Home / Journals / CMC / Vol.76, No.2, 2023
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  • Open AccessOpen Access

    EDITORIAL

    Grad-CAM: Understanding AI Models

    Shuihua Wang1,2, Yudong Zhang2,*
    CMC-Computers, Materials & Continua, Vol.76, No.2, pp. 1321-1324, 2023, DOI:10.32604/cmc.2023.041419
    Abstract This article has no abstract. More >

  • Open AccessOpen Access

    ARTICLE

    A Novel Parallel Computing Confidentiality Scheme Based on Hindmarsh-Rose Model

    Jawad Ahmad1,*, Mimonah Al Qathrady2, Mohammed S. Alshehri3, Yazeed Yasin Ghadi4, Mujeeb Ur Rehman5, Syed Aziz Shah6
    CMC-Computers, Materials & Continua, Vol.76, No.2, pp. 1325-1341, 2023, DOI:10.32604/cmc.2023.040858
    Abstract Due to the inherent insecure nature of the Internet, it is crucial to ensure the secure transmission of image data over this network. Additionally, given the limitations of computers, it becomes even more important to employ efficient and fast image encryption techniques. While 1D chaotic maps offer a practical approach to real-time image encryption, their limited flexibility and increased vulnerability restrict their practical application. In this research, we have utilized a 3D Hindmarsh-Rose model to construct a secure cryptosystem. The randomness of the chaotic map is assessed through standard analysis. The proposed system enhances security… More >

  • Open AccessOpen Access

    ARTICLE

    Deep Learning-Based Action Classification Using One-Shot Object Detection

    Hyun Yoo1, Seo-El Lee2, Kyungyong Chung3,*
    CMC-Computers, Materials & Continua, Vol.76, No.2, pp. 1343-1359, 2023, DOI:10.32604/cmc.2023.039263
    Abstract Deep learning-based action classification technology has been applied to various fields, such as social safety, medical services, and sports. Analyzing an action on a practical level requires tracking multiple human bodies in an image in real-time and simultaneously classifying their actions. There are various related studies on the real-time classification of actions in an image. However, existing deep learning-based action classification models have prolonged response speeds, so there is a limit to real-time analysis. In addition, it has low accuracy of action of each object if multiple objects appear in the image. Also, it needs… More >

  • Open AccessOpen Access

    ARTICLE

    A Stacked Ensemble Deep Learning Approach for Imbalanced Multi-Class Water Quality Index Prediction

    Wen Yee Wong1, Khairunnisa Hasikin1,*, Anis Salwa Mohd Khairuddin2, Sarah Abdul Razak3, Hanee Farzana Hizaddin4, Mohd Istajib Mokhtar5, Muhammad Mokhzaini Azizan6
    CMC-Computers, Materials & Continua, Vol.76, No.2, pp. 1361-1384, 2023, DOI:10.32604/cmc.2023.038045
    Abstract A common difficulty in building prediction models with realworld environmental datasets is the skewed distribution of classes. There are significantly more samples for day-to-day classes, while rare events such as polluted classes are uncommon. Consequently, the limited availability of minority outcomes lowers the classifier’s overall reliability. This study assesses the capability of machine learning (ML) algorithms in tackling imbalanced water quality data based on the metrics of precision, recall, and F1 score. It intends to balance the misled accuracy towards the majority of data. Hence, 10 ML algorithms of its performance are compared. The classifiers… More >

  • Open AccessOpen Access

    ARTICLE

    A Task-Oriented Hybrid Cloud Architecture with Deep Cognition Mechanism for Intelligent Space

    Yongcheng Cui1, Guohui Tian1,*, Xiaochun Cheng2,*
    CMC-Computers, Materials & Continua, Vol.76, No.2, pp. 1385-1408, 2023, DOI:10.32604/cmc.2023.040246
    (This article belongs to the Special Issue: AI Powered Human-centric Computing with Cloud and Edge)
    Abstract Intelligent Space (IS) is widely regarded as a promising paradigm for improving quality of life through using service task processing. As the field matures, various state-of-the-art IS architectures have been proposed. Most of the IS architectures designed for service robots face the problems of fixed-function modules and low scalability when performing service tasks. To this end, we propose a hybrid cloud service robot architecture based on a Service-Oriented Architecture (SOA). Specifically, we first use the distributed deployment of functional modules to solve the problem of high computing resource occupancy. Then, the Socket communication interface layer… More >

  • Open AccessOpen Access

    ARTICLE

    Active Learning Strategies for Textual Dataset-Automatic Labelling

    Sher Muhammad Daudpota1, Saif Hassan1, Yazeed Alkhurayyif2,*, Abdullah Saleh Alqahtani3,4, Muhammad Haris Aziz5
    CMC-Computers, Materials & Continua, Vol.76, No.2, pp. 1409-1422, 2023, DOI:10.32604/cmc.2023.034157
    (This article belongs to the Special Issue: Emerging Techniques on Citation Analysis in Scholarly Articles)
    Abstract The Internet revolution has resulted in abundant data from various sources, including social media, traditional media, etcetera. Although the availability of data is no longer an issue, data labelling for exploiting it in supervised machine learning is still an expensive process and involves tedious human efforts. The overall purpose of this study is to propose a strategy to automatically label the unlabeled textual data with the support of active learning in combination with deep learning. More specifically, this study assesses the performance of different active learning strategies in automatic labelling of the textual dataset at… More >

  • Open AccessOpen Access

    ARTICLE

    Deep Learning Based Cyber Event Detection from Open-Source Re-Emerging Social Data

