- Table View
- List View
Multi-criteria Evaluation of Assortment and Suppliers in E-commerce: Operational Efficiency and Advantage (Palgrave Studies in Logistics and Supply Chain Management)
by Grzegorz ChodakIn a rapidly changing e-commerce environment, effective supplier selection and inventory management are critical to maintaining competitive advantage and operational efficiency. This book introduces innovative multi-criteria decision-making models (MCDMs) and improved inventory classification systems tailored specifically for online retail environments. Combining theory and research-based case studies, the book highlights the importance of comprehensive criteria in supplier and assortment evaluation and the application of artificial intelligence in optimising decision models. It provides valuable insights and practical solutions for improving supply chain performance and inventory management in the e-commerce sector. It will be of great interest to scholars and students of logistics and e-commerce and supply chain management.
Multi-dimensional Optical Storage
by Duanyi XuThis book presents principles and applications to expand the storage space from 2-D to 3-D and even multi-D, including gray scale, color (light with different wavelength), polarization and coherence of light. These actualize the improvements of density, capacity and data transfer rate for optical data storage. Moreover, the applied implementation technologies to make mass data storage devices are described systematically. Some new mediums, which have linear absorption characteristics for different wavelength and intensity to light with high sensitivity, are introduced for multi-wavelength and multi-level optical storage. This book can serve as a useful reference for researchers, engineers, graduate and undergraduate students in material science, information science and optics.
Multi-dimensional Urban Sensing Using Crowdsensing Data (Data Analytics)
by Panlong Yang Fu Xiao Chaocan Xiang Xiaochen FanChaocan Xiang is an Associate Professor at the College of Computer Science, Chongqing University, China. He received his bachelor’s degree and Ph.D. from Nanjing Institute of Communication Engineering, China, in 2009 and 2014, respectively. He subsequently studied at the University of Michigan-Ann Arbor in 2017 (supervised by Prof. Kang G. Shin, IEEE Life Fellow, ACM Fellow). His research interests mainly include UAVs/vehicle-based crowdsensing, urban computing, Internet of Things, Artificial Intelligence, and big data. He has published more than 50 papers, including over 20 in leading venues such as IEEE Transactions on Mobile Computing, IEEE Transactions on Parallel and Distributed Systems, IEEE INFOCOM, and ACM Ubicomp. He has received a best paper award and a best poster award at two international conferences. Panlong Yang is a full Professor at the University of Science and Technology of China. He has been supported by the NSF Jiangsu through a Distinguished Young Scholarship and was honored as a CCF Distinguished Lecturer in 2015. He has published over 150 papers, including 40 in CCF Class A. Since 2012, he has supervised 14 master’s and Ph.D. candidates, including two excellent dissertation winners in Jiangsu Province and the PLA education system. He has been supported by the National Key Development Project and NSFC projects. He has nominated by ACM MobiCom 2009 for the best demo honored mention awards, and won best paper awards at the IEEE MSN and MASS. He has served as general chair of BigCom and TPC chair of IEEE MSN. In addition, he has served as a TPC member of INFOCOM (CCF Class A) and an associate editor of the Journal of Communication of China. He is a Senior Member of the IEEE (2019). Fu Xiao received his Ph.D. in Computer Science and Technology from the Nanjing University of Science and Technology, Nanjing, China, in 2007. He is currently a Professor and Dean of the School of Computer, Nanjing University of Posts and Telecommunications. He has authored more than 60 papers in respected conference proceedings and journals, including IEEE INFOCOM, ACM Mobihoc, IEEE JASC, IEEE/ACM ToN, IEEE TPDS, IEEE TMC, etc. His main research interest is in the Internet of Things. He is a member of the IEEE Computer Society and the Association for Computing Machinery. Xiaochen Fan received his B.S. degree in Computer Science from Beijing Institute of Technology, Beijing, China, in 2013, and his Ph.D. from the University of Technology Sydney, NSW, Australia, in 2021. His research interests include mobile/pervasive computing, deep learning, and Internet of Things (IoT). He has published over 25 peer-reviewed papers in high-quality journals and IEEE/ACM international conference proceedings.
