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Multi-disciplinary Trends in Artificial Intelligence
by Antonis Bikakis Xianghan ZhengThis 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: 13th International Conference, MIWAI 2019, Kuala Lumpur, Malaysia, November 17–19, 2019, Proceedings (Lecture Notes in Computer Science #11909)
by Rapeeporn Chamchong Kok Wai WongThis 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: 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 Manasawee Kaenampornpan Rainer Malaka 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: 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: 16th International Conference, MIWAI 2023, Hyderabad, India, July 21–22, 2023, Proceedings (Lecture Notes in Computer Science #14078)
by Raghava Morusupalli Teja Santosh Dandibhotla Vani Vathsala Atluri David Windridge Pawan Lingras 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
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: 17th International Conference, MIWAI 2024, Pattaya, Thailand, November 11–15, 2024, Proceedings, Part II (Lecture Notes in Computer Science #15432)
by Chattrakul Sombattheera Paul Weng Jun PangThe 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 I (Lecture Notes in Computer Science #15431)
by Chattrakul Sombattheera Paul Weng Jun PangThe 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: 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-Domain Communication Management Systems (Advanced & Emerging Communications Technologies)
by Alex GalisAmong the leading challenges faced by systems managers today is the coherent management of network resources in a multi-domain, multi-environment. The MISA Project - Management of Integrated SDH and ATM Networks - brought together researchers from 17 organizations to explore and advance the state of the art in developing enabling mechanisms for end
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-Fractal Traffic and Anomaly Detection in Computer Communications
by Ming LiThis book provides a comprehensive theory of mono- and multi-fractal traffic, including the basics of long-range dependent time series and 1/f noise, ergodicity and predictability of traffic, traffic modeling and simulation, stationarity tests of traffic, traffic measurement and the anomaly detection of traffic in communications networks. Proving that mono-fractal LRD time series is ergodic, the book exhibits that LRD traffic is stationary. The author shows that the stationarity of multi-fractal traffic relies on observation time scales, and proposes multi-fractional generalized Cauchy processes and modified multi-fractional Gaussian noise. The book also establishes a set of guidelines for determining the record length of traffic in measurement. Moreover, it presents an approach of traffic simulation, as well as the anomaly detection of traffic under distributed-denial-of service attacks. Scholars and graduates studying network traffic in computer science will find the book beneficial.
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-Label Dimensionality Reduction (Chapman & Hall/CRC Machine Learning & Pattern Recognition)
by null Liang Sun null Shuiwang Ji null Jieping YeSimilar to other data mining and machine learning tasks, multi-label learning suffers from dimensionality. An effective way to mitigate this problem is through dimensionality reduction, which extracts a small number of features by removing irrelevant, redundant, and noisy information. The data mining and machine learning literature currently lacks
Multi-Level Bayesian Models for Environment Perception
by Csaba BenedekThis book deals with selected problems of machine perception, using various 2D and 3D imaging sensors. It proposes several new original methods, and also provides a detailed state-of-the-art overview of existing techniques for automated, multi-level interpretation of the observed static or dynamic environment. To ensure a sound theoretical basis of the new models, the surveys and algorithmic developments are performed in well-established Bayesian frameworks. Low level scene understanding functions are formulated as various image segmentation problems, where the advantages of probabilistic inference techniques such as Markov Random Fields (MRF) or Mixed Markov Models are considered. For the object level scene analysis, the book mainly relies on the literature of Marked Point Process (MPP) approaches, which consider strong geometric and prior interaction constraints in object population modeling. In particular, key developments are introduced in the spatial hierarchical decomposition of the observed scenarios, and in the temporal extension of complex MRF and MPP models. Apart from utilizing conventional optical sensors, case studies are provided on passive radar (ISAR) and Lidar-based Bayesian environment perception tasks. It is shown, via several experiments, that the proposed contributions embedded into a strict mathematical toolkit can significantly improve the results in real world 2D/3D test images and videos, for applications in video surveillance, smart city monitoring, autonomous driving, remote sensing, and optical industrial inspection.
