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All Journal IAES International Journal of Artificial Intelligence (IJ-AI)
Al-Dhief, Fahad Taha
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Computer Science & IT 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. 
Enhancing credit card fraud detection with synthetic minority over-sampling technique-integrated extreme learning machine Ajlan, Iman Kadhim; Mahdi, Mohammed Ibrahim; Murad, Hayder; AL-Dhief, Fahad Taha; Safie, Nurhizam; Shakir, Yasir Hussein; Abbas, Ali Hashim
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i6.pp4749-4762

Abstract

Many works in cybersecurity detection suffer from low accuracy rates, particularly in real-world applications, where imbalanced datasets and evolving fraud strategies pose significant hurdles. This study introduces an optimized extreme learning machine (ELM) algorithm to address these challenges by dynamically adjusting hidden nodes ranging from 10 to 100 with an increment step of 10 and integrating two activation functions. The proposed method utilizes the synthetic minority over-sampling technique (SMOTE) to handle class imbalance effectively and incorporates a comprehensive evaluation using descriptive statistics, visualization, and significance testing. The proposed ELM-SMOTE method achieves the highest results including an accuracy of 99.710%, recall of 85.811%, specificity of 99.743%, and G-mean of 92.068%. These outcomes reflect the robustness and adaptability of the proposed ELM algorithm in detecting fraudulent transactions. This study emphasizes the importance of a holistic performance analysis, addressing gaps in existing methods and providing a scalable framework for real-world fraud detection applications.
Co-Authors Abbas, Ali Hashim Ajlan, Iman Kadhim Alwan Tuib, Tabarek Hussein, Yaqdhan Mahmood Mahdi, Mohammed Ibrahim Mandeel, Thulfiqar H. Murad, Hayder Safie, Nurhizam Saoudi, Baydaa Hadi Shakir, Yasir Hussein