scholarly journals Integrated neurosymbolic decision support systems: problems and opportunities

2021 ◽  
Vol 15 (3) ◽  
pp. 7-23
Author(s):  
Alexander Demidovskij ◽  
Eduard Babkin
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Decision Support ◽  
Decision Support Systems ◽  
Support Systems ◽  
Fog Computing ◽  
Key Comparisons ◽  
Multi Agent Systems ◽  
Critical Feature ◽  
Artificial Neural

The current problem of developing new kinds of decision support systems for different categories of management personnel is addressed in this study. A critical feature of such systems is their distributed and decentralized nature, which enables the construction of next-generation information systems in the form of Multi-Agent Systems, Internet of Things, or Fog Computing Architectures. Parallel models of the dynamics of artificial neural networks are produced under such realistic circumstances, demonstrating their potential for addressing a variety of issues. The purpose of this study is to conduct a critical analysis of the problem of integrating Artificial Neural Networks with decision support systems using a corpus of relevant scholarly literature. To tackle this question, the Design Science Research methodology was considered. According to this methodology, a literary search strategy was established, scientific literature was collected and analyzed, and key comparisons between different solutions were emphasized. The study resulted in the presentation of the most important findings, outstanding issues, and potential areas of fundamental and applied solutions. A consistent trend toward the development of decision support systems based on integrated neural-network methods has been observed, which is efficient and cost-effective since it enables the creation of distributed and trainable decision support systems.

scholarly journals WSES project on decision support systems based on artificial neural networks in emergency surgery

2021 ◽  
Vol 16 (1) ◽  
Author(s):  
Andrey Litvin ◽  
Sergey Korenev ◽  
Sophiya Rumovskaya ◽  
Massimo Sartelli ◽  
Gianluca Baiocchi ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Decision Support ◽  
Decision Support Systems ◽  
Emergency Surgery ◽  
Intelligent Systems ◽  
Medical Information ◽  
Support Systems ◽  
Emergency Setting ◽  
Artificial Neural

AbstractThe article is a scoping review of the literature on the use of decision support systems based on artificial neural networks in emergency surgery. The authors present modern literature data on the effectiveness of artificial neural networks for predicting, diagnosing and treating abdominal emergency conditions: acute appendicitis, acute pancreatitis, acute cholecystitis, perforated gastric or duodenal ulcer, acute intestinal obstruction, and strangulated hernia. The intelligent systems developed at present allow a surgeon in an emergency setting, not only to check his own diagnostic and prognostic assumptions, but also to use artificial intelligence in complex urgent clinical cases. The authors summarize the main limitations for the implementation of artificial neural networks in surgery and medicine in general. These limitations are the lack of transparency in the decision-making process; insufficient quality educational medical data; lack of qualified personnel; high cost of projects; and the complexity of secure storage of medical information data. The development and implementation of decision support systems based on artificial neural networks is a promising direction for improving the forecasting, diagnosis and treatment of emergency surgical diseases and their complications.

APPLICATION OF ARTIFICIAL NEURAL NETWORKS IN DECISION SUPPORT SYSTEMS IN LOGISTICS MANAGEMENT

2011 ◽  
Vol 162 (4) ◽  
pp. 270-277
Author(s):  
Artur DUCHACZEK ◽  
Dariusz SKORUPKA
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Decision Support ◽  
Decision Support Systems ◽  
Support Systems ◽  
Computer Applications ◽  
Logistics Management ◽  
Version 2.0 ◽  
Artificial Neural

The article presents the possibility of using a TFannNetwork component, based on the FANN library (version 2.0), for building computer applications used in logistics management. The potential of the component is exemplified with the application of artificial neural networks to estimate the capacity of transport vehicles based on their dimensions.

scholarly journals DEVELOPING AND TRAINING MODEL OF ARTIFICIAL NEURAL NETWORK FOR CREATING DECISION SUPPORT SYSTEMS

2020 ◽  
Vol 2020 (3) ◽  
pp. 61-73
Author(s):  
Anna Olegovna Chupakova ◽  
Sergey Vital'evich Gudin ◽  
Renat Shamil'evich Khabibulin
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Decision Support ◽  
Decision Support Systems ◽  
Support Systems ◽  
Training Data ◽  
Advantages And Disadvantages ◽  
Artificial Neural ◽  
And Training

The article highlights the significant increase of industrial capacities and automation of production, which requires taking effective management decisions by a responsible person. There have been outlined the important achievements of the scientists in application of the artificial neural networks in the various fields of activity and decision support systems involving the information analysis and processing with the results obtained. There has been proposed a review of publications on training artificial neural networks and on their efficient application in solving problems of classification, prediction and control. The most common structures of neural networks, their advantages and disadvantages, as well as the methods used to create training data arrays have been studied. A comparative analysis of using various structures of artificial neural networks and the effectiveness of existing teaching methods and the prospects for their use has been carried out. There has been defined the most preferred neural network topology for solving problems of fire safety management at the production facilities as an active decision support system. Using the analysis results, the most common and effective training methods have been identified, application of which is appropriate for developing and training various types of neural networks. The use of the technology is well grounded for reducing the errors in data processing, the financial costs for ensuring security, as well as for possible using the neural networks in the decision support systems to optimize these systems.

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