He Ni^1,*, Yongqiao Wang¹, Buyun Xu²

Intelligent Automation & Soft Computing, Vol.26, No.2, pp. 227-236, 2020, DOI:10.31209/2019.100000068

Abstract The paper presents a probabilistic clustering approach based on self-organizing learning algorithm and recursive Bayesian estimation. The model is built upon the principle that the market data space is multimodal and can be described by a mixture of Gaussian distributions. The model parameters are approximated by a stochastic recursive Bayesian learning: searches for the maximum a posterior solution at each step, stochastically updates model parameters using a “dualneighbourhood” function with adaptive simulated annealing, and applies profile likelihood confidence interval to avoid prolonged learning. The proposed model is based on a number of pioneer works, such as Mixture Gaussian Autoregressive Model,… More >

Omer Berat Sezer^*, Ahmet Murat Ozbayoglu

Intelligent Automation & Soft Computing, Vol.26, No.2, pp. 323-334, 2020, DOI:10.31209/2018.100000065

Abstract Even though computational intelligence techniques have been extensively utilized in financial trading systems, almost all developed models use the time series data for price prediction or identifying buy-sell points. However, in this study we decided to use 2-D stock bar chart images directly without introducing any additional time series associated with the underlying stock. We propose a novel algorithmic trading model CNN-BI (Convolutional Neural Network with Bar Images) using a 2-D Convolutional Neural Network. We generated 2-D images of sliding windows of 30-day bar charts for Dow 30 stocks and trained a deep Convolutional Neural Network (CNN) model for our… More >

Supreet Kaur^*, Vijay Kumar Joshi

Intelligent Automation & Soft Computing, Vol.26, No.2, pp. 217-226, 2020, DOI:10.31209/2018.100000064

Abstract Wireless sensor networks (WSNs) are susceptible to safety threats due to cumulative dependence upon transmission, computing, and control mechanisms. Therefore, securing the end-to-end communication becomes a major area of research in WSNs. A majority of existing protocols are based upon signature and recommended-based trust evaluation techniques only. However, these techniques are vulnerable to wormhole attacks that happen due to lesser synchronization between the sensor nodes. Therefore, to handle this problem, a novel hybrid crossover-based ant colony optimization-based routing protocol is proposed. An integrated modified signature and recommendationbased trust evaluation protocol for WSNs is presented. Extensive experiments reveal that the proposed… More >

Leyao Chen, Lei Hong^*, Jiayin Liu

Journal of New Media, Vol.2, No.2, pp. 91-98, 2020, DOI:10.32604/jnm.2020.010125

Abstract This paper aims to analyze the microblog data published by the official account in a certain province of China, and finds out the rule of Weibo that is easier to be forwarded in the new police media perspective. In this paper, a new topic-based model is proposed. Firstly, the LDA topic clustering algorithm is used to extract the topic categories with forwarding heat from the microblogs with high forwarding numbers, then the Naive Bayesian algorithm is used to topic categories. The sample data is processed to predict the type of microblog forwarding. In order to evaluate this method, a large… More >

Wangdong Jiang, Dongling Zhang^*, Yapeng Peng, Guang Sun, Ying Cao, Jing Li

Journal of New Media, Vol.2, No.2, pp. 79-90, 2020, DOI:10.32604/jnm.2020.010088

Abstract With the rapid development of science and technology and the increasing popularity of the Internet, the number of network users is gradually expanding, and the behavior of network users is becoming more and more complex. Users’ actual demand for resources on the network application platform is closely related to their historical behavior records. Therefore, it is very important to analyze the user behavior path conversion rate. Therefore, this paper analyses and studies user behavior path based on sales data. Through analyzing the user quality of the website as well as the user’s repurchase rate, repurchase rate and retention rate in… More >

Xiaoyan Chen, Jiahuan Chen^*, Zhongcheng Sha

Journal of New Media, Vol.2, No.2, pp. 61-77, 2020, DOI:10.32604/jnm.2020.010062

Abstract Aiming at the problem that the detection effect of traditional edge detection algorithm is not good, and the problem that the existing edge detection algorithm based on convolution network cannot solve the thick edge problem from the model itself, this paper proposes a new edge detection method based on the generative adversarial network. The confrontation network consists of generator network and discriminator network, generator network is composed of U-net network and discriminator network is composed of five-layer convolution network. In this paper, we use BSDS500 training data set to train the model. Finally, several images are randomly selected from BSDS500… More >

Tong Jiang^*

Journal of New Media, Vol.2, No.2, pp. 45-60, 2020, DOI:10.32604/jnm.2020.09823

Abstract Recently, person Re-identification (person Re-id) has attracted more and more attention, which has become a research focus of computer vision community. Person Re-id is used to ascertain whether the target pedestrians captured by cameras in different positions at different moments are the same person or not. However, due to the influence of various complex factors, person Re-id still has a lot of holes to be filled. In this paper, we first review the research process of person Re-id, and then, two kinds of mainstream methods for person Re-id are introduced respectively, according to the different types of training data they… More >

Yonghong Zhang^1,2, Guangyi Ma^1,2,*, Wei Tian³, Jiangeng Wang⁴, Shiwei Chen^1,2

CMES-Computer Modeling in Engineering & Sciences, Vol.124, No.3, pp. 1149-1165, 2020, DOI:10.32604/cmes.2020.010932

Abstract Snow cover is an important parameter in the fields of computer modeling, engineering technology and energy development. With the extensive growth of novel hardware and software compositions creating smart, cyber physical systems’ (CPS) efficient end-to-end workflows. In order to provide accurate snow detection results for the CPS’s terminal, this paper proposed a snow cover detection algorithm based on the unsupervised Gaussian mixture model (GMM) for the FY-4A satellite data. At present, most snow cover detection algorithms mainly utilize the characteristics of the optical spectrum, which is based on the normalized difference snow index (NDSI) with thresholds in different wavebands. These… More >

Xiaorui Zhang^1,2,*, Xuefeng Yu¹, Wei Sun², Aiguo Song³

CMES-Computer Modeling in Engineering & Sciences, Vol.124, No.3, pp. 1131-1147, 2020, DOI:10.32604/cmes.2020.09735

Abstract In order to solve the problem of high computing cost and low simulation accuracy caused by discontinuity of incision in traditional meshless model, this paper proposes a soft tissue deformation model based on the Marquardt algorithm and enrichment function. The model is based on the element-free Galerkin method, in which Kelvin viscoelastic model and adjustment function are integrated. Marquardt algorithm is applied to fit the relation between force and displacement caused by surface deformation, and the enrichment function is applied to deal with the discontinuity in the meshless method. To verify the validity of the model, the Sensable Phantom Omni… More >

Wanxin Zhang^1,*, Jihong Zhu²

CMES-Computer Modeling in Engineering & Sciences, Vol.124, No.3, pp. 1119-1130, 2020, DOI:10.32604/cmes.2020.010986

Abstract With the increasing demand for the automation of operations and processes in mechatronic systems, fault detection and diagnosis has become a major topic to guarantee the process performance. There exist numerous studies on the topic of applying artificial intelligence methods for fault detection and diagnosis. However, much of the focus has been given on the detection of faults. In terms of the diagnosis of faults, on one hand, assumptions are required, which restricts the diagnosis range. On the other hand, different faults with similar symptoms cannot be distinguished, especially when the model is not trained by plenty of data. In… More >