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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI)
Guennoun, Zouhair
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Computer Science & IT Electrical & Electronics Engineering Engineering

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Toward accurate Amazigh part-of-speech tagging Bani, Rkia; Amri, Samir; Zenkouar, Lahbib; Guennoun, Zouhair
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 1: March 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i1.pp572-580

Abstract

Part-of-speech (POS) tagging is the process of assigning to each word in a text its corresponding grammatical information POS. It is an important pre-processing step in other natural language processing (NLP) tasks, so the objective of finding the most accurate one. The previous approaches were based on traditional machine learning algorithms, later with the development of deep learning, more POS taggers were adopted. If the accuracy of POS tagging reaches 97%, even with the traditional machine learning, for high resourced language like English, French, it’s far the case in low resource language like Amazigh. The most used approaches are traditional machine learning, and the results are far from those for rich language. In this paper, we present a new POS tagger based on bidirectional long short-term memory for Amazigh language and the experiments that have been done on real dataset shows that it outperforms the existing machine learning methods.
Design and experimental validation of a microstrip Vivaldi antenna-based system for breast tumor detection Qanoune, Samiya; Ammor, Hassan; Er-Reguig, Zakaria; Guennoun, Zouhair
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i6.pp5497-5505

Abstract

Breast cancer remains one of the leading causes of death among women worldwide, highlighting the critical need for accurate, non-invasive, and cost-effective diagnostic solutions. In light of this, microwave imaging has surfaced as a promising alternative to conventional diagnostic methods. This approach leverages its capability to differentiate between healthy and cancerous tissues by examining their dielectric properties. This study presents the design, implementation, and experimental assessment of a Vivaldi antenna-based system aimed at breast cancer detection. The antenna is designed to operate within the ultra-wideband frequency range, which facilitates high-resolution imaging and effective deep tissue penetration. Data collected from tissue-mimicking phantoms reveals the system’s proficiency in identifying anomalies, showcasing a significant contrast between malignant and normal tissue regions. We analyze various performance metrics, including signal reflection, penetration depth, and imaging resolution to substantiate the system's efficacy. The results underline the significant potential of Vivaldi antennas in improving early- stage breast cancer detection, thus contributing to advancements in microwave imaging technology.
Co-Authors Ammor, Hassan Amri, Samir Bani, Rkia Er-reguig, Zakaria Qanoune, Samiya Zenkouar, Lahbib