scholarly journals INTEGRATING INTELLIGENT DECISION SUPPORT SERVICES INTO ANALYTICAL MANAGEMENT SYSTEMS

2019 ◽  
pp. 43-48
Keyword(s):  
Decision Making ◽  
Support Services ◽  
Integrated Management ◽  
Analytical Data ◽  
Organizational Management ◽  
Management Systems ◽  
Optimal Solutions ◽  
Intelligent Decision ◽  
Intelligent Decision Support ◽  
Support Decision Making

The article discusses the approach to creating projects of organizational management systems that allow to use the theoretical and practical results of research in the field of artificial intelligence in their design. The importance of using the experience gained to support decision-making in the organizational management system was emphasized. In addition, appropriate recommendations will be developed for the intellectual support of decision-making in integrated management systems, as well as the introduction of service technologies. In the example of supporting management decisions, optimal solutions are put forward for the intellectual processing of information, the creation of services appropriate to expert systems. The article is based on theoretical and analytical data.

A moral analysis of intelligent decision-support systems in diagnostics through the lens of Luciano Floridi’s information ethics

Human Affairs ◽  
2021 ◽  
Vol 31 (2) ◽  
pp. 149-164
Author(s):  
Dmytro Mykhailov
Keyword(s):  
Decision Making ◽  
Decision Support ◽  
Medical Diagnosis ◽  
Moral Agency ◽  
Moral Agent ◽  
Well Being ◽  
Medical Diagnostics ◽  
Intelligent Decision ◽  
Luciano Floridi ◽  
Intelligent Decision Support

Abstract Contemporary medical diagnostics has a dynamic moral landscape, which includes a variety of agents, factors, and components. A significant part of this landscape is composed of information technologies that play a vital role in doctors’ decision-making. This paper focuses on the so-called Intelligent Decision-Support System that is widely implemented in the domain of contemporary medical diagnosis. The purpose of this article is twofold. First, I will show that the IDSS may be considered a moral agent in the practice of medicine today. To develop this idea I will introduce the approach to artificial agency provided by Luciano Floridi. Simultaneously, I will situate this approach in the context of contemporary discussions regarding the nature of artificial agency. It is argued here that the IDSS possesses a specific sort of agency, includes several agent features (e.g. autonomy, interactivity, adaptability), and hence, performs an autonomous behavior, which may have a substantial moral impact on the patient’s well-being. It follows that, through the technology of artificial neural networks combined with ‘deep learning’ mechanisms, the IDSS tool achieves a specific sort of independence (autonomy) and may possess a certain type of moral agency. Second, I will provide a conceptual framework for the ethical evaluation of the moral impact that the IDSS may have on the doctor’s decision-making and, consequently, on the patient’s wellbeing. This framework is the Object-Oriented Model of Moral Action developed by Luciano Floridi. Although this model appears in many contemporary discussions in the field of information and computer ethics, it has not yet been applied to the medical domain. This paper addresses this gap and seeks to reveal the hidden potentialities of the OOP model for the field of medical diagnosis.

Mastering Intelligent Decision Support Systems in Enterprise Information Management

2017 ◽  
pp. 35-56 ◽  
Author(s):  
Kijpokin Kasemsap
Keyword(s):  
Decision Support ◽  
Decision Support Systems ◽  
Information Management ◽  
Support Systems ◽  
Management Systems ◽  
Enterprise Information ◽  
Intelligent Decision ◽  
Intelligent Decision Support Systems ◽  
Information Management Systems ◽  
Intelligent Decision Support

This chapter explains the overview of Intelligent Decision Support Systems (IDSSs); the overview of Enterprise Information Management (EIM); the IDSS techniques for EIM in terms of Expert System (ES), Multi-Agent System (MAS), Fuzzy Logic (FL), Artificial Neural Network (ANN), Evolutionary Computation (EC), and Hybrid System (HS); and the multifaceted applications of IDSSs in EIM. IDSS techniques are rapidly emerging as the modern tools in information management systems and include various techniques, such as ES, MAS, FL, ANN, EC, and HS. IDSS techniques can increase the sensitiveness, flexibility, and accuracy of information management systems. IDSS techniques should be implemented in modern enterprise in order to gain the benefits of using the decision-making process concerning EIM. The chapter argues that utilizing IDSS techniques for EIM has the potential to increase organizational performance and reach strategic goals in global operations.

