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001-es BibID:	BIBFORM126908
035-os BibID:	(Scopus)85207485730
Első szerző:	Ghrabat, Mudhafar Jalil Jassim
Cím:	Utilizing Machine Learning for the Early Detection of Coronary Heart Disease / Mudhafar Jalil Jassim Ghrabat, Siamand Hassan Mohialdin, Luqman Qader Abdulrahman, Murthad Hussein Al-Yoonus, Zaid Ameen Abduljabbar, Dhafer G. Honi, Vincent Omollo Nyangaresi, Iman Qays Abduljaleel, Husam A. Neamah
Dátum:	2024
ISSN:	2241-4487 1792-8036
Tárgyszavak:	Műszaki tudományok Villamosmérnöki tudományok idegen nyelvű folyóiratközlemény külföldi lapban folyóiratcikk
Megjelenés:	Engineering Technology & Applied Science Research. - 14 : 5 (2024), p. 17363-17375. -
További szerzők:	Mohialdin, Siamand Hassan Abdulrahman, Luqman Qader Al-Yoonus, Murthad Hussein Abduljabbar, Zaid Ameen Alshuwaili, Dhafer Gheni Honi (1991) (Informatics)(PhD) Nyangaresi, Vincent Omollo Abduljaleel, Iman Qays Neamah, Husam A. (1990-) (mérnök)
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001-es BibID:	BIBFORM123056
Első szerző:	Mudhafar, Jalil Jassim Ghrabat
Cím:	Utilising Machine Learning for the Early Detection of Coronary Heart Disease / Mudhafar Jalil Jassim Ghrabat, Siamand Hassan Mohialdin, Luqman Qader Abdulrahman, Murthad Hussein Al-Yoonus, Zaid Ameen Abduljabbar, Dhafer G. Honi, Vincent Omollo Nyangaresi, Iman Qays Abduljaleel, Husam A. Neamah
Dátum:	2024
ISSN:	2241-4487 1792-8036
Tárgyszavak:	Műszaki tudományok Informatikai tudományok idegen nyelvű folyóiratközlemény külföldi lapban folyóiratcikk
Megjelenés:	Engineering, Technology and Applied Science Research. - "Accepted by Publisher" : - (2024), p. 1-19. -
További szerzők:	Siamand, Hassan Mohialdin Luqman, Qader Abdulrahman Murthad, Hussein Al-Yoonus Zaid, Ameen Abduljabbar Alshuwaili, Dhafer Gheni Honi (1991) (Informatics)(PhD) Vincent, Omollo Nyangaresi Iman, Qays Abduljaleel Neamah, Husam A. (1990-) (mérnök)
Internet cím:	Intézményi repozitóriumban (DEA) tárolt változat
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001-es BibID:	BIBFORM122701
Első szerző:	Nyangaresi, Vincent Omollo
Cím:	Smart city energy efficient data privacy preservation protocol based on biometrics and fuzzy commitment scheme / Vincent Omollo Nyangaresi, Zaid Ameen Abduljabbar, Keyan Abdul-Aziz Mutlaq, Salim Sabah Bulbul, Junchao Ma, Abdulla J. Y. Aldarwish, Dhafer G. Honi, Mustafa A. Al Sibahee, Husam A. Neamah
Dátum:	2024
ISSN:	2045-2322
Megjegyzések:	Advancements in cloud computing, flying ad?hoc networks, wireless sensor networks, artificial intelligence, big data, 5th generation mobile network and internet of things have led to the development of smart cities. Owing to their massive interconnectedness, high volumes of data are collected and exchanged over the public internet. Therefore, the exchanged messages are susceptible to numerous security and privacy threats across these open public channels. Although many security techniques have been designed to address this issue, most of them are still vulnerable to attacks while some deploy computationally extensive cryptographic operations such as bilinear pairings and blockchain. In this paper, we leverage on biometrics, error correction codes and fuzzy commitment schemes to develop a secure and energy efficient authentication scheme for the smart cities. This is informed by the fact that biometric data is cumbersome to reproduce and hence attacks such as sidechanneling are thwarted. We formally analyze the security of our protocol using the Burrows-AbadiNeedham logic logic, which shows that our scheme achieves strong mutual authentication among the communicating entities. The semantic analysis of our protocol shows that it mitigates attacks such as de?synchronization, eavesdropping, session hijacking, forgery and side?channeling. In addition, its formal security analysis demonstrates that it is secure under the Canetti and Krawczyk attack model. In terms of performance, our scheme is shown to reduce the computation overheads by 20.7% and hence is the most efficient among the state?of?the?art protocols.
