scholarly journals A Novel Hybrid Model For Automated Analysis Of Cardiotocograms Using Machine Learning Algorithms

2021 ◽  
Vol 9 (4) ◽  
pp. 266-272
Author(s):  
Emre Avuclu
Keyword(s):  
Machine Learning ◽  
Hybrid Model ◽  
Learning Algorithms ◽  
Automated Analysis ◽  
Machine Learning Algorithms

scholarly journals I Know Where You Are Going: Predicting Flight Destinations of Corporate and State Aircraft

2021 ◽  
Vol 13 (1) ◽  
pp. 1
Author(s):  
Marc Jourdan ◽  
Karolis Martinkus ◽  
David Roschewitz ◽  
Martin Strohmeier
Keyword(s):  
Machine Learning ◽  
Open Source ◽  
Large Scale ◽  
Historical Data ◽  
Learning Algorithms ◽  
Automated Analysis ◽  
Machine Learning Algorithms ◽  
Commercial Aviation ◽  
Open Source Intelligence

As data of aircraft movements have become freely accessible on a large scale through means of crowdsourcing, their open source intelligence (OSINT) value has been illustrated in many different domains. Potentially sensitive movements of all stakeholders outside commercial aviation are potentially affected, from corporate jets to military and government aircraft. Until now, this OSINT value was shown only on historical data, where automated analysis on flight destinations has been effective to find information on potential mergers & acquisition deals or diplomatic relationships between governments. In practice, obtaining such information as early as possible is crucial. Hence, in this work, we predict the destinations of state and corporate aircraft on live data, while the targets are still in the air. We use machine learning algorithms to predict the area of landing up to 2 h in advance. We evaluate our approach on more than 500,000 flights during 2018 obtained from the OpenSky Network.

Site-scale modeling of surface ozone in Northern Bavaria using machine learning algorithms, regional dynamic models, and a hybrid model

2021 ◽  
Vol 268 ◽  
pp. 115736
Author(s):  
Seyed Omid Nabavi ◽  
Anke C. Nölscher ◽  
Cyrus Samimi ◽  
Christoph Thomas ◽  
Leopold Haimberger ◽  
...  
Keyword(s):  
Machine Learning ◽  
Hybrid Model ◽  
Dynamic Models ◽  
Surface Ozone ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Scale Modeling ◽  
Regional Dynamic

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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