hERG Classification Model Based on a Combination of Support Vector Machine Method and GRIND Descriptors

2008 ◽  
Vol 5 (1) ◽  
pp. 117-127 ◽  
Author(s):  
Qiyuan Li ◽  
Flemming Steen Jørgensen ◽  
Tudor Oprea ◽  
Søren Brunak ◽  
Olivier Taboureau
Keyword(s):  
Support Vector Machine ◽  
Classification Model ◽  
Support Vector ◽  
Machine Method ◽  
Support Vector Machine Method ◽  
Model Based ◽  
Grind Descriptors

scholarly journals Journal Classification Based on Abstract Using Cosine Similarity and Support Vector Machine

2020 ◽  
Vol 4 (3) ◽  
pp. 48
Author(s):  
Muhammad Habibi ◽  
Puji Winar Cahyo
Keyword(s):  
Support Vector Machine ◽  
Cosine Similarity ◽  
Classification Model ◽  
Support Vector ◽  
Weighting Method ◽  
Machine Method ◽  
Support Vector Machine Method ◽  
Performance Accuracy ◽  
Similarity Algorithm ◽  
Journal Editorial

One of the problems related to journal publishing is the process of categorizing entry into journals according to the field of science. A large number of journal documents included in a journal editorial makes it difficult to categorize so that the process of plotting to reviewers requires a long process. The review process in a journal must be done planning according to the expertise of the reviewer, to produce a quality journal. This study aims to create a classification model that can classify journals automatically using the Cosine Similarity algorithm and Support Vector Machine in the classification process and using the TF-IDF weighting method. The object of this research is abstract in scientific journals. The journals will be classified according to the reviewer's field of expertise. Based on the experimental results, the Support Vector Machine method produces better performance accuracy than the Cosine Similarity method. The results of the calculation of the value of precision, recall, and f-score are known that the Support Vector Machine method produces better amounts, in line with the accuracy value.

scholarly journals ANALISIS SENTIMEN GOJEK PADA MEDIA SOSIAL TWITTER DENGAN KLASIFIKASI SUPPORT VECTOR MACHINE (SVM

2020 ◽  
Vol 9 (3) ◽  
pp. 376-390
Author(s):  
Nur Fitriyah ◽  
Budi Warsito ◽  
Di Asih I Maruddani
Keyword(s):  
Support Vector Machine ◽  
Cross Validation ◽  
Classification Model ◽  
Support Vector ◽  
Test Results ◽  
Machine Method ◽  
Support Vector Machine Method ◽  
Rbf Kernel ◽  
Negative Sentiment ◽  
Fold Cross Validation

Appearance of PT Aplikasi Karya Anak Bangsa or as known as Gojek since 2015 give a convenience facility to people in Indonesia especially in daily activities. Sentiment analysis on Twitter social media can be the option to see how Gojek users respond to the services that have been provided. The response was classified into positive sentiment and negative sentiment using Support Vector Machine method with model evaluation 10-fold cross validation. The kernel used is the linear kernel and the RBF kernel. Data labeling can be done with manually and sentiment scoring. The test results showed that the RBF kernel gets overall accuracy and the highest kappa accuracy on manual data labeling and sentiment scoring. On manual data labeling, the overall accuracy is 79.19% and kappa accuracy is 16.52%. While the labeling of data with sentiment scoring obtained overall accuracy of 79.19% and kappa accuracy of 21%. The greater overall accuracy value and kappa accuracy obtained, the better performance of the classification model. Keywords: Gojek, Twitter, Support Vector Machine, overall accuracy, kappa accuracy

scholarly journals A Bayesian least-squares support vector machine method for predicting the remaining useful life of a microwave component

2017 ◽  
Vol 9 (1) ◽  
pp. 168781401668596 ◽  
Author(s):  
Fuqiang Sun ◽  
Xiaoyang Li ◽  
Haitao Liao ◽  
Xiankun Zhang
Keyword(s):  
Support Vector Machine ◽  
Least Squares ◽  
Remaining Useful Life ◽  
Lifetime Prediction ◽  
Support Vector ◽  
Machine Method ◽  
Support Vector Machine Method ◽  
Microwave Component ◽  
Useful Life ◽  
Bayesian Least Squares

Rapid and accurate lifetime prediction of critical components in a system is important to maintaining the system’s reliable operation. To this end, many lifetime prediction methods have been developed to handle various failure-related data collected in different situations. Among these methods, machine learning and Bayesian updating are the most popular ones. In this article, a Bayesian least-squares support vector machine method that combines least-squares support vector machine with Bayesian inference is developed for predicting the remaining useful life of a microwave component. A degradation model describing the change in the component’s power gain over time is developed, and the point and interval remaining useful life estimates are obtained considering a predefined failure threshold. In our case study, the radial basis function neural network approach is also implemented for comparison purposes. The results indicate that the Bayesian least-squares support vector machine method is more precise and stable in predicting the remaining useful life of this type of components.

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