Methods of Support Vector Machine on Classification of Expansive Soils

2012 ◽  
Vol 531 ◽  
pp. 562-565 ◽  
Author(s):  
Hai Ying Yang ◽  
Yun Liu
Keyword(s):  
Support Vector Machine ◽  
Expansive Soils ◽  
Expansive Soil ◽  
Liquid Limit ◽  
Classification Model ◽  
Support Vector ◽  
Large Set ◽  
Practical Engineering ◽  
Machine Analysis

The classification of the grade of shrink and expansion for the expansive soils was the initial and essential work for engineering construction in expansive soil area. Based on the principle of support vector machine analysis, a classification model of expansive was established in this paper, including five indexes reflecting the shrink and expansion of expansive soil, liquid limit, swell-shrink total ratio, plasticity index, water contents and free expansive ratio and functions were obtained through training a large set of expansive samples. It was shown that the classification model of SVM analysis is an effective method performed excellently with high prediction accuracy and could be used in practical engineering.

scholarly journals KLASIFIKASI TEKS SOSIAL MEDIA TWITTER MENGGUNAKAN SUPPORT VECTOR MACHINE (Studi Kasus Penusukan Wiranto)

2019 ◽  
Vol 2 (2) ◽  
pp. 43
Author(s):  
Lalu Mutawalli ◽  
Mohammad Taufan Asri Zaen ◽  
Wire Bagye
Keyword(s):  
Social Media ◽  
Support Vector Machine ◽  
Big Data ◽  
Mass Communication ◽  
Confusion Matrix ◽  
Classification Model ◽  
Support Vector ◽  
Large Set ◽  
Appearance Time ◽  
Data Production

In the era of technological disruption of mass communication, social media became a reference in absorbing public opinion. The digitalization of data is very rapidly produced by social media users because it is an attempt to represent the feelings of the audience. Data production in question is the user posts the status and comments on social media. Data production by the public in social media raises a very large set of data or can be referred to as big data. Big data is a collection of data sets in very large numbers, complex, has a relatively fast appearance time, so that makes it difficult to handle. Analysis of big data with data mining methods to get knowledge patterns in it. This study analyzes the sentiments of netizens on Twitter social media on Mr. Wiranto stabbing case. The results of the sentiment analysis showed 41% gave positive comments, 29% commented neutrally, and 29% commented negatively on events. Besides, modeling of the data is carried out using a support vector machine algorithm to create a system capable of classifying positive, neutral, and negative connotations. The classification model that has been made is then tested using the confusion matrix technique with each result is a precision value of 83%, a recall value of 80%, and finally, as much as 80% obtained in testing the accuracy.

scholarly journals Classification of degenerative parkinsonism subtypes by support-vector-machine analysis and striatal 123I-FP-CIT indices

2019 ◽  
Vol 266 (7) ◽  
pp. 1771-1781 ◽  
Author(s):  
Nicolas Nicastro ◽  
Jennifer Wegrzyk ◽  
Maria Giulia Preti ◽  
Vanessa Fleury ◽  
Dimitri Van de Ville ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Support Vector ◽  
Machine Analysis ◽  
Degenerative Parkinsonism

scholarly journals Individual Classification of Mild Cognitive Impairment Subtypes by Support Vector Machine Analysis of White Matter DTI

2012 ◽  
Vol 34 (2) ◽  
pp. 283-291 ◽  
Author(s):  
S. Haller ◽  
P. Missonnier ◽  
F.R. Herrmann ◽  
C. Rodriguez ◽  
M.-P. Deiber ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
White Matter ◽  
Support Vector ◽  
Machine Analysis

Study on Identification and Classification of Expansive Soil Based on Bayes Discriminant Analysis Method

2013 ◽  
Vol 639-640 ◽  
pp. 573-576
Author(s):  
Yan Jun Qiu ◽  
Chang Ping Wen
Keyword(s):  
Discriminant Analysis ◽  
Expansive Soil ◽  
Liquid Limit ◽  
Analysis Method ◽  
Discriminant Model ◽  
Normal Populations ◽  
Proposed Model ◽  
Practical Engineering ◽  
Bayes Discriminant Analysis

Based on the principle of Bayes discriminant analysis, Bayes discriminant model (BDM) for evaluation of expansive soil in sub-grades is established. Four indexes including free expansive ratio, liquid limit, plasticity index and moisture content of standard absorption are selected as the factors for synthetic evaluation of expansive soil. The grade of shrink and expansion is divided into four grades that are regarded as four normal populations in Bayes discriminant analysis. Bayes discriminant functions obtained through training a set of expansive soil samples are employed to compute the Bayes function values of the evaluating samples, and the maximal function value is used to judge which population the evaluating sample belongs to. The optimality of the proposed model is verified by back-substitution method. The study shows that the prediction accuracy of the proposed model is 100% and could be used in practical engineering.

