Yousef Al-Ohali

Databases for recognition of handwritten Arabic cheques

This paper describes an e0ort towards the development of Arabic cheque databases for research in the recognition of hand-written Arabic cheques. Databases of real-life Arabic legal amounts, Arabic sub-words, courtesy amounts, Indian digits, and Arabic cheques are described. This paper highlights some characteristics of the Arabic language and presents the various steps that have been completed to build these databases including segmentation, binarization and data tagging. It also describes a solid validation procedure including grammars and algorithms used to verify the correctness of the tagging process. Detailed descriptions of the database organization and class distribution are included. These databases aim to facilitate experimental comparisons between various recognition method s, andwill be provid edto all interestedresearchers upon request to

Genetic Bee Colony (GBC) algorithm

Naturally inspired evolutionary algorithms prove effectiveness when used for solving feature selection and classification problems. Artificial Bee Colony (ABC) is a relatively new swarm intelligence method. In this paper, we propose a new hybrid gene selection method, namely Genetic Bee Colony (GBC) algorithm. The proposed algorithm combines the used of a Genetic Algorithm (GA) along with Artificial Bee Colony (ABC) algorithm. The goal is to integrate the advantages of both algorithms. The proposed algorithm is applied to a microarray gene expression profile in order to select the most predictive and informative genes for cancer classification. In order to test the accuracy performance of the proposed algorithm, extensive experiments were conducted. Three binary microarray datasets are use, which include: colon, leukemia, and lung. In addition, another three multi-class microarray datasets are used, which are: SRBCT, lymphoma, and leukemia. Results of the GBC algorithm are compared with our recently proposed technique: mRMR when combined with the Artificial Bee Colony algorithm (mRMR-ABC). We also compared the combination of mRMR with GA (mRMR-GA) and Particle Swarm Optimization (mRMR-PSO) algorithms. In addition, we compared the GBC algorithm with other related algorithms that have been recently published in the literature, using all benchmark datasets. The GBC algorithm shows superior performance as it achieved the highest classification accuracy along with the lowest average number of selected genes. This proves that the GBC algorithm is a promising approach for solving the gene selection problem in both binary and multi-class cancer classification.

mRMR-ABC: A Hybrid Gene Selection Algorithm for Cancer Classification Using Microarray Gene Expression Profiling

An artificial bee colony (ABC) is a relatively recent swarm intelligence optimization approach. In this paper, we propose the first attempt at applying ABC algorithm in analyzing a microarray gene expression profile. In addition, we propose an innovative feature selection algorithm, minimum redundancy maximum relevance (mRMR), and combine it with an ABC algorithm, mRMR-ABC, to select informative genes from microarray profile. The new approach is based on a support vector machine (SVM) algorithm to measure the classification accuracy for selected genes. We evaluate the performance of the proposed mRMR-ABC algorithm by conducting extensive experiments on six binary and multiclass gene expression microarray datasets. Furthermore, we compare our proposed mRMR-ABC algorithm with previously known techniques. We reimplemented two of these techniques for the sake of a fair comparison using the same parameters. These two techniques are mRMR when combined with a genetic algorithm (mRMR-GA) and mRMR when combined with a particle swarm optimization algorithm (mRMR-PSO). The experimental results prove that the proposed mRMR-ABC algorithm achieves accurate classification performance using small number of predictive genes when tested using both datasets and compared to previously suggested methods. This shows that mRMR-ABC is a promising approach for solving gene selection and cancer classification problems.

Arabic Character Recognition: Progress and Challenges

An optical character recognition (OCR) system may provide a solution to the data entry problems, a bottleneck for the data processing industry. Therefore, OCR systems are being developed for almost all major languages and Arabic language is no exception to it. During the past three decades, considerable research and development works have been done towards the development of an efficient Arabic optical character recognition (ACR) system. In this paper we present a comprehensive review of ACR techniques and evaluate the status of the ACR system development and an up to date bibliography.

The Performance of Bio-Inspired Evolutionary Gene Selection Methods for Cancer Classification Using Microarray Dataset

—Microarray based gene expression profiling has become an important and promising dataset for cancer classification that are used for diagnosis and prognosis purposes. It is important to determine the informative genes that cause the cancer to improve early cancer diagnosis and to give effective chemotherapy treatment. Furthermore, find accurate gene selection method that reduce the dimensionality and select informative genes is very significant issue in cancer classification area. In literature, there are several gene selection methods for cancer classification using microarray dataset. However, most of them did not concern on identifying minimum number of informative genes with high classification accuracy. Therefore, in our research study we discuss the performance of Bio-Inspired evolutionary gene selection method in cancer classification using microarray dataset. And, we prove that the Bio-Inspired evolutionary gene selection methods have superior classification accuracy with minimum number of selected genes.

A Comparative Study of Cancer Classification Methods Using Microarray Gene Expression Profile

Microarray based gene expression profiling has been emerged as an efficient technique for cancer classification, as well as for diagnosis, prognosis, and treatment purposes. The primary task of microarray data classification is to determine a computational model from the given microarray data that can determine the class of unknown samples. In recent times, microarray technique has gained more attraction in both scientific and in industrial fields. It is important to determine the informative genes that cause the cancer to improve early cancer diagnosis and to give effective chemotherapy treatment. Classifying cancer microarray gene expression data is a challenging task because microarray is a high dimensional-low sample dataset with lots of noisy or irrelevant genes and missing data. Therefore, finding an accurate and an effective cancer classification approach is very significant issue in medical domain. In this paper, we will make a comparative study and we will categorize the effective binary classification approaches that have been applied for cancer microarray gene expression profile. Then we conclude by identifying the most accurate classification method that has the highest classification accuracy along with the smallest number of effective genes.

Eye-Controlled Games for Behavioral Therapy of Attention Deficit Disorders

This paper describes an eye-controlled game designed for behavioral therapy of ADHD. A user-centered design approach was adopted in the development cycle of these games in close collaboration with domain experts and target user populations. The games have an Arabic language interface and include multimodal interaction. Game scenarios were designed with increasing complexity depicted in visual design, dwell time for controlling elements within the games, and combinations of key presses with eye-control at higher levels of attention training. The visual design, interaction design and the system’s conceptual designs are discussed.

Introducing termination probabilities to HMM

HMM is very well suited to model sequential patterns. This paper introduces a new parameter, called the termination probability, to a hidden Markov model (HMM). The new parameter provides a better initialization for the backward variable during the training and evaluation phases. This improves the discriminatory power of HMM by allowing the system to judge the input observation sequence based on where it is completed. Experimental results show the improvement was achieved by this parameter.

The design and development of an interactive aural rehabilitation therapy program

In this paper, we describe our current work in developing a computer-based aural rehabilitation tool for profoundly deaf children that have recently acquired Cochlear Implants. The software is an interactive program aimed at young Arabic-speaking children, called Rannan. Evaluations of Rannan involved comparing different input modalities and evaluating the effectiveness of sound discrimination activities. Findings show that touch-based interaction facilitated faster response and improved accuracy over cursor-based input modalities. Moreover, usability evaluations suggest that Rannan can be an effective bridge between clinical-based therapy and home-based aural rehabilitation.

Efficient estimation of pen trajectory from off-line handwritten words

This paper presents an easy and efficient method to estimate the pen trajectory based on minimizing the pen movement. Given start and end vertices, the complexity of the proposed algorithm is linear. In addition, the algorithm clearly identifies alternatives that do not affect the overall length of the pen trajectory, making enough room for other criteria, e.g. vision rules, to be applied.