scholarly journals Machine-Learning for Mineral Identification and Ore Estimation From Hyperspectral Imagery in Tin-Tungsten Deposits

2021 ◽  
Author(s):  
Agustin Lobo ◽  
Emma Garcia ◽  
Gisela Barroso ◽  
David Martí ◽  
Jose-Luis Fernandez-Turiel ◽  
...  
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Hyperspectral Images ◽  
Linear Discriminant ◽  
Machine Learning Classification ◽  
Vector Machines ◽  
Ore Mineralization ◽  
Mine Excavation ◽  
Tungsten Mine ◽  
Set Up

This study aims to assess the feasibility of delineating and identifying mineral ores from hyperspectral images of tin-tungsten mine excavation faces using machine-learning classification. We compiled a set of hand samples of minerals of interest from a tin-tungsten mine and analyzed two types of hyperspectral images: 1) images acquired with a laboratory set-up under close-to-optimal conditions; and 2) scan of a simulated mine face using a field set-up, under conditions closer to those in the gallery. We have analyzed the following minerals: cassiterite (tin ore), wolframite (tungsten ore), chalcopyrite, malachite, muscovite, and quartz. Classification (Linear Discriminant Analysis, Singular Vector Machines and Random Forest) of laboratory spectra had a very high overall accuracy rate (98%), slightly lower if the 450 – 950 nm and 950 – 1780 nm ranges are considered independently, and much lower (74.5%) for simulated conventional RGB imagery. Classification accuracy for the simulation was lower than in the laboratory but still high (85%), likely a consequence of the lower spatial resolution. All three classification methods performed similarly in this case, with Random Forest producing results of slightly higher accuracy. The user’s accuracy for wolframite was 85%, but cassiterite was often confused with wolframite (user’s accuracy: 70%). A lumped ore category achieved 94.9% user’s accuracy. Our study confirms the suitability of hyperspectral imaging to record the spatial distribution of ore mineralization in progressing tungsten-tin mine faces.

scholarly journals Machine Learning for Mineral Identification and Ore Estimation from Hyperspectral Imagery in Tin–Tungsten Deposits: Simulation under Indoor Conditions

2021 ◽  
Vol 13 (16) ◽  
pp. 3258 ◽  
Author(s):  
Agustin Lobo ◽  
Emma Garcia ◽  
Gisela Barroso ◽  
David Martí ◽  
Jose-Luis Fernandez-Turiel ◽  
...  
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Hyperspectral Images ◽  
Linear Discriminant ◽  
Machine Learning Classification ◽  
Ore Mineralization ◽  
Mine Excavation ◽  
Tungsten Mine ◽  
Set Up ◽  
Indoor Conditions

This study aims to assess the feasibility of delineating and identifying mineral ores from hyperspectral images of tin–tungsten mine excavation faces using machine learning classification. We compiled a set of hand samples of minerals of interest from a tin–tungsten mine and analyzed two types of hyperspectral images: (1) images acquired with a laboratory set-up under close-to-optimal conditions, and (2) a scan of a simulated mine face using a field set-up, under conditions closer to those in the gallery. We have analyzed the following minerals: cassiterite (tin ore), wolframite (tungsten ore), chalcopyrite, malachite, muscovite, and quartz. Classification (Linear Discriminant Analysis, Singular Vector Machines and Random Forest) of laboratory spectra had a very high overall accuracy rate (98%), slightly lower if the 450–950 nm and 950–1650 nm ranges are considered independently, and much lower (74.5%) for simulated conventional RGB imagery. Classification accuracy for the simulation was lower than in the laboratory but still high (85%), likely a consequence of the lower spatial resolution. All three classification methods performed similarly in this case, with Random Forest producing results of slightly higher accuracy. The user’s accuracy for wolframite was 85%, but cassiterite was often confused with wolframite (user’s accuracy: 70%). A lumped ore category achieved 94.9% user’s accuracy. Our study confirms the suitability of hyperspectral imaging to record the spatial distribution of ore mineralization in progressing tungsten–tin mine faces.

PSIX-15 Assessment of machine learning algorithms for prediction of Aleutian disease in American mink

2021 ◽  
Vol 99 (Supplement_3) ◽  
pp. 264-265
Author(s):  
Duy Ngoc Do ◽  
Guoyu Hu ◽  
Younes Miar
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Linear Models ◽  
American Mink ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Training Data ◽  
Enzyme Linked Immunosorbent Assay ◽  
Linear Discriminant ◽  
Machine Learning Classification