    Farah Mohammad1,*, Saad Al-Ahmadi2, Jalal Al-Muhtadi1,2
    CMC-Computers, Materials & Continua, Vol.76, No.2, pp. 1423-1438, 2023, DOI:10.32604/cmc.2023.035741
    (This article belongs to the Special Issue: Recent Advances in Internet of Things and Emerging Technologies)
    Abstract Social media forums have emerged as the most popular form of communication in the modern technology era, allowing people to discuss and express their opinions. This increases the amount of material being shared on social media sites. There is a wealth of information about the threat that may be found in such open data sources. The security of already-deployed software and systems relies heavily on the timely detection of newly-emerging threats to their safety that can be gleaned from such information. Despite the fact that several models for detecting cybersecurity events have been presented, it… More >

  • Open AccessOpen Access

    ARTICLE

    GRATDet: Smart Contract Vulnerability Detector Based on Graph Representation and Transformer

    Peng Gong1,2,3, Wenzhong Yang2,3,*, Liejun Wang2,3, Fuyuan Wei2,3, KeZiErBieKe HaiLaTi2,3, Yuanyuan Liao2,3
    CMC-Computers, Materials & Continua, Vol.76, No.2, pp. 1439-1462, 2023, DOI:10.32604/cmc.2023.038878
    Abstract Smart contracts have led to more efficient development in finance and healthcare, but vulnerabilities in contracts pose high risks to their future applications. The current vulnerability detection methods for contracts are either based on fixed expert rules, which are inefficient, or rely on simplistic deep learning techniques that do not fully leverage contract semantic information. Therefore, there is ample room for improvement in terms of detection precision. To solve these problems, this paper proposes a vulnerability detector based on deep learning techniques, graph representation, and Transformer, called GRATDet. The method first performs swapping, insertion, and symbolization… More >

  • Open AccessOpen Access

    ARTICLE

    Task Offloading and Resource Allocation in IoT Based Mobile Edge Computing Using Deep Learning

    Ilyоs Abdullaev1, Natalia Prodanova2, K. Aruna Bhaskar3, E. Laxmi Lydia4, Seifedine Kadry5,6,7, Jungeun Kim8,*
    CMC-Computers, Materials & Continua, Vol.76, No.2, pp. 1463-1477, 2023, DOI:10.32604/cmc.2023.038417
    Abstract Recently, computation offloading has become an effective method for overcoming the constraint of a mobile device (MD) using computation-intensive mobile and offloading delay-sensitive application tasks to the remote cloud-based data center. Smart city benefitted from offloading to edge point. Consider a mobile edge computing (MEC) network in multiple regions. They comprise N MDs and many access points, in which every MD has M independent real-time tasks. This study designs a new Task Offloading and Resource Allocation in IoT-based MEC using Deep Learning with Seagull Optimization (TORA-DLSGO) algorithm. The proposed TORA-DLSGO technique addresses the resource management issue More >

  • Open AccessOpen Access

    ARTICLE

    An Improved Honey Badger Algorithm through Fusing Multi-Strategies

    Zhiwei Ye1, Tao Zhao1, Chun Liu1,*, Daode Zhang1, Wanfang Bai2
    CMC-Computers, Materials & Continua, Vol.76, No.2, pp. 1479-1495, 2023, DOI:10.32604/cmc.2023.038787
    (This article belongs to the Special Issue: Optimization Algorithm in Real-World Applications)
    Abstract The Honey Badger Algorithm (HBA) is a novel meta-heuristic algorithm proposed recently inspired by the foraging behavior of honey badgers. The dynamic search behavior of honey badgers with sniffing and wandering is divided into exploration and exploitation in HBA, which has been applied in photovoltaic systems and optimization problems effectively. However, HBA tends to suffer from the local optimum and low convergence. To alleviate these challenges, an improved HBA (IHBA) through fusing multi-strategies is presented in the paper. It introduces Tent chaotic mapping and composite mutation factors to HBA, meanwhile, the random control parameter is More >

  • Open AccessOpen Access

    ARTICLE

    Context Awareness by Noise-Pattern Analysis of a Smart Factory

    So-Yeon Lee1, Jihoon Park1, Dae-Young Kim2,*
    CMC-Computers, Materials & Continua, Vol.76, No.2, pp. 1497-1514, 2023, DOI:10.32604/cmc.2023.034914
    Abstract Recently, to build a smart factory, research has been conducted to perform fault diagnosis and defect detection based on vibration and noise signals generated when a mechanical system is driven using deep-learning technology, a field of artificial intelligence. Most of the related studies apply various audio-feature extraction techniques to one-dimensional raw data to extract sound-specific features and then classify the sound by using the derived spectral image as a training dataset. However, compared to numerical raw data, learning based on image data has the disadvantage that creating a training dataset is very time-consuming. Therefore, we… More >

  • Open AccessOpen Access

    ARTICLE

    A New Privacy-Preserving Data Publishing Algorithm Utilizing Connectivity-Based Outlier Factor and Mondrian Techniques

    Burak Cem Kara1,2,*, Can Eyüpoğlu1
    CMC-Computers, Materials & Continua, Vol.76, No.2, pp. 1515-1535, 2023, DOI:10.32604/cmc.2023.040274
    Abstract Developing a privacy-preserving data publishing algorithm that stops individuals from disclosing their identities while not ignoring data utility remains an important goal to achieve. Because finding the trade-off between data privacy and data utility is an NP-hard problem and also a current research area. When existing approaches are investigated, one of the most significant difficulties discovered is the presence of outlier data in the datasets. Outlier data has a negative impact on data utility. Furthermore, k-anonymity algorithms, which are commonly used in the literature, do not provide adequate protection against outlier data. In this study, a… More >

  • Open AccessOpen Access

    ARTICLE

    Modified Dragonfly Optimization with Machine Learning Based Arabic Text Recognition

