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Personal Information  
Name :Bilal Ibrahim Sowan
Academic Rank :Associate professor
Office No :1525
Phone :
Department :Computer Science
Email :bsowan@zu.edu.jo
CV

 
Qualifications
Bachelor : Applied Science Private University Date : 2020.04.28
Master : Amman Arab University for Graduate Studies Date : 2020.04.28
Ph.D. : University of Bradford Date : 2020.04.28
Research - Journals
Bilal Sowan, Keshav Dahal, M Alamgir Hossain, Li Zhang, Linda Spencer , Fuzzy association rule mining approaches for enhancing prediction performance, Expert Systems with Applications , Volume 40, Issue 17, Pages 6928-6937
12/01/2013
This paper presents an investigation into two fuzzy association rule mining models for enhancing prediction performance. The first model (the FCM–Apriori model) integrates Fuzzy C-Means (FCM) and the Apriori approach for road traffic performance prediction. FCM is used to define the membership functions of fuzzy sets and the Apriori approach is employed to identify the Fuzzy Association Rules (FARs). The proposed model extracts knowledge from a database for a Fuzzy Inference System (FIS) that ca
ملف البحث

Bilal Sowan, Keshav Dahal, M. Alamgir Hossain, Li Zhang, and Linda Spencer. , Fuzzy association rule mining approaches for enhancing prediction performance, Expert Systems with Applications , Vol., 40, Issue 17, pp. 6928-6937
04/01/2020
This paper presents an investigation into two fuzzy association rule mining models for enhancing prediction performance. The first model (the FCM–Apriori model) integrates Fuzzy C-Means (FCM) and the Apriori approach for road traffic performance prediction. FCM is used to define the membership functions of fuzzy sets and the Apriori approach is employed to identify the Fuzzy Association Rules (FARs). The proposed model extracts knowledge from a database for a Fuzzy Inference System (FIS) tha
ملف البحث

Using the interestingness measure lift to generate association rules , Nada Hussein, Abdallah Alashqur, and Bilal Sowan., Journal of Advanced Computer Science & Technology , Vol. 4, Issue 1, pp. 156-162
12/01/2015
In this digital age, organizations have to deal with huge amounts of data, sometimes called Big Data. In recent years, the volume of data has increased substantially. Consequently, finding efficient and automated techniques for discovering useful patterns and relationships in the data becomes very important. In data mining, patterns and relationships can be represented in the form of association rules. Current techniques for discovering association rules rely on measures such as support for find
ملف البحث

Bilal Sowan , A Comparative Analysis of Exam Timetable Using Data Mining Techniques, International Journal of Computer Science and Netw , Vol. 17, Issue 1, pp. 73-80
01/01/2017
Knowledge discovery and data mining is an emerging practice that is applied in a wide range domain fields, for the purpose of extracting implicit knowledge from a huge database. This knowledge helps in making a decision in particular fields. It is one of the important developing applications is the higher education field. This paper proposes a data mining model, which is based on different and well-known classification algorithms. The model is able to extract implicit knowledge from the higher e
ملف البحث

Bilal Sowan and Hazem Qattous , Data Mining of Supervised learning Approach based on K-means Clustering, International Journal of Computer Science and Netw , Vol. 17, Issue 1, pp. 18-24
01/01/2017
A diversity of application fields include a massive number of datasets. Each dataset consists of a number of variables (features). One of these variables that is considered as a dependent variable (target variable) and is used for prediction in data mining of the supervised learning task. Data mining is necessary for building an automatic analysis in order to extract knowledge from datasets. Knowledge extraction is useful for recommendation system and decision making which can be accomplished by
ملف البحث

