scholarly journals Klasifikasi Lukisan Karya Van Gogh Menggunakan Convolutional Neural Network-Support Vector Machine

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Yohannes Yohannes ◽  
Daniel Udjulawa ◽  
Febbiola Febbiola
Keyword(s):  
Feature Extraction ◽  
Extraction Methods ◽  
Support Vector ◽  
Data Set ◽  
Van Gogh ◽  
Network Support ◽  
A Value ◽  
Best Value ◽  
Random Optimization ◽  
Grid Optimization

Painting is a work of art with various strokes, textures, and color gradations so that a painting that is synonymous with beauty is created. The various paintings created have characteristics, such as the paintings by Van Gogh, which have tightly arranged strokes, creating a repetitive and patterned impression. This study classifies paintings by Van Gogh or not by using the VGG-19 and ResNet-50 feature extraction methods. The SVM method is used as a classification method with two optimizations, namely random and grid optimization in the linear kernel. The data set used consisted of 124 Van Gogh paintings and 207 paintings by other painters. The use of VGG-19 feature extraction using grid optimization has the best value of 93,28% using the use of random optimization which has a value of 92,89%. The use of ResNet-50 using grid optimization with the best value of 90,28% using the use of random optimization which has a value of 90,15%. The extraction feature of VGG-19 is better than ResNet-50 in paintings by Van Gogh or not.

A Structural Analysis Based Feature Extraction Method for OCR System For Myanmar Printed Document Images

2012 ◽  
Vol 2 (1) ◽  
pp. 16-41 ◽  
Author(s):  
Htwe Pa Pa Win ◽  
Phyo Thu Thu Khine ◽  
Khin Nwe Ni Tun
Keyword(s):  
Feature Extraction ◽  
Structural Analysis ◽  
Character Recognition ◽  
Optical Character Recognition ◽  
Extraction Method ◽  
Recognition Performance ◽  
Extraction Methods ◽  
Support Vector ◽  
Svm Classifier ◽  
Feature Extraction Method

This paper proposes a new feature extraction method for off-line recognition of Myanmar printed documents. One of the most important factors to achieve high recognition performance in Optical Character Recognition (OCR) system is the selection of the feature extraction methods. Different types of existing OCR systems used various feature extraction methods because of the diversity of the scripts’ natures. One major contribution of the work in this paper is the design of logically rigorous coding based features. To show the effectiveness of the proposed method, this paper assumed the documents are successfully segmented into characters and extracted features from these isolated Myanmar characters. These features are extracted using structural analysis of the Myanmar scripts. The experimental results have been carried out using the Support Vector Machine (SVM) classifier and compare the pervious proposed feature extraction method.

Design of Text Categorization System Based on SVM

2012 ◽  
Vol 532-533 ◽  
pp. 1191-1195 ◽  
Author(s):  
Zhen Yan Liu ◽  
Wei Ping Wang ◽  
Yong Wang
Keyword(s):  
Feature Extraction ◽  
Feature Selection ◽  
Text Categorization ◽  
Feature Selection Method ◽  
Extraction Methods ◽  
Support Vector ◽  
Text Representation ◽  
Text Feature ◽  
Categorization System ◽  
Classifier Training

This paper introduces the design of a text categorization system based on Support Vector Machine (SVM). It analyzes the high dimensional characteristic of text data, the reason why SVM is suitable for text categorization. According to system data flow this system is constructed. This system consists of three subsystems which are text representation, classifier training and text classification. The core of this system is the classifier training, but text representation directly influences the currency of classifier and the performance of the system. Text feature vector space can be built by different kinds of feature selection and feature extraction methods. No research can indicate which one is the best method, so many feature selection and feature extraction methods are all developed in this system. For a specific classification task every feature selection method and every feature extraction method will be tested, and then a set of the best methods will be adopted.

