scholarly journals A Proposed Model to Identify Factors Affecting Asthma using Data Mining

2019 ◽  
Vol 27 (1) ◽  
pp. 203-212
Author(s):  
Marjan Ghazisaeedi ◽  
Abbas Sheikhtaheri ◽  
Nasrin Behniafard ◽  
Fatemehalsadat Aghaei Meybodi ◽  
Rouhallah khara ◽  
...  
Keyword(s):  
Data Mining ◽  
Factors Affecting ◽  
Proposed Model ◽  
Using Data

scholarly journals A simple model for the estimation of the number of fatalities due to floods in central Europe

2014 ◽  
Vol 14 (7) ◽  
pp. 1663-1676 ◽  
Author(s):  
M. Brazdova ◽  
J. Riha
Keyword(s):  
Central Europe ◽  
Pairwise Comparison ◽  
The Czech Republic ◽  
Factors Affecting ◽  
Hazard Exposure ◽  
Proposed Model ◽  
River Floods ◽  
Using Data ◽  
Human Losses ◽  
Significant Factors

Abstract. In this paper a model for the estimation of the number of potential fatalities is proposed based on data from 19 past floods in central Europe. First, the factors contributing to human losses during river floods are listed and assigned to the main risk factors: hazard – exposure – vulnerability. The order of significance of individual factors has been compiled by pairwise comparison based on experience with real flood events. A comparison with factors used in existing models for the estimation of fatalities during floods shows good agreement with the significant factors identified in this study. The most significant factors affecting the number of human losses in floods have been aggregated into three groups and subjected to correlation analysis. A close-fitting regression dependence is proposed for the estimation of loss of life and calibrated using data from selected real floods in central Europe. The application of the proposed model for the estimation of fatalities due to river floods is shown via a flood risk assessment for the locality of Krnov in the Czech Republic.

Quality Control of Micro-Drilled Hole Wall of PWBs Using Data-Mining

2005 ◽  
Author(s):  
Eiichi Aoyama ◽  
Toshiki Hirogaki ◽  
Keiji Ogawa ◽  
Tsuyoshi Otsuka ◽  
Katsutoshi Yamauchi
Keyword(s):  
Data Mining ◽  
Surface Roughness ◽  
Present Report ◽  
Weft Yarn ◽  
Hole Drilling ◽  
Printed Wiring Boards ◽  
Factors Affecting ◽  
Hole Wall ◽  
Using Data ◽  
Through Hole

In the manufacturing of printed wiring boards (PWBs), various methods have been developed in order to improve the circuit packaging density. Micro-drills are generally used to make smaller diameter through-holes in PWBs, which are desired for the miniaturization of equipment. However, a problem has emerged in that copper plating degraded by hole drilling can reduce the reliability of the electrical connection between layers. The surface roughness of drilled hole wall is one of the important factors affecting the plating quality. The purpose of the present report is to apply data-mining to the surface roughness data of drilled through-hole walls, and to elucidate the factors required to control the drilled hole quality. The following conclusions were obtained. (1) The data-mining aided by a computer was found to be effective to control the drilled hole wall quality in the PWBs manufacturing. (2) It was clear that the surface roughness of drilled hole walls depended on three factors: the drill temperature, cutting distance, and the width of the fiber bundle of weft yarn.

A Proposed Model for Predicting Employees’ Performance Using Data Mining Techniques

2020 ◽  
Vol 17 (8) ◽  
pp. 3804-3809
Author(s):  
A. Yovan Felix ◽  
Karthik Reddy Vuyyuru ◽  
Viswas Puli
Keyword(s):  
Data Mining ◽  
Resource Management ◽  
Human Resource Management ◽  
Human Resource ◽  
Human Resource Professionals ◽  
Quality Factors ◽  
Proposed Model ◽  
Opportune Time ◽  
Using Data ◽  
The Impact

Human Resource Management has gotten one of the basic pastimes of supervisors and chiefs in practically wide variety of corporations to include plans for accurately locating profoundly qualified representatives. In similar way, administrations come to be intrigued about the presentation of these representatives. Particularly to guarantee the fitting person apportioned to the beneficial employment on the opportune time. From right here the enthusiasm of statistics in mining process has been growing that its goal is disclosure of facts from huge measures of statistics. Three fundamental Data Mining strategies were applied for building the arrangement version and distinguishing the quality factors that emphatically impact the exhibition. To get a profoundly actual version, a few trials were achieved dependent on the beyond procedures which can be actualized in WEKA tool for empowering leaders and Human Resource professionals to anticipate and improve the exhibition of their representatives. This paper makes use of Hadoop for the remedy of great measure of data with which may be guaranteed to be able to decide the impact.

