scholarly journals Improving fold resistance prediction of HIV-1 against protease and reverse transcriptase inhibitors using artificial neural networks

2017 ◽  
Vol 18 (1) ◽  
Author(s):  
Olivier Sheik Amamuddy ◽  
Nigel T. Bishop ◽  
Özlem Tastan Bishop
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Reverse Transcriptase ◽  
Reverse Transcriptase Inhibitors ◽  
Resistance Prediction ◽  
Artificial Neural ◽  
Hiv 1

HIV-1 Reverse Transcriptase Inhibitor Design Using Artificial Neural Networks

1994 ◽  
Vol 37 (16) ◽  
pp. 2520-2526 ◽  
Author(s):  
Igor V. Tetko ◽  
Vsevolod Yu. Tanchuk ◽  
Neliya P. Chentsova ◽  
Svetlana V. Antonenko ◽  
Gennady I. Poda ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Reverse Transcriptase ◽  
Transcriptase Inhibitor ◽  
Inhibitor Design ◽  
Artificial Neural ◽  
Reverse Transcriptase Inhibitor ◽  
Hiv 1

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

Modeling with Regression Analysis and Artificial Neural Networks the Resistance and Trim of Series 50 Experiments with V-Bottom Motor Boats

2014 ◽  
Vol 30 (04) ◽  
pp. 153-174
Author(s):  
Dejan V. Radojcic ◽  
Michael G. Morabito ◽  
Aleksandar P. Simic ◽  
Antonio B. Zgradic
Keyword(s):  
Neural Networks ◽  
Regression Analysis ◽  
Artificial Neural Networks ◽  
Prediction Method ◽  
Standard Series ◽  
Mathematical Representations ◽  
Resistance Prediction ◽  
Artificial Neural ◽  
Data Tables ◽  
Compare And Contrast

Mathematical representations for the resistance, trim, and wetted length of the Experimental Model Basin Series 50 have been developed using conventional regression analysis techniques as well as artificial neural networks. Series 50 is a standard series of 20 V-bottomed motor boats tested in 1941. These hulls could be representative of today's semidisplacement hulls. Recently, the series has been reanalyzed and published using contemporary planing coefficients, enabling resistance prediction in design stages. In the present study, mathematical representations are developed for the Series 50 as an alternative to using charts or data tables. Two methods are used, regression analysis and artificial neural networks. This study provides a useful resistance prediction method for designers and an opportunity to compare and contrast regression analysis and artificial neural networks applied to standard series. The main finding of the study is that both techniques were capable of developing stable and accurate models. A detailed quantification of the differences between methods is provided.

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