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

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

Algal Research ◽

10.1016/j.algal.2021.102568 ◽

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

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Automatic Classification of ECG Using Multi-Classifiers with a New Fusion Method

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.687-691.3917 ◽

2014 ◽

Vol 687-691 ◽

pp. 3917-3922

Author(s):

Yi Chang Wang ◽

Feng Qi Yan ◽

Yu Fang

Keyword(s):

Traditional Method ◽

Heart Beat ◽

Vector Model ◽

Support Vector ◽

Beat Classification ◽

Computer Aided Analysis ◽

Important Basis ◽

Voting Method ◽

Better Than

ECG signal contains abundant information of human heart activity. It is important basis of doctors’ diagnose. With the development of computer technology, computer aided analysis has been widely applied in the field of ECG analysis. Most of the traditional method is based on single classifier and too complex. Also, the accuracy is not high. This paper focuses on ECG heart beat classification, extracting different types of feature, training different classifiers by vector model and support vector machine (SVM), merging the result of multiple classifiers. In this paper, we used the advanced voting method (voting by weight) to fusion the result of different classifier, having compared it with the traditional voting method.It performed better than traditional method in term of accuracy

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Peringkasan dan Support Vector Machine pada Klasifikasi Dokumen

JURNAL INFOTEL ◽

10.20895/infotel.v9i4.312 ◽

2017 ◽

Vol 9 (4) ◽

pp. 416 ◽

Cited By ~ 1

Author(s):

Nelly Indriani Widiastuti ◽

Ednawati Rainarli ◽

Kania Evita Dewi

Keyword(s):

Support Vector Machine ◽

Naive Bayes ◽

Naïve Bayes ◽

Training Data ◽

Support Vector ◽

Good Reputation ◽

Multiclass Support Vector Machine ◽

Simple Logistic ◽

Better Than

Classification is the process of grouping objects that have the same features or characteristics into several classes. The automatic documents classification use words frequency that appears on training data as features. The large number of documents cause the number of words that appears as a feature will increase. Therefore, summaries are chosen to reduce the number of words that used in classification. The classification uses multiclass Support Vector Machine (SVM) method. SVM was considered to have a good reputation in the classification. This research tests the effect of summary as selection features into documents classification. The summaries reduce text into 50%. A result obtained that the summaries did not affect value accuracy of classification of documents that use SVM. But, summaries improve the accuracy of Simple Logistic Classifier. The classification testing shows that the accuracy of Naïve Bayes Multinomial (NBM) better than SVM

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Modulation Classification of Analog and Digital Signals Using Neural Network and Support Vector Machine

Advances in Neural Networks – ISNN 2007 - Lecture Notes in Computer Science ◽

10.1007/978-3-540-72395-0_47 ◽

2007 ◽

pp. 368-373 ◽

Cited By ~ 3

Author(s):

Cheol-Sun Park ◽

Dae Young Kim

Keyword(s):

Neural Network ◽

Support Vector Machine ◽

Support Vector ◽

Modulation Classification ◽

Digital Signals

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

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.548-549.1265 ◽

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.

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Classification of Hand Manipulation Using BP Neural Network and Support Vector Machine Based on Surface Electromyography Signal**This work was supported in part by the National Natural Science Foundation of China under Grants 61374127 and 61422301, the Natural Science Foundation of Heilongjiang Province of China under Grant F201428, the Scientific and Technology Research Foundation of Heilongjiang Education Department under Grants 12541061 and 12541592, the 12th Five-Year-Plan in Key Science and Technology Research of agricultural bureau in Heilongjiang province of China under Grant HNK125B-04-03, the Doctoral Scientific Research Foundation of Heilongjiang Bayi Agricultural University under Grant XDB2014-12, Jiangsu Provincial Key Laboratory of E-business, Nan- jing University of Finance and Economics JSEB201301.

