Home / Journals / CMC / Vol.77, No.2, 2023
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  • Open AccessOpen Access

    ARTICLE

    Machine Learning Design of Aluminum-Lithium Alloys with High Strength

    Hongxia Wang1,2, Zhiqiang Duan2, Qingwei Guo2, Yongmei Zhang1,2,*, Yuhong Zhao2,3,4,*
    CMC-Computers, Materials & Continua, Vol.77, No.2, pp. 1393-1409, 2023, DOI:10.32604/cmc.2023.045871
    Abstract Due to the large unexplored compositional space, long development cycle, and high cost of traditional trial-anderror experiments, designing high strength aluminum-lithium alloys is a great challenge. This work establishes a performance-oriented machine learning design strategy for aluminum-lithium alloys to simplify and shorten the development cycle. The calculation results indicate that radial basis function (RBF) neural networks exhibit better predictive ability than back propagation (BP) neural networks. The RBF neural network predicted tensile and yield strengths with determination coefficients of 0.90 and 0.96, root mean square errors of 30.68 and 25.30, and mean absolute errors of 28.15 and 19.08, respectively. In… More >

  • Open AccessOpen Access

    ARTICLE

    A Bidimensional Finite Element Study of Crack Propagation in Austempered Ductile Iron

    Gustavo von Zeska de França, Roberto Luís de Assumpção, Marco Antonio Luersen*, Carlos Henrique da Silva
    CMC-Computers, Materials & Continua, Vol.77, No.2, pp. 1411-1424, 2023, DOI:10.32604/cmc.2023.043811
    Abstract Austempered ductile iron (ADI) is composed of an ausferritic matrix with graphite nodules and has a wide range of applications because of its high mechanical strength, fatigue resistance, and wear resistance compared to other cast irons. The amount and size of the nodules can be controlled by the chemical composition and austenitizing temperature. As the nodules have lower stiffness than the matrix and can act as stress concentrators, they influence crack propagation. However, the crack propagation mechanism in ADI is not yet fully understood. In this study, we describe a numerical investigation of crack propagation in ADIs subjected to cyclic… More >

  • Open AccessOpen Access

    ARTICLE

    Phase-Field Simulation of δ Hydride Precipitation with Interfacial Anisotropy

    Hailong Nie1, Xincheng Shi1, Wenkui Yang1, Kaile Wang1, Yuhong Zhao2,1,3,*
    CMC-Computers, Materials & Continua, Vol.77, No.2, pp. 1425-1443, 2023, DOI:10.32604/cmc.2023.044510
    Abstract Previous studies of hydride in zirconium alloys have mainly assumed an isotropic interface. In practice, the difference in crystal structure at the interface between the matrix phase and the precipitate phase results in an anisotropic interface. With the purpose of probing the real evolution of hydrides, this paper couples an anisotropy function in the interfacial energy and interfacial mobility. The influence of anisotropic interfacial energy and interfacial mobility on the morphology of hydride precipitation was investigated using the phase-field method. The results show that the isotropy hydride precipitates a slate-like morphology, and the anisotropic hydride precipitates at the semi-coherent and… More >

  • Open AccessOpen Access

    ARTICLE

    A Machine Learning-Based Attack Detection and Prevention System in Vehicular Named Data Networking

    Arif Hussain Magsi1,*, Ali Ghulam2, Saifullah Memon1, Khalid Javeed3, Musaed Alhussein4, Imad Rida5
    CMC-Computers, Materials & Continua, Vol.77, No.2, pp. 1445-1465, 2023, DOI:10.32604/cmc.2023.040290
    (This article belongs to this Special Issue: Innovations in Pervasive Computing and Communication Technologies)
    Abstract Named Data Networking (NDN) is gaining a significant attention in Vehicular Ad-hoc Networks (VANET) due to its in-network content caching, name-based routing, and mobility-supporting characteristics. Nevertheless, existing NDN faces three significant challenges, including security, privacy, and routing. In particular, security attacks, such as Content Poisoning Attacks (CPA), can jeopardize legitimate vehicles with malicious content. For instance, attacker host vehicles can serve consumers with invalid information, which has dire consequences, including road accidents. In such a situation, trust in the content-providing vehicles brings a new challenge. On the other hand, ensuring privacy and preventing unauthorized access in vehicular (VNDN) is another… More >

  • Open AccessOpen Access

    ARTICLE

    Cross-Domain Authentication Scheme Based on Blockchain and Consistent Hash Algorithm for System-Wide Information Management

    Lizhe Zhang1,2,*, Yongqiang Huang2, Jia Nie2, Kenian Wang1,2
    CMC-Computers, Materials & Continua, Vol.77, No.2, pp. 1467-1488, 2023, DOI:10.32604/cmc.2023.042305
    Abstract System-wide information management (SWIM) is a complex distributed information transfer and sharing system for the next generation of Air Transportation System (ATS). In response to the growing volume of civil aviation air operations, users accessing different authentication domains in the SWIM system have problems with the validity, security, and privacy of SWIM-shared data. In order to solve these problems, this paper proposes a SWIM cross-domain authentication scheme based on a consistent hashing algorithm on consortium blockchain and designs a blockchain certificate format for SWIM cross-domain authentication. The scheme uses a consistent hash algorithm with virtual nodes in combination with a… More >

  • Open AccessOpen Access

    ARTICLE

    Visualization for Explanation of Deep Learning-Based Fault Diagnosis Model Using Class Activation Map

    Youming Guo, Qinmu Wu*
    CMC-Computers, Materials & Continua, Vol.77, No.2, pp. 1489-1514, 2023, DOI:10.32604/cmc.2023.042313
    (This article belongs to this Special Issue: Advances and Applications in Signal, Image and Video Processing)
    Abstract Permanent magnet synchronous motor (PMSM) is widely used in various production processes because of its high efficiency, fast reaction time, and high power density. With the continuous promotion of new energy vehicles, timely detection of PMSM faults can significantly reduce the accident rate of new energy vehicles, further enhance consumers’ trust in their safety, and thus promote their popularity. Existing fault diagnosis methods based on deep learning can only distinguish different PMSM faults and cannot interpret and analyze them. Convolutional neural networks (CNN) show remarkable accuracy in image data analysis. However, due to the “black box” problem in deep learning… More >

