Towards the Machine Learning Algorithms in Telecommunications Business Environment

2020 ◽  
pp. 79-96
Author(s):  
Moisés Loma-Osorio de Andrés ◽  
Aneta Poniszewska-Marańda ◽  
Luis Alfonso Hernández Gómez
Keyword(s):  
Machine Learning ◽  
Learning Algorithms ◽  
Business Environment ◽  
Machine Learning Algorithms

scholarly journals Analyzing the predictive capacity of various machine learning algorithms

2018 ◽  
Vol 7 (2.27) ◽  
pp. 266
Author(s):  
Soly Mathew Biju
Keyword(s):  
Machine Learning ◽  
Nearest Neighbor ◽  
Linear Models ◽  
Learning Algorithms ◽  
New Age ◽  
Business Environment ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Predictive Capacity

The purpose of this study is to deploy and evaluate the performance of the new age machine learning algorithms and their applicability in business environment. Three unique set of datasheets were used to evaluate the true performance of top 4 machine learning algorithms – i.e. Generalized Linear Models (GLM), Support Vector Machine (SVM), K-nearest neighbor (KNN) and Random Forests. The findings of this study revealed that although these algorithms take different way of solving classification and regression problems, they develop quite robust models by understanding and learning the hidden patterns in the datasets. The findings of this study can be used by other companies and individuals while analyzing and solving their respective business problems. Although a number of studies exist where new-age machine learning algorithms are tested and evaluated, there are none where the performance of these algorithms was tested on different size and type of datasets.  

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%.

1290-P: Gut Microbiota in New-Onset Pediatric Patients with Type 1 Diabetes: Machine Learning Algorithms to Classify Microorganisms Disease-Linked

Diabetes ◽  
2020 ◽  
Vol 69 (Supplement 1) ◽  
pp. 1290-P
Author(s):  
GIUSEPPE D’ANNUNZIO ◽  
ROBERTO BIASSONI ◽  
MARGHERITA SQUILLARIO ◽  
ELISABETTA UGOLOTTI ◽  
ANNALISA BARLA ◽  
...  
Keyword(s):  
Machine Learning ◽  
Type 1 Diabetes ◽  
Gut Microbiota ◽  
Pediatric Patients ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
New Onset

The new approach to improvement oil reservoir proxy model predictions using machine learning algorithms

2019 ◽  
Vol 12 ◽  
pp. 60-63
Author(s):  
O.V. Zotkin ◽  
◽  
M.V. Simonov ◽  
A.E. Osokina ◽  
A.M. Andrianova ◽  
...  
Keyword(s):  
Machine Learning ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Oil Reservoir ◽  
New Approach ◽  
Proxy Model ◽  
Model Predictions
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