Home / Journals / CMC / Vol.75, No.3, 2023
Special lssues
Table of Content
  • Open AccessOpen Access

    ARTICLE

    Data Augmentation Using Contour Image for Convolutional Neural Network

    Seung-Yeon Hwang1, Jeong-Joon Kim2,*
    CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 4669-4680, 2023, DOI:10.32604/cmc.2023.031129
    Abstract With the development of artificial intelligence-related technologies such as deep learning, various organizations, including the government, are making various efforts to generate and manage big data for use in artificial intelligence. However, it is difficult to acquire big data due to various social problems and restrictions such as personal information leakage. There are many problems in introducing technology in fields that do not have enough training data necessary to apply deep learning technology. Therefore, this study proposes a mixed contour data augmentation technique, which is a data augmentation technique using contour images, to solve a… More >

  • Open AccessOpen Access

    ARTICLE

    Analysing Various Control Technics for Manipulator Robotic System (Robogymnast)

    Mahmoud Mohamed1,2,*, Bdereddin Abdul Samad1,3, Fatih Anayi1, Michael Packianather1, Khalid Yahya4
    CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 4681-4696, 2023, DOI:10.32604/cmc.2023.035312
    Abstract The Robogymnast is a highly complex, three-link system based on the triple-inverted pendulum and is modelled on the human example of a gymnast suspended by their hands from the high bar and executing larger and larger upswings to eventually rotate fully. The links of the Robogymnast correspond respectively to the arms, trunk, and lower limbs of the gymnast, and from its three joints, one is under passive operation, while the remaining two are powered. The passive top joint poses severe challenges in attaining the smooth movement control needed to operate the Robogymnast effectively. This study More >

  • Open AccessOpen Access

    ARTICLE

    Fine-Grained Features for Image Captioning

    Mengyue Shao1, Jie Feng1,*, Jie Wu1, Haixiang Zhang1, Yayu Zheng2
    CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 4697-4712, 2023, DOI:10.32604/cmc.2023.036564
    Abstract Image captioning involves two different major modalities (image and sentence) that convert a given image into a language that adheres to visual semantics. Almost all methods first extract image features to reduce the difficulty of visual semantic embedding and then use the caption model to generate fluent sentences. The Convolutional Neural Network (CNN) is often used to extract image features in image captioning, and the use of object detection networks to extract region features has achieved great success. However, the region features retrieved by this method are object-level and do not pay attention to fine-grained… More >

  • Open AccessOpen Access

    ARTICLE

    Assessing Secure OpenID-Based EAAA Protocol to Prevent MITM and Phishing Attacks in Web Apps

    Muhammad Bilal1,*, Sandile C. Shongwe2, Abid Bashir3, Yazeed Y. Ghadi4
    CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 4713-4733, 2023, DOI:10.32604/cmc.2023.037071
    Abstract To secure web applications from Man-In-The-Middle (MITM) and phishing attacks is a challenging task nowadays. For this purpose, authentication protocol plays a vital role in web communication which securely transfers data from one party to another. This authentication works via OpenID, Kerberos, password authentication protocols, etc. However, there are still some limitations present in the reported security protocols. In this paper, the presented anticipated strategy secures both Web-based attacks by leveraging encoded emails and a novel password form pattern method. The proposed OpenID-based encrypted Email’s Authentication, Authorization, and Accounting (EAAA) protocol ensure security by relying… More >

  • Open AccessOpen Access

    ARTICLE

    Sea Turtle Foraging Optimization-Based Controller Placement with Blockchain-Assisted Intrusion Detection in Software-Defined Networks

    Sultan Alkhliwi*
    CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 4735-4752, 2023, DOI:10.32604/cmc.2023.037141
    Abstract Software-defined networking (SDN) algorithms are gaining increasing interest and are making networks flexible and agile. The basic idea of SDN is to move the control planes to more than one server’s named controllers and limit the data planes to numerous sending network components, enabling flexible and dynamic network management. A distinctive characteristic of SDN is that it can logically centralize the control plane by utilizing many physical controllers. The deployment of the controller—that is, the controller placement problem (CPP)—becomes a vital model challenge. Through the advancements of blockchain technology, data integrity between nodes can be… More >

  • Open AccessOpen Access

    ARTICLE

    Visualization for Explanation of Deep Learning-Based Defect Detection Model Using Class Activation Map

    Hyunkyu Shin1, Yonghan Ahn2, Mihwa Song3, Heungbae Gil3, Jungsik Choi4,*, Sanghyo Lee5,*
    CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 4753-4766, 2023, DOI:10.32604/cmc.2023.038362
    Abstract Recently, convolutional neural network (CNN)-based visual inspection has been developed to detect defects on building surfaces automatically. The CNN model demonstrates remarkable accuracy in image data analysis; however, the predicted results have uncertainty in providing accurate information to users because of the “black box” problem in the deep learning model. Therefore, this study proposes a visual explanation method to overcome the uncertainty limitation of CNN-based defect identification. The visual representative gradient-weights class activation mapping (Grad-CAM) method is adopted to provide visually explainable information. A visualizing evaluation index is proposed to quantitatively analyze visual representations; this… More >

  • Open AccessOpen Access

    ARTICLE

    Sine Cosine Optimization with Deep Learning-Based Applied Linguistics for Sentiment Analysis on COVID-19 Tweets

