MOESM1 of Deep-learning: investigating deep neural networks hyper-parameters and comparison of performance to shallow methods for modeling bioactivity data
Data set Fukui_2973 ================== Atomic NBO charges for non-hydrogen atoms in 2973 small organic molecules at the B3LYP/6-31G*//DFTB level of theory. Related publication: * Qingyou Zhang, Fangfa
The best set of parameters is {number of conv filters, Nc: 256, number of units in LSTM Nh: 64, dropout probability for Dropout Layer: 0, learning rate: 0.001} for read-level prediction on 5-fold cros
Inspection and diagnosis of construction projects involves health monitoring of buildings and related facilities, and the utilization of renewable energy sources, such as solar energy, is critical to
