scholarly journals Electronic nose: clinical diagnosis based on soft computing methodologies

2003 ◽  
Author(s):  
V.S. Kodogiannis ◽  
P. Chountas ◽  
A. Pavlou ◽  
I. Petrounias ◽  
H.S. Chowdrey ◽  
...  
Keyword(s):  
Clinical Diagnosis ◽  
Soft Computing ◽  
Electronic Nose ◽  
Computing Methodologies

RETRACTED: Wind turbine power coefficient estimation by soft computing methodologies: Comparative study

2014 ◽  
Vol 81 ◽  
pp. 520-526 ◽  
Author(s):  
Shahaboddin Shamshirband ◽  
Dalibor Petković ◽  
Hadi Saboohi ◽  
Nor Badrul Anuar ◽  
Irum Inayat ◽  
...  
Keyword(s):  
Comparative Study ◽  
Wind Turbine ◽  
Soft Computing ◽  
Power Coefficient ◽  
Computing Methodologies ◽  
Coefficient Estimation

Prediction of time-varying maximum scour depth around short abutments using soft computing methodologies - A comparative study

2015 ◽  
Vol 20 (5) ◽  
pp. 2070-2081 ◽  
Author(s):  
Khosrow Hosseini ◽  
Hojjat Karami ◽  
Hossein Hosseinjanzadeh ◽  
Abdollah Ardeshir
Keyword(s):  
Comparative Study ◽  
Soft Computing ◽  
Scour Depth ◽  
Time Varying ◽  
Computing Methodologies

An appraisal of wind speed distribution prediction by soft computing methodologies: A comparative study

2014 ◽  
Vol 84 ◽  
pp. 133-139 ◽  
Author(s):  
Dalibor Petković ◽  
Shahaboddin Shamshirband ◽  
Nor Badrul Anuar ◽  
Hadi Saboohi ◽  
Ainuddin Wahid Abdul Wahab ◽  
...  
Keyword(s):  
Wind Speed ◽  
Comparative Study ◽  
Soft Computing ◽  
Speed Distribution ◽  
Computing Methodologies ◽  
Wind Speed Distribution

Soft-Computing Methodologies for Precipitation Estimation: A Case Study

2014 ◽  
pp. 1-6 ◽  
Author(s):  
Shahaboddin Shamshirband ◽  
Milan Gocic ◽  
Dalibor Petkovic ◽  
Hadi Saboohi ◽  
Tutut Herawan ◽  
...  
Keyword(s):  
Soft Computing ◽  
Computing Methodologies ◽  
Precipitation Estimation

A simulation model for visitors’ thermal comfort at urban public squares using non-probabilistic binary-linear classifier through soft-computing methodologies

Energy ◽  
2016 ◽  
Vol 101 ◽  
pp. 568-580 ◽  
Author(s):  
Shahab Kariminia ◽  
Shahaboddin Shamshirband ◽  
Roslan Hashim ◽  
Ahmadreza Saberi ◽  
Dalibor Petković ◽  
...  
Keyword(s):  
Thermal Comfort ◽  
Simulation Model ◽  
Soft Computing ◽  
Computing Methodologies ◽  
Linear Classifier

Predicting optimum parameters of a protective spur dike using soft computing methodologies – A comparative study

2014 ◽  
Vol 97 ◽  
pp. 168-176 ◽  
Author(s):  
Hossein Basser ◽  
Hojat Karami ◽  
Shahaboddin Shamshirband ◽  
Afshin Jahangirzadeh ◽  
Shatirah Akib ◽  
...  
Keyword(s):  
Comparative Study ◽  
Soft Computing ◽  
Spur Dike ◽  
Computing Methodologies ◽  
Optimum Parameters

scholarly journals Application of soft computing methodologies to predict the 28-day compressive strength of shotcrete: a comparative study of individual and hybrid models

2021 ◽  
Vol 36 (5) ◽  
pp. 33-48
Author(s):  
Mahtab Torkan ◽  
Hamid Kalhori ◽  
Mohammad Hossein Jalalian
Keyword(s):  
Compressive Strength ◽  
Soft Computing ◽  
Clustering Algorithm ◽  
Accurate Method ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Construction Technique ◽  
Computing Methodologies ◽  
Model Tree ◽  
M5p Model Tree

Shotcreting is a popular construction technique with wide-ranging applications in mining and civil engineering. Compressive strength is a primary mechanical property of shotcrete with particular importance for project safety, which highly depends on its mix design. But in practice, there is no reliable and accurate method to predict this strength. In this study, existing experimental data related to shotcretes with 59 different mix designs are used to develop a series of soft computing methodologies, including individual artificial neural network, support vector regression, and M5P model tree and their hybrids with the fuzzy c-means clustering algorithm so as to predict the 28-day compressive strength of shotcrete. Analysis of the results shows the superiority of the hybrid model over the individual models in predicting the compressive strength of shotcrete. Overall, data clustering prior to use of machine learning techniques leads to certain improvement in their performance and reliability and generalizability of their results. In particular, the M5P model tree exhibits excellent capability in anticipating the compressive strength of shotcrete.

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