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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI) Bulletin of Electrical Engineering and Informatics
Zaki, Chamseddine
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Computer Science & IT Electrical & Electronics Engineering Engineering

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Two-way differential strategy for wireless sensor networks Alabed, Samer; Alsaraira, Amer; Mostafa, Nour; Al-Rabayah, Mohammad; Shdefat, Ahmed; Zaki, Chamseddine; A. Saraereh, Omar; Al-Arnaout, Zakwan
Bulletin of Electrical Engineering and Informatics Vol 12, No 6: December 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i6.6121

Abstract

In this paper, a novel optimal two-way amplify and forward (AF) differential beamforming method for wireless sensor network is proposed. The proposed method is an advanced signal processing technique used to enhance the performance and reliability of the communication link by exploiting the diversity provided by multiple antennas. Unlike current state-of-the-art methods which require the knowledge of channel state information (CSI) at both transmitting and receiving antennas or at least at the receiving antennas, the suggested method does not need CSI at any transmitting or receiving antenna. Moreover, the proposed method enjoys high error performance with high diversity and coding gain and has a very low encoding and decoding complexity. Through our simulations, the proposed method is proved to outperform the best known non-coherent multi-antenna methods.
Analytic hierarchy process geographic information system based model for sustainable construction and demolition waste landfill site selection Soussi, Mohamed Ayet Allah Bilel; Madsia, Nermine El; Zaki, Chamseddine; Ramadan, Alaaeddine; Saker, Louai; Ibrahim, Moustafa
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i1.pp803-816

Abstract

Properly managing waste generated by buildings and public works is a significant challenge in Tunisia, particularly in the city of Bizerte. The inadequate disposal of such waste can cause substantial harm to human life, property, and the environment. This paper proposes an multi-criteria decision making (MCDM) that utilizes the analytic hierarchy process (AHP) decision support tool to identify suitable landfill sites for construction and demolition waste (CDW) in Bizerte. The AHP method is widely used in MCDM applications. The approach involves classifying different scenarios based on various exclusion and appreciation criteria to determine the optimal locations for future landfills. Furthermore, the paper develops a conceptual approach for identifying better sites for the disposal of CDW, resulting in a comprehensive database capable of storing, accessing, and extracting information at both conceptual and operational levels. The proposed model considers spatial, technical, and environmental criteria in the selection of a suitable landfill site. The proposed methodology offers an effective and practical solution for properly managing CDW waste in Bizerte, Tunisia, and can be applied to other regions facing similar challenges.
A comparative study of Arabic morphological analyzers Saadiyeh, Omar; Ramadan, Alaaeddine; Zaki, Chamseddine; Hajjar, Mohamad; Bernard, Gilles
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 2: April 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i2.pp1876-1890

Abstract

The field of Arabic natural language processing (NLP) has witnessed significant advancements, driven by the development of various morphological analyzers. This paper compares several major Arabic morphological analyzers and examines their ability to handle word ambiguities, process dialects, operate efficiently, and support downstream NLP tasks. By reviewing previous studies, we identify key gaps, including the limited resources for dialects, the shortage of annotated corpora, and challenges related to system scalability. The study also highlights future directions, such as building larger and more diverse corpora, adapting neural models for dialects, and developing analyzers that are more interpretable and trustworthy. Overall, this comparative overview aims to provide a clearer understanding of the current state of Arabic morphological analyzers, synthesize existing research, and offer practical recommendations for future work in this area.
Co-Authors A. Saraereh, Omar Al-Arnaout, Zakwan Al-Rabayah, Mohammad Alabed, Samer Alsaraira, Amer Bernard, Gilles Hajjar, Mohamad Ibrahim, Moustafa Madsia, Nermine El Mostafa, Nour Ramadan, Alaaeddine Saadiyeh, Omar Saker, Louai Shdefat, Ahmed Soussi, Mohamed Ayet Allah Bilel