Erscheinungsdatum: 22.01.2020, Medium: Buch, Einband: Gebunden, Titel: Biologically Inspired Techniques in Many-Criteria Decision Making, Titelzusatz: International Conference on Biologically Inspired Techniques in Many-Criteria Decision Making (BITMDM-2019), Auflage: 1. Auflage von 1920 // 1st ed. 2020, Redaktion: Cho, Sung-Bae // Dehuri, Satchidananda // Favorskaya, Margarita N. // Mallick, Pradeep Kumar // Mishra, Bhabani Shankar Prasad, Verlag: Springer International Publishing, Sprache: Englisch, Schlagworte: Technologie // allgemein // Datenbanken, Rubrik: Technik allgemein, Seiten: 276, Informationen: HC runder Rücken kaschiert, Gewicht: 582 gr, Verkäufer: averdo
Biologically Inspired Techniques in Many-Criteria Decision Making ab 223.49 € als pdf eBook: International Conference on Biologically Inspired Techniques in Many-Criteria Decision Making (BITMDM-2019). Aus dem Bereich: eBooks, Sachthemen & Ratgeber, Technik,
Biologically Inspired Techniques in Many-Criteria Decision Making ab 235.49 € als gebundene Ausgabe: International Conference on Biologically Inspired Techniques in Many-Criteria Decision Making (BITMDM-2019). 1st ed. 2020. Aus dem Bereich: Bücher, Wissenschaft, Technik,
Biologically Inspired Techniques in Many-Criteria Decision Making ab 235.49 EURO International Conference on Biologically Inspired Techniques in Many-Criteria Decision Making (BITMDM-2019). 1st ed. 2020
Biologically Inspired Techniques in Many-Criteria Decision Making ab 223.49 EURO International Conference on Biologically Inspired Techniques in Many-Criteria Decision Making (BITMDM-2019)
Data collected by multi-modality sensors to detect and characterize behavior of entities and events over a given situation. In order to transform the multi-modality sensors data into useful information leading to actionable information, there is an essential need for a robust data fusion model. A robust fusion model should be able to acquire data from multi-agent sensors and take advantage of spatio-temporal characteristics of multi-modality sensors to create a better situational awareness ability and in particular, assisting with soft fusion of multi-threaded information from variety of sensors under task uncertainties. This book presents a novel Image-based model for multi-modality data fusion. The concept of this fusion model is biologically-inspired by the human brain energy perceptual model. Similar to the human brain having designated regions to map immediate sensory experiences and fusing collective heterogeneous sensory perceptions to create a situational understanding for decision-making, the proposed image-based fusion model follows an analogous data to information fusion scheme for actionable decision-making applied to surveillance intelligent systems.
"Look deep into nature and you will understand everything better." advised Albert Einstein. In recent years, the research communities in Computer Science, Engineering, and other disciplines have taken this message to heart, and a relatively new field of "biologically-inspired computing" has been born. Inspiration is being drawn from nature, from the behaviors of colonies of ants, of swarms of bees and even the human body. This new paradigm in computing takes many simple autonomous objects or agents and lets them jointly perform a complex task, without having the need for centralized control. In this paradigm, these simple objects interact locally with their environment using simple rules. Applications include optimization algorithms, communications networks, scheduling and decision making, supply-chain management, and robotics, to name just a few. There are many disciplines involved in making such systems work: from artificial intelligence to energy aware systems. Often these disciplines have their own field of focus, have their own conferences, or only deal with specialized s- problems (e.g. swarm intelligence, biologically inspired computation, sensor networks). The Second IFIP Conference on Biologically-Inspired Collaborative Computing aims to bridge this separation of the scientific community and bring together researchers in the fields of Organic Computing, Autonomic Computing, Self-Organizing Systems, Pervasive Computing and related areas. We are very pleased to have two very important keynote presentations: Swarm Robotics: The Coordination of Robots via Swarm Intelligence Principles by Marco Dorigo (Université Libre de Bruxelles, Belgium), of which an abstract is included in this volume.
Sequential behavior is essential to intelligence in general and a fundamental part of human activities, ranging from reasoning to language, and from everyday skills to complex problem solving. Sequence learning is an important component of learning in many tasks and application fields: planning, reasoning, robotics natural language processing, speech recognition, adaptive control, time series prediction, financial engineering, DNA sequencing, and so on. This book presents coherently integrated chapters by leading authorities and assesses the state of the art in sequence learning by introducing essential models and algorithms and by examining a variety of applications. The book offers topical sections on sequence clustering and learning with Markov models, sequence prediction and recognition with neural networks, sequence discovery with symbolic methods, sequential decision making, biologically inspired sequence learning models.
Edited by a panel of experts, this book fills a gap in the existing literature by comprehensively covering system, processing, and application aspects of biometrics, based on a wide variety of biometric traits. The book provides an extensive survey of biometrics theory, methods,and applications, making it an indispensable source of information for researchers, security experts, policy makers, engineers, practitioners, and graduate students. The book's wide and in-depth coverage of biometrics enables readers to build a strong, fundamental understanding of theory and methods, and provides a foundation for solutions to many of today's most interesting and challenging biometric problems. Biometric traits covered: Face, Fingerprint, Iris, Gait, Hand Geometry, Signature, Electrocardiogram (ECG), Electroencephalogram (EEG), physiological biometrics. Theory, Methods and Applications covered: Multilinear Discriminant Analysis, Neural Networks for biometrics, classifier design, biometric fusion, Event-Related Potentials, person-specific characteristic feature selection, image and video-based face, recognition/verification, near-infrared face recognition, elastic graph matching, super-resolution of facial images, multimodal solutions, 3D approaches to biometrics, facial aging models for recognition, information theory approaches to biometrics, biologically-inspired methods, biometric encryption, decision-making support in biometric systems, privacy in biometrics.