scholarly journals Investigating factors affecting library visits by university students using data mining

2018 ◽  
Vol 28 (1) ◽  
Author(s):  
Puarungroj Wichai ◽  
Pongpatrakant Pathapong ◽  
Boonsirisumpun Narong ◽  
Phromkhot Suchada
Keyword(s):  
Data Mining ◽  
University Students ◽  
Factors Affecting ◽  
Using Data

scholarly journals Employing Data Mining Techniques for Predicting Opioid Withdrawal in Applicants of Health Centers

2019 ◽  
Vol 3 (2) ◽  
pp. 33
Author(s):  
Raheleh Hamedanizad ◽  
Elham Bahmani ◽  
Mojtaba Jamshidi ◽  
Aso Mohammad Darwesh
Keyword(s):  
Data Mining ◽  
Mean Squared Error ◽  
Opioid Withdrawal ◽  
Health Centers ◽  
Statistical Population ◽  
Data Mining Algorithms ◽  
Proposed Model ◽  
Meta Learning ◽  
Using Data ◽  
Mining Algorithms

   Addiction to narcotics is one of the greatest health challenges in today’s world which has become a serious threat for social, economic, and cultural structures and has ruined a part of an active force of the society and it is one of the main factors of growth of diseases such as HIV and hepatitis. Today, addiction is known as a disease and welfare organization, and many of the dependent centers try to help the addicts treat this disease. In this study, using data mining algorithms and based on data collected from opioid withdrawal applicants referring to welfare organization, a prediction model is proposed to predict the success of opioid withdrawal applicants. In this study, the statistical population is comprised opioid withdrawal applicants in a welfare organization. This statistical population includes 26 features of 793 instances including men and women. The proposed model is a combination of meta-learning algorithms (decorate and bagging) and J48 decision tree implemented in Weka data mining software. The efficiency of the proposed model is evaluated in terms of precision, recall, Kappa, and root mean squared error and the results are compared with algorithms such as multilayer perceptron neural network, Naive Bayes, and Random Forest. The results of various experiments showed that the precision of the proposed model is 71.3% which is superior over the other compared algorithms.

Building Context-Aware E-Commerce Systems

2010 ◽  
pp. 1021-1029
Author(s):  
Anahit Martirosyan ◽  
Thomas Tran ◽  
Azzedine Boukerche
Keyword(s):  
Data Mining ◽  
Social Networks ◽  
Context Aware ◽  
Work Context ◽  
Privacy Issue ◽  
Data Mining Techniques ◽  
Proposed Model ◽  
Use Context ◽  
Using Data ◽  
Different Levels

Context is any information/knowledge about an application and user that can be used by an e-commerce system to provide efficient services to the users of the system. In this article, we propose to extend usage of context as compared to previously designed context-aware e-commerce systems. While in previous work, context was mainly considered for mobile e-commerce systems, we propose to build and use context for e-commerce systems in general. The context is employed to tailor an e-commerce application to the preferences and needs of users and provide insights into purchasing activities of users and particular e-commerce stores by means of using Data Mining techniques. This article proposes a model of context that includes micro-, macro- and domain contexts that constitute knowledge about the application and its user on different levels of granularity. The article also proposes a technique for extracting groups in social networks. This knowledge is part of macro-context in the proposed model of context. Moreover, the article discusses some of the challenges of incorporating context with e-commerce systems, emphasizing on the privacy issue, with an ultimate goal of developing intelligent e-commerce systems.

The Study of Score Analysis for Secondary School Based on Data Mining Technology

2011 ◽  
Vol 219-220 ◽  
pp. 396-399
Author(s):  
Shang Fu Hao ◽  
Zhi Qiang Zhang ◽  
Ying Hui Wei
Keyword(s):  
Data Mining ◽  
Score Distribution ◽  
Mining Technology ◽  
Factors Affecting ◽  
Architecture Model ◽  
Scientific Teaching ◽  
Score Analysis ◽  
Secondary Teaching ◽  
Using Data ◽  
Analysis System

