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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Buletin Ilmiah Sarjana Teknik Elektro Indonesian Journal of Electrical Engineering and Computer Science
Muna Hadi Saleh
University of Baghdad
Computer Science & IT Electrical & Electronics Engineering Engineering

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Evaluation of massive multiple-input multiple-output communication performance under a proposed improved minimum mean squared error precoding Dheyaa Jasim Kadhim; Muna Hadi Saleh; Sadiq Jassim Abou-Loukh
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 12, No 2: June 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v12.i2.pp984-994

Abstract

The fundamental of a downlink massive multiple-input multiple-output (MIMO) energy- issue efficiency strategy is known as minimum mean squared error (MMSE) implementation degrades the performance of a downlink massive MIMO energy-efficiency scheme, so some improvements are adding for this precoding scheme to improve its workthat is called our proposal solution as a proposed improved MMSE precoder (PIMP). The energy efficiency (EE) study has also taken into mind drastically lowering radiated power while maintaining high throughput and minimizing interference issues. We further find the tradeoff between spectral efficiency (SE) and EE although they coincide at the beginning but later their interests become conflicting and divergent then leading EE to decrease so gradually while SE continues increasing logarithmically. The results achieved that for a single-cellular massive MU-MIMO downlink model, our PIMP scheme is the appropriate scenario to achieve higher precoding performance system. Furthermore, both maximum ratio transmission (MRT) and PIMP are suitable for performance improvement in massive MIMO results of EE and SE. So, the main contribution comes with this work that highest EE and SE are belong to use a PIMP which performs better appreciably than MRT at bigger ratio of number of antennas to the number of the users. 
Implementation of Vehicle Ad Hoc Networks for TPBFT on Latency and Fault Tolerance in Blockchain Systems Liqaa Saadi Mezher; Ayam Mohsen Abbass; Muna Hadi Saleh
Buletin Ilmiah Sarjana Teknik Elektro Vol. 8 No. 2 (2026): April
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/biste.v8i2.15227

Abstract

In this paper, examines the combination of Vehicle Ad hoc Networks (VANETs) and a new consensus mechanism called Trust Practical Byzantine Fault Tolerance (TPBFT) that is aimed at improving latency and fault tolerance of decentralized vehicular networks. The VANETs are described by dynamic topology and mobile node and pose special security as well as reliability issues especially in scalable networks. The conventional Byzantine Fault Tolerance (BFT) protocols are ineffective because they incur communication overhead and scaling problems. This paper suggests TPBFT as a powerful consensus mechanism that is suitable to use in vehicular networks and is effective even when malicious or malfunctioning nodes are involved. To model real-life traffic patterns and communication scenarios, the research methodology presupposes extensive simulations based on Simulation of Urban Mobility (SUMO) tool and real-world Open Street Map (OSM) data with the help of the Python program. The performance of TPBFT is strictly tested and compared to the classic Practical Byzantine Fault Tolerance (PBFT) protocol through the analysis of the consensus latency, system throughput, and fault tolerance resilience. The findings indicate TPBFT has a shorter consensus latency (16 to 28 ms) and a greater throughput compared to PBFT and was more effective in time-constrained vehicular usage. The present work makes TPBFT an effective decentralized mechanism that allows achieving low latency, high throughput, and high resistance to Byzantine failures, offering a safe platform to deploy the blockchain technology in smart transportation systems. The optimization of the energy consumption profile of network nodes, as well as the refinement of the consensus process on the application of blockchain-based VANET architecture into practice, will be the subject of future research.
Increasing validation accuracy of a face mask detection by new deep learning model-based classification Mohanad Azeez Joodi; Muna Hadi Saleh; Dheyaa Jasim Kadhim
Indonesian Journal of Electrical Engineering and Computer Science Vol 29, No 1: January 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v29.i1.pp304-314

Abstract

During COVID-19, wearing a mask was globally mandated in various workplaces, departments, and offices. New deep learning convolutional neural network (CNN) based classifications were proposed to increase the validation accuracy of face mask detection. This work introduces a face mask model that is able to recognize whether a person is wearing mask or not. The proposed model has two stages to detect and recognize the face mask; at the first stage, the Haar cascade detector is used to detect the face, while at the second stage, the proposed CNN model is used as a classification model that is built from scratch. The experiment was applied on masked faces (MAFA) dataset with images of 160x160 pixels size and RGB color. The model achieved lower computational complexity and number of layers, while being more reliable compared with other algorithms applied to recognize face masks. The findings reveal that the model's validation accuracy reaches 97.55% to 98.43% at different learning rates and different values of features vector in the dense layer, which represents a neural network layer that is connected deeply of the CNN proposed model training. Finally, the suggested model enhances recognition performance parameters such as precision, recall, and area under the curve (AUC).
Co-Authors Ayam Mohsen Abbass Dheyaa Jasim kadhim Liqaa Saadi Mezher Mohanad Azeez Joodi Sadiq Jassim Abou-Loukh