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  • Open Access

    ARTICLE

    Dental Age Estimation Based on X-ray Images

    Noor Mualla1, Essam H Houssein2, *, M. R. Hassan1

    CMC-Computers, Materials & Continua, Vol.62, No.2, pp. 591-605, 2020, DOI:10.32604/cmc.2020.08580

    Abstract Chronological age estimation using panoramic dental X-ray images is an essential task in forensic sciences. Various statistical approaches have proposed by considering the teeth and mandible. However, building automated dental age estimation based on machine learning techniques needs more research efforts. In this paper, an automated dental age estimation is proposed using transfer learning. In the proposed approach, features are extracted using two deep neural networks namely, AlexNet and ResNet. Several classifiers are proposed to perform the classification task including decision tree, k-nearest neighbor, linear discriminant, and support vector machine. The proposed approach is evaluated More >

  • Open Access

    REVIEW

    Wind Power Forecasting Methods Based on Deep Learning: A Survey

    Xing Deng1, 2, Haijian Shao1, *, Chunlong Hu1, Dengbiao Jiang1, Yingtao Jiang3

    CMES-Computer Modeling in Engineering & Sciences, Vol.122, No.1, pp. 273-301, 2020, DOI:10.32604/cmes.2020.08768 - 01 January 2020

    Abstract Accurate wind power forecasting in wind farm can effectively reduce the enormous impact on grid operation safety when high permeability intermittent power supply is connected to the power grid. Aiming to provide reference strategies for relevant researchers as well as practical applications, this paper attempts to provide the literature investigation and methods analysis of deep learning, enforcement learning and transfer learning in wind speed and wind power forecasting modeling. Usually, wind speed and wind power forecasting around a wind farm requires the calculation of the next moment of the definite state, which is usually achieved… More >

  • Open Access

    ARTICLE

    Multi-Label Learning Based on Transfer Learning and Label Correlation

    Kehua Yang1,*, Chaowei She1, Wei Zhang1, Jiqing Yao2, Shaosong Long1

    CMC-Computers, Materials & Continua, Vol.61, No.1, pp. 155-169, 2019, DOI:10.32604/cmc.2019.05901

    Abstract In recent years, multi-label learning has received a lot of attention. However, most of the existing methods only consider global label correlation or local label correlation. In fact, on the one hand, both global and local label correlations can appear in real-world situation at same time. On the other hand, we should not be limited to pairwise labels while ignoring the high-order label correlation. In this paper, we propose a novel and effective method called GLLCBN for multi-label learning. Firstly, we obtain the global label correlation by exploiting label semantic similarity. Then, we analyze the… More >

  • Open Access

    ARTICLE

    Sentiment Analysis Method Based on Kmeans and Online Transfer Learning

    Shengting Wu1, Yuling Liu1,*, Jingwen Wang2, Qi Li1

    CMC-Computers, Materials & Continua, Vol.60, No.3, pp. 1207-1222, 2019, DOI:10.32604/cmc.2019.05835

    Abstract Sentiment analysis is a research hot spot in the field of natural language processing and content security. Traditional methods are often difficult to handle the problems of large difference in sample distribution and the data in the target domain is transmitted in a streaming fashion. This paper proposes a sentiment analysis method based on Kmeans and online transfer learning in the view of fact that most existing sentiment analysis methods are based on transfer learning and offline transfer learning. We first use the Kmeans clustering algorithm to process data from one or multiple source domains More >

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