Hazem Qattous, Bilal Sowan and Omar AlSheikSalem , Teachme, A Gesture Recognition System with Customization Feature, International Journal of Advanced Computer Science , Vol. 7, Issue 11, pp. 46 -50,
04/01/2016
Many presentation these days are done with the help of a presentation tool. Lecturers at Universities and researchers in conferences use such tools to order the flow of the presentation and to help audiences follow the presentation points. Presenters control the presentation tools using mouse and keyboard which keep the presenters always beside the computer machine to be close enough to the keyboard and mouse. This reduces the ability of the lecturer to move close to the audiences and reduces
ملف البحث

Nada Hussein, Abdallah Alashqur, and Bilal Sowan , Using the interestingness measure lift to generate association rules, Journal of Advanced Computer Science & Technology , Vol. 4, Issue 1, pp. 156-162
12/01/2015
In this digital age, organizations have to deal with huge amounts of data, sometimes called Big Data. In recent years, the volume of data has increased substantially. Consequently, finding efficient and automated techniques for discovering useful patterns and relationships in the data becomes very important. In data mining, patterns and relationships can be represented in the form of association rules. Current techniques for discovering association rules rely on measures such as support for find
ملف البحث

Bilal Sowan, Keshav Dahal, M. Alamgir Hossain, Li Zhang, and Linda Spencer , Fuzzy association rule mining approaches for enhancing prediction performance, Expert Systems with Applications , Vol., 40, Issue 17, pp. 6928-6937
09/01/2013
This paper presents an investigation into two fuzzy association rule mining models for enhancing prediction performance. The first model (the FCM–Apriori model) integrates Fuzzy C-Means (FCM) and the Apriori approach for road traffic performance prediction. FCM is used to define the membership functions of fuzzy sets and the Apriori approach is employed to identify the Fuzzy Association Rules (FARs). The proposed model extracts knowledge from a database for a Fuzzy Inference System (FIS) tha
ملف البحث

Using the interestingness measure lift to generate association rules , Using the interestingness measure lift to generate association rules, Journal of Advanced Computer Science & Technology , Vol. 4, Issue 1, pp. 156-162
12/01/2015
In this digital age, organizations have to deal with huge amounts of data, sometimes called Big Data. In recent years, the volume of data has increased substantially. Consequently, finding efficient and automated techniques for discovering useful patterns and relationships in the data becomes very important. In data mining, patterns and relationships can be represented in the form of association rules. Current techniques for discovering association rules rely on measures such as support for find
Full Text Paper PDF

Bilal Sowan, Keshav Dahal, M. Alamgir Hossain, Li Zhang, and Linda Spencer. , Fuzzy association rule mining approaches for enhancing prediction performance, Expert Systems with Applications , Vol., 40, Issue 17
04/01/2013
This paper presents an investigation into two fuzzy association rule mining models for enhancing prediction performance. The first model (the FCM–Apriori model) integrates Fuzzy C-Means (FCM) and the Apriori approach for road traffic performance prediction. FCM is used to define the membership functions of fuzzy sets and the Apriori approach is employed to identify the Fuzzy Association Rules (FARs). The proposed model extracts knowledge from a database for a Fuzzy Inference System (FIS) tha
Full Text Paper PDF

Using the interestingness measure lift to generate association rules , Nada Hussein, Abdallah Alashqur, and Bilal Sowan, Journal of Advanced Computer Science & Technology , Vol. 4, Issue 1, pp. 156-162
12/01/2015
In this digital age, organizations have to deal with huge amounts of data, sometimes called Big Data. In recent years, the volume of data has increased substantially. Consequently, finding efficient and automated techniques for discovering useful patterns and relationships in the data becomes very important. In data mining, patterns and relationships can be represented in the form of association rules. Current techniques for discovering association rules rely on measures such as support for find
Full Text Paper PDF

Bilal Sowan , A Comparative Analysis of Exam Timetable Using Data Mining Techniques, International Journal of Computer Science and Netw , Vol. 17, Issue 1, pp. 73-80
01/01/2017
Knowledge discovery and data mining is an emerging practice that is applied in a wide range domain fields, for the purpose of extracting implicit knowledge from a huge database. This knowledge helps in making a decision in particular fields. It is one of the important developing applications is the higher education field. This paper proposes a data mining model, which is based on different and well-known classification algorithms. The model is able to extract implicit knowledge from the higher e
Full Text Paper PDF