Ubiquitous Eco Cities

2010 ◽  
Vol 2 (1) ◽  
pp. 1-17 ◽  
Author(s):  
Tan Yigitcanlar ◽  
Jung Hoon Han
Keyword(s):  
Decision Making ◽  
Urban Development ◽  
21St Century ◽  
Integrated Management ◽  
Location Based Services ◽  
Management Systems ◽  
Urban Management ◽  
Telecommunication Infrastructure ◽  
Decision Making System ◽  
Technology Convergence

Efficient and effective urban management systems for Ubiquitous Eco Cities require having intelligent and integrated management mechanisms. This integration includes bringing together economic, socio-cultural and urban development with a well-orchestrated, transparent and open decision-making system and necessary infrastructure and technologies. In Ubiquitous Eco Cities, telecommunication technologies play an important role in monitoring and managing activities via wired and wireless networks. Particularly, technology convergence creates new ways in which information and telecommunication technologies are used and formed the backbone of urban management. The 21st century is an era where information has converged, in which people are able to access a variety of services, including internet- and location- based services, through multi-functional devices and provides new opportunities in the management of Ubiquitous Eco Cities. This paper discusses developments in telecommunication infrastructure and trends in convergence technologies and their implications on the management of Ubiquitous Eco Cities.

scholarly journals Evaluation of the Efficiency of Basic Sanitation Integrated Management in Brazilian Municipalities

2020 ◽  
Vol 17 (24) ◽  
pp. 9244
Author(s):  
Alvaro Cavalcanti ◽  
Arthur Teixeira ◽  
Karen Pontes
Keyword(s):  
Decision Making ◽  
Data Envelopment Analysis ◽  
Integrated Management ◽  
Financial Resources ◽  
Data Envelopment ◽  
Multiple Data ◽  
Basic Sanitation ◽  
Potential Efficiency ◽  
Support Decision Making ◽  
Selection Of

This study aims to evaluate the level of technical efficiency of companies that perform the integrated management of basic sanitation in Brazilian municipalities. A Multiple Data Envelopment Analysis (M-DEA) model was applied to estimate the performance of water supply and sewage services in 1628 municipalities covering more than 56% of the Brazilian population, identifying the factors that most influence the efficiency of the sector in the years 2008 and 2016. The M-DEA methodology is an extension of Data Envelopment Analysis (DEA) with multiple DEA executions considering all combinations of inputs and outputs to calculate efficiency scores. The methodology reduces possible biases in the selection of resources and products of the model, ability to support decision-making in favor of improvements in the sector′s efficiency based on national regulatory framework. The analyses show that the companies analyzed can increase their operating results and attendance coverage by more than 60%, given the current levels of infrastructure, human and financial resources in the sector. Based on the simulation of potential efficiency gains in Brazilian basic sanitation companies, the estimates show that the coverage of the population with access to sanitary sewage would go from the current 59.9% to 76.5%. The evidence found provides indications to subsidize sanitation management in the country at the micro-analytical level, enabling a better competitive position in the sector for the integrated management of basic sanitation and its universalization in Brazil.

scholarly journals Scenario model of intelligent decision support based user digital life models

2021 ◽  
pp. 47-60
Author(s):  
Alexander Smirnov ◽  
Tatiana Levashova
Keyword(s):  
Decision Making ◽  
Decision Support ◽  
Practical Relevance ◽  
Problem Domain ◽  
Active User ◽  
Intelligent Decision ◽  
Support Domain ◽  
Scenario Model ◽  
Intelligent Decision Support ◽  
User Actions

Introduction. In the decision support domain, the practice of using information from user digital traces has not been widespread so far. Earlier, the authors of this paper developed a conceptual framework of intelligent decision support based on user digital life models that was aimed at recommending decisions using information from the user digital traces. The present research is aiming at the development of a scenario model that implements this framework. Purpose: the development of a scenario model of intelligent decision support based on user digital life models and an approach to grouping users with similar preferences and decision-making behaviours. Results: A scenario model of intelligent decision support based on user digital life models has been developed. The model is intended to recommend to the user decisions based on the knowledge about the user decision-maker type, decision support problem, and problem domain. The scenario model enables to process incompletely formulated problems due to taking into account the preferences of users who have preferences and decision-making behaviour similar to the active user. An approach to grouping users with similar preferences and decision-making behaviours has been proposed. The approach enables to group users with similar preferences and decision-making behaviours based on the information about user behavioural segments that exist in various domains, behavioural segmentation rules, and user actions represented in their digital life models. Practical relevance: the research results are beneficial for the development of advanced recommendation systems expected to tracking digital traces.

