Classification of Troso Fabric Using SVM-RBF Multi-class Method with GLCM and PCA Feature Extraction

Gustian, Dinda Aulia; Rohmah, Nur Laelatul; Shidik, Guruh Fajar; Fanani, Ahmad Zainul; Pramunendar, Ricardus Anggi; Pujiono, Pujiono

doi:10.1109/isemantic.2019.8884329

Cited by 15 publications

(7 citation statements)

References 8 publications

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“…Support vector machine (SVM) is a learning system that uses a hypothetical rating in linear functions in a high-dimensional feature space trained with a learning algorithm based on optimisation theory by implementing a learning bias derived from statistical theory [13]. SVM classification attempts to find the best hyperplane that functions as a separator of two data classes in the input space.…”

Section: Support Vector Machinementioning

confidence: 99%

Parking Lots Detection in Static Image Using Support Vector Machine Based on Genetic Algorithm

2021

IJIES

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The increasing ownership of four-wheeled vehicles creates a new problem: difficulty finding available parking spaces in significant places. Issues citizens often experience these in the city centre who use their cars for transportation. This causes the detection of parking lots to attract the attention of researchers. Unfortunately, this system fails when the vehicle occupies more than one place or when the parking lot has a different parking space. In this study, an automation method is proposed that utilises a combination of gray level co-occurrence matrix (GLCM) and support vector machine (SVM) methods with genetic algorithm (GA) optimisation techniques or called SVMGA. The results showed that the SVMGA could provide an accuracy performance of 96.99% for training data and 94.36% for test data. The results of this study are expected to help the queue of people to find a parking space and reduce the density of lines in the parking lot.

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Section: Support Vector Machinementioning

confidence: 99%

Parking Lots Detection in Static Image Using Support Vector Machine Based on Genetic Algorithm

2021

IJIES

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“…Feature extraction utilizes the gray level co-occurrence matrix (GLCM), which applies five quantities, namely angular second moment (ASM), contrast, inverse different moment (IDM), entropi, and correlation [29][30][31]. Examples of the results from feature extraction can be seen in Table 1.…”

Section: Feature Extractionmentioning

confidence: 99%

Application of neural network method for road crack detection

2020

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The study presents a road pavement crack detection system by extracting picture features then classifying them based on image features. The applied feature extraction method is the gray level co-occurrence matrices (GLCM). This method employs two order measurements. The first order utilizes statistical calculations based on the pixel value of the original image alone, such as variance, and does not pay attention to the neighboring pixel relationship. In the second order, the relationship between the two pixel-pairs of the original image is taken into account. Inspired by the recent success in implementing Supervised Learning in computer vision, the applied method for classification is artificial neural network (ANN). Datasets, which are used for evaluation are collected from low-cost smart phones. The results show that feature extraction using GLCM can provide good accuracy that is equal to 90%.

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“…Dey and Dey [ 11 ] utilized a randomized Hough transform in combination with a Canny edge detection technique to obtain optical contours in their research. To diagnose glaucoma, Gustian et al [ 12 ] used a fully convolution network (FCN) and collected patient data and the contours of anatomical structure from case reports, and they were able to produce an image of the fundus. The authors of the work described in this paper used convolution neural networks to distinguish OC and OD pictures in their research, as Htay and Maung [ 13 ] reported.…”

Section: Introductionmentioning

confidence: 99%

Glaucoma Detection Using Image Processing and Supervised Learning for Classification

Joshi

Partibane²,

Hatamleh

et al. 2022

Journal of Healthcare Engineering

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A difficult challenge in the realm of biomedical engineering is the detection of physiological changes occurring inside the human body, which is a difficult undertaking. At the moment, these irregularities are graded manually, which is very difficult, time-consuming, and tiresome due to the many complexities associated with the methods involved in their identification. In order to identify illnesses at an early stage, the use of computer-assisted diagnostics has acquired increased attention as a result of the requirement of a disease detection system. The major goal of this proposed work is to build a computer-aided design (CAD) system to help in the early identification of glaucoma as well as the screening and treatment of the disease. The fundus camera is the most affordable image analysis modality available, and it meets the financial needs of the general public. The extraction of structural characteristics from the segmented optic disc and the segmented optic cup may be used to characterize glaucoma and determine its severity. For this study, the primary goal is to estimate the potential of the image analysis model for the early identification and diagnosis of glaucoma, as well as for the evaluation of ocular disorders. The suggested CAD system would aid the ophthalmologist in the diagnosis of ocular illnesses by providing a second opinion as a judgment made by human specialists in a controlled environment. An ensemble-based deep learning model for the identification and diagnosis of glaucoma is in its early stages now. This method’s initial module is an ensemble-based deep learning model for glaucoma diagnosis, which is the first of its kind ever developed. It was decided to use three pretrained convolutional neural networks for the categorization of glaucoma. These networks included the residual network (ResNet), the visual geometry group network (VGGNet), and the GoogLeNet. It was necessary to use five different data sets in order to determine how well the proposed algorithm performed. These data sets included the DRISHTI-GS, the Optic Nerve Segmentation Database (DRIONS-DB), and the High-Resolution Fundus (HRF). Accuracy of 91.11% for the PSGIMSR data set and the sensitivity of 85.55% and specificity of 95.20% for the suggested ensemble architecture on the PSGIMSR data set were achieved. Similarly, accuracy rates of 95.63%, 98.67%, 95.64%, and 88.96% were achieved using the DRIONS-DB, HRF, DRISHTI-GS, and combined data sets, respectively.

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Classification of Troso Fabric Using SVM-RBF Multi-class Method with GLCM and PCA Feature Extraction

Cited by 15 publications

References 8 publications

Parking Lots Detection in Static Image Using Support Vector Machine Based on Genetic Algorithm

Parking Lots Detection in Static Image Using Support Vector Machine Based on Genetic Algorithm

Application of neural network method for road crack detection

Glaucoma Detection Using Image Processing and Supervised Learning for Classification

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