    Badriyya B. Al-onazi1, Najm Alotaibi2, Jaber S. Alzahrani3, Hussain Alshahrani4, Mohamed Ahmed Elfaki4, Radwa Marzouk5, Mahmoud Othman6, Abdelwahed Motwakel7,*
    CMC-Computers, Materials & Continua, Vol.76, No.2, pp. 1537-1554, 2023, DOI:10.32604/cmc.2023.034196
    Abstract Text classification or categorization is the procedure of automatically tagging a textual document with most related labels or classes. When the number of labels is limited to one, the task becomes single-label text categorization. The Arabic texts include unstructured information also like English texts, and that is understandable for machine learning (ML) techniques, the text is changed and demonstrated by numerical value. In recent times, the dominant method for natural language processing (NLP) tasks is recurrent neural network (RNN), in general, long short term memory (LSTM) and convolutional neural network (CNN). Deep learning (DL) models… More >

  • Open AccessOpen Access

    ARTICLE

    OPT-BAG Model for Predicting Student Employability

    Minh-Thanh Vo1, Trang Nguyen2, Tuong Le3,4,*
    CMC-Computers, Materials & Continua, Vol.76, No.2, pp. 1555-1568, 2023, DOI:10.32604/cmc.2023.039334
    Abstract The use of machine learning to predict student employability is important in order to analyse a student’s capability to get a job. Based on the results of this type of analysis, university managers can improve the employability of their students, which can help in attracting students in the future. In addition, learners can focus on the essential skills identified through this analysis during their studies, to increase their employability. An effective method called OPT-BAG (OPTimisation of BAGging classifiers) was therefore developed to model the problem of predicting the employability of students. This model can help… More >

  • Open AccessOpen Access

    ARTICLE

    Machine Learning-Enabled Communication Approach for the Internet of Medical Things

    Rahim Khan1,3, Abdullah Ghani1, Samia Allaoua Chelloug2,*, Mohammed Amin4, Aamir Saeed5, Jason Teo1
    CMC-Computers, Materials & Continua, Vol.76, No.2, pp. 1569-1584, 2023, DOI:10.32604/cmc.2023.039859
    (This article belongs to the Special Issue: IoMT and Smart Healthcare)
    Abstract The Internet of Medical Things (IoMT) is mainly concerned with the efficient utilisation of wearable devices in the healthcare domain to manage various processes automatically, whereas machine learning approaches enable these smart systems to make informed decisions. Generally, broadcasting is used for the transmission of frames, whereas congestion, energy efficiency, and excessive load are among the common issues associated with existing approaches. In this paper, a machine learning-enabled shortest path identification scheme is presented to ensure reliable transmission of frames, especially with the minimum possible communication overheads in the IoMT network. For this purpose, the… More >

  • Open AccessOpen Access

    ARTICLE

    Accelerate Single Image Super-Resolution Using Object Detection Process

    Xiaolin Xing1, Shujie Yang1,*, Bohan Li2
    CMC-Computers, Materials & Continua, Vol.76, No.2, pp. 1585-1597, 2023, DOI:10.32604/cmc.2023.035415
    Abstract Image Super-Resolution (SR) research has achieved great success with powerful neural networks. The deeper networks with more parameters improve the restoration quality but add the computation complexity, which means more inference time would be cost, hindering image SR from practical usage. Noting the spatial distribution of the objects or things in images, a two-stage local objects SR system is proposed, which consists of two modules, the object detection module and the SR module. Firstly, You Only Look Once (YOLO), which is efficient in generic object detection tasks, is selected to detect the input images for More >

  • Open AccessOpen Access

    ARTICLE

    Double-Layer-Optimizing Method of Hybrid Energy Storage Microgrid Based on Improved Grey Wolf Optimization

    Xianjing Zhong1, Xianbo Sun1,*, Yuhan Wu2
    CMC-Computers, Materials & Continua, Vol.76, No.2, pp. 1599-1619, 2023, DOI:10.32604/cmc.2023.039912
    Abstract To reduce the comprehensive costs of the construction and operation of microgrids and to minimize the power fluctuations caused by randomness and intermittency in distributed generation, a double-layer optimizing configuration method of hybrid energy storage microgrid based on improved grey wolf optimization (IGWO) is proposed. Firstly, building a microgrid system containing a wind-solar power station and electric-hydrogen coupling hybrid energy storage system. Secondly, the minimum comprehensive cost of the construction and operation of the microgrid is taken as the outer objective function, and the minimum peak-to-valley of the microgrid’s daily output is taken as the… More >

  • Open AccessOpen Access

    ARTICLE

    Predicting the Popularity of Online News Based on the Dynamic Fusion of Multiple Features

    Guohui Song1,2, Yongbin Wang1,*, Jianfei Li1, Hongbin Hu1
    CMC-Computers, Materials & Continua, Vol.76, No.2, pp. 1621-1641, 2023, DOI:10.32604/cmc.2023.040095
    (This article belongs to the Special Issue: Optimization for Artificial Intelligence Application)
    Abstract Predicting the popularity of online news is essential for news providers and recommendation systems. Time series, content and meta-feature are important features in news popularity prediction. However, there is a lack of exploration of how to integrate them effectively into a deep learning model and how effective and valuable they are to the model’s performance. This work proposes a novel deep learning model named Multiple Features Dynamic Fusion (MFDF) for news popularity prediction. For modeling time series, long short-term memory networks and attention-based convolution neural networks are used to capture long-term trends and short-term fluctuations… More >

  • Open AccessOpen Access

    ARTICLE

    Developing a Breast Cancer Resistance Protein Substrate Prediction System Using Deep Features and LDA

    Mehdi Hassan1,2, Safdar Ali3, Jin Young Kim2,*, Muhammad Sanaullah4, Hani Alquhayz5, Khushbakht Safdar6
    CMC-Computers, Materials & Continua, Vol.76, No.2, pp. 1643-1663, 2023, DOI:10.32604/cmc.2023.038578
    (This article belongs to the Special Issue: Intelligent Computational Models based on Machine Learning and Deep Learning for Diagnosis System)
    Abstract Breast cancer resistance protein (BCRP) is an important resistance protein that significantly impacts anticancer drug discovery, treatment, and rehabilitation. Early identification of BCRP substrates is quite a challenging task. This study aims to predict early substrate structure, which can help to optimize anticancer drug development and clinical diagnosis. For this study, a novel intelligent approach-based methodology is developed by modifying the ResNet101 model using transfer learning (TL) for automatic deep feature (DF) extraction followed by classification with linear discriminant analysis algorithm (TLRNDF-LDA). This study utilized structural fingerprints, which are exploited by DF contrary to conventional More >

