Review of Machine Learning Algorithms for Automatic Detection of Underground Objects in GPR Images

2022 ◽  
Vol 13 (2) ◽  
Author(s):  
Leila Carolina Martoni Amaral ◽  
Aditya Roshan ◽  
Alireza Bayat
Keyword(s):  
Machine Learning ◽  
Learning Algorithms ◽  
Automatic Detection ◽  
Machine Learning Algorithms

Applying Machine Learning Algorithms for Automatic Detection of Swallowing from Sound

2019 ◽  
Author(s):  
Laura F. Santoso ◽  
Faiz Baqai ◽  
Mary Gwozdz ◽  
Justina Lange ◽  
Matthew G. Rosenberger ◽  
...  
Keyword(s):  
Machine Learning ◽  
Learning Algorithms ◽  
Automatic Detection ◽  
Machine Learning Algorithms

scholarly journals Automatic detection of false positive RFID readings using machine learning algorithms

2018 ◽  
Vol 91 ◽  
pp. 442-451 ◽  
Author(s):  
Haishu Ma ◽  
Yi Wang ◽  
Kesheng Wang
Keyword(s):  
Machine Learning ◽  
False Positive ◽  
Learning Algorithms ◽  
Automatic Detection ◽  
Machine Learning Algorithms

scholarly journals Automatic Detection of Faults in Race Walking: A Comparative Analysis of Machine-Learning Algorithms Fed with Inertial Sensor Data

Sensors ◽  
2019 ◽  
Vol 19 (6) ◽  
pp. 1461 ◽  
Author(s):  
Juri Taborri ◽  
Eduardo Palermo ◽  
Stefano Rossi
Keyword(s):  
Machine Learning ◽  
Inertial Sensors ◽  
Inertial Sensor ◽  
Learning Algorithms ◽  
Automatic Detection ◽  
Machine Learning Algorithms ◽  
Sensor Data ◽  
Support Vector ◽  
Angular Velocities ◽  
Race Walking

The validity of results in race walking is often questioned due to subjective decisions in the detection of faults. This study aims to compare machine-learning algorithms fed with data gathered from inertial sensors placed on lower-limb segments to define the best-performing classifiers for the automatic detection of illegal steps. Eight race walkers were enrolled and linear accelerations and angular velocities related to pelvis, thighs, shanks, and feet were acquired by seven inertial sensors. The experimental protocol consisted of two repetitions of three laps of 250 m, one performed with regular race walking, one with loss-of-contact faults, and one with knee-bent faults. The performance of 108 classifiers was evaluated in terms of accuracy, recall, precision, F1-score, and goodness index. Generally, linear accelerations revealed themselves as more characteristic with respect to the angular velocities. Among classifiers, those based on the support vector machine (SVM) were the most accurate. In particular, the quadratic SVM fed with shank linear accelerations was the best-performing classifier, with an F1-score and a goodness index equal to 0.89 and 0.11, respectively. The results open the possibility of using a wearable device for automatic detection of faults in race walking competition.

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
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