scholarly journals Automatic Gemstone Classification Using Computer Vision

Minerals ◽  
2021 ◽  
Vol 12 (1) ◽  
pp. 60
Author(s):  
Bona Hiu Yan Chow ◽  
Constantino Carlos Reyes-Aldasoro
Keyword(s):  
Computer Vision ◽  
Random Forest ◽  
Local Binary Pattern ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Linear Discriminant ◽  
Matrix Properties ◽  
Occurrence Matrix ◽  
Colour Histograms

This paper presents a computer-vision-based methodology for automatic image-based classification of 2042 training images and 284 unseen (test) images divided into 68 categories of gemstones. A series of feature extraction techniques (33 including colour histograms in the RGB, HSV and CIELAB space, local binary pattern, Haralick texture and grey-level co-occurrence matrix properties) were used in combination with different machine-learning algorithms (Logistic Regression, Linear Discriminant Analysis, K-Nearest Neighbour, Decision Tree, Random Forest, Naive Bayes and Support Vector Machine). Deep-learning classification with ResNet-18 and ResNet-50 was also investigated. The optimal combination was provided by a Random Forest algorithm with the RGB eight-bin colour histogram and local binary pattern features, with an accuracy of 69.4% on unseen images; the algorithms required 0.0165 s to process the 284 test images. These results were compared against three expert gemmologists with at least 5 years of experience in gemstone identification, who obtained accuracies between 42.6% and 66.9% and took 42–175 min to classify the test images. As expected, the human experts took much longer than the computer vision algorithms, which in addition provided, albeit marginal, higher accuracy. Although these experiments included a relatively low number of images, the superiority of computer vision over humans is in line with what has been reported in other areas of study, and it is encouraging to further explore the application in gemmology and related areas.

scholarly journals Extracted features based multi-class classification of orthodontic images

2020 ◽  
Vol 10 (4) ◽  
pp. 3558
Author(s):  
Hicham Riri ◽  
Mohammed Ed-Dhahraouy ◽  
Abdelmajid Elmoutaouakkil ◽  
Abderrahim Beni-Hssane ◽  
Farid Bourzgui
Keyword(s):  
Machine Learning ◽  
Local Binary Pattern ◽  
Principal Component ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Linear Discriminant ◽  
Nearest Neighbours ◽  
Multi Class Classification ◽  
Pca Algorithm

The purpose of this study is to investigate computer vision and machine learning methods for classification of orthodontic images in order to provide orthodontists with a solution for multi-class classification of patients’ images to evaluate the evolution of their treatment. Of which, we proposed three algorithms based on extracted features, such as facial features and skin colour using YCbCrcolour space, assigned to nodes of a decision tree to classify orthodontic images: an algorithm for intra-oral images, an algorithm for mould images and an algorithm for extra-oral images. Then, we compared our method by implementing the Local Binary Pattern (LBP) algorithm to extract textural features from images. After that, we applied the principal component analysis (PCA) algorithm to optimize the redundant parameters in order to classify LBP features with six classifiers; Quadratic Support Vector Machine (SVM), Cubic SVM, Radial Basis Function SVM, Cosine K-Nearest Neighbours (KNN), Euclidian KNN, and Linear Discriminant Analysis (LDA). The presented algorithms have been evaluated on a dataset of images of 98 different patients, and experimental results demonstrate the good performances of our proposed method with a high accuracy compared with machine learning algorithms. Where LDA classifier achieves an accuracy of 84.5%.

Identification and Classification of Fungal Colonies in Moldy Paddy Based on Computer Vision

2018 ◽  
Vol 61 (5) ◽  
pp. 1497-1504
Author(s):  
Zhenjie Wang ◽  
Ke Sun ◽  
Lihui Du ◽  
Jian Yuan ◽  
Kang Tu ◽  
...  
Keyword(s):  
Computer Vision ◽  
Discriminant Analysis ◽  
Texture Features ◽  
Fungal Species ◽  
Projection Algorithm ◽  
Support Vector ◽  
Linear Discriminant ◽  
Sequential Selection ◽  
The Individual

