Open Access
ARTICLE
Xiaorui Zhang1,2,3,*, Rui Jiang1, Wei Sun3,4, Aiguo Song5, Xindong Wei6, Ruohan Meng7
CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 1-17, 2023, DOI:10.32604/cmc.2023.034748
Abstract Robust watermarking requires finding invariant features under multiple attacks to ensure correct extraction. Deep learning has extremely powerful in extracting features, and watermarking algorithms based on deep learning have attracted widespread attention. Most existing methods use small kernel convolution to extract image features and embed the watermarking. However, the effective perception fields for small kernel convolution are extremely confined, so the pixels that each watermarking can affect are restricted, thus limiting the performance of the watermarking. To address these problems, we propose a watermarking network based on large kernel convolution and adaptive weight assignment for loss functions. It uses large-kernel… More >
Open Access
ARTICLE
Monirah Al-Ajlan*, Mourad Ykhlef
CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 19-34, 2023, DOI:10.32604/cmc.2023.033753
Abstract There are several ethical issues that have arisen in recent years due to the ubiquity of the Internet and the popularity of social media and community platforms. Among them is cyberbullying, which is defined as any violent intentional action that is repeatedly conducted by individuals or groups using online channels against victims who are not able to react effectively. An alarmingly high percentage of people, especially teenagers, have reported being cyberbullied in recent years. A variety of approaches have been developed to detect cyberbullying, but they require time-consuming feature extraction and selection processes. Moreover, no approach to date has examined… More >
Open Access
ARTICLE
Naveed Khan1, Zhang Jianbiao1, Intikhab Ullah2, Muhammad Salman Pathan3, Huhnkuk Lim4,*
CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 35-49, 2023, DOI:10.32604/cmc.2023.036189
Abstract Public cloud computing provides a variety of services to consumers via high-speed internet. The consumer can access these services anytime and anywhere on a balanced service cost. Many traditional authentication protocols are proposed to secure public cloud computing. However, the rapid development of high-speed internet and organizations’ race to develop quantum computers is a nightmare for existing authentication schemes. These traditional authentication protocols are based on factorization or discrete logarithm problems. As a result, traditional authentication protocols are vulnerable in the quantum computing era. Therefore, in this article, we have proposed an authentication protocol based on the lattice technique for… More >
Open Access
ARTICLE
Savas Takan1,*, Gokmen Katipoglu2
CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 51-65, 2023, DOI:10.32604/cmc.2023.035282
Abstract Observability and traceability of developed software are crucial to its success in software engineering. Observability is the ability to comprehend a system’s internal state from the outside. Monitoring is used to determine what causes system problems and why. Logs are among the most critical technology to guarantee observability and traceability. Logs are frequently used to investigate software events. In current log technologies, software events are processed independently of each other. Consequently, current logging technologies do not reveal relationships. However, system events do not occur independently of one another. With this perspective, our research has produced a new log design pattern… More >
Open Access
ARTICLE
Wangke Yu, Shuhua Wang*
CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 67-80, 2023, DOI:10.32604/cmc.2023.035337
Abstract Wireless network is the basis of the Internet of things and the intelligent vehicle Internet. Due to the complexity of the Internet of things and intelligent vehicle Internet environment, the nodes of the Internet of things and the intelligent vehicle Internet are more vulnerable to malicious destruction and attacks. Most of the proposed authentication and key agreement protocols for wireless networks are based on traditional cryptosystems such as large integer decomposition and elliptic curves. With the rapid development of quantum computing, these authentication protocols based on traditional cryptography will be more and more threatened, so it is necessary to design… More >
Open Access
ARTICLE
Bahjat Fakieh1, Abdullah S. AL-Malaise AL-Ghamdi1,2,3, Farrukh Saleem1, Mahmoud Ragab2,4,5,6,*
CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 81-97, 2023, DOI:10.32604/cmc.2023.033406
Abstract The outbreak of the pandemic, caused by Coronavirus Disease 2019 (COVID-19), has affected the daily activities of people across the globe. During COVID-19 outbreak and the successive lockdowns, Twitter was heavily used and the number of tweets regarding COVID-19 increased tremendously. Several studies used Sentiment Analysis (SA) to analyze the emotions expressed through tweets upon COVID-19. Therefore, in current study, a new Artificial Bee Colony (ABC) with Machine Learning-driven SA (ABCML-SA) model is developed for conducting Sentiment Analysis of COVID-19 Twitter data. The prime focus of the presented ABCML-SA model is to recognize the sentiments expressed in tweets made upon… More >
Open Access
ARTICLE
Fahad F. Alruwaili*
CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 99-115, 2023, DOI:10.32604/cmc.2023.034752
