Optimization and Computing using Intelligent Data-Driven Approaches for Decision-Making PDF Download
Are you looking for read ebook online? Search for your book and save it on your Kindle device, PC, phones or tablets. Download Optimization and Computing using Intelligent Data-Driven Approaches for Decision-Making PDF full book. Access full book title Optimization and Computing using Intelligent Data-Driven Approaches for Decision-Making by Irfan Ali. Download full books in PDF and EPUB format.
Author: Irfan Ali
Publisher: CRC Press
ISBN: 1040164625
Category : Computers
Languages : en
Pages : 335
Get Book Here
Book Description
This book comprehensively discusses nature‐inspired algorithms, deep learning methods, applications of mathematical programming, and artificial intelligence techniques. It further covers important topics such as the use of machine learning and the Internet of Things and multi‐objective optimization under Fermatean hesitant fuzzy and uncertain environment. This book: Addresses solving practical problems such as supply chain management, smart manufacturing, and healthcare analytics using intelligent computing and discusses solving the fuzzy inference system in ant colony optimization for traveling salesman problem Presents an overview of artificial intelligence (AI) and explainable AI decision‐making (XAIDM) and illustrates a data‐driven optimization concept for modeling environmental and economic sustainability Discusses machine learning‐based multi‐objective optimization technique for load balancing in integrated fog‐cloud environment Explains the use of heuristics and metaheuristics in supply chain networks and the use of fuzzy optimization in sustainable development goals Discusses sustainable transit of hazardous waste, green fractional transportation system, perishable inventory, M‐estimation of functional regression operator, and intuitionistic fuzzy sets applications The text is primarily written for graduate students and academic researchers in diverse fields, including operations research, mathematics, statistics, computer science, information and communication technology, and industrial engineering.
Author: Irfan Ali
Publisher: CRC Press
ISBN: 1040164625
Category : Computers
Languages : en
Pages : 335
Get Book Here
Book Description
This book comprehensively discusses nature‐inspired algorithms, deep learning methods, applications of mathematical programming, and artificial intelligence techniques. It further covers important topics such as the use of machine learning and the Internet of Things and multi‐objective optimization under Fermatean hesitant fuzzy and uncertain environment. This book: Addresses solving practical problems such as supply chain management, smart manufacturing, and healthcare analytics using intelligent computing and discusses solving the fuzzy inference system in ant colony optimization for traveling salesman problem Presents an overview of artificial intelligence (AI) and explainable AI decision‐making (XAIDM) and illustrates a data‐driven optimization concept for modeling environmental and economic sustainability Discusses machine learning‐based multi‐objective optimization technique for load balancing in integrated fog‐cloud environment Explains the use of heuristics and metaheuristics in supply chain networks and the use of fuzzy optimization in sustainable development goals Discusses sustainable transit of hazardous waste, green fractional transportation system, perishable inventory, M‐estimation of functional regression operator, and intuitionistic fuzzy sets applications The text is primarily written for graduate students and academic researchers in diverse fields, including operations research, mathematics, statistics, computer science, information and communication technology, and industrial engineering.
Author: Kalita, Kanak
Publisher: IGI Global
ISBN: 1799872084
Category : Technology & Engineering
Languages : en
Pages : 298
Get Book Here
Book Description
All machining process are dependent on a number of inherent process parameters. It is of the utmost importance to find suitable combinations to all the process parameters so that the desired output response is optimized. While doing so may be nearly impossible or too expensive by carrying out experiments at all possible combinations, it may be done quickly and efficiently by using computational intelligence techniques. Due to the versatile nature of computational intelligence techniques, they can be used at different phases of the machining process design and optimization process. While powerful machine-learning methods like gene expression programming (GEP), artificial neural network (ANN), support vector regression (SVM), and more can be used at an early phase of the design and optimization process to act as predictive models for the actual experiments, other metaheuristics-based methods like cuckoo search, ant colony optimization, particle swarm optimization, and others can be used to optimize these predictive models to find the optimal process parameter combination. These machining and optimization processes are the future of manufacturing. Data-Driven Optimization of Manufacturing Processes contains the latest research on the application of state-of-the-art computational intelligence techniques from both predictive modeling and optimization viewpoint in both soft computing approaches and machining processes. The chapters provide solutions applicable to machining or manufacturing process problems and for optimizing the problems involved in other areas of mechanical, civil, and electrical engineering, making it a valuable reference tool. This book is addressed to engineers, scientists, practitioners, stakeholders, researchers, academicians, and students interested in the potential of recently developed powerful computational intelligence techniques towards improving the performance of machining processes.
Author: Irfan Ali (Statistician)
Publisher:
ISBN: 9781032821207
Category : Artificial intelligence
Languages : en
Pages : 0
Get Book Here
Book Description
"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"--
Author: Asaju La'aro Bolaji
Publisher:
ISBN: 9781032621661
Category : Computers
Languages : en
Pages : 0
Get Book Here
Book Description
This book comprehensively discusses nature-inspired algorithms, deep learning methods, applications of mathematical programming, and artificial intelligence techniques. It further covers important topics such as the use of machine learning and the internet of things, multi-objective optimization under Hesitant Fermatean Fuzzy and Uncertain environment. This Book: Addresses solving practical problems such as supply chain management, smart manufacturing, and healthcare analytics using intelligent computing and discusses solving the Fuzzy Inference System in Ant Colony Optimization for Travelling Salesman Problems Presents an overview of AI and Explainable AI Decision-Making XAIDM and illustrates a data-driven optimization concept for modelling environmental and economic sustainability. Discusses Machine Learning based Multi Objective Optimization Technique for Load Balancing in Integrated Fog Cloud Environment. Explains the use of heuristics and metaheuristics in supply chain networks and the use of fuzzy optimization in sustainable development goals. Discusses Sustainable Transit of Hazardous Waste, Green Fractional Transportation System, Perishable Inventory, M-Estimation of Functional Regression Operator and Intuitionistic Fuzzy Sets applications. The text is primarily written for graduate students, and academic researchers in diverse fields including operations research, mathematics, statistics, computer science, information and communication technology and industrial engineering.