    Abdelwahed Motwakel1,*, Hala J. Alshahrani2, Abdulkhaleq Q. A. Hassan3, Khaled Tarmissi4, Amal S. Mehanna5, Ishfaq Yaseen1, Amgad Atta Abdelmageed1, Mohammad Mahzari6
    CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 4767-4783, 2023, DOI:10.32604/cmc.2023.034840
    Abstract Applied linguistics is an interdisciplinary domain which identifies, investigates, and offers solutions to language-related real-life problems. The new coronavirus disease, otherwise known as Coronavirus disease (COVID-19), has severely affected the everyday life of people all over the world. Specifically, since there is insufficient access to vaccines and no straight or reliable treatment for coronavirus infection, the country has initiated the appropriate preventive measures (like lockdown, physical separation, and masking) for combating this extremely transmittable disease. So, individuals spent more time on online social media platforms (i.e., Twitter, Facebook, Instagram, LinkedIn, and Reddit) and expressed their… More >

  • Open AccessOpen Access

    ARTICLE

    Improved HardNet and Stricter Outlier Filtering to Guide Reliable Matching

    Meng Xu1, Chen Shen2, Jun Zhang2, Zhipeng Wang3, Zhiwei Ruan2, Stefan Poslad1, Pengfei Xu2,*
    CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 4785-4803, 2023, DOI:10.32604/cmc.2023.034053
    Abstract As the fundamental problem in the computer vision area, image matching has wide applications in pose estimation, 3D reconstruction, image retrieval, etc. Suffering from the influence of external factors, the process of image matching using classical local detectors, e.g., scale-invariant feature transform (SIFT), and the outlier filtering approaches, e.g., Random sample consensus (RANSAC), show high computation speed and pool robustness under changing illumination and viewpoints conditions, while image matching approaches with deep learning strategy (such as HardNet, OANet) display reliable achievements in large-scale datasets with challenging scenes. However, the past learning-based approaches are limited to… More >

  • Open AccessOpen Access

    ARTICLE

    Coati Optimization-Based Energy Efficient Routing Protocol for Unmanned Aerial Vehicle Communication

    Hanan Abdullah Mengash1, Hamed Alqahtani2, Mohammed Maray3, Mohamed K. Nour4, Radwa Marzouk1, Mohammed Abdullah Al-Hagery5, Heba Mohsen6, Mesfer Al Duhayyim7,*
    CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 4805-4820, 2023, DOI:10.32604/cmc.2023.037810
    Abstract With the flexible deployment and high mobility of Unmanned Aerial Vehicles (UAVs) in an open environment, they have generated considerable attention in military and civil applications intending to enable ubiquitous connectivity and foster agile communications. The difficulty stems from features other than mobile ad-hoc network (MANET), namely aerial mobility in three-dimensional space and often changing topology. In the UAV network, a single node serves as a forwarding, transmitting, and receiving node at the same time. Typically, the communication path is multi-hop, and routing significantly affects the network’s performance. A lot of effort should be invested… More >

  • Open AccessOpen Access

    ARTICLE

    Machine Learning and Synthetic Minority Oversampling Techniques for Imbalanced Data: Improving Machine Failure Prediction

    Yap Bee Wah1,5,*, Azlan Ismail1,2, Nur Niswah Naslina Azid3, Jafreezal Jaafar4, Izzatdin Abdul Aziz4, Mohd Hilmi Hasan4, Jasni Mohamad Zain1,2
    CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 4821-4841, 2023, DOI:10.32604/cmc.2023.034470
    Abstract Prediction of machine failure is challenging as the dataset is often imbalanced with a low failure rate. The common approach to handle classification involving imbalanced data is to balance the data using a sampling approach such as random undersampling, random oversampling, or Synthetic Minority Oversampling Technique (SMOTE) algorithms. This paper compared the classification performance of three popular classifiers (Logistic Regression, Gaussian Naïve Bayes, and Support Vector Machine) in predicting machine failure in the Oil and Gas industry. The original machine failure dataset consists of 20,473 hourly data and is imbalanced with 19945 (97%) ‘non-failure’ and… More >

  • Open AccessOpen Access

    ARTICLE

    Multi-View & Transfer Learning for Epilepsy Recognition Based on EEG Signals

    Jiali Wang1, Bing Li2, Chengyu Qiu1, Xinyun Zhang1, Yuting Cheng1, Peihua Wang1, Ta Zhou3, Hong Ge2, Yuanpeng Zhang1,3,*, Jing Cai3,*
    CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 4843-4866, 2023, DOI:10.32604/cmc.2023.037457
    Abstract Epilepsy is a central nervous system disorder in which brain activity becomes abnormal. Electroencephalogram (EEG) signals, as recordings of brain activity, have been widely used for epilepsy recognition. To study epileptic EEG signals and develop artificial intelligence (AI)-assist recognition, a multi-view transfer learning (MVTL-LSR) algorithm based on least squares regression is proposed in this study. Compared with most existing multi-view transfer learning algorithms, MVTL-LSR has two merits: (1) Since traditional transfer learning algorithms leverage knowledge from different sources, which poses a significant risk to data privacy. Therefore, we develop a knowledge transfer mechanism that can More >

  • Open AccessOpen Access

    ARTICLE

    Container-Based Internet of Vehicles Edge Application Migration Mechanism

    Sujie Shao, Shihan Tian*, Shaoyong Guo, Xuesong Qiu
    CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 4867-4891, 2023, DOI:10.32604/cmc.2023.038280
    Abstract Internet of Vehicles (IoV) applications integrating with edge computing will significantly drive the growth of IoV. However, the contradiction between the high-speed mobility of vehicles, the delay sensitivity of corresponding IoV applications and the limited coverage and resource capacity of distributed edge servers will pose challenges to the service continuity and stability of IoV applications. IoV application migration is a promising solution that can be supported by application containerization, a technology forseamless cross-edge-server application migration without user perception. Therefore, this paper proposes the container-based IoV edge application migration mechanism, consisting of three parts. The first… More >

