Data Mining for Quality Management of Working Out Detailed Design Documentation Development in a Project Organization

2021 ◽  
Author(s):  
Svetlana Silnova ◽  
Anastasia Karamzina
Keyword(s):  
Data Mining ◽  
Quality Management ◽  
Project Organization ◽  
Detailed Design ◽  
Design Documentation ◽  
Working Out

Quality Management Approach in Qualifications Framework Implementation in Industrial Engineering

2015 ◽  
Vol 760 ◽  
pp. 721-726 ◽  
Author(s):  
Rodica Rohan ◽  
Nicolae Ionescu
Keyword(s):  
Higher Education ◽  
Quality Assurance ◽  
Quality Management ◽  
Conceptual Design ◽  
Industrial Engineering ◽  
Management Approach ◽  
Detailed Design ◽  
National Qualifications Framework ◽  
Implementation Stages

Currently in Romania it has been implemented the European Qualifications Framework (EQF) and its sustainability has become an impending need. In this paper, the authors propose an integrating structure, which involves a relatively large number of organizations for the sustainability of the National Qualifications Framework (CNC), while also providing quality assurance in higher education. Using quality management principles and their implementation stages as generic conceptual solutions, there have been established the specific conceptual solutions for the conceptual design of a system which the authors have called the System of Implementation and Sustainability of the National Qualifications Framework in Higher Education (SIS - CNCIS). It was then prepared the detailed design of the SIS-CNCIS for the particular case of implementation in Industrial Engineering.

scholarly journals Developing data quality management algorithm for Hypertension Patients accompanied with Diabetes Mellitus By Data Mining

2016 ◽  
Vol 14 (7) ◽  
pp. 309-319
Author(s):  
Kyu-Yeon Hwang ◽  
Eun-Sook Lee ◽  
Go-Won Kim ◽  
Sung-Ok Hong ◽  
Jong-Son Park ◽  
...  
Keyword(s):  
Diabetes Mellitus ◽  
Data Mining ◽  
Quality Management ◽  
Data Quality ◽  
Data Quality Management ◽  
Management Algorithm

scholarly journals Total Quality Management, Data Mining and Public Services: Advancing Towards Modernity

2019 ◽  
Vol 6 (6) ◽  
pp. 748-756
Author(s):  
Noemi Bonina ◽  
Marcelo J. Meiriño ◽  
Mirian P. Méxas ◽  
Alexandre Denizot ◽  
Luis Perez Zotes
Keyword(s):  
Data Mining ◽  
Quality Management ◽  
Total Quality Management ◽  
Public Services ◽  
Total Quality

scholarly journals THE EVALUATION OF INTERNAL CONTROL ELEMENTS IN THE QUALITY MANAGEMENT SYSTEM OF EDUCATION ESTABLISHMENTS AT LAW ENFORCEMENT INSTITUTIONS

2020 ◽  
Vol 6 ◽  
pp. 767
Author(s):  
Ivars Zālītis ◽  
Jeļena Davidova ◽  
Svetlana Ignatjeva
Keyword(s):  
Law Enforcement ◽  
Quality Management ◽  
Internal Control ◽  
Management System ◽  
Professional Training ◽  
Quality Management System ◽  
Educational Institution ◽  
Research Process ◽  
Internal Quality ◽  
Working Out

Under the conditions of a growing competition in the field of educational services and the increasing demands of the labor market for well-educated and highly qualified workforce, the quality of professional efficiency assumes a particular significance. Taking into consideration the present situation concerning the security of the EU borders and problems created by the migration as well as the challenges they pose, the quality management systems and their effectiveness are vital also for educational institutions training specialists for the needs of the departments of the Ministry of the Interior. The research is aimed at establishing the most effective principles for working out the quality management system of vocational training in regard to one of the basic elements of the quality system: the resource of internal quality in an educational institution. The paper reflects and provides the analysis of the research process and outcomes that have been obtained from the interviews conducted among twenty Baltic State experts in the field of professional training of border guards and police employees, and by evaluating the resources and elements of quality management system according to the criterion “Impact on the study process quality”. The obtained research and empirical data provide scientifically valid frame for the actions of education establishments of law enforcement institutions on defining the principles at working out the quality management system. 