Nowadays, the contents associated with deep score analysis is rarely involved in the existing secondary teaching management software, which is not conductive to fully develop the information implied by these data,without scientific teaching evaluation. Using data mining technology, multiple aspects of student score distribution will be shown accurately, identifying the regular factors affecting score changes. Standard score as the mathematical model is adopted in the system, choosing the standard SOA architecture model, and a scientific and efficient score analysis system based on JAVA, JSP is developed. The system provides decision support information for academic departments to promote better teaching work, and finally improve the quality of teaching.

scholarly journals A novel academic performance estimation model using two stage feature selection

2020 ◽  
Vol 19 (3) ◽  
pp. 1610
Author(s):  
Pamela Chaudhury ◽  
Hrudaya Kumar Tripathy
Keyword(s):  
Data Mining ◽  
Feature Selection ◽  
Academic Performance ◽  
Educational Data Mining ◽  
Performance Estimation ◽  
Estimation Model ◽  
Proposed Model ◽  
Differential Evolutionary Algorithm ◽  
Performance Estimation Model ◽  
Using Data

<span lang="EN-GB">Educational data mining has gained tremendous interest from researchers across the globe. Using data mining techniques in the field of education several significant findings have been made. Accurate academic performance estimation is a challenging task. In this study we have developed a novel model to estimate the academic performance of students. Techniques like conversion of categorical attributes into dummy variables, classification, two staged feature selection and an improved differential evolutionary algorithm were used. Our proposed model outperformed existing models of students’ academic performance determination and gave a new direction to it. The proposed model can help not only to reduce the number of academic failures but also help to comprehend the factors contributing to a student’s  academic performance (poor, average or outstanding).Computer</span>

scholarly journals Analysis of the factors affecting the iron ore pellet FEO index using data mining technique

2020 ◽  
Vol 4 (2) ◽  
pp. 83-92
Author(s):  
Mahdi Nakhaeinejad ◽  
Farzaneh Zarei
Keyword(s):  
Data Mining ◽  
Iron Oxide ◽  
Optimal Range ◽  
Data Mining Technique ◽  
Factors Affecting ◽  
Five Factors ◽  
Regression Algorithms ◽  
Iron Ore Pellet ◽  
Using Data ◽  
The Ideal

One of the most critical factors affecting iron pellet quality is the reduction in FeO (Iron Oxide) index in the final product. This study aims to predict factors affecting the FeO (Iron Oxide) of iron pellets and find out the contribution of each factor to reduce the pellets FeO (the ideal amount is between 0.4 to 0.6) using data mining tech­niques. When the FeO index's value is in the optimal range, the quality and price of pellets are higher. The data used in this study was collected from the pelletizing plant of Gol-E-Gohar in Sirjan, Iran, and the decision tree and regression algorithms are used in this analysis. Forty-five factors that can affect the FeO (Iron Oxide) index of the final product were studied, showing that the Magnesium Oxide and Airflow of the inlet fan of the indurating machine had the greatest impact on the FeO (Iron Oxide) of iron pellets.

scholarly journals Data Mining Methods for Detecting the Most Significant Factors Affecting Students’ Performance

2020 ◽  
Vol 12 (5) ◽  
pp. 1-13
Author(s):  
Mohammed Abdullah Al-Hagery ◽  
◽  
Maryam Abdullah Alzaid ◽  
Tahani Soud Alharbi ◽  
Moody Abdulrahman Alhanaya
Keyword(s):  
Data Mining ◽  
Educational Data Mining ◽  
Study Finding ◽  
Vital Role ◽  
Educational Institutions ◽  
Factors Affecting ◽  
Educational Environments ◽  
Important Field ◽  
Using Data ◽  
Relationship Of

The field of using Data Mining (DM) techniques in educational environments is typically identified as Educational Data Mining (EDM). EDM is rapidly becoming an important field of research due to its ability to extract valuable knowledge from various educational datasets. During the past decade, an increasing interest has arisen within many practical studies to study and analyze educational data especially students’ performance. The performance of students plays a vital role in higher education institutions. In keeping with this, there is a clear need to investigate factors influencing students’ performance. This study was carried out to identify the factors affecting students’ academic performance. K-means and X-means clustering techniques were applied to analyze the data to find the relationship of the students' performance with these factors. The study finding includes a set of the most influencing personal and social factors on the students’ performance such as parents’ occupation, parents’ qualification, and income rate. Furthermore, it is contributing to improving the education quality, as well as, it motivates educational institutions to benefit and discover the unseen patterns of knowledge in their students' accumulated data.

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