Nouh Alhindawi

Prof. Dr. Nouh Alhindawi

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Research Focus & Metrics

Academic Career Length

14Years (2013-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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25 August 2017 in Neural Computing and Applications
Verified
Journal Article
Closed access
PDF file
OAobaida M. Al-hazaimeh
obaida M. Al-hazaimeh
MAMohammad F. Al-Jamal
Mohammad F. Al-Jamal
NANouh Alhindawi
Nouh Alhindawi
AOAbedalkareem Omari
Abedalkareem Omari

8 April 2017
Verified
Book Chapter
Closed access
PDF file
HAHamad Alsawalqah
Hamad Alsawalqah
HFHossam Faris
Hossam Faris
Ibrahim AljarahIbrahim Aljarah
Ibrahim Aljarah
LALoai Alnemer
Loai Alnemer
NANouh Alhindawi
Nouh Alhindawi

1 October 2019
Verified
Conference Paper
Closed access
SAShadi Alzu'BI
Shadi Alzu'BI
OBOmar Badarneh
Omar Badarneh
BHBilal Hawashin
Bilal Hawashin
MAMahmoud Al-Ayyoub
Mahmoud Al-Ayyoub
NANouh Alhindawi
Nouh Alhindawi
YJYaser Jararweh
Yaser Jararweh
Emotion Analysis (EA)is a process of determining if the text has any emotion. EA spread significantly in the recent years, especially for social media applications as applied to tweets and Facebook posts. An assumption has been presented recently that each social media post has no intensity or has one emotion. Different cases for public posts have been considered in this work, it focuses on several emotions (multi-label)included in a single post. Tweeter posts (Tweets)have been employed to validate the proposed work, it is possible to have different intensities related to each tweet (multi-target). The proposed work focused on Arabic language tweets unlike previously implemented work, which focused on other languages such as English or Chinese. A multi-label multi-target data set of Arabic tweets annotated for emotion analysis has been built, and different experts participated in the annotation process and Cohens Kappa measure was employed to determine their concordance.

19 May 2014 in Journal of Software: Evolution and Process
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Journal Article
Closed access
PDF file
Hakam W AlomariHakam W Alomari
Hakam W Alomari
MCMichael L. Collard
Michael L. Collard
JMJonathan I. Maletic
Jonathan I. Maletic
NANouh Alhindawi
Nouh Alhindawi
OMOmar Meqdadi
Omar Meqdadi

1 September 2013
Verified
Conference Paper
Closed access
NANouh Alhindawi
Nouh Alhindawi
NDNatalia Dragan
Natalia Dragan
MCMichael L. Collard
Michael L. Collard
JMJonathan I. Maletic
Jonathan I. Maletic
A novel approach to improve feature location by enhancing the corpus (i.e., source code) with static information is presented. An information retrieval method, namely Latent Semantic Indexing (LSI), is used for feature location. Adding stereotype information to each method/function enhances the corpus. Stereotypes are terms that describe the abstract role of a method, for example get, set, and predicate are well-known method stereotypes. Each method in the system is automatically stereotyped via a static-analysis approach. Experimental comparisons of using LSI for feature location with, and without, stereotype information are conducted on a set of open-source systems. The results show that the added information improves the recall and precision in the context of feature location. Moreover, the use of stereotype information decreases the total effort that a developer would need to expend to locate relevant methods of the feature.

1 May 2019 in Information and Software Technology
Verified
Journal Article
Closed access
OMOmar Meqdadi
Omar Meqdadi
NANouh Alhindawi
Nouh Alhindawi
JAJamal Alsakran
Jamal Alsakran
ASAhmad Saifan
Ahmad Saifan
HMHatim Migdadi
Hatim Migdadi