Khalid Mohammad Jaber

In this research, approaches to making e-learning accessible to students with impairments were studied based on the authors’ experience at Jordan’s Al-Zaytoonah University. The educational, organizational, and instructional concerns are underlined when analyzing the university’s experience in giving access to online content. Furthermore, these difficulties must be addressed in order to create the necessary organizational change to effectively solve accessibility concerns. This study looked into the e-learning issues that 56 university students with disabilities had reported. Students reported issues with: accessibility to websites and learning course management systems; accessibility to digital audio and video; time management in completing online tasks; trouble handling data during lectures; and a lack of appropriate adaptive strategies in online questionnaires. Students also noted technical issues with using e-learning, instructors’ communication with e-learning, and their treatme...

This paper presents a multi-core programming model that implements the cascade correlation neural networks technique, to enhance the classification phase of any pattern recognition system. It is based on combining the strengths of both approaches in order to construct an efficient Parallel Cascade Correlation Neural Network (P-CC-NN) system. In this work a complex case of pattern recognition system which is a 3D facial data has been used to examine the proposed system and ensure its effectiveness, experimental results are presented using 360 3D facial images, each image contains 96 distinguishable features. Results show significant improvement in execution time about 31 minutes (4.6 times speedup) in comparison with 146.5 minutes for serial time, this topology generated an accuracy of 94 %. This work is the first approach to handle the classification challenges for different pattern recognition applications using multi-core techniques.

The significant growth in biological data has motivated important research by computer scientists into creating fast and accurate algorithms for accessing the database at the fastest possible rates. Therefore, there still remains the need for an efficient indexing approach to speed up the searching time for large biological data, to help the biologists to retrieve the exiting information quickly, from any biological database. Furthermore, the trends in parallel programming models has also encouraged the computer researchers to improve the indexing approaches to cope with this exponential increase. This book adopted a Decision Tree method as an Indexing Model (DTIM), to allow for large databases to be processed. This method of indexing could effectively and rapidly retrieve all similar DNA-Proteins data from a large database for a given query. The decision tree indexing model (PDTIM) was then parallelized, using a hybrid of distributed and shared memory to accelerate the index buildi...

In this paper, we consider surfaces in the 3-dimensional Euclidean space E which are of finite III-type, that is, they are of finite type, in the sense of B.-Y. Chen, corresponding to the third fundamental form. We present an important family of surfaces, namely, tubes in E. We show that tubes are of infinite III-type.

Currently, the size of biological databases has increased significantly with the growing number of users and the rate of queries where some databases are of terabyte size. Hence, there is an increasing need to access databases at the fastest possible rate. Where biologists are concerned, the need is more of a means to fast, scalable and accuracy searching in biological databases. This may seem to be a simple task, given the speed of current available gigabytes processors. However, this is far from the truth as the growing number of data which are deposited into the database are ever increasing. Hence, searching the database becomes a difficult and time-consuming task. Here, the computer scientist can help to organize data in a way that allows biologists to quickly search existing information. In this paper, a decision tree indexing model for DNA and protein sequence datasets is proposed. This method of indexing can effectively and rapidly retrieve all the similar proteins from a lar...

Nowadays, the amount of Arabic documents has increased significantly in different domains, such as news articles, emails, business summary, biomedicine, web sites and social media documents. Some databases have increased in its size to terabyte. Multi-document summarization is the method of creating a summary of a group of interrelated documents. Therefore, the rise of the desire for Arabic multi documents text summarization (at the instant rates possible, coherent, grammatical and meaningful sentences) is increased. Recently, many efforts on multi-document text summarization that is related to the English language have been performed. Arabic multi-document summarization is remained on its early stages. Consequently, the researchers in this paper propose an Arabic Multi-Document Text Summarization (AMD-TS) model based on parallel computing techniques. This model of Arabic text summarization could effectively and rapidly summarize Arabic multi-documents in real time. A conceptual fra...

This paper demonstrates a comparative study of Arabic Multi-Document Summarization System (AMD-SS). These methods are compared and analyzed, aiming to detect which method generates a genuine summary and achieves the best results in comparison with the human summarization techniques. The comparative study shows that there is a lack in the area of Arabic Automatic Text Summarization systems. Therefore, we proposed an Arabic Text Summarization model that built in linear algorithms based on parallel computing techniques. The proposed model is built in order to generate an Arabic Document Summary (ADS) that is fully coherent, grammatical and meaningful Arabic sentences, closing to a human summarization. Recent researches have not provided perfect Arabic summary.

The standard of e-learning is a compound of different quality factors quality dimensions. The researchers looked at the efficiency of e-learning from various angles and viewpoints. However, few works of literature study, specifically, the quality of e-learning portals or software. Besides, the students' or learners' opinions about this issue. In this research, we only focused on the quality of e-learning portals and technology and what factors and impact they have on e-learning in general from the students’ perspectives. The results show that the students are more concerned about the usability and related factors because their learning process become depends fully on the e-learning portal as a result of the COVID-19 pandemic.