  • Open AccessOpen Access

    ARTICLE

    Diagnosis of Autism Spectrum Disorder by Imperialistic Competitive Algorithm and Logistic Regression Classifier

    Shabana R. Ziyad1,*, Liyakathunisa2, Eman Aljohani2, I. A. Saeed3
    CMC-Computers, Materials & Continua, Vol.77, No.2, pp. 1515-1534, 2023, DOI:10.32604/cmc.2023.040874
    Abstract Autism spectrum disorder (ASD), classified as a developmental disability, is now more common in children than ever. A drastic increase in the rate of autism spectrum disorder in children worldwide demands early detection of autism in children. Parents can seek professional help for a better prognosis of the child’s therapy when ASD is diagnosed under five years. This research study aims to develop an automated tool for diagnosing autism in children. The computer-aided diagnosis tool for ASD detection is designed and developed by a novel methodology that includes data acquisition, feature selection, and classification phases. The most deterministic features are… More >

  • Open AccessOpen Access

    ARTICLE

    Design Optimization of Permanent Magnet Eddy Current Coupler Based on an Intelligence Algorithm

    Dazhi Wang*, Pengyi Pan, Bowen Niu
    CMC-Computers, Materials & Continua, Vol.77, No.2, pp. 1535-1555, 2023, DOI:10.32604/cmc.2023.042286
    (This article belongs to this Special Issue: Intelligent Computing Techniques and Their Real Life Applications)
    Abstract The permanent magnet eddy current coupler (PMEC) solves the problem of flexible connection and speed regulation between the motor and the load and is widely used in electrical transmission systems. It provides torque to the load and generates heat and losses, reducing its energy transfer efficiency. This issue has become an obstacle for PMEC to develop toward a higher power. This paper aims to improve the overall performance of PMEC through multi-objective optimization methods. Firstly, a PMEC modeling method based on the Levenberg-Marquardt back propagation (LMBP) neural network is proposed, aiming at the characteristics of the complex input-output relationship and… More >

  • Open AccessOpen Access

    ARTICLE

    A Novel Human Interaction Framework Using Quadratic Discriminant Analysis with HMM

    Tanvir Fatima Naik Bukht1, Naif Al Mudawi2, Saud S. Alotaibi3, Abdulwahab Alazeb2, Mohammed Alonazi4, Aisha Ahmed AlArfaj5, Ahmad Jalal1, Jaekwang Kim6,*
    CMC-Computers, Materials & Continua, Vol.77, No.2, pp. 1557-1573, 2023, DOI:10.32604/cmc.2023.041335
    (This article belongs to this Special Issue: Intelligent Computing Techniques and Their Real Life Applications)
    Abstract Human-human interaction recognition is crucial in computer vision fields like surveillance, human-computer interaction, and social robotics. It enhances systems’ ability to interpret and respond to human behavior precisely. This research focuses on recognizing human interaction behaviors using a static image, which is challenging due to the complexity of diverse actions. The overall purpose of this study is to develop a robust and accurate system for human interaction recognition. This research presents a novel image-based human interaction recognition method using a Hidden Markov Model (HMM). The technique employs hue, saturation, and intensity (HSI) color transformation to enhance colors in video frames,… More >

  • Open AccessOpen Access

    ARTICLE

    Data Analysis of Network Parameters for Secure Implementations of SDN-Based Firewall

    Rizwan Iqbal1,*, Rashid Hussain2, Sheeraz Arif3, Nadia Mustaqim Ansari4, Tayyab Ahmed Shaikh2
    CMC-Computers, Materials & Continua, Vol.77, No.2, pp. 1575-1598, 2023, DOI:10.32604/cmc.2023.042432
    (This article belongs to this Special Issue: Software-defined Internet-of-Vehicles (SD-IoV) leveraging AI, 5G and NFV)
    Abstract Software-Defined Networking (SDN) is a new network technology that uses programming to complement the data plane with a control plane. To enable safe connection, however, numerous security challenges must be addressed. Flooding attacks have been one of the most prominent risks on the internet for decades, and they are now becoming challenging difficulties in SDN networks. To solve these challenges, we proposed a unique firewall application built on multiple levels of packet filtering to provide a flooding attack prevention system and a layer-based packet detection system. This study offers a systematic strategy for wrapping up the examination of SDN operations.… More >

  • Open AccessOpen Access

    ARTICLE

    A Deep Learning Based Sentiment Analytic Model for the Prediction of Traffic Accidents

    Nadeem Malik1,*, Saud Altaf1, Muhammad Usman Tariq2, Ashir Ahmed3, Muhammad Babar4
    CMC-Computers, Materials & Continua, Vol.77, No.2, pp. 1599-1615, 2023, DOI:10.32604/cmc.2023.040455
    (This article belongs to this Special Issue: The Next Generation of Artificial Intelligence and the Intelligent Internet of Things)
    Abstract The severity of traffic accidents is a serious global concern, particularly in developing nations. Knowing the main causes and contributing circumstances may reduce the severity of traffic accidents. There exist many machine learning models and decision support systems to predict road accidents by using datasets from different social media forums such as Twitter, blogs and Facebook. Although such approaches are popular, there exists an issue of data management and low prediction accuracy. This article presented a deep learning-based sentiment analytic model known as Extra-large Network Bi-directional long short term memory (XLNet-Bi-LSTM) to predict traffic collisions based on data collected from… More >

  • Open AccessOpen Access

    ARTICLE

    VGWO: Variant Grey Wolf Optimizer with High Accuracy and Low Time Complexity

    Junqiang Jiang1,2, Zhifang Sun1, Xiong Jiang1, Shengjie Jin1, Yinli Jiang3, Bo Fan1,*
    CMC-Computers, Materials & Continua, Vol.77, No.2, pp. 1617-1644, 2023, DOI:10.32604/cmc.2023.041973
    Abstract The grey wolf optimizer (GWO) is a swarm-based intelligence optimization algorithm by simulating the steps of searching, encircling, and attacking prey in the process of wolf hunting. Along with its advantages of simple principle and few parameters setting, GWO bears drawbacks such as low solution accuracy and slow convergence speed. A few recent advanced GWOs are proposed to try to overcome these disadvantages. However, they are either difficult to apply to large-scale problems due to high time complexity or easily lead to early convergence. To solve the abovementioned issues, a high-accuracy variable grey wolf optimizer (VGWO) with low time complexity… More >

