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Saeed F.,University of Technology Malaysia | Saeed F.,Sanhan Community College | Salim N.,University of Technology Malaysia | Abdo A.,Hodeidah University | Hentabli H.,University of Technology Malaysia
Communications in Computer and Information Science | Year: 2012

Many types of clustering techniques for chemical file structures have been used in the literature, but it is known that any single method will not always give the best results for all types of applications. Recent work on consensus clustering methods is motivated because of the successes of combining multiple classifiers in many areas and the ability of consensus clustering to improve the robustness, novelty, consistency and stability of clustering. In this paper, Cluster-based Similarity Partitioning Algorithm (CSPA) was examined for improving the quality of chemical structures clustering. The effectiveness of clustering was evaluated based on the ability to separate active from inactive molecules in each cluster and compared with the Ward's clustering method. The chemical dataset MDL Drug Data Report (MDDR) database was used for experiments. The results were obtained by combining multiple individual clusterings with different distance measures. Experiments suggest that the effectiveness of consensus partition depends on the consensus generation step so that the effective individual clusterings with different distance measures can obtain more robust and stable consensus clustering. © Springer-Verlag Berlin Heidelberg 2012.


Saeed F.,University of Technology Malaysia | Saeed F.,Sanhan Community College | Salim N.,University of Technology Malaysia | Abdo A.,Hodeidah University | Abdo A.,French Institute for Research in Computer Science and Automation
Molecular Informatics | Year: 2013

Many consensus clustering methods have been applied in different areas such as pattern recognition, machine learning, information theory and bioinformatics. However, few methods have been used for chemical compounds clustering. In this paper, an information theory and voting based algorithm (Adaptive Cumulative Voting-based Aggregation Algorithm A-CVAA) was examined for combining multiple clusterings of chemical structures. The effectiveness of clusterings was evaluated based on the ability of the clustering method to separate active from inactive molecules in each cluster, and the results were compared with Ward's method. The chemical dataset MDL Drug Data Report (MDDR) and the Maximum Unbiased Validation (MUV) dataset were used. Experiments suggest that the adaptive cumulative voting-based consensus method can improve the effectiveness of combining multiple clusterings of chemical structures. Copyright © 2013 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.


Saeed F.,University of Technology Malaysia | Saeed F.,Sanhan Community College | Salim N.,University of Technology Malaysia | Abdo A.,Hodeidah University | And 2 more authors.
Molecular Informatics | Year: 2013

Consensus clustering methods have been successfully used for combining multiple classifiers in many areas such as machine learning, applied statistics, pattern recognition and bioinformatics. In this paper, consensus clustering is used for combining the clusterings of chemical structures to enhance the ability of separating biologically active molecules from inactive ones in each cluster. Two graph-based consensus clustering methods were examined. The Quality Partition Index method (QPI) was used to evaluate the clusterings and the results were compared to the Ward's clustering method. Two homogeneous and heterogeneous subsets DS1-DS2 of MDL Drug Data Report database (MDDR) were used for experiments and represented by two 2D fingerprints. The results, obtained by a combination of multiple runs of an individual clustering and a single run of multiple individual clusterings, showed that graph-based consensus clustering methods can improve the effectiveness of chemical structures clusterings. Copyright © 2013 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.


Saeed F.,University of Technology Malaysia | Saeed F.,Sanhan Community College | Salim N.,University of Technology Malaysia | Abdo A.,Hodeidah University | Abdo A.,French Institute for Research in Computer Science and Automation
Journal of Chemical Information and Modeling | Year: 2013

The goal of consensus clustering methods is to find a consensus partition that optimally summarizes an ensemble and improves the quality of clustering compared with single clustering algorithms. In this paper, an enhanced voting-based consensus method was introduced and compared with other consensus clustering methods, including co-association-based, graph-based, and voting-based consensus methods. The MDDR and MUV data sets were used for the experiments and were represented by three 2D fingerprints: ALOGP, ECFP-4, and ECFC-4. The results were evaluated based on the ability of the clustering method to separate active from inactive molecules in each cluster using four criteria: F-measure, Quality Partition Index (QPI), Rand Index (RI), and Fowlkes-Mallows Index (FMI). The experiments suggest that the consensus methods can deliver significant improvements for the effectiveness of chemical structures clustering. © 2013 American Chemical Society.