Multi-disciplinary Trends in Artificial Intelligence
by Chattrakul Sombattheera Frieder Stolzenburg Fangzhen Lin Abhaya NayakThis volume constitutes the refereed proceedings of the 5th Multi-disciplinary International Workshop On Artificial Intelligence, MIWAI 2011, held in Hyderabad, India, in December 2011. The 38 revised full papers presented were carefully reviewed and selected from 71 submissions. The papers cover the multifarious nature of the Artificial Intelligence research domain, ranging from theoretical to real world applications and address topics such as agent-based simulation, agent-oriented software engineering, agents and Web services, agent-based electronic commerce, auctions and markets, AI in video games, computer vision, constraint satisfaction, data mining, decision theory, distributed AI, e-commerce and AI, game theory, internet/www intelligence, industrial applications of AI, intelligent tutoring, knowledge representation and reasoning, machine learning, multi-agent planning and learning, multi-agent systems and their applications, multi-agent systems and evolving intelligence, natural language processing, neural networks, planning and scheduling, robotics, uncertainty in AI, and Web services.
Multi-disciplinary Trends in Artificial Intelligence
by Xianghan Zheng Antonis BikakisThis book constitutes the refereed conference proceedings of the 9th International Conference on Multi-disciplinary Trends in Artificial Intelligence, MIWAI 2015, held in Fuzhou, China, in November 2015. The 30 revised full papers presented together with 12 short papers were carefully reviewed and selected from 83 submissions. The papers feature a wide range of topics covering knowledge representation, reasoning, and management; multi-agent systems; data mining and machine learning; computer vision; robotics; AI in bioinformatics; AI in security and networks; and other AI applications.
Multi-disciplinary Trends in Artificial Intelligence: 10th International Workshop, MIWAI 2016, Chiang Mai, Thailand, December 7-9, 2016, Proceedings (Lecture Notes in Computer Science #10053)
by Chattrakul Sombattheera, Frieder Stolzenburg, Fangzhen Lin and Abhaya NayakThis book constitutes the refereed conference proceedings of the 10th International Conference on Multi-disciplinary Trends in Artificial Intelligence, MIWAI 2016, held in Chiang Mai, Thailand, in December 2016. The 22 revised full papers presented together with 5 short papers and 2 abstracts of invited talks were carefully reviewed and selected from 50 submissions. The workshop solicits papers from all areas of AI including cognitive science; computational intelligence; computational philosophy; game theory; machine learning; multi-agent systems; natural language; representation and reasoning; speech; vision and the web; as well as applications of AI in big data; bioinformatics; biometrics; decision support; e-commerce; image processing; analysis and retrieval; industrial applications; knowledge management; privacy; recommender systems; security; software engineering; spam filtering; surveillance; telecommunications; and web services.
Multi-disciplinary Trends in Artificial Intelligence: 12th International Conference, MIWAI 2018, Hanoi, Vietnam, November 18–20, 2018, Proceedings (Lecture Notes in Computer Science #11248)
by Rainer Malaka Manasawee Kaenampornpan Duc Dung Nguyen Nicolas SchwindThis book constitutes the refereed conference proceedings of the 12th International Conference on Multi-disciplinary Trends in Artificial Intelligence, MIWAI 2018, held in Hanoi, Vietnam, in November 2018. The 16 full papers presented together with 9 short papers were carefully reviewed and selected from 65 submissions. They are organized in the following topical sections: control, planning and scheduling, pattern recognition, knowledge mining, software applications, strategy games and others.
Multi-disciplinary Trends in Artificial Intelligence: 13th International Conference, MIWAI 2019, Kuala Lumpur, Malaysia, November 17–19, 2019, Proceedings (Lecture Notes in Computer Science #11909)
by Kok Wai Wong Rapeeporn ChamchongThis book constitutes the refereed proceedings of the 13th International Conference on Multi-disciplinary Trends in Artificial Intelligence, MIWAI 2019, held in Kuala Lumpur, Malaysia, in November 2019.The 19 full papers and 6 short papers presented were carefully reviewed and selected from 53 submissions. They cover a wide range of topics in theory, methods, and tools in AI sub-areas such as cognitive science, computational philosophy, computational intelligence, game theory, machine learning, multi-agent systems, natural language, representation and reasoning, data mining, speech, computer vision and the Web as well as their applications in big data, bioinformatics, biometrics, decision support, knowledge management, privacy, recommender systems, security, software engineering, spam filtering, surveillance, telecommunications, Web services, and IoT.