Headnote Prediction Using Machine Learning

2021 ◽  
Vol 18 (5) ◽  
Author(s):  
Sarmad Mahar ◽  
Sahar Zafar ◽  
Kamran Nishat
Keyword(s):  
Machine Learning ◽  
Feature Extraction ◽  
Active Learning ◽  
Text Classification ◽  
Extraction Methods ◽  
Text Summarization ◽  
Training Data ◽  
Second Step ◽  
Support Vector ◽  
Classification Algorithms

Headnotes are the precise explanation and summary of legal points in an issued judgment. Law journals hire experienced lawyers to write these headnotes. These headnotes help the reader quickly determine the issue discussed in the case. Headnotes comprise two parts. The first part comprises the topic discussed in the judgment, and the second part contains a summary of that judgment. In this thesis, we design, develop and evaluate headnote prediction using machine learning, without involving human involvement. We divided this task into a two steps process. In the first step, we predict law points used in the judgment by using text classification algorithms. The second step generates a summary of the judgment using text summarization techniques. To achieve this task, we created a Databank by extracting data from different law sources in Pakistan. We labelled training data generated based on Pakistan law websites. We tested different feature extraction methods on judiciary data to improve our system. Using these feature extraction methods, we developed a dictionary of terminology for ease of reference and utility. Our approach achieves 65% accuracy by using Linear Support Vector Classification with tri-gram and without stemmer. Using active learning our system can continuously improve the accuracy with the increased labelled examples provided by the users of the system.

scholarly journals Superpixel-Based Minimum Noise Fraction Feature Extraction for Classification of Hyperspectral Images

2020 ◽  
Vol 37 (5) ◽  
pp. 812-822
Author(s):  
Behnam Asghari Beirami ◽  
Mehdi Mokhtarzade
Keyword(s):  
Feature Extraction ◽  
Extraction Methods ◽  
Hyperspectral Images ◽  
Support Vector ◽  
Minimum Noise Fraction ◽  
Vector Machines ◽  
Noise Covariance ◽  
Noise Fraction ◽  
Minimum Noise

In this paper, a novel feature extraction technique called SuperMNF is proposed, which is an extension of the minimum noise fraction (MNF) transformation. In SuperMNF, each superpixel has its own transformation matrix and MNF transformation is performed on each superpixel individually. The basic idea behind the SuperMNF is that each superpixel contains its specific signal and noise covariance matrices which are different from the adjacent superpixels. The extracted features, owning spatial-spectral content and provided in the lower dimension, are classified by maximum likelihood classifier and support vector machines. Experiments that are conducted on two real hyperspectral images, named Indian Pines and Pavia University, demonstrate the efficiency of SuperMNF since it yielded more promising results than some other feature extraction methods (MNF, PCA, SuperPCA, KPCA, and MMP).

A Kind of Network Intrusion Detection Method Using Improved Support Vector Machine Based on Ant Colony Algorithm

2012 ◽  
Vol 263-266 ◽  
pp. 2995-2998
Author(s):  
Xiaoqin Zhang ◽  
Guo Jun Jia
Keyword(s):  
Genetic Algorithm ◽  
Support Vector Machine ◽  
Feature Extraction ◽  
Ant Colony Algorithm ◽  
Small Sample ◽  
Ant Colony ◽  
Support Vector ◽  
Algorithm Selection ◽  
Data Set ◽  
Network Intrusion

Support vector machine (SVM) is suitable for the classification problem which is of small sample, nonlinear, high dimension. SVM in data preprocessing phase, often use genetic algorithm for feature extraction, although it can improve the accuracy of classification. But in feature extraction stage the weak directivity of genetic algorithm impact the time and accuracy of the classification. The ant colony algorithm is used in genetic algorithm selection stage, which is better for the data pretreatment, so as to improve the classification speed and accuracy. The experiment in the KDD99 data set shows that this method is feasible.

scholarly journals Detection and Characterization of Physical Activity and Psychological Stress from Wristband Data

Signals ◽  
2020 ◽  
Vol 1 (2) ◽  
pp. 188-208
Author(s):  
Mert Sevil ◽  
Mudassir Rashid ◽  
Mohammad Reza Askari ◽  
Zacharie Maloney ◽  
Iman Hajizadeh ◽  
...  
Keyword(s):  
Physical Activity ◽  
Signal Processing ◽  
Feature Extraction ◽  
Psychological Stress ◽  
Training Data ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Data Set ◽  
Linear Discriminant ◽  
Physiological Variables