IFAC-PapersOnLine ◽

10.1016/j.ifacol.2015.12.239 ◽

2015 ◽

Vol 48 (28) ◽

pp. 869-873 ◽

Cited By ~ 5

Author(s):

Yang Lu ◽

Gang Lu ◽

Xianye Bu ◽

Yajing Yu ◽

Xianye Bu

Keyword(s):

Neural Network ◽

Support Vector Machine ◽

Bp Neural Network ◽

Natural Science ◽

Surface Electromyography ◽

Support Vector ◽

Heilongjiang Province ◽

Technology Research ◽

Education Department

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Classification of fMRI data using support vector machine and convolutional neural network

2017 IEEE International Conference on Signal and Image Processing Applications (ICSIPA) ◽

10.1109/icsipa.2017.8120630 ◽

2017 ◽

Cited By ~ 2

Author(s):

Raheel Zafar ◽

Aamir Saeed Malik ◽

Aliyu Nuhu Shuaibu ◽

M. Javvad ur Rehman ◽

Sarat C. Dass

Keyword(s):

Neural Network ◽

Support Vector Machine ◽

Convolutional Neural Network ◽

Fmri Data ◽

Support Vector

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Application of Support Vector Machine in Springback Forecasting for Steel Structure

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.166-169.1366 ◽

2012 ◽

Vol 166-169 ◽

pp. 1366-1369

Author(s):

Jian Guo Chen ◽

Zhao Guang Li

Keyword(s):

Neural Network ◽

Support Vector Machine ◽

Friction Coefficient ◽

Bp Neural Network ◽

Prediction Error ◽

Steel Structure ◽

Support Vector ◽

Springback Prediction ◽

Better Than

Support vector machine is applied to springback forecasting for steel structure in the paper. In the steel structure, pressure-pad-force, friction coefficient and die filleted corner have a certain influence on springback amount.We employ BP neural network to compare with support vector machine to show the superiority of support vector machine in this study. Finally,we give the comparison of the prediction error of springback for steel structure between support vector machine and BP neural network. Evidently,the springback prediction for steel structure of support vector machine is better than that of BP neural network.

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Analisis Perbandingan Algoritma SVM, KNN, dan CNN untuk Klasifikasi Citra Cuaca

Jurnal Teknologi Informasi dan Ilmu Komputer ◽

10.25126/jtiik.2021824553 ◽

2021 ◽

Vol 8 (2) ◽

pp. 311

Author(s):

Mohammad Farid Naufal

Keyword(s):

Neural Network ◽

Machine Learning ◽

Computer Vision ◽

Support Vector Machine ◽

Convolutional Neural Network ◽

Cross Validation ◽

Nearest Neighbors ◽

Support Vector ◽

Classification Algorithms ◽

K Nearest Neighbors

Cuaca merupakan faktor penting yang dipertimbangkan untuk berbagai pengambilan keputusan. Klasifikasi cuaca manual oleh manusia membutuhkan waktu yang lama dan inkonsistensi. Computer vision adalah cabang ilmu yang digunakan komputer untuk mengenali atau melakukan klasifikasi citra. Hal ini dapat membantu pengembangan self autonomous machine agar tidak bergantung pada koneksi internet dan dapat melakukan kalkulasi sendiri secara real time. Terdapat beberapa algoritma klasifikasi citra populer yaitu K-Nearest Neighbors (KNN), Support Vector Machine (SVM), dan Convolutional Neural Network (CNN). KNN dan SVM merupakan algoritma klasifikasi dari Machine Learning sedangkan CNN merupakan algoritma klasifikasi dari Deep Neural Network. Penelitian ini bertujuan untuk membandingkan performa dari tiga algoritma tersebut sehingga diketahui berapa gap performa diantara ketiganya. Arsitektur uji coba yang dilakukan adalah menggunakan 5 cross validation. Beberapa parameter digunakan untuk mengkonfigurasikan algoritma KNN, SVM, dan CNN. Dari hasil uji coba yang dilakukan CNN memiliki performa terbaik dengan akurasi 0.942, precision 0.943, recall 0.942, dan F1 Score 0.942. AbstractWeather is an important factor that is considered for various decision making. Manual weather classification by humans is time consuming and inconsistent. Computer vision is a branch of science that computers use to recognize or classify images. This can help develop self-autonomous machines so that they are not dependent on an internet connection and can perform their own calculations in real time. There are several popular image classification algorithms, namely K-Nearest Neighbors (KNN), Support Vector Machine (SVM), and Convolutional Neural Network (CNN). KNN and SVM are Machine Learning classification algorithms, while CNN is a Deep Neural Networks classification algorithm. This study aims to compare the performance of that three algorithms so that the performance gap between the three is known. The test architecture is using 5 cross validation. Several parameters are used to configure the KNN, SVM, and CNN algorithms. From the test results conducted by CNN, it has the best performance with 0.942 accuracy, 0.943 precision, 0.942 recall, and F1 Score 0.942.

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