  • Open AccessOpen Access

    ARTICLE

    Digital Image Encryption Algorithm Based on Double Chaotic Map and LSTM

    Luoyin Feng1,*, Jize Du2, Chong Fu1
    CMC-Computers, Materials & Continua, Vol.77, No.2, pp. 1645-1662, 2023, DOI:10.32604/cmc.2023.042630
    (This article belongs to this Special Issue: Advanced Implications of Fuzzy Logic Evolutionary Computation)
    Abstract In the era of network communication, digital image encryption (DIE) technology is critical to ensure the security of image data. However, there has been limited research on combining deep learning neural networks with chaotic mapping for the encryption of digital images. So, this paper addresses this gap by studying the generation of pseudo-random sequences (PRS) chaotic signals using dual logistic chaotic maps. These signals are then predicted using long and short-term memory (LSTM) networks, resulting in the reconstruction of a new chaotic signal. During the research process, it was discovered that there are numerous training parameters associated with the LSTM… More >

  • Open AccessOpen Access

    ARTICLE

    Electromyogram Based Personal Recognition Using Attention Mechanism for IoT Security

    Jin Su Kim, Sungbum Pan*
    CMC-Computers, Materials & Continua, Vol.77, No.2, pp. 1663-1678, 2023, DOI:10.32604/cmc.2023.043998
    (This article belongs to this Special Issue: Innovative Security for the Next Generation Mobile Communication and Internet Systems)
    Abstract As Internet of Things (IoT) technology develops, integrating network functions into diverse equipment introduces new challenges, particularly in dealing with counterfeit issues. Over the past few decades, research efforts have focused on leveraging electromyogram (EMG) for personal recognition, aiming to address security concerns. However, obtaining consistent EMG signals from the same individual is inherently challenging, resulting in data irregularity issues and consequently decreasing the accuracy of personal recognition. Notably, conventional studies in EMG-based personal recognition have overlooked the issue of data irregularities. This paper proposes an innovative approach to personal recognition that combines a siamese fusion network with an auxiliary… More >

  • Open AccessOpen Access

    ARTICLE

    A Fully Adaptive Active Queue Management Method for Congestion Prevention at the Router Buffer

    Ali Alshahrani1, Ahmad Adel Abu-Shareha2,*, Qusai Y. Shambour2, Basil Al-Kasasbeh1
    CMC-Computers, Materials & Continua, Vol.77, No.2, pp. 1679-1698, 2023, DOI:10.32604/cmc.2023.043545
    Abstract Active queue management (AQM) methods manage the queued packets at the router buffer, prevent buffer congestion, and stabilize the network performance. The bursty nature of the traffic passing by the network routers and the slake behavior of the existing AQM methods leads to unnecessary packet dropping. This paper proposes a fully adaptive active queue management (AAQM) method to maintain stable network performance, avoid congestion and packet loss, and eliminate unnecessary packet dropping. The proposed AAQM method is based on load and queue length indicators and uses an adaptive mechanism to adjust the dropping probability based on the buffer status. The… More >

  • Open AccessOpen Access

    ARTICLE

    Interactive Transformer for Small Object Detection

    Jian Wei, Qinzhao Wang*, Zixu Zhao
    CMC-Computers, Materials & Continua, Vol.77, No.2, pp. 1699-1717, 2023, DOI:10.32604/cmc.2023.044284
    Abstract The detection of large-scale objects has achieved high accuracy, but due to the low peak signal to noise ratio (PSNR), fewer distinguishing features, and ease of being occluded by the surroundings, the detection of small objects, however, does not enjoy similar success. Endeavor to solve the problem, this paper proposes an attention mechanism based on cross-Key values. Based on the traditional transformer, this paper first improves the feature processing with the convolution module, effectively maintaining the local semantic context in the middle layer, and significantly reducing the number of parameters of the model. Then, to enhance the effectiveness of the… More >

  • Open AccessOpen Access

    ARTICLE

    The Entity Relationship Extraction Method Using Improved RoBERTa and Multi-Task Learning

    Chaoyu Fan*
    CMC-Computers, Materials & Continua, Vol.77, No.2, pp. 1719-1738, 2023, DOI:10.32604/cmc.2023.041395
    (This article belongs to this Special Issue: Advanced Implications of Fuzzy Logic Evolutionary Computation)
    Abstract There is a growing amount of data uploaded to the internet every day and it is important to understand the volume of those data to find a better scheme to process them. However, the volume of internet data is beyond the processing capabilities of the current internet infrastructure. Therefore, engineering works using technology to organize and analyze information and extract useful information are interesting in both industry and academia. The goal of this paper is to explore the entity relationship based on deep learning, introduce semantic knowledge by using the prepared language model, develop an advanced entity relationship information extraction… More >

  • Open AccessOpen Access

    ARTICLE

    Flexible Global Aggregation and Dynamic Client Selection for Federated Learning in Internet of Vehicles

    Tariq Qayyum1, Zouheir Trabelsi1,*, Asadullah Tariq1, Muhammad Ali2, Kadhim Hayawi3, Irfan Ud Din4
    CMC-Computers, Materials & Continua, Vol.77, No.2, pp. 1739-1757, 2023, DOI:10.32604/cmc.2023.043684
    (This article belongs to this Special Issue: Advanced Artificial Intelligence and Machine Learning Frameworks for Signal and Image Processing Applications)
    Abstract Federated Learning (FL) enables collaborative and privacy-preserving training of machine learning models within the Internet of Vehicles (IoV) realm. While FL effectively tackles privacy concerns, it also imposes significant resource requirements. In traditional FL, trained models are transmitted to a central server for global aggregation, typically in the cloud. This approach often leads to network congestion and bandwidth limitations when numerous devices communicate with the same server. The need for Flexible Global Aggregation and Dynamic Client Selection in FL for the IoV arises from the inherent characteristics of IoV environments. These include diverse and distributed data sources, varying data quality,… More >

  • Open AccessOpen Access

    ARTICLE

    Electroencephalography (EEG) Based Neonatal Sleep Staging and Detection Using Various Classification Algorithms

