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  • Open Access

    ARTICLE

    SmokerViT: A Transformer-Based Method for Smoker Recognition

    Ali Khan1,4, Somaiya Khan2, Bilal Hassan3, Rizwan Khan1,4, Zhonglong Zheng1,4,*

    CMC-Computers, Materials & Continua, Vol.77, No.1, pp. 403-424, 2023, DOI:10.32604/cmc.2023.040251

    Abstract Smoking has an economic and environmental impact on society due to the toxic substances it emits. Convolutional Neural Networks (CNNs) need help describing low-level features and can miss important information. Moreover, accurate smoker detection is vital with minimum false alarms. To answer the issue, the researchers of this paper have turned to a self-attention mechanism inspired by the ViT, which has displayed state-of-the-art performance in the classification task. To effectively enforce the smoking prohibition in non-smoking locations, this work presents a Vision Transformer-inspired model called SmokerViT for detecting smokers. Moreover, this research utilizes a locally… More >

  • Open Access

    ARTICLE

    Intelligent Traffic Surveillance through Multi-Label Semantic Segmentation and Filter-Based Tracking

    Asifa Mehmood Qureshi1, Nouf Abdullah Almujally2, Saud S. Alotaibi3, Mohammed Hamad Alatiyyah4, Jeongmin Park5,*

    CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 3707-3725, 2023, DOI:10.32604/cmc.2023.040738

    Abstract Road congestion, air pollution, and accident rates have all increased as a result of rising traffic density and worldwide population growth. Over the past ten years, the total number of automobiles has increased significantly over the world. In this paper, a novel method for intelligent traffic surveillance is presented. The proposed model is based on multilabel semantic segmentation using a random forest classifier which classifies the images into five classes. To improve the results, mean-shift clustering was applied to the segmented images. Afterward, the pixels given the label for the vehicle were extracted and blob… More >

  • Open Access

    ARTICLE

    Full Scale-Aware Balanced High-Resolution Network for Multi-Person Pose Estimation

    Shaohua Li, Haixiang Zhang*, Hanjie Ma, Jie Feng, Mingfeng Jiang

    CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 3379-3392, 2023, DOI:10.32604/cmc.2023.041538

    Abstract Scale variation is a major challenge in multi-person pose estimation. In scenes where persons are present at various distances, models tend to perform better on larger-scale persons, while the performance for smaller-scale persons often falls short of expectations. Therefore, effectively balancing the persons of different scales poses a significant challenge. So this paper proposes a new multi-person pose estimation model called FSA Net to improve the model’s performance in complex scenes. Our model utilizes High-Resolution Network (HRNet) as the backbone and feeds the outputs of the last stage’s four branches into the DCB module. The More >

  • Open Access

    REVIEW

    Blockchain-Enabled Cybersecurity Provision for Scalable Heterogeneous Network: A Comprehensive Survey

    Md. Shohidul Islam1,*, Md. Arafatur Rahman2, Mohamed Ariff Bin Ameedeen1, Husnul Ajra1, Zahian Binti Ismail1, Jasni Mohamad Zain3

    CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.1, pp. 43-123, 2024, DOI:10.32604/cmes.2023.028687

    Abstract Blockchain-enabled cybersecurity system to ensure and strengthen decentralized digital transaction is gradually gaining popularity in the digital era for various areas like finance, transportation, healthcare, education, and supply chain management. Blockchain interactions in the heterogeneous network have fascinated more attention due to the authentication of their digital application exchanges. However, the exponential development of storage space capabilities across the blockchain-based heterogeneous network has become an important issue in preventing blockchain distribution and the extension of blockchain nodes. There is the biggest challenge of data integrity and scalability, including significant computing complexity and inapplicable latency on… More > Graphic Abstract

    Blockchain-Enabled Cybersecurity Provision for Scalable Heterogeneous Network: A Comprehensive Survey

  • Open Access

    ARTICLE

    The Weighted Basis for PHT-Splines

    Zhiguo Yong1, Hongmei Kang1, Falai Chen2,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.1, pp. 739-760, 2024, DOI:10.32604/cmes.2023.027171

    Abstract PHT-splines are defined as polynomial splines over hierarchical T-meshes with very efficient local refinement properties. The original PHT-spline basis functions constructed by the truncation mechanism have a decay phenomenon, resulting in numerical instability. The non-decay basis functions are constructed as the B-splines that are defined on the 2 × 2 tensor product meshes associated with basis vertices in Kang et al., but at the cost of losing the partition of unity. In the field of finite element analysis and topology optimization, forming the partition of unity is the default ingredient for constructing basis functions of… More > Graphic Abstract

