scholarly journals Eagle View: An Abstract Evaluation of Machine Learning Algorithms based on Data Properties

2021 ◽  
Author(s):  
Dhairya Vyas
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Time Series Data ◽  
Learning Algorithms ◽  
Numerical Data ◽  
Machine Learning Algorithms ◽  
Series Data ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Almost All

In terms of Machine Learning, the majority of the data can be grouped into four categories: numerical data, category data, time-series data, and text. We use different classifiers for different data properties, such as the Supervised; Unsupervised; and Reinforcement. Each Categorises has classifier we have tested almost all machine learning methods and make analysis among them.

scholarly journals Eagle View: An Abstract Evaluation of Machine Learning Algorithms based on Data Properties

2021 ◽  
Author(s):  
Dhairya Vyas
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Time Series Data ◽  
Learning Algorithms ◽  
Numerical Data ◽  
Machine Learning Algorithms ◽  
Series Data ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Almost All

In terms of Machine Learning, the majority of the data can be grouped into four categories: numerical data, category data, time-series data, and text. We use different classifiers for different data properties, such as the Supervised; Unsupervised; and Reinforcement. Each Categorises has classifier we have tested almost all machine learning methods and make analysis among them.

Role of machine learning algorithms in timely prediction of the infectious outbreak: COVID-19 (Preprint)

2020 ◽  
Author(s):  
Atika Qazi ◽  
Khulla Naseer ◽  
Javaria Qazi ◽  
Muhammad Abo
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Time Series Data ◽  
Learning Algorithms ◽  
Epidemiological Data ◽  
Machine Learning Algorithms ◽  
World Health ◽  
Series Data ◽  
Systems Science ◽  
Support Vector

UNSTRUCTURED Well-timed forecast of infectious outbreaks using time-series data can help in proper planning of public health measures. If the forecasts are generated from machine learning algorithms, they can be used to manage resources where most needed. Here we present a support vector machine (SVM) model using epidemiological data provided by Johns Hopkins University Centre for Systems Science and Engineering (JHU CCSE), world health organization (WHO), Center for Disease Control and Prevention (CDC) to predict upcoming data before official declaration by WHO. Our study conducted on the time series data available from 22nd January till 10th March 2020 reveals that COVID-19 was spreading at an alarming rate and progressing towards a pandemic. If machine learning algorithms are used to predict the dynamics of an infectious outbreak future strategies can help in better management. Besides exploratory data analysis (EDA) highlights the importance of quarantine measures taken at the onset of this endemic by China and world leadership in containing the initial COVID-19 transmission. Nevertheless, when quarantine measures were relaxed due to extreme scrutiny a sharp upsurge was seen in COVID-19 transmission. The initial insight that confirmed COVID-19 cases are increasing as these got the highest number of effects for our selected dataset from 22nd January-10th March 2020 i.e. 126,344 (64%). The recovered cases are 68289 (34%) and the death rate is around 2%. The model presented here is flexible and can include uncertainty about outbreak dynamics and can be a significant tool for combating future outbreaks.

scholarly journals Detecting Forest Changes Using Dense Landsat 8 and Sentinel-1 Time Series Data in Tropical Seasonal Forests

2019 ◽  
Vol 11 (16) ◽  
pp. 1899 ◽  
Author(s):  
Katsuto Shimizu ◽  
Tetsuji Ota ◽  
Nobuya Mizoue
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Time Series Data ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Series Data ◽  
Landsat 8 ◽  
Forest Disturbances ◽  
Disturbance Detection ◽  
Seasonal Forests

The accurate and timely detection of forest disturbances can provide valuable information for effective forest management. Combining dense time series observations from optical and synthetic aperture radar satellites has the potential to improve large-area forest monitoring. For various disturbances, machine learning algorithms might accurately characterize forest changes. However, there is limited knowledge especially on the use of machine learning algorithms to detect forest disturbances through hybrid approaches that combine different data sources. This study investigated the use of dense Landsat 8 and Sentinel-1 time series data for detecting disturbances in tropical seasonal forests based on a machine learning algorithm. The random forest algorithm was used to predict the disturbance probability of each Landsat 8 and Sentinel-1 observation using variables derived from a harmonic regression model, which characterized seasonality and disturbance-related changes. The time series disturbance probabilities of both sensors were then combined to detect forest disturbances in each pixel. The results showed that the combination of Landsat 8 and Sentinel-1 achieved an overall accuracy of 83.6% for disturbance detection, which was higher than the disturbance detection using only Landsat 8 (78.3%) or Sentinel-1 (75.5%). Additionally, more timely disturbance detection was achieved by combining Landsat 8 and Sentinel-1. Small-scale disturbances caused by logging led to large omissions of disturbances; however, other disturbances were detected with relatively high accuracy. Although disturbance detection using only Sentinel-1 data had low accuracy in this study, the combination with Landsat 8 data improved the accuracy of detection, indicating the value of dense Landsat 8 and Sentinel-1 time series data for timely and accurate disturbance detection.