Multi-disciplinary Trends in Artificial Intelligence: 14th International Conference, MIWAI 2021, Virtual Event, July 2–3, 2021, Proceedings (Lecture Notes in Computer Science #12832)
by Junmo Kim Phatthanaphong Chomphuwiset Pornntiwa PawaraThis book constitutes the refereed proceedings of the 14th International Conference on Multi-disciplinary Trends in Artificial Intelligence, MIWAI 2021, held online in July 2021.The 13 full papers and 3 short papers presented were carefully reviewed and selected from 33 submissions. They cover a wide range of topics in theory, methods, and tools in AI sub-areas such as cognitive science, computational philosophy, computational intelligence, game theory, machine learning, multi-agent systems, natural language, representation and reasoning, data mining, speech, computer vision and the Web as well as their applications in big data, bioinformatics, biometrics, decision support, knowledge management, privacy, recommender systems, security, software engineering, spam filtering, surveillance, telecommunications, Web services, and IoT.
Multi-disciplinary Trends in Artificial Intelligence: 15th International Conference, MIWAI 2022, Virtual Event, November 17–19, 2022, Proceedings (Lecture Notes in Computer Science #13651)
by Olarik Surinta Kevin Kam Fung YuenThis book constitutes the refereed proceedings of the 15th International Conference on Multi-disciplinary Trends in Artificial Intelligence, MIWAI 2022, held online on November 17–19, 2022.The 14 full papers and 5 short papers presented were carefully reviewed and selected from 42 submissions.
Multi-disciplinary Trends in Artificial Intelligence: 16th International Conference, MIWAI 2023, Hyderabad, India, July 21–22, 2023, Proceedings (Lecture Notes in Computer Science #14078)
by Pawan Lingras Raghava Morusupalli Teja Santosh Dandibhotla Vani Vathsala Atluri David Windridge Venkateswara Rao KomatiThis book constitutes the refereed proceedings of the 16th International Conference on Multi-disciplinary Trends in Artificial Intelligence, MIWAI 2023, held in Hyderabad, India, during July 21–22, 2023, 2022.The 47 full papers and 24 short papers included in this book were carefully reviewed and selected from 245 submissions. They were organized in topical sections as follows: digital life: an advent of transhumanism; traveling salesperson problem; and centrality measures based heuristics for perfect awareness problem in social networks.
Multi-disciplinary Trends in Artificial Intelligence: 17th International Conference, MIWAI 2024, Pattaya, Thailand, November 11–15, 2024, Proceedings, Part I (Lecture Notes in Computer Science #15431)
by Chattrakul Sombattheera Jun Pang Paul WengThe two-volume set LNAI 15431 and 15432 constitutes the refereed proceedings of the 17th International Conference on Multi-disciplinary Trends in Artificial Intelligence, MIWAI 2024, held in Pattaya, Thailand, during November 11–15, 2024. The 68 full papers presented in these proceedings were carefully reviewed and selected from 147 submissions. The papers focus on various topics in AI and its applications, such as deep learning, machine learning, computer vision, pattern recognition, and natural language processing.
Multi-disciplinary Trends in Artificial Intelligence: 17th International Conference, MIWAI 2024, Pattaya, Thailand, November 11–15, 2024, Proceedings, Part II (Lecture Notes in Computer Science #15432)
by Chattrakul Sombattheera Jun Pang Paul WengThe two-volume set LNAI 15431 and 15432 constitutes the refereed proceedings of the 17th International Conference on Multi-disciplinary Trends in Artificial Intelligence, MIWAI 2024, held in Pattaya, Thailand, during November 11–15, 2024. The 68 full papers presented in these proceedings were carefully reviewed and selected from 147 submissions. The papers focus on various topics in AI and its applications, such as deep learning, machine learning, computer vision, pattern recognition, and natural language processing.