Saeed F.,University of Technology Malaysia | Saeed F.,Sanhan Community College | Ahmed A.,Karary University | Shamsir M.S.,University of Technology Malaysia | Salim N.,University of Technology Malaysia
Journal of Computer-Aided Molecular Design | Year: 2014

The cluster-based compound selection is used in the lead identification process of drug discovery and design. Many clustering methods have been used for chemical databases, but there is no clustering method that can obtain the best results under all circumstances. However, little attention has been focused on the use of combination methods for chemical structure clustering, which is known as consensus clustering. Recently, consensus clustering has been used in many areas including bioinformatics, machine learning and information theory. This process can improve the robustness, stability, consistency and novelty of clustering. For chemical databases, different consensus clustering methods have been used including the co-association matrix-based, graph-based, hypergraph-based and voting-based methods. In this paper, a weighted cumulative voting-based aggregation algorithm (W-CVAA) was developed. The MDL Drug Data Report (MDDR) benchmark chemical dataset was used in the experiments and represented by the AlogP and ECPF-4 descriptors. The results from the clustering methods were evaluated by the ability of the clustering to separate biologically active molecules in each cluster from inactive ones using different criteria, and the effectiveness of the consensus clustering was compared to that of Ward's method, which is the current standard clustering method in chemoinformatics. This study indicated that weighted voting-based consensus clustering can overcome the limitations of the existing voting-based methods and improve the effectiveness of combining multiple clusterings of chemical structures. © 2014 Springer International Publishing.


Saeed F.,University of Technology Malaysia | Salim N.,University of Technology Malaysia | Salim N.,French Institute for Research in Computer Science and Automation | Abdo A.,Sanhan Community College | Abdo A.,Alhodaida University
Journal of Cheminformatics | Year: 2012

Background: Although many consensus clustering methods have been successfully used for combining multiple classifiers in many areas such as machine learning, applied statistics, pattern recognition and bioinformatics, few consensus clustering methods have been applied for combining multiple clusterings of chemical structures. It is known that any individual clustering method will not always give the best results for all types of applications. So, in this paper, three voting and graph-based consensus clusterings were used for combining multiple clusterings of chemical structures to enhance the ability of separating biologically active molecules from inactive ones in each cluster. Results: The cumulative voting-based aggregation algorithm (CVAA), cluster-based similarity partitioning algorithm (CSPA) and hyper-graph partitioning algorithm (HGPA) were examined. The F-measure and Quality Partition Index method (QPI) were used to evaluate the clusterings and the results were compared to the Ward's clustering method. The MDL Drug Data Report (MDDR) dataset was used for experiments and was represented by two 2D fingerprints, ALOGP and ECFP-4. The performance of voting-based consensus clustering method outperformed the Ward's method using F-measure and QPI method for both ALOGP and ECFP4 fingerprints, while the graph-based consensus clustering methods outperformed the Ward's method only for ALOGP using QPI. The Jaccard and Euclidean distance measures were the methods of choice to generate the ensembles, which give the highest values for both criteria. Conclusions: The results of the experiments show that consensus clustering methods can improve the effectiveness of chemical structures clusterings. The cumulative voting-based aggregation algorithm (CVAA) was the method of choice among consensus clustering methods. © 2012 Bachrach; licensee Chemistry Central Ltd.


Saeed F.,University of Technology Malaysia | Saeed F.,Sanhan Community College | Hentabli H.,University of Technology Malaysia
Jurnal Teknologi (Sciences and Engineering) | Year: 2013

The consensus clustering has shown capability to improve the robustness, novelty and stability of individual clusterings in many areas including chemoinformatics. In this paper, graph-based consensus method (cluster-based similarity partitioning algorithm CSPA) and soft consensus clustering were examined for combining multiple clusterings of chemical structures. The clustering is evaluated based on the ability to separate active from inactive molecules in each cluster. Experiments suggest that the effectiveness of soft consensus method can obtain better results than the hard consensus method (CSPA). © 2013 Penerbit UTM Press. All rights reserved.


