Deep Learning Applications In Operations Research
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Deep Learning Applications In Operations Research
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Author : Aryan Chaudhary
language : en
Publisher: CRC Press
Release Date : 2024-12-30
Deep Learning Applications In Operations Research written by Aryan Chaudhary and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-12-30 with Computers categories.
The model-based approach for carrying out the classification and identification of tasks has led to progression of the machine learning paradigm in diversified fields of technology. Deep Learning Applications in Operations Research presents the varied applications of this model-based approach. Apart from the classification process, the machine learning (ML) model has become effective enough to predict future trends of any sort of phenomenon. Such fields as object classification, speech recognition, and face detection have sought extensive applications of artificial intelligence (AI) and machine learning as well. The application of AI and ML has also become increasingly common in the domains of agriculture, health sectors, and insurance. Operations research is the branch of mathematics used to perform many operational tasks in other allied domains, and the book explains how the implementation of automated strategies in optimization and parameter selection can be carried out by AI and ML. Operations research has many beneficial aspects to aid in decision making. Arriving at the proper decision depends on a number of factors; this book examines how AI and ML can be used to model equations and define constraints to solve problems more easily and discover proper and valid solutions. This book also looks at how automation plays a significant role in minimizing human labor and thereby minimizes overall time and cost. Case studies examine how to streamline operations and unearth data to make better business decisions. The concepts presented in this book can bring about and guide unique research directions to the future application of AI-enabled technologies.
Deep Learning Concepts In Operations Research
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Author : Biswadip Basu Mallik
language : en
Publisher: CRC Press
Release Date : 2024-08-30
Deep Learning Concepts In Operations Research written by Biswadip Basu Mallik and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-08-30 with Computers categories.
The model-based approach for carrying out classification and identification of tasks has led to the pervading progress of the machine learning paradigm in diversified fields of technology. Deep Learning Concepts in Operations Research looks at the concepts that are the foundation of this model-based approach. Apart from the classification process, the machine learning (ML) model has become effective enough to predict future trends of any sort of phenomena. Such fields as object classification, speech recognition, and face detection have sought extensive application of artificial intelligence (AI) and ML as well. Among a variety of topics, the book examines: An overview of applications and computing devices Deep learning impacts in the field of AI Deep learning as state-of-the-art approach to AI Exploring deep learning architecture for cutting-edge AI solutions Operations research is the branch of mathematics for performing many operational tasks in other allied domains, and the book explains how the implementation of automated strategies in optimization and parameter selection can be carried out by AI and ML. Operations research has many beneficial aspects for decision making. Discussing how a proper decision depends on several factors, the book examines how AI and ML can be used to model equations and define constraints to solve problems and discover proper and valid solutions more easily. It also looks at how automation plays a significant role in minimizing human labor and thereby minimizes overall time and cost.
Handbook Of Research On Ai And Machine Learning Applications In Customer Support And Analytics
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Author : Hossain, Md Shamim
language : en
Publisher: IGI Global
Release Date : 2023-05-02
Handbook Of Research On Ai And Machine Learning Applications In Customer Support And Analytics written by Hossain, Md Shamim and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-05-02 with Computers categories.
In the modern data-driven era, artificial intelligence (AI) and machine learning (ML) technologies that allow a computer to mimic intelligent human behavior are essential for organizations to achieve business excellence and assist organizations in extracting useful information from raw data. AI and ML have existed for decades, but in the age of big data, this sort of analysis is in higher demand than ever, especially for customer support and analytics. The Handbook of Research on AI and Machine Learning Applications in Customer Support and Analytics investigates the applications of AI and ML and how they can be implemented to enhance customer support and analytics at various levels of organizations. This book is ideal for marketing professionals, managers, business owners, researchers, practitioners, academicians, instructors, university libraries, and students, and covers topics such as artificial intelligence, machine learning, supervised learning, deep learning, customer sentiment analysis, data mining, neural networks, and business analytics.
Deep Learning And Blockchain Technology For Smart And Sustainable Cities
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Author : Subramaniyaswamy V
language : en
Publisher: CRC Press
Release Date : 2025-05-22
Deep Learning And Blockchain Technology For Smart And Sustainable Cities written by Subramaniyaswamy V and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-05-22 with Computers categories.
