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All Journal International Journal of Electrical and Computer Engineering
Noor Kadhim Ayoob
University of Babylon
Computer Science & IT Electrical & Electronics Engineering

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A new method for watermarking color images using virtual hiding and El-Gamal ciphering Noor Kadhim Ayoob; Asraa Abdullah Hussein; Rusul Mohammed Neamah
International Journal of Electrical and Computer Engineering (IJECE) Vol 11, No 6: December 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v11i6.pp5251-5258

Abstract

One of the important issues in the era of computer networks and multimedia technology development is to find ways to maintain the reliability, credibility, copyright and non-duplication of digital content transmitted over the internet. For the purpose of protecting images from illegal usage, a watermark is used. A hidden digital watermark is the process of concealing information on a host to prove that this image is owned by a specific person or organization. In this paper, a new method has been proposed to use an RGB logo to protect color images from unlicensed trading. The method depends on retrieving logo data from specific locations in the host to form a logo when the owner claims the rights to those images. These positions are chosen because their pixels match the logo data. The locations of matching pixels are stored in a table that goes through two stages of treatment to ensure confidentiality: First, table compression, second, encoding positions in the compressed table through El-Gamal algorithm. Because the method depends on the idea of keeping host pixels without change, PSNR will always be infinity. After subjecting the host to five types of attack, the results demonstrate that the method can effectively protect the image and hidden logo is retrieved clearly even after the attacks.
Classification of medical datasets using back propagation neural network powered by genetic-based features elector Hussein Attya Lafta; Zainab Falah Hasan; Noor Kadhim Ayoob
International Journal of Electrical and Computer Engineering (IJECE) Vol 9, No 2: April 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (528.434 KB) | DOI: 10.11591/ijece.v9i2.pp1379-1384

Abstract

The classification is a one of the most indispensable domains in   the data mining and machine learning. The classification process has a good reputation in the area of diseases diagnosis by computer systems where the progress in smart technologies of computer can be invested in diagnosing various diseases based on data of real patients documented in databases. The paper introduced a methodology for diagnosing a set of diseases including two types of cancer (breast cancer and lung), two datasets for diabetes and heart attack. Back Propagation Neural Network plays the role of classifier. The performance of neural net is enhanced by using the genetic algorithm which provides the classifier with the optimal features to raise the classification rate to the highest possible. The system showed high efficiency in dealing with databases differs from each other in size, number of features and nature of the data and this is what the results illustrated, where the ratio of the classification reached to 100% in most datasets).
Co-Authors Asraa Abdullah Hussein Hussein Attya Lafta Rusul Mohammed Neamah Zainab Falah Hasan