Tárgyszavak:	Műszaki tudományok Informatikai tudományok idegen nyelvű folyóiratközlemény külföldi lapban folyóiratcikk Authentication Biometrics Fuzzy commitment Security Privacy Efficiency Hamming distance Smart city
Megjelenés:	Scientific Reports. - 14 : 1 (2024), p. 1-17. -
További szerzők:	Abduljabbar, Zaid Ameen Mutlaq, Keyan Abdul-Aziz Bulbul, Salim Sabah Ma, Junchao Aldarwish, Abdulla J. Y. Alshuwaili, Dhafer Gheni Honi (1991) (Informatics)(PhD) Al Sibahee, Mustafa A. Neamah, Husam A. (1990-) (mérnök)
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001-es BibID:	BIBFORM123055
Első szerző:	Rana, Jassim Mohammed
Cím:	A Robust Hybrid Machine and Deep Learning-based Model for Classification And Identification in Chest X-ray Images / Rana Jassim Mohammed, Mudhafar Jalil Jassim Ghrabat, Zaid Ameen Abduljabbar, Vincent Omollo Nyangaresi, Iman Qays Abduljaleel, Ali Hasan Ali, Dhafer G. Honi, Husam A. Neamah
Dátum:	2024
ISSN:	2241-4487 1792-8036
Megjegyzések:	Successful medical treatment for patients with COVID-19 requires rapid and accurate diagnosis. Fighting the COVID-19 pandemic requires an automated system to diagnose the virus on Chest X-Ray (CXR) images. CXR images are frequently used in healthcare as they offer the potential for rapid and accurate disease diagnosis. SARS-CoV-2 targets the respiratory system, resulting in pneumonia with additional symptoms, such as dry cough, fatigue, and fever, which could be misdiagnosed as pneumonia, TB, or lung cancer. There is difficulty in differentiating the features of COVID-19 from other diseases that have similarities in CXR images. Automated Computer-Aided Diagnosis (CAD) systems incorporate machine or deep learning methods to improve efficiency and accuracy. CNNs are among the most widely used methods, as they have shown encouraging accuracy in identifying COVID-19 in CXR images. This study presents a hybrid deep learning model to provide faster diagnosis of COVID-19 infection using CXR images. The Densenet201 model was used for feature extraction and a Multi-Layer Perceptron (MLP) was used for classification. The proposed method achieved 98.82% accuracy and similar sensitivity, specificity, precision, recall, and F1 score. These results are promising when compared to other DL models trained in similar datasets.
Tárgyszavak:	Műszaki tudományok Informatikai tudományok idegen nyelvű folyóiratközlemény külföldi lapban folyóiratcikk COVID-19 Chest X-Ray (CXR) DL ML densenet201 MLP
Megjelenés:	Engineering, Technology and Applied Science Research. - 14 : 5 (2024), p. 16212-16220. -
További szerzők:	Mudhafar, Jalil Jassim Ghrabat Zaid, Ameen Abduljabbar Vincent, Omollo Nyangaresi Iman, Qays Abduljaleel Ali, Ali Hasan (1989-) (matematikus) Alshuwaili, Dhafer Gheni Honi (1991) (Informatics)(PhD) Neamah, Husam A. (1990-) (mérnök)
Internet cím:	Intézményi repozitóriumban (DEA) tárolt változat
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