Yazeed Yasin Ghadi¹, Mohammed S. Alshehri², Sultan Almakdi², Oumaima Saidani^3,*, Nazik Alturki³, Fawad Masood⁴, Muhammad Shahbaz Khan⁵

CMC-Computers, Materials & Continua, Vol., , DOI:10.32604/cmc.2023.042777

Abstract Securing digital image data is a key concern in today’s information-driven society. Effective encryption techniques are required to protect sensitive image data, with the Substitution-box (S-box) often playing a pivotal role in many symmetric encryption systems. This study introduces an innovative approach to creating S-boxes for encryption algorithms. The proposed S-boxes are tested for validity and non-linearity by incorporating them into an image encryption scheme. The nonlinearity measure of the proposed S-boxes is 112. These qualities significantly enhance its resistance to common cryptographic attacks, ensuring high image data security. Furthermore, to assess the robustness of the S-boxes, an encryption system… More >

Awny Sayed¹, Sohair Kinlany², Alaa Zaki², Ahmed Mahfouz^2,3,*

CMC-Computers, Materials & Continua, Vol., , DOI:10.32604/cmc.2023.040256

Abstract Face verification systems are critical in a wide range of applications, such as security systems and biometric authentication. However, these systems are vulnerable to adversarial attacks, which can significantly compromise their accuracy and reliability. Adversarial attacks are designed to deceive the face verification system by adding subtle perturbations to the input images. These perturbations can be imperceptible to the human eye but can cause the system to misclassify or fail to recognize the person in the image. To address this issue, we propose a novel system called VeriFace that comprises two defense mechanisms, adversarial detection, and adversarial removal. The first… More >

Kainat Nazir¹, Tahir Mustafa Madni¹, Uzair Iqbal Janjua¹, Umer Javed², Muhammad Attique Khan³, Usman Tariq⁴, Jae-Hyuk Cha^5,*

CMC-Computers, Materials & Continua, Vol., , DOI:10.32604/cmc.2023.039181

Abstract Brain tumor significantly impacts the quality of life and changes everything for a patient and their loved ones. Diagnosing a brain tumor usually begins with magnetic resonance imaging (MRI). The manual brain tumor diagnosis from the MRO images always requires an expert radiologist. However, this process is time-consuming and costly. Therefore, a computerized technique is required for brain tumor detection in MRI images. Using the MRI, a novel mechanism of the three-dimensional (3D) Kronecker convolution feature pyramid (KCFP) is used to segment brain tumors, resolving the pixel loss and weak processing of multi-scale lesions. A single dilation rate was replaced… More >

Yalong Xie¹, Aiping Li^1,*, Biyin Hu², Liqun Gao¹, Hongkui Tu¹

CMC-Computers, Materials & Continua, Vol., , DOI:10.32604/cmc.2023.037039

Abstract Credit Card Fraud Detection (CCFD) is an essential technology for banking institutions to control fraud risks and safeguard their reputation. Class imbalance and insufficient representation of feature data relating to credit card transactions are two prevalent issues in the current study field of CCFD, which significantly impact classification models’ performance. To address these issues, this research proposes a novel CCFD model based on Multifeature Fusion and Generative Adversarial Networks (MFGAN). The MFGAN model consists of two modules: a multi-feature fusion module for integrating static and dynamic behavior data of cardholders into a unified highdimensional feature space, and a balance module… More >

Kaleem Arshid^1,*, Jianbiao Zhang¹, Muhammad Yaqub¹, Mohammad Daud Awan², Habiba Ijaz³, Imran Shabir Chuhan⁴

CMC-Computers, Materials & Continua, Vol., , DOI:10.32604/cmc.2023.035735

Abstract Channel assignment has emerged as an essential study subject in Cognitive Radio-based Wireless Mesh Networks (CR-WMN). In an era of alarming increase in Multi-Radio Multi-Channel (MRMC) network expansion interference is decreased and network throughput is significantly increased when non-overlapping or partially overlapping channels are correctly integrated. Because of its ad hoc behavior, dynamic channel assignment outperforms static channel assignment. Interference reduces network throughput in the CR-WMN. As a result, there is an extensive research gap for an algorithm that dynamically distributes channels while accounting for all types of interference. This work presents a method for dynamic channel allocations using unsupervised… More >

