Ibrahim Farag

DBCLUM: Density-based Clustering and Merging Algorithm

Clustering is a primary method for DB mining. The clustering process becomes very challenge when the data is different densities, different sizes, different shapes, or has noise and outlier. Many existing algorithms are designed to find clusters. But, these algorithms lack to discover clusters of different shapes, densities and sizes. This paper presents a new algorithm called DBCLUM which is an extension of DBSCAN to discover clusters based on density. DBSCAN can discover clusters with arbitrary shapes. But, fail to discover different-density clusters or adjacent clusters. DBCLUM is developed to overcome these problems. DBCLUM discovers clusters individually then merges them if they are density similar and joined. By this concept, DBCLUM can discover different-densities clusters and adjacent clusters. Experiments revealed that DBCLUM is able to discover adjacent clusters and different-densities clusters and DBCLUM is faster than DBSCAN with speed up ranges from 11% to 52%.

Multistage feature selection approach for high-dimensional cancer data

Cancer is a serious disease that causes death worldwide. DNA methylation (DNAm) is an epigenetic mechanism, which controls the regulation of gene expression and is useful in early detection of cancer. The challenge with DNA methylation microarray datasets is the huge number of CpG sites compared to the number of samples. Recent research efforts attempted to reduce this high dimensionality by different feature selection techniques. This article proposes a multistage feature selection approach to select the optimal CpG sites from three different DNAm cancer datasets (breast, colon and lung). The proposed approach combines three different filter feature selection methods including Fisher Criterion, t-test and Area Under ROC Curve. In addition, as a wrapper feature selection, we apply genetic algorithms with Support Vector Machine Recursive Feature Elimination (SVM-RFE) as its fitness function, and SVM as its evaluator. Using the Incremental Feature Selection (IFS) strategy, subsets of 24, 13 and 27 optimal CpG sites are selected for the breast, colon and lung cancer datasets, respectively. By applying fivefold cross-validation on the training datasets, these subsets of optimal CpG sites showed perfect classification accuracies of 100, 100 and 97.67%, respectively. Moreover, the testing of the three independent cancer datasets by these final subsets resulted in accuracies 96.02, 98.81 and 94.51%, respectively. The experimental results demonstrated high classification performance and small optimal feature subsets. Consequently, the biological significance of the genes corresponding to these feature subsets is validated using enrichment analysis.

Fractional canny edge detection for biomedical applications

This paper presents a comparative study of edge detection algorithms based on integer and fractional order differentiation. A performance comparison of the two algorithms has been proposed. Then, a soft computing technique has been applied to both algorithms for better edge detection. From the simulations, it shows that better performance is obtained compared to the classical approach. The noise performances of those algorithms are analyzed upon the addition of random Gaussian noise, as well as the addition of salt and pepper noise. The performance has been compared to peak signal to noise ratio (PSNR). From results, it is obtained that fractional edge detection with the fuzzy system has better performance.

Arabic Phrase-Level Contextual Polarity Recognition to Enhance Sentiment Arabic Lexical Semantic Database Generation

Most of opinion mining works need lexical resources for opinion which recognize the polarity of words (positive/ negative) regardless their contexts which called prior polarity. The word prior polarity may be changed when it is considered in its contexts, for example, positive words may be used in phrases expressing negative sentiments, or vice versa. In this paper, we aim at generating sentiment Arabic lexical semantic database having the word prior coupled with its contextual polarities and the related phrases. To do that, we study first the prior polarity effects of each word using our Sentiment Arabic Lexical Semantic Database on the sentence-level subjectivity and Support Vector Machine classifier. We then use the seminal English two-step contextual polarity phrase-level recognition approach to enhance word polarities within its contexts. Our results achieve significant improvement over baselines.

Using P System with GPU Model to Design and Implement a Public Key Cryptography

Simulators are limited by the available resources on the GPU as well as the CPU. Simulation of P systems with active membrane using GPUs is a new concept in the development of applications for membrane computing. P systems are an alternative approach to extract all performance available on GPUs due to its parallel nature. In this paper, a design and an implementation of a simulator for a cryptography system using GPU in a P system frame is presented. Then a comparative study is conducted concerning the performance of the GPU model and the CPU model in terms of the needed time to perform encryption /decryption processes. The results show that the proposed GPU system can help in enhancement of encryption /decryption algorithm running in membrane environment.

