scholarly journals PREDICTING ESTROGEN ACTIVITIES OF BISPHENOL A AND ITS ANALOGS USING QUANTUM CHEMISTRY CALCULATIONS AND ARTIFICIAL NEURAL NETWORKS

2019 ◽  
Vol 62 (5) ◽  
pp. 31-37
Author(s):  
Vu Van Dat ◽  
Le Kim Long ◽  
Nguyen Hoang Trang ◽  
Doan Van Phuc ◽  
Nguyen Van Trang ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Artificial Neural Networks ◽  
Quantum Chemistry ◽  
Qsar Model ◽  
Quantum Chemistry Calculations ◽  
Network Method ◽  
Artificial Neural Network Method ◽  
Artificial Neural

This article presents the results of the quantitative structure – activity relationship (QSAR) study of bisphenol A (BPA) and its analogs using quantum chemistry calculations and method of artificial neural networks (ANN). Molecular structural analysis is performed using Density Functional Theory (DFT) at the B3LYP/6-31+G(d) level. The quantum calculations focus on finding the optimized molecular structures, vibrational frequencies, the molecular orbital energies with reasonable accuracy. The study of electron density distribution was carried out in the framework of the natural bond orbital (NBO) methods. The obtained parameters and known observable estrogen activities are used as input data for constructing the QSAR model, using the artificial neural network method. Based on the artificial neural network method the quantum parameters having the strongest impact on the estrogen activity of the compounds were revealed. The internal and external validation methods have been performed to test the performance and the stability of the model. The statistical parameters obtained of the QSAR model were: R2 = 0.99; Q2LOO = 0.98; R2Predict = 0.98. According to the obtained results, our proposed model, constructing by method of artificial neural network using the parameters of quantum chemistry is adequate and may be useful to predict of estrogen activities for unexplored derivatives and BPA analogs with moderate reliability.

scholarly journals Mengidentifikasi Tanaman Beracun pada Pola Daun dengan Jaringan Syaraf Tiruan Learning Vector Quantification

2019 ◽  
Vol 3 (1) ◽  
pp. 7
Author(s):  
Muhammad Jurnalies Habibie
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Pattern Recognition ◽  
Artificial Neural Network ◽  
Poisonous Plants ◽  
Toxic Plants ◽  
Network Method ◽  
Different Types ◽  
Artificial Neural Network Method ◽  
Artificial Neural

Technology nowadays is starting to go very fast, so that all people can use it. Toxic plants are very dangerous if consumed. Therefore to avoid undesirable events, an introduction to the community is needed to find out which plants are poisonous. Plants have many different types to recognize poisonous plants can be seen from the recognition of leaf patterns in these plants. For this reason, in order to determine the use of Learning Vector Quantification artificial neural networks. In this study, the use of input photos obtained from the camera. Photos will be processed later to extract the characteristics. Next, the process of pattern recognition can get the features in the photo. So that later it gets its characteristics. then the classification process uses the Learning Vector Quantification artificial neural network method. This research was conducted to be able to distinguish poisonous plants from those that are not. Which later the data is collected for grouping in accordance with the same data, so that information can be set about the plant.

scholarly journals Optimal Artificial Neural Network Type Selection Method for Usage in Smart House Systems

Sensors ◽  
2020 ◽  
Vol 21 (1) ◽  
pp. 47
Author(s):  
Vasyl Teslyuk ◽  
Artem Kazarian ◽  
Natalia Kryvinska ◽  
Ivan Tsmots
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Artificial Neural Networks ◽  
Input Data ◽  
Optimization Criterion ◽  
Fuzzy Input ◽  
Different Types ◽  
Smart House ◽  
Artificial Neural

In the process of the “smart” house systems work, there is a need to process fuzzy input data. The models based on the artificial neural networks are used to process fuzzy input data from the sensors. However, each artificial neural network has a certain advantage and, with a different accuracy, allows one to process different types of data and generate control signals. To solve this problem, a method of choosing the optimal type of artificial neural network has been proposed. It is based on solving an optimization problem, where the optimization criterion is an error of a certain type of artificial neural network determined to control the corresponding subsystem of a “smart” house. In the process of learning different types of artificial neural networks, the same historical input data are used. The research presents the dependencies between the types of neural networks, the number of inner layers of the artificial neural network, the number of neurons on each inner layer, the error of the settings parameters calculation of the relative expected results.

Sign in / Sign up

Export Citation Format

Share Document