Raed Abu Zitar

Arabic writer identification based on hybrid spectral-statistical measures

Many techniques have been reported for handwriting-based writer identification. None of these techniques assume that the written text is in Arabic. In this paper we present a new technique for feature extraction based on hybrid spectral–statistical measures (SSMs) of texture. We show its effectiveness compared with multiple-channel (Gabor) filters and the grey-level co-occurrence matrix (GLCM), which are well-known techniques yielding a high performance in writer identification in Roman handwriting. Texture features were extracted for wide range of frequency and orientation because of the nature of the spread of Arabic handwriting compared with Roman handwriting, and the most discriminant features were selected with a model for feature selection using hybrid support vector machine–genetic algorithm techniques. Four classification techniques were used: linear discriminant classifier (LDC), support vector machine (SVM), weighted Euclidean distance (WED), and the K nearest neighbours (K_NN) classifier. Experiments were performed using Arabic handwriting samples from 20 different people and very promising results of 90.0% correct identification were achieved.

Development of an efficient neural-based segmentation technique for Arabic handwriting recognition

Off-line Arabic handwriting recognition and segmentation has been a popular field of research for many years. It still remains an open problem. The challenging nature of handwriting recognition and segmentation has attracted the attention of researchers from industry and academic circles. Recognition and segmentation of Arabic handwritten script is a difficult task because the Arabic handwritten characters are naturally both cursive and unconstrained. The analysis of Arabic script is more complicated in comparison with English script. It is believed, good segmentation is one reason for high accuracy character recognition. This paper proposes and investigates four main segmentation techniques. First, a new feature-based Arabic heuristic segmentation AHS technique is proposed for the purpose of partitioning Arabic handwritten words into primitives (over-segmentations) that may then be processed further to provide the best segmentation. Second, a new feature extraction technique (modified direction features-MDF) with modifications in accordant with the characteristics of Arabic scripts is also investigated for the purpose of segmented character classification. Third, a novel neural-based technique for validating prospective segmentation points of Arabic handwriting is proposed and investigated based on direction features. In particular, the vital process of handwriting segmentation is examined in great detail. The classifier chosen for segmentation point validation is a feed-forward neural network trained with the back-propagation algorithm. Many experiments were performed, and their elapsed CPU times and accuracies were reported. Fourth, new fusion equations are proposed and investigation to examine and evaluate a prospective segmentation points by obtaining a fused value from three neural confidence values obtained from right and center character recognition outputs in addition to the segmentation point validation (SPV) output. Confidence values are assigned to each segmentation point located through feature detection. All techniques components are tested on a local benchmark database. High segmentation accuracy is reported in this research along with comparable results for character recognition and segmentation.

Virus detection using clonal selection algorithm with Genetic Algorithm (VDC algorithm)

This paper presents a novel approach for computer viruses detection based on modeling the structures and dynamics of real life paradigm that exists in the bodies of all living creatures. It aims to develop an algorithm based on the concept of the artificial immune system (AIS) for the purpose of detecting viruses. The algorithm is called Virus Detection Clonal algorithm (VDC), and it is derived from the clonal selection algorithm. The VDC algorithm consists of three basic steps: cloning, hyper-mutation and stochastic re-selection. In later stage, the developed VDC algorithm is subjected to validation, which consists of two phases; learning and testing. Two main parameters are determined; one of them is setting the number of signatures per clone (Fat), while the other defines the hypermutation probability (Pm). Later on, the Genetic Algorithm (GA) is used as a tool, to improve the developed algorithm by searching the values of the main parameters (Fat and Pm) to reproduce better results. The results have shown that the detection rate of viruses, by using the developed algorithm, is 94.4%, whereas the detection rate of false positives has reached 0%. These percentages indicate that the VDC algorithm is sufficient and usable in this field. Moreover, the results of employing the GA to optimize the VDC algorithm have shown an improvement in the detection speed of the algorithm.