Data Quality Analysis based Machine Learning models for Credit Card Fraud Detection

2021 ◽  
Vol 23 (06) ◽  
pp. 318-344
Author(s):  
Amit Pundir ◽  
◽  
Rajesh Pandey ◽  
Keyword(s):  
Data Mining ◽  
Random Forest ◽  
Quality Management ◽  
Data Quality ◽  
Decision Trees ◽  
Credit Card ◽  
Fraud Detection ◽  
Data Quality Management ◽  
Credit Card Fraud ◽  
Logistics Regression

Misrepresentation of money is a developing issue in monetary business with far-reaching consequences and keeping in mind that many processes have been found. Data quality management with data mining has been effectively applied to data sets to mechanize the investigation of massive amounts of complex information. Data mining has likewise played a notable role in identifying credit card fraud in online exchanges. Fraud detection in credit cards is a data quality management issue that considered under data mining, tested for two important reasons — first, the profiles of ordinary and false practices habitually change, and also because of the explanation that charge card fraud information is exceptionally slow. This research paper examines the performance of Decision Trees, Logistics Regression, and Random Forest rely strategically on profoundly skewed credit card fraud data. The dataset of credit card transactions is sourced from Kaggle (a publically accessible dataset repository) with 284,807 transactions. These methods are applied to raw data values and data preprocessing techniques. Assessment of the performance of techniques depends on accuracy, sensitivity, specificity, precision, and recall. Results indicate the optimal accuracy for the decision trees, logistics regression, and random forest classifiers with 90.8%, 98.5%, and 99.1% respectively.

Quality improvement calls data mining: the case of the seven new quality tools

2018 ◽  
Vol 25 (1) ◽  
pp. 47-75 ◽  
Author(s):  
Loukas K. Tsironis
Keyword(s):  
Data Mining ◽  
Quality Improvement ◽  
Quality Management ◽  
Rough Sets ◽  
Design Methodology ◽  
Classification Rules ◽  
Content Type ◽  
Useful Knowledge ◽  
Innovative Work ◽  
Quality Tools

Purpose The purpose of this paper is to propose a way of implementing data mining (DM) techniques and algorithms to apply quality improvement (QI) approaches in order to resolve quality issues (Rokach and Maimon, 2006; Köksal et al., 2011; Kahraman and Yanik, 2016). The effectiveness of the proposed methodologies is demonstrated through their application results. The goal of this paper is to develop a DM system based on the seven new QI tools in order to discover useful knowledge, in the form of rules, that are hidden in a vast amount of data and to propose solutions and actions that will lead an organization to improve its quality through the evaluation of the results. Design/methodology/approach Four popular data-mining approaches (rough sets, association rules, classification rules and Bayesian networks) are applied on a set of 12,477 case records concerning vehicle damages. The set of rules and patterns that is produced by each algorithm is used as an input in order to dynamically form each of the seven new quality tools (QTs). Findings The proposed approach enables the creation of the QTs starting from the raw data and passing through the DM process. Originality/value The present paper proposes an innovative work concerning the formation of the seven new QTs of quality management using DM popular algorithms. The resulted seven DM QTs were used to identify patterns and understand, so they can lead even non-experts to draw useful conclusions and make decisions.

The Application of Data Mining Technology in the Remote Open Management System

2014 ◽  
Vol 687-691 ◽  
pp. 1141-1144
Author(s):  
Mei Bai
Keyword(s):  
Data Mining ◽  
Quality Assessment ◽  
Information Management ◽  
Management System ◽  
Information Management System ◽  
Assessment System ◽  
Detailed Design ◽  
Mining Technology ◽  
Open Management ◽  
At Home

This paper introduces the concept of database and data mining, combined with management system of quality assessment system and method of data mining technology. In this paper, applying the data mining skill to the field of remote open management system, introduces the development of data mining in China and the necessity and importance of data mining in remote open information management system. This thesis analyzes the main problems in the remote open management system. On the basis of the relevant researches both at home and abroad, it presents the significance of the application of data mining in remote open management system. It analyzes the needs of the system based on data mining and presents a detailed design and implication of such a system.

Sign in / Sign up

Export Citation Format

Share Document