  • Open AccessOpen Access

    ARTICLE

    Cover Enhancement Method for Audio Steganography Based on Universal Adversarial Perturbations with Sample Diversification

    Jiangchuan Li, Peisong He*, Jiayong Liu, Jie Luo, Qiang Xia
    CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 4893-4915, 2023, DOI:10.32604/cmc.2023.036819
    Abstract Steganography techniques, such as audio steganography, have been widely used in covert communication. However, the deep neural network, especially the convolutional neural network (CNN), has greatly threatened the security of audio steganography. Besides, existing adversarial attacks-based countermeasures cannot provide general perturbation, and the transferability against unknown steganography detection methods is weak. This paper proposes a cover enhancement method for audio steganography based on universal adversarial perturbations with sample diversification to address these issues. Universal adversarial perturbation is constructed by iteratively optimizing adversarial perturbation, which applies adversarial attack techniques, such as Deepfool. Moreover, the sample diversification… More >

  • Open AccessOpen Access

    ARTICLE

    Automated Video-Based Face Detection Using Harris Hawks Optimization with Deep Learning

    Latifah Almuqren1, Manar Ahmed Hamza2,*, Abdullah Mohamed3, Amgad Atta Abdelmageed2
    CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 4917-4933, 2023, DOI:10.32604/cmc.2023.037738
    Abstract Face recognition technology automatically identifies an individual from image or video sources. The detection process can be done by attaining facial characteristics from the image of a subject face. Recent developments in deep learning (DL) and computer vision (CV) techniques enable the design of automated face recognition and tracking methods. This study presents a novel Harris Hawks Optimization with deep learning-empowered automated face detection and tracking (HHODL-AFDT) method. The proposed HHODL-AFDT model involves a Faster region based convolution neural network (RCNN)-based face detection model and HHO-based hyperparameter optimization process. The presented optimal Faster RCNN model… More >

  • Open AccessOpen Access

    ARTICLE

    Pythagorean Neutrosophic Planar Graphs with an Application in Decision-Making

    P. Chellamani1,2,*, D. Ajay1, Mohammed M. Al-Shamiri3,4, Rashad Ismail3,4
    CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 4935-4953, 2023, DOI:10.32604/cmc.2023.036321
    Abstract Graph theory has a significant impact and is crucial in the structure of many real-life situations. To simulate uncertainty and ambiguity, many extensions of graph theoretical notions were created. Planar graphs play a vital role in modelling which has the property of non-crossing edges. Although crossing edges benefit, they have some drawbacks, which paved the way for the introduction of planar graphs. The overall purpose of the study is to contribute to the conceptual development of the Pythagorean Neutrosophic graph. The basic methodology of our research is the incorporation of the analogous concepts of planar… More >

  • Open AccessOpen Access

    ARTICLE

    Google Scholar University Ranking Algorithm to Evaluate the Quality of Institutional Research

    Noor Ul Sabah1, Muhammad Murad Khan1,*, Ramzan Talib1, Muhammad Anwar2, Muhammad Sheraz Arshad Malik3, Puteri Nor Ellyza Nohuddin4
    CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 4955-4972, 2023, DOI:10.32604/cmc.2023.037436
    Abstract Education quality has undoubtedly become an important local and international benchmark for education, and an institute’s ranking is assessed based on the quality of education, research projects, theses, and dissertations, which has always been controversial. Hence, this research paper is influenced by the institutes ranking all over the world. The data of institutes are obtained through Google Scholar (GS), as input to investigate the United Kingdom’s Research Excellence Framework (UK-REF) process. For this purpose, the current research used a Bespoke Program to evaluate the institutes’ ranking based on their source. The bespoke program requires changes… More >

  • Open AccessOpen Access

    ARTICLE

    Survey on Segmentation and Classification Techniques of Satellite Images by Deep Learning Algorithm

    Atheer Joudah1,*, Souheyl Mallat2, Mounir Zrigui1
    CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 4973-4984, 2023, DOI:10.32604/cmc.2023.036483
    Abstract This survey paper aims to show methods to analyze and classify field satellite images using deep learning and machine learning algorithms. Users of deep learning-based Convolutional Neural Network (CNN) technology to harvest fields from satellite images or generate zones of interest were among the planned application scenarios (ROI). Using machine learning, the satellite image is placed on the input image, segmented, and then tagged. In contemporary categorization, field size ratio, Local Binary Pattern (LBP) histograms, and color data are taken into account. Field satellite image localization has several practical applications, including pest management, scene analysis, More >

  • Open AccessOpen Access

    ARTICLE

    Multi-Attack Intrusion Detection System for Software-Defined Internet of Things Network

    Tarcízio Ferrão1,*, Franklin Manene2, Adeyemi Abel Ajibesin3
    CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 4985-5007, 2023, DOI:10.32604/cmc.2023.038276
    Abstract Currently, the Internet of Things (IoT) is revolutionizing communication technology by facilitating the sharing of information between different physical devices connected to a network. To improve control, customization, flexibility, and reduce network maintenance costs, a new Software-Defined Network (SDN) technology must be used in this infrastructure. Despite the various advantages of combining SDN and IoT, this environment is more vulnerable to various attacks due to the centralization of control. Most methods to ensure IoT security are designed to detect Distributed Denial-of-Service (DDoS) attacks, but they often lack mechanisms to mitigate their severity. This paper proposes… More >

