Real-time prediction of COVID-19 patients health situations using Artificial Neural Networks and Fuzzy Interval Mathematical modelling

Mathematical Modelling ◽

Real Time ◽

Time Prediction ◽

Artificial Neural ◽

Fuzzy Interval

REAL TIME PREDICTION OF COASTAL ALGAL BLOOMS USING ARTIFICIAL NEURAL NETWORKS

Hydroinformatics ◽

10.1142/9789812702838_0181 ◽

2004 ◽

pp. 1465-1472 ◽

Cited By ~ 1

Author(s):

J. H. W. LEE ◽

T. M. K. G. FERNANDO ◽

K. T. M. WONG

Keyword(s):

Neural Networks ◽

Real Time ◽

Algal Blooms ◽

Time Prediction ◽

Real-Time Prediction of Rheological Parameters of KCl Water-Based Drilling Fluid Using Artificial Neural Networks

Arabian Journal for Science and Engineering ◽

10.1007/s13369-016-2409-7 ◽

2017 ◽

Vol 42 (4) ◽

pp. 1655-1665 ◽

Cited By ~ 35

Author(s):

Salaheldin Elkatatny

Keyword(s):

Neural Networks ◽

Real Time ◽

Drilling Fluid ◽

Rheological Parameters ◽

Time Prediction ◽

Water Based ◽

Real-Time Prediction of the Dynamic Young’s Modulus from the Drilling Parameters Using the Artificial Neural Networks

Arabian Journal for Science and Engineering ◽

10.1007/s13369-021-05465-2 ◽

2021 ◽

Author(s):

Salaheldin Elkatatny

Keyword(s):

Neural Networks ◽

Real Time ◽

Young’S Modulus ◽

Young's Modulus ◽

Time Prediction ◽

Dynamic Young’S Modulus ◽

Drilling Parameters ◽

Dynamic Young's Modulus ◽

Real time prediction of drilling fluid rheological properties using Artificial Neural Networks visible mathematical model (white box)

Journal of Petroleum Science and Engineering ◽

10.1016/j.petrol.2016.08.021 ◽

2016 ◽

Vol 146 ◽

pp. 1202-1210 ◽

Cited By ~ 54

Author(s):

Salaheldin Elkatatny ◽

Zeeshan Tariq ◽

Mohamed Mahmoud

Keyword(s):

Neural Networks ◽

Mathematical Model ◽

Rheological Properties ◽

Real Time ◽

Drilling Fluid ◽

Time Prediction ◽

Real Time prediction of the Rheological Parameters of NaCl Water-Based Drilling Fluid Using Artificial Neural Networks

10.2118/187976-ms ◽

2017 ◽

Cited By ~ 4

Author(s):

Salaheldin Elkatatny ◽

Mohamed Mahmoud

Keyword(s):

Neural Networks ◽

Real Time ◽

Drilling Fluid ◽

Rheological Parameters ◽

Time Prediction ◽

Water Based ◽

2014 International Conference on Renewable Energy Research and Application (ICRERA) ◽

Use of artificial neural networks for real-time prediction of heave displacement in ocean buoys

10.1109/icrera.2014.7016517 ◽

2014 ◽

Cited By ~ 2

Author(s):

E. Shoori J. Hesam ◽

Bradely Ling ◽

Belinda A. Batten

Keyword(s):

Neural Networks ◽

Real Time ◽

Time Prediction ◽

Lecture Notes in Computer Science - Knowledge-Based Intelligent Information and Engineering Systems ◽

Theory and Application of Artificial Neural Networks for the Real Time Prediction of Ship Motion

10.1007/11552413_151 ◽

2005 ◽

pp. 1064-1069 ◽

Cited By ~ 3

Author(s):

Ameer Khan ◽

Cees Bil ◽

Kaye E. Marion

Keyword(s):

Neural Networks ◽

Real Time ◽

Ship Motion ◽

The Real ◽

Time Prediction ◽

Real Time Prediction of the Rheological Properties of Oil-Based Drilling Fluids Using Artificial Neural Networks

10.2118/192199-ms ◽

2018 ◽

Cited By ~ 8

Author(s):

Khaled Al-Azani ◽

Salaheldin Elkatatny ◽

Abdulaziz Abdulraheem ◽

Mohamed Mahmoud ◽

Dhafer Al-Shehri

Keyword(s):

Neural Networks ◽

Rheological Properties ◽

Real Time ◽

Drilling Fluids ◽

Time Prediction ◽

Real-Time Prediction Of Episodes Of Sleep Disordered Breathing Using LAMSTAR Artificial Neural Networks

10.1164/ajrccm-conference.2012.185.1_meetingabstracts.a6429 ◽

2012 ◽

Author(s):

Jonathan A. Waxman ◽

Dan Graupe ◽

David W. Carley

Keyword(s):

Neural Networks ◽

Real Time ◽

Sleep Disordered Breathing ◽

Time Prediction ◽

Artificial Neural ◽

Disordered Breathing

Real-time prediction of grid voltage and frequency using artificial neural networks: An experimental validation

Sustainable Energy Grids and Networks ◽

10.1016/j.segan.2021.100502 ◽

2021 ◽

pp. 100502

Author(s):

N. Chettibi ◽

A. Massi Pavan ◽

A. Mellit ◽

A.J. Forsyth ◽

R. Todd

Keyword(s):

Neural Networks ◽

Real Time ◽

Experimental Validation ◽

Grid Voltage ◽

Time Prediction ◽