Condition detection of overhaul platform based on image processing

Nowadays, humans tend to carry out activities during the day, both indoors and outdoors. Activities carried out outdoors cause human skin to often receive direct exposure to sunlight, which contains ultraviolet (UV) rays. Direct exposure to UV rays on the skin will harm the skin's health, which is the covering of the human body. Harmful effects on the skin usually include the skin becoming dark and dull, burns, and even causes cancer. One of the skin cancers that may appear on human skin is Actinic Keratosis (AK) cancer. AK cancer is a type of cancer that is classified as benign and can be cured with medical help. However, if this cancer is not caught early, it can become Squamous Cell Carcinoma (SCC), a type of malignant cancer. This research aims to design a system for identifying AK cancer types using color and texture feature extraction. RGB color feature extraction is obtained from image color segmentation and RGB values. The Gray Level Co-occurrence Matrix (GLCM) method is used to determine the texture of the skin cancer. Identification is carried out by a classification process using a Support Vector Machine (SVM), which can recognize the type of AK cancer. This research uses three classification methods: classification with color extraction, classification with texture extraction, and classification with color and texture extraction. Research shows that the highest level of accuracy in cancer recognition reaches 96% by combining color and texture extraction results as classification determinants. So, the system designed has succeeded in recognizing the type of AK cancer early on..

Nowadays, the Structural Building Health Damage Monitoring System (SBHDMS) is a crucial technology for predicting the civil building structures' health. SBHDMS contains abnormal changes in the buildings in terms of damage levels. Natural Disasters like Earthquakes, Floods, and cyclones affect the unusual changes in the buildings. If the building undergoes any natural disaster, the sensors capture the vibration data or change the buildings' structure. Due to the vibration data, these unusual changes can be analyzed. Here sensors or Machine Learning based Building Damage Prediction (MLBDP) are used for capturing and collecting the vibration data. This paper proposes a Novel Rough Set based Artificial Neural Network with Support Vector Machine (RAS) metaheuristic method. RAS method is used to predict the damaged building's vibration data levels captured by the sensors. For the feature reduction subset, we use one of the essential pre-processing method called the Rough set theory (RST) strategy. RAS has two contributions. The first one is the Support Vector Machine (SVM) classification method used for identifying the structures of the buildings. The artificial Neural Network (ANN) method used to predict the buildings' damage levels is the second contribution. The proposed method (RAS) is accurately predicting the conditions of the construction building structure and predicting the damage levels, without human intervention. Comparing the results states that the proposed method accuracy is better than SVM's classification methods, ANN. The prediction analysis depicts that the RAS method can effectively detect the damage levels.

Classifying different types of sea ice plays a significant role in marine navigation, especially on the Northwest Passage in the Arctic. In this paper, a U-Net model is trained to identify multi-year ice (MYI), first-year ice (FYI), and open water (OW) from synthetic aperture radar (SAR) images. 14 Sentinel-1 A/B Extended Wide (EW) mode images, acquired in the Beaufort Sea and Svalbard between January and March 2020, and their gray level co-occurrence matrix (GLCM) texture feature are split into chips to be fed into the model for training and testing. The GLCM is a set of images reflecting the comprehensive information of direction, adjacent interval, and variation amplitude of the gray level of original image. It can also be understood as texture features of original image. It is the basis of analyzing local patterns in an image and their arrangement rules. Dissimilarity of HH (HH-Dis) chosen from 20 kinds of available GLCM texture features is combined with horizontal-vertical (HV) polarization, horizontal-horizontal (HH) polarization, and incident angle as inputs. The LabelMe software creates labels with visual interpretation. The proportions of FYI and MYI data in training data were adjusted to approximately equal, to avoid bias of model prediction results towards dominant categories. The result shows that the combination of HH, HV, incident angle and HH-Dis has the highest precision of 92.10%, although the precision is 89.35% when only HH and HV information combined. This result reflects the potential of adding GLCM for sea ice classification.

Cancer is one of the leading causes of death, and breast cancer is the second leading cause of cancer death in women. One method to realize the level of malignancy of breast cancer from an early age is by classifying the cancer malignancy using data mining. One of the widely used data mining methods with a good level of accuracy is the Support Vector Machine (SVM) and K-Nearest Neighbors (KNN). Evaluation techniques of percentage split and cross-validation were used to evaluate and compare the SVM and KNN classification models. The result was that the accuracy level of the SVM classification method was better than the KNN classification method when using the cross-validation technique, which is 95,7081%. Meanwhile, the KNN classification method was better than the SVM classification method when using the percentage split technique, which is 95,4220%. From the comparison results, it can be seen that the KNN and SVM methods work well in the classification of breast cancer.

