W. Hadi, S. Nawafleh, " The role of e-business in the e-government services implementation " , "International Journal of Academic Research ",Vol.4,No.6, Progress IPS LLC, Baku, Azerbaijan, 10/01/2012
According to information communication technology revolution; governments too realized the benefits of information sharing thus the concept of e-Government emerged and has been implemented in many developed and developing countries, subsequently becoming the topic of studies discussing and identifying the main strength and weakness factors that affect the successful implementation of e-Government programs. This study aimed to identify the reality of the implementation of the e-Government project in the developing countries through discussion and analysis of some of the factors that impact directly on its effectiveness: Laws and legislation, cultural, political and social factors
Nawafleh S. and Hadi, " MULTI-CLASS ASSOCIATIVE CLASSIFICATION TO PREDICTING PHISHING WEBSITES " , "International Journal of Academic Research",Vol.4,No.6, PROGRESS- INTERNET AND POLYGRAPHIC SERVICES LLC, Baku, Azerbaijan, 11/28/2012
Phishing website is a very complex issue to understand and to analyze, since it is joining technical and social problem with each other for which there is no known single silver bullet to solve it entirely. That's why we will try to quantify and qualify all the website phishing characteristics and factors in order to understand where to focus protective measures to prevent or mitigate all the risks and threats that comes from visiting phishing website especially the creation of the “trust crises” which severely affects all the online transaction.
This paper is to create a resilient and effective intelligent model to detect phishing websites to assess whether phishing activity is taking place or not in order to help all users from being deceived or hacked by stealing their personal information and passwords leading to catastrophic consequences. Associative classification (AC) is a data mining approach that uses association rule discovery methods to build classification systems (classifiers).
Several research studies reveal that AC normally generates more accurate classifiers than classic classification data mining approaches such as rule induction, probabilistic and statistical. Experimentations against phishing data sets and using different common AC and traditional learning approaches have been conducted with reference to classification accuracy. The results show that the MAC algorithm is able to derive higher predictive classifiers than SVM, NB, PRISM and RIPPER algorithms.
Abdelhamid N., Ayesh, " MAC: A Multiclass Associative Classification Algorithm " , "Journal of Information & Knowledge Management",Vol.11,No.2, World Scientific Publishing Co., , 06/01/2012
Associative classification (AC) is a data mining approach that uses association rule discovery methods to build classification systems (classifiers). Several research studies reveal that AC normally generates higher accurate classifiers than classic classification data mining approaches such as rule induction, probabilistic and decision trees. This paper proposes a new multiclass AC algorithm called MAC. The proposed algorithm employs a novel method for building the classifier that normally reduces the resulting classifier size in order to enable end-user to more understand and maintain it. Experimentations against 19 different data sets from the UCI data repository and using different common AC and traditional learning approaches have been conducted with reference to classification accuracy and the number of rules derived. The results show that the proposed algorithm is able to derive higher predictive classifiers than rule induction (RIPPER) and decision tree (C4.5) algorithms and very competitive to a known AC algorithm named MCAR. Furthermore, MAC is also able to produce less number of rules than MCAR in normal circumstances (standard support and confidence thresholds) and in sever circumstances (low support and confidence thresholds) and for most of the data sets considered in the experiments.
THABTAH F., HADI W.,, " PREDICTION PHASE IN ASSOCIATIVE CLASSIFICATION MINING " , "International Journal of Software Engineering and Knowledge Engineering",Vol.21,No.6, World Scientific Publishing Co., , 09/01/2011
Associative classification (AC) is an important data mining approach which effectively integrates association rule mining and classification. Prediction of test data is a fundamental step in classification that impacts the outputted system accuracy. In this paper, we present three new prediction methods (Dominant Class Label, Highest Average Confidence per Class, Full Match Rule) and one rule pruning procedure (Partial Matching) in AC. Furthermore, we review current prediction methods in AC.
Experimental results on large English and Arabic text categorisation data collections (Reuters, SPA) using the proposed prediction methods and other popular classification algorithms (SVM, KNN, NB, BCAR, MCAR, C4.5, etc.), have been conducted. The bases of the comparison in the experiments are classification accuracy and the Break-Even-Point (BEP) evaluation measures. The results reveal that our prediction methods are very competitive with reference to BEP if compared with known AC prediction approaches such as those of 2-PS, ARC-BC and BCAR. Moreover, the proposed prediction methods outperform other existing methods in traditional classification approaches such as decision trees, and probabilistic with regards to accuracy. Finally, the results indicate that using the proposed pruning procedure in AC improved the accuracy of the outputted classifier.