Abstract American mink (Neovison vison) is the major source of fur for the fur industries worldwide and Aleutian disease (AD) is causing severe financial losses to the mink industry. Different methods have been used to diagnose the AD in mink, but the combination of several methods can be the most appropriate approach for the selection of AD resilient mink. Iodine agglutination test (IAT) and counterimmunoelectrophoresis (CIEP) methods are commonly employed in test-and-remove strategy; meanwhile, enzyme-linked immunosorbent assay (ELISA) and packed-cell volume (PCV) methods are complementary. However, using multiple methods are expensive; and therefore, hindering the corrected use of AD tests in selection. This research presented the assessments of the AD classification based on machine learning algorithms. The Aleutian disease was tested on 1,830 individuals using these tests in an AD positive mink farm (Canadian Centre for Fur Animal Research, NS, Canada). The accuracy of classification for CIEP was evaluated based on the sex information, and IAT, ELISA and PCV test results implemented in seven machine learning classification algorithms (Random Forest, Artificial Neural Networks, C50Tree, Naive Bayes, Generalized Linear Models, Boost, and Linear Discriminant Analysis) using the Caret package in R. The accuracy of prediction varied among the methods. Overall, the Random Forest was the best-performing algorithm for the current dataset with an accuracy of 0.89 in the training data and 0.94 in the testing data. Our work demonstrated the utility and relative ease of using machine learning algorithms to assess the CIEP information, and consequently reducing the cost of AD tests. However, further works require the inclusion of production and reproduction information in the models and extension of phenotypic collection to increase the accuracy of current methods.

scholarly journals Multiscale Supervised Classification of Point Clouds with Urban and Forest Applications

Sensors ◽  
2019 ◽  
Vol 19 (20) ◽  
pp. 4523 ◽  
Author(s):  
Carlos Cabo ◽  
Celestino Ordóñez ◽  
Fernando Sáchez-Lasheras ◽  
Javier Roca-Pardiñas ◽  
and Javier de Cos-Juez
Keyword(s):  
Random Forest ◽  
Laser Scanning ◽  
Supervised Classification ◽  
Computing Time ◽  
Principal Component ◽  
Point Clouds ◽  
Support Vector ◽  
Linear Discriminant ◽  
Vector Machines ◽  
Input Variables

We analyze the utility of multiscale supervised classification algorithms for object detection and extraction from laser scanning or photogrammetric point clouds. Only the geometric information (the point coordinates) was considered, thus making the method independent of the systems used to collect the data. A maximum of five features (input variables) was used, four of them related to the eigenvalues obtained from a principal component analysis (PCA). PCA was carried out at six scales, defined by the diameter of a sphere around each observation. Four multiclass supervised classification models were tested (linear discriminant analysis, logistic regression, support vector machines, and random forest) in two different scenarios, urban and forest, formed by artificial and natural objects, respectively. The results obtained were accurate (overall accuracy over 80% for the urban dataset, and over 93% for the forest dataset), in the range of the best results found in the literature, regardless of the classification method. For both datasets, the random forest algorithm provided the best solution/results when discrimination capacity, computing time, and the ability to estimate the relative importance of each variable are considered together.

scholarly journals Classifications of Breast Cancer Diagnosis using Machine Learning

2020 ◽  
Vol 14 ◽  
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Random Forest ◽  
Breast Cancer Diagnosis ◽  
Performance Comparison ◽  
Support Vector ◽  
Breast Cancer Dataset ◽  
K Nearest Neighbors ◽  
Cancer Dataset ◽  
Machine Learning Classification

Breast Cancer (BC) is amongst the most common and leading causes of deaths in women throughout the world. Recently, classification and data analysis tools are being widely used in the medical field for diagnosis, prognosis and decision making to help lower down the risks of people dying or suffering from diseases. Advanced machine learning methods have proven to give hope for patients as this has helped the doctors in early detection of diseases like Breast Cancer that can be fatal, in support with providing accurate outcomes. However, the results highly depend on the techniques used for feature selection and classification which will produce a strong machine learning model. In this paper, a performance comparison is conducted using four classifiers which are Multilayer Perceptron (MLP), Support Vector Machine (SVM), K-Nearest Neighbors (KNN) and Random Forest on the Wisconsin Breast Cancer dataset to spot the most effective predictors. The main goal is to apply best machine learning classification methods to predict the Breast Cancer as benign or malignant using terms such as accuracy, f-measure, precision and recall. Experimental results show that Random forest is proven to achieve the highest accuracy of 99.26% on this dataset and features, while SVM and KNN show 97.78% and 97.04% accuracy respectively. MLP shows the least accuracy of 94.07%. All the experiments are conducted using RStudio as the data mining tool platform.

scholarly journals Hyperspectral reflectance for non-invasive early detection of black shank disease in flue-cured tobacco

2021 ◽  
Author(s):  
Austin Hayes ◽  
T. David Reed
Keyword(s):  
Machine Learning ◽  
Broad Band ◽  
Phytophthora Nicotianae ◽  
Hyperspectral Reflectance ◽  
Black Shank ◽  
Linear Discriminant ◽  
Machine Learning Classification ◽  
Band Index ◽  
Intensively Managed ◽  
Reflectance Data