The segmentation of MRI brain tumors utilizes computer technology to segment and label tumors and normal tissues automatically on multimodal brain images, which plays an important role in disease diagnosis, treatment planning, and surgical navigation. We propose a solution using gray-level co-occurrence matrix (GLCM) texture and an ensemble Support Vector Machine (SVM) structure.This manuscript per the authors focus on the effects of GLCM texture on brain tumor segmentation. The result is different from the application of the GLCM texture in other types of image processing.The experimental material was a dataset called BraTs2015. The segmented five different labels are normal brain, necrosis, edema, non-enhancing tumor, and enhancing tumor. The proposed model was verified with the Dice coefficient. The result demonstrated that this method has a better capacity and higher segmentation accuracy with a low computation cost.

This research aims to detect subtle changes by combining binary change analysis, the Iteratively Reweighted Multivariate Alteration Detection (IRMAD), over dual polarimetric Advanced Land Observing Satellite (ALOS) backscatter with augmented data for post-classification change analysis. The accuracy of change detection was iteratively evaluated based on thresholds composed of mean and a range constant of standard deviation. Four datasets were examined for post-classification change analysis including the dual polarimetric backscatter as the benchmark and its augmented data with indices, entropy alpha decomposition and selected texture features. Variable importance was then evaluated to build a best subset model employing seven classifiers, including Bagged Classification and Regression Tree (CAB), Extreme Learning Machine Neural Network (ENN), Bagged Multivariate Adaptive Regression Spline (MAB), Regularised Random Forest (RFG), Original Random Forest (RFO), Support Vector Machine (SVM), and Extreme Gradient Boosting Tree (XGB). The best accuracy was 98.8%, which resulted from thresholding MAD variate-2 with constants at 1.7. The highest improvement of classification accuracy was obtained by amending the grey level co-occurrence matrix (GLCM) texture. The identification of variable importance (VI) confirmed that selected GLCM textures (mean and variance of HH or HV) were equally superior, while the contribution of index and decomposition were negligible. The best model produced similar classification accuracy at about 90% for both years 2007 and 2010. Tree-based algorithms including RFO, RFG and XGB were more robust than SVM and ENN. Subtle changes indicated by binary change analysis were somewhat hidden in post-classification analysis. Reclassification by combining all important variables and adding five classes to include subtle changes assisted by Google Earth yielded an accuracy of 82%.

In this study, we used visible/near-infrared (Vis/NIR) hyperspectral imaging technology to measure the reflectance and gray-level texture features of grape leaf surfaces, and we predicted water content based on those features for more effective irrigation control. Grape vine specimens were potted in the actual field environment, and five levels of water treatment with two replicates were applied to create controlled water stresses. The reflectance and gray-level co-occurrence matrix (GLCM) texture features were used to develop partial least squares (PLS) models for water content prediction. The results showed that when using both reflectance and GLCM texture features, the results achieved a correlation coefficient (rp), root mean square error of prediction (RMSEP), bias, and residual predictive deviation (RPD) of 0.900, 0.826, -2.213e-04, and 2.084, respectively. The experimental results indicated that using a Vis/NIR hyperspectral imaging system could enable this combined method of reflectance and GLCM texture to predict water content of grape vines and be beneficial for grape vine management.

The prediction of discharge is a challenging task for compound channel due to exchange of momentum at the junction of main channel and floodplain. From the literature, it has been found that, using apparent shear force (ASF) concept at the interface of floodplain and main channel, the accurate discharge can be predicted. ASF is a function of various non-dimensional parameters such as channel width ratio (α), relative flow depth (β), main channel aspect ratio (δ), relative roughness (γ), bed slope (S0), Froude number (Fr), and side slope (m). In this paper, an attempt has been made to model ASF by considering the aforementioned non-dimensional parameters. A total of 152 datasets have been collected from various literatures related to ASF. In the recent days, artificial intelligence (AI) and machine learning (ML) techniques are widely used to model hydrological and hydraulic flow problems. In this research work, two soft computing techniques such as support vector machine (SVM) and adaptive neuro fuzzy inference system (ANFIS) have been utilized to model apparent shear force at the two-stage channel. First, SVM has been successfully used in classification and then extended for regression analysis. The SVM classification methods are based on the principle of optimal separation of classes. The computation is critically dependent upon the number of training pattern and selection of hyper-parameters for regression. There are four types of SVM named as cubic SVM, fine Gaussian SVM, medium Gaussian SVM, and coarse Gaussian SVM. In ANFIS modeling, different membership functions have been utilized. All types SVMs modelling and ANFIS modelling are performed using MATLAB tool. Sensitivity analysis has also been performed to check the strength of developed model. The present model is compared with other existing models and found to provide satisfactory results with coefficient of determination (R2) value greater than 0.93.

Lithology prediction by support vector classifiers using inverted seismic attributes data and petrophysical logs as a new approach and investigation of training data set size effect on its performance in a heterogeneous carbonate reservoir

Breast cancer is the leading cancer among females, the key technology of preventing breast cancers is early detection. Based on the advantage of support vector machine (SVM), finding global solution and possessing higher generalization capability on dealing with the small sample, a new method of diagnosing breast cancer by CAD is proposed in this paper. Firstly, a principal component analysis is used to represent the information of ROI image, which account for most of the variance of the original data set while significantly reducing the data dimension. After the extraction of principal components, only those data of which account for most part of variance were retained as the feature vector and input into a SVM classification and BP neural network classification to classify. Finally, the results of experiment show that the accuracy and specificity for the diagnosis of breast cancer using SVM classification is good.

