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All Journal International Journal of Robotics and Control Systems ASEAN Journal for Science and Engineering in Materials
Jaber, Alaa Abdulhady
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Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Control & Systems Engineering Electrical & Electronics Engineering Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mechanical Engineering

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Robust Multi-State EEG Cognitive Classification via Optimized Time-Domain Features and CatBoost Nassir, Layla M.; Ramadhan, Ali J.; Al-Sharify, Noor T.; Khalaf, Mohammed I.; Ogaili, Ahmed Ali Farhan; Jaber, Alaa Abdulhady; Al-Sharify, Zainab T.
International Journal of Robotics and Control Systems Vol 5, No 2 (2025)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v5i2.1799

Abstract

This study introduces a novel framework for classifying multi-state cognitive processes using electroencephalogram (EEG) signals. By integrating optimized time-domain feature extraction with ensemble learning techniques, the proposed method achieves exceptional accuracy in distinguishing eight distinct cognitive states. The preprocessing pipeline employs finite impulse response (FIR) bandpass filtering (0.5–45 Hz) and Independent Component Analysis (ICA) for artifact removal, while feature extraction leverages Hjorth parameters and statistical measures. A comparative analysis of classification algorithms reveals CatBoost as the top performer, achieving 93.4% accuracy, followed by Neural Network (91.3%), SVM (89.7%), and AdaBoost (88.9%). CatBoost excels in discriminating complex states with computational efficiency, processing times ranging from 18 ms (SVM) to 32 ms (CatBoost), supporting real-time applications. The framework demonstrates robustness under varying signal quality, maintaining 91% accuracy at 10 dB SNR. These advancements set new benchmarks for EEG-based cognitive monitoring, with implications for adaptive systems requiring real-time neural feedback.
Integrated Experimental, Statistical, and Finite Element Analysis of Nanoparticle-Reinforced Polymer Composites for Advanced Structural Applications Completed with Bibliometric Analysis Nassir, Layla M.; Jweri, Abdul-Rasool Kareem; Al-Ameen, Ehsan Sabah; Ogai-li, Ahmed Ali Farhan; Njim, Emad Kadum; Al-Maliky, Firas Thair; Jaber, Alaa Abdulhady; Al-Haddad, Luttfi A.; Al-Karkhi, Mustafa I.
ASEAN Journal for Science and Engineering in Materials Vol 5, No 2 (2026): AJSEM: Volume 5, Issue 2, September 2026
Publisher : Bumi Publikasi Nusantara

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Abstract

This study investigates the mechanical and tribological behavior of polyvinylidene fluoride (PVDF)/unsaturated polyester resin (UPR) composites reinforced with 1–5% multi-walled carbon nanotubes (MWCNTs). Research on PVDF-based nanocomposites has increased significantly, according to a quick bibliometric screening of Scopus-indexed publications. MWCNTs were found to be one of the most influential reinforcement keywords, indicating a high level of interest in mechanically optimized polymer systems worldwide. In this study, specimens were tested for tensile, flexural, hardness, impact, wear, and reversed-bending fatigue performance. Results reveal that 3% MWCNT provides optimal strengthening, improving tensile and flexural properties, hardness, wear resistance, and fatigue life while reducing void content. Finite element simulations using ANSYS aligned with experimental findings, showing deviations below 10%. Statistical analysis (ANOVA) confirmed significant effects of MWCNT content. Overall, PVDF/UPR–MWCNT composites demonstrate excellent potential for advanced lightweight structural applications.
Co-Authors Al-Ameen, Ehsan Sabah Al-Haddad, Luttfi A. Al-Karkhi, Mustafa I. Al-Maliky, Firas Thair Al-Sharify, Noor T. Al-Sharify, Zainab T. Jweri, Abdul-Rasool Kareem Khalaf, Mohammed I. Nassir, Layla M. Njim, Emad Kadum Ogai-li, Ahmed Ali Farhan Ogaili, Ahmed Ali Farhan Ramadhan, Ali J.