  • Open AccessOpen Access

    ARTICLE

    Index-adaptive Triangle-Based Graph Local Clustering

    Zhe Yuan*, Zhewei Wei, Ji-rong Wen
    CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 5009-5026, 2023, DOI:10.32604/cmc.2023.038531
    Abstract Motif-based graph local clustering (MGLC) algorithms are generally designed with the two-phase framework, which gets the motif weight for each edge beforehand and then conducts the local clustering algorithm on the weighted graph to output the result. Despite correctness, this framework brings limitations on both practical and theoretical aspects and is less applicable in real interactive situations. This research develops a purely local and index-adaptive method, Index-adaptive Triangle-based Graph Local Clustering (TGLC+), to solve the MGLC problem w.r.t. triangle. TGLC+ combines the approximated Monte-Carlo method Triangle-based Random Walk (TRW) and deterministic Brute-Force method Triangle-based Forward Push More >

  • Open AccessOpen Access

    ARTICLE

    Fine-Grained Multivariate Time Series Anomaly Detection in IoT

    Shiming He1,4, Meng Guo1, Bo Yang1, Osama Alfarraj2, Amr Tolba2, Pradip Kumar Sharma3, Xi’ai Yan4,*
    CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 5027-5047, 2023, DOI:10.32604/cmc.2023.038551
    Abstract Sensors produce a large amount of multivariate time series data to record the states of Internet of Things (IoT) systems. Multivariate time series timestamp anomaly detection (TSAD) can identify timestamps of attacks and malfunctions. However, it is necessary to determine which sensor or indicator is abnormal to facilitate a more detailed diagnosis, a process referred to as fine-grained anomaly detection (FGAD). Although further FGAD can be extended based on TSAD methods, existing works do not provide a quantitative evaluation, and the performance is unknown. Therefore, to tackle the FGAD problem, this paper first verifies that… More >

  • Open AccessOpen Access

    REVIEW

    Technologies Behind the Smart Grid and Internet of Things: A System Survey

    Kuldeep Sharma1, Arun Malik1, Isha Batra1, A. S. M. Sanwar Hosen2, Md Abdul Latif Sarker3, Dong Seog Han4,*
    CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 5049-5072, 2023, DOI:10.32604/cmc.2023.035638
    Abstract Electric smart grids enable a bidirectional flow of electricity and information among power system assets. For proper monitoring and controlling of power quality, reliability, scalability and flexibility, there is a need for an environmentally friendly system that is transparent, sustainable, cost-saving, energy-efficient, agile and secure. This paper provides an overview of the emerging technologies behind smart grids and the internet of things. The dependent variables are identified by analyzing the electricity consumption patterns for optimal utilization and planning preventive maintenance of their legacy assets like power distribution transformers with real-time parameters to ensure an uninterrupted More >

  • Open AccessOpen Access

    ARTICLE

    A Novel Approach for Security Enhancement of Data Encryption Standard

    Dawood Shah1,*, Tariq Shah1, Sajjad Shaukat Jamal2, Mohammad Mazyad Hazzazi2, Amer Aljaedi3, Adel R. Alharbi3
    CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 5073-5086, 2023, DOI:10.32604/cmc.2023.020513
    Abstract Data Encryption Standard (DES) is a symmetric key cryptosystem that is applied in different cryptosystems of recent times. However, researchers found defects in the main assembling of the DES and declared it insecure against linear and differential cryptanalysis. In this paper, we have studied the faults and made improvements in their internal structure and get the new algorithm for Improved DES. The improvement is being made in the substitution step, which is the only nonlinear component of the algorithm. This alteration provided us with great outcomes and increase the strength of DES. Accordingly, a novel More >

  • Open AccessOpen Access

    ARTICLE

    GaitDONet: Gait Recognition Using Deep Features Optimization and Neural Network

    Muhammad Attique Khan1, Awais Khan1, Majed Alhaisoni2, Abdullah Alqahtani3, Ammar Armghan4, Sara A. Althubiti5, Fayadh Alenezi4, Senghour Mey6, Yunyoung Nam6,*
    CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 5087-5103, 2023, DOI:10.32604/cmc.2023.033856
    Abstract Human gait recognition (HGR) is the process of identifying a subject (human) based on their walking pattern. Each subject is a unique walking pattern and cannot be simulated by other subjects. But, gait recognition is not easy and makes the system difficult if any object is carried by a subject, such as a bag or coat. This article proposes an automated architecture based on deep features optimization for HGR. To our knowledge, it is the first architecture in which features are fused using multiset canonical correlation analysis (MCCA). In the proposed method, original video frames… More >

  • Open AccessOpen Access

    ARTICLE

    A Transfer Learning Approach Based on Ultrasound Images for Liver Cancer Detection

    Murtada K. Elbashir1, Alshimaa Mahmoud2, Ayman Mohamed Mostafa1,*, Eslam Hamouda1, Meshrif Alruily1, Sadeem M. Alotaibi1, Hosameldeen Shabana3,4, Mohamed Ezz1,*
    CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 5105-5121, 2023, DOI:10.32604/cmc.2023.037728
    Abstract The convolutional neural network (CNN) is one of the main algorithms that is applied to deep transfer learning for classifying two essential types of liver lesions; Hemangioma and hepatocellular carcinoma (HCC). Ultrasound images, which are commonly available and have low cost and low risk compared to computerized tomography (CT) scan images, will be used as input for the model. A total of 350 ultrasound images belonging to 59 patients are used. The number of images with HCC is 202 and 148, respectively. These images were collected from ultrasound cases.info (28 Hemangiomas patients and 11 HCC… More >

  • Open AccessOpen Access

    ARTICLE

    Human Gait Recognition Based on Sequential Deep Learning and Best Features Selection

