A Novel Chinese Points of Interest Classification Method Based on Weighted Quadratic Surface Support Vector Machine

2022 ◽  
Author(s):  
An Luo ◽  
Xin Yan ◽  
Jian Luo
Keyword(s):  
Support Vector Machine ◽  
Classification Method ◽  
Support Vector ◽  
Points Of Interest ◽  
Quadratic Surface

scholarly journals A two-stage classification method for borehole-wall images with support vector machine

PLoS ONE ◽  
2018 ◽  
Vol 13 (6) ◽  
pp. e0199749
Author(s):  
Zhaopeng Deng ◽  
Maoyong Cao ◽  
Laxmisha Rai ◽  
Wei Gao
Keyword(s):  
Support Vector Machine ◽  
Borehole Wall ◽  
Classification Method ◽  
Support Vector ◽  
Two Stage ◽  
Stage Classification

scholarly journals Developing Support Vector Machine with New Fuzzy Selection for the Infringement of a Patent Rights Problem

Mathematics ◽  
2020 ◽  
Vol 8 (8) ◽  
pp. 1263
Author(s):  
Chih-Yao Chang ◽  
Kuo-Ping Lin
Keyword(s):  
Support Vector Machine ◽  
Data Quality ◽  
Classification Performance ◽  
Patent Infringement ◽  
Classification Method ◽  
Training Dataset ◽  
Support Vector ◽  
Classification Problems ◽  
Data Quality Management ◽  
Patent Rights

Classification problems are very important issues in real enterprises. In the patent infringement issue, accurate classification could help enterprises to understand court decisions to avoid patent infringement. However, the general classification method does not perform well in the patent infringement problem because there are too many complex variables. Therefore, this study attempts to develop a classification method, the support vector machine with new fuzzy selection (SVMFS), to judge the infringement of patent rights. The raw data are divided into training and testing sets. However, the data quality of the training set is not easy to evaluate. Effective data quality management requires a structural core that can support data operations. This study adopts new fuzzy selection based on membership values, which are generated from fuzzy c-means clustering, to select appropriate data to enhance the classification performance of the support vector machine (SVM). An empirical example based on the SVMFS shows that the proposed SVMFS can obtain a superior accuracy rate. Moreover, the new fuzzy selection also verifies that it can effectively select the training dataset.

scholarly journals Label Self-Advised Support Vector Machine (LSA-SVM)—Automated Classification of Foot Drop Rehabilitation Case Study

Biosensors ◽  
2019 ◽  
Vol 9 (4) ◽  
pp. 114 ◽  
Author(s):  
Sahar Adil Abboud ◽  
Saba Al-Wais ◽  
Salma Hameedi Abdullah ◽  
Fady Alnajjar ◽  
Adel Al-Jumaily
Keyword(s):  
Support Vector Machine ◽  
Motor Neurons ◽  
Experimental Results ◽  
Foot Drop ◽  
Classification Method ◽  
Support Vector ◽  
Major Health Problem ◽  
Rehabilitation Devices ◽  
Predictive Data Mining ◽  
Ankle And Foot

Stroke represents a major health problem in our society. One of the effects of stroke is foot drop. Foot drop (FD) is a weakness that occurs in specific muscles in the ankle and foot such as the anterior tibialis, gastrocnemius, plantaris and soleus muscles. Foot flexion and extension are normally generated by lower motor neurons (LMN). The affected muscles impact the ankle and foot in both downward and upward motions. One possible solution for FD is to investigate the movement based on the bio signal (myoelectric signal) of the muscles. Bio signal control systems like electromyography (EMG) are used for rehabilitation devices that include foot drop. One of these systems is function electrical stimulation (FES). This paper proposes new methods and algorithms to develop the performance of myoelectric pattern recognition (M-PR), to improve automated rehabilitation devices, to test these methodologies in offline and real-time experimental datasets. Label classifying is a predictive data mining application with multiple applications in the world, including automatic labeling of resources such as videos, music, images and texts. We combine the label classification method with the self-advised support vector machine (SA-SVM) to create an adapted and altered label classification method, named the label self-advised support vector machine (LSA-SVM). For the experimental data, we collected data from foot drop patients using the sEMG device, in the Metro Rehabilitation Hospital in Sydney, Australia using Ethical Approval (UTS HREC NO. ETH15-0152). The experimental results for the EMG dataset and benchmark datasets exhibit its benefits. Furthermore, the experimental results on UCI datasets indicate that LSA-SVM achieves the best performance when working together with SA-SVM and SVM. This paper describes the state-of-the-art procedures for M-PR and studies all the conceivable structures.

scholarly journals Gender Classification Based on Geometry Features of Palm Image

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
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.

scholarly journals Detection Of Spam Comments On Instagram Using Complementary Naïve Bayes

2019 ◽  
Vol 13 (3) ◽  
pp. 263
Author(s):  
Nur Azizul Haqimi ◽  
Nur Rokhman ◽  
Sigit Priyanta
Keyword(s):  
Social Media ◽  
Support Vector Machine ◽  
Test Data ◽  
Training Data ◽  
Classification Method ◽  
Support Vector ◽  
Test Results ◽  
Imbalanced Dataset ◽  
Web Based ◽  
F Measure

Instagram (IG) is a web-based and mobile social media application where users can share photos or videos with available features. Upload photos or videos with captions that contain an explanation of the photo or video that can reap spam comments. Comments on spam containing comments that are not relevant to the caption and photos. The problem that arises when identifying spam is non-spam comments are more dominant than spam comments so that it leads to the problem of the imbalanced dataset. A balanced dataset can influence the performance of a classification method. This is the focus of research related to the implementation of the CNB method in dealing with imbalance datasets for the detection of Instagram spam comments. The study used TF-IDF weighting with Support Vector Machine (SVM) as a comparison classification. Based on the test results with 2500 training data and 100 test data on the imbalanced dataset (25% spam and 75% non-spam), the CNB accuracy was 92%, precision 86% and f-measure 93%. Whereas SVM produces 87% accuracy, 79% precision, 88% f-measure. In conclusion, the CNB method is more suitable for detecting spam comments in cases of imbalanced datasets.

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