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All Journal International Journal of Advances in Applied Sciences Indonesian Journal of Electrical Engineering and Computer Science
Fakhreldin, Mohammad
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Earth & Planetary Sciences Environmental Science Materials Science & Nanotechnology Mathematics Physics

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A set of embedding rules in IWT for watermark embedding in image watermarking Hafidz, Muhammad Afnan; Ernawan, Ferda; Bakar, Suraya Abu; Fakhreldin, Mohammad
Indonesian Journal of Electrical Engineering and Computer Science Vol 36, No 3: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v36.i3.pp1512-1520

Abstract

The development of new technologies has made image watermarking crucial in the digital era to preserve and protect illegal distribution of images against unauthorized users. This paper presents a robust image watermarking technique that employs a set of embedding rules in the three-level of integer wavelet transform (IWT). The proposed method aims to achieve high robustness of image watermarking while maintaining the imperceptibility. The proposed scheme divides the red and green layers into non-overlapping 16×16 blocks. Three levels of IWT are applied to obtain 2×2 LL sub-band, four coefficients of IWT are then modified based on the proposed set of rules for embedding watermark. The experimental results demonstrate a comparison of the proposed embedding and the existing methods. The proposed scheme produced an average NC value of 0.965 against the median filter. The results also showed the imperceptibility of the the image with a PSNR of 45.1760 db and SSIM of 0.9995.
Deep learning approach for monkeypox virus prediction: leveraging DensetNet-121 and image data Chinthala, Kishor Kumar Reddy; Kaza, Vijaya Sindhoori; Ashritha, Pilly; Shuaib, Mohammed; Alam, Shadab; Fakhreldin, Mohammad
International Journal of Advances in Applied Sciences Vol 14, No 2: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijaas.v14.i2.pp439-453

Abstract

The Mpox virus, sometimes referred to as monkeypox, causes flu-like symptoms and rashes. The variola virus, which causes smallpox, is linked to the virus that causes monkeypox. Smallpox symptoms are more severe than those of Mpox, and the illness is rarely deadly. There is no connection between Mpox and chickenpox. The variola virus of smallpox and the vaccinia virus being used in the smallpox vaccine both belong to the Orthopoxvirus genus, which also includes the uncommon viral disease known as monkeypox. This study aims to increase the effectiveness of monkeypox virus (MPV) identification by utilizing global historical records. This study examines several approaches and determines which produces the best results for the input data. Performance metrics have been used to compare the efficiency to current models. The underlying patterns and correlations in the data are then taught to Dense-Net-121 through the use of the training set. The remarkable results are as follows: accuracy at 96.12%, precision at 93.2%, recall at 90%, F1-score at 91%, the area under the curve-receiver operating characteristic (AUC-ROC) at 94.5%, and specificity at 94%, outperforming the existing methods.
Co-Authors Alam, Shadab Ashritha, Pilly Bakar, Suraya Abu Chinthala, Kishor Kumar Reddy Ferda Ernawan Hafidz, Muhammad Afnan Kaza, Vijaya Sindhoori Shuaib, Mohammed