Abstract The Internet of Things (IoT) paradigm enables end users to access networking services amongst diverse kinds of electronic devices. IoT security mechanism is a technology that concentrates on safeguarding the devices and networks connected in the IoT environment. In recent years, False Data Injection Attacks (FDIAs) have gained considerable interest in the IoT environment. Cybercriminals compromise the devices connected to the network and inject the data. Such attacks on the IoT environment can result in a considerable loss and interrupt normal activities among the IoT network devices. The FDI attacks have been effectively overcome so far by conventional threat detection… More >
Open Access
ARTICLE
Xinliang Tang1, Caixing Wang1, Jingfang Su1,*, Cecilia Taylor2
CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 117-131, 2023, DOI:10.32604/cmc.2023.033327
Abstract Fast recognition of elevator buttons is a key step for service robots to ride elevators automatically. Although there are some studies in this field, none of them can achieve real-time application due to problems such as recognition speed and algorithm complexity. Elevator button recognition is a comprehensive problem. Not only does it need to detect the position of multiple buttons at the same time, but also needs to accurately identify the characters on each button. The latest version 5 of you only look once algorithm (YOLOv5) has the fastest reasoning speed and can be used for detecting multiple objects in… More >
Open Access
ARTICLE
Abdullah M. Basahel1, Mohammad Yamin1, Sulafah M. Basahel2, Mona M. Abusurrah3, K.Vijaya Kumar4, E. Laxmi Lydia5,*
CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 133-148, 2023, DOI:10.32604/cmc.2023.031786
Abstract Osteosarcoma is one of the rare bone cancers that affect the individuals aged between 10 and 30 and it incurs high death rate. Early diagnosis of osteosarcoma is essential to improve the survivability rate and treatment protocols. Traditional physical examination procedure is not only a time-consuming process, but it also primarily relies upon the expert’s knowledge. In this background, the recently developed Deep Learning (DL) models can be applied to perform decision making. At the same time, hyperparameter optimization of DL models also plays an important role in influencing overall classification performance. The current study introduces a novel Symbiotic Organisms… More >
Open Access
ARTICLE
K. Kalyani1, Sara A Althubiti2, Mohammed Altaf Ahmed3, E. Laxmi Lydia4, Seifedine Kadry5, Neunggyu Han6, Yunyoung Nam6,*
CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 149-164, 2023, DOI:10.32604/cmc.2023.033005
Abstract Melanoma is a skin disease with high mortality rate while early diagnoses of the disease can increase the survival chances of patients. It is challenging to automatically diagnose melanoma from dermoscopic skin samples. Computer-Aided Diagnostic (CAD) tool saves time and effort in diagnosing melanoma compared to existing medical approaches. In this background, there is a need exists to design an automated classification model for melanoma that can utilize deep and rich feature datasets of an image for disease classification. The current study develops an Intelligent Arithmetic Optimization with Ensemble Deep Transfer Learning Based Melanoma Classification (IAOEDTT-MC) model. The proposed IAOEDTT-MC… More >
Open Access
ARTICLE
Muhammad Azhar1,*, Sehat Ullah1, Khalil Ullah2, Habib Shah3, Abdallah Namoun4, Khaliq Ur Rahman5
CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 165-182, 2023, DOI:10.32604/cmc.2023.034605
Abstract Human biometric analysis has gotten much attention due to its widespread use in different research areas, such as security, surveillance, health, human identification, and classification. Human gait is one of the key human traits that can identify and classify humans based on their age, gender, and ethnicity. Different approaches have been proposed for the estimation of human age based on gait so far. However, challenges are there, for which an efficient, low-cost technique or algorithm is needed. In this paper, we propose a three-dimensional real-time gait-based age detection system using a machine learning approach. The proposed system consists of training… More >
Open Access
ARTICLE
Samah Alshathri1, Ezz El-Din Hemdan2, Walid El-Shafai3,4,*, Amged Sayed5,6
CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 183-196, 2023, DOI:10.32604/cmc.2023.034048
Abstract In recent years, Digital Twin (DT) has gained significant interest from academia and industry due to the advanced in information technology, communication systems, Artificial Intelligence (AI), Cloud Computing (CC), and Industrial Internet of Things (IIoT). The main concept of the DT is to provide a comprehensive tangible, and operational explanation of any element, asset, or system. However, it is an extremely dynamic taxonomy developing in complexity during the life cycle that produces a massive amount of engendered data and information. Likewise, with the development of AI, digital twins can be redefined and could be a crucial approach to aid the… More >
Open Access
ARTICLE
Qiuying Shen1, Wentao Zhang1, Mofei Song2,*
CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 197-217, 2023, DOI:10.32604/cmc.2023.036365
Abstract With the rapid development of the internet of things (IoT), electricity consumption data can be captured and recorded in the IoT cloud center. This provides a credible data source for enterprise credit scoring, which is one of the most vital elements during the financial decision-making process. Accordingly, this paper proposes to use deep learning to train an enterprise credit scoring model by inputting the electricity consumption data. Instead of predicting the credit rating, our method can generate an absolute credit score by a novel deep ranking model–ranking extreme gradient boosting net (rankXGB). To boost the performance, the rankXGB model combines… More >