Matching algorithms are working to find the exact or the approximate matching between text “T” and pattern “P”, due to the development of a computer processor, which currently contains a set of multi-cores, multitasks can be performed simultaneously. This technology makes these algorithms work in parallel to improve their speed matching performance. Several exact string matching and approximate matching algorithms have been developed to work in parallel to find the correspondence between text “T” and pattern “P”. This paper proposed two models: First, parallelized the Direct Matching Algorithm (PDMA) in multi-cores architecture using OpenMP technology. Second, the PDMA implemented in Network Intrusion Detection Systems (NIDS) to enhance the speed of the NIDS detection engine. The PDMA can be achieved more than 19.7% in parallel processing time compared with sequential matching processing. In addition, the performance of the NIDS detection engine improved for more than 8% compared to...

ABSTRACT Finding the optimal parameters that can reproduce experimental data (such as the velocity–density relation and the specific flow rate) is a very important component of the validation and calibration of microscopic crowd dynamic models. Heavy computational demand during parameter search is a known limitation that exists in a previously developed model known as the Harmony Search-Based Social Force Model (HS-SFM). In this paper, a parallel-based mechanism is proposed to reduce the computational time and memory resource utilisation required to find these parameters. More specifically, two MATLAB-based multicore techniques (parfor and create independent jobs) using shared memory are developed by taking advantage of the multithreading capabilities of parallel computing, resulting in a new framework called the Parallel Harmony Search-Based Social Force Model (P-HS-SFM). The experimental results show that the parfor-based P-HS-SFM achieved a better computational time of about 26 h, an efficiency improvement of 54% and a speedup factor of 2.196 times in comparison with the HS-SFM sequential processor. The performance of the P-HS-SFM using the create independent jobs approach is also comparable to parfor with a computational time of 26.8 h, an efficiency improvement of about 30% and a speedup of 2.137 times.

This paper explores the possibility of using multi-core programming model that implements the Cascade correlation neural networks technique (CCNNs), to enhance the classification phase of 3D facial recognition system, after extracting robust and distinguishable features. This research pro-vides a comprehensive summary of the 3D facial recognition systems, as well as the state-of-the-art for the Parallel Cascade Correlation Neural Networks methods (PCCNNs). Moreover, it high-lights the lack of literature that combined between distributed and shared memory model which leads to novel possibility of taking advantage of the strengths of both approaches in order to con-struct an efficient parallel computing system for 3D facial recognition.

Exponential growth of the biological data raises a fundamental problem for meaningful extraction of information from the huge GenBank databases. The Bioinformatics computational methods have been efficiently applied for extracting, searching, integrating and analyzing the biological data. In this paper we propose an innovative extraction method, called Bio-BM, which uses the Boyer-Moore search algorithm to solve the problem of biological data selection and analysis. Experimental performance and evaluation process is utilized to compare the new Bio-BM method to the state-of-the-art biological search methods using the real benchmark datasets. The effectiveness and high-performance of the proposed method show more accurate results over the real biological datasets from GenBank

... Science and Information Technology, Al-Zaytoonah University of Jordan Rosni Abdullah, School of Computer Sciences, Universiti Sains Malaysia Nur'Aini Abdul Rashid ... In real life situations for any given query data in a large database, the need to quickly retrieve all data which ...

2010 IEEE EMBS Conference on Biomedical Engineering & Sciences (IECBES 2010), Kuala Lumpur, Malaysia, 30th November - 2nd December 2010. ... A Framework for Decision Tree-Based Method to ... Index Data from Large Protein Sequence Databases

... approaches require storing the sequences in the main memory and resulting in a reduction in the ... BLAST is more time efficient than FAST A by searching only for the more significant patterns in the ... The total sum of the sub-matrices and the contact-pattern type are indexed by an ...

ABSTRACT This paper introduces the Hybrid Decision Tree with Harmony Search (HDT-HS) optimization algorithm to improve the rate of accuracy for the decision tree algorithm so as to apply it to DNA data sets. The hybridization includes operating the decision tree method after the Improvisation step of the harmony search algorithm in order to navigate for several solutions at the same time. This is to improve the accuracy of the final results for the decision tree. The results show that the hybrid algorithm achieved better accuracy of about 96.73% compared to classifier algorithms such as Nave (94.8%), MBBC (95.99%); optimization algorithms such as bagging (94.5%) and boosting (94.7%); hybrid decision tree with genetic algorithm (70.7%) and another version from the decision tree such as C4.5 (94.3%) and PCL (94.4%).

In recent times, the size of biological databases has increased significantly, with the continuous growth in the number of users and rate of queries; such that some databases have reached the terabyte size. There is therefore, the increasing need to access databases at the fastest rates possible. In this paper, the decision tree indexing model (PDTIM) was parallelised, using a hybrid of distributed and shared memory on resident database; with horizontal and vertical growth through Message Passing Interface (MPI) and POSIX Thread (PThread), to accelerate the index building time. The PDTIM was implemented using 1, 2, 4 and 5 processors on 1, 2, 3 and 4 threads respectively. The results show that the hybrid technique improved the speedup, compared to a sequential version. It could be concluded from results that the proposed PDTIM is appropriate for large data sets, in terms of index building time.

... approaches require storing the sequences in the main memory and resulting in a reduction in the ... BLAST is more time efficient than FAST A by searching only for the more significant patterns in the ... The total sum of the sub-matrices and the contact-pattern type are indexed by an ...