scholarly journals Disease Detection of Dragon Fruit Stem Based on The Combined Features of Color and Texture

2021 ◽  
Vol 5 (2) ◽  
pp. 161-175
Author(s):  
Lutfi Hakim ◽  
Sepyan Purnama Kristanto ◽  
Dianni Yusuf ◽  
Mohammad Nur Shodiq ◽  
Wahyu Ade Setiawan
Keyword(s):  
Support Vector Machine ◽  
Feature Extraction ◽  
Texture Feature ◽  
Extraction Methods ◽  
Support Vector ◽  
Identification System ◽  
Accuracy Score ◽  
K Nearest Neighbors ◽  
Dragon Fruit ◽  
Combined Features

Dragon fruit is one of the favorite commodities in Banyuwangi Regency's agriculture. In 2019, this commodity had the fourth largest harvest area among other fruit commodities in Banyuwangi until it was exported to China. However, disease attacks often appeared in several dragon fruit plantations in Banyuwangi, and the identification system was still conventional. Many farmers did not know the types of disease and how to handle it, causing the quality and quantity of their crops to decline. Therefore, this study implemented two feature extraction methods. Both methods include color feature extraction using the color moments method and texture feature extraction using gray level co-occurrence matrices (GLCM). The methods used to develop a system that recognized or detected the three types of dragon fruit stem based on digital image processing using Support Vector Machine and k-Nearest Neighbors methods as comparison methods. The results obtained from this study indicated that the combination of the two proposed feature extraction methods could distinguish between stem rot, smallpox, and insect stings with an optimal accuracy score of 87.5% obtained by using Support Vector Machine as a classification method.

scholarly journals Diagnosis of Melanoma Based on the Sparse Auto-Encoder for Feature Extraction

2020 ◽  
pp. 220-230
Author(s):  
Nadia Smaoui Zghal ◽  
Marwa Zaabi ◽  
Houda Derbel
Keyword(s):  
Support Vector Machine ◽  
Feature Extraction ◽  
Support Vector Machine Classifier ◽  
Nearest Neighbors ◽  
Support Vector ◽  
Suspected Case ◽  
K Nearest Neighbors ◽  
Unsupervised Deep Learning ◽  
Sparse Autoencoder ◽  
Experimental Findings

Aims: Skin cancer is a fairly critical disease all over the world and especially in Western countries and America. However, if it is perceived and treated early, it is quite often curable. The main risk factors for melanoma are exposure to UV rays, the presence of many moles, and heredity. For this reason, this work focuses on the issue of automatic diagnosis of melanoma. The aim is to extract significant features from pixels of the images based on an unsupervised deep learning technique which is the sparse autoencoder method. Methodology: A preprocessing phase is required to remove the artifacts and enhance the contrast of the images before proceeding with the feature extraction. Once the characteristics are extracted automatically, the support vector machine classifier and the k-nearest neighbors are applied for the classification phase. The objective is to differentiate between 3 categories: melanoma, suspected case, and non-melanoma. Finally, the PH2 database is used to test the proposed approaches (200 images are presented in this dataset: 80 atypical nevi, 80 common nevi, and 40 melanoma). Results: The obtained results in terms of specificity, accuracy, and sensitivity present noticeable performances with the support vector machine classifier (achieved 94 % overall accuracy) and the k-nearest neighbors (92 %). Conclusion: This study's experimental findings showed that the best performance was obtained by the approach based on a deep sparse autoencoder combined with support vector machine.

scholarly journals Analyzing the Effectiveness of the Brain–Computer Interface for Task Discerning Based on Machine Learning

Sensors ◽  
2020 ◽  
Vol 20 (8) ◽  
pp. 2403
Author(s):  
Jakub Browarczyk ◽  
Adam Kurowski ◽  
Bozena Kostek
Keyword(s):  
Feature Extraction ◽  
Principal Component ◽  
Component Analysis ◽  
Mental States ◽  
Extraction Methods ◽  
Support Vector ◽  
Discrete Wavelet ◽  
K Nearest Neighbors ◽  
Vector Machines

The aim of the study is to compare electroencephalographic (EEG) signal feature extraction methods in the context of the effectiveness of the classification of brain activities. For classification, electroencephalographic signals were obtained using an EEG device from 17 subjects in three mental states (relaxation, excitation, and solving logical task). Blind source separation employing independent component analysis (ICA) was performed on obtained signals. Welch’s method, autoregressive modeling, and discrete wavelet transform were used for feature extraction. Principal component analysis (PCA) was performed in order to reduce the dimensionality of feature vectors. k-Nearest Neighbors (kNN), Support Vector Machines (SVM), and Neural Networks (NN) were employed for classification. Precision, recall, F1 score, as well as a discussion based on statistical analysis, were shown. The paper also contains code utilized in preprocessing and the main part of experiments.

scholarly journals Plasmodium Falciparum Identification in Thick Blood Preparations Using GLCM and Support Vector Machine (SVM)

2017 ◽  
Vol 2 (1) ◽  
pp. 12-20 ◽  
Author(s):  
Farah Zakiyah Rahmanti ◽  
Novita Kurnia Ningrum ◽  
Septian Enggar Sukmana ◽  
Prajanto Wahyu Adi
Keyword(s):  
Support Vector Machine ◽  
Plasmodium Falciparum ◽  
Research Result ◽  
Texture Feature ◽  
Rapid Identification ◽  
Blood Film ◽  
Support Vector ◽  
Identification System ◽  
Risk Of Death ◽  
Occurrence Matrix

