Drill-Core Mineral Abundance Estimation Using Hyperspectral and High-Resolution Mineralogical Data

Due to the extensive drilling performed every year in exploration campaigns for the discovery and evaluation of ore deposits, drill-core mapping is becoming an essential step. While valuable mineralogical information is extracted during core logging by on-site geologists, the process is time consuming and dependent on the observer and individual background. Hyperspectral short-wave infrared (SWIR) data is used in the mining industry as a tool to complement traditional logging techniques and to provide a rapid and non-invasive analytical method for mineralogical characterization. Additionally, Scanning Electron Microscopy-based image analyses using a Mineral Liberation Analyser (SEM-MLA) provide exhaustive high-resolution mineralogical maps, but can only be performed on small areas of the drill-cores. We propose to use machine learning algorithms to combine the two data types and upscale the quantitative SEM-MLA mineralogical data to drill-core scale. This way, quasi-quantitative maps over entire drill-core samples are obtained. Our upscaling approach increases result transparency and reproducibility by employing physical-based data acquisition (hyperspectral imaging) combined with mathematical models (machine learning). The procedure is tested on 5 drill-core samples with varying training data using random forests, support vector machines and neural network regression models. The obtained mineral abundance maps are further used for the extraction of mineralogical parameters such as mineral association.

Download Full-text

Automatic Grading of Stroke Symptoms for Rapid Assessment Using Optimized Machine Learning and 4-Limb Kinematics: Clinical Validation Study (Preprint)

10.2196/preprints.20641 ◽

2020 ◽

Author(s):

Eunjeong Park ◽

Kijeong Lee ◽

Taehwa Han ◽

Hyo Suk Nam

Keyword(s):

Machine Learning ◽

Medical Staff ◽

Machine Learning Algorithms ◽

Training Data ◽

Support Vector ◽

Grading System ◽

Operating Characteristics ◽

Stroke Patients ◽

Motor Weakness ◽

Automatic Grading

BACKGROUND Subtle abnormal motor signs are indications of serious neurological diseases. Although neurological deficits require fast initiation of treatment in a restricted time, it is difficult for nonspecialists to detect and objectively assess the symptoms. In the clinical environment, diagnoses and decisions are based on clinical grading methods, including the National Institutes of Health Stroke Scale (NIHSS) score or the Medical Research Council (MRC) score, which have been used to measure motor weakness. Objective grading in various environments is necessitated for consistent agreement among patients, caregivers, paramedics, and medical staff to facilitate rapid diagnoses and dispatches to appropriate medical centers. OBJECTIVE In this study, we aimed to develop an autonomous grading system for stroke patients. We investigated the feasibility of our new system to assess motor weakness and grade NIHSS and MRC scores of 4 limbs, similar to the clinical examinations performed by medical staff. METHODS We implemented an automatic grading system composed of a measuring unit with wearable sensors and a grading unit with optimized machine learning. Inertial sensors were attached to measure subtle weaknesses caused by paralysis of upper and lower limbs. We collected 60 instances of data with kinematic features of motor disorders from neurological examination and demographic information of stroke patients with NIHSS 0 or 1 and MRC 7, 8, or 9 grades in a stroke unit. Training data with 240 instances were generated using a synthetic minority oversampling technique to complement the imbalanced number of data between classes and low number of training data. We trained 2 representative machine learning algorithms, an ensemble and a support vector machine (SVM), to implement auto-NIHSS and auto-MRC grading. The optimized algorithms performed a 5-fold cross-validation and were searched by Bayes optimization in 30 trials. The trained model was tested with the 60 original hold-out instances for performance evaluation in accuracy, sensitivity, specificity, and area under the receiver operating characteristics curve (AUC). RESULTS The proposed system can grade NIHSS scores with an accuracy of 83.3% and an AUC of 0.912 using an optimized ensemble algorithm, and it can grade with an accuracy of 80.0% and an AUC of 0.860 using an optimized SVM algorithm. The auto-MRC grading achieved an accuracy of 76.7% and a mean AUC of 0.870 in SVM classification and an accuracy of 78.3% and a mean AUC of 0.877 in ensemble classification. CONCLUSIONS The automatic grading system quantifies proximal weakness in real time and assesses symptoms through automatic grading. The pilot outcomes demonstrated the feasibility of remote monitoring of motor weakness caused by stroke. The system can facilitate consistent grading with instant assessment and expedite dispatches to appropriate hospitals and treatment initiation by sharing auto-MRC and auto-NIHSS scores between prehospital and hospital responses as an objective observation.