Open Access
ARTICLE
Ansam A. Abdulhussien1,2,*, Mohammad F. Nasrudin1, Saad M. Darwish3, Zaid A. Alyasseri1
CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 219-242, 2023, DOI:10.32604/cmc.2023.033331
Abstract Signature verification is regarded as the most beneficial behavioral characteristic-based biometric feature in security and fraud protection. It is also a popular biometric authentication technology in forensic and commercial transactions due to its various advantages, including noninvasiveness, user-friendliness, and social and legal acceptability. According to the literature, extensive research has been conducted on signature verification systems in a variety of languages, including English, Hindi, Bangla, and Chinese. However, the Arabic Offline Signature Verification (OSV) system is still a challenging issue that has not been investigated as much by researchers due to the Arabic script being distinguished by changing letter shapes,… More >
Open Access
ARTICLE
Umer Waqas, Jesse Wiebe Visser, Hana Choe, Donghun Lee*
CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 243-258, 2023, DOI:10.32604/cmc.2023.035716
Abstract The exponential increase in data over the past few years, particularly in images, has led to more complex content since visual representation became the new norm. E-commerce and similar platforms maintain large image catalogues of their products. In image databases, searching and retrieving similar images is still a challenge, even though several image retrieval techniques have been proposed over the decade. Most of these techniques work well when querying general image databases. However, they often fail in domain-specific image databases, especially for datasets with low intraclass variance. This paper proposes a domain-specific image similarity search engine based on a fused… More >
Open Access
ARTICLE
Wenhui Li1, Dazhi Wang1,*, Shuo Cao2, Deshan Kong1, Sihan Wang1, Zhong Hua1
CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 259-276, 2023, DOI:10.32604/cmc.2023.034622
Abstract In this paper, the axial-flux permanent magnet driver is modeled and analyzed in a simple and novel way under three-dimensional cylindrical coordinates. The inherent three-dimensional characteristics of the device are comprehensively considered, and the governing equations are solved by simplifying the boundary conditions. The axial magnetization of the sector-shaped permanent magnets is accurately described in an algebraic form by the parameters, which makes the physical meaning more explicit than the purely mathematical expression in general series forms. The parameters of the Bessel function are determined simply and the magnetic field distribution of permanent magnets and the air-gap is solved. Furthermore,… More >
Open Access
ARTICLE
Mohd Aminudin Jamlos1,*, Nur Amirah Othman1, Wan Azani Mustafa2, Mohd Faizal Jamlos3, Mohamad Nur Khairul Hafizi Rohani2
CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 277-292, 2023, DOI:10.32604/cmc.2023.032840
Abstract Metamaterials (MTM) can enhance the properties of microwaves and also exceed some limitations of devices used in technical practice. Note that the antenna is the element for realizing a microwave imaging (MWI) system since it is where signal transmission and absorption occur. Ultra-Wideband (UWB) antenna superstrates with MTM elements to ensure the signal transmitted from the antenna reaches the tumor and is absorbed by the same antenna. The lack of conventional head imaging techniques, for instance, Magnetic Resonance Imaging (MRI) and Computerized Tomography (CT)-scan, has been demonstrated in the paper focusing on the point of failure of these techniques for… More >
Open Access
ARTICLE
Mingshuai Sheng1, Jingbing Li1,2,*, Uzair Aslam Bhatti1,2,3, Jing Liu4, Mengxing Huang1,5, Yen-Wei Chen6
CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 293-309, 2023, DOI:10.32604/cmc.2023.036438
Abstract Medical images are used as a diagnostic tool, so protecting their confidentiality has long been a topic of study. From this, we propose a Resnet50-DCT-based zero watermarking algorithm for use with medical images. To begin, we use Resnet50, a pre-training network, to draw out the deep features of medical images. Then the deep features are transformed by DCT transform and the perceptual hash function is used to generate the feature vector. The original watermark is chaotic scrambled to get the encrypted watermark, and the watermark information is embedded into the original medical image by XOR operation, and the logical key… More >
Open Access
ARTICLE
Sumbal Zahoor1, Ishtiaq Ahmad1, Ateeq Ur Rehman2, Elsayed Tag Eldin3, Nivin A. Ghamry4, Muhammad Shafiq5,*
CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 311-329, 2023, DOI:10.32604/cmc.2023.035960
Abstract The development of the Next-Generation Wireless Network
(NGWN) is becoming a reality. To conduct specialized processes more, rapid
network deployment has become essential. Methodologies like Network
Function Virtualization (NFV), Software-Defined Networks (SDN), and
cloud computing will be crucial in addressing various challenges that 5G
networks will face, particularly adaptability, scalability, and reliability. The
motivation behind this work is to confirm the function of virtualization
and the capabilities offered by various virtualization platforms, including
