most citedCoronary Artery Disease Diagnosis; Ranking the Significant Features Using Random Trees Model

148 citations · 148 across the 5 of their papers we have counts for

collaborators

5 papers

eess.SP2020

Comparative Analysis of Single and Hybrid Neuro-Fuzzy-Based Models for an Industrial Heating Ventilation and Air Conditioning Control System

Sina Ardabili, Bertalan Beszedes, Laszlo Nadai +3

Hybridization of machine learning methods with soft computing techniques is an essential approach to improve the performance of the prediction models. Hybrid machine learning model…

eess.SP2020

Performance Analysis of Combine Harvester using Hybrid Model of Artificial Neural Networks Particle Swarm Optimization

Laszlo Nadai, Felde Imre, Sina Ardabili +3

Novel applications of artificial intelligence for tuning the parameters of industrial machines for optimal performance are emerging at a fast pace. Tuning the combine harvesters an…

physics.med-ph2020148 cited

Coronary Artery Disease Diagnosis; Ranking the Significant Features Using Random Trees Model

Javad Hassannataj Joloudari, Edris Hassannataj Joloudari, Hamid Saadatfar +6

Heart disease is one of the most common diseases in middle-aged citizens. Among the vast number of heart diseases, the coronary artery disease (CAD) is considered as a common cardi…

physics.ao-ph2020

Modeling Climate Change Impact on Wind Power Resources Using Adaptive Neuro-Fuzzy Inference System

Narjes Nabipour, Amir Mosavi, Eva Hajnal +3

Climate change impacts and adaptations are the subjects to ongoing issues that attract the attention of many researchers. Insight into the wind power potential in an area and its p…

cs.LG2020

Prediction of flow characteristics in the bubble column reactor by the artificial pheromone-based communication of biological ants

Shahab Shamshirband, Meisam Babanezhad, Amir Mosavi +4

In order to perceive the behavior presented by the multiphase chemical reactors, the ant colony optimization algorithm was combined with computational fluid dynamics (CFD) data. Th…