Ibrahim Aljarah

Abstract: �University Courses Timetabling problem has been extensively researched in the last decade. Therefore, numerous approaches were proposed to solve UCT problem. This paper proposes a new approach to process a sequence of meetings between instructors, rooms, and students in predefined periods of time with satisfying a set of constraints divided in variety of types. In addition, this paper proposes new representation for courses timetabling and conflict-free for each time slot by mining instructor preferences from previous schedules to avoid undesirable times for instructors. Experiments on different real data showed the approach achieved increased satisfaction degree for each instructor and gives feasible schedule with satisfying all hard constraints in construction operation. The generated schedules have high satisfaction degrees comparing with schedules created manually. The research conducts experiments on collected data gathered from the computer science department and other related departments in Jordan University of Science and Technology- Jordan.

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Abstract: �In optimization problems, such as highly multimodal functions, many iterations involving complex function evaluations are required. Glowworm Swarm Optimization (GSO) has to be parallelized for such functions when large populations capturing the complete function space, are used. However, largescale parallel algorithms must communicate ef�ciently, involve load balancing across all available computer nodes, and resolve parallelization problems such as the failure of nodes. In this paper, we outline how GSO can be modeled based on the MapReduce parallel programming model. We describe MapReduce and present how GSO can be naturally expressed in this model, without having to explicitly handle the parallelization details. We use highly multimodal benchmark functions for evaluating our MR-GSO algorithm. Furthermore, we demonstrate that MRGSO is appropriate for optimizing dif�cult evaluation functions, and show that high function peak capture rates are achieved. We show with the experiments that adding more nodes would help to solve larger problems without any modi�cations to the algorithm structure.

Abstract: The growing data trac in large networks faces new challenges requiring ecient intrusion detection systems. The analysis of this high volume of data trac to discover attacks has to be done very quickly. However, in order to be able to process large data, new distributed and parallel methods need to be developed. Several approaches are proposed to build intrusion systems using clustering approaches. In this paper, we introduce an intrusion detection system based on a parallel particle swarm optimization clustering algorithm using the MapReduce framework. The proposed system is scalable in processing large data on commodity hardware.

Abstract: �The increasing volume of data in large networks to be analyzed imposes new challenges to an intrusion detection system. Since data in computer networks is growing rapidly, the analysis of these large amounts of data to discover anomaly fragments has to be done within a reasonable amount of time. Some of the past and current intrusion detection systems are based on a clustering approach. However, in order to cope with the increasing amount of data, new parallel methods need to be developed in order to make the algorithms scalable. In this paper, we propose an intrusion detection system based on a parallel particle swarm optimization clustering algorithm using the MapReduce methodology. The use of particle swarm optimization for the clustering task is a very ef�cient way since particle swarm optimization avoids the sensitivity problem of initial cluster centroids as well as premature convergence. The proposed intrusion detection system processes large data sets on commodity hardware. The experimental results on a real intrusion data set demonstrate that the proposed intrusion detection system scales very well with increasing data set sizes. Moreover, it achieves close to the linear speedup by improving the intrusion detection and false alarm rates.

Abstract: High-quality clustering techniques are required for the effective analysis of the growing data. Clustering is a common data mining technique used to analyze homogeneous data instance groups based on their speci�cations. The clustering based nature-inspired optimization algorithms have received much attention as they have the ability to �nd better solutions for clustering analysis problems. Glowworm Swarm Optimization (GSO) is a recent nature-inspired optimization algorithm that simulates the behavior of the lighting worms. GSO algorithm is useful for a simultaneous search of multiple solutions, having different or equal objective function values. In this paper, a clustering based GSO is proposed (CGSO), where the GSO is adjusted to solve the data clustering problem to locate multiple optimal centroids based on the multimodal search capability of the GSO. The CGSO process ensures that the similarity between the cluster members is maximized and the similarity among members from different clusters is minimized. Furthermore, three special �tness functions are proposed to evaluate the goodness of the GSO individuals in achieving high quality clusters. The proposed algorithm is tested by arti�cial and real-world data sets. The better performance of our proposed algorithm over four popular clustering algorithms is demonstrated on most data sets. The results reveal that CGSO can ef�ciently be used for data clustering.

Abstract: Large scale data sets are dif�cult to manage. Dif�culties include capture, storage, search, analysis, and visualization of large data. In particular, clustering of large scale data has received considerable attention in the last few years and many application areas such as bioinformatics and social networking are in urgent need of scalable approaches. The new techniques need to make use of parallel computing concepts in order to be able to scale with increasing data set sizes. In this paper, we propose a parallel particle swarm optimization clustering (MR-CPSO) algorithm that is based on MapReduce. The experimental results reveal that MR-CPSO scales very well with increasing data set sizes and achieves a very close to the linear speedup while maintaining the clustering quality. The results also demonstrate that the proposed MR-CPSO algorithm can ef�ciently process large data sets on commodity hardware.

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Abstract: Functional module discovery aims to find well-connected subnetworks which can serve as candidate protein complexes. Advances in High-throughput proteomic technologies have enabled the collection of large amount of interaction data as well as gene expression data. We propose, CLARM, a clustering algorithm that integrates gene expression profiles and protein protein interaction network for biological modules discovery. The main premise is that by enriching the interaction network by adding interactions between genes which are highly co-expressed over a wide range of biological and environmental conditions, we can improve the quality of the discovered modules. Protein protein interactions, known protein complexes, and gene expression profiles for diverse environmental conditions from the yeast Saccharomyces cerevisiae were used for evaluate the biological significance of the reported modules. Our experiments show that the CLARM approach is competitive to well-established module discovery methods.

Abstract: Background. The bug assignment problem is the problem of triaging new bug reports to the most qualified developer. The qualified developer is the one who has enough knowledge in a specific area that is relevant to the reported bug. In recent years, bug triaging has received a considerable amount of attention from researchers. In previous work, bugs were represented as vectors of terms extracted from the bug reports' description. Once the bugs are represented as vectors in the terms space, traditional machine learning techniques are employed for the bug assignment. Most of the previous algorithms are marred by low accuracy values. Aims. This paper formulates the bug assignment problem as a classification task, and then examines the impact of several term selection approaches on the classification effectiveness. Method. Three variants selection methods that are based on the Log Odds Ratio (LOR) score are compared against methods that are based on the Information Gain (IG) score and Latent Semantic Analysis (LSA). The main difference in the methods that are based on the LOR score is in the process of selecting the terms. Results. Term selection techniques that are based on the Log Odds Ratio achieved up to 30% improvement in the precision and up to 5% higher in recall compared to other term selection methods such as Latent Semantic Analysis and Information Gain. Conclusions. Experimental results showed that the effectiveness of bug assignment methods is directly affected by the selected terms that are used in the classification methods.

Abstract: Entity discovery has become an important topic of study in recent years due to its wide range of applications. In this paper, we focus on examining the e�ectiveness of various classi�cation techniques on entity discovery and their application to the opinion mining task. The initial and most important step in opinion mining is to identify and extract highly speci�c product related and opinion related entities from product reviews. We formulate this problem as a classi�cation task and present a comprehensive study of classi�cation techniques on identifying entities of interest. The impacts of linguistic features such as part-of-speech (POS), and context features such as surrounding contextual clues of words on the classi�cation performance are carefully evaluated. The experimental results show that good classi�cation performance is closely related to the use of classi�cation techniques, linguistic features, and context features. The evaluation is presented based on processing the online product reviews from Amazon.

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