Bilgisayar Mühendisliği Bölümü Yayın Koleksiyonu

Permanent URI for this collectionhttps://hdl.handle.net/20.500.12416/253

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  • Article
    Citation - WoS: 21
    Citation - Scopus: 27
    Reporting and Analyzing Alternative Clustering Solutions by Employing Multi-Objective Genetic Algorithm and Conducting Experiments on Cancer Data
    (Elsevier, 2014) Peng, Peter; Addam, Omer; Ozyer, Sibel T.; Elzohbi, Mohamad; Elhajj, Ahmad; Gao, Shang; Alhajj, Reda
    Clustering is an essential research problem which has received considerable attention in the research community for decades. It is a challenge because there is no unique solution that fits all problems and satisfies all applications. We target to get the most appropriate clustering solution for a given application domain. In other words, clustering algorithms in general need prior specification of the number of clusters, and this is hard even for domain experts to estimate especially in a dynamic environment where the data changes and/or become available incrementally. In this paper, we described and analyze the effectiveness of a robust clustering algorithm which integrates multi-objective genetic algorithm into a framework capable of producing alternative clustering solutions; it is called Multi-objective K-Means Genetic Algorithm (MOKGA). We investigate its application for clustering a variety of datasets, including microarray gene expression data. The reported results are promising. Though we concentrate on gene expression and mostly cancer data, the proposed approach is general enough and works equally to cluster other datasets as demonstrated by the two datasets Iris and Ruspini. After running MOKGA, a pareto-optimal front is obtained, and gives the optimal number of clusters as a solution set. The achieved clustering results are then analyzed and validated under several cluster validity techniques proposed in the literature. As a result, the optimal clusters are ranked for each validity index. We apply majority voting to decide on the most appropriate set of validity indexes applicable to every tested dataset. The proposed clustering approach is tested by conducting experiments using seven well cited benchmark data sets. The obtained results are compared with those reported in the literature to demonstrate the applicability and effectiveness of the proposed approach. (C) 2013 Elsevier B.V. All rights reserved.
  • Article
    Citation - WoS: 8
    Citation - Scopus: 9
    Power Aware Routing Protocols in Wireless Sensor Network
    (Ieice-inst Electronics information Communications Eng, 2016) Oztoprak, Kasim; Hassanpour, Reza; Alsultan, Mohammed
    Wireless Sensor Networks (WSNs) have gained importance with a rapid growth in their applications during the past decades. There has also been a rise in the need for energy-efficient and scalable routing along with the data aggregation protocols for the large scale deployments of sensor networks. The traditional routing algorithms suffer from drawbacks such as the presence of one hop long distance data transmissions, very large or very small clusters within a network at the same moment, over-accumulated energy consumption within the cluster-heads (CHs) etc. The lifetime of WSNs is also decreased due to these drawbacks. To overcome them, we have proposed a new method for the Multi Hop, Far-Zone and Load-Balancing Hierarchical-Based Routing Algorithm for Wireless Sensor Network (MFLHA). Various improvements have been brought forward by MFLHA. The first contribution of the proposed method is the existence of a large probability for the nodes with higher energy to become the CH through the introduction of the energy decision condition and energy-weighted factor within the electing threshold of the CH. Secondly, MFLHA forms a Far-Zone, which is defined as a locus where the sensors can reach the CH with an energy less than a threshold. Finally, the energy consumption by CHs is reduced by the introduction of a minimum energy cost method called the Multi-Hop Inter-Cluster routing algorithm. Our experimental results indicate that MFLHA has the ability to balance the network energy consumption effectively as well as extend the lifetime of the networks. The proposed method outperforms the competitors especially in the middle range distances.