Malaria is one of the serious diseases that require rapid handling, otherwise it can lead to death. One of the causes of malaria parasites is plasmodium falciparum which can cause severe or fatal malaria. Handling a medical late can increase the risk of death. Therefore, it takes a rapid identification system with a high percentage of accuracy to reduce the risk of death. This research aims to build an identification system of plasmodium falciparum in thick blood film using Gray Level Co-occurrence Matrix (GLCM) and Support Vector Machine (SVM). The GLCM is used to get texture feature values such as contrast, correlations, energy, and homogeneity from images. Those values is processed and as an input of classification using SVM. The research result using SVM for accuracy value of  plasmodium falciparum identification can reach 93.33%.

scholarly journals Enhanced Epileptic Seizure diagnosis using EEG Signals with Support vector machine and Bagging Classifiers

2021 ◽  
Vol 13 (3) ◽  
Author(s):  
Rana Alrawashdeh ◽  
Mohammad Al-Fawa'reh ◽  
Wail Mardini
Keyword(s):  
Support Vector Machine ◽  
Feature Extraction ◽  
Wavelet Transforms ◽  
Support Vector ◽  
Discrete Wavelet ◽  
Eeg Signals ◽  
K Nearest Neighbors ◽  
Data Set ◽  
Artificial Neural Network Ann ◽  
Manual Testing

Many approaches have been proposed using Electroencephalogram (EEG) to detect epilepsy seizures in their early stages. Epilepsy seizure is a severe neurological disease. Practitioners continue to rely on manual testing of EEG signals. Artificial intelligence (AI) and Machine Learning (ML) can effectively deal with this problem. ML can be used to classify EEG signals employing feature extraction techniques. This work focuses on automated detection for epilepsy seizures using ML techniques. Various algorithms are investigated, such as  Bagging, Decision Tree (DT), Adaboost, Support vector machine (SVM), K-nearest neighbors(KNN), Artificial neural network(ANN), Naïve Bayes, and Random Forest (RF) to distinguish injected signals from normal ones with high accuracy. In this work, 54 Discrete wavelet transforms (DWTs) are used for feature extraction, and the similarity distance is applied to identify the most powerful features. The features are then selected to form the features matrix. The matrix is subsequently used to train ML. The proposed approach is evaluated through different metrics such as F-measure, precision, accuracy, and Recall. The experimental results show that the SVM and Bagging classifiers in some data set combinations, outperforming all other classifiers

scholarly journals Comparison of Parametric and Non-parametric EEG Feature Extraction Methods in Detection of Pediatric Migraine without Aura

2018 ◽  
Author(s):  
S Kazemi ◽  
P Katibeh
Keyword(s):  
Support Vector Machine ◽  
Feature Extraction ◽  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Extraction Methods ◽  
Dominant Frequency ◽  
Support Vector ◽  
Classification Methods ◽  
Linear Discriminant ◽  
Non Parametric

Background: Migraine headache without aura is the most common type of migraine especially among pediatric patients. It has always been a great challenge of migraine diagnosis using quantitative electroencephalography measurements through feature classification. It has been proven that different feature extraction and classification methods vary in terms of performance regarding detection and diagnostic accuracy. Previous work on the subject was controversial, hence a comparison of these methods seems necessary.Objectives: The aim of this research is to compare two parametric and non-parametric feature extraction methods and also two classification methods in order to obtain optimal combinations of diagnostic accuracy.Materials and Methods: Having recorded background EEG from 24 pediatric migraineurs and 19 control subjects, data was processed by Welch’s and Yule-Walker’s methods. Features were selected using genetic algorithm, and then given to a support vector machine and the linear discriminant analysis for the classification. Accuracy was calculated for all combinations having the dominant frequency and the correlated absolute power of each EEG wave band (theta, alpha, and beta) and for all wave bands combined.Results: The highest migraine detection accuracy of 93% was obtained utilizing Welch’s method for EEG feature extraction alongside support vector machine for a classifier. Besides, Yule-Walker autoregressive method showed better performance than Welch’s, when only power bands (and not the dominant frequency) were used as classification input.Conclusion: The superiority of Welch’s method over Yule-Walker’s and the support vector machine over linear discriminant analysis can be great help for further researches on computer aided EEG-based diagnosis of migraine

Intelligent sports feature recognition system based on texture feature extraction and SVM parameter selection

2020 ◽  
Vol 39 (4) ◽  
pp. 4847-4858
Author(s):  
Lei Wang ◽  
Jinhai Sun ◽  
Tuojian Li
Keyword(s):  
Support Vector Machine ◽  
Feature Extraction ◽  
Extraction Method ◽  
Feature Recognition ◽  
Texture Feature ◽  
Texture Features ◽  
Spatial Domain ◽  
Image Texture ◽  
Support Vector ◽  
Texture Feature Extraction

Feature extraction is the basis of texture analysis. How to obtain texture features with small feature dimension, simple calculation and comprehensive representation of images is a hot spot and a difficult point in feature extraction. The traditional image texture feature extraction method is to process the image in the spatial domain. However, due to its high computational complexity, its practical application is restricted. Based on this, this study studies the extraction method of texture features, and deeply analyzes the principle of non-subsampled Contourlet transform. Moreover, this study uses NSCT to transform the image from the spatial domain to the frequency domain and extracts the texture features of the decomposed low frequency sub-band, intermediate frequency sub-band and high frequency sub-band image respectively. In addition, this study selects the appropriate parameters to establish the support vector machine model and applies the extracted texture features into the support vector machine for recognition and applies it to the sports feature recognition. Finally, this study designed a controlled experiment to analyze the performance of the algorithm. The results show that the proposed method has certain effects.

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