Noor Mualla¹, Essam H Houssein^{2, *}, 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 using a number of suitable… More >

Xing Deng^{1, 2}, Haijian Shao^{1, *}, 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

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 based on the state of… More >

Kehua Yang^1,*, 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 pairwise labels in the label… More >

Shengting Wu¹, Yuling Liu^1,*, 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 and select the data similar… More >