Download Full-text

Android Malware Detection using Machine Learning

International Journal of Recent Technology and Engineering - 2 ◽

10.35940/ijrte.b1011.0982s1219 ◽

2020 ◽

Vol 8 (2S12) ◽

pp. 65-70

Keyword(s):

Machine Learning ◽

Nearest Neighbor ◽

Machine Learning Algorithms ◽

Training Data ◽

Machine Learning Techniques ◽

Support Vector ◽

K Nearest Neighbor ◽

User Interest ◽

Android Malware ◽

Android Malware Detection

Machine Learning is empowering many aspects of day-to-day lives from filtering the content on social networks to suggestions of products that we may be looking for. This technology focuses on taking objects as image input to find new observations or show items based on user interest. The major discussion here is the Machine Learning techniques where we use supervised learning where the computer learns by the input data/training data and predict result based on experience. We also discuss the machine learning algorithms: Naïve Bayes Classifier, K-Nearest Neighbor, Random Forest, Decision Tress, Boosted Trees, Support Vector Machine, and use these classifiers on a dataset Malgenome and Drebin which are the Android Malware Dataset. Android is an operating system that is gaining popularity these days and with a rise in demand of these devices the rise in Android Malware. The traditional techniques methods which were used to detect malware was unable to detect unknown applications. We have run this dataset on different machine learning classifiers and have recorded the results. The experiment result provides a comparative analysis that is based on performance, accuracy, and cost.

Download Full-text

A Comparison of Machine Learning Algorithms for the Segmentation and Classification of Snow Micro Penetrometer Profiles on Arctic Sea Ice

10.5194/egusphere-egu21-15637 ◽

2021 ◽

Author(s):

Julia Kaltenborn ◽

Viviane Clay ◽

Amy R. Macfarlane ◽

Joshua Michael Lloyd King ◽

Martin Schneebeli

Keyword(s):

Machine Learning ◽

Sea Ice ◽

Arctic Sea Ice ◽

Machine Learning Algorithms ◽

Training Data ◽

Support Vector ◽

Snow Layer ◽

Arctic Sea ◽

Execution Speed

Snow-layer classification is an essential diagnostic task for a wide variety of cryospheric science and climate research applications. Traditionally, these measurements are made in snow pits, requiring trained operators and a substantial time commitment. The SnowMicroPen (SMP), a portable high-resolution snow penetrometer, has been demonstrated as a capable tool for rapid snow grain classification and layer type segmentation through statistical inversion of its mechanical signal. The manual classification of the SMP profiles requires time and training and becomes infeasible for large datasets.Here, we introduce a novel set of SMP measurements collected during the MOSAiC expedition and apply Machine Learning (ML) algorithms to automatically classify and segment SMP profiles of snow on Arctic sea ice. To this end, different supervised and unsupervised ML methods, including Random Forests, Support Vector Machines, Artificial Neural Networks, and k-means Clustering, are compared. A subsequent segmentation of the classified data results in distinct layers and snow grain markers for the SMP profiles. The models are trained with the dataset by King et al. (2020) and the MOSAiC SMP dataset. The MOSAiC dataset is a unique and extensive dataset characterizing seasonal and spatial variation of snow on the central Arctic sea-ice.We will test and compare the different algorithms and evaluate the algorithms&#8217; effectiveness based on the need for initial dataset labeling, execution speed, and ease of implementation. In particular, we will compare supervised to unsupervised methods, which are distinguished by their need for labeled training data.The implementation of different ML algorithms for SMP profile classification could provide a fast and automatic grain type classification and snow layer segmentation. Based on the gained knowledge from the algorithms&#8217; comparison, a tool can be built to provide scientists from different fields with an immediate SMP profile classification and segmentation.&#160;&#160;King, J., Howell, S., Brady, M., Toose, P., Derksen, C., Haas, C., & Beckers, J. (2020). Local-scale variability of snow density on Arctic sea ice. The Cryosphere, 14(12), 4323-4339, https://doi.org/10.5194/tc-14-4323-2020.

