scholarly journals A Credit Conflict Detection Model Based on Decision Distance and Probability Matrix

2022 ◽  
Vol 2022 ◽  
pp. 1-7
Author(s):  
Xiaodong Zhang ◽  
Congdong Lv ◽  
Zhoubao Sun
Keyword(s):  
Influencing Factors ◽  
Elderly Care ◽  
Distance Matrix ◽  
Conflict Detection ◽  
Support Vector ◽  
Similarity Matrix ◽  
Detection Model ◽  
Model Based ◽  
Internet Finance ◽  
The Impact

Considering the credit index calculation differences, semantic differences, false data, and other problems between platforms such as Internet finance, e-commerce, and health and elderly care, which lead to the credit deviation from the trusted range of credit subjects and the lack of related information of credit subjects, in this paper, we proposed a crossplatform service credit conflict detection model based on the decision distance to support the migration and application of crossplatform credit information transmission and integration. Firstly, we give a scoring table of influencing factors. Score is the probability of the impact of this factor on credit. Through this probability, the distance matrix between influencing factors is generated. Secondly, the similarity matrix is calculated from the distance matrix. Thirdly, the support vector is calculated through the similarity matrix. Fourth, the credit vector is calculated by the support vector. Finally, the credibility is calculated by the credit vector and probability.

scholarly journals Active Learning for Constrained Document Clustering with Uncertainty Region

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-16
Author(s):  
M. A. Balafar ◽  
R. Hazratgholizadeh ◽  
M. R. F. Derakhshi
Keyword(s):  
Iterative Process ◽  
Clustering Algorithm ◽  
State Of The Art ◽  
Distance Matrix ◽  
Support Vector ◽  
Constrained Clustering ◽  
Similarity Matrix ◽  
Improve Accuracy ◽  
Degree Of Similarity ◽  
Accuracy And Stability

Constrained clustering is intended to improve accuracy and personalization based on the constraints expressed by an Oracle. In this paper, a new constrained clustering algorithm is proposed and some of the informative data pairs are selected during an iterative process. Then, they are presented to the Oracle and their relation is answered with “Must-link (ML) or Cannot-link (CL).” In each iteration, first, the support vector machine (SVM) is utilized based on the label produced by the current clustering. According to the distance of each document from the hyperplane, the distance matrix is created. Also, based on cosine similarity of word2vector of each document, the similarity matrix is created. Two types of probability (similarity and degree of similarity) are calculated and they are smoothed for belonging to neighborhoods. Neighborhoods form the samples that are labeled by Oracle, to be in the same cluster. Finally, at the end of each iteration, the data with a greater level of uncertainty (in term of probability) is selected for questioning the oracle. In order to evaluate, the proposed method is compared with famous state-of-the-art methods based on two criteria and over a standard dataset. The result demonstrates an increased accuracy and stability of the obtained result with fewer questions.

scholarly journals Establishment of Landslide Groundwater Level Prediction Model Based on GA-SVM and Influencing Factor Analysis

Sensors ◽  
2020 ◽  
Vol 20 (3) ◽  
pp. 845 ◽  
Author(s):  
Ying Cao ◽  
Kunlong Yin ◽  
Chao Zhou ◽  
Bayes Ahmed
Keyword(s):  
Prediction Model ◽  
Influencing Factors ◽  
Groundwater Level ◽  
Influencing Factor ◽  
Hybrid Genetic Algorithm ◽  
Early Warning Systems ◽  
Three Gorges Reservoir Area ◽  
Support Vector ◽  
Single Factor ◽  
The Impact

The monitoring and prediction of the landslide groundwater level is a crucial part of landslide early warning systems. In this study, Tangjiao landslide in the Three Gorges Reservoir area (TGRA) in China was taken as a case study. Three groundwater level monitoring sensors were installed in different locations of the landslide. The monitoring data indicated that the fluctuation of groundwater level is significantly consistent with rainfall and reservoir level in time, but there is a lag. In addition, there is a spatial difference in the impact of reservoir levels on the landslide groundwater level. The data of two monitoring locations were selected for establishing the prediction model of groundwater. Combined with the qualitative and quantitative analysis, the influencing factors were selected, respectively, to establish the hybrid Genetic Algorithm-Support Vector Machine (GA-SVM) prediction model. The single-factor GA-SVM without considering influencing factors and the backpropagation neural network (BPNN) model were adopted to make comparisons. The results showed that the multi-factor GA-SVM performed the best, followed by multi-factor BPNN and single-factor GA-SVM. We found that the prediction accuracy can be improved by considering the influencing factor. The proposed GA-SVM model combines the advantages of each algorithm; it can effectively construct the response relationship between groundwater level fluctuations and influencing factors. Above all, the multi-factor GA-SVM is an effective method for the prediction of landslides groundwater in the TGRA.

