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Recent Theories and Applications for Multi-Criteria Decision-Making
In an increasingly complex world, decision-makers face the challenge of optimizing multiple conflicting objectives across various scenarios. Multi-Criteria Decision-Making (MCDM) techniques have emerged as essential tools for addressing these challenges and offer methods to evaluate alternatives and minimize subjectivity. As the landscape of MCDM evolves with new approaches such as fuzzy set theory, rough set theory, and neutrosophic set theory, decision-making in situations involving varied and complex data becomes more reliable and consistent. Recent Theories and Applications for Multi-Criteria Decision-Making explores the latest trends and innovations in this field. The book includes thought-provoking input from renowned researchers who cover case studies, real-world applications, challenges, and cutting-edge methodologies. It highlights the integration of advanced technologies such as AI, big data, and IoT with MCDM, while offering practical insights into strategic decision-making in today's digital age. This volume serves as a valuable resource for scholars, practitioners, and researchers keen to improve their decision-making capacity.
AI-Driven Innovation in Healthcare Data Analytics
As the healthcare industry continues to rely on data to enhance patient outcomes and streamline operations, artificial intelligence (AI) becomes a powerful tool for complex dataset analysis using improved speed and accuracy. From predictive modeling in disease outbreak management to personalized treatment plans for individual patient profiles, AI technologies are reshaping clinical decision-making and resource allocation. Harnessing the potential of machine learning and advanced analytics may allow healthcare providers to uncover insights that drive innovation, improve patient care, and optimize operational efficiency. AI-Driven Innovation in Healthcare Data Analytics explores the intersection of AI and healthcare data analytics. It examines the application of AI-driven techniques, including machine learning, deep learning, and data mining, in addressing complex challenges in healthcare management. This book covers topics such as data science, medical diagnosis, and patient care, and is a useful resource for healthcare professionals, data scientists, computer engineers, business owners, academicians, and researchers.
Applications of New Technology in Operations and Supply Chain Management
The International Data Corporation (IDC) has unveiled a series of transformative predictions to reshape operations and supply chain management, leading companies to re-assess their processes. Applications of New Technology in Operations and Supply Chain Management offers an in-depth exploration of how emerging technologies are positioned to revolutionize the way businesses execute and coordinate their operations. The book delves into the adoption of digital technologies, the shift to cloud technology, and the emergence of real-time operational insights that can be accessed from anywhere. For instance, 2026 ushers in integrating digital tools for measuring carbon footprints and the increased use of robots in unconventional domains, such as remote inspection and maintenance. By 2027, augmented reality technology will take center stage, reducing operator and field worker errors. Furthermore, remote operations embrace satellite-based artificial intelligence or machine learning technologies, revolutionizing data collection and analysis at the edge.
Transportation, Logistics, and Supply Chain Management in Home Healthcare: Emerging Research and Opportunities
With advancing technology and the digitization of the modern era, businesses are required to adopt the latest innovations computer science and information technology have to offer. The field of home healthcare must utilize the finest available operations management systems in order to remain relevant in a globalized world while also providing the best treatment possible to its patients. Transportation, Logistics, and Supply Chain Management in Home Healthcare: Emerging Research and Opportunities is an essential reference source that provides theoretical and empirical research on logistics management and transportation and scheduling routing and their applications in home healthcare and logistics. While highlighting topics such as hybrid energy, scheduling optimization, and forecasting techniques, this book is ideally designed for outpatient doctors and nurses, transportation professionals, logisticians, home healthcare managers, computer scientists, logistic engineers, health practitioners, academicians, researchers, and students.
Machine Learning for Societal Improvement, Modernization, and Progress
Learning has been fundamental to the growth and evolution of humanity and civilization. The same concepts of learning, applied to the tasks that machines can perform, are having a similar effect now. Machine learning is evolving computation and its applications like never before. It is now widely recognized that machine learning is playing a similar role to electricity in the late 19th and early 20th centuries in modernizing the world. From simple high school science projects to large-scale radio astronomy, machine learning has revolutionized it all—however, a few of the applications clearly stand out as transforming the world and opening up a new era. Machine Learning for Societal Improve...
Advances in Computational Intelligence
This two-volume set LNCS 6691 and 6692 constitutes the refereed proceedings of the 11th International Work-Conference on Artificial Neural Networks, IWANN 2011, held in Torremolinos-Málaga, Spain, in June 2011. The 154 revised papers were carefully reviewed and selected from 202 submissions for presentation in two volumes. The first volume includes 69 papers organized in topical sections on mathematical and theoretical methods in computational intelligence; learning and adaptation; bio-inspired systems and neuro-engineering; hybrid intelligent systems; applications of computational intelligence; new applications of brain-computer interfaces; optimization algorithms in graphic processing units; computing languages with bio-inspired devices and multi-agent systems; computational intelligence in multimedia processing; and biologically plausible spiking neural processing.
Supply Chain Social Sustainability for Manufacturing
This book highlights a number of social sustainability issues at different stages of the supply chain, and demonstrates how these issues can be addressed by adopting social sustainability practices in the manufacturing supply chain. In the wake of emerging social issues in developing countries, research on social sustainability has gained importance for academics and practitioners alike. The three distinguishable social sustainability dimensions in manufacturing that emerge as a result of this research provide insights for supply chain managers and practitioners who might otherwise be unaware of what constitutes social sustainability. A better understanding allows supply chain managers to ad...
Integrating Machine Learning Into HPC-Based Simulations and Analytics
Researchers are increasingly using machine learning (ML) models to analyze data and simulate complex systems and phenomena. Small-scale computing systems used for training, validation, and testing of these ML models are no longer sufficient for grand-challenge problems characterized by large volumes of data generated at a much higher rate than before, surpassing by far the computing capabilities currently available in many cyberinfrastructure platforms. By associating high-performance computing (HPC) with ML environments, scientists and engineers would be able to enhance not only the scalability but also the performance of their predictive ML models. The Handbook of Research on Integrating Machine Learning Into HPC-Based Simulations and Analytics presents recent research efforts in designing and using ML techniques on HPC systems and discusses some of the results achieved thus far by cutting-edge relevant contributions. Covering topics such as data analytics, deep learning, and networking, this major reference work is ideal for computer scientists, academicians, engineers, researchers, scholars, practitioners, librarians, instructors, and students.
Preservation and Restoration Techniques for Ancient Egyptian Textiles
Preservation and restoration techniques are essential in maintaining the integrity of historic artifacts, including textiles, and specifically, the materials of the textile industry in Egypt. The technologies, methods, and advancements in preserving these ancient artifacts are growing areas of research and important factors in increasing knowledge of the conservation process. By offering and increasing the knowledge field with practical applications of preservation and restoration techniques both old and new, the industry will continue to advance. Preservation and Restoration Techniques for Ancient Egyptian Textiles provides critical research on the history, technology, and materials of the ...
Multiple Attribute Decision Making
This mono graph is intended for an advanced undergraduate or graduate course as weIl as for the researchers who want a compilation of developments in this rapidly growing field of operations research. This is a sequel to our previous work entitled "Multiple Objective Decision Making--Methods and Applications: A State-of-the-Art Survey," (No. 164 of the Lecture Notes). The literature on methods and applications of Multiple Attribute Decision Making (MADM) has been reviewed and classified systematically. This study provides readers with a capsule look into the existing methods, their char acteristics, and applicability to analysis of MADM problems. The basic MADM concepts are defined and a sta...