Multi-faceted Deep Learning: Models and Data
by Jenny Benois-Pineau Akka ZemmariThis book covers a large set of methods in the field of Artificial Intelligence - Deep Learning applied to real-world problems. The fundamentals of the Deep Learning approach and different types of Deep Neural Networks (DNNs) are first summarized in this book, which offers a comprehensive preamble for further problem–oriented chapters. The most interesting and open problems of machine learning in the framework of Deep Learning are discussed in this book and solutions are proposed. This book illustrates how to implement the zero-shot learning with Deep Neural Network Classifiers, which require a large amount of training data. The lack of annotated training data naturally pushes the researchers to implement low supervision algorithms. Metric learning is a long-term research but in the framework of Deep Learning approaches, it gets freshness and originality. Fine-grained classification with a low inter-class variability is a difficult problem for any classification tasks. This book presents how it is solved, by using different modalities and attention mechanisms in 3D convolutional networks. Researchers focused on Machine Learning, Deep learning, Multimedia and Computer Vision will want to buy this book. Advanced level students studying computer science within these topic areas will also find this book useful.
Multi-finger Haptic Interaction
by Manuel Ferre Ignacio GalianaMulti-finger Haptic Interaction presents a panorama of technologies and methods for multi-finger haptic interaction, together with an analysis of the benefits and implications of adding multiple-fingers to haptic applications. Research topics covered include: design and control of advanced haptic devices;multi-contact point simulation algorithms;interaction techniques and implications in human perception when interacting with multiple fingers.These multi-disciplinary results are integrated into applications such as medical simulators for training manual skills, simulators for virtual prototyping and precise manipulations in remote environments. Multi-finger Haptic Interaction presents the current and potential applications that can be developed with these systems, and details the systems' complexity. The research is focused on enhancing haptic interaction by providing multiple contact points to the user. This state-of-the-art volume is oriented towards researchers who are involved in haptic device design, rendering methods and perception studies, as well as readers from different disciplines who are interested in applying multi-finger haptic technologies and methods to their field of interest.
Multi-hop Routing in Wireless Sensor Networks
by Shalli Rani Syed Hassan AhmedThis brief provides an overview of recent developments in multi-hop routing protocols for Wireless Sensor Networks (WSNs). It introduces the various classifications of routing protocols and lists the pros and cons of each category, going beyond the conceptual overview of routing classifications offered in other books. Recently many researchers have proposed numerous multi-hop routing protocols and thereby created a need for a book that provides its readers with an up-to-date road map of this research paradigm. The authors present some of the most relevant results achieved by applying an algorithmic approach to the research on multi-hop routing protocols. The book covers measurements, experiences and lessons learned from the implementation of multi-hop communication prototypes. Furthermore, it describes future research challenges and as such serves as a useful guide for students and researchers alike.
Multi-modal Hash Learning: Efficient Multimedia Retrieval and Recommendations (Synthesis Lectures on Information Concepts, Retrieval, and Services)
by Lei Zhu Jingjing Li Weili GuanThis book systemically presents key concepts of multi-modal hashing technology, recent advances on large-scale efficient multimedia search and recommendation, and recent achievements in multimedia indexing technology. With the explosive growth of multimedia contents, multimedia retrieval is currently facing unprecedented challenges in both storage cost and retrieval speed. The multi-modal hashing technique can project high-dimensional data into compact binary hash codes. With it, the most time-consuming semantic similarity computation during the multimedia retrieval process can be significantly accelerated with fast Hamming distance computation, and meanwhile the storage cost can be reduced greatly by the binary embedding. The authors introduce the categorization of existing multi-modal hashing methods according to various metrics and datasets. The authors also collect recent multi-modal hashing techniques and describe the motivation, objective formulations, and optimization steps for context-aware hashing methods based on the tag-semantics transfer.
Multi-objective Evolutionary Optimisation for Product Design and Manufacturing
by Lihui Wang Kalyanmoy Deb Amos H. NgWith the increasing complexity and dynamism in today's product design and manufacturing, more optimal, robust and practical approaches and systems are needed to support product design and manufacturing activities. Multi-objective Evolutionary Optimisation for Product Design and Manufacturing presents a focused collection of quality chapters on state-of-the-art research efforts in multi-objective evolutionary optimisation, as well as their practical applications to integrated product design and manufacturing. Multi-objective Evolutionary Optimisation for Product Design and Manufacturing consists of two major sections. The first presents a broad-based review of the key areas of research in multi-objective evolutionary optimisation. The second gives in-depth treatments of selected methodologies and systems in intelligent design and integrated manufacturing. Recent developments and innovations in multi-objective evolutionary optimisation make Multi-objective Evolutionary Optimisation for Product Design and Manufacturing a useful text for a broad readership, from academic researchers to practicing engineers.