Research on the Passive Detection Model Based on 3D-HBF and FSVDD for Underwater High-Speed Small Targets and its Application

2011 ◽  
Vol 317-319 ◽  
pp. 1282-1288
Author(s):  
Qiao Hu ◽  
Bao An Hao ◽  
Hong Yi ◽  
Yun Chuan Yang
Keyword(s):  
Target Detection ◽  
High Speed ◽  
Beam Width ◽  
Target Strength ◽  
Support Vector ◽  
Support Vector Data Description ◽  
Passive Detection ◽  
Detection Model ◽  
Model Based ◽  
Small Targets

Due to the high-speed, short-time countermeasure and small target strength of underwater high-speed small targets (UHSST), it is difficult to use a traditional method to accurately detect UHSST. So a novel passive detection model based on three-dimensional hyperbeam forming (3D-HBF) and fuzzy support vector data description (FSVDD) is proposed, where these advantages of beam width reduction and side lobe suppression for 3D-HBF and excellent target-detection capability for FSVDD are combined. The model consists of two stages. In the first stage, 3D-HBF is carried out to obtain the beam respond vectors (BSV) from original underwater acoustic signals. In the second stage, the BSV are input into the detector based on FSVDD to detect and locate the underwater targets intelligently. This model is applied to target detection of UHSST, and these testing results show that the proposed model has better detection performance than the conventional beam forming method, with a high detection success rate and localization capability.

Long-term Runoff Forecasting Models Based on the Teleconnection coupled with Machine Learning

2020 ◽  
Author(s):  
Teng Zhang ◽  
Zhongjing Wang ◽  
Zixiong Zhang
Keyword(s):  
Support Vector Machine ◽  
Water Resources ◽  
Model Performance ◽  
Support Vector ◽  
Wet Season ◽  
Model Based ◽  
Runoff Forecasting ◽  
Monthly Runoff ◽  
Highly Correlated ◽  
The Impact

<p>Runoff forecast with high precision is important for the efficient utilization of water resources and regional sustainable development, especially in the arid area. The monthly runoff of Changmabao (CMB) station has an upwards trend and an abrupt point in 1998. The impact factor analysis shows that it is highly correlated with the current precipitation and temperature in the wet season while the previous runoff and previous global land temperature in the dry season. Three models including the time-series decomposition model, the model based on teleconnection coupled with the support vector machine, and the model based on teleconnection coupled with the artificial neural network are used to predict the runoff of CMB station. An indicator β is constructed with the correlation coefficient (R) and mean relative deviation (rBias) to evaluate the model performance more conveniently and intuitively. The results suggest that the model based on teleconnection coupled with the support vector machine preforms best. This forecasting method could be applied to the management and dispatch of water resources in arid areas.</p>

scholarly journals An Android Malware Detection Model Based on DT-SVM

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Min Yang ◽  
Xingshu Chen ◽  
Yonggang Luo ◽  
Hang Zhang
Keyword(s):  
Decision Tree ◽  
Malware Detection ◽  
Small Sample ◽  
Support Vector ◽  
Android Malware ◽  
Detection Model ◽  
Model Based ◽  
Android Malware Detection ◽  
Svm Algorithm ◽  
N Gram

In order to improve the accuracy and efficiency of Android malware detection, an Android malware detection model based on decision tree (DT) with support vector machine (SVM) algorithm (DT-SVM) is proposed. Firstly, the original opcode, Dalvik opcode, is extracted by reversing Android software, and the eigenvector of the sample is generated by using the n-gram model. Then, a decision tree is generated via training the sample and updating decision nodes as SVM nodes from the bottom up according to the evaluation result of the test set in the decision path. The model effectively combines DT with SVM. Under the premise of maintaining a high-accuracy decision path, SVM is used to effectively reduce the overfitting problem in DT and thus improve the generalization ability, and maintain the superiority of SVM for the small sample training set. Finally, to test our approach, several simulation experiments are carried out, and the results demonstrate that the improved algorithm has better accuracy and higher speed as compared with other malware detection approaches.

Sign in / Sign up

Export Citation Format

Share Document