Abstract. In this study, computer vision was used for the identification and classification of fungi on moldy paddy. To develop a rapid and efficient method for the classification of common fungal species found in stored paddy, computer vision was used to acquire images of individual colonies of growing fungi for three consecutive days. After image processing, the color, shape, and texture features were acquired and used in a subsequent discriminant analysis. Both linear (i.e., linear discriminant analysis and partial least squares discriminant analysis) and nonlinear (i.e., random forest and support vector machine [SVM]) pattern recognition models were employed for the classification of fungal colonies, and the results were compared. The results indicate that when using all of the features for three consecutive days, the performance of the nonlinear tools was superior to that of the linear tools, especially in the case of the SVM models, which achieved an accuracy of 100% on the calibration sets and an accuracy of 93.2% to 97.6% on the prediction sets. After sequential selection of projection algorithm, ten common features were selected for building the classification models. The results showed that the SVM model achieved an overall accuracy of 95.6%, 98.3%, and 99.0% on the prediction sets on days 2, 3, and 4, respectively. This work demonstrated that computer vision with several features is suitable for the identification and classification of fungi on moldy paddy based on the form of the individual colonies at an early growth stage during paddy storage. Keywords: Classification, Computer vision, Fungal colony, Feature selection, SVM.

Damage Classification of Composites Using Machine Learning

2019 ◽  
Author(s):  
Shweta Dabetwar ◽  
Stephen Ekwaro-Osire ◽  
João Paulo Dias
Keyword(s):  
Machine Learning ◽  
Composite Materials ◽  
Random Forest ◽  
Condition Monitoring ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Damage Classification ◽  
Combining Data ◽  
Ultrasonic Measurements

Abstract Composite materials have tremendous and ever-increasing applications in complex engineering systems; thus, it is important to develop non-destructive and efficient condition monitoring methods to improve damage prediction, thereby avoiding catastrophic failures and reducing standby time. Nondestructive condition monitoring techniques when combined with machine learning applications can contribute towards the stated improvements. Thus, the research question taken into consideration for this paper is “Can machine learning techniques provide efficient damage classification of composite materials to improve condition monitoring using features extracted from acousto-ultrasonic measurements?” In order to answer this question, acoustic-ultrasonic signals in Carbon Fiber Reinforced Polymer (CFRP) composites for distinct damage levels were taken from NASA Ames prognostics data repository. Statistical condition indicators of the signals were used as features to train and test four traditional machine learning algorithms such as K-nearest neighbors, support vector machine, Decision Tree and Random Forest, and their performance was compared and discussed. Results showed higher accuracy for Random Forest with a strong dependency on the feature extraction/selection techniques employed. By combining data analysis from acoustic-ultrasonic measurements in composite materials with machine learning tools, this work contributes to the development of intelligent damage classification algorithms that can be applied to advanced online diagnostics and health management strategies of composite materials, operating under more complex working conditions.

scholarly journals Five Typical Stenches Detection Using an Electronic Nose

Sensors ◽  
2020 ◽  
Vol 20 (9) ◽  
pp. 2514 ◽  
Author(s):  
Wei Jiang ◽  
Daqi Gao
Keyword(s):  
Electronic Nose ◽  
Detection System ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Odor Concentration ◽  
Linear Discriminant ◽  
Vector Machines ◽  
Components Analysis ◽  
Olfactory Organs

This paper deals with the classification of stenches, which can stimulate olfactory organs to discomfort people and pollute the environment. In China, the triangle odor bag method, which only depends on the state of the panelist, is widely used in determining odor concentration. In this paper, we propose a stenches detection system composed of an electronic nose and machine learning algorithms to discriminate five typical stenches. These five chemicals producing stenches are 2-phenylethyl alcohol, isovaleric acid, methylcyclopentanone, γ-undecalactone, and 2-methylindole. We will use random forest, support vector machines, backpropagation neural network, principal components analysis (PCA), and linear discriminant analysis (LDA) in this paper. The result shows that LDA (support vector machine (SVM)) has better performance in detecting the stenches considered in this paper.

scholarly journals Computer Vision Classification of Barley Flour Based on Spatial Pyramid Partition Ensemble

Sensors ◽  
2019 ◽  
Vol 19 (13) ◽  
pp. 2953 ◽  
Author(s):  
Jessica Fernandes Lopes ◽  
Leniza Ludwig ◽  
Douglas Fernandes Barbin ◽  
Maria Victória Eiras Grossmann ◽  
Sylvio Barbon
Keyword(s):  
Machine Learning ◽  
Computer Vision ◽  
Image Features ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Human Consumption ◽  
Barley Flour ◽  
Quality Aspects ◽  
Spatial Pyramid