In this digital era, a smart city can become an intelligent society by using advances in emerging technologies. Specifically, the rapid adoption of deep learning (DL) in fusion with blockchain technology has led to a new digital smart city ecosystem. A broad spectrum of DL and blockchain applications promises solutions for problems in areas ranging from risk management and financial services to cryptocurrency to public and social services. Furthermore, the convergence of artificial intelligence and blockchain technology is revolutionizing the smart city network architecture to build sustainable ecosystems. However, these advances in technology bring both opportunities and challenges in creating sustainable smart cities. To help planners and developers to meet these challenges and exploit these opportunities, Deep Learning and Blockchain Technology for Smart and Sustainable Cities takes a deep dive into the technologies and applications that enable smart and sustainable cities. It provides a comprehensive literature review of the security issues and problems that impact the deployment of blockchain systems in smart cities. It presents a detailed discussion of key factors in the convergence of blockchain and DL technologies that help form sustainable smart societies. This book also discusses blockchain security enhancement solutions and summarizes main key points necessary for developing various blockchain and DL‐based intelligent transportation systems. This book concludes with a discussion of open issues and future research direction.These include new security suggestions and guidelines for a sustainable smart city ecosystem. Also discussed is 6G‐enabled DL and blockchain in real‐time applications.
Deep Learning Applications Volume 3
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Author : M. Arif Wani
language : en
Publisher: Springer Nature
Release Date : 2021-11-12
Deep Learning Applications Volume 3 written by M. Arif Wani and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-11-12 with Technology & Engineering categories.
This book presents a compilation of extended version of selected papers from the 19th IEEE International Conference on Machine Learning and Applications (IEEE ICMLA 2020) and focuses on deep learning networks in applications such as pneumonia detection in chest X-ray images, object detection and classification, RGB and depth image fusion, NLP tasks, dimensionality estimation, time series forecasting, building electric power grid for controllable energy resources, guiding charities in maximizing donations, and robotic control in industrial environments. Novel ways of using convolutional neural networks, recurrent neural network, autoencoder, deep evidential active learning, deep rapid class augmentation techniques, BERT models, multi-task learning networks, model compression and acceleration techniques, and conditional Feature Augmented and Transformed GAN (cFAT-GAN) for the above applications are covered in this book. Readers will find insights to help them realize novel ways of using deep learning architectures and algorithms in real-world applications and contexts, making the book an essential reference guide for academic researchers, professionals, software engineers in the industry, and innovative product developers.
Artificial Intelligence And Machine Learning In Management Science Emerging Research And Applications
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Author : Ms. Meenu Shukla
language : en
Publisher: NC Publishers
Release Date : 2025-07-01
Artificial Intelligence And Machine Learning In Management Science Emerging Research And Applications written by Ms. Meenu Shukla and has been published by NC Publishers this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-07-01 with Antiques & Collectibles categories.
As the global business environment continues to evolve, artificial intelligence (AI) and machine learning (ML) have emerged as powerful tools for enhancing decision-making, optimizing operations, and fostering innovation across various sectors. This book brings together a collection of scholarly contributions from researchers and practitioners who are at the forefront of integrating these technologies with managerial practices. The chapters offer both theoretical insights and practical applications, covering domains such as operations research, strategic planning, supply chain optimization, marketing analytics, financial forecasting, and human resource management.
Operational Research In The Era Of Digital Transformation And Business Analytics
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Author : Nikolaos F. Matsatsinis
language : en
Publisher: Springer Nature
Release Date : 2023-04-04
Operational Research In The Era Of Digital Transformation And Business Analytics written by Nikolaos F. Matsatsinis and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-04-04 with Business & Economics categories.
This proceedings volume presents new methods and applications in Operational Research and Management Science with a special focus on Business Analytics. Featuring selected contributions from the XIV Balkan Conference on Operational Research held in Thessaloniki, Greece in 2020 (BALCOR 2020), it addresses applications and methodological tools or techniques in various areas of Operational Research, such as agent-based modelling, big data and business analytics, data envelopment analysis, data mining, decision support systems, fuzzy systems, game theory, heuristics, metaheuristics and nature inspired optimization algorithms, linear and nonlinear programming, machine learning, multiple criteria decision analysis, network design and optimization, queuing theory, simulation and statistics.
Digital Transformation For Sustainability
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Author : Jorge Marx Gómez
language : en
Publisher: Springer Nature
Release Date : 2022-12-01
Digital Transformation For Sustainability written by Jorge Marx Gómez and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-12-01 with Business & Economics categories.
This book presents case studies to analyse the relationship between sustainability – environmental, social, institutional and economic – and digital innovation. The respective contributions offer a contextualisation of the main present and future trends concerning these two elements, and present analyses from economic, technical, managerial, and social perspectives alike. The individual sections of the book focus on interactions between sustainability and digital innovation in existing organisations and highlight the new opportunities, challenges and threats that may emerge as a result. The contributions are mainly based on case studies and research conducted in Europe and Africa, with a few focusing on Southeast Asia and Central America, and were prepared by experts in the fields of Information Systems, Computer Science, Social Development, and Economics.
