Publications by authors named "Laszlo Nadai"

2 Publications

  • Page 1 of 1

Adaptive Neuro-Fuzzy Inference System and a Multilayer Perceptron Model Trained with Grey Wolf Optimizer for Predicting Solar Diffuse Fraction.

Entropy (Basel) 2020 Oct 22;22(11). Epub 2020 Oct 22.

Environmental Quality, Atmospheric Science and Climate Change Research Group, Ton Duc Thang University, Ho Chi Minh City, Vietnam.

The accurate prediction of the solar diffuse fraction (DF), sometimes called the diffuse ratio, is an important topic for solar energy research. In the present study, the current state of Diffuse irradiance research is discussed and then three robust, machine learning (ML) models are examined using a large dataset (almost eight years) of hourly readings from Almeria, Spain. The ML models used herein, are a hybrid adaptive network-based fuzzy inference system (ANFIS), a single multi-layer perceptron (MLP) and a hybrid multi-layer perceptron grey wolf optimizer (MLP-GWO). These models were evaluated for their predictive precision, using various solar and DF irradiance data, from Spain. The results were then evaluated using frequently used evaluation criteria, the mean absolute error (MAE), mean error (ME) and the root mean square error (RMSE). The results showed that the MLP-GWO model, followed by the ANFIS model, provided a higher performance in both the training and the testing procedures.
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http://dx.doi.org/10.3390/e22111192DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7711824PMC
October 2020

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

Int J Environ Res Public Health 2020 01 23;17(3). Epub 2020 Jan 23.

Kalman Kando Faculty of Electrical Engineering, Obuda University, 1034 Budapest, Hungary.

Heart disease is one of the most common diseases in middle-aged citizens. Among the vast number of heart diseases, coronary artery disease (CAD) is considered as a common cardiovascular disease with a high death rate. The most popular tool for diagnosing CAD is the use of medical imaging, e.g., angiography. However, angiography is known for being costly and also associated with a number of side effects. Hence, the purpose of this study is to increase the accuracy of coronary heart disease diagnosis through selecting significant predictive features in order of their ranking. In this study, we propose an integrated method using machine learning. The machine learning methods of random trees (RTs), decision tree of C5.0, support vector machine (SVM), and decision tree of Chi-squared automatic interaction detection (CHAID) are used in this study. The proposed method shows promising results and the study confirms that the RTs model outperforms other models.
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http://dx.doi.org/10.3390/ijerph17030731DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7037941PMC
January 2020