Imaging sensors are largely employed in the food processing industry for quality control. Flour from malting barley varieties is a valuable ingredient in the food industry, but its use is restricted due to quality aspects such as color variations and the presence of husk fragments. On the other hand, naked varieties present superior quality with better visual appearance and nutritional composition for human consumption. Computer Vision Systems (CVS) can provide an automatic and precise classification of samples, but identification of grain and flour characteristics require more specialized methods. In this paper, we propose CVS combined with the Spatial Pyramid Partition ensemble (SPPe) technique to distinguish between naked and malting types of twenty-two flour varieties using image features and machine learning. SPPe leverages the analysis of patterns from different spatial regions, providing more reliable classification. Support Vector Machine (SVM), k-Nearest Neighbors (k-NN), J48 decision tree, and Random Forest (RF) were compared for samples’ classification. Machine learning algorithms embedded in the CVS were induced based on 55 image features. The results ranged from 75.00% (k-NN) to 100.00% (J48) accuracy, showing that sample assessment by CVS with SPPe was highly accurate, representing a potential technique for automatic barley flour classification.

scholarly journals Cumulant Features based Classification of Brain MR Images using ANN and LS-SVM Algorithm

2019 ◽  
Vol 8 (11) ◽  
pp. 4008-4012
Keyword(s):  
Brain Tumour ◽  
Automatic Classification ◽  
Machine Learning Algorithms ◽  
Least Square ◽  
Support Vector ◽  
Discrete Wavelet ◽  
Mr Images ◽  
Mr Image ◽  
Linear Discriminant

Automatic classification of magnetic resonance (MR) brain images using machine learning algorithms has a significant role in clinical diagnosis of brain tumour. The higher order spectra cumulant features are powerful and competent tool for automatic classification. The study proposed an effective cumulant-based features to predict the severity of brain tumour. The study at first stage implicates the one-level classification of 2-D discrete wavelet transform (DWT) of taken brain MR image. The cumulants of every sub-bands are then determined to calculate the primary feature vector. Linear discriminant analysis is adopted to extract the discriminative features derived from the primary ones. A three layer feed-forward artificial neural network (ANN) and least square based support vector machine (LS-SVM) algorithms are considered to compute that the brain MR image is either belongs to normal or to one of seven other diseases (eight-class scenario). Furthermore, in one more classification problem, the input MR image is categorized as normal or abnormal (two-class scenario). The correct classification rate (CCR) of LS-SVM is superior than the ANN algorithm thereby the proposed study with LS-SVM attains higher accuracy rate in both classification scenarios of MR images.

scholarly journals Comparison of Machine Learning Algorithms Using WEKA and Sci-Kit Learn in Classifying Online Shopper Intention

2019 ◽  
Vol 3 (1) ◽  
pp. 58
Author(s):  
Yefta Christian
Keyword(s):  
Support Vector Machine ◽  
Random Forest ◽  
Kappa Statistic ◽  
Absolute Error ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Test Results ◽  
Online Shoppers ◽  
Online Stores

<p class="8AbstrakBahasaIndonesia"><span>The growth of online stores nowadays is very rapid. This is supported by faster and better internet infrastructure. The increasing growth of online stores makes the competition more difficult in this business field. It is necessary for online stores to have a website or an application that is able to measure and classify consumers’ spending intentions, so that the consumers will have eyes on things on the sites and applications to make purchases eventually. Classification of online shoppers’ intentions can be done by using several algorithms, such as Naïve Bayes, Multi-Layer Perceptron, Support Vector Machine, Random Forest and J48 Decision Trees. In this case, the comparison of algorithms is done with two tools, WEKA and Sci-Kit Learn by comparing the values of F1-Score, accuracy, Kappa Statistic and mean absolute error. There is a difference between the test results using WEKA and Sci-Kit Learn on the Support Vector Machine algorithm. Based on this research, the Random Forest algorithm is the most appropriate algorithm to be used as an algorithm for classifying online shoppers’ intentions.</span></p>

scholarly journals Electrodermal Activity Based Emotion Recognition using Time-Frequency Methods and Machine Learning Algorithms

2021 ◽  
Vol 7 (2) ◽  
pp. 863-866
Author(s):  
Yedukondala Rao Veeranki ◽  
Nagarajan Ganapathy ◽  
Ramakrishnan Swaminathan
Keyword(s):  
Random Forest ◽  
Electrodermal Activity ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Emotional States ◽  
Time Frequency ◽  
Suggested Technique ◽  
Clinical Conditions ◽  
Short Time