    Ch Avais Hanif1, Muhammad Ali Mughal1,*, Muhammad Attique Khan2, Usman Tariq3, Ye Jin Kim4, Jae-Hyuk Cha4
    CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 5123-5140, 2023, DOI:10.32604/cmc.2023.038120
    Abstract Gait recognition is an active research area that uses a walking theme to identify the subject correctly. Human Gait Recognition (HGR) is performed without any cooperation from the individual. However, in practice, it remains a challenging task under diverse walking sequences due to the covariant factors such as normal walking and walking with wearing a coat. Researchers, over the years, have worked on successfully identifying subjects using different techniques, but there is still room for improvement in accuracy due to these covariant factors. This paper proposes an automated model-free framework for human gait recognition in this… More >

  • Open AccessOpen Access

    ARTICLE

    An Effective Security Comparison Protocol in Cloud Computing

    Yuling Chen1,2, Junhong Tao1, Tao Li1,*, Jiangyuan Cai3, Xiaojun Ren4
    CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 5141-5158, 2023, DOI:10.32604/cmc.2023.037783
    Abstract With the development of cloud computing technology, more and more data owners upload their local data to the public cloud server for storage and calculation. While this can save customers’ operating costs, it also poses privacy and security challenges. Such challenges can be solved using secure multi-party computation (SMPC), but this still exposes more security issues. In cloud computing using SMPC, clients need to process their data and submit the processed data to the cloud server, which then performs the calculation and returns the results to each client. Each client and server must be honest.… More >

  • Open AccessOpen Access

    ARTICLE

    Residual Feature Attentional Fusion Network for Lightweight Chest CT Image Super-Resolution

    Kun Yang1,2, Lei Zhao1, Xianghui Wang1, Mingyang Zhang1, Linyan Xue1,2, Shuang Liu1,2, Kun Liu1,2,3,*
    CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 5159-5176, 2023, DOI:10.32604/cmc.2023.036401
    Abstract The diagnosis of COVID-19 requires chest computed tomography (CT). High-resolution CT images can provide more diagnostic information to help doctors better diagnose the disease, so it is of clinical importance to study super-resolution (SR) algorithms applied to CT images to improve the resolution of CT images. However, most of the existing SR algorithms are studied based on natural images, which are not suitable for medical images; and most of these algorithms improve the reconstruction quality by increasing the network depth, which is not suitable for machines with limited resources. To alleviate these issues, we propose… More >

  • Open AccessOpen Access

    ARTICLE

    A New Hybrid Model for Segmentation of the Skin Lesion Based on Residual Attention U-Net

    Saleh Naif Almuayqil1, Reham Arnous2,*, Noha Sakr3, Magdy M. Fadel3
    CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 5177-5192, 2023, DOI:10.32604/cmc.2023.038625
    Abstract Skin segmentation participates significantly in various biomedical applications, such as skin cancer identification and skin lesion detection. This paper presents a novel framework for segmenting the skin. The framework contains two main stages: The first stage is for removing different types of noises from the dermoscopic images, such as hair, speckle, and impulse noise, and the second stage is for segmentation of the dermoscopic images using an attention residual U-shaped Network (U-Net). The framework uses variational Autoencoders (VAEs) for removing the hair noises, the Generative Adversarial Denoising Network (DGAN-Net), the Denoising U-shaped U-Net (D-U-NET), and… More >

  • Open AccessOpen Access

    ARTICLE

    Research and Implementation of Credit Investigation Sharing Platform Based on Double Blockchain

    Yanyan Han1,2, Wanqi Wei2,*, Kaili Dou3, Peng Li2
    CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 5193-5211, 2023, DOI:10.32604/cmc.2023.037756
    Abstract As the development of the modern economy is increasingly inseparable from credit support, the traditional credit investigation mode has yet to meet this demand. Because of the difficulties in conventional credit data sharing among credit investigation agencies, poor data portability, and centralized supervision, this paper proposes a data-sharing scheme for credit investigation agencies based on a double blockchain. Given the problems such as difficult data sharing, difficult recovery of damaged data, and accessible data leakage between institutions and users with non-traditional credit investigation data other than credit, this paper proposes a data-sharing scheme for credit… More >

  • Open AccessOpen Access

    ARTICLE

    Deep Learning ResNet101 Deep Features of Portable Chest X-Ray Accurately Classify COVID-19 Lung Infection

    Sobia Nawaz1, Sidra Rasheed2, Wania Sami3, Lal Hussain4,5,*, Amjad Aldweesh6,*, Elsayed Tag eldin7, Umair Ahmad Salaria8,9, Mohammad Shahbaz Khan10
    CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 5213-5228, 2023, DOI:10.32604/cmc.2023.037543
    Abstract This study is designed to develop Artificial Intelligence (AI) based analysis tool that could accurately detect COVID-19 lung infections based on portable chest x-rays (CXRs). The frontline physicians and radiologists suffer from grand challenges for COVID-19 pandemic due to the suboptimal image quality and the large volume of CXRs. In this study, AI-based analysis tools were developed that can precisely classify COVID-19 lung infection. Publicly available datasets of COVID-19 (N = 1525), non-COVID-19 normal (N = 1525), viral pneumonia (N = 1342) and bacterial pneumonia (N = 2521) from the Italian Society of Medical and… More >

  • Open AccessOpen Access

    ARTICLE

    APST-Flow: A Reversible Network-Based Artistic Painting Style Transfer Method

    Meng Wang*, Yixuan Shao, Haipeng Liu
    CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 5229-5254, 2023, DOI:10.32604/cmc.2023.036631
    Abstract In recent years, deep generative models have been successfully applied to perform artistic painting style transfer (APST). The difficulties might lie in the loss of reconstructing spatial details and the inefficiency of model convergence caused by the irreversible en-decoder methodology of the existing models. Aiming to this, this paper proposes a Flow-based architecture with both the en-decoder sharing a reversible network configuration. The proposed APST-Flow can efficiently reduce model uncertainty via a compact analysis-synthesis methodology, thereby the generalization performance and the convergence stability are improved. For the generator, a Flow-based network using Wavelet additive coupling… More >