Author: Gloria Phillips-Wren
Publisher: Springer Science & Business Media
ISBN: 3540768289
Category : Mathematics
Languages : en
Pages : 414
Get Book Here
Book Description
Intelligent Decision Support Systems have the potential to transform human decision making by combining research in artificial intelligence, information technology, and systems engineering. The field of intelligent decision making is expanding rapidly due, in part, to advances in artificial intelligence and network-centric environments that can deliver the technology. Communication and coordination between dispersed systems can deliver just-in-time information, real-time processing, collaborative environments, and globally up-to-date information to a human decision maker. At the same time, artificial intelligence techniques have demonstrated that they have matured sufficiently to provide computational assistance to humans in practical applications. This book includes contributions from leading researchers in the field beginning with the foundations of human decision making and the complexity of the human cognitive system. Researchers contrast human and artificial intelligence, survey computational intelligence, present pragmatic systems, and discuss future trends. This book will be an invaluable resource to anyone interested in the current state of knowledge and key research gaps in the rapidly developing field of intelligent decision support.
Author: Dimitris Mourtzis
Publisher: Elsevier
ISBN: 0443139237
Category : Technology & Engineering
Languages : en
Pages : 526
Get Book Here
Book Description
Manufacturing from Industry 4.0 to Industry 5.0: Advances and Applications unfolds establishing three main pillars: (i) it investigates the theoretical background of the current industrial practice within the framework of industry 4.0 by presenting its key definitions and backbone technologies; (ii) it discusses the methods and state-of-the-art developments employed in the ongoing digital transformation of companies worldwide to promote more resilient, sustainable, and human-centric smart manufacturing and production networks; and (iii) it outlines a strategic plan for the transition from industry 4.0 to industry 5.0. Written by an international group of expert scientists, this volume offers an overview of the most recent research in the field and provides actionable insights to benefit audiences in both academia and industry. - Appeals to readers with its systematic and coherent approach that includes fundamental theoretical concepts as well as applied practical knowledge - Includes state-of-the-art information on disruptive smart manufacturing technologies, real-life case studies of their impact in business scenarios, and gap analysis, creating an evidence-based path to recognize the opportunities and challenges originating from an industry 4.0 to industry 5.0 transition - Serves as a guide to the next generation of engineers and facilitates making the next manufacturing paradigm a reality
Author: Ting Yu
Publisher: CRC Press
ISBN: 1439895953
Category : Business & Economics
Languages : en
Pages : 443
Get Book Here
Book Description
Going beyond performing simple analyses, researchers involved in the highly dynamic field of computational intelligent data analysis design algorithms that solve increasingly complex data problems in changing environments, including economic, environmental, and social data. Computational Intelligent Data Analysis for Sustainable Development present
Author: Amir Sasson
Publisher:
ISBN: 9788215031408
Category :
Languages : en
Pages : 274
Get Book Here
Book Description
Author: Lopa Mandal
Publisher: Springer Nature
ISBN: 9811916578
Category : Technology & Engineering
Languages : en
Pages : 466
Get Book Here
Book Description
This book includes selected papers presented at International Conference on Computational Intelligence, Data Science,, and Cloud Computing (IEM-ICDC 2021), organized by the Department of Information Technology Institute of Engineering and Management, Kolkata, India, during December 22 – 24, 2021. It covers substantial new findings about AI and robotics, image processing and NLP, cloud computing and big data analytics as well as in cyber-security, blockchain and IoT, and various allied fields. The book serves as a reference resource for researchers and practitioners in academia and industry.
Author: Su-Min Yu
Publisher: Springer Nature
ISBN: 9811678898
Category : Business & Economics
Languages : en
Pages : 195
Get Book Here
Book Description
This book explores clustering operations in the context of social networks and consensus-reaching paths that take into account non-cooperative behaviors. This book focuses on the two key issues in large-scale group decision-making: clustering and consensus building. Clustering aims to reduce the dimension of a large group. Consensus reaching requires that the divergent individual opinions of the decision makers converge to the group opinion. This book emphasizes the similarity of opinions and social relationships as important measurement attributes of clustering, which makes it different from traditional clustering methods with single attribute to divide the original large group without requiring a combination of the above two attributes. The proposed consensus models focus on the treatment of non-cooperative behaviors in the consensus-reaching process and explores the influence of trust loss on the consensus-reaching process.The logic behind is as follows: firstly, a clustering algorithm is adopted to reduce the dimension of decision-makers, and then, based on the clusters’ opinions obtained, a consensus-reaching process is carried out to obtain a decision result acceptable to the majority of decision-makers. Graduates and researchers in the fields of management science, computer science, information management, engineering technology, etc., who are interested in large-scale group decision-making and consensus building are potential audience of this book. It helps readers to have a deeper and more comprehensive understanding of clustering analysis and consensus building in large-scale group decision-making.
