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| 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 | |
| Mobility Prediction Based on LSTM Multi-Layer Using GPS Phone Data | |
| Nabaa Kareem Mhalhal, and Suhad Faisal Behadili | |
| Version of record online: 28 February 2025 | DOI: 10.37917/ijeee.21.2.25 | Full Text (PDF) | |
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Precise Prediction of activity location is an essential element in numerous mobility applications and is especially necessary for the development of tailored sustainable transportation systems. Next-location prediction, which involves predicting a user’s future position based on their past movement patterns, has significant implications in various domains, including urban planning, geo-marketing, disease transmission, Performance wireless network, Recommender Systems, and many other areas. In recent years, various predictors have been suggested to tackle this issue, including state-of-the-art ones that utilize deep learning techniques. This study introduces a robust Model for predicting the future location path of a user based on their known previous locations. The study proposes the use of a Long Short-Term Memory (LSTM) prediction scheme, which is well-suited for learning from sequential data; then a fully connected neuron is employed to decrease the sparsity of the data, resulting in accurate predictions for the path of the user’s next location. The suggested strategy demonstrates superior prediction accuracy compared to a state-of-the-art method, with improvements of up to a loss error of 0.002 based on real-life datasets (Geolife). The results demonstrate that the reliability of forecasts is excellent, indicating the accuracy of the predictions.| Open Access | |
| Design of Hand Gesture Classification System Based on High Density-Surface Electromyography Accompanied Force Myography | |
| Alya Ghazi Darweesh, and Mofeed Turky Rashid | |
| Version of record online: 28 February 2025 | DOI: 10.37917/ijeee.21.2.24 | Full Text (PDF) | |
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A robust system that classifies various hand gestures would greatly help those using prosthetic limbs. Recently, emphasis has been placed on extracted features from the High Density – surface Electromyography (HD-sEMG) signals and the size of segmentation windows which augment the recognition accuracy. This paper proposes a hand gestures identification system, in which HD-sEMG signals are employed, and is supported by Force Myography (FMG) signals for this mission. Several feature types have been extracted from FMG and HD-sEMG signals such as MEAN, RMS, MAD, STD, and Variance, these features have been validated under some classifiers such as decision tree (DT), linear discriminant analysis (LDA), support vector machine SVM, and k-nearest neighbor (KNN), in which results showing that MEAN and RMS features are superior to others, while the best classifier is SVM. Several experiments have been achieved by the MATLAB platform to validate the proposed system, in which, a database of HD-sEMG signals comprising 65 isometric hand gestures is employed, where two (8×8) electrodes and 9 force sensors are used to collect the FMG data. This data was derived from 20 intact participants, the first preprocessing step was applied during the recording stage. Ten gestures are chosen to be classified from the 65 hand gestures. Results show the success of the proposed system while the classification accuracy arrived at 99.1%.| Open Access | |
| Performance of Sparse Code Multiple Access Communication System Based on Logarithmic Message Passing Algorithm and Low-Density Parity Check Code | |
| Mustafa Safwan Moafaq, and Maher K. Mahmood Al-Azawi | |
| Version of record online: 28 February 2025 | DOI: 10.37917/ijeee.21.2.23 | Full Text (PDF) | |
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The performance of Sparse Code Multiple Access (SCMA) communication system with Logarithmic Message Passing Algorithm (log-MPA) decoder is introduced. To boost the performance, a Low-Density Parity-Check Code LDPC is used together with Belief Propagation (BP) decoder. LDPC is chosen due to its sparsity property that complements the sparsity nature of SCMA for maximum efficiency and minimum complexity. Three distinct SCMA configurations are used. These are: A (4 x 4 x 6), B (4 x 16 x 6), and C (5 x 4 x 10) where the (K x M x V) are numbers of resources, codewords and users respectively. The performance of these configuration is shown in various channel conditions, various LDPC code rates and various numbers of SCMA iterations (N_SCMA), to find the local minimum value of log-MPA. Simulation results showed that the LDPC greatly boosted the performance in mentioned configurations: In A configuration, a gain of 13 dB was observed. Configuration B experienced a substantial improvement of 23.5 dB, while C achieved a gain of 20.5 dB. Notably, configuration B stood out with the highest gain, attributed to LDPC’s exceptional performance with high data rates, as the data transmitted in B was double that of A.|
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