hypervisors, clouds, and containers, which will serve as a guide to dealing
with the stimulating environment of 5G. This is particularly crucial when
implementing network operations at… More >
Open Access
ARTICLE
Nihat Arslan1, Kali Gurkahraman2,*
CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 331-350, 2023, DOI:10.32604/cmc.2023.035087
Abstract Parametric curves such as Bézier and B-splines, originally developed for the design of automobile bodies, are now also used in image processing and computer vision. For example, reconstructing an object shape in an image, including different translations, scales, and orientations, can be performed using these parametric curves. For this, Bézier and B-spline curves can be generated using a point set that belongs to the outer boundary of the object. The resulting object shape can be used in computer vision fields, such as searching and segmentation methods and training machine learning algorithms. The prerequisite for reconstructing the shape with parametric curves… More >
Open Access
ARTICLE
Wu-Chun Chung1, Yung-Chin Chang1, Ching-Hsien Hsu2,3, Chih-Hung Chang4, Che-Lun Hung4,5,*
CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 351-371, 2023, DOI:10.32604/cmc.2023.035720
Abstract Federated learning is an emerging machine learning technique that enables clients to collaboratively train a deep learning model without uploading raw data to the aggregation server. Each client may be equipped with different computing resources for model training. The client equipped with a lower computing capability requires more time for model training, resulting in a prolonged training time in federated learning. Moreover, it may fail to train the entire model because of the out-of-memory issue. This study aims to tackle these problems and propose the federated feature concatenate (FedFC) method for federated learning considering heterogeneous clients. FedFC leverages the model… More >
Open Access
ARTICLE
Shujuan Tian1,2,3, Wenjian Ding1,2,3, Gang Liu4, Yuxia Sun5, Saiqin Long5, Jiang Zhu1,2,3,*
CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 373-391, 2023, DOI:10.32604/cmc.2023.034770
Abstract In edge computing, a reasonable edge resource bidding mechanism can enable edge providers and users to obtain benefits in a relatively fair fashion. To maximize such benefits, this paper proposes a dynamic multi-attribute resource bidding mechanism (DMRBM). Most of the previous work mainly relies on a third-party agent to exchange information to gain optimal benefits. It is worth noting that when edge providers and users trade with third-party agents which are not entirely reliable and trustworthy, their sensitive information is prone to be leaked. Moreover, the privacy protection of edge providers and users must be considered in the dynamic pricing/transaction… More >
Open Access
ARTICLE
Sachin Sharma1,*, Meena Malik2, Chander Prabha3, Amal Al-Rasheed4, Mona Alduailij4, Sultan Almakdi5
CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 393-407, 2023, DOI:10.32604/cmc.2023.033536
Abstract Watermarking of digital images is required in diversified applications ranging from medical imaging to commercial images used over the web.
Usually, the copyright information is embossed over the image in the form of
a logo at the corner or diagonal text in the background. However, this form
of visible watermarking is not suitable for a large class of applications. In all
such cases, a hidden watermark is embedded inside the original image as proof
of ownership. A large number of techniques and algorithms are proposed
by researchers for invisible watermarking. In this paper, we focus on issues
that are critical… More >
Open Access
ARTICLE
Mohammad Yamin1, Abdullah M. Basahel1, Mona Abusurrah2, Sulafah M Basahel3, Sachi Nandan Mohanty4, E. Laxmi Lydia5,*
CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 409-425, 2023, DOI:10.32604/cmc.2023.032432
Abstract White blood cells (WBC) or leukocytes are a vital component of the blood which forms the immune system, which is accountable to fight foreign elements. The WBC images can be exposed to different data analysis approaches which categorize different kinds of WBC. Conventionally, laboratory tests are carried out to determine the kind of WBC which is erroneous and time consuming. Recently, deep learning (DL) models can be employed for automated investigation of WBC images in short duration. Therefore, this paper introduces an Aquila Optimizer with Transfer Learning based Automated White Blood Cells Classification (AOTL-WBCC) technique. The presented AOTL-WBCC model executes… More >
Open Access
ARTICLE
Reem Alkanhel1, El-Sayed M. El-kenawy2,3, D. L. Elsheweikh4, Abdelaziz A. Abdelhamid5,6, Abdelhameed Ibrahim7, Doaa Sami Khafaga8,*
CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 427-442, 2023, DOI:10.32604/cmc.2023.032885
Abstract Traffic prediction of wireless networks attracted many researchers and practitioners during the past decades. However, wireless traffic frequently exhibits strong nonlinearities and complicated patterns, which makes it challenging to be predicted accurately. Many of the existing approaches for predicting wireless network traffic are unable to produce accurate predictions because they lack the ability to describe the dynamic spatial-temporal correlations of wireless network traffic data. In this paper, we proposed a novel meta-heuristic optimization approach based on fitness grey wolf and dipper throated optimization algorithms for boosting the prediction accuracy of traffic volume. The proposed algorithm is employed to optimize the… More >