    Hafza Ayesha Siddiqa1, Muhammad Irfan1, Saadullah Farooq Abbasi2,*, Wei Chen1
    CMC-Computers, Materials & Continua, Vol.77, No.2, pp. 1759-1778, 2023, DOI:10.32604/cmc.2023.041970
    Abstract Automatic sleep staging of neonates is essential for monitoring their brain development and maturity of the nervous system. EEG based neonatal sleep staging provides valuable information about an infant’s growth and health, but is challenging due to the unique characteristics of EEG and lack of standardized protocols. This study aims to develop and compare 18 machine learning models using Automated Machine Learning (autoML) technique for accurate and reliable multi-channel EEG-based neonatal sleep-wake classification. The study investigates autoML feasibility without extensive manual selection of features or hyperparameter tuning. The data is obtained from neonates at post-menstrual age 37 ± 05 weeks.… More >

  • Open AccessOpen Access

    ARTICLE

    Gate-Attention and Dual-End Enhancement Mechanism for Multi-Label Text Classification

    Jieren Cheng1,2, Xiaolong Chen1,*, Wenghang Xu3, Shuai Hua3, Zhu Tang1, Victor S. Sheng4
    CMC-Computers, Materials & Continua, Vol.77, No.2, pp. 1779-1793, 2023, DOI:10.32604/cmc.2023.042980
    Abstract In the realm of Multi-Label Text Classification (MLTC), the dual challenges of extracting rich semantic features from text and discerning inter-label relationships have spurred innovative approaches. Many studies in semantic feature extraction have turned to external knowledge to augment the model’s grasp of textual content, often overlooking intrinsic textual cues such as label statistical features. In contrast, these endogenous insights naturally align with the classification task. In our paper, to complement this focus on intrinsic knowledge, we introduce a novel Gate-Attention mechanism. This mechanism adeptly integrates statistical features from the text itself into the semantic fabric, enhancing the model’s capacity… More >

  • Open AccessOpen Access

    ARTICLE

    Force Sensitive Resistors-Based Real-Time Posture Detection System Using Machine Learning Algorithms

    Arsal Javaid1, Areeb Abbas1, Jehangir Arshad1, Mohammad Khalid Imam Rahmani2,*, Sohaib Tahir Chauhdary3, Mujtaba Hussain Jaffery1, Abdulbasid S. Banga2,*
    CMC-Computers, Materials & Continua, Vol.77, No.2, pp. 1795-1814, 2023, DOI:10.32604/cmc.2023.044140
    Abstract To detect the improper sitting posture of a person sitting on a chair, a posture detection system using machine learning classification has been proposed in this work. The addressed problem correlates to the third Sustainable Development Goal (SDG), ensuring healthy lives and promoting well-being for all ages, as specified by the World Health Organization (WHO). An improper sitting position can be fatal if one sits for a long time in the wrong position, and it can be dangerous for ulcers and lower spine discomfort. This novel study includes a practical implementation of a cushion consisting of a grid of 3… More >

  • Open AccessOpen Access

    ARTICLE

    BLECA: A Blockchain-Based Lightweight and Efficient Cross-Domain Authentication Scheme for Smart Parks

    Fengting Luo, Ruwei Huang*, Yuyue Chen
    CMC-Computers, Materials & Continua, Vol.77, No.2, pp. 1815-1835, 2023, DOI:10.32604/cmc.2023.041676
    Abstract Smart parks serve as integral components of smart cities, where they play a pivotal role in the process of urban modernization. The demand for cross-domain cooperation among smart devices from various parks has witnessed a significant increase. To ensure secure communication, device identities must undergo authentication. The existing cross-domain authentication schemes face issues such as complex authentication paths and high certificate management costs for devices, making it impractical for resource-constrained devices. This paper proposes a blockchain-based lightweight and efficient cross-domain authentication protocol for smart parks, which simplifies the authentication interaction and requires every device to maintain only one certificate. To… More >

  • Open AccessOpen Access

    ARTICLE

    ChainApparel: A Trustworthy Blockchain and IoT-Based Traceability Framework for Apparel Industry 4.0

    Muhammad Shakeel Faridi1, Saqib Ali1,2,*, Guojun Wang2,*, Salman Afsar Awan1, Muhammad Zafar Iqbal3
    CMC-Computers, Materials & Continua, Vol.77, No.2, pp. 1837-1854, 2023, DOI:10.32604/cmc.2023.041929
    Abstract Trustworthiness and product traceability are essential factors in the apparel industry 4.0 for establishing successful business relationships among stakeholders such as customers, manufacturers, suppliers, and consumers. Each stakeholder has implemented different technology-based systems to record and track product transactions. However, these systems work in silos, and there is no intra-system communication, leading to a lack of complete supply chain traceability for all apparel stakeholders. Moreover, apparel stakeholders are reluctant to share their business information with business competitors; thus, they involve third-party auditors to ensure the quality of the final product. Furthermore, the apparel manufacturing industry faces challenges with counterfeit products,… More >

  • Open AccessOpen Access

    ARTICLE

    Optimized Deep Learning Approach for Efficient Diabetic Retinopathy Classification Combining VGG16-CNN

    Heba M. El-Hoseny1,*, Heba F. Elsepae2, Wael A. Mohamed2, Ayman S. Selmy2
    CMC-Computers, Materials & Continua, Vol.77, No.2, pp. 1855-1872, 2023, DOI:10.32604/cmc.2023.042107
    Abstract Diabetic retinopathy is a critical eye condition that, if not treated, can lead to vision loss. Traditional methods of diagnosing and treating the disease are time-consuming and expensive. However, machine learning and deep transfer learning (DTL) techniques have shown promise in medical applications, including detecting, classifying, and segmenting diabetic retinopathy. These advanced techniques offer higher accuracy and performance. Computer-Aided Diagnosis (CAD) is crucial in speeding up classification and providing accurate disease diagnoses. Overall, these technological advancements hold great potential for improving the management of diabetic retinopathy. The study’s objective was to differentiate between different classes of diabetes and verify the… More >

  • Open AccessOpen Access

    ARTICLE

    Recognition of Human Actions through Speech or Voice Using Machine Learning Techniques

