-driven systems, and novel application ?elds, such as complex - stems, ?nance, econo-physics and population evolution.
Online Social Media Content Delivery
| Author | : Zhi Wang |
| Publisher | : Springer |
| Total Pages | : 117 |
| Release | : 2018-07-31 |
| Genre | : Computers |
| ISBN | : 9811027749 |
This book explains how to use a data-driven approach to design strategies for social media content delivery. It first introduces readers to how social information can be effectively gathered for big data analysis, which provides content delivery intelligence. Secondly, the book describes data-driven models to capture information diffusion in online social networks and social media content propagation and popularity, before presenting prediction models for social media content delivery. By addressing the resource allocation and content replication aspects of social media content delivery, the book presents the latest data-driven strategies. In closing, it outlines a number of potential research directions regarding social media content delivery.
Surrogate-Based Modeling and Optimization
| Author | : Slawomir Koziel |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 413 |
| Release | : 2013-06-06 |
| Genre | : Mathematics |
| ISBN | : 1461475511 |
Contemporary engineering design is heavily based on computer simulations. Accurate, high-fidelity simulations are used not only for design verification but, even more importantly, to adjust parameters of the system to have it meet given performance requirements. Unfortunately, accurate simulations are often computationally very expensive with evaluation times as long as hours or even days per design, making design automation using conventional methods impractical. These and other problems can be alleviated by the development and employment of so-called surrogates that reliably represent the expensive, simulation-based model of the system or device of interest but they are much more reasonable and analytically tractable. This volume features surrogate-based modeling and optimization techniques, and their applications for solving difficult and computationally expensive engineering design problems. It begins by presenting the basic concepts and formulations of the surrogate-based modeling and optimization paradigm and then discusses relevant modeling techniques, optimization algorithms and design procedures, as well as state-of-the-art developments. The chapters are self-contained with basic concepts and formulations along with applications and examples. The book will be useful to researchers in engineering and mathematics, in particular those who employ computationally heavy simulations in their design work.
Advances in Artificial Intelligence, Software and Systems Engineering
| Author | : Tareq Ahram |
| Publisher | : Springer Nature |
| Total Pages | : 624 |
| Release | : 2020-07-03 |
| Genre | : Technology & Engineering |
| ISBN | : 3030513289 |
This book addresses emerging issues concerning the integration of artificial intelligence systems in our daily lives. It focuses on the cognitive, visual, social and analytical aspects of computing and intelligent technologies, and highlights ways to improve the acceptance, effectiveness, and efficiency of said technologies. Topics such as responsibility, integration and training are discussed throughout. The book also reports on the latest advances in systems engineering, with a focus on societal challenges and next-generation systems and applications for meeting them. Based on the AHFE 2020 Virtual Conference on Software and Systems Engineering, and the AHFE 2020 Virtual Conference on Artificial Intelligence and Social Computing, held on July 16–20, 2020, it provides readers with extensive information on current research and future challenges in these fields, together with practical insights into the development of innovative services for various purposes.
Advances in Social Computing
| Author | : Sun-Ki Chai |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 437 |
| Release | : 2010-04 |
| Genre | : Business & Economics |
| ISBN | : 3642120784 |
This book constitutes the refereed proceedings of the Third International Conference on Social Computing, Behavioral Modeling, and Prediction, SBP 2010, held in Bethseda, MD, USA, in March 2010. The 26 revised full papers and 23 revised poster papers presented together with 4 invited and keynote papers were carefully reviewed and selected from 78 initial submissions. The papers cover a wide range of interesting topics such as social network analysis, modeling, machine learning and data mining, social behaviors, public health, cultural aspects, effects and search.