Open Access
ARTICLE
S. Balaji*, S. Karthik
CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 443-459, 2023, DOI:10.32604/cmc.2023.035275
Abstract The Internet of Things (IoT) technology has been developed for directing and maintaining the atmosphere in smart buildings in real time. In order to optimise the power generation sector and schedule routine maintenance, it is crucial to predict future energy demand. Electricity demand forecasting is difficult because of the complexity of the available demand patterns. Establishing a perfect prediction of energy consumption at the building’s level is vital and significant to efficiently managing the consumed energy by utilising a strong predictive model. Low forecast accuracy is just one of the reasons why energy consumption and prediction models have failed to… More >
Open Access
ARTICLE
Safdar Ali1, Saad Asad1, Zeeshan Asghar1, Atif Ali1, Dohyeun Kim2,*
CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 461-475, 2023, DOI:10.32604/cmc.2023.035672
Abstract The extent of the peril associated with cancer can be perceived from the lack of treatment, ineffective early diagnosis techniques, and most importantly its fatality rate. Globally, cancer is the second leading cause of death and among over a hundred types of cancer; lung cancer is the second most common type of cancer as well as the leading cause of cancer-related deaths. Anyhow, an accurate lung cancer diagnosis in a timely manner can elevate the likelihood of survival by a noticeable margin and medical imaging is a prevalent manner of cancer diagnosis since it is easily accessible to people around… More >
Open Access
ARTICLE
Cheng Zhao1, Shuyi Yang2, Chu Qin3, Jie Zhou4, Longxiang Chen5,*
CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 477-491, 2023, DOI:10.32604/cmc.2023.034933
Abstract Rule-based portfolio construction strategies are rising as investment demand grows, and smart beta strategies are becoming a trend among institutional investors. Smart beta strategies have high transparency, low management costs, and better long-term performance, but are at the risk of severe short-term declines due to a lack of Risk Control tools. Although there are some methods to use historical volatility for Risk Control, it is still difficult to adapt to the rapid switch of market styles. How to strengthen the Risk Control management of the portfolio while maintaining the original advantages of smart beta has become a new issue of… More >
Open Access
ARTICLE
Yuxuan Gu, Meng Wu*, Qian Wang, Siguang Chen, Lijun Yang
CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 493-512, 2023, DOI:10.32604/cmc.2023.035974
Abstract In this paper, a deep learning-based method is proposed for crowd-counting problems. Specifically, by utilizing the convolution kernel density map, the ground truth is generated dynamically to enhance the feature-extracting ability of the generator model. Meanwhile, the “cross stage partial” module is integrated into congested scene recognition network (CSRNet) to obtain a lightweight network model. In addition, to compensate for the accuracy drop owing to the lightweight model, we take advantage of “structured knowledge transfer” to train the model in an end-to-end manner. It aims to accelerate the fitting speed and enhance the learning ability of the student model. The… More >
Open Access
ARTICLE
Niladri Dey1, T. Gunasekhar1, K. Purnachand2,*
CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 513-529, 2023, DOI:10.32604/cmc.2023.035139
Abstract Virtual Machines are the core of cloud computing and are utilized to get the benefits of cloud computing. Other essential features include portability, recovery after failure, and, most importantly, creating the core mechanism for load balancing. Several study results have been reported in enhancing load-balancing systems employing stochastic or biogenetic optimization methods. It examines the underlying issues with load balancing and the limitations of present load balance genetic optimization approaches. They are criticized for using higher-order probability distributions, more complicated solution search spaces, and adding factors to improve decision-making skills. Thus, this paper explores the possibility of summarizing load characteristics.… More >
Open Access
ARTICLE
Shahad Alyousif1,2, Mohammed Dauwed3,*, Rafal Nader4, Mohammed Hasan Ali5, Mustafa Musa Jabar6,7, Ahmed Alkhayyat8
CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 531-546, 2023, DOI:10.32604/cmc.2023.034329
Abstract The number of mobile devices accessing wireless networks is skyrocketing due to the rapid advancement of sensors and wireless communication technology. In the upcoming years, it is anticipated that mobile data traffic would rise even more. The development of a new cellular network paradigm is being driven by the Internet of Things, smart homes, and more sophisticated applications with greater data rates and latency requirements. Resources are being used up quickly due to the steady growth of smartphone devices and multimedia apps. Computation offloading to either several distant clouds or close mobile devices has consistently improved the performance of mobile… More >
Open Access
ARTICLE
Hussein Ibrahim Hussein1, Said Amirul Anwar2,*, Muhammad Imran Ahmad2
CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 547-564, 2023, DOI:10.32604/cmc.2023.036025
