activity
20192022
most citedPredicting and Mapping of Soil Organic Carbon Using Machine Learning Algorithms in Northern Iran

268 citations · 1.7k across the 52 of their papers we have counts for

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Showing 2019Show all

12 papers · 1 filter

physics.flu-dyn2019★ 1 cited

Simulation of Turbulent Flow around a Generic High-Speed Train using Hybrid Models of RANS Numerical Method with Machine Learning

Alireza Hajipour, Arash Mirabdolah Lavasani, Mohammad Eftekhari Yazdi +3

In the present paper, an aerodynamic investigation of a high-speed train is performed. In the first section of this article, a generic high-speed train against a turbulent flow is…

physics.flu-dyn2019

Shear Stress Distribution Prediction in Symmetric Compound Channels Using Data Mining and Machine Learning Models

Zohreh Sheikh Khozani, Khabat Khosravi, Mohammadamin Torabi +3

Shear stress distribution prediction in open channels is of utmost importance in hydraulic structural engineering as it directly affects the design of stable channels. In this stud…

physics.chem-ph2019★ 25 cited

Applying ANN, ANFIS, and LSSVM Models for Estimation of Acid Solvent Solubility in Supercritical CO

Amin Bemani, Alireza Baghban, Shahaboddin Shamshirband +3

In the present work, a novel and the robust computational investigation is carried out to estimate solubility of different acids in supercritical carbon dioxide. Four different alg…

eess.IV2019

Hybrid Machine Learning Model of Extreme Learning Machine Radial basis function for Breast Cancer Detection and Diagnosis; a Multilayer Fuzzy Expert System

Sanaz Mojrian, Gergo Pinter, Javad Hassannataj Joloudari +4

Mammography is often used as the most common laboratory method for the detection of breast cancer, yet associated with the high cost and many side effects. Machine learning predict…

cs.LG2019★ 15 cited

Developing an ANFIS PSO Model to Estimate Mercury Emission in Combustion Flue Gases

Shahaboddin Shamshirband, Masoud Hadipoor, Alireza Baghban +3

Accurate prediction of mercury content emitted from fossil fueled power stations is of utmost important for environmental pollution assessment and hazard mitigation. In this paper,…

cs.CV2019★ 52 cited

Deep Learning for Detecting Building Defects Using Convolutional Neural Networks

Husein Perez, Joseph H. M. Tah, Amir Mosavi

Clients are increasingly looking for fast and effective means to quickly and frequently survey and communicate the condition of their buildings so that essential repairs and mainte…