Yonghong Xie^{1, 3}, Liangyuan Hu^{1, 3}, Xingxing Chen^{2, 3}, Jim Feng⁴, Dezheng Zhang^{1, 3, *}

CMC-Computers, Materials & Continua, Vol.65, No.1, pp. 481-494, 2020, DOI:10.32604/cmc.2020.010297 - 23 July 2020

Abstract As one of the most valuable assets in China, traditional medicine has a long history and contains pieces of knowledge. The diagnosis and treatment of Traditional Chinese Medicine (TCM) has benefited from the natural language processing technology. This paper proposes a knowledge-based syndrome reasoning method in computerassisted diagnosis. This method is based on the established knowledge graph of TCM and this paper introduces the reinforcement learning algorithm to mine the hidden relationship among the entities and obtain the reasoning path. According to this reasoning path, we could infer the path from the symptoms to the More >

Shengchun Wang¹, Xiaozhong Yu¹, Lianye Liu², Jingui Huang^{1, *}, Tsz Ho Wong³, Chengcheng Jiang¹

CMC-Computers, Materials & Continua, Vol.65, No.1, pp. 459-479, 2020, DOI:10.32604/cmc.2020.010627 - 23 July 2020

Abstract Radar quantitative precipitation estimation (QPE) is a key and challenging task for many designs and applications with meteorological purposes. Since the Z-R relation between radar and rain has a number of parameters on different areas, and the rainfall varies with seasons, the traditional methods are incapable of achieving high spatial and temporal resolution and thus difficult to obtain a refined rainfall estimation. This paper proposes a radar quantitative precipitation estimation algorithm based on the spatiotemporal network model (ST-QPE), which designs a convolutional time-series network QPE-Net8 and a multi-scale feature fusion time-series network QPE-Net22 to address More >

Yiming Huang^{1, *}, Hang Lei¹, Xiaoyu Li^{1, *}, Qingsheng Zhu², Wanghao Ren³, Xusheng Liu^{2, 4}

CMC-Computers, Materials & Continua, Vol.65, No.1, pp. 445-458, 2020, DOI:10.32604/cmc.2020.010390 - 23 July 2020

Abstract In recent years, an increasing number of studies about quantum machine learning not only provide powerful tools for quantum chemistry and quantum physics but also improve the classical learning algorithm. The hybrid quantum-classical framework, which is constructed by a variational quantum circuit (VQC) and an optimizer, plays a key role in the latest quantum machine learning studies. Nevertheless, in these hybridframework-based quantum machine learning models, the VQC is mainly constructed with a fixed structure and this structure causes inflexibility problems. There are also few studies focused on comparing the performance of quantum generative models with… More >

Shi Qiu¹, Bin Li^{2, *}, Keyang Cheng³, Xiao Zhang², Guifang Duan⁴, Feng Li⁵

CMC-Computers, Materials & Continua, Vol.65, No.1, pp. 433-443, 2020, DOI:10.32604/cmc.2020.09708 - 23 July 2020

Abstract of view. It can be applied in virtual reality, smart homes and other fields as well. A multi-directional reconstruction algorithm for panoramic camera is proposed in this paper according to the imaging principle of dome camera, as the distortion inevitably exists in the captured panorama. First, parameters of a panoramic image are calculated. Then, a weighting operator with location information is introduced to solve the problem of rough edges by taking full advantage of pixels. Six directions of the mapping model are built, which include up, down, left, right, front and back, according to More >

Yu Wang¹, Chen Liu¹, Lei Wang², Lihua Zhu^{1, *}

CMC-Computers, Materials & Continua, Vol.65, No.1, pp. 419-432, 2020, DOI:10.32604/cmc.2020.010911 - 23 July 2020

Abstract The internal structure of the inertial measurement unit (IMU) in active state is easily damaged in the high overload environment. So that the IMU is usually required to be powered within the disappearance of the high overload. In this paper, a mechanical switch is designed to enable the IMU based on the analysis of the impact of high overload on the power-supply circuit. In which, parameters of mechanical switch are determined through theoretical calculation and data analysis. The innovation of the proposed structure lies in that the mechanical switch is triggered through the high overload More >

