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All Journal International Journal of Electrical and Computer Engineering
Usama A. Badawi
Imam Abdulrahman Bin Faisal University
Computer Science & IT Electrical & Electronics Engineering

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Comparison of specific segmentation methods used for copy move detection Eman Abdulazeem Ahmed; Malek Alzaqebah; Sana Jawarneh; Jehad Saad Alqurni; Fahad A. Alghamdi; Hayat Alfagham; Lubna Mahmoud Abdel Jawad; Usama A. Badawi; Mutasem K. Alsmadi; Ibrahim Almarashdeh
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 2: April 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i2.pp2363-2374

Abstract

In this digital age, the widespread use of digital images and the availability of image editors have made the credibility of images controversial. To confirm the credibility of digital images many image forgery detection types are arises, copy-move forgery is consisting of transforming any image by duplicating a part of the image, to add or hide existing objects. Several methods have been proposed in the literature to detect copy-move forgery, these methods use the key point-based and block-based to find the duplicated areas. However, the key point-based and block-based have a drawback of the ability to handle the smooth region. In addition, image segmentation plays a vital role in changing the representation of the image in a meaningful form for analysis. Hence, we execute a comparison study for segmentation based on two clustering algorithms (i.e., k-means and super pixel segmentation with density-based spatial clustering of applications with noise (DBSCAN)), the paper compares methods in term of the accuracy of detecting the forgery regions of digital images. K-means shows better performance compared with DBSCAN and with other techniques in the literature.
Susceptible exposed infectious recovered-machine learning for COVID-19 prediction in Saudi Arabia Mutasem K. Alsmadi; Ghaith M. Jaradat; Sami A. Abahussain; Mohammed Fahed Tayfour; Usama A. Badawi; Hayat Alfagham; Muneerah Ebrahem Alshabanah; Daniah Abdulrahman Alrajhi; Hanouf Naif ALkhaldi; Njoud Ahmad Altuwaijri; Hany Answer ShoShan; Hayah Mohamed Abouelnaga; Ahmed Baz Mohamed Metwally
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 4: August 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i4.pp4761-4776

Abstract

Susceptible exposed infectious recovered (SEIR) is among the epidemiological models used in forecasting the spread of disease in large populations. SEIR is a fitting model for coronavirus disease (COVID-19) spread prediction. Somehow, in its original form, SEIR could not measure the impact of lockdowns. So, in the SEIR equations system utilized in this study, a variable was included to evaluate the impact of varying levels of social distance on the transmission of COVID-19. Additionally, we applied artificial intelligence utilizing the deep neural network machine learning (ML) technique. On the initial spread data for Saudi Arabia that were available up to June 25th, 2021, this improved SEIR model was used. The study shows possible infection to around 3.1 million persons without lockdown in Saudi Arabia at the peak of spread, which lasts for about 3 months beginning from the lockdown date (March 21st). On the other hand, the Kingdom's current partial lockdown policy was estimated to cut the estimated number of infections to 0.5 million over nine months. The data shows that stricter lockdowns may successfully flatten the COVID-19 graph curve in Saudi Arabia. We successfully predicted the COVID-19 epidemic's peaks and sizes using our modified deep neural network (DNN) and SEIR model.
Co-Authors Ahmed Baz Mohamed Metwally Daniah Abdulrahman Alrajhi Eman Abdulazeem Ahmed Fahad A. Alghamdi Ghaith M. Jaradat Hanouf Naif ALkhaldi Hany Answer ShoShan Hayah Mohamed Abouelnaga Hayat Alfagham Ibrahim Almarashdeh Jehad Saad Alqurni Lubna Mahmoud Abdel Jawad Malek Alzaqebah Mohammed Fahed Tayfour Muneerah Ebrahem Alshabanah Mutasem K. Alsmadi Njoud Ahmad Altuwaijri Sami A. Abahussain Sana Jawarneh