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 >

Ye Zhai^{1, *}, Dongsheng Liu¹, Celimuge Wu², Rongrong She¹

CMC-Computers, Materials & Continua, Vol.64, No.3, pp. 1999-2012, 2020, DOI:10.32604/cmc.2020.09950

Abstract Reusing code fragments by copying and pasting them with or without minor adaptation is a common activity in software development. As a result, software systems often contain sections of code that are very similar, called code clones. Code clones are beneficial in reducing software development costs and development risks. However, recent studies have indicated some negative impacts as a result. In order to effectively manage and utilize the clones, we design an approach for recommending refactoring clones based on a Bayesian network. Firstly, clone codes are detected from the source code. Secondly, the clones that need to be refactored are… More >

Yan Li^1,2, Dayou Liu^1,2, Yungang Zhu^1,2, Jie Liu^1,2,*

CMES-Computer Modeling in Engineering & Sciences, Vol.124, No.1, pp. 303-313, 2020, DOI:10.32604/cmes.2020.09353

Abstract Under different conditions, gene regulatory networks (GRNs) of the same gene set could be similar but different. The differential analysis of GRNs under different conditions is important for understanding condition-specific gene regulatory relationships. In a naive approach, existing GRN inference algorithms can be used to separately estimate two GRNs under different conditions and identify the differences between them. However, in this way, the similarities between the pairwise GRNs are not taken into account. Several joint differential analysis algorithms have been proposed recently, which were proved to outperform the naive approach apparently. In this paper, we model the GRNs under different… More >

Kun Zhu¹, Nana Zhang¹, Shi Ying^{1, *}, Xu Wang²

CMC-Computers, Materials & Continua, Vol.63, No.2, pp. 891-910, 2020, DOI:10.32604/cmc.2020.08096

Abstract With the continuous expansion of software scale, software update and maintenance have become more and more important. However, frequent software code updates will make the software more likely to introduce new defects. So how to predict the defects quickly and accurately on the software change has become an important problem for software developers. Current defect prediction methods often cannot reflect the feature information of the defect comprehensively, and the detection effect is not ideal enough. Therefore, we propose a novel defect prediction model named ITNB (Improved Transfer Naive Bayes) based on improved transfer Naive Bayesian algorithm in this paper, which… More >

Sasan Saqaeeyan¹, Hamid Haj Seyyed Javadi^1,2,*, Hossein Amirkhani^1,3

CMES-Computer Modeling in Engineering & Sciences, Vol.121, No.3, pp. 815-834, 2019, DOI:10.32604/cmes.2019.07848

Abstract Anomaly detection in smart homes provides support to enhance the health and safety of people who live alone. Compared to the previous studies done on this topic, less attention has been given to hybrid methods. This paper presents a two-steps hybrid probabilistic anomaly detection model in the smart home. First, it employs various algorithms with different characteristics to detect anomalies from sensory data. Then, it aggregates their results using a Bayesian network. In this Bayesian network, abnormal events are detected through calculating the probability of abnormality given anomaly detection results of base methods. Experimental evaluation of a real dataset indicates… More >

Hoheok Kim¹, Tatsuki Yamamoto², Yushi Sato¹, Junya Inoue^1,3,4,*

The International Conference on Computational & Experimental Engineering and Sciences, Vol.22, No.2, pp. 135-135, 2019, DOI:10.32604/icces.2019.05453

Abstract The viability of establishing low-cost surrogate structure-property (S-P) linkages which applies a Bayesian model selection method to the Materials Knowledge System (MKS) homogenization framework is studied. The MKS framework employs the n-point correlation function, principal component analysis, and regression techniques for mapping between the structural factors and the property of a material. However, the framework chooses the factors not by their influence on the property but by their amount of inherent microstructural information. This also makes it difficult to find out which microstructural morphology affects the property. In the present work, we introduced a Bayesian model selection method to choose… More >

Yoh-ichi Mototake¹, Hitoshi Izuno², Kenji Nagata^3,4, Masahiko Demura² , Masato Okada^1,2,*

The International Conference on Computational & Experimental Engineering and Sciences, Vol.22, No.2, pp. 129-130, 2019, DOI:10.32604/icces.2019.05389

Abstract The creep deformation process is constructed by complex interactions of multiple factors, and the measurement of creep deformation requires enormous economic costs and a long experimental time, so there is a small amount of measurement data. In such a situation, multiple models are often proposed to explain the same experimental data. The coexistence of multiple models based on different physical assumptions makes it difficult to understand the creep deformation process.
The purpose of this study is to construct a framework to compare and evaluate coexistence models based on measurement data using the Bayesian model selection framework. Basically, in the… More >

Yue Zhao¹, Jianjian Yue¹, Wei Song^1,*, Xiaona Xu¹, Xiali Li¹, Licheng Wu¹, Qiang Ji²

Journal on Internet of Things, Vol.1, No.1, pp. 17-23, 2019, DOI:10.32604/jiot.2019.05866

Abstract We proposed a method using latent regression Bayesian network (LRBN) to extract the shared speech feature for the input of end-to-end speech recognition model. The structure of LRBN is compact and its parameter learning is fast. Compared with Convolutional Neural Network, it has a simpler and understood structure and less parameters to learn. Experimental results show that the advantage of hybrid LRBN/Bidirectional Long Short-Term Memory-Connectionist Temporal Classification architecture for Tibetan multi-dialect speech recognition, and demonstrate the LRBN is helpful to differentiate among multiple language speech sets. More >

D. C. Knupp^1,2, A. J. Silva Neto³

CMES-Computer Modeling in Engineering & Sciences, Vol.96, No.5, pp. 339-360, 2013, DOI:10.3970/cmes.2013.096.339

Abstract Inverse radiative transfer problems in heterogeneous participating media applications include determining gas properties in combustion chambers, estimating environmental and atmospheric conditions, and remote sensing, among others. In recent papers the spatially variable single scattering albedo has been estimated by expanding this unknown function as a series of known functions, and then estimating the expansion coefficients with parameter estimation techniques. In the present work we assume that there is no prior information on the functional form of the unknown spatially variable albedo and, making use of the Bayesian approach, we propose the development of a posterior probability density, which is explored… More >

W. Zhang¹, X. Han^1,2, J. Liu¹, R. Chen¹

CMES-Computer Modeling in Engineering & Sciences, Vol.86, No.5, pp. 385-408, 2012, DOI:10.3970/cmes.2012.086.385

Abstract A novel inverse technique is presented for quantifying the uncertainty of the identified the results in an encounter condition identification problem. In this technique, the polynomial response surface method based on the structure-selection technique is first adopted to construct the approximation model of the projectile/target system, so as to reduce the computational cost. The Markov Chain Monte Carlo method is then used to identify the encounter condition parameters and their confidence intervals based on this cheap approximation model with Bayesian perspective. The results are demonstrated through the simulated test cases, which show the utility and efficiency of the proposed technique.… More >