    The Weighted Basis for PHT-Splines

  • Open Access

    ARTICLE

    Intelligent Fish Behavior Classification Using Modified Invasive Weed Optimization with Ensemble Fusion Model

    B. Keerthi Samhitha*, R. Subhashini

    Intelligent Automation & Soft Computing, Vol.37, No.3, pp. 3125-3142, 2023, DOI:10.32604/iasc.2023.040643

    Abstract Accurate and rapid detection of fish behaviors is critical to perceive health and welfare by allowing farmers to make informed management decisions about recirculating the aquaculture system while decreasing labor. The classic detection approach involves placing sensors on the skin or body of the fish, which may interfere with typical behavior and welfare. The progress of deep learning and computer vision technologies opens up new opportunities to understand the biological basis of this behavior and precisely quantify behaviors that contribute to achieving accurate management in precision farming and higher production efficacy. This study develops an… More >

  • Open Access

    ARTICLE

    Relevant Visual Semantic Context-Aware Attention-Based Dialog

    Eugene Tan Boon Hong1, Yung-Wey Chong1,*, Tat-Chee Wan1, Kok-Lim Alvin Yau2

    CMC-Computers, Materials & Continua, Vol.76, No.2, pp. 2337-2354, 2023, DOI:10.32604/cmc.2023.038695

    Abstract The existing dataset for visual dialog comprises multiple rounds of questions and a diverse range of image contents. However, it faces challenges in overcoming visual semantic limitations, particularly in obtaining sufficient context from visual and textual aspects of images. This paper proposes a new visual dialog dataset called Diverse History-Dialog (DS-Dialog) to address the visual semantic limitations faced by the existing dataset. DS-Dialog groups relevant histories based on their respective Microsoft Common Objects in Context (MSCOCO) image categories and consolidates them for each image. Specifically, each MSCOCO image category consists of top relevant histories extracted… More >

  • Open Access

    ARTICLE

    Increasing Crop Quality and Yield with a Machine Learning-Based Crop Monitoring System

    Anas Bilal1,*, Xiaowen Liu1, Haixia Long1,*, Muhammad Shafiq2, Muhammad Waqar3

    CMC-Computers, Materials & Continua, Vol.76, No.2, pp. 2401-2426, 2023, DOI:10.32604/cmc.2023.037857

    Abstract Farming is cultivating the soil, producing crops, and keeping livestock. The agricultural sector plays a crucial role in a country’s economic growth. This research proposes a two-stage machine learning framework for agriculture to improve efficiency and increase crop yield. In the first stage, machine learning algorithms generate data for extensive and far-flung agricultural areas and forecast crops. The recommended crops are based on various factors such as weather conditions, soil analysis, and the amount of fertilizers and pesticides required. In the second stage, a transfer learning-based model for plant seedlings, pests, and plant leaf disease More >

  • Open Access

    PROCEEDINGS

    Acoustic Topology Optimization of Sound Absorbing Materials Directly from Subdivision Surfaces with IGA-FEM/BEM

    Yanming Xu1,2, Leilei Chen1,2,*, Haojie Lian3

    The International Conference on Computational & Experimental Engineering and Sciences, Vol.26, No.1, pp. 1-1, 2023, DOI:10.32604/icces.2023.010581

    Abstract An isogeometric coupling algorithm based on the finite element method and the boundary element method (IGA-FEM/BEM) is proposed for the simulation of acoustic fluid-structure interaction and structuralacoustic topology optimization using the direct differentiation method. The geometries are constructed from triangular control meshes through Loop subdivision scheme. The effect of sound-absorbing materials on the acoustic response is characterized by acoustic impedance boundary conditions. The optimization problem is formulated in the framework of Solid Isotropic Material with Penalization methods and the sound absorption coefficients on elements are selected as design variables. Numerical examples are presented to demonstrate More >

  • Open Access

    ARTICLE

    A Triplet-Branch Convolutional Neural Network for Part-Based Gait Recognition

    Sang-Soo Yeo1, Seungmin Rho2,*, Hyungjoon Kim3, Jibran Safdar4, Umar Zia5, Mehr Yahya Durrani5

    Computer Systems Science and Engineering, Vol.47, No.2, pp. 2027-2047, 2023, DOI:10.32604/csse.2023.040327

    Abstract Intelligent vision-based surveillance systems are designed to deal with the gigantic volume of videos captured in a particular environment to perform the interpretation of scenes in form of detection, tracking, monitoring, behavioral analysis, and retrievals. In addition to that, another evolving way of surveillance systems in a particular environment is human gait-based surveillance. In the existing research, several methodological frameworks are designed to use deep learning and traditional methods, nevertheless, the accuracies of these methods drop substantially when they are subjected to covariate conditions. These covariate variables disrupt the gait features and hence the recognition… More >

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