  • Open AccessOpen Access

    ARTICLE

    Optimal Synergic Deep Learning for COVID-19 Classification Using Chest X-Ray Images

    José Escorcia-Gutierrez1,*, Margarita Gamarra1, Roosvel Soto-Diaz2, Safa Alsafari3, Ayman Yafoz4, Romany F. Mansour5
    CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 5255-5270, 2023, DOI:10.32604/cmc.2023.033731
    Abstract A chest radiology scan can significantly aid the early diagnosis and management of COVID-19 since the virus attacks the lungs. Chest X-ray (CXR) gained much interest after the COVID-19 outbreak thanks to its rapid imaging time, widespread availability, low cost, and portability. In radiological investigations, computer-aided diagnostic tools are implemented to reduce intra- and inter-observer variability. Using lately industrialized Artificial Intelligence (AI) algorithms and radiological techniques to diagnose and classify disease is advantageous. The current study develops an automatic identification and classification model for CXR pictures using Gaussian Filtering based Optimized Synergic Deep Learning using… More >

  • Open AccessOpen Access

    ARTICLE

    Classification of Electroencephalogram Signals Using LSTM and SVM Based on Fast Walsh-Hadamard Transform

    Saeed Mohsen1,2,*, Sherif S. M. Ghoneim3, Mohammed S. Alzaidi3, Abdullah Alzahrani3, Ashraf Mohamed Ali Hassan4
    CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 5271-5286, 2023, DOI:10.32604/cmc.2023.038758
    Abstract Classification of electroencephalogram (EEG) signals for humans can be achieved via artificial intelligence (AI) techniques. Especially, the EEG signals associated with seizure epilepsy can be detected to distinguish between epileptic and non-epileptic regions. From this perspective, an automated AI technique with a digital processing method can be used to improve these signals. This paper proposes two classifiers: long short-term memory (LSTM) and support vector machine (SVM) for the classification of seizure and non-seizure EEG signals. These classifiers are applied to a public dataset, namely the University of Bonn, which consists of 2 classes –seizure and… More >

  • Open AccessOpen Access

    ARTICLE

    Designing Pair of Nonlinear Components of a Block Cipher over Gaussian Integers

    Muhammad Sajjad1,*, Tariq Shah1, Robinson Julian Serna2
    CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 5287-5305, 2023, DOI:10.32604/cmc.2023.035347
    Abstract In block ciphers, the nonlinear components, also known as substitution boxes (S-boxes), are used with the purpose of inducing confusion in cryptosystems. For the last decade, most of the work on designing S-boxes over the points of elliptic curves has been published. The main purpose of these studies is to hide data and improve the security levels of crypto algorithms. In this work, we design pair of nonlinear components of a block cipher over the residue class of Gaussian integers (GI). The fascinating features of this structure provide S-boxes pair at a time by fixing More >

  • Open AccessOpen Access

    ARTICLE

    Cyberbullying Detection and Recognition with Type Determination Based on Machine Learning

    Khalid M. O. Nahar1,*, Mohammad Alauthman2, Saud Yonbawi3, Ammar Almomani4,5
    CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 5307-5319, 2023, DOI:10.32604/cmc.2023.031848
    Abstract Social media networks are becoming essential to our daily activities, and many issues are due to this great involvement in our lives. Cyberbullying is a social media network issue, a global crisis affecting the victims and society as a whole. It results from a misunderstanding regarding freedom of speech. In this work, we proposed a methodology for detecting such behaviors (bullying, harassment, and hate-related texts) using supervised machine learning algorithms (SVM, Naïve Bayes, Logistic regression, and random forest) and for predicting a topic associated with these text data using unsupervised natural language processing, such as More >

  • Open AccessOpen Access

    ARTICLE

    A Model for Helmet-Wearing Detection of Non-Motor Drivers Based on YOLOv5s

    Hongyu Lin, Feng Jiang*, Yu Jiang, Huiyin Luo, Jian Yao, Jiaxin Liu
    CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 5321-5336, 2023, DOI:10.32604/cmc.2023.036893
    Abstract Detecting non-motor drivers’ helmets has significant implications for traffic control. Currently, most helmet detection methods are susceptible to the complex background and need more accuracy and better robustness of small object detection, which are unsuitable for practical application scenarios. Therefore, this paper proposes a new helmet-wearing detection algorithm based on the You Only Look Once version 5 (YOLOv5). First, the Dilated convolution In Coordinate Attention (DICA) layer is added to the backbone network. DICA combines the coordinated attention mechanism with atrous convolution to replace the original convolution layer, which can increase the perceptual field of… More >

  • Open AccessOpen Access

    ARTICLE

    An Efficient Three-Party Authenticated Key Exchange Procedure Using Chebyshev Chaotic Maps with Client Anonymity

    Akshaykumar Meshram1,2, Monia Hadj Alouane-Turki3, N. M. Wazalwar2, Chandrashekhar Meshram4,*
    CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 5337-5353, 2023, DOI:10.32604/cmc.2023.037324
    Abstract Internet of Things (IoT) applications can be found in various industry areas, including critical infrastructure and healthcare, and IoT is one of several technological developments. As a result, tens of billions or possibly hundreds of billions of devices will be linked together. These smart devices will be able to gather data, process it, and even come to decisions on their own. Security is the most essential thing in these situations. In IoT infrastructure, authenticated key exchange systems are crucial for preserving client and data privacy and guaranteeing the security of data-in-transit (e.g., via client identification… More >

  • Open AccessOpen Access

    ARTICLE

    Ensemble Deep Learning Framework for Situational Aspects-Based Annotation and Classification of International Student’s Tweets during COVID-19

