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All Journal International Journal of Power Electronics and Drive Systems (IJPEDS) IAES International Journal of Artificial Intelligence (IJ-AI)
Hussein, Yaqdhan Mahmood
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering

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Convolutional neural network with binary moth flame optimization for emotion detection in electroencephalogram Alwan Tuib, Tabarek; Saoudi, Baydaa Hadi; Hussein, Yaqdhan Mahmood; Mandeel, Thulfiqar H.; Al-Dhief, Fahad Taha
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 1: March 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i1.pp1172-1178

Abstract

Electroencephalograph (EEG) signals have the ability of real-time reflecting brain activities. Utilizing the EEG signal for analyzing human emotional states is a common study. The EEG signals of the emotions aren’t distinctive and it is different from one person to another as every one of them has different emotional responses to same stimuli. Which is why, the signals of the EEG are subject dependent and proven to be effective for the subject dependent detection of the Emotions. For the purpose of achieving enhanced accuracy and high true positive rate, the suggested system proposed a binary moth flame optimization (BMFO) algorithm for the process of feature selection and convolutional neural networks (CNNs) for classifications. In this proposal, optimum features are chosen with the use of accuracy as objective function. Ultimately, optimally chosen features are classified after that with the use of a CNN for the purpose of discriminating different emotion states. 
Enhanced voltage stability in power distribution networks through optimal reconfiguration using hybrid metaheuristic algorithms Azeez, Mohammed Zuhair; Atiyah, Abbas Swayeh; Hussein, Yaqdhan Mahmood; Hanoosh, Hatem Oday
International Journal of Power Electronics and Drive Systems (IJPEDS) Vol 16, No 4: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijpeds.v16.i4.pp2582-2591

Abstract

An optimal network reconfiguration (ONR) is used in distribution power systems to improve voltage decreases within the permitted period and minimize real power losses. Consequently, attaining optimal reconfiguration in distribution systems is regarded as the primary objective of numerous researchers. Conventional heuristic techniques such as genetic algorithms (GA), ant colony optimization (ACO), and particle swarm optimization (PSO) can reduce active power losses and enhance network stability. These algorithms indicate a greater number of difficulties, including inadequate convergence characteristics, a reduction in power loss, and an increase in bus voltage. This research proposes effective optimization strategies utilizing the salp swarm algorithm (SSA) and whale optimization algorithm (WOA) to augment bus voltage, reduce distribution losses, and improve network dependability. The proposed algorithms are executed and evaluated on the IEEE 33-bus and 69-bus networks to determine the ideal network architecture. The efficacy of the examined methodologies is illustrated through MATLAB under steady-state conditions, showcasing benefits in the reduction of active power loss relative to current algorithms. The comparison indicates that the SSA algorithm exhibits superior performance in terms of power losses and bus voltage enhancement relative to the WOA method. due to its enhanced exploration and exploitation capabilities, which help avoid local optima and ensure a more effective search for optimal solutions. SSA's adaptive mechanism and cooperative behavior improve convergence speed and solution accuracy, making it more efficient for optimization in network reconfiguration.
Co-Authors Al-Dhief, Fahad Taha Alwan Tuib, Tabarek Atiyah, Abbas Swayeh Azeez, Mohammed Zuhair Hanoosh, Hatem Oday Mandeel, Thulfiqar H. Saoudi, Baydaa Hadi