Detection for Eggshell Crack Based on Acoustic Feature and Support Vector Machine

2011 ◽  
Vol 58-60 ◽  
pp. 227-232
Author(s):  
Li Rong Xiong
Keyword(s):  
Support Vector Machine ◽  
Power Spectrum ◽  
Crack Detection ◽  
Recognition Rate ◽  
Average Power ◽  
Kernel Functions ◽  
Support Vector ◽  
Acoustic Feature ◽  
Advantages And Disadvantages ◽  
Spectrum Area

The paper has proposed a new method based on acoustic feature and support vector machine. A sound signal acquisition system is designed based on microcontroller, the power spectra is received for good shell eggs and crack eggs. 4 parameters, such as the average power spectrum area (x1), power spectrum area of range value (x2), the first average formant amplitude (x3) and the first formant amplitude range value (x4), are extracted. These 4 parameters are regarded as input vector for support vector machine (SVM). The advantages and disadvantages for classification performance because of different kernel functions and different training sample size are compared, and ultimately the radial basis function (RBF) function is regarded as the best kernel function for the optimal classification results, and then the penalty coefficient C and the normalization coefficient are optimized, the overall recognition rate reached 97.37% or more, running time is about 0. 3s.The results show that SVM has a perfect performance in eggshell crack detection.

scholarly journals Detection and Classification of Advanced Persistent Threats and Attacks Using the Support Vector Machine

2019 ◽  
Vol 9 (21) ◽  
pp. 4579 ◽  
Author(s):  
Wen-Lin Chu ◽  
Chih-Jer Lin ◽  
Ke-Neng Chang
Keyword(s):  
Support Vector Machine ◽  
Information Search ◽  
Detection Efficiency ◽  
Detection System ◽  
Rapid Development ◽  
Recognition Rate ◽  
Attack Detection ◽  
Support Vector ◽  
Advantages And Disadvantages ◽  
The Impact

Traditional network attack and hacking models are constantly evolving to keep pace with the rapid development of network technology. Advanced persistent threat (APT), usually organized by a hacker group, is a complex and targeted attack method. A long period of strategic planning and information search usually precedes an attack on a specific goal. Focus is on a targeted object and customized specific methods are used to launch the attack and obtain confidential information. This study offers an attack detection system that enables early discovery of the APT attack. The system uses the NSL-KDD database for attack detection and verification. The main method uses principal component analysis (PCA) for feature sampling and the enhancement of detection efficiency. The advantages and disadvantages of using the classifiers are then compared to detect the dataset, the classifier supports the vector machine, naive Bayes classification, the decision tree and neural networks. Results of the experiments show the support vector machine (SVM) to have the highest recognition rate, reaching 97.22% (for the trained subdata A). The purpose of this study was to establish an APT early warning model mechanism, that could be used to reduce the impact and influence of APT attacks.

Component Spectrum Recognition for Mixed Gas Based on SVM

2011 ◽  
Vol 128-129 ◽  
pp. 557-560
Author(s):  
Peng Bai ◽  
Juan Zao Ji ◽  
Peng Liu ◽  
Dao Tian Geng
Keyword(s):  
Support Vector Machine ◽  
Natural Gas ◽  
Dimensional Space ◽  
Recognition Rate ◽  
Kernel Functions ◽  
Support Vector ◽  
Mixed Gas ◽  
Characteristic Spectrum ◽  
Component Identification ◽  
Training Samples

As for the problem that component gas characteristic spectrum lines overlaps seriously in the identification of Mixed Gas, Support Vector Machine is introduced for the identification, and an one-by-one identification methods for Mixed Gas classification based on the binary category identification model based on the support vector machine is proposed in this article. One-by-one category identification is carried out for each mixed gas when the characteristic spectrum lines are overlapped seriously and is transformed in high dimensional space into linear by SVM kernel function transformation. In the experiment for gas component identification of a natural gas, we compare the recognition results affected by different kernel functions, data preprocessing, feature extraction, numbers of training samples and other conditions. The results show that the method has the correct recognition rate of over 97% for the natural gas whose concentration is over 1%, and it has a great promotional value both in theory and practical application.