Computational Intelligence In Industry 4 0 And 5 0 Applications
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Author : JOSEPH BAMIDELE AWOTUNDE
language : en
Publisher: CRC Press
Release Date : 2025-02-06
Computational Intelligence In Industry 4 0 And 5 0 Applications written by JOSEPH BAMIDELE AWOTUNDE and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-02-06 with Computers categories.
Industry 4.0 and 5.0 applications will revolutionize production, enabling smart manufacturing machines to interact with their environments. These machines will become self-aware, self-learning, and capable of real-time data interpretation for self-diagnosis and prevention of production issues. They will also self-calibrate and prioritize tasks to enhance production quality and efficiency. Computational Intelligence in Industry 4.0 and 5.0 Applications examines applications that merge three key disciplines: computational intelligence (CI), Industry 4.0, and Industry 5.0. It presents solutions using Industrial Internet of Things (IIoT) technologies, augmented by CI-based techniques, modeling, controls, estimations, applications, systems, and future scopes. These applications use data from smart sensors, processed through enhanced CI methods, to make smart automation more effective. Industry 4.0 integrates data and intelligent automation into manufacturing, using technologies like CI, the IoT, the IIoT, and cloud computing. It transforms data into actionable insights for decision-making and process optimization, essential for modern competitive businesses managing high-speed data integration in production processes. Currently, Industries 4.0 and 5.0 are undergoing significant transformations due to advances in applying artificial intelligence (AI), big data analytics, telecommunication technologies, and control theory. These applications are increasingly multidisciplinary, integrating mechanical, control, and information technologies. However, they face such technical challenges as parametric uncertainties, external disturbances, sensor noise, and mechanical failures. To address these, this book examines such CI technologies as fuzzy logic, neural networks, and reinforcement learning and their application to modeling, control, and estimation. It also covers recent advancements in IIoT sensors, microcontrollers, and big data analytics that further enhance CI-based solutions in Industry 4.0 and 5.0 systems.
Artificial Intelligence And Machine Learning For Enhancing Resilience Concepts Applications And Future Directions
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Author :
language : en
Publisher: Deep Science Publishing
Release Date : 2025-07-01
Artificial Intelligence And Machine Learning For Enhancing Resilience Concepts Applications And Future Directions written by and has been published by Deep Science Publishing this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-07-01 with Computers categories.
As contemporary societies face unprecedented challenges such as mounting mental health issues, environmental crises, and socioeconomic insecurity, the urgency of developing objective, scalable, and dynamic methodologies to study resilience has never been greater. This book arises at the intersection of cutting-edge technology and human insight. It focuses on the possibility for AI and ML to transform resilience assessment, prediction, and interventions across the individual, organizational, and ecological levels. The chapters included in this book represent an organized synthesis of cutting-edge science, pragmatic applications, and prospective potential. With machine learning algorithms to estimate psychological resilience and AI-based models for climate change adaptation and ecosystem management, this book demonstrates the rich innovations that are emerging at the cross-sector of technology and resilience science. Perhaps most importantly, this book does not gloss over the urgent ethical, technical, and regulatory issues that arise when AI is introduced to sensitive topics such as mental health and environmental management. Questions about data privacy, algorithmic bias, model interpretability, and equitable technology deployment are thoroughly investigated, providing lessons learned and suggestions for moving ahead. A significant strength of this work is its global focus. Showcasing work from contributors of various methodologies and regions provides the latest views on new methodologies, strategies for practical implementation, and on what still needs to be invented. This guarantees that the publication engages with the messy socio-cultural and environmental contexts in which these interventions work and that it doesn’t just mirror technological possibilities. For academicians, practitioners, technologists, and policymakers, this book is both a fundamental reference and an outlook resource. It provides: Holistic examination of AI and ML in the context of psychological, organizational, and ecological resilience. In-depth reviews on methodological innovations, such as deep learning, natural language processing, and sensor-based assessments. Unprecedented appraisals of barriers to implementation, with ethical and regulatory considerations. We trust that this book will inspire conversation, fuel innovation, and support a future in which technology supplements, rather than replaces, human ability to adapt, recover, and flourish. We encourage readers to critique the content, to reflect on how AI, ML, and resilience intersect in their particular contexts, and to join us in shaping a future where technological and human resilience evolve together.