scholarly journals Predicting Assembly Quality of Complex Structures Using Data Mining

2006 ◽  
pp. 263-268
Author(s):  
Ekaterina S. Ponomareva ◽  
Kesheng Wang ◽  
Terje K. Lien
Keyword(s):  
Data Mining ◽  
Complex Structures ◽  
Assembly Quality ◽  
Using Data

scholarly journals Using Data Mining to Analyze Dwell Time and Nonstop Running Time in Road-Based Mass Transit Systems

Proceedings ◽  
2018 ◽  
Vol 2 (19) ◽  
pp. 1217
Author(s):  
Teresa Cristóbal ◽  
Gabino Padrón ◽  
Alexis Quesada ◽  
Francisco Alayón ◽  
Gabriel de Blasio ◽  
...  
Keyword(s):  
Data Mining ◽  
Travel Time ◽  
Dwell Time ◽  
Mass Transit ◽  
Transit Systems ◽  
Running Time ◽  
Bus Stop ◽  
Transport Line ◽  
Using Data

Travel Time plays a key role in the quality of service in road-based mass transit systems. In this type of mass transit systems, travel time of a public transport line is the sum of the dwell time at each bus stop and the nonstop running time between pair of consecutives bus stops of the line. The aim of the methodology presented in this paper is to obtain the behavior patterns of these times. Knowing these patterns, it would be possible to reduce travel time or its variability to make more reliable travel time predictions. To achieve this goal, the methodology uses data related to check-in and check-out movements of the passengers and vehicles GPS positions, processing this data by Data Mining techniques. To illustrate the validity of the proposal, the results obtained in a case of use in presented.

scholarly journals Improving Quality of Educational Processes Providing New Knowledge Using Data Mining Techniques

2014 ◽  
Vol 147 ◽  
pp. 390-397 ◽  
Author(s):  
Manolis Chalaris ◽  
Stefanos Gritzalis ◽  
Manolis Maragoudakis ◽  
Cleo Sgouropoulou ◽  
Anastasios Tsolakidis
Keyword(s):  
Data Mining ◽  
Data Mining Techniques ◽  
Educational Processes ◽  
New Knowledge ◽  
Using Data

Optimizing and Managing Digital Telecommunication Systems Using Data Mining and Knowledge Discovery Approaches

2011 ◽  
pp. 2360-2379
Author(s):  
Adnan I. Al Rabea ◽  
Ibrahiem M. M. El Emary
Keyword(s):  
Data Mining ◽  
Quality Of Service ◽  
Knowledge Discovery ◽  
Self Organizing Map ◽  
Performance Measurements ◽  
Operating Personnel ◽  
Telecommunication Systems ◽  
Original Measurement ◽  
Using Data

This chapter is interested in discussing and reporting how one can be benefited by using Data Mining and Knowledge Discovery techniques in achieving an acceptable level of quality of service of telecommunication systems. The quality of service is defined as the metrics which is predicated by using the data mining techniques, decision tree, association rules and neural networks. Digital telecommunication networks are highly complex systems and thus their planning, management and optimization are challenging tasks. The user expectations constitute the Quality of Service (QoS). To gain a competitive edge on other operators, the operating personnel have to measure the network in terms of QoS. In current times, there are three data mining methods applied to actual GSM network performance measurements, in which the methods were chosen to help the operating staff to find the essential information in network quality performance measurements. The results of Pekko (2004) show that the analyst can make good use of Rough Sets and Classification and Regression Trees (CART), because their information can be expressed in plain language rules that preserve the variable names of the original measurement. In addition, the CART and the Self-Organizing Map (SOM) provide effective visual means for interpreting the data set.

Using Data Mining Techniques to Discover Patterns in an Airline's Flight Hours Assignments

2021 ◽  
pp. 107-126
Author(s):  
Francisco Javier Villar Martín ◽  
Jose Luis Castillo Sequera ◽  
Miguel Angel Navarro Huerga
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Information System ◽  
Predictive Modeling ◽  
Data Model ◽  
Free Software ◽  
Data Mining Techniques ◽  
Visualization Tools ◽  
Using Data

The quality of a company's information system is essential and also its physical data model. In this article, the authors apply data mining techniques in order to generate knowledge from the information system's data model, and also to discover and understand hidden patterns within data that regulate the planning of flight hours of pilots and copilots in an airline. With this objective, they use Weka free software which offers a set of algorithms and visualization tools geared to data analysis and predictive modeling of information systems. Firstly, they apply clustering to study the information system and analyze data model; secondly, they apply association rules to discover connection patterns in data; and finally, they generate a decision tree to classify and extract more specific patterns. The authors suggest conclusions according these information system's data to improve future decision making in an airline's flight hours assignments.

scholarly journals Predictive Modelling for Quality Prediction and Assurance of Extrusion Blow Molding

2019 ◽  
Vol 8 (11) ◽  
pp. 1364-1368
Keyword(s):  
Data Mining ◽  
Predictive Modelling ◽  
Molding Process ◽  
K Nearest Neighbors ◽  
Blow Molding ◽  
Data Mining Approach ◽  
Complex Relationships ◽  
Using Data ◽  
Extrusion Blow Molding