Application of machine learning methods to assess progressive cavity pumps (PCPs) performance in coal seam gas (CSG) wells

2020 ◽  
Vol 60 (1) ◽  
pp. 197
Author(s):  
Fahd Saghir ◽  
M. E. Gonzalez Perdomo ◽  
Peter Behrenbruch
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Real Time ◽  
Time Series Data ◽  
Series Data ◽  
Coal Seam Gas ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Artificial Lift ◽  
Image Conversion

In Queensland, progressive cavity pumps (PCPs) are the artificial lift method of choice in coal seam gas (CSG) wells, and this choice of artificial lift production stems from the ability of PCPs to better manage the production of liquids with suspended solids. As with any mechanical pumping system, PCPs are prone to natural wear and tear over their operational life, and with the production of coal fines and inter-burden, the run life of PCPs in CSG wells is significantly reduced. Another factor to consider with the use of PCPs is their reliability. As per the CSG production data available through the Queensland Government Data Portal, there are approximately 6400 wells operational in the state as of December 2018. This number is expected to grow significantly over the next decade to meet both international and domestic gas utilisation requirements. Operators supervising these wells rely on a reactive or exception-based approach to manage well performance. In order to efficiently operate thousands of PCP wells, it is pertinent that a benchmark methodology is devised to autonomously monitor PCP performance and allow operators to manage wells by exception. In this study, we will cover the application of machine learning methods to understand anomalous PCP behaviour and overall pump performance based on the analysis of multivariate time-series data. An innovative time-series data approximation and image conversion technique will be discussed in this paper, along with machine learning methods, which will focus on a scalable and autonomous approach to cluster PCP performance and detection of anomalous pump behaviour in near real-time. Results from this study show that clustering real-time data based on converted time-series images helps to pro-actively detect change in PCP performance. Discovery of anomalous multivariate events is also achieved through time-series image conversion. This study also demonstrates that clustering time-series data noticeably improves the real-time monitoring capabilities of PCP performance through improved visual analytics.

Detecting Interesting and Anomalous Patterns In Multivariate Time-Series Data in an Offshore Platform Using Unsupervised Learning

2021 ◽  
Author(s):  
Ilan Sousa Figueirêdo ◽  
Tássio Farias Carvalho ◽  
Wenisten José Dantas Silva ◽  
Lílian Lefol Nani Guarieiro ◽  
Erick Giovani Sperandio Nascimento
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Anomaly Detection ◽  
Unsupervised Learning ◽  
Time Series Data ◽  
Multivariate Time Series ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Series Data ◽  
Unsupervised Machine Learning

Abstract Detection of anomalous events in practical operation of oil and gas (O&G) wells and lines can help to avoid production losses, environmental disasters, and human fatalities, besides decreasing maintenance costs. Supervised machine learning algorithms have been successful to detect, diagnose, and forecast anomalous events in O&G industry. Nevertheless, these algorithms need a large quantity of annotated dataset and labelling data in real world scenarios is typically unfeasible because of exhaustive work of experts. Therefore, as unsupervised machine learning does not require an annotated dataset, this paper intends to perform a comparative evaluation performance of unsupervised learning algorithms to support experts for anomaly detection and pattern recognition in multivariate time-series data. So, the goal is to allow experts to analyze a small set of patterns and label them, instead of analyzing large datasets. This paper used the public 3W database of three offshore naturally flowing wells. The experiment used real data of production of O&G from underground reservoirs with the following anomalous events: (i) spurious closure of Downhole Safety Valve (DHSV) and (ii) quick restriction in Production Choke (PCK). Six unsupervised machine learning algorithms were assessed: Cluster-based Algorithm for Anomaly Detection in Time Series Using Mahalanobis Distance (C-AMDATS), Luminol Bitmap, SAX-REPEAT, k-NN, Bootstrap, and Robust Random Cut Forest (RRCF). The comparison evaluation of unsupervised learning algorithms was performed using a set of metrics: accuracy (ACC), precision (PR), recall (REC), specificity (SP), F1-Score (F1), Area Under the Receiver Operating Characteristic Curve (AUC-ROC), and Area Under the Precision-Recall Curve (AUC-PRC). The experiments only used the data labels for assessment purposes. The results revealed that unsupervised learning successfully detected the patterns of interest in multivariate data without prior annotation, with emphasis on the C-AMDATS algorithm. Thus, unsupervised learning can leverage supervised models through the support given to data annotation.

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