    Oscar Peña-Cáceres1,2,*, Henry Silva-Marchan3, Manuela Albert4, Miriam Gil1
    CMC-Computers, Materials & Continua, Vol.77, No.2, pp. 1873-1891, 2023, DOI:10.32604/cmc.2023.043176
    Abstract The development of artificial intelligence (AI) and smart home technologies has driven the need for speech recognition-based solutions. This demand stems from the quest for more intuitive and natural interaction between users and smart devices in their homes. Speech recognition allows users to control devices and perform everyday actions through spoken commands, eliminating the need for physical interfaces or touch screens and enabling specific tasks such as turning on or off the light, heating, or lowering the blinds. The purpose of this study is to develop a speech-based classification model for recognizing human actions in the smart home. It seeks… More >

  • Open AccessOpen Access

    ARTICLE

    A Memory-Guided Anomaly Detection Model with Contrastive Learning for Multivariate Time Series

    Wei Zhang1, Ping He2,*, Ting Li2, Fan Yang1, Ying Liu3
    CMC-Computers, Materials & Continua, Vol.77, No.2, pp. 1893-1910, 2023, DOI:10.32604/cmc.2023.044253
    (This article belongs to this Special Issue: Development and Industrial Application of AI Technologies)
    Abstract Some reconstruction-based anomaly detection models in multivariate time series have brought impressive performance advancements but suffer from weak generalization ability and a lack of anomaly identification. These limitations can result in the misjudgment of models, leading to a degradation in overall detection performance. This paper proposes a novel transformer-like anomaly detection model adopting a contrastive learning module and a memory block (CLME) to overcome the above limitations. The contrastive learning module tailored for time series data can learn the contextual relationships to generate temporal fine-grained representations. The memory block can record normal patterns of these representations through the utilization of… More >

  • Open AccessOpen Access

    ARTICLE

    DM Code Key Point Detection Algorithm Based on CenterNet

    Wei Wang1, Xinyao Tang2,*, Kai Zhou1, Chunhui Zhao1, Changfa Liu3
    CMC-Computers, Materials & Continua, Vol.77, No.2, pp. 1911-1928, 2023, DOI:10.32604/cmc.2023.043233
    (This article belongs to this Special Issue: Advanced Artificial Intelligence and Machine Learning Frameworks for Signal and Image Processing Applications)
    Abstract Data Matrix (DM) codes have been widely used in industrial production. The reading of DM code usually includes positioning and decoding. Accurate positioning is a prerequisite for successful decoding. Traditional image processing methods have poor adaptability to pollution and complex backgrounds. Although deep learning-based methods can automatically extract features, the bounding boxes cannot entirely fit the contour of the code. Further image processing methods are required for precise positioning, which will reduce efficiency. Because of the above problems, a CenterNet-based DM code key point detection network is proposed, which can directly obtain the four key points of the DM code.… More >

  • Open AccessOpen Access

    ARTICLE

    A Lightweight Road Scene Semantic Segmentation Algorithm

    Jiansheng Peng1,2,*, Qing Yang1, Yaru Hou1
    CMC-Computers, Materials & Continua, Vol.77, No.2, pp. 1929-1948, 2023, DOI:10.32604/cmc.2023.043524
    (This article belongs to this Special Issue: Advanced Artificial Intelligence and Machine Learning Frameworks for Signal and Image Processing Applications)
    Abstract In recent years, with the continuous deepening of smart city construction, there have been significant changes and improvements in the field of intelligent transportation. The semantic segmentation of road scenes has important practical significance in the fields of automatic driving, transportation planning, and intelligent transportation systems. However, the current mainstream lightweight semantic segmentation models in road scene segmentation face problems such as poor segmentation performance of small targets and insufficient refinement of segmentation edges. Therefore, this article proposes a lightweight semantic segmentation model based on the LiteSeg model improvement to address these issues. The model uses the lightweight backbone network… More >

  • Open AccessOpen Access

    ARTICLE

    C2Net-YOLOv5: A Bidirectional Res2Net-Based Traffic Sign Detection Algorithm

    Xiujuan Wang1, Yiqi Tian1,*, Kangfeng Zheng2, Chutong Liu3
    CMC-Computers, Materials & Continua, Vol.77, No.2, pp. 1949-1965, 2023, DOI:10.32604/cmc.2023.042224
    (This article belongs to this Special Issue: Machine Vision Detection and Intelligent Recognition)
    Abstract Rapid advancement of intelligent transportation systems (ITS) and autonomous driving (AD) have shown the importance of accurate and efficient detection of traffic signs. However, certain drawbacks, such as balancing accuracy and real-time performance, hinder the deployment of traffic sign detection algorithms in ITS and AD domains. In this study, a novel traffic sign detection algorithm was proposed based on the bidirectional Res2Net architecture to achieve an improved balance between accuracy and speed. An enhanced backbone network module, called C2Net, which uses an upgraded bidirectional Res2Net, was introduced to mitigate information loss in the feature extraction process and to achieve information… More >

  • Open AccessOpen Access

    ARTICLE

    Real-Time Prediction Algorithm for Intelligent Edge Networks with Federated Learning-Based Modeling

    Seungwoo Kang1, Seyha Ros1, Inseok Song1, Prohim Tam1, Sa Math2, Seokhoon Kim1,3,*
    CMC-Computers, Materials & Continua, Vol.77, No.2, pp. 1967-1983, 2023, DOI:10.32604/cmc.2023.045020
    Abstract Intelligent healthcare networks represent a significant component in digital applications, where the requirements hold within quality-of-service (QoS) reliability and safeguarding privacy. This paper addresses these requirements through the integration of enabler paradigms, including federated learning (FL), cloud/edge computing, software-defined/virtualized networking infrastructure, and converged prediction algorithms. The study focuses on achieving reliability and efficiency in real-time prediction models, which depend on the interaction flows and network topology. In response to these challenges, we introduce a modified version of federated logistic regression (FLR) that takes into account convergence latencies and the accuracy of the final FL model within healthcare networks. To establish… More >

  • Open AccessOpen Access

    ARTICLE

    Fine-Grained Classification of Remote Sensing Ship Images Based on Improved VAN

    Guoqing Zhou, Liang Huang, Qiao Sun*
    CMC-Computers, Materials & Continua, Vol.77, No.2, pp. 1985-2007, 2023, DOI:10.32604/cmc.2023.040902
    (This article belongs to this Special Issue: Optimization for Artificial Intelligence Application)
    Abstract The remote sensing ships’ fine-grained classification technology makes it possible to identify certain ship types in remote sensing images, and it has broad application prospects in civil and military fields. However, the current model does not examine the properties of ship targets in remote sensing images with mixed multi-granularity features and a complicated backdrop. There is still an opportunity for future enhancement of the classification impact. To solve the challenges brought by the above characteristics, this paper proposes a Metaformer and Residual fusion network based on Visual Attention Network (VAN-MR) for fine-grained classification tasks. For the complex background of remote… More >

