Additionally, we compare several machine learning algorithms, including random forest (RF), support vector machine (SVM), long short term memory (LSTM), and gated recurrent unit (GRU), to find the best algorithm to predict lane changes. We evaluate the performance of these four algorithms on ...
There are three key issues about online classification: observation window size, feature selection, and classification algorithms.In this paper, by collecting five types of typical network flow data as the experiment sample data, the authorsfound observation window size 7 is the best for the sample...
Machine learning algorithms only depend on the training data to predict the outputs; hence, we can detect the symbol even without the use of cyclic prefix or channel estimation which can save a lot of time and data if the input data is large. A comparative study on the performance of ...
In our study, we use a closed-source data set for our analysis contrary to the majority of studies in the literature. We obtain five projects from the company’s Jira interface for analysis. We focus exclusively on the main language of the issues. To prepare the data set for this study,...
In this paper, the efficiency of five Machine Learning (ML) methods consisting of Deep Learning (DL), Support Vector Machine (SVM), Random Forest (RF), Decision Tree (DT), and Gradient Tree Booting (GTB) for regression and classification of the Ultimate Load Factor (ULF) of nonlinear inela...
The algorithms and techniques deployed in machine learning (ML) can be framed within a more general process known as knowledge discovery in databases or simply data mining. Some of these techniques were described more than 50 years ago [1], however in recent years interest in and about them...
With the aim of enhancing predictive performance, we employ ensemble methods that leverage a diverse range of machine-learning algorithms. To evaluate the effectiveness of our system, we employ widely recognized metrics such as accuracy, precision, recall, and F1-score which serve as indicators of ...
Previous studies of Brain Computer Interfaces (BCI) based on scalp electroencephalography (EEG) have demonstrated the feasibility of decoding kinematics for lower limb movements during walking. In this computational study, we investigated offline decodin
A tie between four algorithms (NB, LR, SVM, ANN) obtained the highest AUC (0.93). The expert-built model (Expert) in Configuration 3, where all missing values were left unassigned, obtained the lowest AUC (0.8). The expert-built model obtained the lowest AUC in all configurations. ...
1.2. Machine learning algorithm theory 1.2.1. Naive Bayes Naive Bayes (NB) is a well known statistical learning algorithm recommended as a base level classifier for comparison with other algorithms (Guyon, 2009, Henery, 1994). NB estimates class-conditional probabilities by “naively” assuming th...