GESTURE RECOGNITION SYSTEM FOR NIGERIAN TRIBAL GREETING POSTURES USING SUPPORT VECTOR MACHINE

2020 ◽  
Vol 5 (2) ◽  
pp. 609
Author(s):  
Segun Aina ◽  
Kofoworola V. Sholesi ◽  
Aderonke R. Lawal ◽  
Samuel D. Okegbile ◽  
Adeniran I. Oluwaranti
Keyword(s):  
Support Vector Machine ◽  
Gesture Recognition ◽  
Recognition Rate ◽  
Recognition Task ◽  
Recognition System ◽  
Human Interaction ◽  
Support Vector ◽  
System A ◽  
Extraction Algorithm ◽  
Gaussian Blur

This paper presents the application of Gaussian blur filters and Support Vector Machine (SVM) techniques for greeting recognition among the Yoruba tribe of Nigeria. Existing efforts have considered different recognition gestures. However, tribal greeting postures or gestures recognition for the Nigerian geographical space has not been studied before. Some cultural gestures are not correctly identified by people of the same tribe, not to mention other people from different tribes, thereby posing a challenge of misinterpretation of meaning. Also, some cultural gestures are unknown to most people outside a tribe, which could also hinder human interaction; hence there is a need to automate the recognition of Nigerian tribal greeting gestures. This work hence develops a Gaussian Blur – SVM based system capable of recognizing the Yoruba tribe greeting postures for men and women. Videos of individuals performing various greeting gestures were collected and processed into image frames. The images were resized and a Gaussian blur filter was used to remove noise from them. This research used a moment-based feature extraction algorithm to extract shape features that were passed as input to SVM. SVM is exploited and trained to perform the greeting gesture recognition task to recognize two Nigerian tribe greeting postures. To confirm the robustness of the system, 20%, 25% and 30% of the dataset acquired from the preprocessed images were used to test the system. A recognition rate of 94% could be achieved when SVM is used, as shown by the result which invariably proves that the proposed method is efficient.

scholarly journals Pioneer dataset and recognition of Handwritten Pashto characters using Convolution Neural Networks

2020 ◽  
pp. 002029402096482
Author(s):  
Sulaiman Khan ◽  
Abdul Hafeez ◽  
Hazrat Ali ◽  
Shah Nazir ◽  
Anwar Hussain
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Support Vector Machine ◽  
Research Work ◽  
Recognition Rate ◽  
Recognition Task ◽  
Convolution Neural Network ◽  
Medium Size ◽  
Support Vector ◽  
Artificial Neural

This paper presents an efficient OCR system for the recognition of offline Pashto isolated characters. The lack of an appropriate dataset makes it challenging to match against a reference and perform recognition. This research work addresses this problem by developing a medium-size database that comprises 4488 samples of handwritten Pashto character; that can be further used for experimental purposes. In the proposed OCR system the recognition task is performed using convolution neural network. The performance analysis of the proposed OCR system is validated by comparing its results with artificial neural network and support vector machine based on zoning feature extraction technique. The results of the proposed experiments shows an accuracy of 56% for the support vector machine, 78% for artificial neural network, and 80.7% for the proposed OCR system. The high recognition rate shows that the OCR system based on convolution neural network performs best among the used techniques.

Forecasting Repair and Maintenance Services of Medical Devices Using Support Vector Machine

2021 ◽  
Author(s):  
Hao-yu Liao ◽  
Willie Cade ◽  
Sara Behdad
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Medical Devices ◽  
Kernel Functions ◽  
Support Vector ◽  
Quality Level ◽  
Learning Tools ◽  
Physical Damage ◽  
Repair Service ◽  
Product Failure

Abstract Accurate prediction of product failures and the need for repair services become critical for various reasons, including understanding the warranty performance of manufacturers, defining cost-efficient repair strategies, and compliance with safety standards. The purpose of this study is to use machine learning tools to analyze several parameters crucial for achieving a robust repair service system, including the number of repairs, the time of the next repair ticket or product failure, and the time to repair. A large dataset of over 530,000 repairs and maintenance of medical devices has been investigated by employing the Support Vector Machine (SVM) tool. SVM with four kernel functions is used to forecast the timing of the next failure or repair request in the system for two different products and two different failure types, namely random failure and physical damage. A frequency analysis is also conducted to explore the product quality level based on product failure and the time to repair it. Besides, the best probability distributions are fitted for the number of failures, the time between failures, and the time to repair. The results reveal the value of data analytics and machine learning tools in analyzing post-market product performance and the cost of repair and maintenance operations.