Multi-objective Optimization Techniques in Engineering Applications: Advanced Methods for Solving Complex Engineering Problems (Studies in Computational Intelligence #1184)
by Lagouge K. TartibuThis essential book bridges theory and practice, exploring advanced multi-objective optimization methods applied across engineering fields like manufacturing, renewable energy, and thermal management. This book presents a comprehensive, hands-on guide for engineers, researchers, and students seeking to harness the power of optimization in diverse, real-world scenarios. Through expertly crafted chapters, this book illuminates the strengths of state-of-the-art metaheuristic algorithms—such as the Harris hawk optimization, whale optimization, gray wolf optimization, sunflower optimization, imperialistic competitive optimization, jaya optimization, thermal exchange optimization, grasshopper optimization, and cuckoo search optimization. These algorithms tackle complex, high-dimensional challenges, giving readers invaluable tools to boost performance and efficiency. Case studies breathe life into these methods, showcasing their adaptability in systems with multiple conflicting objectives. Readers will find practical MATLAB and GAMS models, enabling immediate experimentation and application. In an era where efficiency and sustainability are paramount, this book equips engineers to solve today’s toughest optimization problems, making it an indispensable resource for those committed to innovation. Whether focused on energy systems, structural design, or computational mechanics, this book serves as a trusted guide to achieving breakthrough solutions across multiple disciplines.
Multi-objective Swarm Intelligence
by Satchidananda Dehuri Alok Kumar Jagadev Mrutyunjaya PandaThe aim of this book is to understand the state-of-the-art theoretical and practical advances of swarm intelligence. It comprises seven contemporary relevant chapters. In chapter 1, a review of Bacteria Foraging Optimization (BFO) techniques for both single and multiple criterions problem is presented. A survey on swarm intelligence for multiple and many objectives optimization is presented in chapter 2 along with a topical study on EEG signal analysis. Without compromising the extensive simulation study, a comparative study of variants of MOPSO is provided in chapter 3. Intractable problems like subset and job scheduling problems are discussed in chapters 4 and 7 by different hybrid swarm intelligence techniques. An attempt to study image enhancement by ant colony optimization is made in chapter 5. Finally, chapter 7 covers the aspect of uncertainty in data by hybrid PSO.
Multi-objective, Multi-class and Multi-label Data Classification with Class Imbalance: Theory and Practices (Springer Tracts in Nature-Inspired Computing)
by Sanjay Chakraborty Lopamudra DeyThis book explores intricate world of data classification with 'Multi-Objective, Multi-Class, and Multi-Label Data Classification.' This book studies sophisticated methods and strategies for working with complicated data sets, tackling the difficulties of various classes, many objectives, and complicated labelling tasks. This resource fosters a deeper grasp of multi-dimensional data analysis in today's data-driven world by providing readers with the skills and insights needed to navigate the subtleties of modern classification jobs, from algorithmic techniques to practical applications.
Multi-photon Quantum Secure Communication (Signals and Communication Technology)
by Pramode K. Verma Mayssaa El Rifai Kam Wai ChanThis book explores alternative ways of accomplishing secure information transfer with incoherent multi-photon pulses in contrast to conventional Quantum Key Distribution techniques. Most of the techniques presented in this book do not need conventional encryption. Furthermore, the book presents a technique whereby any symmetric key can be securely transferred using the polarization channel of an optical fiber for conventional data encryption. The work presented in this book has largely been practically realized, albeit in a laboratory environment, to offer proof of concept rather than building a rugged instrument that can withstand the rigors of a commercial environment.