Abstract Imbalanced data classification is one of the major problems in machine learning. This imbalanced dataset typically has significant differences in the number of data samples between its classes. In most cases, the performance of the machine learning algorithm such as Support Vector Machine (SVM) is affected when dealing with an imbalanced dataset. The classification accuracy is mostly skewed toward the majority class and poor results are exhibited in the prediction of minority-class samples. In this paper, a hybrid approach combining data pre-processing technique and SVM algorithm based on improved Simulated Annealing (SA) was proposed. Firstly, the data pre-processing technique which… More >
Open Access
ARTICLE
Wenxing Zhang1, Jingbing Li1,2,*, Uzair Aslam Bhatti1,2, Jing Liu3, Junhua Zheng1, Yen-Wei Chen4
CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 565-586, 2023, DOI:10.32604/cmc.2023.036317
Abstract The field of medical images has been rapidly evolving since the advent of the digital medical information era. However, medical data is susceptible to leaks and hacks during transmission. This paper proposed a robust multi-watermarking algorithm for medical images based on GoogLeNet transfer learning to protect the privacy of patient data during transmission and storage, as well as to increase the resistance to geometric attacks and the capacity of embedded watermarks of watermarking algorithms. First, a pre-trained GoogLeNet network is used in this paper, based on which the parameters of several previous layers of the network are fixed and the… More >
Open Access
ARTICLE
M. V. Narayana1, Vadla Pradeep Kumar2, Ashok Kumar Nanda2,*, Hanumantha Rao Jalla3, Subba Reddy Chavva4
CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 587-607, 2023, DOI:10.32604/cmc.2023.034773
Abstract Mobile ad hoc networks (MANETs) are subjected to attack detection for transmitting and creating new messages or existing message modifications. The attacker on another node evaluates the forging activity in the message directly or indirectly. Every node sends short packets in a MANET environment with its identifier, location on the map, and time through beacons. The attackers on the network broadcast the warning message using faked coordinates, providing the appearance of a network collision. Similarly, MANET degrades the channel utilization performance. Performance highly affects network performance through security algorithms. This paper developed a trust management technique called Enhanced Beacon Trust… More >
Open Access
ARTICLE
Duan Xue1,2, Yan Guo1,*, Ning Li1, Xiaoxiang Song1
CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 609-631, 2023, DOI:10.32604/cmc.2023.035177
Abstract The traditional multi-access edge computing (MEC) capacity is overwhelmed by the increasing demand for vehicles, leading to acute degradation in task offloading performance. There is a tremendous number of resource-rich and idle mobile connected vehicles (CVs) in the traffic network, and vehicles are created as opportunistic ad-hoc edge clouds to alleviate the resource limitation of MEC by providing opportunistic computing services. On this basis, a novel scalable system framework is proposed in this paper for computation task offloading in opportunistic CV-assisted MEC. In this framework, opportunistic ad-hoc edge cloud and fixed edge cloud cooperate to form a novel hybrid cloud.… More >
Open Access
ARTICLE
Mohammad Aldossary*
CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 633-649, 2023, DOI:10.32604/cmc.2023.035414
Abstract In the smart city paradigm, the deployment of Internet of Things (IoT) services and solutions requires extensive communication and computing resources to place and process IoT applications in real time, which consumes a lot of energy and increases operational costs. Usually, IoT applications are placed in the cloud to provide high-quality services and scalable resources. However, the existing cloud-based approach should consider the above constraints to efficiently place and process IoT applications. In this paper, an efficient optimization approach for placing IoT applications in a multi-layer fog-cloud environment is proposed using a mathematical model (Mixed-Integer Linear Programming (MILP)). This approach… More >
Open Access
ARTICLE
Sarah Alzahrani1, Joud Alderaan1, Dalya Alatawi1, Bandar Alotaibi1,2,*
CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 651-667, 2023, DOI:10.32604/cmc.2023.035173
Abstract Internet of Things (IoT) devices incorporate a large amount of data in several fields, including those of medicine, business, and engineering. User authentication is paramount in the IoT era to assure connected devices’ security. However, traditional authentication methods and conventional biometrics-based authentication approaches such as face recognition, fingerprints, and password are vulnerable to various attacks, including smudge attacks, heat attacks, and shoulder surfing attacks. Behavioral biometrics is introduced by the powerful sensing capabilities of IoT devices such as smart wearables and smartphones, enabling continuous authentication. Artificial Intelligence (AI)-based approaches introduce a bright future in refining large amounts of homogeneous biometric… More >
Open Access
ARTICLE
Muhammad Awais1, Shahid Bashir1, Awais Khan1,2, Muhammad Asif2, Nasim Ullah3,*, Hend I. Alkhammash4
CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 669-681, 2023, DOI:10.32604/cmc.2022.033939
Abstract This paper presents a compact Multiple Input Multiple Output
(MIMO) antenna with WLAN band notch for Ultra-Wideband (UWB)
applications. The antenna is designed on 0.8 mm thick low-cost FR-4 substrate
having a compact size of 22 mm × 30 mm. The proposed antenna comprises
of two monopole patches on the top layer of substrate while having a shared