AN INTELLIGENT DECISION SUPPORT BASED ON A SUBTRACTIVE CLUSTERING AND FUZZY INFERENCE SYSTEM FOR MULTIOBJECTIVE OPTIMIZATION PROBLEM IN SERIOUS GAME

2011 ◽  
Vol 10 (05) ◽  
pp. 793-810 ◽  
Author(s):  
I. G. P. ASTO BUDITJAHJANTO ◽  
HAJIME MIYAUCHI
Keyword(s):  
Decision Support ◽  
Multiobjective Optimization ◽  
Optimization Problem ◽  
Fuzzy Inference ◽  
Subtractive Clustering ◽  
Multiobjective Optimization Problem ◽  
Optimal Solutions ◽  
Inference System ◽  
Intelligent Decision ◽  
Intelligent Decision Support

Learning decision making through playing a game is an interesting activity for the decision maker or player. In this paper, a multiobjective optimization problem for economic and emission dispatch in which the player can learn about the tradeoff between fuel cost (economic) and emission problems to achieve optimal decisions is considered. A nonplayer character (NPC) is an entity that is built to provide intelligent decision support for the player. The proposed approach is carried out in two stages for the NPC module: the first stage uses the nondominated sorting genetic algorithm II method to solve the multiobjective optimization problem; this stage produces some optimal solutions. The next stage uses subtractive clustering to cluster optimal solutions; furthermore, these clusters are used to build a fuzzy inference system based on the Mamdani type. In this stage, players can select the best decision offered by the NPC.

Improving Intelligent Decision Making in Urban Planning

2021 ◽  
Vol 8 (3) ◽  
pp. 40-58
Author(s):  
Abderrazak Khediri ◽  
Mohamed Ridda Laouar ◽  
Sean B. Eom
Keyword(s):  
Decision Making ◽  
Decision Support ◽  
Urban Planning ◽  
Agglomerative Clustering ◽  
New Techniques ◽  
Intelligent Decision Support System ◽  
Intelligent Decision Making ◽  
Intelligent Decision ◽  
Urban Issues ◽  
Intelligent Decision Support

Generally, decision making in urban planning has progressively become difficult due to the uncertain, convoluted, and multi-criteria nature of urban issues. Even though there has been a growing interest to this domain, traditional decision support systems are no longer able to effectively support the decision process. This paper aims to elaborate an intelligent decision support system (IDSS) that provides relevant assistance to urban planners in urban projects. This research addresses the use of new techniques that contribute to intelligent decision making: machine learning classifiers, naïve Bayes classifier, and agglomerative clustering. Finally, a prototype is being developed to concretize the proposition.

An Intelligent Support System Integrating Data Mining and Online Analytical Processing

2011 ◽  
pp. 141-156
Author(s):  
Rahul Singh ◽  
Richard T. Redmond ◽  
Victoria Yoon
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Decision Support ◽  
Decision Makers ◽  
Online Analytical Processing ◽  
Intelligent Decision Making ◽  
Course Of Action ◽  
Intelligent Decision ◽  
Analytical Processing ◽  
Intelligent Decision Support

Intelligent decision support requires flexible, knowledge-driven analysis of data to solve complex decision problems faced by contemporary decision makers. Recently, online analytical processing (OLAP) and data mining have received much attention from researchers and practitioner alike, as components of an intelligent decision support environment. Little that has been done in developing models to integrate the capabilities of data mining and online analytical processing to provide a systematic model for intelligent decision making that allows users to examine multiple views of the data that are generated using knowledge about the environment and the decision problem domain. This paper presents an integrated model in which data mining and online analytical processing complement each other to support intelligent decision making for data rich environments. The integrated approach models system behaviors that are of interest to decision makers; predicts the occurrence of such behaviors; provides support to explain the occurrence of such behaviors and supports decision making to identify a course of action to manage these behaviors.

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