    Shabir Hussain1, Muhammad Ayoub2, Yang Yu1, Junaid Abdul Wahid1, Akmal Khan3, Dietmar P. F. Moller4, Hou Weiyan1,*
    CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 5355-5377, 2023, DOI:10.32604/cmc.2023.036779
    Abstract As the COVID-19 pandemic swept the globe, social media platforms became an essential source of information and communication for many. International students, particularly, turned to Twitter to express their struggles and hardships during this difficult time. To better understand the sentiments and experiences of these international students, we developed the Situational Aspect-Based Annotation and Classification (SABAC) text mining framework. This framework uses a three-layer approach, combining baseline Deep Learning (DL) models with Machine Learning (ML) models as meta-classifiers to accurately predict the sentiments and aspects expressed in tweets from our collected Student-COVID-19 dataset. Using the… More >

  • Open AccessOpen Access

    ARTICLE

    Traffic Management in Internet of Vehicles Using Improved Ant Colony Optimization

    Abida Sharif1, Imran Sharif1, Muhammad Asim Saleem2, Muhammad Attique Khan3, Majed Alhaisoni4, Marriam Nawaz5,6, Abdullah Alqahtani7, Ye Jin Kim8, Byoungchol Chang9,*
    CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 5379-5393, 2023, DOI:10.32604/cmc.2023.034413
    Abstract The Internet of Vehicles (IoV) is a networking paradigm related to the intercommunication of vehicles using a network. In a dynamic network, one of the key challenges in IoV is traffic management under increasing vehicles to avoid congestion. Therefore, optimal path selection to route traffic between the origin and destination is vital. This research proposed a realistic strategy to reduce traffic management service response time by enabling real-time content distribution in IoV systems using heterogeneous network access. Firstly, this work proposed a novel use of the Ant Colony Optimization (ACO) algorithm and formulated the path More >

  • Open AccessOpen Access

    ARTICLE

    An Erebus Attack Detection Method Oriented to Blockchain Network Layer

    Qianyi Dai1,2,*, Bin Zhang1,2, Kaiyong Xu1,2, Shuqin Dong1,2
    CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 5395-5431, 2023, DOI:10.32604/cmc.2023.036033
    Abstract Recently, the Erebus attack has proved to be a security threat to the blockchain network layer, and the existing research has faced challenges in detecting the Erebus attack on the blockchain network layer. The cloud-based active defense and one-sidedness detection strategies are the hindrances in detecting Erebus attacks. This study designs a detection approach by establishing a ReliefF_WMRmR-based two-stage feature selection algorithm and a deep learning-based multimodal classification detection model for Erebus attacks and responding to security threats to the blockchain network layer. The goal is to improve the performance of Erebus attack detection methods,… More >

  • Open AccessOpen Access

    ARTICLE

    Hybrid Metaheuristics with Deep Learning Enabled Automated Deception Detection and Classification of Facial Expressions

    Haya Alaskar*
    CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 5433-5449, 2023, DOI:10.32604/cmc.2023.035266
    Abstract Automatic deception recognition has received considerable attention from the machine learning community due to recent research on its vast application to social media, interviews, law enforcement, and the military. Video analysis-based techniques for automated deception detection have received increasing interest. This study develops a new self-adaptive population-based firefly algorithm with a deep learning-enabled automated deception detection (SAPFF-DLADD) model for analyzing facial cues. Initially, the input video is separated into a set of video frames. Then, the SAPFF-DLADD model applies the MobileNet-based feature extractor to produce a useful set of features. The long short-term memory (LSTM) More >

  • Open AccessOpen Access

    ARTICLE

    Deep Learning Based Underground Sewer Defect Classification Using a Modified RegNet

    Yu Chen1, Sagar A. S. M. Sharifuzzaman2, Hangxiang Wang1, Yanfen Li1, L. Minh Dang3, Hyoung-Kyu Song3, Hyeonjoon Moon1,*
    CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 5455-5473, 2023, DOI:10.32604/cmc.2023.033787
    Abstract The sewer system plays an important role in protecting rainfall and treating urban wastewater. Due to the harsh internal environment and complex structure of the sewer, it is difficult to monitor the sewer system. Researchers are developing different methods, such as the Internet of Things and Artificial Intelligence, to monitor and detect the faults in the sewer system. Deep learning is a promising artificial intelligence technology that can effectively identify and classify different sewer system defects. However, the existing deep learning based solution does not provide high accuracy prediction and the defect class considered for… More >

  • Open AccessOpen Access

    ARTICLE

    Enhanced Water Quality Control Based on Predictive Optimization for Smart Fish Farming

    Azimbek Khudoyberdiev1, Mohammed Abdul Jaleel1, Israr Ullah2, DoHyeun Kim3,*
    CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 5471-5499, 2023, DOI:10.32604/cmc.2023.036898
    Abstract The requirement for high-quality seafood is a global challenge in today’s world due to climate change and natural resource limitations. Internet of Things (IoT) based Modern fish farming systems can significantly optimize seafood production by minimizing resource utilization and improving healthy fish production. This objective requires intensive monitoring, prediction, and control by optimizing leading factors that impact fish growth, including temperature, the potential of hydrogen (pH), water level, and feeding rate. This paper proposes the IoT based predictive optimization approach for efficient control and energy utilization in smart fish farming. The proposed fish farm control… More >

  • Open AccessOpen Access

    ARTICLE

    WiFi6 Dynamic Channel Optimization Method for Fault Tolerance in Power Communication Network