Lihua Zhu¹, Zhiqiang Wu¹, Lei Wang², Yu Wang^{1, *}

CMC-Computers, Materials & Continua, Vol.65, No.1, pp. 405-418, 2020, DOI:10.32604/cmc.2020.010124 - 23 July 2020

Abstract The wind as a natural phenomenon would cause the derivation of the pesticide drops during the operation of agricultural unmanned aerial vehicles (UAV). In particular, the changeable wind makes it difficult for the precision agriculture. For accurate spraying of pesticide, it is necessary to estimate the real-time wind parameters to provide the correction reference for the UAV path. Most estimation algorithms are model based, and as such, serious errors can arise when the models fail to properly fit the physical wind motions. To address this problem, a robust estimation model is proposed in this paper. More >

Xinyue Lin¹, Jin Liu^{1, *}, Jianming Zhang², Se-Jung Lim³

CMC-Computers, Materials & Continua, Vol.65, No.1, pp. 387-404, 2020, DOI:10.32604/cmc.2020.010984 - 23 July 2020

Abstract Neural Machine Translation (NMT) is an end-to-end learning approach for automated translation, overcoming the weaknesses of conventional phrase-based translation systems. Although NMT based systems have gained their popularity in commercial translation applications, there is still plenty of room for improvement. Being the most popular search algorithm in NMT, beam search is vital to the translation result. However, traditional beam search can produce duplicate or missing translation due to its target sequence selection strategy. Aiming to alleviate this problem, this paper proposed neural machine translation improvements based on a novel beam search evaluation function. And we More >

Haijiang Liu¹, Lianwei Cui², Xuebin Ma^{1, *}, Celimuge Wu³

CMC-Computers, Materials & Continua, Vol.65, No.1, pp. 369-385, 2020, DOI:10.32604/cmc.2020.010987 - 23 July 2020

Abstract Frequent itemset mining is an essential problem in data mining and plays a key role in many data mining applications. However, users’ personal privacy will be leaked in the mining process. In recent years, application of local differential privacy protection models to mine frequent itemsets is a relatively reliable and secure protection method. Local differential privacy means that users first perturb the original data and then send these data to the aggregator, preventing the aggregator from revealing the user’s private information. We propose a novel framework that implements frequent itemset mining under local differential privacy More >

Ye Wang¹, Bixin Liu², Hongjia Wu¹, Shan Zhao¹, Zhiping Cai^{1, *}, Donghui Li^{3, *}, Cheang Chak Fong⁴

CMC-Computers, Materials & Continua, Vol.65, No.1, pp. 355-367, 2020, DOI:10.32604/cmc.2020.09835 - 23 July 2020

Abstract With the continuous development of e-commerce, consumers show increasing interest in posting comments on consumption experience and quality of commodities. Meanwhile, people make purchasing decisions relying on other comments much more than ever before. So the reliability of commodity comments has a significant impact on ensuring consumers’ equity and building a fair internet-trade-environment. However, some unscrupulous online-sellers write fake praiseful reviews for themselves and malicious comments for their business counterparts to maximize their profits. Those improper ways of self-profiting have severely ruined the entire online shopping industry. Aiming to detect and prevent these deceptive comments More >

Tao Li¹, Yongzhen Ren^{1, *}, Yongjun Ren², Jinyue Xia³

CMC-Computers, Materials & Continua, Vol.65, No.1, pp. 337-354, 2020, DOI:10.32604/cmc.2020.06462 - 23 July 2020

Abstract Apriori algorithm is often used in traditional association rules mining, searching for the mode of higher frequency. Then the correlation rules are obtained by detected the correlation of the item sets, but this tends to ignore low-support high-correlation of association rules. In view of the above problems, some scholars put forward the positive correlation coefficient based on Phi correlation to avoid the embarrassment caused by Apriori algorithm. It can dig item sets with low-support but high-correlation. Although the algorithm has pruned the search space, it is not obvious that the performance of the running time… More >