  • Open AccessOpen Access

    ARTICLE

    Shadow Extraction and Elimination of Moving Vehicles for Tracking Vehicles

    Kalpesh Jadav1, Vishal Sorathiya1,*, Walid El-Shafai2, Torki Altameem3, Moustafa H. Aly4, Vipul Vekariya5, Kawsar Ahmed6, Francis M. Bui6
    CMC-Computers, Materials & Continua, Vol.77, No.2, pp. 2009-2030, 2023, DOI:10.32604/cmc.2023.043168
    Abstract Shadow extraction and elimination is essential for intelligent transportation systems (ITS) in vehicle tracking application. The shadow is the source of error for vehicle detection, which causes misclassification of vehicles and a high false alarm rate in the research of vehicle counting, vehicle detection, vehicle tracking, and classification. Most of the existing research is on shadow extraction of moving vehicles in high intensity and on standard datasets, but the process of extracting shadows from moving vehicles in low light of real scenes is difficult. The real scenes of vehicles dataset are generated by self on the Vadodara–Mumbai highway during periods… More >

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    Deep Learning-Enhanced Brain Tumor Prediction via Entropy-Coded BPSO in CIELAB Color Space

    Mudassir Khalil1, Muhammad Imran Sharif2,*, Ahmed Naeem3, Muhammad Umar Chaudhry1, Hafiz Tayyab Rauf4,*, Adham E. Ragab5
    CMC-Computers, Materials & Continua, Vol.77, No.2, pp. 2031-2047, 2023, DOI:10.32604/cmc.2023.043687
    Abstract Early detection of brain tumors is critical for effective treatment planning. Identifying tumors in their nascent stages can significantly enhance the chances of patient survival. While there are various types of brain tumors, each with unique characteristics and treatment protocols, tumors are often minuscule during their initial stages, making manual diagnosis challenging, time-consuming, and potentially ambiguous. Current techniques predominantly used in hospitals involve manual detection via MRI scans, which can be costly, error-prone, and time-intensive. An automated system for detecting brain tumors could be pivotal in identifying the disease in its earliest phases. This research applies several data augmentation techniques… More >

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    An Intelligent Approach for Intrusion Detection in Industrial Control System

    Adel Alkhalil1,*, Abdulaziz Aljaloud1, Diaa Uliyan1, Mohammed Altameemi1, Magdy Abdelrhman2,3, Yaser Altameemi4, Aakash Ahmad5, Romany Fouad Mansour6
    CMC-Computers, Materials & Continua, Vol.77, No.2, pp. 2049-2078, 2023, DOI:10.32604/cmc.2023.044506
    Abstract Supervisory control and data acquisition (SCADA) systems are computer systems that gather and analyze real-time data, distributed control systems are specially designed automated control system that consists of geographically distributed control elements, and other smaller control systems such as programmable logic controllers are industrial solid-state computers that monitor inputs and outputs and make logic-based decisions. In recent years, there has been a lot of focus on the security of industrial control systems. Due to the advancement in information technologies, the risk of cyberattacks on industrial control system has been drastically increased. Because they are so inextricably tied to human life,… More >

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    ARTICLE

    Fake News Detection Using Machine Learning and Deep Learning Methods

    Ammar Saeed1,*, Eesa Al Solami2
    CMC-Computers, Materials & Continua, Vol.77, No.2, pp. 2079-2096, 2023, DOI:10.32604/cmc.2023.030551
    Abstract The evolution of the internet and its accessibility in the twenty-first century has resulted in a tremendous increase in the use of social media platforms. Some social media sources contribute to the propagation of fake news that has no real validity, but they accumulate over time and begin to appear in the feed of every consumer producing even more ambiguity. To sustain the value of social media, such stories must be distinguished from the true ones. As a result, an automated system is required to save time and money. The classification of fake news and misinformation from social media data… More >

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    ARTICLE

    Fusion of Region Extraction and Cross-Entropy SVM Models for Wheat Rust Diseases Classification

    Deepak Kumar1, Vinay Kukreja1, Ayush Dogra1,*, Bhawna Goyal2, Talal Taha Ali3
    CMC-Computers, Materials & Continua, Vol.77, No.2, pp. 2097-2121, 2023, DOI:10.32604/cmc.2023.044287
    Abstract Wheat rust diseases are one of the major types of fungal diseases that cause substantial yield quality losses of 15%–20% every year. The wheat rust diseases are identified either through experienced evaluators or computerassisted techniques. The experienced evaluators take time to identify the disease which is highly laborious and too costly. If wheat rust diseases are predicted at the development stages, then fungicides are sprayed earlier which helps to increase wheat yield quality. To solve the experienced evaluator issues, a combined region extraction and cross-entropy support vector machine (CE-SVM) model is proposed for wheat rust disease identification. In the proposed… More >

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    ARTICLE

    Detecting Android Botnet Applications Using Convolution Neural Network

    Mamona Arshad1, Ahmad Karim1, Salman Naseer2, Shafiq Ahmad3, Mejdal Alqahtani3, Akber Abid Gardezi4, Muhammad Shafiq5,*, Jin-Ghoo Choi5
    CMC-Computers, Materials & Continua, Vol.77, No.2, pp. 2123-2135, 2023, DOI:10.32604/cmc.2022.028680
    Abstract The exponential growth in the development of smartphones and handheld devices is permeated due to everyday activities i.e., games applications, entertainment, online banking, social network sites, etc., and also allow the end users to perform a variety of activities. Because of activities, mobile devices attract cybercriminals to initiate an attack over a diverse range of malicious activities such as theft of unauthorized information, phishing, spamming, Distributed Denial of Services (DDoS), and malware dissemination. Botnet applications are a type of harmful attack that can be used to launch malicious activities and has become a significant threat in the research area. A… More >

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    Notes on Convergence and Modeling for the Extended Kalman Filter