Download Full-text

Identification of Leukemia Subtypes from Microscopic Images Using Convolutional Neural Network

Diagnostics ◽

10.3390/diagnostics9030104 ◽

2019 ◽

Vol 9 (3) ◽

pp. 104 ◽

Cited By ~ 11

Author(s):

Ahmed ◽

Yigit ◽

Isik ◽

Alpkocak

Keyword(s):

Machine Learning ◽

Data Augmentation ◽

Nearest Neighbor ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Training Data ◽

Support Vector ◽

K Nearest Neighbor ◽

Data Set ◽

Leukemia Data

Leukemia is a fatal cancer and has two main types: Acute and chronic. Each type has two more subtypes: Lymphoid and myeloid. Hence, in total, there are four subtypes of leukemia. This study proposes a new approach for diagnosis of all subtypes of leukemia from microscopic blood cell images using convolutional neural networks (CNN), which requires a large training data set. Therefore, we also investigated the effects of data augmentation for an increasing number of training samples synthetically. We used two publicly available leukemia data sources: ALL-IDB and ASH Image Bank. Next, we applied seven different image transformation techniques as data augmentation. We designed a CNN architecture capable of recognizing all subtypes of leukemia. Besides, we also explored other well-known machine learning algorithms such as naive Bayes, support vector machine, k-nearest neighbor, and decision tree. To evaluate our approach, we set up a set of experiments and used 5-fold cross-validation. The results we obtained from experiments showed that our CNN model performance has 88.25% and 81.74% accuracy, in leukemia versus healthy and multiclass classification of all subtypes, respectively. Finally, we also showed that the CNN model has a better performance than other wellknown machine learning algorithms.

Download Full-text

Rotor Unbalance Kind and Severity Identification by Current Signature Analysis with Adaptative Update to Multiclass Machine Learning Algorithms

Studies in Engineering and Technology ◽

10.11114/set.v8i1.5213 ◽

2021 ◽

Vol 8 (1) ◽

pp. 28

Author(s):

S. L. Ávila ◽

H. M. Schaberle ◽

S. Youssef ◽

F. S. Pacheco ◽

C. A. Penz

Keyword(s):

Machine Learning ◽

Machine Learning Algorithms ◽

Training Data ◽

Machine Learning Techniques ◽

Support Vector ◽

Signature Analysis ◽

Data Set ◽

Learning Techniques ◽

Environmental Variations ◽

Current Signature

The health of a rotating electric machine can be evaluated by monitoring electrical and mechanical parameters. As more information is available, it easier can become the diagnosis of the machine operational condition. We built a laboratory test bench to study rotor unbalance issues according to ISO standards. Using the electric stator current harmonic analysis, this paper presents a comparison study among Support-Vector Machines, Decision Tree classifies, and One-vs-One strategy to identify rotor unbalance kind and severity problem – a nonlinear multiclass task. Moreover, we propose a methodology to update the classifier for dealing better with changes produced by environmental variations and natural machinery usage. The adaptative update means to update the training data set with an amount of recent data, saving the entire original historical data. It is relevant for engineering maintenance. Our results show that the current signature analysis is appropriate to identify the type and severity of the rotor unbalance problem. Moreover, we show that machine learning techniques can be effective for an industrial application.

Download Full-text

A Novel GIS-Based Random Forest Machine Algorithm for the Spatial Prediction of Shallow Landslide Susceptibility

Forests ◽

10.3390/f11010118 ◽

2020 ◽

Vol 11 (1) ◽

pp. 118 ◽

Cited By ~ 6

Author(s):

Viet-Hung Dang ◽

Nhat-Duc Hoang ◽

Le-Mai-Duyen Nguyen ◽

Dieu Tien Bui ◽

Pijush Samui

Keyword(s):

Machine Learning ◽

Random Forest ◽

Landslide Susceptibility ◽

Spatial Prediction ◽

Shallow Landslide ◽

Machine Learning Algorithms ◽

Training Data ◽

Support Vector ◽

Conditioning Factors ◽

Susceptibility Modeling

This study developed and verified a new hybrid machine learning model, named random forest machine (RFM), for the spatial prediction of shallow landslides. RFM is a hybridization of two state-of-the-art machine learning algorithms, random forest classifier (RFC) and support vector machine (SVM), in which RFC is used to generate subsets from training data and SVM is used to build decision functions for these subsets. To construct and verify the hybrid RFM model, a shallow landslide database of the Lang Son area (northern Vietnam) was prepared. The database consisted of 101 shallow landslide polygons and 14 conditioning factors. The relevance of these factors for shallow landslide susceptibility modeling was assessed using the ReliefF method. Experimental results pointed out that the proposed RFM can help to achieve the desired prediction with an F1 score of roughly 0.96. The performance of the RFM was better than those of benchmark approaches, including the SVM, RFC, and logistic regression. Thus, the newly developed RFM is a promising tool to help local authorities in shallow landslide hazard mitigations.