ground on its bottom layer. The mutual coupling between adjacent patches
has been reduced by using a novel stub with shared ground structure. The stub
consists of complementary rectangular slots that disturb the surface current
direction and thus result in reducing mutual… More >
Open Access
ARTICLE
Young-Man Kwon, Sunghoon Bae, Dong-Keun Chung, Myung-Jae Lim*
CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 683-696, 2023, DOI:10.32604/cmc.2023.033370
Abstract Recently, semantic segmentation has been widely applied to image processing, scene understanding, and many others. Especially, in deep learning-based semantic segmentation, the U-Net with convolutional encoder-decoder architecture is a representative model which is proposed for image segmentation in the biomedical field. It used max pooling operation for reducing the size of image and making noise robust. However, instead of reducing the complexity of the model, max pooling has the disadvantage of omitting some information about the image in reducing it. So, this paper used two diagonal elements of down-sampling operation instead of it. We think that the down-sampling feature maps… More >
Open Access
ARTICLE
Samra Rehman1, Muhammad Attique Khan1, Majed Alhaisoni2, Ammar Armghan3, Usman Tariq4, Fayadh Alenezi3, Ye Jin Kim5, Byoungchol Chang6,*
CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 697-714, 2023, DOI:10.32604/cmc.2023.035183
Abstract Identifying fruit disease manually is time-consuming, expert-required, and expensive; thus, a computer-based automated system is widely required. Fruit diseases affect not only the quality but also the quantity. As a result, it is possible to detect the disease early on and cure the fruits using computer-based techniques. However, computer-based methods face several challenges, including low contrast, a lack of dataset for training a model, and inappropriate feature extraction for final classification. In this paper, we proposed an automated framework for detecting apple fruit leaf diseases using CNN and a hybrid optimization algorithm. Data augmentation is performed initially to balance the… More >
Open Access
ARTICLE
Tianliang Lu*, Yuxuan Bao, Lanting Li
CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 715-740, 2023, DOI:10.32604/cmc.2023.034963
Abstract Rapid development of deepfake technology led to the spread of forged audios and videos across network platforms, presenting risks for numerous countries, societies, and individuals, and posing a serious threat to cyberspace security. To address the problem of insufficient extraction of spatial features and the fact that temporal features are not considered in the deepfake video detection, we propose a detection method based on improved CapsNet and temporal–spatial features (iCapsNet–TSF). First, the dynamic routing algorithm of CapsNet is improved using weight initialization and updating. Then, the optical flow algorithm is used to extract interframe temporal features of the videos to… More >
Open Access
ARTICLE
Mohd Annuar Isa1, Mohamad Nur Khairul Hafizi Rohani1,*, Baharuddin Ismail1, Mohamad Kamarol Jamil1, Muzamir Isa1, Afifah Shuhada Rosmi1, Mohd Aminudin Jamlos2, Wan Azani Mustafa1, Nurulbariah Idris3, Abdullahi Abubakar Mas’ud4
CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 741-760, 2023, DOI:10.32604/cmc.2023.036077
Abstract Electrical trees are an aging mechanism most associated with partial discharge (PD) activities in crosslinked polyethylene (XLPE) insulation of high-voltage (HV) cables. Characterization of electrical tree structures gained considerable attention from researchers since a deep understanding of the tree morphology is required to develop new insulation material. Two-dimensional (2D) optical microscopy is primarily used to examine tree structures and propagation shapes with image segmentation methods. However, since electrical trees can emerge in different shapes such as bush-type or branch-type, treeing images are complicated to segment due to manifestation of convoluted tree branches, leading to a high misclassification rate during segmentation.… More >
Open Access
ARTICLE
Faizan Rasheed1, Yasir Saleem2, Kok-Lim Alvin Yau3,*, Yung-Wey Chong4,*, Sye Loong Keoh5
CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 761-784, 2023, DOI:10.32604/cmc.2023.034988
Abstract In today’s smart city transportation, traffic congestion is a vexing issue, and vehicles seeking parking spaces have been identified as one of the causes leading to approximately 40% of traffic congestion. Identifying parking spaces alone is insufficient because an identified available parking space may have been taken by another vehicle when it arrives, resulting in the driver’s frustration and aggravating traffic jams while searching for another parking space. This explains the need to predict the availability of parking spaces. Recently, deep learning (DL) has been shown to facilitate drivers to find parking spaces efficiently, leading to a promising performance enhancement… More >
Open Access
ARTICLE
Kamalrulnizam Bin Abu Bakar1, Fatima Tul Zuhra2,*, Babangida Isyaku1,3, Fuad A. Ghaleb1
CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 785-798, 2023, DOI:10.32604/cmc.2023.034540
Abstract The Internet of Medical Things (IoMT) emerges with the vision of the Wireless Body Sensor Network (WBSN) to improve the health monitoring systems and has an enormous impact on the healthcare system for recognizing the levels of risk/severity factors (premature diagnosis, treatment, and supervision of chronic disease i.e., cancer) via wearable/electronic health sensor i.e., wireless endoscopic capsule. However, AI-assisted endoscopy plays a very significant role in the detection of gastric cancer. Convolutional Neural Network (CNN) has been widely used to diagnose gastric cancer based on various feature extraction models, consequently, limiting the identification and categorization performance in terms of cancerous… More >
