Analysis of HCV’s Linearity Using Support Vector Machine (SVM)

2014 ◽  
Vol 548-549 ◽  
pp. 1265-1269
Author(s):  
Yun Sik Hwang ◽  
Byeong Joo Jun ◽  
Tae Seon Yoon
Keyword(s):  
Neural Network ◽  
Support Vector Machine ◽  
Hepatitis C Virus ◽  
Data Analysis ◽  
Hepatitis C ◽  
Amino Acid ◽  
Support Vector ◽  
Svm Algorithm ◽  
Genetic Patterns

As the stage of bioinformatics has been upgraded, classification of certain pathogen has been improved into a new manner. The main topic of this research is genetic singularity of HCV (Hepatitis C Virus) and our objective is to assay features of the HCV's amino acid under usage of Support Vector Machine (SVM) algorithm. HCV data used in our experiment has 10 kinds of sequences and 257 kinds of data. According to data analysis, some peculiar genetic patterns of HCV’s linearity that discord pre-existing neural network and C5.0 were found.

Convolutional neural network - Support vector machine based approach for classification of cyanobacteria and chlorophyta microalgae groups

2021 ◽  
pp. 102568
Author(s):  
Mesut Ersin Sonmez ◽  
Numan Eczacıoglu ◽  
Numan Emre Gumuş ◽  
Muhammet Fatih Aslan ◽  
Kadir Sabanci ◽  
...  
Keyword(s):  
Neural Network ◽  
Support Vector Machine ◽  
Convolutional Neural Network ◽  
Support Vector ◽  
Network Support

scholarly journals Auto Classification of Biomass through Characterization of their Pyrolysis Behaviors by using Thermogravimetric Analysis with Support Vector Machine Algorithm: Case Study for Tobacco

2020 ◽  
Author(s):  
Chao Yin ◽  
Xiaohua Deng ◽  
Zhiqiang Yu ◽  
Ruting Chen ◽  
Hongxiang Zhong ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Thermogravimetric Analysis ◽  
Oil Quality ◽  
Support Vector ◽  
Analysis Method ◽  
Biomass Pyrolysis ◽  
Svm Algorithm ◽  
Bio Oil

Abstract Background: During the biomass-to-bio-oil conversion process, many researches focus on the study of the association between the biomass and the bio-products by using near infrared spectra (NIR) and chemical analysis method. However, the characterization of biomass pyrolysis behaviors by using thermogravimetric analysis (TGA) with support vector machine (SVM) algorithm has not been reported. In this study, tobacco was chosen as the object for biomass, because the cigarette smoke (including water, tar and gases) released by tobacco pyrolysis reactions decide the sensory quality, which is similar to the use of biomass as a renewable resource through the pyrolysis process. Results: Support vector machine (SVM) has been employed to automatically classify the planting area and growing position of tobacco leaves by using thermogravimetric analysis data as the information source for the first time. 88 single-grade tobacco samples belonging to 4 grades and 8 categories were split into the training, validation and blind testing set. Our model showed excellent performances in both the training and validation set as well as in the blind test, with accuracy over 91.67%. Throughout the whole dataset of 88 samples, our model not only provides precise results on the planting area of tobacco leave, but also accurately distinguishes the major grades among the upper, lower and middle positions. Error only occurs in the classification of subgrades of the middle position. Conclusions: Our results not only validated the feasibility of using thermogravimetric analysis with SVM algorithm as an objective and rapid method for automatic classification of tobacco planting area and growing position, but also showed this new analysis method would be a promising way to exploring bio-oil quality prior to biomass pyrolysis production.

Classification of Medical Text Data Using Convolutional Neural Network-Support Vector Machine Method

2020 ◽  
Vol 10 (7) ◽  
pp. 1746-1753
Author(s):  
Lan Liu ◽  
Xiankun Sun ◽  
Chengfan Li ◽  
Yongmei Lei
Keyword(s):  
Neural Network ◽  
Support Vector Machine ◽  
Convolutional Neural Network ◽  
Support Vector ◽  
Svm Classifier ◽  
Text Data ◽  
Machine Method ◽  
Medical Text ◽  
Conventional Methods

Conventional methods of medical text data classification, neglect of context among different words and semantic information, has a poor text description, classification effect and generalization capability and robustness. To tackle the inefficiencies and low precision in the classification of medical text data, in this paper, we presented a new classification method with improved convolutional neural network (CNN) and support vector machine (SVM), i.e., CNN-SVM method. In the method, some convolution kernel filters that contribute greatly to the CNN model are first selected by the average response energy (ARE) value, and then used to simplify and reconstruct the CNN model. Next, the SVM classifier was optimized by firefly algorithm (FA) and context information to overcome the disadvantages of over-saturation and over-training in SVM classification. Finally, the presented CNN-SVM method is tested by the simulation experiment and the true classification of medical text data. The experimental results show that the presented CNN-SVM method in this paper can significantly reduce the complexity and amount of computation compared to the conventional methods, and further promote the computational efficiency and classification accuracy of medical text data.

Study on Detection Method for Crack in Eggs Based on Computer Vision and Support Vector Machine Neural Network

2014 ◽  
Vol 472 ◽  
pp. 176-179 ◽  
Author(s):  
Jian Yang ◽  
Ying Shi ◽  
Wei Zhou ◽  
Yong Shun Che
Keyword(s):  
Neural Network ◽  
Computer Vision ◽  
Support Vector Machine ◽  
Traditional Method ◽  
Detection Method ◽  
Support Vector ◽  
Characteristic Parameters ◽  
Sample Data ◽  
Better Than

To improve the accuracy of detection and classification of egg with cracks, this paper is to add Support Vector Machine to neural network to automatically identify and classify the eggs with cracks. Firstly process the egg images with light-transmitting were obtained by the computer vision device including denoising, threshold segmentation. Five characteristic parameters of crack areas and noise areas were acquired. Secondly train SVM Neural Network and identify the eggs with cracks by five parameters data as the sample data. The correct discerning rate of grading table eggs is 98.07%. It proves better than traditional method in terms of prediction accuracy and robustness. The generalization ability of SVM Neural Network is strengthened.

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