Download Full-text

Spatial–Temporal Analysis of Land Cover Change at the Bento Rodrigues Dam Disaster Area Using Machine Learning Techniques

Remote Sensing ◽

10.3390/rs11212548 ◽

2019 ◽

Vol 11 (21) ◽

pp. 2548

Author(s):

Dong Luo ◽

Douglas G. Goodin ◽

Marcellus M. Caldas

Keyword(s):

Machine Learning ◽

Support Vector Machine ◽

Land Cover ◽

Decision Tree ◽

Machine Learning Algorithms ◽

Training Data ◽

Machine Learning Techniques ◽

Support Vector ◽

Disaster Area ◽

Mine Sites

Disasters are an unpredictable way to change land use and land cover. Improving the accuracy of mapping a disaster area at different time is an essential step to analyze the relationship between human activity and environment. The goals of this study were to test the performance of different processing procedures and examine the effect of adding normalized difference vegetation index (NDVI) as an additional classification feature for mapping land cover changes due to a disaster. Using Landsat ETM+ and OLI images of the Bento Rodrigues mine tailing disaster area, we created two datasets, one with six bands, and the other one with six bands plus the NDVI. We used support vector machine (SVM) and decision tree (DT) algorithms to build classifier models and validated models performance using 10-fold cross-validation, resulting in accuracies higher than 90%. The processed results indicated that the accuracy could reach or exceed 80%, and the support vector machine had a better performance than the decision tree. We also calculated each land cover type’s sensitivity (true positive rate) and found that Agriculture, Forest and Mine sites had higher values but Bareland and Water had lower values. Then, we visualized land cover maps in 2000 and 2017 and found out the Mine sites areas have been expanded about twice of the size, but Forest decreased 12.43%. Our findings showed that it is feasible to create a training data pool and use machine learning algorithms to classify a different year’s Landsat products and NDVI can improve the vegetation covered land classification. Furthermore, this approach can provide a venue to analyze land pattern change in a disaster area over time.

Download Full-text

Aligning text mining and machine learning algorithms with best practices for study selection in systematic literature reviews

Systematic Reviews ◽

10.1186/s13643-020-01520-5 ◽

2020 ◽

Vol 9 (1) ◽

Author(s):

E. Popoff ◽

M. Besada ◽

J. P. Jansen ◽

S. Cope ◽

S. Kanters

Keyword(s):

Machine Learning ◽

Text Mining ◽

Sensitivity And Specificity ◽

Full Text ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Training Data ◽

Support Vector ◽

Study Selection ◽

Literature Reviews

Abstract Background Despite existing research on text mining and machine learning for title and abstract screening, the role of machine learning within systematic literature reviews (SLRs) for health technology assessment (HTA) remains unclear given lack of extensive testing and of guidance from HTA agencies. We sought to address two knowledge gaps: to extend ML algorithms to provide a reason for exclusion—to align with current practices—and to determine optimal parameter settings for feature-set generation and ML algorithms. Methods We used abstract and full-text selection data from five large SLRs (n = 3089 to 12,769 abstracts) across a variety of disease areas. Each SLR was split into training and test sets. We developed a multi-step algorithm to categorize each citation into the following categories: included; excluded for each PICOS criterion; or unclassified. We used a bag-of-words approach for feature-set generation and compared machine learning algorithms using support vector machines (SVMs), naïve Bayes (NB), and bagged classification and regression trees (CART) for classification. We also compared alternative training set strategies: using full data versus downsampling (i.e., reducing excludes to balance includes/excludes because machine learning algorithms perform better with balanced data), and using inclusion/exclusion decisions from abstract versus full-text screening. Performance comparisons were in terms of specificity, sensitivity, accuracy, and matching the reason for exclusion. Results The best-fitting model (optimized sensitivity and specificity) was based on the SVM algorithm using training data based on full-text decisions, downsampling, and excluding words occurring fewer than five times. The sensitivity and specificity of this model ranged from 94 to 100%, and 54 to 89%, respectively, across the five SLRs. On average, 75% of excluded citations were excluded with a reason and 83% of these citations matched the reviewers’ original reason for exclusion. Sensitivity significantly improved when both downsampling and abstract decisions were used. Conclusions ML algorithms can improve the efficiency of the SLR process and the proposed algorithms could reduce the workload of a second reviewer by identifying exclusions with a relevant PICOS reason, thus aligning with HTA guidance. Downsampling can be used to improve study selection, and improvements using full-text exclusions have implications for a learn-as-you-go approach.

