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

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Visualization of hyperspectral images on parallel and distributed platform: Apache Spark Zbakh, Abdelali; Bennani, Mohamed Taj; Souri, Adnan; Hichami, Outman El
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 6: December 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i6.pp7115-7124

Abstract

The field of hyperspectral image storage and processing has undergone a remarkable evolution in recent years. The visualization of these images represents a challenge as the number of bands exceeds three bands, since direct visualization using the trivial system red, green and blue (RGB) or hue, saturation and lightness (HSL) is not feasible. One potential solution to resolve this problem is the reduction of the dimensionality of the image to three dimensions and thereafter assigning each dimension to a color. Conventional tools and algorithms have become incapable of producing results within a reasonable time. In this paper, we present a new distributed method of visualization of hyperspectral image based on the principal component analysis (PCA) and implemented in a distributed parallel environment (Apache Spark). The visualization of the big hyperspectral images with the proposed method is made in a smaller time and with the same performance as the classical method of visualization.
Arabic text diacritization using transformers: a comparative study Zubeiri, Iman; Souri, Adnan; El Mohajir, Badr Eddine
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i1.pp702-711

Abstract

The Arabic language presents challenges for natural language processing (NLP) tasks. One such challenge is diacritization, which involves adding diacritical marks to Arabic text to enhance readability and disambiguation. Diacritics play a crucial role in determining the correct pronunciation, meaning, and grammatical structure of words and sentences. However, Arabic texts are often written without diacritics, making NLP tasks more complex. This study investigates the efficacy of advanced machine learning models in automatic Arabic text diacritization, with a concentrated focus on the Arabic bidirectional encoder representations from transformers (AraBERT) and bidirectional long short-term memory (Bi-LSTM) models. AraBERT, a bidirectional encoder representation from transformers (BERT) derivative, leverages the transformer architecture to exploit contextual subtleties and discern linguistic patterns within a substantial corpus. Our comprehensive evaluation benchmarks the performance of these models, revealing that AraBERT significantly outperforms the Bi-LSTM with a diacritic error rate (DER) of only 0.81% and an accuracy rate of 98.15%, against the Bi-LSTM's DER of 1.02% and accuracy of 93.88%. The study also explores various optimization strategies to amplify model performance, setting a precedent for future research to enhance Arabic diacritization and contribute to the advancement of Arabic NLP.
Co-Authors bennani, Mohamed Taj EL Mohajir, Badr Eddine Hichami, Outman El Zbakh, Abdelali Zubeiri, iman