Saeed F.,University of Technology Malaysia | Saeed F.,Sanhan Community College | Salim N.,University of Technology Malaysia
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Year: 2013

Many consensus clustering methods have been applied for combining multiple clusterings of chemical structures such as co-association matrix-based, graph-based, hypergraph-based and voting-based methods. However, the voting-based consensus methods showed the best performance among these methods. In this paper, a Weighted Cumulative Voting-based Aggregation Algorithm (W-CVAA) was developed for enhancing the effectiveness of combining multiple clusterings of chemical structures. The effectiveness of clusterings was evaluated based on the ability of clustering to separate active from inactive molecules in each cluster and the results were compared to Ward's method, which is the standard clustering method for chemoinformatics applications. The chemical dataset MDL Drug Data Report (MDDR) was used. Experimental results suggest that the weighted cumulative voting-based consensus method can improve the effectiveness of combining multiple clustering of chemical structures. © 2013 Springer-Verlag.


Al-Aghbari A.,University of Technology Malaysia | Abu-Ulbeh W.,University of Technology Malaysia | Ibrahim O.,University of Technology Malaysia | Saeed F.,University of Technology Malaysia | Saeed F.,Sanhan Community College
Jurnal Teknologi | Year: 2015

E-government refers to the use of Information technology to efficiently enhance government services that are provided to citizens, employees, businesses and agencies. The achievement of high level of E-government readiness is increasingly heralded as one of the top priorities for the countries in the world, especially in developing countries. Yemen is one of the developing countries that seeks to improve E-government implementation and services. Currently, Yemen government decided to revive the E-government by 2014-2015; but many challenges stand on the way from achieving this goal. This paper surveyed the E-government readiness ranking for Yemen from 2003 to 2014 using three factors, which are e-readiness rank, online services index and telecommunication infrastructure index; and compared the ranking of all factors with neighbored countries. In addition, this paper investigated the challenges that limit improving E-government in Yemen. These challenges are divided to three categories: organizational, technical and adoption challenges. © 2015 Penerbit UTM Press. All rights reserved.


Al-Dhubhani R.,King Abdulaziz University | Idris N.B.,University of Technology Malaysia | Saeed F.,University of Technology Malaysia | Saeed F.,Sanhan Community College
Jurnal Teknologi | Year: 2015

Network Intrusion Detection System (NIDS) is considered as one of the last defense mechanisms for any organization. NIDS can be broadly classified into two approaches: misuse-based detection and anomaly-based detection. Misuse-based intrusion detection builds a database of the well-defined patterns of the attacks that exploit weaknesses in systems and network protocols, and uses that database to identify the intrusions. Although this approach can detect all the attacks included in the database, it leads to false negative errors where any new attack not included in that database can’t be detected. The other approach is the anomaly-based NIDS which is developed to emulate the Human Immune System (HIS) and overcome the limitation of the misuse-based approach. The anomaly-based detection approach is based on Negative Selection (NS) mechanism. NS is based on building a database of the normal self patterns, and identifying any pattern not included in that database as a non-self pattern and hence the intrusion is detected. Unfortunately, NS concept has also its drawbacks. Although any attack pattern can be detected as a non-self pattern and this leads to low false negative rate, non-self patterns would not necessarily indicate the existence of intrusions. So, NS has a high false positive error rate caused from that assumption. Danger Theory (DT) is a new concept in HIS, which shows that the response mechanism in HIS is more complicated and beyond the simple NS concept. So, is it possible to utilize the DT to minimize the high false positive detection rate of NIDS? This paper answers this question by developing a prototype for NIDS based on DT and evaluating that prototype using DARPA99 Intrusion Detection dataset. © 2015 Penerbit UTM Press. All rights reserved.

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