scholarly journals Machine Learning—A Review of Applications in Mineral Resource Estimation

Energies ◽  
2021 ◽  
Vol 14 (14) ◽  
pp. 4079
Author(s):  
Nelson K. Dumakor-Dupey ◽  
Sampurna Arya
Keyword(s):  
Machine Learning ◽  
Mineral Resource ◽  
Mineral Resources ◽  
Mineral Deposit ◽  
Machine Learning Techniques ◽  
Superior Performance ◽  
Estimation Methods ◽  
Computer Algorithms ◽  
Resource Estimation ◽  
Learning Techniques

Mineral resource estimation involves the determination of the grade and tonnage of a mineral deposit based on its geological characteristics using various estimation methods. Conventional estimation methods, such as geometric and geostatistical techniques, remain the most widely used methods for resource estimation. However, recent advances in computer algorithms have allowed researchers to explore the potential of machine learning techniques in mineral resource estimation. This study presents a comprehensive review of papers that have employed machine learning to estimate mineral resources. The review covers popular machine learning techniques and their implementation and limitations. Papers that performed a comparative analysis of both conventional and machine learning techniques were also considered. The literature shows that the machine learning models can accommodate several geological parameters and effectively approximate complex nonlinear relationships among them, exhibiting superior performance over the conventional techniques.

scholarly journals Reaction Wheels Fault Isolation Onboard 3-Axis Controlled Satel-lite using Enhanced Random Forest with Multidomain Features

2021 ◽  
Vol 12 (2) ◽  
Author(s):  
Afshin Rahimi ◽  
Mofiyinoluwa O. Folami
Keyword(s):  
Machine Learning ◽  
Fault Isolation ◽  
Data Driven ◽  
Machine Learning Techniques ◽  
Superior Performance ◽  
Alternative Methods ◽  
Model Parameters ◽  
High Fidelity ◽  
Novel Approach ◽  
Learning Techniques

As the number of satellite launches increases each year, it is only natural that an interest in the safety and monitoring of these systems would increase as well. However, as a system becomes more complex, generating a high-fidelity model that accurately describes the system becomes complicated. Therefore, imploring a data-driven method can provide to be more beneficial for such applications. This research proposes a novel approach for data-driven machine learning techniques on the detection and isolation of nonlinear systems, with a case-study for an in-orbit closed loop-controlled satellite with reaction wheels as actuators. High-fidelity models of the 3-axis controlled satellite are employed to generate data for both nominal and faulty conditions of the reaction wheels. The generated simulation data is used as input for the isolation method, after which the data is pre-processed through feature extraction from a temporal, statistical, and spectral domain. The pre-processed features are then fed into various machine learning classifiers. Isolation results are validated with cross-validation, and model parameters are tuned using hyperparameter optimization. To validate the robustness of the proposed method, it is tested on three characterized datasets and three reaction wheel configurations, including standard four-wheel, three-orthogonal, and pyramid. The results prove superior performance isolation accuracy for the system under study compared to previous studies using alternative methods (Rahimi & Saadat, 2019, 2020).

scholarly journals Estimating Biomechanical Time-Series with Wearable Sensors: A Systematic Review of Machine Learning Techniques

2019 ◽  
Author(s):  
Reed D. Gurchiek ◽  
Nicholas Cheney ◽  
Ryan S. McGinnis
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Wearable Sensors ◽  
Sensor Data ◽  
Machine Learning Techniques ◽  
Superior Performance ◽  
Estimation Accuracy ◽  
Accurate Estimation ◽  
Practical Implementation ◽  
Learning Techniques

Wearable sensors have the potential to enable comprehensive patient characterization and optimized clinical intervention. Critical to realizing this vision is accurate estimation of biomechanical time-series in daily-life, including joint, segment, and muscle kinetics and kinematics, from wearable sensor data. The use of physical models for estimation of these quantities often requires many wearable devices making practical implementation more difficult. However, regression techniques may provide a viable alternative by allowing the use of a reduced number of sensors for estimating biomechanical time-series. Herein, we review 46 articles that used regression algorithms to estimate joint, segment, and muscle kinematics and kinetics. We present a high-level comparison of the many different techniques identified and discuss the implications of our findings concerning practical implementation and further improving estimation accuracy. In particular, we found that several studies report the incorporation of domain knowledge often yielded superior performance. Further, most models were trained on small datasets in which case nonparametric regression often performed best. No models were open-sourced, and most were subject-specific and not validated on impaired populations. Future research should focus on developing open-source algorithms using complementary physics-based and machine learning techniques that are validated in clinically impaired populations. This approach may further improve estimation performance and reduce barriers to clinical adoption.

scholarly journals Impact of Machine Learning in Neurosurgery: A Systematic Review of Related Literature

2020 ◽  
Vol 8 (1) ◽  
pp. 13-20
Author(s):  
Praveen Kumar Donepudi
Keyword(s):  
Machine Learning ◽  
Surgical Planning ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Clinical Settings ◽  
Clinical Expertise ◽  
Computer Algorithms ◽  
Learning Techniques ◽  
Decision Making Capacity ◽  
Applications Of Machine Learning

