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2021 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)
FUZZ-IEEE 2021 will represent a unique meeting point for scientists and engineers, both from academia and industry, to interact and discuss the latest enhancements and innovations in the field. The topics of the conference will cover all the aspects of theory and applications of fuzzy sets, fuzzy logic and associated approaches (e.g. aggregation operators such as the Fuzzy Integral), as well as their hybridizations with other artificial and computational intelligence techniques.
The conference program will consist of plenary lectures, symposia, workshops and invitedsessions of the latest significant findings and developments in all the major fields of biomedical engineering.Submitted papers will be peer reviewed. Accepted high quality papers will be presented in oral and postersessions, will appear in the Conference Proceedings and will be indexed in PubMed/MEDLINE
The International Conference on Image Processing (ICIP), sponsored by the IEEE SignalProcessing Society, is the premier forum for the presentation of technological advances andresearch results in the fields of theoretical, experimental, and applied image and videoprocessing. ICIP 2020, the 27th in the series that has been held annually since 1994, bringstogether leading engineers and scientists in image and video processing from around the world.
The International Conference on Robotics and Automation (ICRA) is the IEEE Robotics and Automation Society’s biggest conference and one of the leading international forums for robotics researchers to present their work.
The 2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC 2020) will be held in Metro Toronto Convention Centre (MTCC), Toronto, Ontario, Canada. SMC 2020 is the flagship conference of the IEEE Systems, Man, and Cybernetics Society. It provides an international forum for researchers and practitioners to report most recent innovations and developments, summarize state-of-the-art, and exchange ideas and advances in all aspects of systems science and engineering, human machine systems, and cybernetics. Advances in these fields have increasing importance in the creation of intelligent environments involving technologies interacting with humans to provide an enriching experience and thereby improve quality of life. Papers related to the conference theme are solicited, including theories, methodologies, and emerging applications. Contributions to theory and practice, including but not limited to the following technical areas, are invited.
The theory, design and application of Control Systems. It shall encompass components, and the integration of these components, as are necessary for the construction of such systems. The word `systems' as used herein shall be interpreted to include physical, biological, organizational and other entities and combinations thereof, which can be represented through a mathematical symbolism. The Field of Interest: shall ...
The IEEE Transactions on Automation Sciences and Engineering (T-ASE) publishes fundamental papers on Automation, emphasizing scientific results that advance efficiency, quality, productivity, and reliability. T-ASE encourages interdisciplinary approaches from computer science, control systems, electrical engineering, mathematics, mechanical engineering, operations research, and other fields. We welcome results relevant to industries such as agriculture, biotechnology, healthcare, home automation, maintenance, manufacturing, pharmaceuticals, retail, ...
Computer, the flagship publication of the IEEE Computer Society, publishes peer-reviewed technical content that covers all aspects of computer science, computer engineering, technology, and applications. Computer is a resource that practitioners, researchers, and managers can rely on to provide timely information about current research developments, trends, best practices, and changes in the profession.
Both general and technical articles on current technologies and methods used in biomedical and clinical engineering; societal implications of medical technologies; current news items; book reviews; patent descriptions; and correspondence. Special interest departments, students, law, clinical engineering, ethics, new products, society news, historical features and government.
Theory and application of fuzzy systems with emphasis on engineering systems and scientific applications. (6) (IEEE Guide for Authors) Representative applications areas include:fuzzy estimation, prediction and control; approximate reasoning; intelligent systems design; machine learning; image processing and machine vision;pattern recognition, fuzzy neurocomputing; electronic and photonic implementation; medical computing applications; robotics and motion control; constraint propagation and optimization; civil, chemical and ...
Second International Conference on Intelligent Systems Engineering, 1994, 1994
IEE Colloquium on Reasoning Under Uncertainty, 1990
IEE Colloquium on Grammatical Inference: Theory, Applications and Alternatives, 1993
1997 IEEE International Conference on Intelligent Processing Systems (Cat. No.97TH8335), 1997
This medical expert system is applied to the diagnosis, treatment, and teaching of diabetes. It adopts a forward, backward and forward-backward chaining inference mechanism and an uncertainty handling method which can quickly and efficiently, based on the patient's symptoms, judge the possibility of illness, its severity, and its potential complications. Based upon this assessment the system gives prescriptions for treatment ...
Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 1988
The use of model-based reasoning, combined with locally adaptive selection of segmentation procedures, has already been found productive in expert-system- guided scene segmentation of histopathologic imagery. It applies human understanding of segmentation problems, with suitable remedial procedures, and knowledge of the structure of the tissues to the segmentation. Expert-system- guided scene segmentation thus implements certain aspects of image understanding to ...
