scholarly journals Do machine learning methods used in data mining enhance the potential of decision support systems? A review for the urban water sector

2016 ◽  
Vol 29 (6) ◽  
pp. 747-756 ◽  
Author(s):  
Antonia Hadjimichael ◽  
Joaquim Comas ◽  
Lluís Corominas
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Decision Support ◽  
Decision Support Systems ◽  
Support Systems ◽  
Urban Water ◽  
Water Sector ◽  
Learning Methods ◽  
Machine Learning Methods

scholarly journals The Future of Health care: Machine Learning

2018 ◽  
Vol 7 (4.6) ◽  
pp. 23 ◽  
Author(s):  
M. A.Jabbar ◽  
Shirina Samreen ◽  
Rajanikanth Aluvalu
Keyword(s):  
Machine Learning ◽  
Health Care ◽  
Decision Support ◽  
Decision Support Systems ◽  
Precision Medicine ◽  
Support Systems ◽  
Personalized Care ◽  
Machine Learning Methods ◽  
Effective Diagnosis ◽  
Change Health Care

Machine learning (ML) is a rising field. Machine learning is to find patterns automatically and reason about data.ML enables personalized care called precision medicine. Machine learning methods have made advances in healthcare domain. This paper discuss about application of machine learning in health care. Machine learning will change health care within a few years. In future ML and AI will transform health care, but quality ML and AI decision support systems (DSS) Should Require to address the problems faced by patients and physicians in effective diagnosis. 

Review of classification studies for machine learning in the development of intelligent management decision support systems

2020 ◽  
Vol 89 ◽  
pp. 20-29
Author(s):  
Sh. K. Kadiev ◽  
◽  
R. Sh. Khabibulin ◽  
P. P. Godlevskiy ◽  
V. L. Semikov ◽  
...  
Keyword(s):  
Machine Learning ◽  
Decision Support ◽  
Mathematical Models ◽  
Decision Support Systems ◽  
Support Systems ◽  
Management Decision ◽  
Classification Methods ◽  
Advantages And Disadvantages ◽  
Intelligent Management ◽  
Management Decision Support

Introduction. An overview of research in the field of classification as a method of machine learning is given. Articles containing mathematical models and algorithms for classification were selected. The use of classification in intelligent management decision support systems in various subject areas is also relevant. Goal and objectives. The purpose of the study is to analyze papers on the classification as a machine learning method. To achieve the objective, it is necessary to solve the following tasks: 1) to identify the most used classification methods in machine learning; 2) to highlight the advantages and disadvantages of each of the selected methods; 3) to analyze the possibility of using classification methods in intelligent systems to support management decisions to solve issues of forecasting, prevention and elimination of emergencies. Methods. To obtain the results, general scientific and special methods of scientific knowledge were used - analysis, synthesis, generalization, as well as the classification method. Results and discussion thereof. According to the results of the analysis, studies with a mathematical formulation and the availability of software developments were identified. The issues of classification in the implementation of machine learning in the development of intelligent decision support systems are considered. Conclusion. The analysis revealed that enough algorithms were used to perform the classification while sorting the acquired knowledge within the subject area. The implementation of an accurate classification is one of the fundamental problems in the development of management decision support systems, including for fire and emergency prevention and response. Timely and effective decision by officials of operational shifts for the disaster management is also relevant. Key words: decision support, analysis, classification, machine learning, algorithm, mathematical models.

scholarly journals Data Mining and Machine Learning Methods Applied to A Numerical Clinching Model

2018 ◽  
Vol 117 (3) ◽  
pp. 387-423
Author(s):  
Marco Götz ◽  
Ferenc Leichsenring ◽  
Thomas Kropp ◽  
Peter Müller ◽  
Tobias Falk ◽  
...  
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Learning Methods ◽  
Machine Learning Methods

Benefits and Risks of Machine Learning Decision Support Systems

JAMA ◽  
2017 ◽  
Vol 318 (23) ◽  
pp. 2353 ◽  
Author(s):  
Eta S. Berner ◽  
Bunyamin Ozaydin
Keyword(s):  
Machine Learning ◽  
Decision Support ◽  
Decision Support Systems ◽  
Support Systems

Business Intelligence and Data Mining

2013 ◽  
pp. 57-70
Author(s):  
Zsolt T. Kardkovács
Keyword(s):  
Data Mining ◽  
Decision Support ◽  
Strategic Planning ◽  
Decision Support Systems ◽  
Business Intelligence ◽  
Business Processes ◽  
Support Systems ◽  
Decision Makers ◽  
Key Factors ◽  
Short Introduction

Whenever decision makers find out that they want to know more about how the business works and progresses, or why customers do what they do, then data miners are summoned, and business intelligence is to be built or altered. Data mining aims at retrieving valid, interesting, explicable connection between key factors for either operative reporting or supporting strategic planning. While data mining discovers static connections between factors, business intelligence visualizes relevant data for decision makers in order to make them identify fast changes and analyze precisely business states. In this chapter, the authors give a short introduction for data oriented decision support systems with data mining and business intelligence in it. While these techniques are widely used in business processes, there are much more bad practices than good ones. We try to make an attempt to demystify and clear the myths about these technologies, and determine who should and how (not) to use them.

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