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4. Deep Learning Based Approach: An Efficient Anomaly Detecting from Various Medical Signals and Medical Images Using CNNs

Author

Abbass, Mohamed Y., primary, Kwon, Ki-Chul, additional, Kim, Nam, additional, Haggag, Nehad, additional, Ibrahim, Fatma, additional, Mahrous, Yasser, additional, Seddik, Ahmad, additional, Khalil, Ali, additional, Elsherbeeny, Zeinab, additional, El-Shafai, Walid, additional, Rihan, Mohamad, additional, El-Banby, Ghada, additional, Soltan, Eman, additional, soliman, nagla, additional, Algarni, Abeer, additional, alhanafy, waleed, additional, Elfishawy, Adel, additional, El-Rabaie, El-Sayed, additional, Al-Nuaimy, Waleed, additional, Dessouky, Moawad, additional, Saleeb, Adel, additional, Messiha, Nagy, additional, El-Dokany, Ibrahim, additional, Sayed, fathi, additional, Khalaf, Ashraf, additional, and El-Bendary, Mohsen, additional

Published
2022
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5. Efficient anomaly detection from medical signals and images with convolutional neural networks for Internet of medical things (IoMT) systems

Author

Khalil, Ali A., primary, Ibrahim, Fatma, additional, Abbass, Mohamed Y., additional, Haggag, Nehad, additional, Mahrous, Yasser, additional, Sedik, Ahmed, additional, Elsherbeeny, Zeinab, additional, Khalaf, Ashraf A. M., additional, Rihan, Mohamad, additional, El‐Shafai, Walid, additional, El‐Banby, Ghada M., additional, Soltan, Eman, additional, Soliman, Naglaa F., additional, Algarni, Abeer D., additional, Al‐Hanafy, Waleed, additional, El‐Fishawy, Adel S., additional, El‐Rabaie, El‐Sayed M., additional, Al‐Nuaimy, Waleed, additional, Dessouky, Moawad I., additional, Saleeb, Adel A., additional, Messiha, Nagy W., additional, El‐Dokany, Ibrahim M., additional, El‐Bendary, Mohsen A. M., additional, and Abd El‐Samie, Fathi E., additional

Published
2021
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6. Anomaly Detection from Medical Signals and Images Using Advanced Convolutional Neural Network

Author

Abbass, Mohammed, primary, Kwon, Ki-Chul, additional, Kim, Nam, additional, Abdelwahab, Safey A., additional, Haggag, Nehad, additional, Ibrahim, Fatma, additional, Mahrous, Yasser, additional, Seddik, Ahmad, additional, Khalil, Ali, additional, Elsherbeeny, Zeinab, additional, El-Shafai, Walid, additional, Rihan, Mohamad, additional, El-Banby, Ghada, additional, Soltan, Eman, additional, Soliman, Naglaa, additional, Algarni, Abeer, additional, Alhanafy, Waleed, additional, El-Fishawy, Adel, additional, El-Rabaie, El-Sayed M., additional, Al-Nuaimy, Waleed, additional, Dessouky, Moawad I., additional, Saleeb, Adel A., additional, Messiha, Nagy W., additional, El-Dokany, Ibrahim M., additional, El-Samie, Fathi E. Abd, additional, and Khalaf, Ashraf A.M., additional

Published
2020
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7. Efficient anomaly detection from medical signals and images with convolutional neural networks for Internet of medical things (IoMT) systems.

Author

Khalil, Ali A., E. Ibrahim, Fatma, Abbass, Mohamed Y., Haggag, Nehad, Mahrous, Yasser, Sedik, Ahmed, Elsherbeeny, Zeinab, Khalaf, Ashraf A. M., Rihan, Mohamad, El‐Shafai, Walid, El‐Banby, Ghada M., Soltan, Eman, Soliman, Naglaa F., Algarni, Abeer D., Al‐Hanafy, Waleed, El‐Fishawy, Adel S., El‐Rabaie, El‐Sayed M., Al‐Nuaimy, Waleed, Dessouky, Moawad I., and Saleeb, Adel A.

Subjects
DEEP learning, CONVOLUTIONAL neural networks, DIAGNOSTIC imaging, INTERNET of things, ARTIFICIAL intelligence
Abstract

Deep learning is one of the most promising machine learning techniques that revolutionalized the artificial intelligence field. The known traditional and convolutional neural networks (CNNs) have been utilized in medical pattern recognition applications that depend on deep learning concepts. This is attributed to the importance of anomaly detection (AD) in automatic diagnosis systems. In this paper, the AD is performed on medical electroencephalography (EEG) signal spectrograms and medical corneal images for Internet of medical things (IoMT) systems. Deep learning based on the CNN models is employed for this task with training and testing phases. Each input image passes through a series of convolution layers with different kernel filters. For the classification task, pooling and fully‐connected layers are utilized. Computer simulation experiments reveal the success and superiority of the proposed models for automated medical diagnosis in IoMT systems. [ABSTRACT FROM AUTHOR]

Published
2022
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8. The tribal world in the early Islamic age: the tribe 'Amila up to the late Umayyad period

Author

Rihan, Mohamad Abbas

Subjects
ComputerApplications_COMPUTERSINOTHERSYSTEMS, ComputingMethodologies_GENERAL
Abstract

This thesis was digitised by the British Library from microfilm. You can acquire a single copy of this thesis for research purposes by clicking on the padlock icon on the thesis file. Please be aware that the text in the supplied thesis pdf file may not be as clear as text in a thesis that was born digital or digitised directly from paper due to the conversion in format. However, all of the theses in Apollo that were digitised from microfilm are readable and have been processed by optical character recognition (OCR) technology which means the reader can search and find text within the document. If you are the author of this thesis and would like to make your work openly available, please contact us: thesis@repository.cam.ac.uk

Published
2018
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9. Editorial.

Author

RIHAN, Mohamad and DARWICHE, Frank

Subjects
*GOD in Christianity, *SCHOLARLY periodicals, *ISLAM & politics
Published
2020

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