Raheem Ogla^1,*, Eman Shakar Mahmood¹, Rasha I. Ahmed¹, Abdul Monem S. Rahma²

CMC-Computers, Materials & Continua, Vol., , DOI:10.32604/cmc.2023.039818

Abstract The transmission of video content over a network raises various issues relating to copyright authenticity, ethics, legality, and privacy. The protection of copyrighted video content is a significant issue in the video industry, and it is essential to find effective solutions to prevent tampering and modification of digital video content during its transmission through digital media. However, there are still many unresolved challenges. This paper aims to address those challenges by proposing a new technique for detecting moving objects in digital videos, which can help prove the credibility of video content by detecting any fake objects inserted by hackers. The… More >

Sayyid Kamran Hussain¹, Ali Haider Khan^2,*, Malek Alrashidi³, Sajid Iqbal⁴, Qazi Mudassar Ilyas⁴, Kamran Shah⁵

CMC-Computers, Materials & Continua, Vol., , DOI:10.32604/cmc.2023.041722

Abstract As ocular computer-aided diagnostic (CAD) tools become more widely accessible, many researchers are developing deep learning (DL) methods to aid in ocular disease (OHD) diagnosis. Common eye diseases like cataracts (CATR), glaucoma (GLU), and age-related macular degeneration (AMD) are the focus of this study, which uses DL to examine their identification. Data imbalance and outliers are widespread in fundus images, which can make it difficult to apply many DL algorithms to accomplish this analytical assignment. The creation of effcient and reliable DL algorithms is seen to be the key to further enhancing detection performance. Using the analysis of images of… More >

Jingyao Liu^1,2, Qinghe Feng⁴, Jiashi Zhao^2,3, Yu Miao^2,3, Wei He², Weili Shi^2,3, Zhengang Jiang^2,3,*

CMC-Computers, Materials & Continua, Vol., , DOI:10.32604/cmc.2023.038891

Abstract The coronavirus disease 2019 (COVID-19) has severely disrupted both human life and the health care system. Timely diagnosis and treatment have become increasingly important; however, the distribution and size of lesions vary widely among individuals, making it challenging to accurately diagnose the disease. This study proposed a deep-learning disease diagnosis model based on weakly supervised learning and clustering visualization (W_CVNet) that fused classification with segmentation. First, the data were preprocessed. An optimizable weakly supervised segmentation preprocessing method (O-WSSPM) was used to remove redundant data and solve the category imbalance problem. Second, a deep-learning fusion method was used for feature extraction… More >

Fayaz Muhammad¹, Jahangir Khan¹, Asad Ullah¹, Fasee Ullah¹, Razaullah Khan², Inayat Khan², Mohammed ElAffendi³, Gauhar Ali^3,*

CMC-Computers, Materials & Continua, Vol., , DOI:10.32604/cmc.2023.038748

Abstract IIF (Indirect Immune Florescence) has gained much attention recently due to its importance in medical sciences. The primary purpose of this work is to highlight a step-by-step methodology for detecting autoimmune diseases. The use of IIF for detecting autoimmune diseases is widespread in different medical areas. Nearly 80 different types of autoimmune diseases have existed in various body parts. The IIF has been used for image classification in both ways, manually and by using the Computer-Aided Detection (CAD) system. The data scientists conducted various research works using an automatic CAD system with low accuracy. The diseases in the human body… More >

Shaohua Li, Haixiang Zhang^*, Hanjie Ma, Jie Feng, Mingfeng Jiang

CMC-Computers, Materials & Continua, Vol., , DOI:10.32604/cmc.2023.041538

Abstract Scale variation is a major challenge in multi-person pose estimation. In scenes where persons are present at various distances, models tend to perform better on larger-scale persons, while the performance for smaller-scale persons often falls short of expectations. Therefore, effectively balancing the persons of different scales poses a significant challenge. So this paper proposes a new multi-person pose estimation model called FSA Net to improve the model’s performance in complex scenes. Our model utilizes High-Resolution Network (HRNet) as the backbone and feeds the outputs of the last stage’s four branches into the DCB module. The dilated convolution-based (DCB) module employs… More >