Home / Journals / CMC / Vol.75, No.1, 2023
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  • Open AccessOpen Access

    ARTICLE

    LKAW: A Robust Watermarking Method Based on Large Kernel Convolution and Adaptive Weight Assignment

    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… More >

  • Open AccessOpen Access

    ARTICLE

    Firefly-CDDL: A Firefly-Based Algorithm for Cyberbullying Detection Based on Deep Learning

    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… More >

  • Open AccessOpen Access

    ARTICLE

    Lattice-Based Authentication Scheme to Prevent Quantum Attack in Public Cloud Environment

    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 More >

  • Open AccessOpen Access

    ARTICLE

    Relational Logging Design Pattern

    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… More >

  • Open AccessOpen Access

    ARTICLE

    Attribute-Based Authentication Scheme from Partial Encryption for Lattice with Short Key

    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… More >

  • Open AccessOpen Access

    ARTICLE

    Optimal Machine Learning Driven Sentiment Analysis on COVID-19 Twitter Data

    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 More >

  • Open AccessOpen Access

    ARTICLE

    Optimal Hybrid Deep Learning Enabled Attack Detection and Classification in IoT Environment

    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… More >

  • Open AccessOpen Access

    ARTICLE

    An Elevator Button Recognition Method Combining YOLOv5 and OCR

    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… More >

  • Open AccessOpen Access

    ARTICLE

    Symbiotic Organisms Search with Deep Learning Driven Biomedical Osteosarcoma Detection and Classification

    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… More >

  • Open AccessOpen Access

    ARTICLE

    Arithmetic Optimization with Ensemble Deep Transfer Learning Based Melanoma Classification

    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… More >

  • Open AccessOpen Access

    ARTICLE

    A Three-Dimensional Real-Time Gait-Based Age Detection System Using Machine Learning

    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… More >

  • Open AccessOpen Access

    ARTICLE

    Digital Twin-Based Automated Fault Diagnosis in Industrial IoT Applications

    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… More >

  • Open AccessOpen Access

    ARTICLE

    RankXGB-Based Enterprise Credit Scoring by Electricity Consumption in Edge Computing Environment

    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… More >

  • Open AccessOpen Access

    ARTICLE

    One-Class Arabic Signature Verification: A Progressive Fusion of Optimal Features

    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… More >

  • Open AccessOpen Access

    ARTICLE

    Multimodal Fused Deep Learning Networks for Domain Specific Image Similarity Search

    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… More >

  • Open AccessOpen Access

    ARTICLE

    Three-Dimensional Analytical Modeling of Axial-Flux Permanent Magnet Drivers

    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… More >

  • Open AccessOpen Access

    ARTICLE

    Zero-Index Metamaterial Superstrates UWB Antenna for Microwave Imaging Detection

    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… More >

  • Open AccessOpen Access

    ARTICLE

    Zero Watermarking Algorithm for Medical Image Based on Resnet50-DCT

    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… More >

  • Open AccessOpen Access

    ARTICLE

    Performance Evaluation of Virtualization Methodologies to Facilitate NFV Deployment

    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 More >

  • Open AccessOpen Access

    ARTICLE

    A Novel Contour Tracing Algorithm for Object Shape Reconstruction Using Parametric Curves

    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… More >

  • Open AccessOpen Access

    ARTICLE

    Federated Feature Concatenate Method for Heterogeneous Computing in Federated Learning

    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… More >

  • Open AccessOpen Access

    ARTICLE

    A Dynamic Multi-Attribute Resource Bidding Mechanism with Privacy Protection in Edge Computing

    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… More >

  • Open AccessOpen Access

    ARTICLE

    Robust Image Watermarking Using LWT and Stochastic Gradient Firefly Algorithm

    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… More >

  • Open AccessOpen Access

    ARTICLE

    Automated Artificial Intelligence Empowered White Blood Cells Classification Model

