IRAQI JOURNAL FOR ELECTRICAL AND ELECTRONIC ENGINEERING (IJEEE)

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The June issue is now online! For electronic browsing click here

IJEEE welcomes scientifically and technically valid articles from all areas of electrical, electronic engineering, and computer science.

With a broad scope, the journal is meant to provide a unified and reputable outlet for rigorously peer-reviewed and well-conducted scientific research. See the full Aims & Scope here.

As well as original articles, IJEEE publishes comprehensive review articles and short articles.

The Iraqi Journal of Electrical and Electronic Engineering (IJEEE) is a peer-reviewed open access journal that undergoes a rigorous evaluation process and is freely accessible to the public. As of January 1, 2024, the publishing processing fee is set at 300,000 IQD (200 $). More details can be found here.

Most Recent Articles

Open Access
The Beam Squint Effects in Antenna Arrays at Millimeter Bands
Mariam Q. Abdalrazak, Asmaa H. Majeed, and Raed A. Abd-Alhameed
Version of record online: 13 October 2024 \| DOI: 10.37917/ijeee.21.1.2 \| Full Text (PDF)
Beam squint phenomenon is considered one of the most drawbacks that limit the use of (mm-waves) array antennas; which causes significant degradation in the BER of the system. In this paper, a uniform linear array (ULA) system is exemplified at millimeter (mm-waves) frequency bands to realize the effects of beam squint phenomena from different directions on an equivalent gain response to represent the channel performance in terms of bit error rate (BER). A simple QPSK passband signal model is developed and tested according to the proposed antenna array with beam squint. The computed results show that increasing the passband bandwidth and the number of antenna elements, have a significant degradation in BER at the receiver when the magnitude and phase errors caused by the beam squint at 26 GHz with various spectrum bandwidths.

Open Access
Integration of Fuzzy Logic and Neural Networks for Enhanced MPPT in PV Systems Under Partial Shading Conditions
Hayder Dakhil Atiya, Mohamed Boukattaya, and Fatma Ben Salem
Version of record online: 19 September 2024 \| DOI: 10.37917/ijeee.21.1.1 \| Full Text (PDF)
Efficient energy collection from photovoltaic (PV) systems in environments that change is still a challenge, especially when partial shading conditions (PSC) come into play. This research shows a new method called Maximum Power Point Tracking (MPPT) that uses fuzzy logic and neural networks to make PV systems more flexible and accurate when they are exposed to PSC. Our method uses a fuzzy logic controller (FLC) that is specifically made to deal with uncertainty and imprecision. This is different from other MPPT methods that have trouble with the nonlinearity and transient dynamics of PSC. At the same time, an artificial neural network (ANN) is taught to guess where the Global Maximum Power Point (GMPP) is most likely to be by looking at patterns of changes in irradiance and temperature from the past. The fuzzy controller fine-tunes the ANN’s prediction, ensuring robust and precise MPPT operation. We used MATLAB/Simulink to run a lot of simulations to make sure our proposed method would work. The results showed that combining fuzzy logic with neural networks is much better than using traditional MPPT algorithms in terms of speed, stability, and response to changing shading patterns. This innovative technique proposes a dual-layered control mechanism where the robustness of fuzzy logic and the predictive power of neural networks converge to form a resilient and efficient MPPT system, marking a significant advancement in PV technology.

Open Access
A Hybrid Lung Cancer Model for Diagnosis and Stage Classification from Computed Tomography Images
Abdalbasit Mohammed Qadir, Peshraw Ahmed Abdalla, and Dana Faiq Abd
Version of record online: 12 August 2024 \| DOI: 10.37917/ijeee.20.2.23 \| Full Text (PDF)
Detecting pulmonary cancers at early stages is difficult but crucial for patient survival. Therefore, it is essential to develop an intelligent, autonomous, and accurate lung cancer detection system that shows great reliability compared to previous systems and research. In this study, we have developed an innovative lung cancer detection system known as the Hybrid Lung Cancer Stage Classifier and Diagnosis Model (Hybrid-LCSCDM). This system simplifies the complex task of diagnosing lung cancer by categorizing patients into three classes: normal, benign, and malignant, by analyzing computed tomography (CT) scans using a two-part approach: First, feature extraction is conducted using a pre-trained model called VGG-16 for detecting key features in lung CT scans indicative of cancer. Second, these features are then classified using a machine learning technique called XGBoost, which sorts the scans into three categories. A dataset, IQ-OTH/NCCD – Lung Cancer, is used to train and evaluate the proposed model to show its effectiveness. The dataset consists of the three aforementioned classes containing 1190 images. Our suggested strategy achieved an overall accuracy of 98.54 %, while the classification precision among the three classes was 98.63 %. Considering the accuracy, recall, and precision as well as the F1-score evaluation metrics, the results indicated that when using solely computed tomography scans, the proposed (Hybrid-LCSCDM) model outperforms all previously published models.