Arabic document layout analysis

Document layout analysis is a key step in the process of converting document images into text. Arabic language script is cursive and written in different styles which cause some challenges in the analysis of Arabic text documents. In this paper, we introduce an approach for Arabic documents layout analysis. In that approach, the document is segmented into set of zones using morphological operations. The segmented zones are classified as text or non-text ones using a support vector machine classifier. Features used in zone classification are combination between texture-based features and connected component-based features. The textural-based feature vector size is reduced using genetic algorithm. Classified text zones are clustered, using adaptive sample set clustering algorithm, into lines. Each segmented line is segmented into words by clustering inter- and intra-spaces. The proposed system was evaluated against two other systems that represent the best available tools for the Arabic documents analysis, and evaluation results show that the proposed system works well on multi-font and multi-size documents with a variety of layouts even on some historical documents.

An Algorithm for Browsing the Referentially-compressed Genomes

Genome resequencing produces enormous amount of data daily. Biologists need to frequently mine this data with the provided processing and storage resources. Therefore, it becomes very critical to professionally store this data in order to efficiently browse it in a frequent manner. Reference-based Compression algorithms (RbCs) showed significant genome compression results compared to the traditional text compression algorithms. By avoiding the complete decompression of the compressed genomes, they can be browsed by performing partial decompressions at specific regions, taking lower runtime and storage resources. This paper introduces the inCompressi algorithm that is designed and implemented to efficiently pick sequences from genomes, that are compressed by an existing Reference-based Compression algorithm (RbC), through partial decompressions. Moreover, inCompressi performs a more efficient complete genome decompression compared to the original decompression algorithm. The experimental results showed a significant reduction in both runtime and memory consumption compared to the original algorithm.

A P System Simulator for Logic Gates

The objectives of this paper are representing a simulator for the logic gates using P systems with priorities rules, and making use of the P system parallel computing in order to reduce the time used to test or evaluate a logic circuit (set of logic gates), which may change the vision of the current logic gates systems. Also, introducing the basic logic gates and how they work together, the development of the appropriate P system models for these gates are represented, and putting all together in order to get logic circuits which are P system based, finally a simulation and a test for them using a P Lingua language simulator, and an example is introduced to illustrate and making test of the model.

A novel approach for detecting and correcting segmentation and recognition errors in arabic OCR systems

In this paper, we propose a new approach for detecting and correcting segmentation and recognition errors in Arabic OCR systems. The approach is suitable for both typewritten and handwritten script recognition systems. Error detection is based on rules of the Arabic language and a morphology analyzer. This type of analysis has the advantage of limiting the size of the dictionary to a practical size. Thus, a complete dictionary for roots, which does not exceed 5641 roots, the morphological rules and all valid patterns can be kept in a moderate size file. Recognition channel characteristics are modeled using a set of probabilistic finite state machines. Contextual information is utilized in the form of transitional probabilities between letters of previously defined vocabulary (finite lexicon) and transitional probabilities of garbled text. The developed detection and correction modules have been incorporated as a post-processing phase in an Arabic handwritten cursive script recognition system. Experimental results show a considerable enhancement in performance.

Intelligent real-time scheduling of dynamic processes in MPI

The Message Passing Interface (MPI), which appeared in 1994, introduced many powerful functionalities for parallel processing positioning the MPI among the top preferred packages for parallel processing for researchers and developers. However, MPI-1 did not provide mechanisms for creating runtime processes. Although MPI-2 included spawn process creation, it did not specify how those dynamically created processes may be assigned to hardware processors. This resulted in poor resource allocation, load balancing, and consequently bad performance too. In this paper, the researchers discuss this problem proposing a fuzzy scheduling algorithm for dynamic processes, which proved to vastly outperform the native MPI scheduler.