Research of Diver Sonar Image Recognition Based on Support Vector Machine

To recognize small diver target from the dim special diver sonar images accurately, the Support Vector Machine method is used as classifier. According to the main characteristics of diver, five feature parameters, including Average-scale, Velocity, Shape, Direction, Included angle, are chosen as the input of characteristics vectors to train the net. And then the testing images are classified and identified. The experimental results show that accuracy rate of recognition reaches 94.5% for as many as 200 testing images. The experiment indicates that small object recognition from complex sonar images based on the right selection of feature parameters is of good performance by using the SVM method as well as good engineering foreground.

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Particle Swarm Optimization-based Support Vector Machine Method for Sentiment Analysis in OVO Digital Payment Applications

JURNAL TEKNOLOGI DAN OPEN SOURCE ◽

10.36378/jtos.v4i2.1776 ◽

2021 ◽

Vol 4 (2) ◽

pp. 232-239

Author(s):

Retno Sari ◽

Ratih Yulia Hayuningtyas

Keyword(s):

Support Vector Machine ◽

Particle Swarm Optimization ◽

Sentiment Analysis ◽

Particle Swarm ◽

Support Vector ◽

In Ovo ◽

Machine Method ◽

Swarm Optimization ◽

Support Vector Machine Method ◽

The Right

Sentiment analysis is used to analyze reviews of a place or item from an application or website that then classified the review into positive reviews or negative reviews. reviews from users are considered very important because it contains information that can make it easier for new users who want to choose the right digital payment. Reviews about digital payment ovo are so much that it is difficult for prospective users of ovo digital payment applications to draw conclusions about ovo digital payment information. For this reason, a classification method is needed in this study using support vector machine and PSO methods. In this study, we used 400 data that were reduced to 200 positive reviews and 200 negative reviews. The accuracy obtained by using the support vector machine method of 76.50% is in the fair classification, while the accuracy obtained by using the support vector machine and Particle Swarm Optimization (PSO) method is 82.75% which is in good classification.

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Selection of Parameters of the Support Vector Machine Method to the Problem of Subsidence Modelling Due to Drainage

XVIII International Coal Preparation Congress ◽

10.1007/978-3-319-40943-6_100 ◽

2016 ◽

pp. 651-656

Author(s):

Wojciech T. Witkowski ◽

Ryszard Hejmanowski

Keyword(s):

Support Vector Machine ◽

Support Vector ◽

Machine Method ◽

Support Vector Machine Method ◽

Selection Of

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Modified multiscale weighted permutation entropy and optimized support vector machine method for rolling bearing fault diagnosis with complex signals

ISA Transactions ◽

10.1016/j.isatra.2020.12.054 ◽

2021 ◽

Author(s):

Zhenya Wang ◽

Ligang Yao ◽

Gang Chen ◽

Jiaxin Ding

Keyword(s):

Support Vector Machine ◽

Fault Diagnosis ◽

Rolling Bearing ◽

Permutation Entropy ◽

Support Vector ◽

Complex Signals ◽

Machine Method ◽

Support Vector Machine Method ◽

Bearing Fault ◽

Bearing Fault Diagnosis

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Prediction of the resilient modulus of non-cohesive subgrade soils and unbound subbase materials using a hybrid support vector machine method and colliding bodies optimization algorithm

Construction and Building Materials ◽

10.1016/j.conbuildmat.2020.122140 ◽

2021 ◽

Vol 275 ◽

pp. 122140

Author(s):

Nasrin Heidarabadizadeh ◽

Ali Reza Ghanizadeh ◽

Ali Behnood

Keyword(s):

Support Vector Machine ◽

Optimization Algorithm ◽

Resilient Modulus ◽

Support Vector ◽

Machine Method ◽

Support Vector Machine Method ◽

Subgrade Soils ◽

Colliding Bodies Optimization

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Personality Classification of Facebook Users According to Big Five Personality Using SVM (Support Vector Machine) Method

Procedia Computer Science ◽

10.1016/j.procs.2020.12.023 ◽

2021 ◽

Vol 179 ◽

pp. 177-184

Author(s):

Ninda Anggoro Utami ◽

Warih Maharani ◽

Imelda Atastina

Keyword(s):

Support Vector Machine ◽

Big Five ◽

Support Vector ◽

Big Five Personality ◽

Machine Method ◽

Support Vector Machine Method ◽

Personality Classification

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

Advances in Mechanical Engineering ◽

10.1177/1687814016685963 ◽

2017 ◽

Vol 9 (1) ◽

pp. 168781401668596 ◽

Cited By ~ 9

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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