    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.… More >

  • Open AccessOpen Access

    ARTICLE

    Metaheuristic Optimization of Time Series Models for Predicting Networks Traffic

    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 More >

  • Open AccessOpen Access

    ARTICLE

    Energy Prediction in IoT Systems Using Machine Learning Models

    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… More >

  • Open AccessOpen Access

    ARTICLE

    End-to-End 2D Convolutional Neural Network Architecture for Lung Nodule Identification and Abnormal Detection in Cloud

    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… More >

  • Open AccessOpen Access

    ARTICLE

    A Novel Smart Beta Optimization Based on Probabilistic Forecast

    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… More >

  • Open AccessOpen Access

    ARTICLE

    A Deep Learning-Based Crowd Counting Method and System Implementation on Neural Processing Unit Platform

    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… More >

  • Open AccessOpen Access

    ARTICLE

    ACO-Inspired Load Balancing Strategy for Cloud-Based Data Centre with Predictive Machine Learning Approach

    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 More >

  • Open AccessOpen Access

    ARTICLE

    An Optimal Algorithm for Resource Allocation in D2D Communication

    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… More >

  • Open AccessOpen Access

    ARTICLE

    Imbalanced Data Classification Using SVM Based on Improved Simulated Annealing Featuring Synthetic Data Generation and Reduction

    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,… More >

  • Open AccessOpen Access

    ARTICLE

    Robust Multi-Watermarking Algorithm for Medical Images Based on GoogLeNet and Henon Map

    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… More >

  • Open AccessOpen Access

    ARTICLE

    Enhanced Energy Efficient with a Trust Aware in MANET for Real-Time Applications

    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… More >

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    ARTICLE

    Connected Vehicles Computation Task Offloading Based on Opportunism in Cooperative Edge Computing

    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… More >

  • Open AccessOpen Access

    ARTICLE

    Multi-Layer Fog-Cloud Architecture for Optimizing the Placement of IoT Applications in Smart Cities

    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… More >

  • Open AccessOpen Access

    ARTICLE

    Continuous Mobile User Authentication Using a Hybrid CNN-Bi-LSTM Approach

    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… More >

  • Open AccessOpen Access

    ARTICLE

    A Novel Compact Highly Isolated UWB MIMO Antenna with WLAN Notch

    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… More >

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    ARTICLE

    Semantic Segmentation by Using Down-Sampling and Subpixel Convolution: DSSC-UNet

    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… More >

  • Open AccessOpen Access

    ARTICLE

    A Framework of Deep Optimal Features Selection for Apple Leaf Diseases Recognition

    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… More >

  • Open AccessOpen Access

    ARTICLE

    Deepfake Video Detection Based on Improved CapsNet and Temporal–Spatial Features

    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… More >

  • Open AccessOpen Access

    ARTICLE

    Electrical Tree Image Segmentation Using Hybrid Multi Scale Line Tracking Algorithm

    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… More >

  • Open AccessOpen Access

    ARTICLE

    The Role of Deep Learning in Parking Space Identification and Prediction Systems

    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… More >

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    ARTICLE

    Optimized Identification with Severity Factors of Gastric Cancer for Internet of Medical Things

    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… More >

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    ARTICLE

    Prediction of Traffic Volume of Motor Vehicles Based on Mobile Phone Signaling Technology

    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.… More >

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    ARTICLE

    Optimal Location to Use Solar Energy in an Urban Situation

    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… More >

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    ARTICLE

    A Dual Model Watermarking Framework for Copyright Protection in Image Processing Networks

    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… More >

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    ARTICLE

    Experimental Evaluation of Trilateration-Based Outdoor Localization with LoRaWAN

    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… More >

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    ARTICLE

    Federated Learning Based on Data Divergence and Differential Privacy in Financial Risk Control Research

    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… More >

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    ARTICLE

    Towards Robust Rain Removal with Unet++

    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 More >

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