Selected Mathematical Theories Underpinning Decision Models

2016 ◽  
pp. 95-121
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Decision Support ◽  
Decision Support Systems ◽  
Markov Decision Processes ◽  
Support Systems ◽  
Decision Processes ◽  
Decision Models ◽  
Rule Based ◽  
Markov Decision

In Chapter 4, various decision support systems have been examined. The rational for Chapter 4 was to appraise the diiferent decision-support systems that have been used in construction without necessarily detailing the complexities and mathematical underpinnings. This chapter will provide the theory that underpins some selected decision support systems. These are regression models (RLM), artificial neural networks (ANN), Matrices, Markov decision processes (MDP) and the ontology rule-based decision support systems.

Overview of Decision Support Systems Applied to Construction

2016 ◽  
pp. 77-94
Keyword(s):  
Decision Support ◽  
Decision Support Systems ◽  
Markov Decision Processes ◽  
Support Systems ◽  
Decision Processes ◽  
Rule Based ◽  
Markov Decision ◽  
Knowledge Intensive ◽  
The Cost ◽  
Rule Based Systems

The domain of construction is a very knowledge-intensive domain with so many factors involved. This implies undertaking any action requires an understanding of the different factors and how best to combine them to achieve a favourable and optimal outcome. Thus decision-making has been extensively used in the domain of construction. The aim of this chapter is to undertake a review of various decision support systems and to provide insights into their applications in the domain of construction. Specifically, the principle of cost index, sub-work chaining diagram method, linear regression and cost over-runs in time-overrun context (CCOTOV) model and Markov decision processes (MDP), ontology and rule-based systems have been reviewed. Based on the review the Markov decision processes (MDP), ontology and rule-based systems were chosen as the more suitable for the cost control case considered in this study.

scholarly journals Intelligent medical systems in diagnosing and treating inflammatory diseases of the pancreas (systematic review)

2018 ◽  
Vol 39 (1) ◽  
pp. 10-15
Author(s):  
A. A. Litvin
Keyword(s):  
Systematic Review ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Decision Support ◽  
Decision Support Systems ◽  
Support Systems ◽  
Inflammatory Diseases ◽  
Review Of The Literature ◽  
Medical Systems ◽  
Pancreatic Diseases

This paper is a systematic review of the literature on the use of intelligent medical systems in the diagnosis and treatment of acute inflammatory pancreatic diseases. The author provides modern literature data on the efficacy of decision support systems based on artificial neural networks to determine the severity, diagnosis and outcome prognosis of pancreatitis and complications.

Rule-Based Ontology Decision Model

2016 ◽  
pp. 271-291
Keyword(s):  
Decision Support ◽  
Decision Support Systems ◽  
Decision Process ◽  
Support Systems ◽  
Rule Based ◽  
The Matrix ◽  
Markov Decision ◽  
Minimization Technique ◽  
Complex Decision

In chapter 7, we examined some selected case study applications of some decision support systems. Those considered were the matrix-based used in determining labour cost, sub-chaining method, linear regression, optimization (i.e. minimization) technique and Markov decision process. As earlier discussed, our focus will be on rule-based decision support systems. This is because rule-based systems are more encompassing and can easily be employed to deal with complex decision about construction activities. Hence in this chapter, an overview of rule-based decision system will be examined.

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.

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