Multi-run Memory Tests for Pattern Sensitive Faults
by Ireneusz MrozekThis book describes efficient techniques for production testing as well as for periodic maintenance testing (specifically in terms of multi-cell faults) in modern semiconductor memory. The author discusses background selection and address reordering algorithms in multi-run transparent march testing processes. Formal methods for multi-run test generation and many solutions to increase their efficiency are described in detail. All methods presented ideas are verified by both analytical investigations and numerical simulations.Provides the first book related exclusively to the problem of multi-cell fault detection by multi-run tests in memory testing process;Presents practical algorithms for design and implementation of efficient multi-run tests;Demonstrates methods verified by analytical and experimental investigations.
Multi-scale Simulation of Composite Materials: Results from the Project MuSiKo (Mathematical Engineering)
by Stefan Diebels Sergej RjasanowDue to their high stiffness and strength and their good processing properties short fibre reinforced thermoplastics are well-established construction materials.Up to now, simulation of engineering parts consisting of short fibre reinforced thermoplastics has often been based on macroscopic phenomenological models, but deformations, damage and failure of composite materials strongly depend on their microstructure. The typical modes of failure of short fibre thermoplastics enriched with glass fibres are matrix failure, rupture of fibres and delamination, and pure macroscopic consideration is not sufficient to predict those effects. The typical predictive phenomenological models are complex and only available for very special failures. A quantitative prediction on how failure will change depending on the content and orientation of the fibres is generally not possible, and the direct involvement of the above effects in a numerical simulation requires multi-scale modelling.One the one hand, this makes it possible to take into account the properties of the matrix material and the fibre material, the microstructure of the composite in terms of fibre content, fibre orientation and shape as well as the properties of the interface between fibres and matrix. On the other hand, the multi-scale approach links these local properties to the global behaviour and forms the basis for the dimensioning and design of engineering components. Furthermore, multi-scale numerical simulations are required to allow efficient solution of the models when investigating three-dimensional problems of dimensioning engineering parts.Bringing together mathematical modelling, materials mechanics, numerical methods and experimental engineering, this book provides a unique overview of multi-scale modelling approaches, multi-scale simulations and experimental investigations of short fibre reinforced thermoplastics. The first chapters focus on two principal subjects: the mathematical and mechanical models governing composite properties and damage description. The subsequent chapters present numerical algorithms based on the Finite Element Method and the Boundary Element Method, both of which make explicit use of the composite’s microstructure. Further, the results of the numerical simulations are shown and compared to experimental results.Lastly, the book investigates deformation and failure of composite materials experimentally, explaining the applied methods and presenting the results for different volume fractions of fibres.This book is a valuable resource for applied mathematics, theoretical and experimental mechanical engineers as well as engineers in industry dealing with modelling and simulation of short fibre reinforced composites.
Multi-sensor Fusion for Autonomous Driving
by Jun Li Li Wang Huaping Liu Mo Zhou Zhiwei Li Xinyu Zhang Zhenhong ZouAlthough sensor fusion is an essential prerequisite for autonomous driving, it entails a number of challenges and potential risks. For example, the commonly used deep fusion networks are lacking in interpretability and robustness. To address these fundamental issues, this book introduces the mechanism of deep fusion models from the perspective of uncertainty and models the initial risks in order to create a robust fusion architecture. This book reviews the multi-sensor data fusion methods applied in autonomous driving, and the main body is divided into three parts: Basic, Method, and Advance. Starting from the mechanism of data fusion, it comprehensively reviews the development of automatic perception technology and data fusion technology, and gives a comprehensive overview of various perception tasks based on multimodal data fusion. The book then proposes a series of innovative algorithms for various autonomous driving perception tasks, to effectively improve the accuracy and robustness of autonomous driving-related tasks, and provide ideas for solving the challenges in multi-sensor fusion methods. Furthermore, to transition from technical research to intelligent connected collaboration applications, it proposes a series of exploratory contents such as practical fusion datasets, vehicle-road collaboration, and fusion mechanisms. In contrast to the existing literature on data fusion and autonomous driving, this book focuses more on the deep fusion method for perception-related tasks, emphasizes the theoretical explanation of the fusion method, and fully considers the relevant scenarios in engineering practice. Helping readers acquire an in-depth understanding of fusion methods and theories in autonomous driving, it can be used as a textbook for graduate students and scholars in related fields or as a reference guide for engineers who wish to apply deep fusion methods.