Flue-cured tobacco (Nicotiana tabacum L.) is a high value-per-acre crop that is intensively managed to optimise the yield of high-quality cured leaf. A 15-day study assessed the potential of hyperspectral reflectance data for detecting Phytophthora nicotianae (black shank) incidence in flue-cured tobacco. Hyperspectral reflectance data were taken from a commercial flue-cured tobacco field with a progressing black shank infestation. The effort encompassed two key objectives. First, develop hyperspectral indices and/or machine learning classification models capable of detecting Phytophthora nicotianae (black shank) incidence in flue-cured tobacco. Second, evaluate the model’s ability to separate pre-symptomatic plants from healthy plants. Two hyperspectral indices were developed to detect black shank incidence based on differences in the spectral profiles of asymptomatic flue-cured tobacco plants compared to those with black shank symptoms. While one of the indices is a broad-band index and the other uses narrow wavelength values, the statistical difference between the two indices was not significant and both provided an accurate classification of symptomatic plants. Further analysis of the indices showed significant differences between the index values of healthy and symptomatic plants (α = 0.05). In addition, the indices were able to detect black shank symptoms pre-symptomatically (α = 0.09). Subspace linear discriminant analysis, a machine learning classification, was also used for prediction of black shank incidence with up to 85.7% classification accuracy. The implications of using either spectral indices or machine learning for classification for future black shank research are discussed.

scholarly journals Machine Learning Approaches for Detecting Tropical Cyclone Formation Using Satellite Data

2019 ◽  
Vol 11 (10) ◽  
pp. 1195 ◽  
Author(s):  
Minsang Kim ◽  
Myung-Sook Park ◽  
Jungho Im ◽  
Seonyoung Park ◽  
Myong-In Lee
Keyword(s):  
Machine Learning ◽  
Tropical Cyclone ◽  
Surface Wind ◽  
Support Vector ◽  
Learning Approaches ◽  
Yearly Variation ◽  
Linear Discriminant ◽  
Forecast Lead Time ◽  
Vector Machines ◽  
First Time

This study compared detection skill for tropical cyclone (TC) formation using models based on three different machine learning (ML) algorithms-decision trees (DT), random forest (RF), and support vector machines (SVM)-and a model based on Linear Discriminant Analysis (LDA). Eight predictors were derived from WindSat satellite measurements of ocean surface wind and precipitation over the western North Pacific for 2005–2009. All of the ML approaches performed better with significantly higher hit rates ranging from 94 to 96% compared with LDA performance (~77%), although false alarm rate by MLs is slightly higher (21–28%) than that by LDA (~13%). Besides, MLs could detect TC formation at the time as early as 26–30 h before the first time diagnosed as tropical depression by the JTWC best track, which was also 5 to 9 h earlier than that by LDA. The skill differences across MLs were relatively smaller than difference between MLs and LDA. Large yearly variation in forecast lead time was common in all models due to the limitation in sampling from orbiting satellite. This study highlights that ML approaches provide an improved skill for detecting TC formation compared with conventional linear approaches.

scholarly journals Which PHQ-9 Items Can Effectively Screen for Suicide? Machine Learning Approaches

2021 ◽  
Vol 18 (7) ◽  
pp. 3339
Author(s):  
Sunhae Kim ◽  
Hye-Kyung Lee ◽  
Kounseok Lee
Keyword(s):  
Machine Learning ◽  
Primary Care ◽  
Suicidal Ideation ◽  
Random Forest ◽  
Suicide Ideation ◽  
Area Under The Curve ◽  
Learning Approaches ◽  
K Nearest Neighbors ◽  
Predictive Values ◽  
Linear Discriminant

(1) Background: The Patient Health Questionnaire-9 (PHQ-9) is a tool that screens patients for depression in primary care settings. In this study, we evaluated the efficacy of PHQ-9 in evaluating suicidal ideation (2) Methods: A total of 8760 completed questionnaires collected from college students were analyzed. The PHQ-9 was scored in combination with and evaluated against four categories (PHQ-2, PHQ-8, PHQ-9, and PHQ-10). Suicidal ideations were evaluated using the Mini-International Neuropsychiatric Interview suicidality module. Analyses used suicide ideation as the dependent variable, and machine learning (ML) algorithms, k-nearest neighbors, linear discriminant analysis (LDA), and random forest. (3) Results: Random forest application using the nine items of the PHQ-9 revealed an excellent area under the curve with a value of 0.841, with 94.3% accuracy. The positive and negative predictive values were 84.95% (95% CI = 76.03–91.52) and 95.54% (95% CI = 94.42–96.48), respectively. (4) Conclusion: This study confirmed that ML algorithms using PHQ-9 in the primary care field are reliably accurate in screening individuals with suicidal ideation.