    Dah-Jing Jwo*
    CMC-Computers, Materials & Continua, Vol.77, No.2, pp. 2137-2155, 2023, DOI:10.32604/cmc.2023.034308
    Abstract The goal of this work is to provide an understanding of estimation technology for both linear and nonlinear dynamical systems. A critical analysis of both the Kalman filter (KF) and the extended Kalman filter (EKF) will be provided, along with examples to illustrate some important issues related to filtering convergence due to system modeling. A conceptual explanation of the topic with illustrative examples provided in the paper can help the readers capture the essential principles and avoid making mistakes while implementing the algorithms. Adding fictitious process noise to the system model assumed by the filter designers for convergence assurance is… More >

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    Detecting and Mitigating DDOS Attacks in SDNs Using Deep Neural Network

    Gul Nawaz1, Muhammad Junaid1, Adnan Akhunzada2, Abdullah Gani2,*, Shamyla Nawazish3, Asim Yaqub3, Adeel Ahmed1, Huma Ajab4
    CMC-Computers, Materials & Continua, Vol.77, No.2, pp. 2157-2178, 2023, DOI:10.32604/cmc.2023.026952
    Abstract Distributed denial of service (DDoS) attack is the most common attack that obstructs a network and makes it unavailable for a legitimate user. We proposed a deep neural network (DNN) model for the detection of DDoS attacks in the Software-Defined Networking (SDN) paradigm. SDN centralizes the control plane and separates it from the data plane. It simplifies a network and eliminates vendor specification of a device. Because of this open nature and centralized control, SDN can easily become a victim of DDoS attacks. We proposed a supervised Developed Deep Neural Network (DDNN) model that can classify the DDoS attack traffic… More >

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    ARTICLE

    Efficient Cloud Resource Scheduling with an Optimized Throttled Load Balancing Approach

    V. Dhilip Kumar1, J. Praveenchandar2, Muhammad Arif3,*, Adrian Brezulianu4, Oana Geman5, Atif Ikram3,6
    CMC-Computers, Materials & Continua, Vol.77, No.2, pp. 2179-2188, 2023, DOI:10.32604/cmc.2023.034764
    Abstract Cloud Technology is a new platform that offers on-demand computing Peripheral such as storage, processing power, and other computer system resources. It is also referred to as a system that will let the consumers utilize computational resources like databases, servers, storage, and intelligence over the Internet. In a cloud network, load balancing is the process of dividing network traffic among a cluster of available servers to increase efficiency. It is also known as a server pool or server farm. When a single node is overwhelmed, balancing the workload is needed to manage unpredictable workflows. The load balancer sends the load… More >

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    Modelling and Performance Analysis of Visible Light Communication System in Industrial Implementations

    Mohammed S. M. Gismalla1,2, Asrul I. Azmi1,2, Mohd R. Salim1,2, Farabi Iqbal1,2, Mohammad F. L. Abdullah3, Mosab Hamdan4,5, Muzaffar Hamzah4,*, Abu Sahmah M. Supa’at1,2
    CMC-Computers, Materials & Continua, Vol.77, No.2, pp. 2189-2204, 2023, DOI:10.32604/cmc.2023.035250
    Abstract Visible light communication (VLC) has a paramount role in industrial implementations, especially for better energy efficiency, high speed-data rates, and low susceptibility to interference. However, since studies on VLC for industrial implementations are in scarcity, areas concerning illumination optimisation and communication performances demand further investigation. As such, this paper presents a new modelling of light fixture distribution for a warehouse model to provide acceptable illumination and communication performances. The proposed model was evaluated based on various semi-angles at half power (SAAHP) and different height levels for several parameters, including received power, signal to noise ratio (SNR), and bit error rate… More >

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    ARTICLE

    Addressing the Issues and Developments Correlated with Envisaged 5G Mobile Technologies: Comprehensive Solutions

    Idrees Sarhan Kocher*
    CMC-Computers, Materials & Continua, Vol.77, No.2, pp. 2205-2223, 2023, DOI:10.32604/cmc.2023.039392
    (This article belongs to this Special Issue: Artificial Intelligence, Big Data, Sensors and 5G for Smart Cities)
    Abstract Fifth Generation (5G) communications are regarded as the cornerstone to household consumer experience improvements and smart manufacturing revolution from the standpoint of industries' objectives. It is anticipated that Envisaged 5G (E5G) mobile technology would be operational in certain developed countries by 2023. The Internet of Things (IoTs) will transform how humans live when combined with smart and integrated sensing devices, such as in-home sensing devices. Recent research is being carried out all over the world to produce a new technique that can be crucial in the success of the anticipated 5G mobile technology. High output, reduced latency, highly reliable, greater… More >

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    ARTICLE

    Fake News Classification: Past, Current, and Future

    Muhammad Usman Ghani Khan1, Abid Mehmood2, Mourad Elhadef2, Shehzad Ashraf Chaudhry2,3,*
    CMC-Computers, Materials & Continua, Vol.77, No.2, pp. 2225-2249, 2023, DOI:10.32604/cmc.2023.038303
    Abstract The proliferation of deluding data such as fake news and phony audits on news web journals, online publications, and internet business apps has been aided by the availability of the web, cell phones, and social media. Individuals can quickly fabricate comments and news on social media. The most difficult challenge is determining which news is real or fake. Accordingly, tracking down programmed techniques to recognize fake news online is imperative. With an emphasis on false news, this study presents the evolution of artificial intelligence techniques for detecting spurious social media content. This study shows past, current, and possible methods that… More >

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    ARTICLE

    A Nonstandard Computational Investigation of SEIR Model with Fuzzy Transmission, Recovery and Death Rates

    Ahmed H. Msmali1, Fazal Dayan2,*, Muhammad Rafiq3, Nauman Ahmed4, Abdullah Ali H. Ahmadini1, Hassan A. Hamali5
    CMC-Computers, Materials & Continua, Vol.77, No.2, pp. 2251-2269, 2023, DOI:10.32604/cmc.2023.040266
    (This article belongs to this Special Issue: Advanced Implications of Fuzzy Logic Evolutionary Computation)
    Abstract In this article, a Susceptible-Exposed-Infectious-Recovered (SEIR) epidemic model is considered. The equilibrium analysis and reproduction number are studied. The conventional models have made assumptions of homogeneity in disease transmission that contradict the actual reality. However, it is crucial to consider the heterogeneity of the transmission rate when modeling disease dynamics. Describing the heterogeneity of disease transmission mathematically can be achieved by incorporating fuzzy theory. A numerical scheme nonstandard, finite difference (NSFD) approach is developed for the studied model and the results of numerical simulations are presented. Simulations of the constructed scheme are presented. The positivity, convergence and consistency of the… More >