  • Open AccessOpen Access

    ARTICLE

    CNN Accelerator Using Proposed Diagonal Cyclic Array for Minimizing Memory Accesses

    Hyun-Wook Son1, Ali A. Al-Hamid1,2, Yong-Seok Na1, Dong-Yeong Lee1, Hyung-Won Kim1,*
    CMC-Computers, Materials & Continua, Vol.76, No.2, pp. 1665-1687, 2023, DOI:10.32604/cmc.2023.038760
    Abstract This paper presents the architecture of a Convolution Neural Network (CNN) accelerator based on a new processing element (PE) array called a diagonal cyclic array (DCA). As demonstrated, it can significantly reduce the burden of repeated memory accesses for feature data and weight parameters of the CNN models, which maximizes the data reuse rate and improve the computation speed. Furthermore, an integrated computation architecture has been implemented for the activation function, max-pooling, and activation function after convolution calculation, reducing the hardware resource. To evaluate the effectiveness of the proposed architecture, a CNN accelerator has been… More >

  • Open AccessOpen Access

    ARTICLE

    Developed Fall Detection of Elderly Patients in Internet of Healthcare Things

    Omar Reyad1,2, Hazem Ibrahim Shehata1,3, Mohamed Esmail Karar1,4,*
    CMC-Computers, Materials & Continua, Vol.76, No.2, pp. 1689-1700, 2023, DOI:10.32604/cmc.2023.039084
    (This article belongs to the Special Issue: Intelligent Computational Models based on Machine Learning and Deep Learning for Diagnosis System)
    Abstract Falling is among the most harmful events older adults may encounter. With the continuous growth of the aging population in many societies, developing effective fall detection mechanisms empowered by machine learning technologies and easily integrable with existing healthcare systems becomes essential. This paper presents a new healthcare Internet of Health Things (IoHT) architecture built around an ensemble machine learning-based fall detection system (FDS) for older people. Compared to deep neural networks, the ensemble multi-stage random forest model allows the extraction of an optimal subset of fall detection features with minimal hyperparameters. The number of cascaded… More >

  • Open AccessOpen Access

    ARTICLE

    Explainable Artificial Intelligence-Based Model Drift Detection Applicable to Unsupervised Environments

    Yongsoo Lee, Yeeun Lee, Eungyu Lee, Taejin Lee*
    CMC-Computers, Materials & Continua, Vol.76, No.2, pp. 1701-1719, 2023, DOI:10.32604/cmc.2023.040235
    Abstract Cybersecurity increasingly relies on machine learning (ML) models to respond to and detect attacks. However, the rapidly changing data environment makes model life-cycle management after deployment essential. Real-time detection of drift signals from various threats is fundamental for effectively managing deployed models. However, detecting drift in unsupervised environments can be challenging. This study introduces a novel approach leveraging Shapley additive explanations (SHAP), a widely recognized explainability technique in ML, to address drift detection in unsupervised settings. The proposed method incorporates a range of plots and statistical techniques to enhance drift detection reliability and introduces a… More >

  • Open AccessOpen Access

    ARTICLE

    Fusion of Feature Ranking Methods for an Effective Intrusion Detection System

    Seshu Bhavani Mallampati1, Seetha Hari2,*
    CMC-Computers, Materials & Continua, Vol.76, No.2, pp. 1721-1744, 2023, DOI:10.32604/cmc.2023.040567
    Abstract Expanding internet-connected services has increased cyberattacks, many of which have grave and disastrous repercussions. An Intrusion Detection System (IDS) plays an essential role in network security since it helps to protect the network from vulnerabilities and attacks. Although extensive research was reported in IDS, detecting novel intrusions with optimal features and reducing false alarm rates are still challenging. Therefore, we developed a novel fusion-based feature importance method to reduce the high dimensional feature space, which helps to identify attacks accurately with less false alarm rate. Initially, to improve training data quality, various preprocessing techniques are… More >

  • Open AccessOpen Access

    ARTICLE

    PLDMLT: Multi-Task Learning of Diabetic Retinopathy Using the Pixel-Level Labeled Fundus Images

    Hengyang Liu, Chuncheng Huang*
    CMC-Computers, Materials & Continua, Vol.76, No.2, pp. 1745-1761, 2023, DOI:10.32604/cmc.2023.040710
    (This article belongs to the Special Issue: Recent Advances in Ophthalmic Diseases Diagnosis using AI)
    Abstract In the field of medical images, pixel-level labels are time-consuming and expensive to acquire, while image-level labels are relatively easier to obtain. Therefore, it makes sense to learn more information (knowledge) from a small number of hard-to-get pixel-level annotated images to apply to different tasks to maximize their usefulness and save time and training costs. In this paper, using Pixel-Level Labeled Images for Multi-Task Learning (PLDMLT), we focus on grading the severity of fundus images for Diabetic Retinopathy (DR). This is because, for the segmentation task, there is a finely labeled mask, while the severity… More >

  • Open AccessOpen Access

    ARTICLE

    Classification of Human Protein in Multiple Cells Microscopy Images Using CNN

    Lina Al-joudi, Muhammad Arif*
    CMC-Computers, Materials & Continua, Vol.76, No.2, pp. 1763-1780, 2023, DOI:10.32604/cmc.2023.039413
    Abstract The subcellular localization of human proteins is vital for understanding the structure of human cells. Proteins play a significant role within human cells, as many different groups of proteins are located in a specific location to perform a particular function. Understanding these functions will help in discovering many diseases and developing their treatments. The importance of imaging analysis techniques, specifically in proteomics research, is becoming more prevalent. Despite recent advances in deep learning techniques for analyzing microscopy images, classification models have faced critical challenges in achieving high performance. Most protein subcellular images have a significant… More >

