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  • Open Access


    Quick Weighing of Passing Vehicles Using the Transfer-Learning-Enhanced Convolutional Neural Network

    Wangchen Yan1,*, Jinbao Yang1, Xin Luo2

    CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.3, pp. 2507-2524, 2024, DOI:10.32604/cmes.2023.044709

    Abstract Transfer learning could reduce the time and resources required by the training of new models and be therefore important for generalized applications of the trained machine learning algorithms. In this study, a transfer learning-enhanced convolutional neural network (CNN) was proposed to identify the gross weight and the axle weight of moving vehicles on the bridge. The proposed transfer learning-enhanced CNN model was expected to weigh different bridges based on a small amount of training datasets and provide high identification accuracy. First of all, a CNN algorithm for bridge weigh-in-motion (B-WIM) technology was proposed to identify… More >

  • Open Access


    A Multimodel Transfer-Learning-Based Car Price Prediction Model with an Automatic Fuzzy Logic Parameter Optimizer

    Ping-Huan Kuo1,2, Sing-Yan Chen1, Her-Terng Yau1,2,*

    Computer Systems Science and Engineering, Vol.46, No.2, pp. 1577-1596, 2023, DOI:10.32604/csse.2023.036292

    Abstract Cars are regarded as an indispensable means of transportation in Taiwan. Several studies have indicated that the automotive industry has witnessed remarkable advances and that the market of used cars has rapidly expanded. In this study, a price prediction system for used BMW cars was developed. Nine parameters of used cars, including their model, registration year, and transmission style, were analyzed. The data obtained were then divided into three subsets. The first subset was used to compare the results of each algorithm. The predicted values produced by the two algorithms with the most satisfactory results… More >

  • Open Access


    An Improved Transfer-Learning for Image-Based Species Classification of Protected Indonesians Birds

    Chao-Lung Yang1, Yulius Harjoseputro2,3, Yu-Chen Hu4, Yung-Yao Chen2,*

    CMC-Computers, Materials & Continua, Vol.73, No.3, pp. 4577-4593, 2022, DOI:10.32604/cmc.2022.031305

    Abstract This research proposed an improved transfer-learning bird classification framework to achieve a more precise classification of Protected Indonesia Birds (PIB) which have been identified as the endangered bird species. The framework takes advantage of using the proposed sequence of Batch Normalization Dropout Fully-Connected (BNDFC) layers to enhance the baseline model of transfer learning. The main contribution of this work is the proposed sequence of BNDFC that can be applied to any Convolutional Neural Network (CNN) based model to improve the classification accuracy, especially for image-based species classification problems. The experiment results show that the proposed More >

  • Open Access


    Covid-19 Symptoms Periods Detection Using Transfer-Learning Techniques

    Fahad Albogamy1, Mohammed Faisal2,3,*, Mohammed Arafah4, Hebah ElGibreen3,5

    Intelligent Automation & Soft Computing, Vol.32, No.3, pp. 1921-1937, 2022, DOI:10.32604/iasc.2022.022559

    Abstract The inflationary illness caused by extreme acute respiratory syndrome coronavirus in 2019 (COVID-19) is an infectious and deadly disease. COVID-19 was first found in Wuhan, China, in December 2019, and has since spread worldwide. Globally, there have been more than 198 M cases and over 4.22 M deaths, as of the first of Augest, 2021. Therefore, an automated and fast diagnosis system needs to be introduced as a simple, alternative diagnosis choice to avoid the spread of COVID-19. The main contributions of this research are 1) the COVID-19 Period Detection System (CPDS), that used to… More >

  • Open Access


    COVID-19 Diagnosis Using Transfer-Learning Techniques

    Mohammed Faisal1,*, Fahad Albogamy2, Hebah ElGibreen3, Mohammed Algabri3, Syed Ahad M. Alvi1, Mansour Alsulaiman3

    Intelligent Automation & Soft Computing, Vol.29, No.3, pp. 649-667, 2021, DOI:10.32604/iasc.2021.017898

    Abstract COVID-19 was first discovered in Wuhan, China, in December 2019 and has since spread worldwide. An automated and fast diagnosis system needs to be developed for early and effective COVID-19 diagnosis. Hence, we propose two- and three-classifier diagnosis systems for classifying COVID-19 cases using transfer-learning techniques. These systems can classify X-ray images into three categories: healthy, COVID-19, and pneumonia cases. We used two X-ray image datasets (DATASET-1 and DATASET-2) collected from state-of-the-art studies and train the systems using deep learning architectures, such as VGG-19, NASNet, and MobileNet2, on these datasets. According to the validation and More >

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