Abstract In this work, the feasibility of time-frequency methods, namely short-time Fourier transform, Choi Williams distribution, and smoothed pseudo-Wigner-Ville distribution in the classification of happy and sad emotional states using Electrodermal activity signals have been explored. For this, the annotated happy and sad signals are obtained from an online public database and decomposed into phasic components. The time-frequency analysis has been performed on the phasic components using three different methods. Four statistical features, namely mean, variance, kurtosis, and skewness are extracted from each method. Four classifiers, namely logistic regression, Naive Bayes, random forest, and support vector machine, have been used for the classification. The combination of the smoothed pseudo-Wigner-Ville distribution and random forest yields the highest F-measure of 68.74% for classifying happy and sad emotional states. Thus, it appears that the suggested technique could be helpful in the diagnosis of clinical conditions linked to happy and sad emotional states.

scholarly journals RNA editing-based classification of diffuse gliomas: predicting isocitrate dehydrogenase mutation and chromosome 1p/19q codeletion

2019 ◽  
Vol 20 (S19) ◽  
Author(s):  
Sean Chun-Chang Chen ◽  
Chung-Ming Lo ◽  
Shih-Hua Wang ◽  
Emily Chia-Yu Su
Keyword(s):  
Random Forest ◽  
Rna Editing ◽  
Isocitrate Dehydrogenase ◽  
Prediction Models ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Genome Wide ◽  
Diffuse Gliomas ◽  
Editing Level

Abstract Background Accurate classification of diffuse gliomas, the most common tumors of the central nervous system in adults, is important for appropriate treatment. However, detection of isocitrate dehydrogenase (IDH) mutation and chromosome1p/19q codeletion, biomarkers to classify gliomas, is time- and cost-intensive and diagnostic discordance remains an issue. Adenosine to inosine (A-to-I) RNA editing has emerged as a novel cancer prognostic marker, but its value for glioma classification remains largely unexplored. We aim to (1) unravel the relationship between RNA editing and IDH mutation and 1p/19q codeletion and (2) predict IDH mutation and 1p/19q codeletion status using machine learning algorithms. Results By characterizing genome-wide A-to-I RNA editing signatures of 638 gliomas, we found that tumors without IDH mutation exhibited higher total editing level compared with those carrying it (Kolmogorov-Smirnov test, p < 0.0001). When tumor grade was considered, however, only grade IV tumors without IDH mutation exhibited higher total editing level. According to 10-fold cross-validation, support vector machines (SVM) outperformed random forest and AdaBoost (DeLong test, p < 0.05). The area under the receiver operating characteristic curve (AUC) of SVM in predicting IDH mutation and 1p/19q codeletion were 0.989 and 0.990, respectively. After performing feature selection, AUCs of SVM and AdaBoost in predicting IDH mutation were higher than that of random forest (0.985 and 0.983 vs. 0.977; DeLong test, p < 0.05), but AUCs of the three algorithms in predicting 1p/19q codeletion were similar (0.976–0.982). Furthermore, 67% of the six continuously misclassified samples by our 1p/19q codeletion prediction models were misclassifications in the original labelling after inspection of 1p/19q status and/or pathology report, highlighting the accuracy and clinical utility of our models. Conclusions The study represents the first genome-wide analysis of glioma editome and identifies RNA editing as a novel prognostic biomarker for glioma. Our prediction models provide standardized, accurate, reproducible and objective classification of gliomas. Our models are not only useful in clinical decision-making, but also able to identify editing events that have the potential to serve as biomarkers and therapeutic targets in glioma management and treatment.

Classification of Sentiment of Reviews using Supervised Machine Learning Techniques

2017 ◽  
Vol 4 (1) ◽  
pp. 56-74 ◽  
Author(s):  
Abinash Tripathy ◽  
Santanu Kumar Rath
Keyword(s):  
Machine Learning ◽  
Sentiment Analysis ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Performance Parameters ◽  
Linear Discriminant ◽  
Learning Techniques

Sentiment analysis helps to determine hidden intention of the concerned author of any topic and provides an evaluation report on the polarity of any document. The polarity may be positive, negative or neutral. It is observed that very often the data associated with the sentiment analysis consist of the feedback given by various specialists on any topic or product. Thus, the review may be categorized properly into any sort of class based on the polarity, in order to have a good knowledge about the product. This article proposes an approach to classify the review dataset made on basis of sentiment analysis into different polarity groups. Four machine learning algorithms viz., Naive Bayes (NB), Support Vector Machine (SVM), Random Forest, and Linear Discriminant Analysis (LDA) have been considered in this paper for classification process. The obtained result on values of accuracy of the algorithms are critically examined by using different performance parameters, applied on two different datasets.

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