  • Open AccessOpen Access

    ARTICLE

    Regional Economic Development Trend Prediction Method Based on Digital Twins and Time Series Network

    Runguo Xu*, Xuehan Yu, Xiaoxue Zhao
    CMC-Computers, Materials & Continua, Vol.76, No.2, pp. 1781-1796, 2023, DOI:10.32604/cmc.2023.037293
    Abstract At present, the interpretation of regional economic development (RED) has changed from a simple evaluation of economic growth to a focus on economic growth and the optimization of economic structure, the improvement of economic relations, and the change of institutional innovation. This article uses the RED trend as the research object and constructs the RED index to conduct the theoretical analysis. Then this paper uses the attention mechanism based on digital twins and the time series network model to verify the actual data. Finally, the regional economy is predicted according to the theoretical model. The… More >

  • Open AccessOpen Access

    ARTICLE

    Enhancement of UAV Data Security and Privacy via Ethereum Blockchain Technology

    Sur Singh Rawat1,*, Youseef Alotaibi2, Nitima Malsa1, Vimal Gupta1
    CMC-Computers, Materials & Continua, Vol.76, No.2, pp. 1797-1815, 2023, DOI:10.32604/cmc.2023.039381
    Abstract Unmanned aerial vehicles (UAVs), or drones, have revolutionized a wide range of industries, including monitoring, agriculture, surveillance, and supply chain. However, their widespread use also poses significant challenges, such as public safety, privacy, and cybersecurity. Cyberattacks, targeting UAVs have become more frequent, which highlights the need for robust security solutions. Blockchain technology, the foundation of cryptocurrencies has the potential to address these challenges. This study suggests a platform that utilizes blockchain technology to manage drone operations securely and confidentially. By incorporating blockchain technology, the proposed method aims to increase the security and privacy of drone… More >

  • Open AccessOpen Access

    ARTICLE

    Deep Facial Emotion Recognition Using Local Features Based on Facial Landmarks for Security System

    Youngeun An, Jimin Lee, EunSang Bak*, Sungbum Pan*
    CMC-Computers, Materials & Continua, Vol.76, No.2, pp. 1817-1832, 2023, DOI:10.32604/cmc.2023.039460
    (This article belongs to the Special Issue: Advances in Information Security Application)
    Abstract Emotion recognition based on facial expressions is one of the most critical elements of human-machine interfaces. Most conventional methods for emotion recognition using facial expressions use the entire facial image to extract features and then recognize specific emotions through a pre-trained model. In contrast, this paper proposes a novel feature vector extraction method using the Euclidean distance between the landmarks changing their positions according to facial expressions, especially around the eyes, eyebrows, nose, and mouth. Then, we apply a new classifier using an ensemble network to increase emotion recognition accuracy. The emotion recognition performance was More >

  • Open AccessOpen Access

    ARTICLE

    Fully Automated Density-Based Clustering Method

    Bilal Bataineh*, Ahmad A. Alzahrani
    CMC-Computers, Materials & Continua, Vol.76, No.2, pp. 1833-1851, 2023, DOI:10.32604/cmc.2023.039923
    Abstract Cluster analysis is a crucial technique in unsupervised machine learning, pattern recognition, and data analysis. However, current clustering algorithms suffer from the need for manual determination of parameter values, low accuracy, and inconsistent performance concerning data size and structure. To address these challenges, a novel clustering algorithm called the fully automated density-based clustering method (FADBC) is proposed. The FADBC method consists of two stages: parameter selection and cluster extraction. In the first stage, a proposed method extracts optimal parameters for the dataset, including the epsilon size and a minimum number of points thresholds. These parameters More >

  • Open AccessOpen Access

    ARTICLE

    Priority Detector and Classifier Techniques Based on ML for the IoMT

    Rayan A. Alsemmeari1,*, Mohamed Yehia Dahab2, Badraddin Alturki1, Abdulaziz A. Alsulami3
    CMC-Computers, Materials & Continua, Vol.76, No.2, pp. 1853-1870, 2023, DOI:10.32604/cmc.2023.038589
    Abstract Emerging telemedicine trends, such as the Internet of Medical Things (IoMT), facilitate regular and efficient interactions between medical devices and computing devices. The importance of IoMT comes from the need to continuously monitor patients’ health conditions in real-time during normal daily activities, which is realized with the help of various wearable devices and sensors. One major health problem is workplace stress, which can lead to cardiovascular disease or psychiatric disorders. Therefore, real-time monitoring of employees’ stress in the workplace is essential. Stress levels and the source of stress could be detected early in the fog… More >

  • Open AccessOpen Access

    ARTICLE

    Blockchain-Based Secure and Fair IoT Data Trading System with Bilateral Authorization

    Youngho Park1, Mi Hyeon Jeon2, Sang Uk Shin3,*
    CMC-Computers, Materials & Continua, Vol.76, No.2, pp. 1871-1890, 2023, DOI:10.32604/cmc.2023.039462
    (This article belongs to the Special Issue: Advances in Information Security Application)
    Abstract These days, data is regarded as a valuable asset in the era of the data economy, which demands a trading platform for buying and selling data. However, online data trading poses challenges in terms of security and fairness because the seller and the buyer may not fully trust each other. Therefore, in this paper, a blockchain-based secure and fair data trading system is proposed by taking advantage of the smart contract and matchmaking encryption. The proposed system enables bilateral authorization, where data trading between a seller and a buyer is accomplished only if their policies,… More >

  • Open AccessOpen Access

    ARTICLE

    CNN-Based RF Fingerprinting Method for Securing Passive Keyless Entry and Start System

