scholarly journals DATA FILTERING TECHNIQUES IN DECISION SUPPORT SYSTEMS

2021 ◽  
Author(s):  
Roman L. Pantyeyev ◽  
Oksana L. Timoshchuk ◽  
Vira H. Huskova ◽  
Petro I. Bidyuk
Keyword(s):  
Parameter Estimation ◽  
Decision Support ◽  
Data Analysis ◽  
Decision Support Systems ◽  
Statistical Data ◽  
Support Systems ◽  
Nonstationary Processes ◽  
Data Filtering ◽  
High Quality ◽  
Filtering Techniques

Background. The majority of modern dynamic processes in economy, finances, ecology, technologies and many other areas of studies exhibit short- and long-term nonlinear and nonstationary behavior. That is why it is required to create for their thorough analysis modern highly developed specialized instrumentation providing for appropriate preliminary statistical data processing, simulation state and parameter estimation and quality forecasting their evolution in time to be used in decision support systems (DSS). Objective. The purpose of the paper is to perform introductory analysis of some modern methods for filtering statistical and experimental data; to consider modern filtering techniques on the basis of probabilistic Bayesian approach, that provide a possibility for preparing the data to adequate simulation, computing high quality state and forecast estimates for dynamic systems in stochastic environment and availability of measurement errors. Methods. To implement modern data filtering techniques appropriate simulation and optimization procedures, probabilistic Bayesian methods of data analysis are utilized; simulation algorithms for parameter estimation, and criteria bases for analyzing quality of intermediate and final results in the frames of DSS are used. Results. A set of data filtering techniques is presented to be used together with the models describing formally selected processes dynamics. The methodology is considered for implementation of probabilistic Bayesian filter based upon modern statistical data analysis techniques including application of appropriate simulation procedures. Conclusions. Development of effective means for simulation, state estimation and forecasting dynamics of nonlinear nonstationary processes in various areas of human activities provides a possibility for high quality state and parameter estimation and compute short and middle term forecasts for their future evolution. The methods of optimal Kalman and probabilistic Bayesian filtering considered in the review provide a possibility for performing appropriate analysis of nonlinear nonstationary processes, compute forecasts and provide for managerial decision support on the basis of the forecast estimates.

scholarly journals Clinical Decision Support Systems

2018 ◽  
pp. 153-169 ◽  
Author(s):  
A. T. M. Wasylewicz ◽  
A. M. J. W. Scheepers-Hoeks
Keyword(s):  
Decision Support ◽  
Decision Support Systems ◽  
Clinical Decision Support ◽  
Data Science ◽  
Support Systems ◽  
Critical Evaluation ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
High Quality ◽  
Normal Limits

AbstractClinical decision support (CDS) includes a variety of tools and interventions computerized as well as non- computerized. High-quality clinical decision support systems (CDSS), computerized CDS, are essential to achieve the full benefits of electronic health records and computerized physician order entry. A CDSS can take into account all data available in the EHR making it possible to notice changes outside the scope of the professional and notice changes specific for a certain patient, within normal limits. However, to use of CDSS in practice, it is important to understand the basic requirements of these systems.This chapter shows in what way CDSS can support the use of clinical data science in daily clinical practice. Moreover, it explains what types of CDSS are available and how such systems can be used. However, to achieve high-quality CDSS which is effective in use requires thoughtful design, implementation and critical evaluation. Therefore, challenges surrounding implementation of a CDSS are discussed, as well as a strategies to develop and validate CDSS.

scholarly journals Sistem Pendukung Keputusan Spesifikasi Biji Jagung Berkualitas Terbaik dengan Metode Multi Attribute Utility Theory

KOMTEKINFO ◽  
2019 ◽  
Vol 5 (3) ◽  
pp. 10-19
Author(s):  
Chairul Imam ◽  
Julius Santony ◽  
Yuhandri
Keyword(s):  
Decision Support ◽  
Decision Support Systems ◽  
Utility Theory ◽  
Support Systems ◽  
High Quality ◽  
Multi Attribute Utility Theory ◽  
Corn Kernels ◽  
Total Price

Farmers sell corn kernels in the company of PT Charoen Pokphan Indonesia Tbk Medan, corn kernels are used to mix feed ingredients to meet the protein values and nutrition of these feeds to be of high quality. The company PT Charoen Pokphan Indonesia Tbk Medan buys corn kernels to farmers by specifying the best quality corn kernels, so they know the total price of corn kernels is in accordance with the quality needed. This research determines the criteria of the best quality types of corn kernels and how to apply the Multi Attribute Utility Theory to decision support systems to determine the quality of corn kernels, to be able to help the company PT Charoen Pokphan Indonesia Tbk Medan in determining the quality of corn kernels. Based on the criteria set out in the company PT Charoen Pokphan Indonesia Tbk Medan to obtain the best quality corn kernels using grade 1 to grade 4 and ranking. The results of testing these methods are produced a decision on an alternative with a total value of 86.7%. So this method is needed to evaluate the determination of the best quality corn kernels to produce the best decisions