Wearable devices continuously measure multiple physiological variables to inform users of health and behavior indicators. The computed health indicators must rely on informative signals obtained by processing the raw physiological variables with powerful noise- and artifacts-filtering algorithms. In this study, we aimed to elucidate the effects of signal processing techniques on the accuracy of detecting and discriminating physical activity (PA) and acute psychological stress (APS) using physiological measurements (blood volume pulse, heart rate, skin temperature, galvanic skin response, and accelerometer) collected from a wristband. Data from 207 experiments involving 24 subjects were used to develop signal processing, feature extraction, and machine learning (ML) algorithms that can detect and discriminate PA and APS when they occur individually or concurrently, classify different types of PA and APS, and estimate energy expenditure (EE). Training data were used to generate feature variables from the physiological variables and develop ML models (naïve Bayes, decision tree, k-nearest neighbor, linear discriminant, ensemble learning, and support vector machine). Results from an independent labeled testing data set demonstrate that PA was detected and classified with an accuracy of 99.3%, and APS was detected and classified with an accuracy of 92.7%, whereas the simultaneous occurrences of both PA and APS were detected and classified with an accuracy of 89.9% (relative to actual class labels), and EE was estimated with a low mean absolute error of 0.02 metabolic equivalent of task (MET).The data filtering and adaptive noise cancellation techniques used to mitigate the effects of noise and artifacts on the classification results increased the detection and discrimination accuracy by 0.7% and 3.0% for PA and APS, respectively, and by 18% for EE estimation. The results demonstrate the physiological measurements from wristband devices are susceptible to noise and artifacts, and elucidate the effects of signal processing and feature extraction on the accuracy of detection, classification, and estimation of PA and APS.

scholarly journals Vehicle Price Prediction using SVM Techniques

2020 ◽  
Vol 9 (8) ◽  
pp. 398-401
Keyword(s):  
Machine Learning ◽  
Research Area ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Data Set ◽  
Network Support ◽  
Java Application ◽  
Learning Techniques ◽  
The Individual

The prediction of price for a vehicle has been more popular in research area, and it needs predominant effort and information about the experts of this particular field. The number of different attributes is measured and also it has been considerable to predict the result in more reliable and accurate. To find the price of used vehicles a well defined model has been developed with the help of three machine learning techniques such as Artificial Neural Network, Support Vector Machine and Random Forest. These techniques were used not on the individual items but for the whole group of data items. This data group has been taken from some web portal and that same has been used for the prediction. The data must be collected using web scraper that was written in PHP programming language. Distinct machine learning algorithms of varying performances had been compared to get the best result of the given data set. The final prediction model was integrated into Java application

scholarly journals Multi-Stage Feature Extraction and Classification for Ship-Radiated Noise

Sensors ◽  
2021 ◽  
Vol 22 (1) ◽  
pp. 112
Author(s):  
Hamada Esmaiel ◽  
Dongri Xie ◽  
Zeyad A. H. Qasem ◽  
Haixin Sun ◽  
Jie Qi ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Recognition Rate ◽  
Extraction Methods ◽  
Permutation Entropy ◽  
Support Vector ◽  
Intrinsic Mode Functions ◽  
Radiated Noise ◽  
Passive Sonar ◽  
Multi Stage ◽  
Mode Decomposition

Due to the complexity and unique features of the hydroacoustic channel, ship-radiated noise (SRN) detected using a passive sonar tends mostly to distort. SRN feature extraction has been proposed to improve the detected passive sonar signal. Unfortunately, the current methods used in SRN feature extraction have many shortcomings. Considering this, in this paper we propose a new multi-stage feature extraction approach to enhance the current SRN feature extractions based on enhanced variational mode decomposition (EVMD), weighted permutation entropy (WPE), local tangent space alignment (LTSA), and particle swarm optimization-based support vector machine (PSO-SVM). In the proposed method, first, we enhance the decomposition operation of the conventional VMD by decomposing the SRN signal into a finite group of intrinsic mode functions (IMFs) and then calculate the WPE of each IMF. Then, the high-dimensional features obtained are reduced to two-dimensional ones by using the LTSA method. Finally, the feature vectors are fed into the PSO-SVM multi-class classifier to realize the classification of different types of SRN sample. The simulation and experimental results demonstrate that the recognition rate of the proposed method overcomes the conventional SRN feature extraction methods, and it has a recognition rate of up to 96.6667%.

