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Amer Al-Nassiri

Bio: Amer Al-Nassiri is an academic researcher from Ajman University of Science and Technology. The author has contributed to research in topics: Canopy clustering algorithm & Bidirectional associative memory. The author has an hindex of 4, co-authored 5 publications receiving 42 citations.

Papers
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Journal Article
TL;DR: A printing device for a time recorder in which printing operation is performed on a preselected printing column of a time card by means of a dot printer, while the latter is transversely displaced by a distance equal to the width of a printing column.
Abstract: A printing device for a time recorder in which printing operation is performed on a preselected printing column of a time card by means of a dot printer, while the latter is transversely displaced by a distance equal to the width of a printing column of the time card inserted into a card pocket.

20 citations

Journal Article
TL;DR: A worm detection system that leverages the reliability of IP-Flow and the effectiveness of learning machines and uses the classification accuracy, false alarm rates, and training time as metrics of performance to conclude which algorithm is superior to another.
Abstract: We present a worm detection system that leverages the reliability of IP-Flow and the effectiveness of learning machines. Typically, a host infected by a scanning or an email worm initiates a significant amount of traffic that does not rely on DNS to translate names into numeric IP addresses. Based on this fact, we capture and classify NetFlow records to extract feature patterns for each PC on the network within a certain period of time. A feature pattern includes: No of DNS requests, no of DNS responses, no of DNS normals, and no of DNS anomalies. Two learning machines are used, K-Nearest Neighbors (KNN) and Naive Bayes (NB), for the purpose of classification. Solid statistical tests, the cross-validation and paired t-test, are conducted to compare the individual performance between the KNN and NB algorithms. We used the classification accuracy, false alarm rates, and training time as metrics of performance to conclude which algorithm is superior to another. The data set used in training and testing the algorithms is created by using 18 real-life worm variants along with a big amount of benign flows.

10 citations

Journal ArticleDOI
TL;DR: In this paper, the authors proposed a kNN-based evolving fuzzy clustering method (kEFCM), which employs the least squares method in determining the cluster center and influential area, as well as the Euclidean distance in identifying the membership degree.
Abstract: Despite the recent emergence of research, creating an evolving fuzzy clustering method that intelligently copes with huge amount of data streams in the present high-speed networks involves a lot of difficulties. Several efforts have been devoted to enhance traditional clustering techniques into on-line evolving fuzzy able to learn and develop continuously. In line with these efforts, we propose kEFCM, kNN-based evolving fuzzy clustering method. kEFCM overcomes the problems of computational cost, dynamic fuzzy evolving, and clustering complexity of traditional kNN. It employs the least-squares method in determining the cluster center and influential area, as well as the Euclidean distance in identifying the membership degree. It enhances the traditional kNN algorithm by involving only cluster centers in making classification decisions and evolving on-line the clusters when a new data arrives. For evaluation purpose, the experimental results on a collection of benchmark datasets are compared against other well-known clustering methods. The evaluation results approve a good competitive level of kEFCM.

7 citations

Proceedings Article
27 May 2006
TL;DR: The main theme of this paper is the recognition of isolated Arabic speech phonemes using artificial neural networks, as most of the researches on speech recognition (SR) are based on Hidden Markov Models (HMM).
Abstract: The main theme of this paper is the recognition of isolated Arabic speech phonemes using artificial neural networks, as most of the researches on speech recognition (SR) are based on Hidden Markov Models (HMM). The technique in this paper can be divided into three major steps: firstly the preprocessing in which the original speech is transformed into digital form. Two methods for preprocessing have been applied, FIR filter and Normalization. Secondly, the global features of the Arabic speech phoneme are then extracted using Cepstral coefficients, with frame size of 512 samples, 170 overlapping, and hamming window. Finally, recognition of Arabic speech phoneme using supervised learning method and Multi Layer Perceptron Neural Network MLP, based on Feed Forward Backprobagation. The proposed system achieved a recognition rate within 96.3% for most of the 34 phonemes. The database used in this paper is KAPD (King AbdulAziz Phonetics Database), and the algorithms were written in MATLAB.

5 citations

Journal ArticleDOI
TL;DR: A new data and text lossless compression method, based on the combination of BWT1 and GBAM2 approaches, is presented that was tested on many texts in different formats (ASCII and RTF).
Abstract: In this paper we considered a theoretical evaluation of data and text compression algorithm based on the Burrows–Wheeler Transform (BWT) and General Bidirectional Associative Memory (GBAM). A new data and text lossless compression method, based on the combination of BWT1 and GBAM2 approaches, is presented. The algorithm was tested on many texts in different formats (ASCII and RTF). The compression ratio achieved is fairly good, on average 28–36%. Decompression is fast.