Open Access
ARTICLE
Jin Shang1,*, Hailong Su2,*, Kai Hu3, Xin Guo3, Defa Sun3
CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 799-814, 2023, DOI:10.32604/cmc.2023.035729
Abstract Urban traffic volume detection is an essential part of traffic planning in terms of urban planning in China. To improve the statistics efficiency of road traffic volume, this thesis proposes a method for predicting motor vehicle traffic volume on urban roads in small and medium-sized cities during the traffic peak hour by using mobile signal technology. The method is verified through simulation experiments, and the limitations and the improvement methods are discussed. This research can be divided into three parts: Firstly, the traffic patterns of small and medium-sized cities are obtained through a questionnaire survey. A total of 19745 residents… More >
Open Access
ARTICLE
Ngakan Ketut Acwin Dwijendra1,*, Indrajit Patra2, N. Bharath Kumar3, Iskandar Muda4, Elsayed M. Tag El Din5
CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 815-829, 2023, DOI:10.32604/cmc.2023.034297
Abstract This study conducted in Lima, Peru, a combination of spatial decision making system and machine learning was utilized to identify potential solar power plant construction sites within the city. Sundial measurements of solar radiation, precipitation, temperature, and altitude were collected for the study. Gene Expression Programming (GEP), which is based on the evolution of intelligent models, and Artificial Neural Networks (ANN) were both utilized in this investigation, and the results obtained from each were compared. Eighty percent of the data was utilized during the training phase, while the remaining twenty percent was utilized during the testing phase. On the basis… More >
Open Access
ARTICLE
Yuhang Meng1, Xianyi Chen1,*, Xingming Sun1, Yu Liu1, Guo Wei2
CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 831-844, 2023, DOI:10.32604/cmc.2023.033700
Abstract Image processing networks have gained great success in many fields, and thus the issue of copyright protection for image processing networks has become a focus of attention. Model watermarking techniques are widely used in model copyright protection, but there are two challenges: (1) designing universal trigger sample watermarking for different network models is still a challenge; (2) existing methods of copyright protection based on trigger s watermarking are difficult to resist forgery attacks. In this work, we propose a dual model watermarking framework for copyright protection in image processing networks. The trigger sample watermark is embedded in the training process… More >
Open Access
ARTICLE
Saeed Ahmed Magsi1,2,*, Mohd Haris Bin Md Khir1, Illani Bt Mohd Nawi1, Muath Al Hasan3, Zaka Ullah3, Fasih Ullah Khan4, Abdul Saboor5, Muhammad Aadil Siddiqui1,2
CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 845-862, 2023, DOI:10.32604/cmc.2023.033636
Abstract Long Range Wide Area Network (LoRaWAN) in the Internet of Things (IoT) domain has been the subject of interest for researchers. There is an increasing demand to localize these IoT devices using LoRaWAN due to the quickly growing number of IoT devices. LoRaWAN is well suited to support localization applications in IoTs due to its low power consumption and long range. Multiple approaches have been proposed to solve the localization problem using LoRaWAN. The Expected Signal Power (ESP) based trilateration algorithm has the significant potential for localization because ESP can identify the signal’s energy below the noise floor with no… More >
Open Access
ARTICLE
Mao Yuxin, Wang Honglin*
CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 863-878, 2023, DOI:10.32604/cmc.2023.034879
Abstract In the financial sector, data are highly confidential and sensitive, and ensuring data privacy is critical. Sample fusion is the basis of horizontal federation learning, but it is suitable only for scenarios where customers have the same format but different targets, namely for scenarios with strong feature overlapping and weak user overlapping. To solve this limitation, this paper proposes a federated learning-based model with local data sharing and differential privacy. The indexing mechanism of differential privacy is used to obtain different degrees of privacy budgets, which are applied to the gradient according to the contribution degree to ensure privacy without… More >
Open Access
ARTICLE
Boxia Hu1,2,*, Yaqi Sun3, Yufei Yang1,4, Ze Ouyang3, Feng Zhang3
CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 879-890, 2023, DOI:10.32604/cmc.2023.035858
Abstract Image deraining has become a hot topic in the field of computer vision. It is the process of removing rain streaks from an image to reconstruct a high-quality background. This study aims at improving the performance of image rain streak removal and reducing the disruptive effects caused by rain. To better fit the rain removal task, an innovative image deraining method is proposed, where a kernel prediction network with Unet++ is designed and used to filter rainy images, and rainy-day images are used to estimate the pixel-level kernel for rain removal. To minimize the gap between synthetic and real data… More >
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