Abdelhamid N., Ayesh, " Multi-Label Rules Algorithm Based Associative Classification " , "Parallel Processing Letters",Vol.24,No.1, World Scientific Publishing Company, , 03/01/2014
Hadi W., " Classification of Arabic Social Media Data " , "Advances in Computational Sciences and Technology",Vol.8,No.1, Research India Publications, , 02/17/2015
Hadi W., " ECAR: A New Enhanced Class Association Rule " , "Advances in Computational Sciences and Technology",Vol.8,No.1, Research India Publications, , 03/02/2015
Hadi W., " The Relationship between CRM Strategies Stage on Competitive Advantage: An Analytical Perspective " , "International Journal of Business and Management",Vol.10,No.8, Canadian Center of Science and Education, , 08/01/2015
Allozi A., Alryalat, and Hadi W. , " Applying Electronic Customer Processes to Electronic Customer Retention (Field Study in Jordanian Telecommunication Sector) " , "International Journal of Business and Management",Vol.11,No.1, Canadian Center of Science and Education, , 01/02/2016
W Hadi, F Aburub, S Alhawari, " A new fast associative classification algorithm for detecting phishing websites " , "Applied Soft Computing",Vol.,No., Elsevier, , 11/30/2016
Associative classification (AC) is a new, effective supervised learning approach that aims to predict unseen instances. AC effectively integrates association rule mining and classification, and produces more accurate results than other traditional data mining classification algorithms. In this paper
Wa'el Hadi, Ghassan Issa, Abdelraouf Ishtaiwi, " ACPRISM: Associative classification based on PRISM algorithm " , "Information Sciences",Vol.,No., Elsevier, , 11/01/2017
Associative classification (AC) is an integration between association rules and classification tasks that aim to predict unseen samples. Several studies indicate that the AC algorithms produce more accurate results than classical data mining algorithms. However, current AC algorithms inherit from association rules two major drawbacks resulting in a massive set of generated rules, in addition to a very large number of models (classifiers). In response to these two drawbacks, a new AC algorithm based on PRISM algorithm (ACPRISM) is proposed which employs the power of the PRISM algorithm to decrease the number of generated rules.
To investigate the efficiency and the performance of the proposed algorithm, five different algorithms were tested, namely FACA, CBA, MAC, PRISM and RIPPER. Two experiments were conducted on groundwater and 16 different well-known datasets using predictive accuracy (%), number of generated rules and time taken to build the model (learning times).
Our experimental results show that the ACPRISM produced the lowest number of rules, and is much more efficient and more scalable than all considered algorithms with regard to learning times. Finally, the ACPRISM outperformed the CBA, MCAR, PRISM and RIPPER algorithms in terms of predictive accuracy, and produced comparable results to the FACA algorithm.
Wa'el Hadi, Qasem A Al-Radaideh, Samer Alhawari, " Integrating associative rule-based classification with Naïve Bayes for text classification " , "Applied Soft Computing",Vol.,No., Elsevier, , 08/01/2018
Associative classification (AC) integrates the task of mining association rules with the classification task to increase the efficiency of the classification process. AC algorithms produce accurate classification and generate easy to understand rules. However, AC algorithms suffer from two drawbacks
Wa'el Hadi, Nuha El-Khalili, May AlNashashibi, Ghassan Issa, Abed Alkarim AlBanna, " Application of data mining algorithms for improving stress prediction of automobile drivers: A case study in Jordan " , "Computers in Biology and Medicine",Vol.114,No., Elsevier, United Kingdom, 10/01/2019
In this paper, the problem of predicting automobile drivers' stress levels, as experienced during actual driving, is investigated through the application of five different data mining algorithms, namely K-Nearest Neighbour (KNN), Decision Tree (J48), Random Forest (RF), Support Vector Machine (SVM),
Mohammed Abu-arqoub, Wael Hadi, Abdelraouf Ishtaiwi, " ACRIPPER: A New Associative Classification Based on RIPPER Algorithm " , "Journal of information and knowledge management ",Vol.20,No., World Scientific Publishing Company, USA , 03/06/2021
Associative Classification (AC) classifiers are of substantial interest due to their ability to be utilised for mining vast sets of rules. However, researchers over the decades have shown that a large number of these mined rules are trivial, irrelevant, redundant, and some
May Al-Nashashibi, Wa’el Hadi, Nuha El-Khalili, Ghassan Issa, Abed Alkarim AlBanna, " A New Two-step Ensemble Learning Model for Improving Stress Prediction of Automobile Drivers " , "IAJIT ",Vol.18,No., IAJIT , JORDAN , 11/06/2021
Commuting when there is a significant volume of traffic congestion has been acknowledged as one of the key factors
causing stress. Significant levels of stress whilst driving are seen to have a profoundly negative effect on the actions and ability
of a driver; this has the capacity to result in
Abedal-Kareem Al-Banna, Eran Edirisinghe, Hui Fang and Wael Hadi, " Stuttering Disfluency Detection Using Machine Learning Approaches " , "Journal of Information & Knowledge Management",Vol.21,No., World Scientific Publishing Company, USA, 04/01/2022
Stuttering is a neurodevelopmental speech disorder wherein people suffer from disfluency in speech generation. Recent research has applied machine learning and deep learning approaches to stuttering disfluency recognition and classification. However, these studies have focussed on small datasets, ge