Download Full-text

Using Machine Learning Methods To Identify Coal Pay Zones from Drilling and Logging-While-Drilling (LWD) Data

SPE Journal ◽

10.2118/198288-pa ◽

2020 ◽

Vol 25 (03) ◽

pp. 1241-1258 ◽

Cited By ~ 2

Author(s):

Ruizhi Zhong ◽

Raymond L. Johnson ◽

Zhongwei Chen

Keyword(s):

Machine Learning ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Training Data ◽

Support Vector ◽

Learning Methods ◽

Well Completion ◽

Machine Learning Methods ◽

Extreme Gradient Boosting ◽

Logging While Drilling

Summary Accurate coal identification is critical in coal seam gas (CSG) (also known as coalbed methane or CBM) developments because it determines well completion design and directly affects gas production. Density logging using radioactive source tools is the primary tool for coal identification, adding well trips to condition the hole and additional well costs for logging runs. In this paper, machine learning methods are applied to identify coals from drilling and logging-while-drilling (LWD) data to reduce overall well costs. Machine learning algorithms include logistic regression (LR), support vector machine (SVM), artificial neural network (ANN), random forest (RF), and extreme gradient boosting (XGBoost). The precision, recall, and F1 score are used as evaluation metrics. Because coal identification is an imbalanced data problem, the performance on the minority class (i.e., coals) is limited. To enhance the performance on coal prediction, two data manipulation techniques [naive random oversampling (NROS) technique and synthetic minority oversampling technique (SMOTE)] are separately coupled with machine learning algorithms. Case studies are performed with data from six wells in the Surat Basin, Australia. For the first set of experiments (single-well experiments), both the training data and test data are in the same well. The machine learning methods can identify coal pay zones for sections with poor or missing logs. It is found that rate of penetration (ROP) is the most important feature. The second set of experiments (multiple-well experiments) uses the training data from multiple nearby wells, which can predict coal pay zones in a new well. The most important feature is gamma ray. After placing slotted casings, all wells have coal identification rates greater than 90%, and three wells have coal identification rates greater than 99%. This indicates that machine learning methods (either XGBoost or ANN/RF with NROS/SMOTE) can be an effective way to identify coal pay zones and reduce coring or logging costs in CSG developments.

Download Full-text

USE OF MULTIVARIATE MACHINE LEARNING ANALYSIS TECHNIQUES FOR FLOOD RISK PREVENTION

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xlii-3-w4-549-2018 ◽

2018 ◽

Vol XLII-3/W4 ◽

pp. 549-554

Author(s):

D. Vito

Keyword(s):

Machine Learning ◽

Flood Risk ◽

Multivariate Analyses ◽

Regional Climate ◽

Learning Algorithms ◽

Warning System ◽

Machine Learning Algorithms ◽

Training Data ◽

Support Vector ◽

Multiple Variables

Abstract. Natural disasters such as flood are regarded to be caused by extreme weather conditions as well as changes in global and regional climate. The prediction of flood incoming is a key factor to ensure civil protection in case of emergency and to provide effective early warning system. The risk of flood is affected by several factors such as land use, meteorological events, hydrology and the topology of the land. Predict such a risk implies the use of data coming from different sources such satellite images, water basin levels, meteorological and GIS data, that nowadays are easily produced by the availability new satellite portals as SENTINEL and distributed sensor networks on the field. In order to have a comprehensive and accurate prediction of flood risk is essential to perform a selective and multivariate analyses among the different types of inputs. Multivariate Analysis refers to all statistical techniques that simultaneously analyse multiple variables. Among multivariate analyses, Machine learning to provide increasing levels of accuracy precision and efficiency by discovering patterns in large and heterogeneous input datasets. Basically, machine learning algorithms automatically acquire experience information from data. This is done by the process of learning, by which the algorithm can generalize beyond the examples given by training data in input. Machine learning is interesting for predictions because it adapts the resolution strategies to the features of the data. This peculiarity can be used to predict extreme from high variable data, as in the case of floods. This work propose strategies and case studies on the application on machine learning algorithms on floods events prediction. Particullarly the study will focus on the application of Support Vector Machines and Artificial Neural Networks on a multivariate set of data related to river Seveso, in order to propose a more general framework from the case study.

Download Full-text