Testing machine learning algorithms for the prediction of depositional fluxes of the radionuclides 7Be, 210Pb and 40K.

2021 ◽  
Author(s):  
Pedro De la Torre Luque ◽  
Concepcion Duenas ◽  
Elisa Gordo ◽  
Sergio Cañete
Keyword(s):  
Machine Learning ◽  
Learning Algorithms ◽  
Testing Machine ◽  
Machine Learning Algorithms

Testing Machine Learning Algorithms for Drilling Incidents Detection on a Pilot Small-Scale Drilling Rig

2021 ◽  
Vol 143 (12) ◽  
Author(s):  
Erik Andreas Løken ◽  
Jens Løkkevik ◽  
Dan Sui
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Learning Algorithms ◽  
Testing Machine ◽  
Field Testing ◽  
Machine Learning Algorithms ◽  
Small Scale ◽  
Operational Parameters ◽  
Novel Technologies ◽  
Drilling Rig

Abstract In recent years, drilling digitalization and automation have advanced from being automation of rig floor equipment to an idea that is starting to be applied to entire drilling processes. However it is very costly in terms of field testing and validating developed novel technologies. To address this limitation, we take advantage of a laboratory drilling rig to run a large number of drilling tests. By introducing various drilling scenarios while drilling different formations using various combinations of the operational parameters, we could be able to collect a large amount of data for data-driven methods development and testing. The main study in this article is to develop machine learning algorithms for identifying abnormal drilling and test these algorithms on the rig based on the responses of the rig sensors in real-time operations. The idea also helps us determine what the most important parameters or their combinations for drilling incidents detection are, which we could pay greatest attention to make right decisions with the help of drilling data during real-time operations.

scholarly journals Development of a Human Flow Generation Module for Testing Machine Learning Algorithms

2021 ◽  
Vol 54 ◽  
pp. 783-792
Author(s):  
Kirill Kulagin ◽  
Aleksandr Leksashov ◽  
Marina Bolsunovskaya ◽  
Svetlana Shirokova
Keyword(s):  
Machine Learning ◽  
Learning Algorithms ◽  
Testing Machine ◽  
Machine Learning Algorithms ◽  
Flow Generation ◽  
Human Flow

Forecasting US movies box office performances in Turkey using machine learning algorithms

2020 ◽  
Vol 39 (5) ◽  
pp. 6579-6590
Author(s):  
Sandy Çağlıyor ◽  
Başar Öztayşi ◽  
Selime Sezgin
Keyword(s):  
Machine Learning ◽  
Global Economy ◽  
Learning Algorithms ◽  
Forecast Model ◽  
Machine Learning Algorithms ◽  
Gradient Boosting ◽  
High Stakes ◽  
Box Office ◽  
Industry Forecast ◽  
The Impact

The motion picture industry is one of the largest industries worldwide and has significant importance in the global economy. Considering the high stakes and high risks in the industry, forecast models and decision support systems are gaining importance. Several attempts have been made to estimate the theatrical performance of a movie before or at the early stages of its release. Nevertheless, these models are mostly used for predicting domestic performances and the industry still struggles to predict box office performances in overseas markets. In this study, the aim is to design a forecast model using different machine learning algorithms to estimate the theatrical success of US movies in Turkey. From various sources, a dataset of 1559 movies is constructed. Firstly, independent variables are grouped as pre-release, distributor type, and international distribution based on their characteristic. The number of attendances is discretized into three classes. Four popular machine learning algorithms, artificial neural networks, decision tree regression and gradient boosting tree and random forest are employed, and the impact of each group is observed by compared by the performance models. Then the number of target classes is increased into five and eight and results are compared with the previously developed models in the literature.

Intelligent system of English composition scoring model based on improved machine learning algorithm

2020 ◽  
pp. 1-11
Author(s):  
Jie Liu ◽  
Lin Lin ◽  
Xiufang Liang
Keyword(s):  
Machine Learning ◽  
Evaluation System ◽  
Intelligent System ◽  
Learning Algorithm ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Assessment System ◽  
English Composition ◽  
Region Extraction ◽  
Constraint Model

The online English teaching system has certain requirements for the intelligent scoring system, and the most difficult stage of intelligent scoring in the English test is to score the English composition through the intelligent model. In order to improve the intelligence of English composition scoring, based on machine learning algorithms, this study combines intelligent image recognition technology to improve machine learning algorithms, and proposes an improved MSER-based character candidate region extraction algorithm and a convolutional neural network-based pseudo-character region filtering algorithm. In addition, in order to verify whether the algorithm model proposed in this paper meets the requirements of the group text, that is, to verify the feasibility of the algorithm, the performance of the model proposed in this study is analyzed through design experiments. Moreover, the basic conditions for composition scoring are input into the model as a constraint model. The research results show that the algorithm proposed in this paper has a certain practical effect, and it can be applied to the English assessment system and the online assessment system of the homework evaluation system algorithm system.

The Unlearnable Checkerboard Pattern

2019 ◽  
Vol 1 (2) ◽  
pp. 78-80
Author(s):  
Eric Holloway
Keyword(s):  
Machine Learning ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Checkerboard Pattern ◽  
Simple Task

Detecting some patterns is a simple task for humans, but nearly impossible for current machine learning algorithms.  Here, the "checkerboard" pattern is examined, where human prediction nears 100% and machine prediction drops significantly below 50%.

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