KNN-BLOCK DBSCAN: Fast Clustering for Large-Scale Data

Chen, Yewang; Zhou, Lida; Pei, Songwen; Yu, Zhiwen; Chen, Yi; Liu, Xin; Du, Ji-Xiang; Xiong, Naixue

doi:10.1109/tsmc.2019.2956527

Cited by 127 publications

(44 citation statements)

References 47 publications

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“…The reason why our five-layer model is better than that six-layer model [ 9 ] is threefold: (i) We choose SP to improve the performance of our deep learning model; (ii) We fine-tune the hyperparameters (such as , , number of filters at each CB, number of neurons at each FCB); (iii) Our model was particularly designed for detecting COVID-19, while the 6L-CNN-F [ 9 ] was designed for fingerspelling recognition. In the future, we shall try to use clustering techniques [ 28 , 29 ] to help improve the performance. Figure 10 shows the comparison bar plot of all seven methods.…”

Section: Experiments Results and Discussionmentioning

confidence: 99%

A five-layer deep convolutional neural network with stochastic pooling for chest CT-based COVID-19 diagnosis

Zhang

Satapathy

Liu

et al. 2020

Machine Vision and Applications

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Till August 17, 2020, COVID-19 has caused 21.59 million confirmed cases in more than 227 countries and territories, and 26 naval ships. Chest CT is an effective way to detect COVID-19. This study proposed a novel deep learning model that can diagnose COVID-19 on chest CT more accurately and swiftly. Based on traditional deep convolutional neural network (DCNN) model, we proposed three improvements: (i) We introduced stochastic pooling to replace average pooling and max pooling; (ii) We combined conv layer with batch normalization layer and obtained the conv block (CB); (iii) We combined dropout layer with fully connected layer and obtained the fully connected block (FCB). Our algorithm achieved a sensitivity of 93.28% ± 1.50%, a specificity of 94.00% ± 1.56%, and an accuracy of 93.64% ± 1.42%, in identifying COVID-19 from normal subjects. We proved using stochastic pooling yields better performance than average pooling and max pooling. We compared different structure configurations and proved our 3CB + 2FCB yields the best performance. The proposed model is effective in detecting COVID-19 based on chest CT images.

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Section: Experiments Results and Discussionmentioning

confidence: 99%

A five-layer deep convolutional neural network with stochastic pooling for chest CT-based COVID-19 diagnosis

Zhang

Satapathy

Liu

et al. 2020

Machine Vision and Applications

View full text Add to dashboard Cite

show abstract

“…Our future work is to develop a new version of the proposed method by using fast clustering [28]- [30] and CNN [31] based time series data mining to deal with complex consultation. Also, we would optimize the depth of the decision tree, and prevent the tree building from overfitting.…”

Section: Discussionmentioning

confidence: 99%

Automatic Dialogue System of Marriage Law Based on the Parallel C4.5 Decision Tree

Wang

Chen

et al. 2020

IEEE Access

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In order to realize real-time marriage legal consultation automatically, a marriage legal dialogue system based on the parallel C4.5 decision tree was designed in this paper. Firstly, the legal consultation problem is transformed into a classification task. Secondly, a legal consultation classification prediction model based on the parallel C4.5 decision tree algorithm was trained with MapReduce by collected data. Finally, a model based on the SVM algorithm, which has a strategy that was designed to provide automatic interaction for users, was designed to extract attribute value from the user's input. When a new user comes to consult, an automatic legal dialogue is launched to respond to user intelligently. The proposed system works well in some real applications, such as the divorce problem, and the experimental results show that it is outperforming the SVM and NB algorithm and more applicable than the other two algorithms. Moreover, the system can return consultation results with fewer questions asked to the user than some automatic legal consultation websites, which improves the efficiency of consultation. INDEX TERMS Automatic dialogue system, parallel C4.5 decision tree, SVM, attribute value extraction, marriage law.

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“…In the future, we will focus on how to combine the different features more effectively and select a better classification model for the combined feature. Moreover, we will improve the proposed method by data preparation techniques, such as clustering [51][52][53], normalization, and data cleaning.…”

Section: Discussionmentioning

confidence: 99%

Steganalysis of Adaptive Multi-Rate Speech Based on Extreme Gradient Boosting

et al. 2020

Self Cite

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Steganalysis of adaptive multi-rate (AMR) speech is a hot topic for controlling cybercrimes grounded in steganography in related speech streams. In this paper, we first present a novel AMR steganalysis model, which utilizes extreme gradient boosting (XGBoost) as the classifier, instead of support vector machines (SVM) adopted in the previous schemes. Compared with the SVM-based model, this new model can facilitate the excavation of potential information from the high-dimensional features and can avoid overfitting. Moreover, to further strengthen the preceding features based on the statistical characteristics of pulse pairs, we present the convergence feature based on the Markov chain to reflect the global characterization of pulse pairs, which is essentially the final state of the Markov transition matrix. Combining the convergence feature with the preceding features, we propose an XGBoost-based steganalysis scheme for AMR speech streams. Finally, we conducted a series of experiments to assess our presented scheme and compared it with previous schemes. The experimental results demonstrate that the proposed scheme is feasible, and can provide better performance in terms of detecting the existing steganography methods based on AMR speech streams.

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KNN-BLOCK DBSCAN: Fast Clustering for Large-Scale Data

Cited by 127 publications

References 47 publications

A five-layer deep convolutional neural network with stochastic pooling for chest CT-based COVID-19 diagnosis

A five-layer deep convolutional neural network with stochastic pooling for chest CT-based COVID-19 diagnosis

Automatic Dialogue System of Marriage Law Based on the Parallel C4.5 Decision Tree

Steganalysis of Adaptive Multi-Rate Speech Based on Extreme Gradient Boosting

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