The major hurdle for segmenting lung lobes in computed tomographic (CT) images is to identify fissure regions, which encase lobar fissures. Accurate identification of these regions is difficult due to the variable shape and appearance of the fissures, along with the low contrast and high noise associated with CT images. This paper studies the effectiveness of two texture analysis methods - the gray level co-occurrence matrix (GLCM) and the gray level run length matrix (GLRLM) - in identifying fissure regions from isotropic CT image stacks. To classify GLCM and GLRLM texture features, we applied a feed-forward back-propagation neural network and achieved the best classification accuracy utilizing 16 quantized levels for computing the GLCM and GLRLM texture features and 64 neurons in the input/hidden layers of the neural network. Tested on isotropic CT image stacks of 24 patients with the pathologic lungs, we obtained accuracies of 86% and 87% for identifying fissure regions using the GLCM and GLRLM methods, respectively. These accuracies compare favorably with surgeons/radiologists' accuracy of 80% for identifying fissure regions in clinical settings. This shows promising potential for segmenting lung lobes using the GLCM and GLRLM methods.

Lung cancer is a common type of cancer that requires early diagnosis. Computer systems by particular different image processing techniques can use for increase the speed and accuracy of lung nodule detection. CT images used in this work in order to process medical images. In this paper proposed an automatic lung nodule detection algorithm using active contour method and SVM classification method. In proposed method, at first in order to achieve better results, lung CT image pre-processing is performed. Then the lung area is segmented by thresholding method followed by some reconstruction techniques to transfer non-isolated nodules into isolated ones. In the next step the nodule candidates are determined using active contour method. Then, nodules are detected by the support vector machine (SVM) classifier using efficient 2D stochastic and 3D anatomical features. In the result, nodules are detected with an overall detection rate of 87%; the number of false positive is 7.5/scan and the location of all detected nodules are recognized correctly.

Abstract: This work explores a novel approach to accurately identify brain tumors from magnetic resonance imaging (MRI) images by utilizing the complementary capabilities of fuzzy C-Means (FCM) clustering and support vector machines (SVM). Using the soft clustering approach, MRI images are meticulously segregated in the first stage, known as FCM clustering. By giving individual voxels varying degrees of membership, FCM deftly handles the inherent ambiguities and complexity of medical pictures to produce a nuanced representation of tissue kinds. SVM, a potent supervised learning technique for binary classification applications, comes after segmentation. uses this information in the second phase of classification. Through the creation of a hyperplane that divides tumor and non-tumor regions to the greatest extent possible, support vector machines (SVM) are able to optimize the margin between these classes and improve their ability to generalize. We deliberately integrate FCM clustering with SVM classification in order to take use of the complementary characteristics that each of these techniques possesses. While SVM's strong classification skills enable it to fine-tune the identification of tumor regions, FCM's sophisticated segmentation captures the spatial distribution of tissues.

Current studies document the effectiveness of multi-sensing technique implementation to mimic or to complement human senses. This work demonstrated the successful application of multi-sensing techniques such electronic tongue (e-tongue), electronic nose (e-nose) and Fourier Transform Infrared Spectroscopy (FTIR). The fusion of these modalities enhance the classification of pure Tualang honey using Linear Discriminant Analysis (LDA), Probabilistic Neural Network (PNN), Support Vector Machine (SVM) and k-Nearest Neighbour (KNN). KNN and PNN are able to classify between pure and adulterated honey samples, outperform LDA and SVM. By performing data fusion, SVM and LDA classifier can achieved more than 80% accuracy, while KNN and PNN obtained greater precision, up to 96% correct classification. The findings confirmed that, multi-sensing technique; either KNN or PNN was significantly superior compared to SVM and LDA classification methods. Thus, both analyses are able to discriminate between pure and adulterated honey.

This paper investigates whether the combination of airborne hyperspectral imagery (Aisa EAGLE II) and image classification methods (MLC, SVM) using feature extraction can discriminate among species and clones of energy trees. The trees examined have similar morphological traits due to limitation of detection. The image classification was applied on a spectrally selected and transformed (PCA, MNF) dataset. A binary tree SVM classifier was developed in accordance with the principle of SVM, based on the Jeffries-Matusita (JM) separability measure of selected classes. The adaptive binary tree SVM on MNF-transformed dataset provided more accurate results than applied MLC and multiclass SVM methods. The primary outcome of this study was a comparison of support vector machines (SVM) classification methods to evaluate species or clones of energy plants. In this paper, an adaptive binary tree SVM classifier (ABTSVM) is proposed to increase the accuracy of subspecies level.