Square-IT and Plan-Merge: Decision Support Systems for High Quality Manufacturing Setup Sequences

2002 ◽  
Author(s):  
Caroline C. Hayes ◽  
Amit S. Pande
Keyword(s):  
Decision Support ◽  
Decision Support Systems ◽  
Process Planning ◽  
Support Systems ◽  
Software Agents ◽  
High Quality ◽  
Automated Process Planning ◽  
Machined Parts ◽  
Automated Process ◽  
Future Work

Square-IT and Plan-Merge are software agents which assist in the process of creating manufacturing plans for small batch prismatic machined parts. Squaring is the process of generating a sequence of operations that prepares the stock so all surfaces are parallel or perpendicular to each other. Most automated process planning programs, and many machinists, perform squaring as a separate process completed prior to manufacturing the part. Although this practice simplifies planning, many opportunities for efficiency are lost when squaring and shaping the part into its final form are achieved in two completely separate plans. A far more efficient plan (i.e. fewer setups) can be achieved when squaring and feature cutting are treated as two types of operations included in a single, more globally efficient plan. Square-IT and Plan-Merge together perform this process of merging these two types of operations. The efficiency savings are dramatic. Setups can be reduced by 10 to 40 percent. Reducing set-ups also increases accuracy of the part by reducing opportunities for error. In future work we hope to adapt Square-IT and Plan-Merge so that they can be used as training tools that teach proper squaring and planning efficiency principles.

scholarly journals Prospects for Using of Decision Support Systems for Assessment and Control of Technogenic Pressure on the Environment

2019 ◽  
Keyword(s):  
Decision Support ◽  
Data Analysis ◽  
Decision Support Systems ◽  
Spatial Data ◽  
Support Systems ◽  
Main Tool ◽  
Spatial Data Analysis ◽  
Integral Index ◽  
Mathematical Algorithms ◽  
State And Local

Purpose. Designing the database concerning level of technogenic load on the environment. Development of the software for database control and zoning Ukrainian area by the techonogenic load. Methods. The GIS free software QGIS is used as main tool for spatial data analysis and designing the digital maps. The secondary tool is Environmental Decision Support Systems software which has been developed by author of the research. The main mathematical algorithms are cluster and factor analysis. Results. The comprehensive approach to multidimensional zoning has been introduced. The integral index of technogenic load on the environment has been defined. The integral index is based on particular indexes which describes technodenic impacts on atmosphere, water and soils. The territory of Ukraine has been zoned by the level of technogenic load on the environment. There has been calculated comprehensive map of spatial distribution for technogenic load on the environment of Ukraine. There have been designed The digital map database, which describes conditions of the environment of Ukraine, and appropriate database control system. Author has developed the comprehensive software Envoronmental Decision Support systems by utilizing objectice-oriented language C++. The core of the application is geoinrormational models and appropriate mathematical algorithms for spatial data analysis. Conclusions. The areas with high levels of technogenic load on the environment have been outlined. The developed approach and software might be useful for state and local authority institutions control activities which directed to reduction of negative impacts on the environment.

scholarly journals Using clinical reasoning ontologies to make smarter clinical decision support systems: a systematic review and data synthesis

2019 ◽  
Vol 27 (1) ◽  
pp. 159-174 ◽  
Author(s):  
Pavithra I Dissanayake ◽  
Tiago K Colicchio ◽  
James J Cimino
Keyword(s):  
Decision Support ◽  
Decision Support Systems ◽  
Clinical Decision Support ◽  
Clinical Reasoning ◽  
Support Systems ◽  
Medical Knowledge ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
High Quality ◽  
Current Practices

Abstract Objective The study sought to describe the literature describing clinical reasoning ontology (CRO)–based clinical decision support systems (CDSSs) and identify and classify the medical knowledge and reasoning concepts and their properties within these ontologies to guide future research. Methods MEDLINE, Scopus, and Google Scholar were searched through January 30, 2019, for studies describing CRO-based CDSSs. Articles that explored the development or application of CROs or terminology were selected. Eligible articles were assessed for quality features of both CDSSs and CROs to determine the current practices. We then compiled concepts and properties used within the articles. Results We included 38 CRO-based CDSSs for the analysis. Diversity of the purpose and scope of their ontologies was seen, with a variety of knowledge sources were used for ontology development. We found 126 unique medical knowledge concepts, 38 unique reasoning concepts, and 240 unique properties (137 relationships and 103 attributes). Although there is a great diversity among the terms used across CROs, there is a significant overlap based on their descriptions. Only 5 studies described high quality assessment. Conclusion We identified current practices used in CRO development and provided lists of medical knowledge concepts, reasoning concepts, and properties (relationships and attributes) used by CRO-based CDSSs. CRO developers reason that the inclusion of concepts used by clinicians’ during medical decision making has the potential to improve CDSS performance. However, at present, few CROs have been used for CDSSs, and high-quality studies describing CROs are sparse. Further research is required in developing high-quality CDSSs based on CROs.

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