Nuha El-Khalili, May Alnashashibi, Wael Hadi, Abed Alkarim Banna and Ghassan Issa, " Data Engineering for Affective Understanding Systems " , "Data",Vol.4,No., MDPI AG, Basel, Switzerland, Faculty of Information Technology, University of Petra, Amman 11196, Jordan, 04/18/2019
Abstract:
Affective understanding is an area of affective computing which is concerned with advancing the ability of a computer to understand the affective state of its user. This area continues to receive attention in order to improve the human-computer interactions of automated systems and services. Systems
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Wa'el Hadia, Nuha El-Khalili, May AlNashashibi, Ghassan Issa, Abed Alkarim AlBanna, " Application of data mining algorithms for improving stress prediction of automobile drivers: A case study in Jordan " , "Computers in Biology and Medicine journal",Vol.114,No., ELSEVIER, https://doi.org/10.1016/j.compbiomed.2019.103474, 09/26/2019
Abstract:
Driving daily through traffic congestion has been recognised as a major cause of stress. High levels of stress while driving negatively impact the driver’s decisions which could potentially lead to accidents and other long-term health hazards. Accordingly, there is a great need to determine stress l
Y. Swan, K. Botros, , " Improved High Tc Superconductivity above 125oK in Y-Ba-Cu-O System " , "American Physical Society Meeting",Vol.,No., , , 03/01/1989
Abstract:
Improved High Tc Superconductivity above 125oK in Y-Ba-Cu-O System
May Y. Al-Nashashibi, " An improved root extraction technique for Arabic words " , "The second International Conference on Computer Technology and Development",Vol.,No., IEEE, Cairo, Egypt, 11/02/2010
Abstract:
algorithm to handle weak, eliminated-long-vowel, hamzated, and geminated words since the linguistic approach does not handle such cases and a reasonably large portion of Arabic words in texts are irregular. The accuracy of the extracted roots is determined by comparing them with a predefined list of 5,405 triliteral and quadriliteral roots. The linguistic approach performance (with and without the proposed correction algorithm) was tested on an in-house text collection of eight categories. The proposed correction algorithm improved the accuracy of the linguistic one by about 14%.
May Y. Al-Nashashibi, " Stemming Techniques for Arabic Words: A Comparative Study " , "The Second International Conference on Computer Technology and Development",Vol.,No., IEEE, Cairo, Egypt, 11/02/2010
Abstract:
Text interpretation depends among other things on a pre-processing stage in extracting effectively a correct stem or root. Since there is no available standard stemmer for Arabic, we address here five methods for extracting Arabic roots and the outcomes of the approach with best results will be used later on. Four of these methods are based on a positional-letter-ranking approach where such an approach is investigated along with an adjustment, and two proposed variants. The fifth one is a rule-based approach. An algorithm for correcting irregular words is applied for all methods and a comparison is made between all approaches. The accuracy of these methods was found by comparing extracted roots with a predefined list of roots using an in-house text collection. Results show that the correction algorithm improved the accuracy of the rule-based one by about 14% and the positional letter ranking based algorithms by 7% to 10%. The adjusted positional letter ranking method proved to be the highest in accuracy among all five algorithms but slightly higher than the rule-based one. However, the rule-based algorithm was found to be the approach with the highest accuracy among all ten algorithms when the correction algorithm was included in it.
Dr. May Al-Nashashibi , Dr. Nuha El-Khalili, Dr. Wael Hadi, and Dr. Ghassan Issa, " A STUDY OF THE SUBJECTIVE FACTORS OF STRESS WHILE DRIVING " , "8th Traffic Safety Conference, Dec. 12-13, 2017",Vol.-,No., Public Security Directorate, Jordan Traffic Institute, Amman, Jordan, 12/12/2017
Abstract:
Nowadays with the increasing number of cars, daily driving activity has become a source of stress. The increase in drivers' stress level may affect their decisions while driving causing accidents or it may have a long term effect on their health. Therefore, it is important to be aware of factors tha
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May Al-Nashashibi, " ARABIC LANGUAGE PROCESSING FOR TEXT CLASSIFICATION Contributions to Arabic Root Extraction Techniques, Building An Arabic Corpus, and to Arabic Text Classification Techniques " , "https://bradscholars.brad.ac.uk/handle/10454/6326",Vol.-,No., University of Bradford, Bradford, UK, 02/01/2014
Abstract:
The impact and dynamics of Internet-based resources for Arabic-speaking users is increasing in significance, depth and breadth at highest pace than ever, and thus requires updated mechanisms for computational processing of Arabic texts. Arabic is a complex language and as such requires in depth inve
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May Y. Al-Nashashibi, " 20th meeting of Computational Linguistics In the Netherlands - Universiteit Utrecht - Poster Session " , "CLIN 20",Vol.,No., , , 02/05/2010
Abstract:
Arabic text interpretation depends among other things on a pre-processing stage in extracting a correct stem or root. We address in this work a linguistic approach for root extraction as a pre-processing step for Arabic text mining. The linguistic approach is composed of a rule-based light stemmer and a pattern-based infix remover. Since this approach does not handle defective, vocalized words and slightly handles geminated words, we propose an algorithm to handle such cases by using 5737 possible correction cases in 71 predefined lists. This algorithm is proposed since there is reasonably large portion of Arabic words that are defective and most available stemmers of Arabic words either don't handle such words or handle them poorly. The accuracy of the extracted roots is determined by comparing them with a predefined list of 5404 triliteral and quadriliteral roots. The linguistic approach performance (with and without the proposed correction algorithm)was tested on an original text collection of eight categories. The accuracies of the linguistic algorithm along with the proposed correction one are reported here. The proposed correction algorithm improved the performance of the linguistic one by about 14%.
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