The past years have seen a rapid development in the area of image compression techniques, mainly due to the need of fast and efficient techniques for storage and transmission of data among individuals. Compression is the process of representing the data in a compact form rather than in its original or incompact form. In this paper, integer implementation of Arithmetic Coding (AC) and Discreet Cosine Transform (DCT) were applied to colored images. The DCT was applied using the YCbCr color model. The transformed image was then quantized with the standard quantization tables for luminance and chrominance. The quantized coefficients were scanned by zigzag scan and the output was encoded using AC. The results showed a decent compression ratio

Images hold important information, especially in military and commercial surveillance as well as in industrial inspection and communication. Therefore, the protection of the image from abuse, unauthorized access, and damage became a significant demand. This paper introduces a new Beta chaotic map for encrypting and confusing the color image with Deoxyribonucleic Acid (DNA) sequence. First, the DNA addition operation is used for diffusing each component of the plain image. Then, a new Beta chaotic map is used for shuffling the DNA color image. In addition, two chaotic maps, namely the proposed new Beta and Sine chaotic maps, are used for key generation. Finally, the DNA XOR operation is applied between the generated key and shuffled DNA i

The emergence of SARS-CoV-2, the virus responsible for the COVID-19 pandemic, has resulted in a global health crisis leading to widespread illness, death, and daily life disruptions. Having a vaccine for COVID-19 is crucial to controlling the spread of the virus which will help to end the pandemic and restore normalcy to society. Messenger RNA (mRNA) molecules vaccine has led the way as the swift vaccine candidate for COVID-19, but it faces key probable restrictions including spontaneous deterioration. To address mRNA degradation issues, Stanford University academics and the Eterna community sponsored a Kaggle competition.This study aims to build a deep learning (DL) model which will predict deterioration rates at each base of the mRNA

One of the significant stages in computer vision is image segmentation which is fundamental for different applications, for example, robot control and military target recognition, as well as image analysis of remote sensing applications. Studies have dealt with the process of improving the classification of all types of data, whether text or audio or images, one of the latest studies in which researchers have worked to build a simple, effective, and high-accuracy model capable of classifying emotions from speech data, while several studies dealt with improving textual grouping. In this study, we seek to improve the classification of image division using a novel approach depending on two methods used to segment the images. The first

     Face detection systems are based on the assumption that each individual has a unique face structure and that computerized face matching is possible using facial symmetry. Face recognition technology has been employed for security purposes in many organizations and businesses throughout the world. This research examines the classifications in machine learning approaches using feature extraction for the facial image detection system. Due to its high level of accuracy and speed, the Viola-Jones method is utilized for facial detection using the MUCT database. The LDA feature extraction method is applied as an input to three algorithms of machine learning approaches, which are the J48, OneR, and JRip classifiers.  The experiment’s

HM Al-Dabbas, RA Azeez, AE Ali, Iraqi Journal of Science, 2023

      Nowadays, 3D content is becoming an essential part of multimedia applications, when the 3D content is not protected, hackers may attack and steal it. This paper introduces a proposed scheme that provides high protection for 3D content by implementing multiple levels of security with preserving the original size using weight factor (w). First level of security is implemented by encrypting the texture map based on a 2D Logistic chaotic map. Second level is implemented by shuffling vertices (confusion) based on a 1D Tent chaotic map. Third level is implemented by modifying the vertices values (diffusion) based on a 3D Lorenz chaotic map. Results illustrate that the proposed scheme is completely deform the entire 3D content accord