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
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
Ababneh, J., Thabtah, " Evaluating the Performance of Active Queue Management Using Discrete-Time Analytical Model " , "Technology Engineering and Management in Aviation: Advancements and Discoveries",Vol.,No., IGI Global, Hershey, Pennsylvania , USA, 07/11/2012
Congestion in networks is considered a serious problem; in order to manage and control this phenomena in early stages before it occurs, a derivation of a new discrete-time queuing network analytical model based on dynamic random early drop (DRED) algorithm is derived to present analytical expressions to calculate three performance measures: average queue length (Qavg,j), packet-loss rate (Ploss,j), and packet dropping probability (pd(j)). Many scenarios can be implemented to analyze the effectiveness and flexibility of the model. We compare between the three queue nodes of the proposed model using the derived performance measures to identify which queue node provides better performance. Results show that queue node one provides highest Qavg,j, Ploss,j, and (pd(j)) than queue nodes two and three, since it has the highest priority than other nodes. All the above results of performance measure are obtained only based on the queuing network setting parameters.