A New Enhanced Arabic Light Stemmer for IR in Medical Documents

Al-Khatib, Ra’ed M.; Zerrouki, Taha; Shquier, Mohammed M. Abu; Balla, Amar; Al-Khateeb, Asef

doi:10.32604/cmc.2021.016155

Cited by 4 publications

(3 citation statements)

References 12 publications

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“…In the process of testing, the task is given a pair of face images, then we verify whether both faces belong to the same person or not. The accuracy of our proposed algorithm is tested by generating the confusion matrix [46], [47]. We compute the related accuracy measures such as Sensitivity, Specificity and F1-score.…”

Section: Experiments and Discussionmentioning

confidence: 99%

Twins and Similar Faces Recognition Using Geometric and Photometric Features with Transfer Learning

Nahar¹,

Abul-Huda²,

bataineh³

et al. 2022

IJCDS

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Twins recognition and identification is one of the important challenges in the field of image processing. The strong similarity between identical twins makes it hard to distinguish the twin from his/her sibling. Similarities come from biometric, geometric, and photometric features. In biometric patterns, the fingerprints found to be identical in some cases, geometrically, the twins' faces rarely differ which confuses people. Photometric features are very close to each other even though they rarely success in twins' recognition. We tackle this challenge by a model for twin's face recognition (FR) where our solution is based on deep transfer learning in terms of residual neural networks including two VGG16 trained networks, which are considered to be one of the powerful and deeply learned neural networks. For comparison purposes, we check other approaches to solve the twins' problem including iris, fingerprints, and lip corners. The data used was collected from Google which is a challenge. Data contains 4-pairs of twins with the 17-different position for each one which produces 5×2×17 (170) different images. Collected images were used for comparisons between features. Results show that geometrical features gave 85% of success while photometric features gave 96%. By hybridizing geometrical and photometric features together, the results reach 98% of accuracy. Biometric measures, in this research, prove the superiority of deeply transferring learning over traditional methods. The newly achieved method could be replaced to assist authentication systems that fully depend on biometric features.

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Section: Experiments and Discussionmentioning

confidence: 99%

Twins and Similar Faces Recognition Using Geometric and Photometric Features with Transfer Learning

Nahar¹,

Abul-Huda²,

bataineh³

et al. 2022

IJCDS

View full text Add to dashboard Cite

show abstract

“…In another project, we worked on applying the stemming algorithm to tackle the information retrieval problem in medical documents. (Al-Khatib et al, 2021).…”

Section: Statement Of Needmentioning

confidence: 99%

“…Tashaphyne has also been utilized for building resources such as corpora (Kuppevelt et al, 2018) and ontologies (Albukhitan et al, 2017), question answering (Abdul Salam, 2022Ezzeldin, 2014;Ezzeldin et al, 2015), and information retrieval (Al-Khatib et al, 2021;Mortaja, 2017;S & R, 2022).…”

Section: Mentionmentioning

confidence: 99%

Tashaphyne: A Python package for Arabic Light Stemming

Zerrouki

2024

JOSS

Self Cite

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Stemming is an important task in natural language processing that involves reducing a word to its root form, or stem. In many cases, stemming can significantly improve the accuracy and efficiency of text analysis tasks such as information retrieval, text classification, and sentiment analysis. For the Arabic language, which has a rich morphology with a large number of prefixes and suffixes, stemming is particularly challenging. Tashaphyne provides an effective solution to this challenge, making it a valuable tool for researchers and practitioners working with Arabic text data.

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