scholarly journals APPLICATION OF THE METHODS OF HYBRID ARTIFICIAL INTELLIGENCE TO DETERMINE THE LOCATION OF OBJECTS BY THEIR PHOTOGRAPHY

2019 ◽  
Author(s):  
Dmitry V. Berezkin ◽  
Andrew V. Proletarsky ◽  
Valentin Yu. Sidorov ◽  
Ilya E. Sergeev
Keyword(s):  
Artificial Intelligence ◽  
Hybrid Artificial Intelligence

scholarly journals A review of the hybrid artificial intelligence and optimization modelling of hydrological streamflow forecasting

2021 ◽  
Author(s):  
Karim Sherif Mostafa Hassan Ibrahim ◽  
Yuk Feng Huang ◽  
Ali Najah Ahmed ◽  
Chai Hoon Koo ◽  
Ahmed El-Shafie
Keyword(s):  
Artificial Intelligence ◽  
Streamflow Forecasting ◽  
Hybrid Artificial Intelligence

A hybrid artificial intelligence method to classify trichotillomania and obsessive compulsive disorder

2015 ◽  
Vol 161 ◽  
pp. 220-228 ◽  
Author(s):  
Turker Tekin Erguzel ◽  
Serhat Ozekes ◽  
Gokben Hizli Sayar ◽  
Oguz Tan ◽  
Nevzat Tarhan
Keyword(s):  
Artificial Intelligence ◽  
Obsessive Compulsive Disorder ◽  
Obsessive Compulsive ◽  
Compulsive Disorder ◽  
Hybrid Artificial Intelligence

scholarly journals Hybrid Artificial Intelligence Methods for Predicting Air Demand in Dam Bottom Outlet

2021 ◽  
Author(s):  
Aliakbar Narimani ◽  
Moghimi ◽  
Amir Mosavi
Keyword(s):  
Artificial Intelligence ◽  
Economic Value ◽  
Air Entrainment ◽  
Operating Conditions ◽  
Successful Operation ◽  
Volume Rate ◽  
Hydraulic Equipment ◽  
Bottom Outlets ◽  
Hybrid Artificial Intelligence ◽  
Bottom Outlet

In large infrastructures such as dams, which have a relatively high economic value, ensuring the proper operation of the associated hydraulic facilities in different operating conditions is of utmost importance. To ensure the correct and successful operation of the dam's hydraulic equipment and prevent possible damages, including gates and downstream tunnel, to build laboratory models and perform some tests are essential (the advancement of the smart sensors based on artificial intelligence is essential). One of the causes of damage to dam bottom outlets is cavitation in downstream and between the gates, which can impact on dam facilities, and air aeration can be a solution to improve it. In the present study, six dams in different provinces in Iran has been chosen to evaluate the air entrainment in the downstream tunnel experimentally. Three artificial neural networks (ANN) based machine learning (ML) algorithms are used to model and predict the air aeration in the bottom outlet. The proposed models are trained with genetic algorithms (GA), particle swarm optimization (PSO), i.e., ANN-GA, ANN-PSO, and ANFIS-PSO. Two hydrodynamic variables, namely volume rate and opening percentage of the gate, are used as inputs into all bottom outlet models. The results showed that the most optimal model is ANFIS-PSO to predict the dependent value compared with ANN-GA and ANN-PSO. The importance of the volume rate and opening percentage of the dams' gate parameters is more effective for suitable air aeration.

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