Deep learning model for protein disease classification

2021 ◽  
Vol 16 ◽  
Author(s):  
Farida Alaaeldin Mostafa ◽  
Yasmine Mohamed Afify ◽  
Rasha Mohamed Ismail ◽  
Nagwa Lotfy Badr
Keyword(s):  
Neural Network ◽  
Support Vector Machine ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Protein Analysis ◽  
Kernel Functions ◽  
Support Vector ◽  
Feature Descriptors

Background: Protein sequence analysis helps in the prediction of protein functions. As the number of proteins increases, it gives the bioinformaticians a challenge to analyze and study the similarity between them. Most of the existing protein analysis methods use Support Vector Machine. Deep learning did not receive much attention regarding protein analysis as it is noted that little work focused on studying the protein diseases classification. Objective: The contribution of this paper is to present a deep learning approach that classifies protein diseases based on protein descriptors. Methods: Different protein descriptors are used and decomposed into modified feature descriptors. Uniquely, we introduce using Convolutional Neural Network model to learn and classify protein diseases. The modified feature descriptors are fed to the Convolutional Neural Network model on a dataset of 1563 protein sequences classified into 3 different disease classes: Aids, Tumor suppressor, and Proto oncogene. Results: The usage of the modified feature descriptors shows a significant increase in the performance of the Convolutional Neural Network model over Support Vector Machine using different kernel functions. One modified feature descriptor improved by 19.8%, 27.9%, 17.6%, 21.5%, 17.3%, and 22% for evaluation metrics: Area Under the Curve, Matthews Correlation Coefficient, Accuracy, F1-score, Recall, and Precision, respectively. Conclusion: Results show that the prediction of the proposed modified feature descriptors significantly surpasses that of Support Vector Machine model.

Recognition of 3D Objects from 2D Views Features

2015 ◽  
Vol 13 (2) ◽  
pp. 50-58
Author(s):  
R. Khadim ◽  
R. El Ayachi ◽  
Mohamed Fakir
Keyword(s):  
Neural Network ◽  
Support Vector Machine ◽  
Nearest Neighbor ◽  
Color Image ◽  
Recognition Rate ◽  
Experimental Results ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
3D Objects ◽  
Color Descriptor

This paper focuses on the recognition of 3D objects using 2D attributes. In order to increase the recognition rate, the present an hybridization of three approaches to calculate the attributes of color image, this hybridization based on the combination of Zernike moments, Gist descriptors and color descriptor (statistical moments). In the classification phase, three methods are adopted: Neural Network (NN), Support Vector Machine (SVM), and k-nearest neighbor (KNN). The database COIL-100 is used in the experimental results.

AUTOMATED RECOGNITION OF SINGLE & HYBRID POWER QUALITY DISTURBANCES USING WAVELET TRANSFORM BASED SUPPORT VECTOR MACHINE

2016 ◽  
Vol 79 (1) ◽  
Author(s):  
Suhail Khokhar ◽  
A. A. Mohd Zin ◽  
M. A. Bhayo ◽  
A. S. Mokhtar
Keyword(s):  
Neural Network ◽  
Support Vector Machine ◽  
Wavelet Transform ◽  
Power Quality ◽  
Probabilistic Neural Network ◽  
Recognition Rate ◽  
Classification Performance ◽  
Support Vector ◽  
Discrete Wavelet ◽  
Feed Forward Neural Network

The monitoring of power quality (PQ) disturbances in a systematic and automated way is an important issue to prevent detrimental effects on power system. The development of new methods for the automatic recognition of single and hybrid PQ disturbances is at present a major concern. This paper presents a combined approach of wavelet transform based support vector machine (WT-SVM) for the automatic classification of single and hybrid PQ disturbances. The proposed approach is applied by using synthetic models of various single and hybrid PQ signals. The suitable features of the PQ waveforms were first extracted by using discrete wavelet transform. Then SVM classifies the type of PQ disturbances based on these features. The classification performance of the proposed algorithm is also compared with wavelet based radial basis function neural network, probabilistic neural network and feed-forward neural network. The experimental results show that the recognition rate of the proposed WT-SVM based classification system is more accurate and much better than the other classifiers. 

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