Bilal Sowan and Hazem Qattous , A Data Mining of Supervised learning Approach based on K-means Clustering, International Journal of Computer Science and Netw , Vol. 17, Issue 1, pp. 18-24
01/01/2017
A diversity of application fields include a massive number of datasets. Each dataset consists of a number of variables (features). One of these variables that is considered as a dependent variable (target variable) and is used for prediction in data mining of the supervised learning task. Data mining is necessary for building an automatic analysis in order to extract knowledge from datasets. Knowledge extraction is useful for recommendation system and decision making which can be accomplished by
Full Text Paper PDF

Hazem Qattous, Bilal Sowan and Omar AlSheikSalem , Teachme, A Gesture Recognition System with Customization Feature, International Journal of Advanced Computer Science , Vol. 7, Issue 11, pp. 46 -50
04/01/2016
Many presentation these days are done with the help of a presentation tool. Lecturers at Universities and researchers in conferences use such tools to order the flow of the presentation and to help audiences follow the presentation points. Presenters control the presentation tools using mouse and keyboard which keep the presenters always beside the computer machine to be close enough to the keyboard and mouse. This reduces the ability of the lecturer to move close to the audiences and reduces
Full Text Paper PDF

Nada Hussein, Abdallah Alashqur, and Bilal Sowan , Using the interestingness measure lift to generate association rules, Journal of Advanced Computer Science & Technology , Vol. 4, Issue 1, pp. 156-162
12/01/2015
In this digital age, organizations have to deal with huge amounts of data, sometimes called Big Data. In recent years, the volume of data has increased substantially. Consequently, finding efficient and automated techniques for discovering useful patterns and relationships in the data becomes very important. In data mining, patterns and relationships can be represented in the form of association rules. Current techniques for discovering association rules rely on measures such as support for find
Full Text Paper PDF

Bilal Sowan, Keshav Dahal, M. Alamgir Hossain, Li Zhang, and Linda Spencer , Fuzzy association rule mining approaches for enhancing prediction performance, Expert Systems with Applications , Vol., 40, Issue 17, pp. 6928-6937
09/01/2013
This paper presents an investigation into two fuzzy association rule mining models for enhancing prediction performance. The first model (the FCM–Apriori model) integrates Fuzzy C-Means (FCM) and the Apriori approach for road traffic performance prediction. FCM is used to define the membership functions of fuzzy sets and the Apriori approach is employed to identify the Fuzzy Association Rules (FARs). The proposed model extracts knowledge from a database for a Fuzzy Inference System (FIS) tha
Full Text Paper PDF

Conferences
A Prediction Model Based on Multiple Support and Associative Classification Approaches
Proceedings of International conference on Softwar
Thailand
2013.09.28
In this paper, a Fuzzy Classification Association Rule (FCAR) model is proposed based on the improved G-K algorithm, improved multiple supports and the proposed Fuzzy Associative Classification Rules (FACR) approaches to improve the prediction accuracy. The improved G-K algorithm is used to define the membership functions of fuzzy sets, while FACR improves current associative classification approaches by adapting the improved multiple support algorithm. The proposed FCAR model can provide a gene
ملف البحث

A Prediction Model Based on Multiple Support and Associative Classification Approaches
Proceedings of International conference on Softwar
Thailand
2013.09.28
In this paper, a Fuzzy Classification Association Rule (FCAR) model is proposed based on the improved G-K algorithm, improved multiple supports and the proposed Fuzzy Associative Classification Rules (FACR) approaches to improve the prediction accuracy. The improved G-K algorithm is used to define the membership functions of fuzzy sets, while FACR improves current associative classification approaches by adapting the improved multiple support algorithm. The proposed FCAR model can provide a gene
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