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Jiahui He¹, Chaozhi Wang¹, Hongyu Wu¹, Leiming Yan^1,*, Christian Lu²
Journal of New Media, Vol.1, No.2, pp. 51-61, 2019, DOI:10.32604/jnm.2019.06238
Abstract Multi-label text categorization refers to the problem of categorizing text through a multi-label learning algorithm. Text classification for Asian languages such as Chinese is different from work for other languages such as English which use spaces to separate words. Before classifying text, it is necessary to perform a word segmentation operation to convert a continuous language into a list of separate words and then convert it into a vector of a certain dimension. Generally, multi-label learning algorithms can be divided into two categories, problem transformation methods and adapted algorithms. This work will use customer's comments More >

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Fangming Bi^1,2, Xin Ma^1,2, Wei Chen^1,2,*, Weidong Fang³, Huayi Chen^1,2, Jingru Li^1,2, Biruk Assefa^1,4
Journal of New Media, Vol.1, No.2, pp. 63-74, 2019, DOI:10.32604/jnm.2019.06253
Abstract Video object tracking is an important research topic of computer vision, which finds a wide range of applications in video surveillance, robotics, human-computer interaction and so on. Although many moving object tracking algorithms have been proposed, there are still many difficulties in the actual tracking process, such as illumination change, occlusion, motion blurring, scale change, self-change and so on. Therefore, the development of object tracking technology is still challenging. The emergence of deep learning theory and method provides a new opportunity for the research of object tracking, and it is also the main theoretical framework More >

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ARTICLE

Xiaoli Li^1,*, Chao Ye¹, Yujia Yan², Zhenlong Du¹
Journal of New Media, Vol.1, No.2, pp. 75-85, 2019, DOI:10.32604/jnm.2019.06259
Abstract In order to improve the quality of low-dose computational tomography (CT) images, the paper proposes an improved image denoising approach based on WGAN-gp with Wasserstein distance. For improving the training and the convergence efficiency, the given method introduces the gradient penalty term to WGAN network. The novel perceptual loss is introduced to make the texture information of the low-dose images sensitive to the diagnostician eye. The experimental results show that compared with the state-of-art methods, the time complexity is reduced, and the visual quality of low-dose CT images is significantly improved. More >

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Jingcheng Chen¹, Zhili Zhou^1,2,*, Zhaoqing Pan¹, Ching-nung Yang³
Journal of New Media, Vol.1, No.2, pp. 87-99, 2019, DOI:10.32604/jnm.2019.06582
Abstract Recently, image representations derived by convolutional neural networks (CNN) have achieved promising performance for instance retrieval, and they outperform the traditional hand-crafted image features. However, most of existing CNN-based features are proposed to describe the entire images, and thus they are less robust to background clutter. This paper proposes a region of interest (RoI)-based deep convolutional representation for instance retrieval. It first detects the region of interests (RoIs) from an image, and then extracts a set of RoI-based CNN features from the fully-connected layer of CNN. The proposed RoI-based CNN feature describes the patterns of More >

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ARTICLE

Qi Liu^1,2, Zhiyun Yang¹, Xiaodong Liu³, Scholas Mbonihankuye^4,*
Journal of New Media, Vol.1, No.2, pp. 101-113, 2019, DOI:10.32604/jnm.2019.06958
Abstract This paper aims to study energy consumption in a house. Home energy man-agement system (HEMS) has become very important, because energy consumption of a residential sector accounts for a significant amount of total energy consumption. However, a conventional HEMS has some architectural limitations among dimensional variables reusability and interoperability. Furthermore, the cost of implementation in HEMS is very expensive, which leads to the disturbance of the spread of a HEMS. Therefore, this study proposes an Internet of Things (IoT) based HEMS with lightweight photovoltaic (PV) system over dynamic home area networks (DHANs), which enables the More >

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