Machine learning is a domain within artificial intelligence that allows for computer algorithms to be learned from experience without them having being programmed. The objective of this study is to summarize the neurosurgical applications of machine learning when compared to clinical expertise. This study uses a systematic search to review articles from the PubMed and Embase databases in comparing various machine learning studies approaches to that of the clinical experts. For this study, 23 studies were identified which used machine learning algorithms for the diagnosis, pre-surgical planning, and outcome prediction. In conclusion, this study identifies that machine learning models can augment decision-making capacity for the surgeons and clinicians in neurosurgical applications. Despite this, there still exist hurdles that involve creation, validation, and the deployment of the machine learning techniques in clinical settings.  

scholarly journals Estimating Biomechanical Time-Series with Wearable Sensors: A Systematic Review of Machine Learning Techniques

Sensors ◽  
2019 ◽  
Vol 19 (23) ◽  
pp. 5227 ◽  
Author(s):  
Reed D. Gurchiek ◽  
Nick Cheney ◽  
Ryan S. McGinnis
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Wearable Sensors ◽  
Sensor Data ◽  
Machine Learning Techniques ◽  
Superior Performance ◽  
Estimation Accuracy ◽  
Accurate Estimation ◽  
Practical Implementation ◽  
Learning Techniques

Wearable sensors have the potential to enable comprehensive patient characterization and optimized clinical intervention. Critical to realizing this vision is accurate estimation of biomechanical time-series in daily-life, including joint, segment, and muscle kinetics and kinematics, from wearable sensor data. The use of physical models for estimation of these quantities often requires many wearable devices making practical implementation more difficult. However, regression techniques may provide a viable alternative by allowing the use of a reduced number of sensors for estimating biomechanical time-series. Herein, we review 46 articles that used regression algorithms to estimate joint, segment, and muscle kinematics and kinetics. We present a high-level comparison of the many different techniques identified and discuss the implications of our findings concerning practical implementation and further improving estimation accuracy. In particular, we found that several studies report the incorporation of domain knowledge often yielded superior performance. Further, most models were trained on small datasets in which case nonparametric regression often performed best. No models were open-sourced, and most were subject-specific and not validated on impaired populations. Future research should focus on developing open-source algorithms using complementary physics-based and machine learning techniques that are validated in clinically impaired populations. This approach may further improve estimation performance and reduce barriers to clinical adoption.

scholarly journals Supervised Machine Learning in Oncology: A Clinician's Guide

2020 ◽  
Vol 04 (01) ◽  
pp. 073-081
Author(s):  
Nikitha Murali ◽  
Ahmet Kucukkaya ◽  
Alexandra Petukhova ◽  
John Onofrey ◽  
Julius Chapiro
Keyword(s):  
Machine Learning ◽  
Clinical Information ◽  
Supervised Machine Learning ◽  
Machine Learning Techniques ◽  
Computer Algorithms ◽  
Volume Data ◽  
Health Records ◽  
Processing Technologies ◽  
Learning Techniques ◽  
Practice Of Medicine

AbstractThe widespread adoption of electronic health records has resulted in an abundance of imaging and clinical information. New data-processing technologies have the potential to revolutionize the practice of medicine by deriving clinically meaningful insights from large-volume data. Among those techniques is supervised machine learning, the study of computer algorithms that use self-improving models that learn from labeled data to solve problems. One clinical area of application for supervised machine learning is within oncology, where machine learning has been used for cancer diagnosis, staging, and prognostication. This review describes a framework to aid clinicians in understanding and critically evaluating studies applying supervised machine learning methods. Additionally, we describe current studies applying supervised machine learning techniques to the diagnosis, prognostication, and treatment of cancer, with a focus on gastroenterological cancers and other related pathologies.

scholarly journals Use of machine learning techniques on airborne geophysical data for mineral resources exploration in Burkina Faso

2019 ◽  
Vol 2019 (1) ◽  
pp. 1-4
Author(s):  
Nasreddine Bournas ◽  
Abdoulaye Touré ◽  
Mahamoud Balboné ◽  
Palalé Sama Zagré ◽  
Abdoulaye Ouédraogo ◽  
...  
Keyword(s):  
Machine Learning ◽  
Burkina Faso ◽  
Mineral Resources ◽  
Geophysical Data ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Airborne Geophysical Data

Using machine learning techniques to reduce data annotation time

2006 ◽  
Author(s):  
Christopher Schreiner ◽  
Kari Torkkola ◽  
Mike Gardner ◽  
Keshu Zhang
Keyword(s):  
Machine Learning ◽  
Machine Learning Techniques ◽  
Data Annotation ◽  
Learning Techniques

Using Machine Learning Algorithms on Prediction of Stock Price

2020 ◽  
Vol 12 (2) ◽  
pp. 84-99
Author(s):  
Li-Pang Chen
Keyword(s):  
Machine Learning ◽  
Stock Price ◽  
Short Term Memory ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Short Term ◽  
Learning Techniques ◽  
Historical Database ◽  
Long Short Term Memory

In this paper, we investigate analysis and prediction of the time-dependent data. We focus our attention on four different stocks are selected from Yahoo Finance historical database. To build up models and predict the future stock price, we consider three different machine learning techniques including Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN) and Support Vector Regression (SVR). By treating close price, open price, daily low, daily high, adjusted close price, and volume of trades as predictors in machine learning methods, it can be shown that the prediction accuracy is improved.

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