1 citations


Cited by
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Journal ArticleDOI
TL;DR: A comprehensive survey of recent developments in Arabic handwriting recognition, including a summary of the characteristics of Arabic text, followed by a general model for an Arabic text recognition system.
Abstract: Research in offline Arabic handwriting recognition has increased considerably in the past few years. This is evident from the numerous research results published recently in major journals and conferences in the area of handwriting recognition. Features and classifications techniques utilized in recent research work have diversified noticeably compared to the past. Moreover, more efforts have been diverted, in last few years, to construct different databases for Arabic handwriting recognition. This article provides a comprehensive survey of recent developments in Arabic handwriting recognition. The article starts with a summary of the characteristics of Arabic text, followed by a general model for an Arabic text recognition system. Then the used databases for Arabic text recognition are discussed. Research works on preprocessing phase, like text representation, baseline detection, line, word, character, and subcharacter segmentation algorithms, are presented. Different feature extraction techniques used in Arabic handwriting recognition are identified and discussed. Different classification approaches, like HMM, ANN, SVM, k-NN, syntactical methods, etc., are discussed in the context of Arabic handwriting recognition. Works on Arabic lexicon construction and spell checking are presented in the postprocessing phase. Several summary tables of published research work are provided for used Arabic text databases and reported results on Arabic character, word, numerals, and text recognition. These tables summarize the features, classifiers, data, and reported recognition accuracy for each technique. Finally, we discuss some future research directions in Arabic handwriting recognition.

135 citations

Journal ArticleDOI
TL;DR: This paper surveys existing studies about security-related data collection and analytics for the purpose of measuring the Internet security and proposes several additional requirements for security- related data analytics in order to make the analytics flexible and scalable.
Abstract: Attacks over the Internet are becoming more and more complex and sophisticated. How to detect security threats and measure the security of the Internet arises a significant research topic. For detecting the Internet attacks and measuring its security, collecting different categories of data and employing methods of data analytics are essential. However, the literature still lacks a thorough review on security-related data collection and analytics on the Internet. Therefore, it becomes a necessity to review the current state of the art in order to gain a deep insight on what categories of data should be collected and which methods should be used to detect the Internet attacks and to measure its security. In this paper, we survey existing studies about security-related data collection and analytics for the purpose of measuring the Internet security. We first divide the data related to network security measurement into four categories: 1) packet-level data; 2) flow-level data; 3) connection-level data; and 4) host-level data. For each category of data, we provide a specific classification and discuss its advantages and disadvantages with regard to the Internet security threat detection. We also propose several additional requirements for security-related data analytics in order to make the analytics flexible and scalable. Based on the usage of data categories and the types of data analytic methods, we review current detection methods for distributed denial of service flooding and worm attacks by applying the proposed requirements to evaluate their performance. Finally, based on the completed review, a list of open issues is outlined and future research directions are identified.

82 citations

Journal ArticleDOI
TL;DR: A survey of Arabic character recognition systems which are classified into the character recognition categories: printed and handwritten and examines the literature on the most significant work in handwritten text recognition without segmentation.
Abstract: Off-line recognition of text play a significant role in several application such as the automatic sorting of postal mail or editing old documents. It is the ability of the computer to distinguish characters and words. Automatic off-line recognition of text can be divided into the recognition of printed and handwritten characters. Off-line Arabic handwriting recognition still faces great challenges. This paper provides a survey of Arabic character recognition systems which are classified into the character recognition categories: printed and handwritten. Also, it examines the literature on the most significant work in handwritten text recognition without segmentation and discusses algorithms which split the words into characters.

64 citations

Journal Article
TL;DR: A new network is designed to recognize a set of handwritten arabic characters and consists of two stages, the first is to recognize the main shape of the character, and the second stage is for dots recognition.
Abstract: Character recognition has served as one of the principal proving grounds for neural network methods and has emerged as one of the most successful applications of this technology. In this paper, a new network is designed to recognize a set of handwritten arabic characters. This new network consists of two stages. The first is to recognize the main shape of the character, and the second stage is for dots recognition. Also, the characteristics, structure, and the training algorithm for the network are presented.

64 citations

Journal ArticleDOI
TL;DR: A noise robust IFS (NR-IFS) is defined for an image and a novel noise robust multiobjective evolutionary intuitionistic fuzzy clustering algorithm ( NR-MOEIFC) is presented, which behaves well in noise robustness and segmentation performance while requiring a low time cost.
Abstract: Images are always contaminated by noise, increasing uncertainty. Fuzzy set (FS) theory is a useful tool for dealing with uncertainty in images. When comparing with the FS, an intuitionistic fuzzy set (IFS) can better describe the blurred characteristic in images due to the membership, nonmembership, and hesitation degrees. However, when applied to an image segmentation, the IFS cannot completely overcome the influence of noise. With the aim of performing noisy image segmentation under several criteria, this paper defines a noise robust IFS (NR-IFS) for an image and then presents a novel noise robust multiobjective evolutionary intuitionistic fuzzy clustering algorithm (NR-MOEIFC). A majority dominated suppressed similarity measure using the neighborhood statistics and the competitive learning is proposed to obtain the NR-IFS representation for the image corrupted by noise. Then, the NR-IFS is fully used to motivate the whole process of multiobjective evolutionary clustering: first, computing a three-parameter intuitionistic fuzzy distance measure; second, constructing intuitionistic fuzzy fitness functions; third, designing a nonuniform intuitionistic fuzzy mutation operator; and forth, defining an intuitionistic fuzzy cluster validity index to select the optimal solution from the final nondominated solution set. The histogram statistics of NR-IFS are adopted in the NR-MOEIFC to greatly reduce the computational complexity. Experimental results on Berkeley and real magnetic resonance images reveal that the NR-MOEIFC behaves well in noise robustness and segmentation performance while requiring a low time cost.

56 citations