Cervical cancer survival prediction using hybrid of SMOTE, CART and smooth support vector machine

Urban traffic flow prediction has always been an important realm for smart city build-up. With the development of edge computing technology in recent years, the network edge nodes of smart cities are able to collect and process various types of urban traffic data in real time, which leads to the possibility of deploying intelligent traffic prediction technology with real-time analysis and timely feedback on the edge. In view of the strong nonlinear characteristics of urban traffic flow, multiple dynamic and static influencing factors involved, and increasing difficulty of short-term traffic flow prediction in a metropolitan area, this paper proposes an urban traffic flow prediction model based on chaotic particle swarm optimization algorithm-smooth support vector machine (CPSO/SSVM). The prediction model has built a new second-order smooth function to achieve better approximation and regression effects and has further improved the computational efficiency of the smooth support vector machine algorithm through chaotic particle swarm optimization. Simulation experiment results show that this model can accurately predict urban traffic flow.

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A gastric cancer LncRNAs model for MSI and survival prediction based on support vector machine

BMC Genomics ◽

10.1186/s12864-019-6135-x ◽

2019 ◽

Vol 20 (1) ◽

Cited By ~ 5

Author(s):

Tao Chen ◽

Cangui Zhang ◽

Yingqiao Liu ◽

Yuyun Zhao ◽

Dingyi Lin ◽

...

Keyword(s):

Gastric Cancer ◽

Support Vector Machine ◽

Overall Survival ◽

Transcriptional Regulation ◽

Monoclonal Antibodies ◽

Tumor Therapy ◽

Disease Free Survival ◽

Stage I ◽

Support Vector ◽

Survival Prediction

Abstract Background Recent studies have shown that long non-coding RNAs (lncRNAs) play a crucial role in the induction of cancer through epigenetic regulation, transcriptional regulation, post-transcriptional regulation and other aspects, thus participating in various biological processes such as cell proliferation, differentiation and apoptosis. As a new nova of anti-tumor therapy, immunotherapy has been shown to be effective in many tumors of which PD-1/PD-L1 monoclonal antibodies has been proofed to increase overall survival rate in advanced gastric cancer (GC). Microsatellite instability (MSI) was known as a biomarker of response to PD-1/PD-L1 monoclonal antibodies therapy. The aim of this study was to identify lncRNAs signatures able to classify MSI status and create a predictive model associated with MSI for GC patients. Methods Using the data of Stomach adenocarcinoma from The Cancer Genome Atlas (TCGA), we developed and validated a lncRNAs model for automatic MSI classification using a machine learning technology – support vector machine (SVM). The C-index was adopted to evaluate its accuracy. The prognostic values of overall survival (OS) and disease-free survival (DFS) were also assessed in this model. Results Using the SVM, a lncRNAs model was established consisting of 16 lncRNA features. In the training cohort with 94 GC patients, accuracy was confirmed with AUC 0.976 (95% CI, 0.952 to 0.999). Veracity was also confirmed in the validation cohort (40 GC patients) with AUC 0.950 (0.889 to 0.999). High predicted score was correlated with better DFS in the patients with stage I-III and lower OS with stage I-IV. Conclusion This study identify 16 LncRNAs signatures able to classify MSI status. The correlation between lncRNAs and MSI status indicates the potential roles of lncRNAs interacting in immunotherapy for GC patients. The pathway of these lncRNAs which might be a target in PD-1/PD-L1 immunotherapy are needed to be further study.

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Gender Classification Based on Geometry Features of Palm Image

The Scientific World JOURNAL ◽

10.1155/2014/734564 ◽

2014 ◽

Vol 2014 ◽

pp. 1-7 ◽

Cited By ~ 7

Author(s):

Ming Wu ◽

Yubo Yuan

Keyword(s):

Image Processing ◽

Support Vector Machine ◽

The Other ◽

Classification Method ◽

Support Vector ◽

Gender Classification ◽

Gender Recognition ◽

Classification Rate ◽

Classification Approach ◽

Smooth Support Vector Machine

This paper presents a novel gender classification method based on geometry features of palm image which is simple, fast, and easy to handle. This gender classification method based on geometry features comprises two main attributes. The first one is feature extraction by image processing. The other one is classification system with polynomial smooth support vector machine (PSSVM). A total of 180 palm images were collected from 30 persons to verify the validity of the proposed gender classification approach and the results are satisfactory with classification rate over 85%. Experimental results demonstrate that our proposed approach is feasible and effective in gender recognition.

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