scholarly journals Lateral–torsional buckling resistance prediction model for steel cellular beams generated by Artificial Neural Networks (ANN)

2022 ◽  
Vol 170 ◽  
pp. 108592
Author(s):  
Felipe Piana Vendramell Ferreira ◽  
Rabee Shamass ◽  
Vireen Limbachiya ◽  
Konstantinos Daniel Tsavdaridis ◽  
Carlos Humberto Martins
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Prediction Model ◽  
Lateral Torsional Buckling ◽  
Torsional Buckling ◽  
Buckling Resistance ◽  
Resistance Prediction ◽  
Artificial Neural

Prediction Modeling of PM-10 in Chiangmai City Moat by Using Artificial Neural Networks

2015 ◽  
Vol 781 ◽  
pp. 628-631 ◽  
Author(s):  
Rati Wongsathan ◽  
Issaravuth Seedadan ◽  
Metawat Kavilkrue
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Prediction Model ◽  
Chiang Mai ◽  
Adverse Health Effects ◽  
High Pollution ◽  
Artificial Neural ◽  
Input Variables ◽  
Pm 10 ◽  
Respiratory Conditions

A mathematical prediction model has been developed in order to detect particles with a diameter of 10 micrometers or less (PM-10) that are responsible for adverse health effects because of their ability to cause serious respiratory conditions in areas of high pollution such as Chiang Mai City moat area. The prediction model is based on 3 types of Artificial Neural Networks (ANNs), including Multi-layer perceptron (MLP-NN), Radial basis function (RBF-NN), and hybrid of RBF and Genetic algorithm (RBF-NN-GA). The model uses 8 input variables to predict PM-10, consisting of 4 air pollution substances ( CO, O3, NO2 and SO2) and 4 meteorological variables related PM-10 (wind speed, temperature, atmospheric pressure and relative humidity). These 3 types of ANN have proved efficient instrument in predicting the PM-10. However, the performance of RBF-NN was superior in comparison with MLP-NN and RBF-NN-GA respectively.

Artificial Neural Networks Based Dengue Diagnosis Prediction Model

2021 ◽  
Author(s):  
Lathesparan Ramachandran ◽  
Rm Kapila Tharanga Rathnayaka ◽  
Wiraj Udara Wickramaarachchi
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Prediction Model ◽  
Artificial Neural
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