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Your search keyword '"Khalifa, Hany S."' showing total 14 results
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14 results on '"Khalifa, Hany S."'

2. Detection of Myocardial Infarction Based on Novel Deep Transfer Learning Methods for Urban Healthcare in Smart Cities

Author

Alghamdi, Ahmed, Hammad, Mohamed, Ugail, Hassan, Abdel-Raheem, Asmaa, Muhammad, Khan, Khalifa, Hany S., and El-Latif, Ahmed A. Abd

Subjects
Computer Science - Machine Learning, Electrical Engineering and Systems Science - Signal Processing, Statistics - Machine Learning
Abstract

In this paper, an effective computer-aided diagnosis (CAD) system is presented to detect MI signals using the convolution neural network (CNN) for urban healthcare in smart cities. Two types of transfer learning techniques are employed to retrain the pre-trained VGG-Net (Fine-tuning and VGG-Net as fixed feature extractor) and obtained two new networks VGG-MI1 and VGG-MI2. In the VGG-MI1 model, the last layer of the VGG-Net model is replaced with a specific layer according to our requirements and various functions are optimized to reduce overfitting. In the VGG-MI2 model, one layer of the VGG-Net model is selected as a feature descriptor of the ECG images to describe it with informative features. Considering the limited availability of dataset, ECG data is augmented which has increased the classification performance. Physikalisch-technische bundesanstalt (PTB) Diagnostic ECG database is used for experimentation, which has been widely employed in MI detection studies. In case of using VGG-MI1, we achieved an accuracy, sensitivity, and specificity of 99.02%, 98.76%, and 99.17%, respectively and we achieved an accuracy of 99.22%, a sensitivity of 99.15%, and a specificity of 99.49% with VGG-MI2 model. Experimental results validate the efficiency of the proposed system in terms of accuracy sensitivity, and specificity.

Published
2019

14. Classifying electroencephalogram signals using an innovative and effective machine learning method based on chaotic elephant herding optimum.

Author

Alqahtani, Ali, Alqahtani, Nayef, Alsulami, Abdulaziz A., Ojo, Stephen, Shukla, Prashant Kumar, Pandit, Shraddha V., Pareek, Piyush Kumar, and khalifa, Hany S.

Abstract

The field of electroencephalography (EEG) has made significant contributions to our understanding of the brain, our understanding of neurological diseases, and our ability to treat such diseases. Epileptic seizures, strokes, and even death can all be detected with the use of the electroencephalogram, a diagnostic technique used to record electrical activity in the brain. This research suggests using binary classification for automated epilepsy diagnosis. Patients' EEG signals are pre‐processed after being recorded. On the basis of the results of the feature extraction technique, the best traits are picked for further examination by means of a structured genetic algorithm. The EEG data are analysed and categorized as either seizure‐free or epileptic seizure‐related based on the assumption of feature optimization utilizing the support vector classifier. As a result, categorizing EEG signals is an ideal application for the suggested technique. For this purpose of accelerating the implementation of distributed computing, a CEHOC (Chaotic Elephant Herding Optimization based Classification) is used to classify the vast scope of various datasets. The results show that the CEHOC algorithm is more effective than previous versions. Precision, recall, F score, sensitivity, specificity, and accuracy are some of the metrics used to assess the effectiveness of the work provided here. The suggested work has a 99.3019% accuracy rate, a 98.2018% sensitivity rate, and a 99.1125% specificity rate. There was an F score of 99.3204%, a precision of 99.1019%, and a recall of 98.3015%. These numbers indicate that the planned action was successful. [ABSTRACT FROM AUTHOR]

Published
2023
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