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All Journal Journal of Robotics and Control (JRC)
Ahmed, Ali Saadoon
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Aerospace Engineering Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Mechanical Engineering

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Journal : Journal of Robotics and Control (JRC)

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Advanced Estimation of Brain Age Using Pre-trained 2D Convolutional Neural Networks on a Public Dataset Al-kubaisi, Mohannad; Ahmed, Ali Saadoon; Al-Barzinji, Shokhan M.; Khaleel, Arshad M.
Journal of Robotics and Control (JRC) Vol 5, No 4 (2024)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v5i4.22006

Abstract

This work introduces a brand-new approach to be employed for correctly assessing healthy person’s brain aging, masking what constitutes the most serious challenge in the fight against age-related cognitive decline. An approach is serviced by 2D CNNs, a simpler technology to state-of-the-art 3D model, to yield close to accurate forecast. Our algorithm improves telling in two respects. By virtue of this, we will utilize well-known ImageNet-pre-trained classifiers to suggest initial brain age predictions. This process sets the tone of the core of our business model in terms of dependability and reliability. Next, we improve the networks’ performance through progressively expanding their capacity via fine-tuning on pre-segmentation tasks using the neuroimaging datasets which are openly available. This stage increases the predictive accuracy in addition to ensuring that there is transparency and flexibility because it utilizes open datasets. Our research's strength is that it encompasses all techniques and fields necessary for brain age estimation and adopts justifiable evaluation metrics. As a result, this conduct adds to the literature. Our study not only points out deficiencies in private datasets but reels out the validity of our approach by using the public data instead to give the results another direction of accessibility and reproducibility.
Enhancing Voice Authentication with a Hybrid Deep Learning and Active Learning Approach for Deepfake Detection Ahmed, Ali Saadoon; Khaleel, Arshad M.
Journal of Robotics and Control (JRC) Vol. 5 No. 6 (2024)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v5i6.23502

Abstract

This paper explores the application of active learning to enhance machine learning classifiers for spoofing detection in automatic speaker verification (ASV) systems. Leveraging the ASVspoof 2019 database, we integrate an active learning framework with traditional machine learning workflows, specifically focusing on Random Forest (RF) and Multilayer Perceptron (MLP) classifiers. The active learning approach was implemented by initially training models on a small subset of data and iteratively selecting the most uncertain samples for further training, which allowed the classifiers to refine their predictions effectively. Experimental results demonstrate that while the MLP initially outperformed RF with an accuracy of 95.83% compared to 91%, the incorporation of active learning significantly improved RF's performance to 94%, narrowing the performance gap between the two models. After applying active learning, both classifiers showed enhanced precision, recall, and F1-scores, with improvements ranging from 3% to 5%. This study provides valuable insights into the role of active learning in boosting the efficiency of machine learning models for dynamic spoofing scenarios in ASV systems. Future research should focus on designing advanced active learning techniques and exploring their integration with other machine learning paradigms to further enhance ASV security.
Co-Authors Al-Barzinji, Shokhan M. Al-kubaisi, Mohannad Khaleel, Arshad M.