scholarly journals Improved feature extraction using structured Fisher discrimination sparse coding scheme for machinery fault diagnosis

2016 ◽  
Vol 8 (12) ◽  
pp. 168781401668308 ◽  
Author(s):  
Shuangyuan Wang ◽  
Yixiang Huang ◽  
Liang Gong ◽  
Lin Li ◽  
Chengliang Liu
Keyword(s):  
Feature Extraction ◽  
Fault Diagnosis ◽  
Sparse Coding ◽  
Extraction Procedure ◽  
Vibration Signal ◽  
Support Vector ◽  
Data Set ◽  
Vibration Signals ◽  
Efficiency And Effectiveness ◽  
Gear Fault

Vibration signals reflecting different kinds of machinery conditions are very useful for fault diagnosis. However, vibration signal characteristics are not the same for different types of equipment and patterns of failure. This available information is often lost in structureless condition diagnosis models. We propose a structured Fisher discrimination sparse coding–based fault diagnosis scheme to improve the feature extraction procedure considering both efficiency and effectiveness. There are three major components: (1) a structured dictionary for synthesizing the vibration signals that is learned by structure Fisher discrimination dictionary learning, (2) a tree-structured sparse coding to extract sparse representation coefficients from vibration signals to represent fault features, and (3) a support vector machine’s classifier on the features to recognize different faults. The proposed algorithm is verified on a standard bearing fault data set and a worm gear fault experiment. Test results have proved that the proposed method can achieve better performance with considerable efficiency and generalization ability.

scholarly journals An Automatic Text Document Classification using Modified Weight and Semantic Method

2019 ◽  
Vol 8 (12) ◽  
pp. 2608-2622
Keyword(s):  
Feature Extraction ◽  
Text Mining ◽  
Crime Rate ◽  
Semantic Analysis ◽  
Extraction Methods ◽  
Support Vector ◽  
Text Document ◽  
Use Of Resources ◽  
Benchmark Datasets ◽  
Text Document Classification

Text mining is the process of transformation of useful information from the structured or unstructured sources. In text mining, feature extraction is one of the vital parts. This paper analyses some of the feature extraction methods and proposed the enhanced method for feature extraction. Term Frequency-Inverse Document Frequency(TF-IDF) method only assigned weight to the term based on the occurrence of the term. Now, it is enlarged to increases the weight of the most important words and decreases the weight of the less important words. This enlarged method is called as M-TF-IDF. This method does not consider the semantic similarity between the terms. Hence, Latent Semantic Analysis(LSA) method is used for feature extraction and dimensionality reduction. To analyze the performance of the proposed feature extraction methods, two benchmark datasets like Reuter-21578-R8 and 20 news group and two real time datasets like descriptive type answer dataset and crime news dataset are used. This paper used this proposed method for descriptive type answer evaluation. Manual evaluation of descriptive type paper may lead to discrepancy in the mark. It is eliminated by using this type of evaluation. The proposed method has been tested with answers written by learners of our department. It allows more accurate assessment and more effective evaluation of the learning process. This method has a lot of benefits such as reduced time and effort, efficient use of resources, reduced burden on the faculty and increased reliability of results. This proposed method also used to analyze the documents which contain the details about in and around Madurai city. Madurai is a sensitive place in the southern area of Tamilnadu in India. It has been collected from the Hindu archives. This news document has been classified like crime or not. It is also used to check in which month most crime rate occurs. This analysis used to reduce the crime rate in future. The classification algorithm Support Vector Machine(SVM) used to classify the dataset. The experimental analysis and results show that the performances of the proposed feature extraction methods are outperforming the existing feature extraction methods.

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