… comparing the predictive performance of five well-known machine learning models: SVM [22, …
the benchmark dataset in [20]. Also, we analyze the advantage and capacity of each model …

… Recently, several machine learning packages based on edge devices have been … In
this paper, we make a performance comparison of several state-of-the-art machine learning …

… Intelligence techniques such as machine learning based intrusion detection and prevention
systems. Many machine learning models … based on supervised machine learning algorithms. …

We set out in this study to review a vast amount of recent literature on machine learning (ML)
approaches to predicting financial distress (FD), including supervised, unsupervised and …

… Moreover, among deep learning algorithms, we analyze the performance of Artificial Neural
… -Term Memory (LSTM) [28] as the most renowned type of recurrent neural networks model. …

… was to introduce a Deep Learning Neural Network (DLNN) model in landslide susceptibility
… and compare its predictive performance with state-of-the-art machine learning models. The …

… method, we would give more weight to the rankings obtained for the other preprocessing
procedures, when judging the overall performance of the machine learning models. …

… To boost the performance of machine learning models, we … analysis results of the machine
learning models RF, XGBoost, … We compare the model performance between SS1 and SS2. …

Machine learning (ML) has become a popular tool for mining functional neuroimaging data, …
Towards this goal, we compared accuracy of six different ML algorithms applied to …

… In this paper, a comparative study is carried out with machine learning techniques in order …
machine learning and pattern recognition communities. We applied multiple machine learning …