Extrusion Blow Molding process plays an important role in manufacturing of hollow products with wide variety of materials like polyethylene (PE), polypropylene (PP), polyvinylchloride (PVC). Extrusion blow molded products are rejected due to the occurrence of defects such as die lines, blowouts, shrinkage, over weight of part. The complex relationships that exist between the process variables, and causes of defects are investigated for 1 litre container made of highdensity polyethylene (HDPE) using data mining techniques in order to reduce scrap. In this paper Data Mining approach is implemented by applying Decision Tree, k-Nearest Neighbors, Rule Induction and Vote techniques in RapidMiner for quality assurance and prediction of the quality of the extrusion blow molded product

scholarly journals Implementation of K-Means Technique in Data Mining to Cluster Researchers Google Scholar Profile

2019 ◽  
Vol 9 (1) ◽  
pp. 3654-3660
Keyword(s):  
Data Mining ◽  
Organizational Management ◽  
Google Scholar ◽  
Scientific Publications ◽  
R Language ◽  
Quality Of Higher Education ◽  
Main Indicator ◽  
The Common ◽  
Using Data

A university usually has many Lecturers that have an important role in improving the quality of Higher Education. The Lecturers should produce scientific publications at least 1 publication on each semester. The achievements of a Lecturer in research and publication become the main indicator that describes the professionalism of lecturers as scientists. Monitoring the improvement of publication trends is very important to do as an evaluation for organizational management in choosing the best strategy to strengthen the quality of publication, and one of the common tools used for analyzing the publication data is the Google Scholar system. This paper attempts to analyze the Google scholar data using Data Mining techniques (Text Mining) by R language, to collect Lecturer’s profile and list of publications in a real-time, the aim of this research is to allow the Management for identifying the Cluster from total 1039 Lecturers on University of Lampung. The results of this research shown there were 5 Cluster of scholar profile data, with member details C0=102, C1=924, C2=1, C3=1, C4=11, total 88.93% of Lecturers are on cluster C1 with, centroid data was h_index=1.942, total_cites=20.89, i10_index=0.417.

Role of Data Mining and Knowledge Discovery in Managing Telecommunication Systems

2010 ◽  
pp. 14-29
Author(s):  
Ibrahiem Mahmoud Mohamed El Emary
Keyword(s):  
Data Mining ◽  
Quality Of Service ◽  
Expert Systems ◽  
Telecommunications Industry ◽  
Data Mining Techniques ◽  
Telecommunication Systems ◽  
The Status ◽  
Using Data

This chapter is interested in discussing how to use data mining techniques to assist in achieving an acceptable level of quality of service of telecommunication systems. The quality of service is defined as the metrics which are predicated by using the data mining techniques, decision tree, association rules and neural networks. Routing algorithms can use this metric for optimal path selection which in turn will affect positively on the system performance. Also, in this chapter management axis using data mining techniques were handled, i.e., check the status of the telecommunication networks, role of data mining in obtaining optimal configuration, how to use data mining technique to assure high level of security for the telecommunication. The popularity of data mining in the telecommunications industry can be viewed as an extension of the use of expert systems in the telecommunications industry. These systems were developed to address the complexity associated with maintaining a huge network infrastructure and the need to maximize network reliability while minimizing labor costs (Liebowitz, J. 1988). The problem with these expert systems is that they are expensive to develop because it is both difficult and time consuming to elicit the requisite domain knowledge from experts.

Optimizing and Managing Digital Telecommunication Systems Using Data Mining and Knowledge Discovery Approaches

2010 ◽  
pp. 66-84
Author(s):  
Adnan I. Al Rabea ◽  
Ibrahiem M. M. El Emary
Keyword(s):  
Data Mining ◽  
Quality Of Service ◽  
Knowledge Discovery ◽  
Self Organizing Map ◽  
Performance Measurements ◽  
Operating Personnel ◽  
Telecommunication Systems ◽  
Original Measurement ◽  
Using Data

This chapter is interested in discussing and reporting how one can be benefited by using Data Mining and Knowledge Discovery techniques in achieving an acceptable level of quality of service of telecommunication systems. The quality of service is defined as the metrics which is predicated by using the data mining techniques, decision tree, association rules and neural networks. Digital telecommunication networks are highly complex systems and thus their planning, management and optimization are challenging tasks. The user expectations constitute the Quality of Service (QoS). To gain a competitive edge on other operators, the operating personnel have to measure the network in terms of QoS. In current times, there are three data mining methods applied to actual GSM network performance measurements, in which the methods were chosen to help the operating staff to find the essential information in network quality performance measurements. The results of Pekko (2004) show that the analyst can make good use of Rough Sets and Classification and Regression Trees (CART), because their information can be expressed in plain language rules that preserve the variable names of the original measurement. In addition, the CART and the Self-Organizing Map (SOM) provide effective visual means for interpreting the data set.

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