scholarly journals Classification of hand gestures from forearm electromyogram signatures from support vector machine

2021 ◽  
Vol 24 (1) ◽  
pp. 260
Author(s):  
Diaa Albitar ◽  
R. Jailani ◽  
Megat Syahirul Amin Megat Ali ◽  
Anwar P. P. Abdul Majeed
Keyword(s):  
Support Vector Machine ◽  
Classification Model ◽  
Support Vector ◽  
Enabling Technology ◽  
Hand Gestures ◽  
Different Types ◽  
Specific Effector ◽  
Intelligent Model ◽  
Vital Element

<p>Robotic prosthetics is increasingly adopted as an enabling technology for amputees. These are vital not only for activities of daily living but to display expression and affection. A vital element to this system is an intelligent model that can identify signatures from the remaining limb that can be mapped to specific effector movements. Therefore, the study proposes the use of forearm electromyogram to classify between different types of hand gestures; fingers spread, wave out, wave in, fist, double tap, and relaxed state. Data are acquired from 32 subjects using Myo armband. Initially, a total of 248 time-and frequency-domain features are extracted from the eightchannel device. Neighborhood component analysis has reduced them to a total of fourteen features. A hand gesture classification model based on electromyogram signal has been successfully developed using support vector machine with overall accuracy of 97.4% for training, and 88.0% for testing.</p>

scholarly journals Deteksi Penyakit Dengue Hemorrhagic Fever dengan Pendekatan One Class Classification

2017 ◽  
Vol 3 (2) ◽  
pp. 129
Author(s):  
Zida Ziyan Azkiya ◽  
Fatma Indriani ◽  
Heru Kartika Chandra
Keyword(s):  
Support Vector Machine ◽  
Dengue Hemorrhagic Fever ◽  
Hemorrhagic Fever ◽  
Classification Problem ◽  
Training Data ◽  
Classification Model ◽  
Support Vector ◽  
Single Class ◽  
One Class Classification

Abstrak— Pada kasus deteksi penderita penyakit demam berdarah (Dengue Hemorrhagic Fever- DHF), data training yang tersedia umumnya hanya data pasien penderita positif. Sedangkan data orang normal (data negatif) tidak tersedia secara khusus. Pada makalah ini dipaparkan pembangunan model klasifikasi untuk deteksi DHF dengan pendekatan One Class Classification (OCC). Data yang digunakan pada penelitian ini adalah hasil uji darah dari laboratorium dari pasien penderita penyakit demam berdarah. Metode yang diteliti adalah One-class Support Vector Machine dan K-Means. Hasil yang diperoleh pada penelitian ini adalah untuk metode SVM memiliki nilai precision = 1,0, recall = 0,993, f-1 score = 0,997, dan tingkat akurasi sebesar 99,7%  sedangkan dengan metode K-Means diperoleh nilai precision = 0,901, recall = 0,973, f-1 score = 0,936, dan tingkat akurasi sebesar 93,3%. Hal ini  menunjukkan bahwa metode SVM sedikit lebih unggul dibandingkan dengan K-Means untuk kasus ini. Kata Kunci— demam berdarah, Dengue Hemorrhagic Fever, K-Means, One Class Classification, OSVMAbstract— Two class classification problem maps input into two target classes. In certain cases, training data is available only in the form of a single class, as in the case of Dengue Hemorrhagic Fever (DHF) patients, where only data of positive patients is available. In this paper, we report our experiment in building a classification model for detecting DHF infection using One Class Classification (OCC) approach. Data from this study is sourced from laboratory tests of patients with dengue fever. The OCC methods compared are One-Class Support Vector Machine and One-Class K-Means. The result shows SVM method obtained precision value = 1.0, recall = 0.993, f-1 score = 0.997, and accuracy of 99.7% while the K-Means method obtained precision value = 0.901, recall = 0.973, f- 1 score = 0.936, and accuracy of 93.3%. This indicates that the SVM method is slightly superior to K-Means for One-Class Classification of DHF patients. Keywords— Dengue Hemorrhagic Fever, K-Means, One Class Classification, OSVM

Classification of Fatigued Bills Based on a Support Vector Machine by Using the Amplitude Difference Features of Acoustic Signals

2011 ◽  
Vol 131 (8) ◽  
pp. 1495-1501
Author(s):  
Dongshik Kang ◽  
Masaki Higa ◽  
Hayao Miyagi ◽  
Ikugo Mitsui ◽  
Masanobu Fujita ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Acoustic Signals ◽  
Support Vector ◽  
Amplitude Difference
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