scholarly journals Teknik Resampling untuk Mengatasi Ketidakseimbangan Kelas pada Klasifikasi Penyakit Diabetes Menggunakan C4.5, Random Forest, dan SVM

Techno Com ◽  
2021 ◽  
Vol 20 (3) ◽  
pp. 352-361
Author(s):  
Wahyu Nugraha ◽  
Raja Sabaruddin
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Random Forest ◽  
Area Under Curve ◽  
Support Vector ◽  
Pima Indians ◽  
R Language ◽  
Level Data ◽  
Vector Machines ◽  
Under Sampling

Penderita diabetes di seluruh dunia terus mengalami peningkatan dengan angka kematian sebesar 4,6 juta pada tahun 2011 dan diperkirakan akan terus meningkat secara global menjadi 552 juta pada tahun 2030. Pencegahan Penyakit diabetes mungkin dapat dilakukan secara efektif dengan cara mendeteksinya sejak dini. Data mining dan machine learning terus dikembangkan agar menjadi alat yang handal dalam membangun model komputasi untuk mengidentifikasi penyakit diabetes pada tahap awal. Namun, masalah yang sering dihadapi dalam menganalisis penyakit diabetes ialah masalah ketidakseimbangan class. Kelas yang tidak seimbang membuat model pembelajaran akan sulit melakukan prediksi karena model pembelajaran didominasi oleh instance kelas mayoritas sehingga mengabaikan prediksi kelas minoritas. Pada penelitian ini kami mencoba menganalisa dan mencoba mengatasi masalah ketidakseimbangan kelas dengan menggunakan pendekatan level data yaitu teknik resampling data. Eksperimen ini menggunakan R language dengan library ROSE (version 0.0-4). Dataset Pima Indians dipilih pada penelitian ini karena merupakan salah satu dataset yang mengalami ketidakseimbangan kelas. Model pengklasifikasian pada penelitian ini menggunakan algoritma decision tree C4.5, RF (Random Forest), dan SVM (Support Vector Machines). Dari hasil eksperimen yang dilakukan model klasifikasi SVM dengan teknik resampling yang menggabungkan over dan under-sampling menjadi model yang memiliki performa terbaik dengan nilai AUC (Area Under Curve) sebesar 0.80

scholarly journals Enhanced Changeover Detection in Industry 4.0 Environments with Machine Learning

Sensors ◽  
2021 ◽  
Vol 21 (17) ◽  
pp. 5896
Author(s):  
Eddi Miller ◽  
Vladyslav Borysenko ◽  
Moritz Heusinger ◽  
Niklas Niedner ◽  
Bastian Engelmann ◽  
...  
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Binary Classification ◽  
Model Performance ◽  
Support Vector ◽  
Milling Machine ◽  
Vector Machines ◽  
Changeover Times ◽  
Flow Power

Changeover times are an important element when evaluating the Overall Equipment Effectiveness (OEE) of a production machine. The article presents a machine learning (ML) approach that is based on an external sensor setup to automatically detect changeovers in a shopfloor environment. The door statuses, coolant flow, power consumption, and operator indoor GPS data of a milling machine were used in the ML approach. As ML methods, Decision Trees, Support Vector Machines, (Balanced) Random Forest algorithms, and Neural Networks were chosen, and their performance was compared. The best results were achieved with the Random Forest ML model (97% F1 score, 99.72% AUC score). It was also carried out that model performance is optimal when only a binary classification of a changeover phase and a production phase is considered and less subphases of the changeover process are applied.

scholarly journals Prediction of Stock Prices using Random Forest and Support Vector Machines

2019 ◽  
Vol 8 (4) ◽  
pp. 473-477
Keyword(s):  
Machine Learning ◽  
Support Vector Machines ◽  
Random Forest ◽  
Stock Prices ◽  
Support Vector ◽  
Future Cost ◽  
Financial Instrument ◽  
Vector Machines ◽  
The Future ◽  
Future Value

Prediction of stock markets is the act of attempting to determine the future value of an inventory of a business or other financial instrument traded on an economic exchange.Effectively foreseeing the future cost of a stock will amplify the benefits of the financial specialist.This article suggests a model of machine learning to forecast the price of the stock market.During the way toward considering various techniques and factors that should be considered, we found that strategy, for example, random forest, support vector machines were not completely used in past structures. In this article, we will present and audit an increasingly suitable strategy for anticipating more prominent exactness stock oscillations.The primary thing we thought about was the securities exchange estimating informational index from yahoo stocks. We will audit the utilization of random forest after pre-handling the data, help the vector machine on the informational index and the outcomes it produces.The powerful stock gauge will be a superb resource for financial exchange associations and will give genuine options in contrast to the difficulties confronting the stock speculator.

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