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All Journal International Journal of Electrical and Computer Engineering
Desuky, Abeer S.
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Computer Science & IT Electrical & Electronics Engineering

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An enhanced Giza Pyramids construction for solving optimization problems Omar, Asmaa Hekal; Mostafa, Naglaa M.; Desuky, Abeer S.; Bakrawy, Lamiaa M. El
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 5: October 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i5.pp5672-5680

Abstract

Many real-world optimization problems can be solved by various algorithms that are not fast in convergence or gain enough accuracy. Meta-heuristic algorithms are used to solve optimization problems and have achieved their effectiveness in solving several real-world optimization problems. Meta-heuristic algorithms try to find the best solution out of all available solutions in the possible shortest time. A good meta-heuristic algorithm is characterized by its accuracy, convergence speed, and ability to solve high dimensions’ problems. Giza Pyramids construction (GPC) has recently been introduced as a physics-inspired optimization method. This paper suggests an enhanced Giza Pyramids construction (EGPC) by adding a new parameter based on the step length of each individual and iteratively revises the individual’ position. The EGPC algorithm is suggested for improving the GPC exploitation and exploration. Experiments were performed on 23 benchmark functions and four IEEE CEC 2019 benchmarks to test the performance of the proposed EGPC algorithm. The experimental results show the high competitiveness of the EGPC algorithm compared to the basic GPC algorithm and another four well known optimizers in terms of improved exploration, exploitation, convergence’ rate, and the avoidance of local optima.
Optimizing internet of things based gas sensors: deep learning and performance optimization strategies Abdellatif, Mariam M.; Çifçi, Mehmet Akif; Ibrahim, Asmaa A.; Harb, Hany M.; Desuky, Abeer S.
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 5: October 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i5.pp4813-4828

Abstract

The rapid growth of industrialization and internet of things (IoT) driven advancements in Industry 5.0 necessitates efficient and user-friendly engineering solutions. Gas leakage incidents in coal mines, chemical enterprises, and households pose significant risks to ecosystems and human safety, emphasizing the need for automated and rapid gas-type detection. Traditional detection methods rely on single-source data and focus on isolated spatial or temporal features, limiting accuracy. This paper proposes a multimodal artificial intelligence (AI) fusion technique combining pre-trained convolutional neural networks (CNNs), such as VGG16, with a deep neural network (DNN) model. The particle swarm optimization (PSO) algorithm optimizes CNN hyperparameters, outperforming traditional trial-and-error methods. The system addresses challenges posed by gases being odorless, colorless, and tasteless, which limit conventional human detection methods. By leveraging sensor fusion, the late fusion technique integrates distinct network architectures for unified gas identification. Experimental results demonstrate 95% accuracy using DNN with gas sensor data, 96% with optimized VGG16 using thermal imaging, and 99.5% through multimodal late fusion. This IoT-enhanced solution outperforms single-sensor approaches, offering a robust and reliable gas leakage detection system suitable for industrial and smart city applications.
Hybrid CNBLA architecture for accurate earthquake magnitude forecasting Shams, Somia A.; Mohamed, Asmaa; Desuky, Abeer S.; A. Elsharawy, Gaber; El-Sayed, Rania Salah
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i6.pp5879-5893

Abstract

Earthquake prediction in seismology is challenging due to sudden events and lack of warnings, requiring rapid detection and accurate parameter estimation for real-time applications. This study proposed a novel automatic earthquake detection model to enhance the processing and analysis of seismic data. The hybrid model comprises convolutional layers, normalization techniques, bidirectional long short-term memory (Bi-LSTM) networks, and attention mechanisms, collectively referred to as the hybrid convolutional–normalization–BiLSTM–attention (CNBLA) model. The attention mechanism allows the model to focus on critical segments of seismic sequences, while layer normalization stabilizes training by normalizing activations, thus reducing the effects of input scale variations. This dual approach mitigates the impact of input scale variations and enhances the model’s ability to effectively decode complex temporal patterns. The hybrid CNBLA model optimizes the extraction and processing of temporal features from raw waveforms recorded at single stations, thereby improving the accuracy and efficiency of seismic magnitude estimation. The proposed model is evaluated using two datasets: the STEAD and USGS achieving a mean square error (MSE) values 0.054 and 0.0843 and a mean absolute error (MAE) 0.15 and 0.2526 respectively. The hybrid CNBLA model outperforms two baseline models and five state-of-the-art approaches in earthquake magnitude estimation, improving seismic monitoring and early warning systems.
Co-Authors A. Elsharawy, Gaber Abdellatif, Mariam M. Bakrawy, Lamiaa M. El Cifci, Mehmet Akif El-Sayed, Rania Salah Harb, Hany M. Ibrahim, Asmaa A. Mohamed, Asmaa Mostafa, Naglaa M. Omar, Asmaa Hekal Shams, Somia A.