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Your search keyword '"Ayub, Rashid"' showing total 15 results
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15 results on '"Ayub, Rashid"'

9. EEGDepressionNet: A Novel Self Attention-Based Gated DenseNet With Hybrid Heuristic Adopted Mental Depression Detection Model Using EEG Signals

Author

Abidi, Mustufa Haider, Moiduddin, Khaja, Ayub, Rashid, Mohammed, Muneer Khan, Shankar, Achyut, and Shiaeles, Stavros

Abstract

World Health Organization (WHO) has identified depression as a significant contributor to global disability, creating a complex thread in both public and private health. Electroencephalogram (EEG) can accurately reveal the working condition of the human brain, and it is considered an effective tool for analyzing depression. However, manual depression detection using EEG signals is time-consuming and tedious. To address this, fully automatic depression identification models have been designed using EEG signals to assist clinicians. In this study, we propose a novel automated deep learning-based depression detection system using EEG signals. The required EEG signals are gathered from publicly available databases, and three sets of features are extracted from the original EEG signal. Firstly, spectrogram images are generated from the original EEG signal, and 3-dimensional Convolutional Neural Networks (3D-CNN) are employed to extract deep features. Secondly, 1D-CNN is utilized to extract deep features from the collected EEG signal. Thirdly, spectral features are extracted from the collected EEG signal. Following feature extraction, optimal weights are fused with the three sets of features. The selection of optimal features is carried out using the developed Chaotic Owl Invasive Weed Search Optimization (COIWSO) algorithm. Subsequently, the fused features undergo analysis using the Self-Attention-based Gated Densenet (SA-GDensenet) for depression detection. The parameters within the detection network are optimized with the assistance of the same COIWSO. Finally, implementation results are analyzed in comparison to existing detection models. The experimentation findings of the developed model show 96% of accuracy. Throughout the empirical result, the findings of the developed model show better performance than traditional approaches.

Published
2024
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10. Role of artificial intelligence (Google bard) in morphological, histopathological, and radiological image identifications: Objective Structured Practical Examination (OSPE) type-based performance.

Author

Meo, Sultan A., AbuKhalaf, Abdulelah A., Meo, Muhammad Zain S., Meo, Muhammad Omair S., Ayub, Rashid, ElToukhy, Riham A., Usmani, Adnan M., and Hajjar, Waseem M.

Subjects
GEMINI (Chatbot), ARTIFICIAL intelligence, ENDOCRINE glands, MEDICAL sciences, IMAGE analysis
Abstract

Objectives: To evaluate the role of artificial intelligence (Google Bard) in figures, scans, and image identifications and interpretations in medical education and healthcare sciences through an Objective Structured Practical Examination (OSPE) type of performance. Methods: The OSPE type of question bank was created with a pool of medical sciences figures, scans, and images. For assessment, 60 figures, scans and images were selected and entered into the given area of the Google Bard to evaluate the knowledge level. Results: The marks obtained by Google Bard in brain structures, morphological and radiological images 7/10 (70%); bone structures, radiological images 9/10 (90%); liver structure and morphological, pathological images 4/10 (40%); kidneys structure and morphological images 2/7 (28.57%); neuro-radiological images 4/7 (57.14%); and endocrine glands including the thyroid, pancreas, breast morphological and radiological images 8/16 (50%). The overall total marks obtained by Google Bard in various OSPE figures, scans, and image identification questions were 34/60 (56.7%). Conclusion: Google Bard scored satisfactorily in morphological, histopathological, and radiological image identifications and their interpretations. Google Bard may assist medical students, faculty in medical education and physicians in healthcare settings. [ABSTRACT FROM AUTHOR]

Published
2024
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15. Electrical transport properties of [(1 - x )succinonitrile: x poly(ethylene oxide)]-LiCF 3 SO 3 -Co[tris-(2,2'-bipyridine)] 3 (TFSI) 2 -Co[tris-(2,2'-bipyridine)] 3 (TFSI) 3 solid redox mediators.

Author

Gupta RK, Shaikh H, Imran A, Bedja I, Ajaj AF, Aldwayyan AS, Khan A, and Ayub R

Abstract

A solid redox mediator (solid electrolyte) with an electrical conductivity ( σ 25°C ) greater than 10 -4 S cm -1 is an essential requirement for a dye-sensitized solar cell in the harsh weather of Gulf countries. This paper reports the electrical properties of solid redox mediators prepared using highly dissociable ionic salts: Co[tris-(2,2'-bipyridine)] 3 (TFSI) 2 , Co[tris-(2,2'-bipyridine)] 3 (TFSI) 3 , and LiCF 3 SO 3 as a source of Co 2+ , Co 3+ , and Li + ions, respectively, in a solid matrix: [(1 - x )succinonitrile: x poly(ethylene oxide)], where x = 0, 0.5, and 1 in weight fraction. In the presence of large size of cations (Co 2+ and Co 3+ ) and large-sized and weakly-coordinated anions (TFSI - and CF 3 SO 3 - ), only the succinonitrile-poly(ethylene oxide) blend ( x = 0.5) resulted in highly conductive amorphous regions with σ 25°C of 4.7 × 10 -4 S cm -1 for EO/Li + = 108.4 and 3.1 × 10 -4 S cm -1 for EO/Li + = 216.8. These values are slightly lower than 1.5 × 10 -3 S cm -1 for x = 0 and higher than 6.3 × 10 -7 S cm -1 for x = 1. Only blend-based electrolytes exhibited a downward curve in the log  σ - T -1 plot, a low value of pseudo-activation energy (0.06 eV), a high degree of transparency, and high thermal stability, making it useful for device applications., Competing Interests: There are no conflicts to declare., (This journal is © The Royal Society of Chemistry.)

Published
2024
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