    Hyeon Park1, SeoYeon Kim2, Seok Min Ko1, TaeGuen Kim2,*
    CMC-Computers, Materials & Continua, Vol.76, No.2, pp. 1891-1909, 2023, DOI:10.32604/cmc.2023.039464
    (This article belongs to the Special Issue: Advances in Information Security Application)
    Abstract The rapid growth of modern vehicles with advanced technologies requires strong security to ensure customer safety. One key system that needs protection is the passive key entry system (PKES). To prevent attacks aimed at defeating the PKES, we propose a novel radio frequency (RF) fingerprinting method. Our method extracts the cepstral coefficient feature as a fingerprint of a radio frequency signal. This feature is then analyzed using a convolutional neural network (CNN) for device identification. In evaluation, we conducted experiments to determine the effectiveness of different cepstral coefficient features and the convolutional neural network-based model. More >

  • Open AccessOpen Access

    ARTICLE

    A Novel S-Box Generation Methodology Based on the Optimized GAN Model

    Runlian Zhang1,*, Rui Shu1, Yongzhuang Wei1, Hailong Zhang2, Xiaonian Wu1
    CMC-Computers, Materials & Continua, Vol.76, No.2, pp. 1911-1927, 2023, DOI:10.32604/cmc.2023.041187
    Abstract S-boxes can be the core component of block ciphers, and how to efficiently generate S-boxes with strong cryptographic properties appears to be an important task in the design of block ciphers. In this work, an optimized model based on the generative adversarial network (GAN) is proposed to generate 8-bit S-boxes. The central idea of this optimized model is to use loss function constraints for GAN. More specially, the Advanced Encryption Standard (AES) S-box is used to construct the sample dataset via the affine equivalence property. Then, three models are respectively built and cross-trained to generate… More >

  • Open AccessOpen Access

    ARTICLE

    A Lightweight ABE Security Protection Scheme in Cloud Environment Based on Attribute Weight

    Lihong Guo*, Jie Yang, Haitao Wu
    CMC-Computers, Materials & Continua, Vol.76, No.2, pp. 1929-1946, 2023, DOI:10.32604/cmc.2023.039170
    Abstract Attribute-based encryption (ABE) is a technique used to encrypt data, it has the flexibility of access control, high security, and resistance to collusion attacks, and especially it is used in cloud security protection. However, a large number of bilinear mappings are used in ABE, and the calculation of bilinear pairing is time-consuming. So there is the problem of low efficiency. On the other hand, the decryption key is not uniquely associated with personal identification information, if the decryption key is maliciously sold, ABE is unable to achieve accountability for the user. In practical applications, shared… More >

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    ARTICLE

    Hyper-Tuned Convolutional Neural Networks for Authorship Verification in Digital Forensic Investigations

    Asif Rahim1, Yanru Zhong2, Tariq Ahmad3,*, Sadique Ahmad4,*, Mohammed A. ElAffendi4
    CMC-Computers, Materials & Continua, Vol.76, No.2, pp. 1947-1976, 2023, DOI:10.32604/cmc.2023.039340
    Abstract Authorship verification is a crucial task in digital forensic investigations, where it is often necessary to determine whether a specific individual wrote a particular piece of text. Convolutional Neural Networks (CNNs) have shown promise in solving this problem, but their performance highly depends on the choice of hyperparameters. In this paper, we explore the effectiveness of hyperparameter tuning in improving the performance of CNNs for authorship verification. We conduct experiments using a Hyper Tuned CNN model with three popular optimization algorithms: Adaptive Moment Estimation (ADAM), Stochastic Gradient Descent (SGD), and Root Mean Squared Propagation (RMSPROP).… More >

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    ARTICLE

    A Positive Influence Maximization Algorithm in Signed Social Networks

    Wenlong Zhu1,2,*, Yang Huang1, Shuangshuang Yang3, Yu Miao1, Chongyuan Peng1
    CMC-Computers, Materials & Continua, Vol.76, No.2, pp. 1977-1994, 2023, DOI:10.32604/cmc.2023.040998
    Abstract The influence maximization (IM) problem aims to find a set of seed nodes that maximizes the spread of their influence in a social network. The positive influence maximization (PIM) problem is an extension of the IM problem, which consider the polar relation of nodes in signed social networks so that the positive influence of seeds can be the most widely spread. To solve the PIM problem, this paper proposes the polar and decay related independent cascade (IC-PD) model to simulate the influence propagation of nodes and the decay of information during the influence propagation in… More >

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    ARTICLE

    Eye-Tracking Based Autism Spectrum Disorder Diagnosis Using Chaotic Butterfly Optimization with Deep Learning Model

    Tamilvizhi Thanarajan1, Youseef Alotaibi2, Surendran Rajendran3,*, Krishnaraj Nagappan4
    CMC-Computers, Materials & Continua, Vol.76, No.2, pp. 1995-2013, 2023, DOI:10.32604/cmc.2023.039644
    Abstract Autism spectrum disorder (ASD) can be defined as a neurodevelopmental condition or illness that can disturb kids who have heterogeneous characteristics, like changes in behavior, social disabilities, and difficulty communicating with others. Eye tracking (ET) has become a useful method to detect ASD. One vital aspect of moral erudition is the aptitude to have common visual attention. The eye-tracking approach offers valuable data regarding the visual behavior of children for accurate and early detection. Eye-tracking data can offer insightful information about the behavior and thought processes of people with ASD, but it is important to… More >