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    A Novel Unsupervised MRI Synthetic CT Image Generation Framework with Registration Network

    Liwei Deng1, Henan Sun1, Jing Wang2, Sijuan Huang3, Xin Yang3,*
    CMC-Computers, Materials & Continua, Vol.77, No.2, pp. 2271-2287, 2023, DOI:10.32604/cmc.2023.039062
    Abstract In recent years, radiotherapy based only on Magnetic Resonance (MR) images has become a hot spot for radiotherapy planning research in the current medical field. However, functional computed tomography (CT) is still needed for dose calculation in the clinic. Recent deep-learning approaches to synthesized CT images from MR images have raised much research interest, making radiotherapy based only on MR images possible. In this paper, we proposed a novel unsupervised image synthesis framework with registration networks. This paper aims to enforce the constraints between the reconstructed image and the input image by registering the reconstructed image with the input image… More >

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    Convolutional Neural Network Model for Fire Detection in Real-Time Environment

    Abdul Rehman, Dongsun Kim*, Anand Paul
    CMC-Computers, Materials & Continua, Vol.77, No.2, pp. 2289-2307, 2023, DOI:10.32604/cmc.2023.036435
    (This article belongs to this Special Issue: Recent Advances in Internet of Things and Emerging Technologies)
    Abstract Disasters such as conflagration, toxic smoke, harmful gas or chemical leakage, and many other catastrophes in the industrial environment caused by hazardous distance from the peril are frequent. The calamities are causing massive fiscal and human life casualties. However, Wireless Sensors Network-based adroit monitoring and early warning of these dangerous incidents will hamper fiscal and social fiasco. The authors have proposed an early fire detection system uses machine and/or deep learning algorithms. The article presents an Intelligent Industrial Monitoring System (IIMS) and introduces an Industrial Smart Social Agent (ISSA) in the Industrial SIoT (ISIoT) paradigm. The proffered ISSA empowers smart… More >

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    PoIR: A Node Selection Mechanism in Reputation-Based Blockchain Consensus Using Bidirectional LSTM Regression Model

    Jauzak Hussaini Windiatmaja, Delphi Hanggoro, Muhammad Salman, Riri Fitri Sari*
    CMC-Computers, Materials & Continua, Vol.77, No.2, pp. 2309-2339, 2023, DOI:10.32604/cmc.2023.041152
    Abstract This research presents a reputation-based blockchain consensus mechanism called Proof of Intelligent Reputation (PoIR) as an alternative to traditional Proof of Work (PoW). PoIR addresses the limitations of existing reputation-based consensus mechanisms by proposing a more decentralized and fair node selection process. The proposed PoIR consensus combines Bidirectional Long Short-Term Memory (BiLSTM) with the Network Entity Reputation Database (NERD) to generate reputation scores for network entities and select authoritative nodes. NERD records network entity profiles based on various sources, i.e., Warden, Blacklists, DShield, AlienVault Open Threat Exchange (OTX), and MISP (Malware Information Sharing Platform). It summarizes these profile records into… More >

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    Leveraging Blockchain with Optimal Deep Learning-Based Drug Supply Chain Management for Pharmaceutical Industries

    Shanthi Perumalsamy, Venkatesh Kaliyamurthy*
    CMC-Computers, Materials & Continua, Vol.77, No.2, pp. 2341-2357, 2023, DOI:10.32604/cmc.2023.040269
    Abstract Due to its complexity and involvement of numerous stakeholders, the pharmaceutical supply chain presents many challenges that companies must overcome to deliver necessary medications to patients efficiently. The pharmaceutical supply chain poses different challenging issues, encompasses supply chain visibility, cold-chain shipping, drug counterfeiting, and rising prescription drug prices, which can considerably surge out-of-pocket patient costs. Blockchain (BC) offers the technical base for such a scheme, as it could track legitimate drugs and avoid fake circulation. The designers presented the procedure of BC with fabric for creating a secured drug supply-chain management (DSCM) method. With this motivation, the study presents a… More >

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    Automated Video Generation of Moving Digits from Text Using Deep Deconvolutional Generative Adversarial Network

    Anwar Ullah1, Xinguo Yu1,*, Muhammad Numan2
    CMC-Computers, Materials & Continua, Vol.77, No.2, pp. 2359-2383, 2023, DOI:10.32604/cmc.2023.041219
    (This article belongs to this Special Issue: Cognitive Computing and Systems in Education and Research)
    Abstract Generating realistic and synthetic video from text is a highly challenging task due to the multitude of issues involved, including digit deformation, noise interference between frames, blurred output, and the need for temporal coherence across frames. In this paper, we propose a novel approach for generating coherent videos of moving digits from textual input using a Deep Deconvolutional Generative Adversarial Network (DD-GAN). The DD-GAN comprises a Deep Deconvolutional Neural Network (DDNN) as a Generator (G) and a modified Deep Convolutional Neural Network (DCNN) as a Discriminator (D) to ensure temporal coherence between adjacent frames. The proposed research involves several steps.… More >

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    An Enhanced Equilibrium Optimizer for Solving Optimization Tasks

    Yuting Liu1, Hongwei Ding1,*, Zongshan Wang1,*, Gaurav Dhiman2,3,4, Zhijun Yang1, Peng Hu5
    CMC-Computers, Materials & Continua, Vol.77, No.2, pp. 2385-2406, 2023, DOI:10.32604/cmc.2023.039883
    (This article belongs to this Special Issue: Optimization for Artificial Intelligence Application)
    Abstract The equilibrium optimizer (EO) represents a new, physics-inspired metaheuristic optimization approach that draws inspiration from the principles governing the control of volume-based mixing to achieve dynamic mass equilibrium. Despite its innovative foundation, the EO exhibits certain limitations, including imbalances between exploration and exploitation, the tendency to local optima, and the susceptibility to loss of population diversity. To alleviate these drawbacks, this paper introduces an improved EO that adopts three strategies: adaptive inertia weight, Cauchy mutation, and adaptive sine cosine mechanism, called SCEO. Firstly, a new update formula is conceived by incorporating an adaptive inertia weight to reach an appropriate balance… More >

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