Predicting attributes based movie success through ensemble machine learning

Estimation of fluid saturation is an important step in dynamic reservoir characterization. Machine learning techniques have been increasingly used in recent years for reservoir saturation prediction workflows. However, most of these studies require input parameters derived from cores, petrophysical logs, or seismic data, which may not always be readily available. Additionally, very few studies incorporate the production data, which is an important reflection of the dynamic reservoir properties and also typically the most frequently and reliably measured quantity throughout the life of a field. In this research, the random forest ensemble machine learning algorithm is implemented that uses the field-wide production and injection data (both measured at the surface) as the only input parameters to predict the time-lapse oil saturation profiles at well locations. The algorithm is optimized using feature selection based on feature importance score and Pearson correlation coefficient, in combination with geophysical domain-knowledge. The workflow is demonstrated using the actual field data from a structurally complex, heterogeneous, and heavily faulted offshore reservoir. The random forest model captures the trends from three and a half years of historical field production, injection, and simulated saturation data to predict future time-lapse oil saturation profiles at four deviated well locations with over 90% R-square, less than 6% Root Mean Square Error, and less than 7% Mean Absolute Percentage Error, in each case.

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Prediction of Cesarean Childbirth using Ensemble Machine Learning Methods

Proceedings of the 22nd International Conference on Information Integration and Web-based Applications & Services ◽

10.1145/3428757.3429138 ◽

2020 ◽

Cited By ~ 1

Author(s):

Nafiz Imtiaz Khan ◽

Tahasin Mahmud ◽

Muhammad Nazrul Islam ◽

Sumaiya Nuha Mustafina

Keyword(s):

Machine Learning ◽

Learning Methods ◽

Machine Learning Methods ◽

Ensemble Machine Learning

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Enhanced Twitter bot detection using ensemble machine learning

2021 6th International Conference on Inventive Computation Technologies (ICICT) ◽

10.1109/icict50816.2021.9358734 ◽

2021 ◽

Author(s):

Hrushikesh Shukla ◽

Nakshatra Jagtap ◽

Balaji Patil

Keyword(s):

Machine Learning ◽

Ensemble Machine Learning ◽

Bot Detection

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Cloud based ensemble machine learning approach for smart detection of epileptic seizures using higher order spectral analysis

Physical and Engineering Sciences in Medicine ◽

10.1007/s13246-021-00970-y ◽

2021 ◽

Author(s):

Kuldeep Singh ◽

Jyoteesh Malhotra

Keyword(s):

Machine Learning ◽

Spectral Analysis ◽

Epileptic Seizures ◽

Higher Order ◽

Learning Approach ◽

Ensemble Machine Learning ◽

Machine Learning Approach

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Chemometrics‐based models hyphenated with ensemble machine learning for retention time simulation of Isoquercitrin in Coriander sativum L. using high performance liquid chromatography

Journal of Separation Science ◽

10.1002/jssc.202000890 ◽

2020 ◽

Author(s):

Abdullahi Garba Usman ◽

Selin Işik ◽

Sani Isah Abba ◽

Filiz Meriçli

Keyword(s):

Machine Learning ◽

High Performance Liquid Chromatography ◽

Liquid Chromatography ◽

Retention Time ◽

High Performance ◽

Ensemble Machine Learning ◽

Time Simulation

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Comparison of Ensemble Machine Learning Methods for Soil Erosion Pin Measurements

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi10010042 ◽

2021 ◽

Vol 10 (1) ◽

pp. 42

Author(s):

Kieu Anh Nguyen ◽

Walter Chen ◽

Bor-Shiun Lin ◽

Uma Seeboonruang

Keyword(s):

Machine Learning ◽

Soil Erosion ◽

Ensemble Methods ◽

Machine Learning Algorithms ◽

Multivariate Adaptive Regression Splines ◽

Gradient Boosting ◽

Support Vector ◽

Ensemble Machine Learning ◽

Boosting Method ◽

Bagging Method

Although machine learning has been extensively used in various fields, it has only recently been applied to soil erosion pin modeling. To improve upon previous methods of quantifying soil erosion based on erosion pin measurements, this study explored the possible application of ensemble machine learning algorithms to the Shihmen Reservoir watershed in northern Taiwan. Three categories of ensemble methods were considered in this study: (a) Bagging, (b) boosting, and (c) stacking. The bagging method in this study refers to bagged multivariate adaptive regression splines (bagged MARS) and random forest (RF), and the boosting method includes Cubist and gradient boosting machine (GBM). Finally, the stacking method is an ensemble method that uses a meta-model to combine the predictions of base models. This study used RF and GBM as the meta-models, decision tree, linear regression, artificial neural network, and support vector machine as the base models. The dataset used in this study was sampled using stratified random sampling to achieve a 70/30 split for the training and test data, and the process was repeated three times. The performance of six ensemble methods in three categories was analyzed based on the average of three attempts. It was found that GBM performed the best among the ensemble models with the lowest root-mean-square error (RMSE = 1.72 mm/year), the highest Nash-Sutcliffe efficiency (NSE = 0.54), and the highest index of agreement (d = 0.81). This result was confirmed by the spatial comparison of the absolute differences (errors) between model predictions and observations using GBM and RF in the study area. In summary, the results show that as a group, the bagging method and the boosting method performed equally well, and the stacking method was third for the erosion pin dataset considered in this study.

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An Optimized Stacking Ensemble Model for Phishing Websites Detection

Electronics ◽

10.3390/electronics10111285 ◽

2021 ◽

Vol 10 (11) ◽

pp. 1285

Author(s):

Mohammed Al-Sarem ◽

Faisal Saeed ◽

Zeyad Ghaleb Al-Mekhlafi ◽

Badiea Abdulkarem Mohammed ◽

Tawfik Al-Hadhrami ◽

...

Keyword(s):

Machine Learning ◽

Random Forests ◽

Ensemble Method ◽

Detection Methods ◽

Detection Accuracy ◽

Ensemble Model ◽

Security Attacks ◽

Data Set ◽

Machine Learning Methods ◽

Ensemble Machine Learning

Security attacks on legitimate websites to steal users’ information, known as phishing attacks, have been increasing. This kind of attack does not just affect individuals’ or organisations’ websites. Although several detection methods for phishing websites have been proposed using machine learning, deep learning, and other approaches, their detection accuracy still needs to be enhanced. This paper proposes an optimized stacking ensemble method for phishing website detection. The optimisation was carried out using a genetic algorithm (GA) to tune the parameters of several ensemble machine learning methods, including random forests, AdaBoost, XGBoost, Bagging, GradientBoost, and LightGBM. The optimized classifiers were then ranked, and the best three models were chosen as base classifiers of a stacking ensemble method. The experiments were conducted on three phishing website datasets that consisted of both phishing websites and legitimate websites—the Phishing Websites Data Set from UCI (Dataset 1); Phishing Dataset for Machine Learning from Mendeley (Dataset 2, and Datasets for Phishing Websites Detection from Mendeley (Dataset 3). The experimental results showed an improvement using the optimized stacking ensemble method, where the detection accuracy reached 97.16%, 98.58%, and 97.39% for Dataset 1, Dataset 2, and Dataset 3, respectively.

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