Genetic optimized artificial immune system in spam detection: a review and a model

Spam is a serious universal problem which causes problems for almost all computer users. This issue affects not only normal users of the internet, but also causes a big problem for companies and organizations since it costs a huge amount of money in lost productivity, wasting users’ time and network bandwidth. Many studies on spam indicate that spam cost organizations billions of dollars yearly. This work presents a machine learning method inspired by the human immune system called Artificial Immune System (AIS) which is a new emerging method that still needs further exploration. Core modifications were applied on the standard AIS with the aid of the Genetic Algorithm. Also an Artificial Neural Network for spam detection is applied with a new manner. SpamAssassin corpus is used in all our simulations.

POLYNOMIAL NETWORKS VERSUS OTHER TECHNIQUES IN TEXT CATEGORIZATION

Many techniques and algorithms for automatic text categorization had been devised and proposed in the literature. However, there is still much space for researchers in this area to improve existing algorithms or come up with new techniques for text categorization (TC). Polynomial Networks (PNs) were never used before in TC. This can be attributed to the huge datasets used in TC, as well as the technique itself which has high computational demands. In this paper, we investigate and propose using PNs in TC. The proposed PN classifier has achieved a competitive classification performance in our experiments. More importantly, this high performance is achieved in one shot training (noniteratively) and using just 0.25%–0.5% of the corpora features. Experiments are conducted on the two benchmark datasets in TC: Reuters-21578 and the 20 Newsgroups. Five well-known classifiers are experimented on the same data and feature subsets: the state-of-the-art Support Vector Machines (SVM), Logistic Regression (LR), the k-nearest-neighbor (kNN), Naive Bayes (NB), and the Radial Basis Function (RBF) networks.

Random-guided search algorithm for complex functions

Optimization is a general goal that has many applications in Engineering, Business, computer science and almost in every operation in life. Devising ways for handling problem optimization is an important yet a challenging task. We look for techniques that are efficient, accurate, and applicable. The search space could have any nature and could have discontinuity or multi-local optima. In this paper, we address this challenge by offering an algorithm that combines the random search techniques with both an effective mapping and a dynamic adjustment of its search behavior. Our proposed algorithm automatically builds two types of triangles over the unity intervals: principal and marginal. These triangles guide the search within both the effective regions of the search domain that most likely contain the optima and the marginal regions of the search domain that less likely contain the optima. Experiments with our prototype implementation showed that our method can effectively find the global optima for rather complicated mathematical functions chosen from well-known benchmarks and perform better than other algorithms.

An Evolutionary FIR Filter Design Method

In this introductory chapter, we present an evolutionary-based technique for designing one-dimensional and two-dimensional Finite Impulse Response (FIR) filters. Typically, the required filter has a given set of specifications to be met. The specifications may include cut-off frequency, band-stop region, band-pass region and ripple factors. The evolutionary method we are using is a modified version of the Genetic Algorithm (GA), which we call Flexible Genetic Algorithm (FGA). It is an optimization algorithm with high capabilities to span the space of filter parameters. FIR filters are highly required in different applications that process signals or images. A review of the state-of-the-art of filter optimization using evolutionary techniques is presented in this chapter. The aim of this work is to simply give a basic example of how filters can be designed using evolutionary techniques. As a matter of fact, medical applications require high linearity in the filter phase function to prevent undesired distortions in the detected signals. The proposed technique is based on minimizing a cost function that uses the weighted squared difference between the optimum filter specifications and the solutions generated by the evolutionary method. Comparisons between FGA-designed filter and standard method designed filters are implemented. Testing of filters is done using different noisy artificial ECG signals and selected images. We used Hermite functions to build the artificial ECG signals.

Application of cepstrum algorithms for speech recognition

This paper proposes a novel method for speech recognition. This is method based on the calculation of correlation coefficients of cepestrum by linear prediction (LP). The results of the method is compared with other methods. The real cepstrum is used to separate/estimate the spectral content of the speech from its pitch frequencies. Some notes regarding the efficiency of the processors used in the simulations are also concluded.