Random Sparse Adaptation for Accurate Inference with Inaccurate RRAM Arrays - IEEE Rebooting Computing 2017
Learning Method of the SIC Fuzzy Inference Model - Genki Ohashi - ICRC San Mateo, 2019
Octopus-Inspired Robot Can Grasp, Crawl and Swim -- IEEE Spectrum Report
Deep Learning & Machine Learning Inference - Ashish Sirasao - LPIRC 2019
Introduction: Emerging Technology for Probabilistic Inference - Pierre Bessiere at INC 2019
Part 3: Workshop on Benchmarking Quantum Computational Devices and Systems - ICRC 2018
FPGA demonstrator of a Programmable ML Inference Accelerator - Martin Foltin - ICRC San Mateo, 2019
Brain Inspired Computing Systems - Luping Shi: 2016 International Conference on Rebooting Computing
Stochastic Sampling Machine for Bayesian Inference - Raphael Frisch at INC 2019
Data for Good: Data Science at Columbia - Jeannette Wing - IEEE Sarnoff Symposium, 2019
The Art of MobileNet Design - Andrew Howard - LPIRC 2019
ICASSP 2010 - Advances in Neural Engineering
Welcome & Overview - Emerging Technology for Probabilistic Inference - Arvind Kumar at INC 2019
IEEE Themes - Social dynamics in peer-to-peer sharing networks
The Era of AI Hardware - 2018 IEEE Industry Summit on the Future of Computing
Emerging Technologies for the Control of Human Brain Dynamics: IEEE TechEthics Keynote with Danielle Bassett
A Conversation with Danielle Bassett: IEEE TechEthics Interview
IROS TV 2019-STAR LAB at the University of Surrey Space Technology for Autonomous systems & Robotics
EMBC 2011 - Course: Virtual Reality and Robotics in Neurorehabilitation-Sergei Adamovich, PhD
This medical expert system is applied to the diagnosis, treatment, and teaching of diabetes. It adopts a forward, backward and forward-backward chaining inference mechanism and an uncertainty handling method which can quickly and efficiently, based on the patient's symptoms, judge the possibility of illness, its severity, and its potential complications. Based upon this assessment the system gives prescriptions for treatment and makes useful suggestions. The system can also be used in teaching practice.
The use of model-based reasoning, combined with locally adaptive selection of segmentation procedures, has already been found productive in expert-system- guided scene segmentation of histopathologic imagery. It applies human understanding of segmentation problems, with suitable remedial procedures, and knowledge of the structure of the tissues to the segmentation. Expert-system- guided scene segmentation thus implements certain aspects of image understanding to attain robustness. For diagnostic expert systems, though, image understanding in a much broader sense is required. A pathologist's verbal description of histopathologic patterns must be related to specific information extraction and analytic processes, which are to be executed by the automated system.<<ETX>>
The complexity of histopathologic imagery presents substantial difficulties for scene segmentation preceding diagnostic information extraction. An expert- system-guided segmentation strategy founded on model-based reasoning allows a locally adaptive selection of segmentation procedures and performance control. This introduces increased stability and reliability to the process.<<ETX>>
This paper presents an approach by graphs for the recognition of temporal scenarios, that represent models of the dynamical behavior of a system. The aim of the presented work is to analyze the relative situation of a scenario and an effective behavior of the system, called a session. Different symbolic levels of recognition are proposed to qualify this status. All these levels, as well as most of the properties, are formulated in terms of graphs of temporal constraints. Different contexts are analyzed, where the session is either statically built, when considered as a history, or dynamically built, when information is treated in an incremental manner or on-line. In a second phase, each status is refined using a numeric estimation of the proximity between a scenario and a session. This estimation is performed by calculating an overlapping index or a temporal difference index between the volumes of the domains corresponding to the temporal graphs of the scenario and the session.
In our paper we considered dynamic tasks scheduling problem. To solve this problem we try applying both characteristics of rough sets and neighborhood theories (theory name introduced for temporary use). Among rough sets characteristics we can quote proposal of Slovinski et al.: outranking parameter, reference ranking, cumulated preference etc. Among neighborhood characteristics we can appoint Jaron, Nikodem et al. terms such as subordinate, tolerance and collision. The tasks succeeding process has an asynchronous character. However, tasks assigning and loading to actual processor is done in regular time intervals. The tasks choice problem is supported by several approximate scheduling algorithm. Usually the complexity of this kind of algorithms is much lower than of precise algorithms. According to tasks placements appointed by every algorithm a dynamic data table is created. This data table provides information on estimations of both characteristics types, which support inference process of tasks sequencing in a form of final scheduling list. Using consolidated approach we simplify inference organization and obtain tools for taking into account dispersed tasks locations according to different algorithms.
Summary form only given, as follows. A bidirectional network model is described for inheritance reasoning which processes queries by combinations of top-down and bottom-up reasoning. The model, which is based on theoretical work in nonmonotonic reasoning, permits multiple inheritance paths in acyclic inheritance theories and allows an arbitrary preference relation among the inferences in the theory (to handle exceptions, for example). Unlike other inheritance models which compute extensions serially (maximally consistent models), the network gains substantially more parallelism by simultaneously reasoning in multiple extensions when possible.<>
The following topics are dealt with: temporal representation and reasoning in AI; time management in databases; temporal logic in computer science; and temporal logic theory.
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