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    ARTICLE

    Underwater Waste Recognition and Localization Based on Improved YOLOv5

    Jinxing Niu1,*, Shaokui Gu1, Junmin Du2, Yongxing Hao1
    CMC-Computers, Materials & Continua, Vol.76, No.2, pp. 2015-2031, 2023, DOI:10.32604/cmc.2023.040489
    Abstract With the continuous development of the economy and society, plastic pollution in rivers, lakes, oceans, and other bodies of water is increasingly severe, posing a serious challenge to underwater ecosystems. Effective cleaning up of underwater litter by robots relies on accurately identifying and locating the plastic waste. However, it often causes significant challenges such as noise interference, low contrast, and blurred textures in underwater optical images. A weighted fusion-based algorithm for enhancing the quality of underwater images is proposed, which combines weighted logarithmic transformations, adaptive gamma correction, improved multi-scale Retinex (MSR) algorithm, and the contrast… More >

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    ARTICLE

    Asymmetric Key Cryptosystem for Image Encryption by Elliptic Curve over Galois Field

    Mohammad Mazyad Hazzazi1, Hafeez Ur Rehman2,*, Tariq Shah2, Hajra Younas2
    CMC-Computers, Materials & Continua, Vol.76, No.2, pp. 2033-2060, 2023, DOI:10.32604/cmc.2023.040629
    Abstract Protecting the integrity and secrecy of digital data transmitted through the internet is a growing problem. In this paper, we introduce an asymmetric key algorithm for specifically processing images with larger bit values. To overcome the separate flaws of elliptic curve cryptography (ECC) and the Hill cipher (HC), we present an approach to picture encryption by combining these two encryption approaches. In addition, to strengthen our scheme, the group laws are defined over the rational points of a given elliptic curve (EC) over a Galois field (GF). The exclusive-or (XOR) function is used instead of More >

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    ARTICLE

    An Efficient Cyber Security and Intrusion Detection System Using CRSR with PXORP-ECC and LTH-CNN

    Nouf Saeed Alotaibi*
    CMC-Computers, Materials & Continua, Vol.76, No.2, pp. 2061-2078, 2023, DOI:10.32604/cmc.2023.039446
    (This article belongs to the Special Issue: Advances in Information Security Application)
    Abstract Intrusion Detection System (IDS) is a network security mechanism that analyses all users’ and applications’ traffic and detects malicious activities in real-time. The existing IDS methods suffer from lower accuracy and lack the required level of security to prevent sophisticated attacks. This problem can result in the system being vulnerable to attacks, which can lead to the loss of sensitive data and potential system failure. Therefore, this paper proposes an Intrusion Detection System using Logistic Tanh-based Convolutional Neural Network Classification (LTH-CNN). Here, the Correlation Coefficient based Mayfly Optimization (CC-MA) algorithm is used to extract the… More >

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    ARTICLE

    A Deep CNN-LSTM-Based Feature Extraction for Cyber-Physical System Monitoring

    Alaa Omran Almagrabi*
    CMC-Computers, Materials & Continua, Vol.76, No.2, pp. 2079-2093, 2023, DOI:10.32604/cmc.2023.039683
    Abstract A potential concept that could be effective for multiple applications is a “cyber-physical system” (CPS). The Internet of Things (IoT) has evolved as a research area, presenting new challenges in obtaining valuable data through environmental monitoring. The existing work solely focuses on classifying the audio system of CPS without utilizing feature extraction. This study employs a deep learning method, CNN-LSTM, and two-way feature extraction to classify audio systems within CPS. The primary objective of this system, which is built upon a convolutional neural network (CNN) with Long Short Term Memory (LSTM), is to analyze the… More >

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    ARTICLE

    MBB-IoT: Construction and Evaluation of IoT DDoS Traffic Dataset from a New Perspective

    Yi Qing1, Xiangyu Liu2, Yanhui Du2,*
    CMC-Computers, Materials & Continua, Vol.76, No.2, pp. 2095-2119, 2023, DOI:10.32604/cmc.2023.039980
    Abstract Distributed Denial of Service (DDoS) attacks have always been a major concern in the security field. With the release of malware source codes such as BASHLITE and Mirai, Internet of Things (IoT) devices have become the new source of DDoS attacks against many Internet applications. Although there are many datasets in the field of IoT intrusion detection, such as Bot-IoT, Constrained Application Protocol–Denial of Service (CoAP-DoS), and LATAM-DDoS-IoT (some of the names of DDoS datasets), which mainly focus on DDoS attacks, the datasets describing new IoT DDoS attack scenarios are extremely rare, and only N-BaIoT… More >

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    ARTICLE

    AID4I: An Intrusion Detection Framework for Industrial Internet of Things Using Automated Machine Learning

    Anıl Sezgin1,2,*, Aytuğ Boyacı3
    CMC-Computers, Materials & Continua, Vol.76, No.2, pp. 2121-2143, 2023, DOI:10.32604/cmc.2023.040287
    Abstract By identifying and responding to any malicious behavior that could endanger the system, the Intrusion Detection System (IDS) is crucial for preserving the security of the Industrial Internet of Things (IIoT) network. The benefit of anomaly-based IDS is that they are able to recognize zero-day attacks due to the fact that they do not rely on a signature database to identify abnormal activity. In order to improve control over datasets and the process, this study proposes using an automated machine learning (AutoML) technique to automate the machine learning processes for IDS. Our ground-breaking architecture, known… More >

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    ARTICLE

    Multi-Model Fusion Framework Using Deep Learning for Visual-Textual Sentiment Classification

    Israa K. Salman Al-Tameemi1,3, Mohammad-Reza Feizi-Derakhshi1,*, Saeed Pashazadeh2, Mohammad Asadpour2
    CMC-Computers, Materials & Continua, Vol.76, No.2, pp. 2145-2177, 2023, DOI:10.32604/cmc.2023.040997
    Abstract Multimodal Sentiment Analysis (SA) is gaining popularity due to its broad application potential. The existing studies have focused on the SA of single modalities, such as texts or photos, posing challenges in effectively handling social media data with multiple modalities. Moreover, most multimodal research has concentrated on merely combining the two modalities rather than exploring their complex correlations, leading to unsatisfactory sentiment classification results. Motivated by this, we propose a new visual-textual sentiment classification model named Multi-Model Fusion (MMF), which uses a mixed fusion framework for SA to effectively capture the essential information and the… More >

