Open Access
ARTICLE
Yixia Chen1,2, Mingwei Lin1,2,*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2023.040970
Abstract The consensus scheme is an essential component in the real blockchain environment. The Delegated Proof of Stake
(DPoS) is a competitive consensus scheme that can decrease energy costs, promote decentralization, and increase
eciency, respectively. However, how to study the knowledge representation of the collective voting information
and then select delegates is a new open problem. To ensure the fairness and eectiveness of transactions in the
blockchain, in this paper, we propose a novel More >
Open Access
ARTICLE
Arif Hussain Magsi1,*, Ali Ghulam2, Saifullah Memon1, Khalid Javeed3, Musaed Alhussein4, Imad Rida5
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2023.040290
（This article belongs to this Special Issue: Innovations in Pervasive Computing and Communication Technologies)
Abstract Named Data Networking (NDN) is gaining a significant attention in Vehicular Ad-hoc Networks (VANET) due
to its in-network content caching, name-based routing, and mobility-supporting characteristics. Nevertheless,
existing NDN faces three significant challenges, including security, privacy, and routing. In particular, security
attacks, such as Content Poisoning Attacks (CPA), can jeopardize legitimate vehicles with malicious content. For
instance, attacker host vehicles can serve consumers with invalid information, which has dire consequences, including road accidents. In such a situation, trust in the content-providing vehicles brings a new challenge. On the other
hand, ensuring privacy and preventing unauthorized access in vehicular (VNDN) is another… More >
Open Access
ARTICLE
Yazeed Yasin Ghadi1, Mohammed S. Alshehri2, Sultan Almakdi2, Oumaima Saidani3,*, Nazik Alturki3, Fawad Masood4, Muhammad Shahbaz Khan5
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2023.042777
（This article belongs to this Special Issue: Multimedia Encryption and Information Security)
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 >
Open Access
ARTICLE
Awny Sayed1, Sohair Kinlany2, Alaa Zaki2, Ahmed Mahfouz2,3,*
CMC-Computers, Materials & Continua, 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 >
Open Access
ARTICLE
Kainat Nazir1, Tahir Mustafa Madni1, Uzair Iqbal Janjua1, Umer Javed2, Muhammad Attique Khan3, Usman Tariq4, Jae-Hyuk Cha5,*
CMC-Computers, Materials & Continua, 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 >
Open Access
ARTICLE
Yalong Xie1, Aiping Li1,*, Biyin Hu2, Liqun Gao1, Hongkui Tu1
CMC-Computers, Materials & Continua, 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 >
Open Access
ARTICLE
Kaleem Arshid1,*, Jianbiao Zhang1, Muhammad Yaqub1, Mohammad Daud Awan2, Habiba Ijaz3, Imran Shabir Chuhan4
CMC-Computers, Materials & Continua, 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 >
Open Access
ARTICLE
Raheem Ogla1,*, Eman Shakar Mahmood1, Rasha I. Ahmed1, Abdul Monem S. Rahma2
CMC-Computers, Materials & Continua, 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 >
Open Access
ARTICLE
Sayyid Kamran Hussain1, Ali Haider Khan2,*, Malek Alrashidi3, Sajid Iqbal4, Qazi Mudassar Ilyas4, Kamran Shah5
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2023.041722
（This article belongs to this Special Issue: Recent Advances in Ophthalmic Diseases Diagnosis using AI)
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 >
Open Access
ARTICLE
Jingyao Liu1,2, Qinghe Feng4, Jiashi Zhao2,3, Yu Miao2,3, Wei He2, Weili Shi2,3, Zhengang Jiang2,3,*
CMC-Computers, Materials & Continua, 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 >
Open Access
ARTICLE
Fayaz Muhammad1, Jahangir Khan1, Asad Ullah1, Fasee Ullah1, Razaullah Khan2, Inayat Khan2, Mohammed ElAffendi3, Gauhar Ali3,*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2023.038748
（This article belongs to this Special Issue: Emerging Trends, Advances and Challenges of IoT in Healthcare and Education)
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 >
Open Access
ARTICLE
Shaohua Li, Haixiang Zhang*, Hanjie Ma, Jie Feng, Mingfeng Jiang
CMC-Computers, Materials & Continua, 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 >
Open Access
ARTICLE
Abdul Rehman, Dongsun Kim*, Anand Paul
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2023.036435
（This article belongs to this Special Issue: Recent Advances in Internet of Things and Emerging Technologies)
Abstract Disasters such as conflagration, toxic smoke, harmful gas or chemical leakage, and many other catastrophes in the
industrial environment caused by hazardous distance from the peril are frequent. The calamities are causing massive
fiscal and human life casualties. However, Wireless Sensors Network-based adroit monitoring and early warning
of these dangerous incidents will hamper fiscal and social fiasco. The authors have proposed an early fire detection
system uses machine and/or deep learning algorithms. The article presents an Intelligent Industrial Monitoring
System (IIMS) and introduces an Industrial Smart Social Agent (ISSA) in the Industrial SIoT (ISIoT) paradigm.
The proffered ISSA empowers smart… More >
Open Access
ARTICLE
Ali Haider Khan1,2,*, Hassaan Malik2, Wajeeha Khalil3, Sayyid Kamran Hussain4, Tayyaba Anees5, Muzammil Hussain2
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2023.039518
Abstract To prevent irreversible damage to one’s eyesight, ocular diseases
(ODs) need to be recognized and treated immediately. Color fundus imaging
(CFI) is a screening technology that is both effective and economical. According to CFIs, the early stages of the disease are characterized by a paucity of
observable symptoms, which necessitates the prompt creation of automated
and robust diagnostic algorithms. The traditional research focuses on imagelevel diagnostics that attend to the left and right eyes in isolation without making use of pertinent correlation data between the two sets of eyes. In addition,
they usually only target one or a few different… More >
Open Access
ARTICLE
Jeongha Lee1, Hyokyung Bahn2,*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2023.039236
Abstract Due to the recent trend of software intelligence in the Fourth
Industrial Revolution, deep learning has become a mainstream workload for
modern computer systems. Since the data size of deep learning increasingly
grows, managing the limited memory capacity efficiently for deep learning
workloads becomes important. In this paper, we analyze memory accesses
in deep learning workloads and find out some unique characteristics differentiated from traditional workloads. First, when comparing instruction and
data accesses, data access accounts for 96%–99% of total memory accesses in
deep learning workloads, which is quite different from traditional workloads.
Second, when comparing read and write accesses,… More >
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