    Hong Zhu1, Lisha Gao1, Lei Wei1, Guangchang Yang2,*, Sujie Shao2
    CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 5501-5519, 2023, DOI:10.32604/cmc.2023.035759
    Abstract As the scale of power networks has expanded, the demand for multi-service transmission has gradually increased. The emergence of WiFi6 has improved the transmission efficiency and resource utilization of wireless networks. However, it still cannot cope with situations such as wireless access point (AP) failure. To solve this problem, this paper combines orthogonal frequency division multiple access (OFDMA) technology and dynamic channel optimization technology to design a fault-tolerant WiFi6 dynamic resource optimization method for achieving high quality wireless services in a wirelessly covered network even when an AP fails. First, under the premise of AP… More >

  • Open AccessOpen Access

    ARTICLE

    Temperature-Triggered Hardware Trojan Based Algebraic Fault Analysis of SKINNY-64-64 Lightweight Block Cipher

    Lei Zhu, Jinyue Gong, Liang Dong*, Cong Zhang
    CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 5521-5537, 2023, DOI:10.32604/cmc.2023.037336
    Abstract SKINNY-64-64 is a lightweight block cipher with a 64-bit block length and key length, and it is mainly used on the Internet of Things (IoT). Currently, faults can be injected into cryptographic devices by attackers in a variety of ways, but it is still difficult to achieve a precisely located fault attacks at a low cost, whereas a Hardware Trojan (HT) can realize this. Temperature, as a physical quantity incidental to the operation of a cryptographic device, is easily overlooked. In this paper, a temperature-triggered HT (THT) is designed, which, when activated, causes a specific… More >

  • Open AccessOpen Access

    ARTICLE

    Robust Watermarking Algorithm for Medical Images Based on Non-Subsampled Shearlet Transform and Schur Decomposition

    Meng Yang1, Jingbing Li1,2,*, Uzair Aslam Bhatti1,2, Chunyan Shao1, Yen-Wei Chen3
    CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 5539-5554, 2023, DOI:10.32604/cmc.2023.036904
    Abstract With the development of digitalization in healthcare, more and more information is delivered and stored in digital form, facilitating people’s lives significantly. In the meanwhile, privacy leakage and security issues come along with it. Zero watermarking can solve this problem well. To protect the security of medical information and improve the algorithm’s robustness, this paper proposes a robust watermarking algorithm for medical images based on Non-Subsampled Shearlet Transform (NSST) and Schur decomposition. Firstly, the low-frequency subband image of the original medical image is obtained by NSST and chunked. Secondly, the Schur decomposition of low-frequency blocks… More >

  • Open AccessOpen Access

    ARTICLE

    Research on PM2.5 Concentration Prediction Algorithm Based on Temporal and Spatial Features

    Song Yu*, Chen Wang
    CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 5555-5571, 2023, DOI:10.32604/cmc.2023.038162
    Abstract PM2.5 has a non-negligible impact on visibility and air quality as an important component of haze and can affect cloud formation and rainfall and thus change the climate, and it is an evaluation indicator of air pollution level. Achieving PM2.5 concentration prediction based on relevant historical data mining can effectively improve air pollution forecasting ability and guide air pollution prevention and control. The past methods neglected the impact caused by PM2.5 flow between cities when analyzing the impact of inter-city PM2.5 concentrations, making it difficult to further improve the prediction accuracy. However, factors including geographical information such… More >

  • Open AccessOpen Access

    ARTICLE

    MSEs Credit Risk Assessment Model Based on Federated Learning and Feature Selection

    Zhanyang Xu1, Jianchun Cheng1,*, Luofei Cheng1, Xiaolong Xu1,2, Muhammad Bilal3
    CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 5573-5595, 2023, DOI:10.32604/cmc.2023.037287
    Abstract Federated learning has been used extensively in business innovation scenarios in various industries. This research adopts the federated learning approach for the first time to address the issue of bank-enterprise information asymmetry in the credit assessment scenario. First, this research designs a credit risk assessment model based on federated learning and feature selection for micro and small enterprises (MSEs) using multi-dimensional enterprise data and multi-perspective enterprise information. The proposed model includes four main processes: namely encrypted entity alignment, hybrid feature selection, secure multi-party computation, and global model updating. Secondly, a two-step feature selection algorithm based… More >

  • Open AccessOpen Access

    ARTICLE

    Strategy for Rapid Diabetic Retinopathy Exposure Based on Enhanced Feature Extraction Processing

    V. Banupriya1,*, S. Anusuya2
    CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 5597-5613, 2023, DOI:10.32604/cmc.2023.038696
    Abstract In the modern world, one of the most severe eye infections brought on by diabetes is known as diabetic retinopathy (DR), which will result in retinal damage, and, thus, lead to blindness. Diabetic retinopathy (DR) can be well treated with early diagnosis. Retinal fundus images of humans are used to screen for lesions in the retina. However, detecting DR in the early stages is challenging due to the minimal symptoms. Furthermore, the occurrence of diseases linked to vascular anomalies brought on by DR aids in diagnosing the condition. Nevertheless, the resources required for manually identifying… More >

  • Open AccessOpen Access

    ARTICLE

    MFF-Net: Multimodal Feature Fusion Network for 3D Object Detection

    Peicheng Shi1,*, Zhiqiang Liu1, Heng Qi1, Aixi Yang2
    CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 5615-5637, 2023, DOI:10.32604/cmc.2023.037794
    Abstract In complex traffic environment scenarios, it is very important for autonomous vehicles to accurately perceive the dynamic information of other vehicles around the vehicle in advance. The accuracy of 3D object detection will be affected by problems such as illumination changes, object occlusion, and object detection distance. To this purpose, we face these challenges by proposing a multimodal feature fusion network for 3D object detection (MFF-Net). In this research, this paper first uses the spatial transformation projection algorithm to map the image features into the feature space, so that the image features are in the… More >

Per Page:

Share Link