scholarly journals Decision Support System for an Art University

2021 ◽  
Vol 1 (2) ◽  
pp. 8-12
Author(s):  
Svetlana E. Vecherskaya
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Special Needs ◽  
Support System ◽  
Learning Process ◽  
Performance Indicators ◽  
Educational Process ◽  
Learning Plan ◽  
Specific Performance ◽  
Talented Students

A prototype of an automated decision support system for creative universities has been developed, which will allow assessing the achievements particularly talented students and identifying the needs in the learning process in order to help organize the educational process in accordance with identified capabilities. Use of a decision support system based on the Bayesian classifier is suggested which will allow to evaluate factors contributing to the progress in teaching students particular techniques, and in perspective to assess the possible resources that will be required to make changes to the learning. The list of specific performance indicators is given. The system should contribute to the formation of the learning plan, taking into account the capabilities of both a group art workshop as a whole, and special needs of an individual to develop, if necessary an individual approach.

scholarly journals Decision Support System for an Art University

2021 ◽  
pp. 8-12
Author(s):  
Svetlana E. Vecherskaya ◽  
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Special Needs ◽  
Support System ◽  
Learning Process ◽  
Performance Indicators ◽  
Educational Process ◽  
Learning Plan ◽  
Specific Performance ◽  
Talented Students

A prototype of an automated decision support system for creative universities has been developed, which will allow assessing the achievements particularly talented students and identifying the needs in the learning process in order to help organize the educational process in accordance with identified capabilities. Use of a decision support system based on the Bayesian classifier is suggested which will allow to evaluate factors contributing to the progress in teaching students particular techniques, and in perspective to assess the possible resources that will be required to make changes to the learning. The list of specific performance indicators is given. The system should contribute to the formation of the learning plan, taking into account the capabilities of both a group art workshop as a whole, and special needs of an individual to develop, if necessary an individual approach.

Selection of criteria for a decision support system for an art university

2021 ◽  
pp. 56-62
Author(s):  
S. Е. Vecherskaya
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Learning Process ◽  
Performance Indicators ◽  
Educational Process ◽  
Learning Plan ◽  
Specific Performance ◽  
Talented Students ◽  
Selection Of

Automation of decision support is proposed by including special criteria with the overall configuration of an automated decision support system for creative universities. A prototype of an automated decision support system for creative universities has been developed, which will allow assessing the achievements particularly talented students and identifying the needs in the learning process in order to help organize the educational process in accordance with identified capabilities. Use of a decision support system based on the Bayesian classifier is suggested to assess and evaluate factors contributing to the progress in teaching students particular techniques, and in perspective to assess the possible resources that will be required to make changes to the learning plan. The existing approaches to the assessment of students academic performance are analyzed. The list of specific performance indicators, which are important to be taken into account when assessing the achievements of students of creative specialties, is given. The system should contribute to the formation of the learning plan, taking into account the capabilities of both a group art workshop as a whole, and special needs of an individual to develop, if necessary an individual approach. 

scholarly journals Sistem Pendukung Keputusan Klinis dengan Memanfaatkan Jaringan Syaraf Tiruan Untuk Mendeteksi Stadium Penderita Kanker Paru-Paru Jenis Karsinoma Bukan Sel Kecil

2011 ◽  
Vol 5 (2) ◽  
Author(s):  
Rio Kurniawan ◽  
Sri Hartati
Keyword(s):  
Neural Network ◽  
Lung Cancer ◽  
Decision Support ◽  
Decision Support System ◽  
Clinical Decision Support ◽  
Support System ◽  
Learning Process ◽  
Clinical Decision Support System ◽  
Early Stage ◽  
Clinical Decision

Abstract-- Lung cancer is leading cause of death in the cancer group. In general, lung cancer has some symptoms, but at an early stage, symptoms are not perceived by the patient. As a result, when patients go to hospital, lung cancer has been diagnosed in middle or high stage. For early detection of lung cancer, necessary a decision support system based on computerized technology that can be utilized by doctor needed to detection lung cancer. The clinical decision support system will help to determine specific medical treatment. The clinical decision support system capable to know data input and produce output result by learning process. The learning process is  part of process in artificial neural network (ANN). Many methods used in ANN as Backpropagation (BP)learning algorithm. BP used to produce output result in decision support system. Keywords-- lung cancer, stage, clinical decision support systems, neural network, multilayer perceptron, backpropagation algorithm

Specifications of a queuing model-driven decision support system for predicting the healthcare performance indicators pertaining to the patients flow

2022 ◽  
Vol 14 (1) ◽  
pp. 0-0
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Performance Indicators ◽  
Performance Indicator ◽  
Dynamic Change ◽  
Public Hospitals ◽  
Queuing Model ◽  
Model Driven ◽  
Effective System

This article has developed specifications for a new model-driven decision support system (DSS) that aids the key stakeholders of public hospitals in estimating and tracking a set of crucial performance indicators pertaining to the patients flow. The developed specifications have considered several requirements for ensuring an effective system, including tracking the performance indicator on the level of the entire patients flow system, paying attention to the dynamic change of the values of the indicator’s parameters, and considering the heterogeneity of the patients. According to these requirements, the major components of the proposed system, which include a comprehensive object-based queuing model and an object-oriented database, have been specified. In addition to these components, the system comprises the equations that produce the required predictions. From the system output perspective, these predictions act as a foundation for evaluating the performance indicators as well as developing policies for managing the patients flow in the public hospitals.

scholarly journals Academic Decision Support System for Choosing Information Systems Sub Majors Programs using Decision Tree Algorithm

2019 ◽  
Vol 5 (1) ◽  
pp. 57
Author(s):  
Cut Fiarni ◽  
Evasaria M. Sipayung ◽  
Prischilia B.T. Tumundo
Keyword(s):  
Data Mining ◽  
Factor Analysis ◽  
Decision Support ◽  
Decision Tree ◽  
Decision Support System ◽  
Support System ◽  
Educational Data Mining ◽  
Educational Process ◽  
Student Interest ◽  
The Impact

Background: Educational data mining is an emerging trend, especially in today Big Data Era. Numerous method and technique already been implemented in order  to improve its process to gain better understanding of the educational process and to extract knowledge from various related data, but the implementation of these methods into Decision support system (DSS) application still limited, especially regarding help to choose university sub majors .Objective: To design an academic decision support system (DSS) by adopting Theory of Reasoned Action (TRA) concept and using Data Mining as a factor analytic apporach to extract rules for its knowledge model.Methods: We implemented factor analysis method and decision tree method  of C.45 to produce rules of the impact course of the sub- majors and the job interest as the basic rules of the DSS.Results: The proposed academic decision support system able to give sub majors recommendations in accordance with student interest and competence, with 79.03% of precision and 61.11% of recall. Moreover, the system also has a dashboard feature that shows the information about the statistic of students in each sub majors.Conclusion: C.45 algorithm and factor analysis are suitable to build a knowledge model for Academic Decision Support System for Choosing Information System Sub Majors Bachelor Programs. This system could also help the academic adviser on monitoring and make decision accordance with that academic information

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