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    ARTICLE

    An IoT-Based Aquaculture Monitoring System Using Firebase

    Wen-Tsai Sung1, Indra Griha Tofik Isa1,2, Sung-Jung Hsiao3,*
    CMC-Computers, Materials & Continua, Vol.76, No.2, pp. 2179-2200, 2023, DOI:10.32604/cmc.2023.041022
    (This article belongs to the Special Issue: Development and Industrial Application of AI Technologies)
    Abstract Indonesia is a producer in the fisheries sector, with production reaching 14.8 million tons in 2022. The production potential of the fisheries sector can be optimally optimized through aquaculture management. One of the most important issues in aquaculture management is how to efficiently control the fish pond water conditions. IoT technology can be applied to support a fish pond aquaculture monitoring system, especially for catfish species (Siluriformes), in real-time and remotely. One of the technologies that can provide this convenience is the IoT. The problem of this study is how to integrate IoT devices with Firebase’s… More >

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    ARTICLE

    SNSVM: SqueezeNet-Guided SVM for Breast Cancer Diagnosis

    Jiaji Wang1, Muhammad Attique Khan2, Shuihua Wang1,3, Yudong Zhang1,3,*
    CMC-Computers, Materials & Continua, Vol.76, No.2, pp. 2201-2216, 2023, DOI:10.32604/cmc.2023.041191
    (This article belongs to the Special Issue: Telehealth Monitoring with Man-Computer Interface for Medical Processing)
    Abstract Breast cancer is a major public health concern that affects women worldwide. It is a leading cause of cancer-related deaths among women, and early detection is crucial for successful treatment. Unfortunately, breast cancer can often go undetected until it has reached advanced stages, making it more difficult to treat. Therefore, there is a pressing need for accurate and efficient diagnostic tools to detect breast cancer at an early stage. The proposed approach utilizes SqueezeNet with fire modules and complex bypass to extract informative features from mammography images. The extracted features are then utilized to train… More >

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    ARTICLE

    Redundant Transmission Control Algorithm for Information-Centric Vehicular IoT Networks

    Abdur Rashid Sangi1,*, Satish Anamalamudi2, Mohammed S. Alkatheiri3, Murali Krishna Enduri2, Anil Carie2, Mohammed A. Alqarni4
    CMC-Computers, Materials & Continua, Vol.76, No.2, pp. 2217-2234, 2023, DOI:10.32604/cmc.2023.038305
    Abstract Vehicular Adhoc Networks (VANETs) enable vehicles to act as mobile nodes that can fetch, share, and disseminate information about vehicle safety, emergency events, warning messages, and passenger infotainment. However, the continuous dissemination of information from vehicles and their one-hop neighbor nodes, Road Side Units (RSUs), and VANET infrastructures can lead to performance degradation of VANETs in the existing host-centric IP-based network. Therefore, Information Centric Networks (ICN) are being explored as an alternative architecture for vehicular communication to achieve robust content distribution in highly mobile, dynamic, and error-prone domains. In ICN-based Vehicular-IoT networks, consumer mobility is… More >

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    ARTICLE

    A Machine Learning-Based Distributed Denial of Service Detection Approach for Early Warning in Internet Exchange Points

    Salem Alhayani*, Diane R. Murphy
    CMC-Computers, Materials & Continua, Vol.76, No.2, pp. 2235-2259, 2023, DOI:10.32604/cmc.2023.038003
    Abstract The Internet service provider (ISP) is the heart of any country’s Internet infrastructure and plays an important role in connecting to the World Wide Web. Internet exchange point (IXP) allows the interconnection of two or more separate network infrastructures. All Internet traffic entering a country should pass through its IXP. Thus, it is an ideal location for performing malicious traffic analysis. Distributed denial of service (DDoS) attacks are becoming a more serious daily threat. Malicious actors in DDoS attacks control numerous infected machines known as botnets. Botnets are used to send numerous fake requests to… More >

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    ARTICLE

    Improved Transportation Model with Internet of Things Using Artificial Intelligence Algorithm

    Ayman Khallel Al-Ani1,*, Shams Ul Arfeen Laghari2, Hariprasath Manoharan3, Shitharth Selvarajan4, Mueen Uddin5
    CMC-Computers, Materials & Continua, Vol.76, No.2, pp. 2261-2279, 2023, DOI:10.32604/cmc.2023.038534
    Abstract In this paper, the application of transportation systems in real-time traffic conditions is evaluated with data handling representations. The proposed method is designed in such a way as to detect the number of loads that are present in a vehicle where functionality tasks are computed in the system. Compared to the existing approach, the design model in the proposed method is made by dividing the computing areas into several cluster regions, thereby reducing the complex monitoring system where control errors are minimized. Furthermore, a route management technique is combined with Artificial Intelligence (AI) algorithm to More >

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    ARTICLE

    Intermediary RRT*-PSO: A Multi-Directional Hybrid Fast Convergence Sampling-Based Path Planning Algorithm

    Loc Q. Huynh1, Ly V. Tran1, Phuc N. K. Phan1, Zhiqiu Yu2, Son V. T. Dao1,2,*
    CMC-Computers, Materials & Continua, Vol.76, No.2, pp. 2281-2300, 2023, DOI:10.32604/cmc.2023.034872
    Abstract Path planning is a prevalent process that helps mobile robots find the most efficient pathway from the starting position to the goal position to avoid collisions with obstacles. In this paper, we propose a novel path planning algorithm–Intermediary RRT*-PSO-by utilizing the exploring speed advantages of Rapidly exploring Random Trees and using its solution to feed to a metaheuristic-based optimizer, Particle swarm optimization (PSO), for fine-tuning and enhancement. In Phase 1, the start and goal trees are initialized at the starting and goal positions, respectively, and the intermediary tree is initialized at a random unexplored region… More >

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