Call for Papers Special Transactions Issue on Medical Applications of Artificial Intelligence and Information Systems

1987 ◽  
Vol BME-34 (4) ◽  
pp. 314-314
Keyword(s):  
Artificial Intelligence ◽  
Information Systems ◽  
Medical Applications

scholarly journals Cognitive Information Systems for Protection of Museum Complexes

2018 ◽  
Vol 7 (4.38) ◽  
pp. 82
Author(s):  
Aleksey Valentinovich Bogdanov ◽  
Igor Gennadievich Malygin
Keyword(s):  
Artificial Intelligence ◽  
Information Systems ◽  
Information Security ◽  
Security System ◽  
Structural Scheme ◽  
Cognitive Information ◽  
Technological Platform ◽  
Network Technologies ◽  
Cognitive Information Systems ◽  
Stratified Model

The paper considers the conceptual provisions of building a promising cognitive information security system of the museum complex on a cyber-physical basis. The stratified model of cognitive information security system of the museum complex was presented. It was shown that the key technological platform for the security of the museum complex is information and network technologies integrated (converged) with the technologies of industrial artificial intelligence. The generalized structural scheme of the cognitive cycle of the information security system of the museum complex was considered. The characteristic of the basic processes realized in a cognitive contour was given.   

scholarly journals Deep learning-enabled medical computer vision

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Andre Esteva ◽  
Katherine Chou ◽  
Serena Yeung ◽  
Nikhil Naik ◽  
Ali Madani ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Computer Vision ◽  
Deep Learning ◽  
Medical Imaging ◽  
Real World ◽  
Recent Progress ◽  
Medical Applications ◽  
Modern Computer ◽  
Medical Computer

AbstractA decade of unprecedented progress in artificial intelligence (AI) has demonstrated the potential for many fields—including medicine—to benefit from the insights that AI techniques can extract from data. Here we survey recent progress in the development of modern computer vision techniques—powered by deep learning—for medical applications, focusing on medical imaging, medical video, and clinical deployment. We start by briefly summarizing a decade of progress in convolutional neural networks, including the vision tasks they enable, in the context of healthcare. Next, we discuss several example medical imaging applications that stand to benefit—including cardiology, pathology, dermatology, ophthalmology–and propose new avenues for continued work. We then expand into general medical video, highlighting ways in which clinical workflows can integrate computer vision to enhance care. Finally, we discuss the challenges and hurdles required for real-world clinical deployment of these technologies.

scholarly journals Devices for Continuous Recording of "Integral ECG" for Determining Vital Physiological Indicators of a Person's State and Their use in Bio Cybernetic Complexes

2020 ◽  
Author(s):  
Bogatyrev Evgeniy ◽  
Kodkin Vladimir
Keyword(s):  
Artificial Intelligence ◽  
Information Systems ◽  
Physical Nature ◽  
Continuous Recording ◽  
Accurate Information ◽  
Physiological Indicators ◽  
Research Areas ◽  
The Creation

One of the rapidly developing research areas is the creation of systems. which are commonly referred to as cyberphysical complexes. In such systems, devices and complexes interact with a completely different physical nature. The role of a person in such systems usually consists in the formation of final tasks for “artificial intelligence” and executive mechanisms. The functioning of actuators is controlled by accurate information systems.

A Survey on Ontology Creation Methodologies

2011 ◽  
pp. 98-122 ◽  
Author(s):  
Matteo Cristani ◽  
Roberta Cuel
Keyword(s):  
Artificial Intelligence ◽  
Knowledge Management ◽  
Information Systems ◽  
Current Literature ◽  
A Priori ◽  
Top Down ◽  
Specific Context ◽  
Systematic Analysis ◽  
General Terms ◽  
The Right

In the current literature of knowledge management and artificial intelligence, several different approaches to the problem have been carried out of developing domain ontologies from scratch. All these approaches deal fundamentally with three problems: (1) providing a collection of general terms describing classes and relations to be employed in the description of the domain itself; (2) organizing the terms into a taxonomy of the classes by the ISA relation; and (3) expressing in an explicit way the constraints that make the ISA pairs meaningful. Though a number of such approaches can be found, no systematic analysis of them exists which can be used to understand the inspiring motivation, the applicability context, and the structure of the approaches. In this paper, we provide a framework for analyzing the existing methodologies that compares them to a set of general criteria. In particular, we obtain a classification based upon the direction of ontology construction; bottom-up are those methodologies that start with some descriptions of the domain and obtain a classification, while top-down ones start with an abstract view of the domain itself, which is given a priori. The resulting classification is useful not only for theoretical purposes but also in the practice of deployment of ontologies in Information Systems, since it provides a framework for choosing the right methodology to be applied in the specific context, depending also on the needs of the application itself.

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