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-Modal Sentiment Analysis
by Hua XuThe natural interaction ability between human and machine mainly involves human-machine dialogue ability, multi-modal sentiment analysis ability, human-machine cooperation ability, and so on. To enable intelligent computers to have multi-modal sentiment analysis ability, it is necessary to equip them with a strong multi-modal sentiment analysis ability during the process of human-computer interaction. This is one of the key technologies for efficient and intelligent human-computer interaction. This book focuses on the research and practical applications of multi-modal sentiment analysis for human-computer natural interaction, particularly in the areas of multi-modal information feature representation, feature fusion, and sentiment classification. Multi-modal sentiment analysis for natural interaction is a comprehensive research field that involves the integration of natural language processing, computer vision, machine learning, pattern recognition, algorithm, robot intelligent system, human-computer interaction, etc. Currently, research on multi-modal sentiment analysis in natural interaction is developing rapidly. This book can be used as a professional textbook in the fields of natural interaction, intelligent question answering (customer service), natural language processing, human-computer interaction, etc. It can also serve as an important reference book for the development of systems and products in intelligent robots, natural language processing, human-computer interaction, and related fields.
Multi-Modality Imaging: Applications and Computational Techniques
by Mauren Abreu de Souza Humberto Remigio Gamba Helio PedriniThis book presents different approaches on multi-modality imaging with a focus on biomedical applications. Medical imaging can be divided into two categories: functional (related to physiological body measurements) and anatomical (structural) imaging modalities.In particular, this book covers imaging combinations coming from the usual popular modalities (such as the anatomical modalities, e.g. X-ray, CT and MRI), and it also includes some promising and new imaging modalities that are still being developed and improved (such as infrared thermography (IRT) and photoplethysmography imaging (PPGI)), implying potential approaches for innovative biomedical applications.Moreover, this book includes a variety of tools on computer vision, imaging processing, and computer graphics, which led to the generation and visualization of 3D models, making the most recent advances in this area possible. This is an ideal book for students and biomedical engineering researchers covering the biomedical imaging field.
Multi Modality State-of-the-Art Medical Image Segmentation and Registration Methodologies: Volume II
by Jasjit S. Suri Ayman S. El-Baz Rajendra Acharya U Andrew F. LaineWith the advances in image guided surgery for cancer treatment, the role of image segmentation and registration has become very critical. The central engine of any image guided surgery product is its ability to quantify the organ or segment the organ whether it is a magnetic resonance imaging (MRI) and computed tomography (CT), X-ray, PET, SPECT, Ultrasound, and Molecular imaging modality. Sophisticated segmentation algorithms can help the physicians delineate better the anatomical structures present in the input images, enhance the accuracy of medical diagnosis and facilitate the best treatment planning system designs. The focus of this book in towards the state of the art techniques in the area of image segmentation and registration.
Multi Modality State-of-the-Art Medical Image Segmentation and Registration Methodologies. Volume 1
by Majid Mirmehdi Ayman S. El-Baz Rajendra Acharya U Jasjit S. SuriWith the advances in image guided surgery for cancer treatment, the role of image segmentation and registration has become very critical. The central engine of any image guided surgery product is its ability to quantify the organ or segment the organ whether it is a magnetic resonance imaging (MRI) and computed tomography (CT), X-ray, PET, SPECT, Ultrasound, and Molecular imaging modality. Sophisticated segmentation algorithms can help the physicians delineate better the anatomical structures present in the input images, enhance the accuracy of medical diagnosis and facilitate the best treatment planning system designs. The focus of this book in towards the state of the art techniques in the area of image segmentation and registration.
Multi-Net Optimization of VLSI Interconnect
by Konstantin Moiseev Avinoam Kolodny Shmuel WimerThis book covers layout design and layout migration methodologies for optimizing multi-net wire structures in advanced VLSI interconnects. Scaling-dependent models for interconnect power, interconnect delay and crosstalk noise are covered in depth, and several design optimization problems are addressed, such as minimization of interconnect power under delay constraints, or design for minimal delay in wire bundles within a given routing area. A handy reference or a guide for design methodologies and layout automation techniques, this book provides a foundation for physical design challenges of interconnect in advanced integrated circuits.
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, 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.