Shahaboddin Shamshirband

Ton Duc Thang University

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Co-Authors

Dalibor Petković

University of Nis

141

Amir Mosavi

Obuda University

Kasra Mohammadi

University of Utah

Nor Badrul Anuar

University of Malaya

Narjes Nabipour

Duy Tan University

Kwok‐wing Chau

Hong Kong Polytechnic University

Miss Laiha Mat Kiah

University of Malaya

Shervin Motamedi

Coventry University

Shatirah Akib

Nottingham Trent University

Roslan Hashim

University of Malaya

Cooperative Institutions

University of Malaya

146

University of Tehran

Information Technology University

University of Tabriz

Islamic Azad University, Tehran

COMSATS University Islamabad

Amirkabir University of Technology

University of Technology Malaysia

Islamic Azad University, Science and Research Branch

Duy Tan University

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

arXiv (Cornell University) (2020)

A. Hajipour Arash Mirabdolah Lavasani Mohammad Eftekhari Yazdi Amir Mosavi Shahaboddin Shamshirband

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 simulated, numerically. The Reynolds-Averaged Navier-Stokes (RANS) equations combined with the turbulence model are applied to solve incompressible turbulent flow around a high-speed train. Flow structure, velocity and pressure contours and streamlines at some typical wind directions are the most important results of this simulation. The maximum and minimum values are specified and discussed. Also, the pressure coefficient for some critical points on the train surface is evaluated. In the following, the wind direction influence the aerodynamic key parameters as drag, lift, and side forces at the mentioned wind directions are analyzed and compared. Moreover, the effects of velocity changes (50, 60, 70, 80 and 90 m/s) are estimated and compared on the above flow and aerodynamic parameters. In the second section of the paper, various data-driven methods including Gene Expression Programming (GEP), Gaussian Process Regression (GPR), and random forest (RF), are applied for predicting output parameters. So, drag, lift, and side forces and also minimum and a maximum of pressure coefficients for mentioned wind directions and velocity are predicted and compared using statistical parameters. Obtained results indicated that RF in all coefficients of wind direction and most coefficients of free stream velocity provided the most accurate predictions. As a conclusion, RF may be recommended for the prediction of aerodynamic coefficients.

Lift (data mining)

Aerodynamic force

Pressure coefficient

10.48550/arxiv.2001.01569

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Citations (1)

Evaluation of electrical efficiency of photovoltaic thermal solar collector.

arXiv (Cornell University) (2020)

Mohammad Hossein Ahmadi Alireza Baghban Milad Sadeghzadeh Mohammad Zamen Amir Mosavi

Solar energy is a renewable resource of energy that is broadly utilized and has the least emissions among renewable energies. In this study, machine learning methods of artificial neural networks (ANNs), least squares support vector machines (LSSVM), and neuro-fuzzy are used for advancing prediction models for the thermal performance of a photovoltaic-thermal solar collector (PV/T). In the proposed models, the inlet temperature, flow rate, heat, solar radiation, and the sun heat have been considered as the inputs variables. Data set has been extracted through experimental measurements from a novel solar collector system. Different analyses are performed to examine the credibility of the introduced approaches and evaluate their performance. The proposed LSSVM model outperformed ANFIS and ANNs models. LSSVM model is reported suitable when the laboratory measurements are costly and time-consuming, or achieving such values requires sophisticated interpretations.

Source

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Citations (0)

RETRACTED: Application of adaptive neuro-fuzzy technique to determine the most relevant variables for daily soil temperature prediction at different depths

CATENA (2016)

Kasra Mohammadi Shahaboddin Shamshirband Amirrudin Kamsin Raja Jamilah Raja Yusof

Dew point

Scientific Publishing

10.1016/j.catena.2016.06.008

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Citations (2)

Groundwater Quality Assessment for Sustainable Drinking and Irrigation

Sustainability (2019)

Esmaeil Asadi Mohammad Isazadeh Saeed Samadianfard Mohammad Firuz Ramli Amir Mosavi

Identification and management of the groundwater quality are of utmost importance for maintaining freshwater resources in arid and semi-arid areas, which is essential for sustainable development. Based on the quality of the groundwater in various areas, local policymakers and water resource managers can allocate the usage of resources for either drinking or agricultural purposes. This research aims to identify suitable areas of water pumping for drinking and agricultural harvest in the Tabriz aquifer, located in East Azerbaijan province, northwest Iran. A groundwater compatibility study was conducted by analyzing Electrical conductivity (EC), total dissolved solids (TDS), Chloride (Cl), Calcium (Ca), Magnesium (Mg), Sodium (Na), Potassium (K), Sulfate (SO4), Total hardness (TH), Bicarbonate (HCO3), pH, carbonate (CO3), the and Sodium Adsorption Ratio (SAR) obtained from 39 wells in the time period from 2003 to 2014. The Water Quality Index (WQI) and irrigation water quality (IWQ) index are respectively utilized due to their high importance in identifying the quality of water resources for irrigation and drinking purposes. The WQI index zoning for drinking classified water as excellent, good, or poor. The study concludes that most drinking water harvested for urban and rural areas is ‘excellent water’ or ‘good water’. The IWQ index average for the study area is reported to be in the range of 25.9 to 34.55. The results further revealed that about 37 percent (296 km2) of groundwater has high compatibility, and 63 percent of the study area (495 km2) has average compatibility for agricultural purposes. The trend of IWQ and WQI indexes demonstrates that groundwater quality has been declining over time.

Sodium adsorption ratio

Total dissolved solids

10.3390/su12010177

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Citations (176)

Retraction Note: An architecture of agent-based multi-layer interactive e-learning and e-testing platform

Quality & Quantity (2019)

Muhammad Arif Manzoor Illahi Ahmad Karim Shahaboddin Shamshirband Khubaib Amjad Alam

10.1007/s11135-019-00948-9

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An Automated System for Skeletal Maturity Assessment by Extreme Learning Machines

PLoS ONE (2015)

Marjan Mansourvar Shahaboddin Shamshirband Ram Gopal Raj Roshan Gunalan Iman Mazinani

Assessing skeletal age is a subjective and tedious examination process. Hence, automated assessment methods have been developed to replace manual evaluation in medical applications. In this study, a new fully automated method based on content-based image retrieval and using extreme learning machines (ELM) is designed and adapted to assess skeletal maturity. The main novelty of this approach is it overcomes the segmentation problem as suffered by existing systems. The estimation results of ELM models are compared with those of genetic programming (GP) and artificial neural networks (ANNs) models. The experimental results signify improvement in assessment accuracy over GP and ANN, while generalization capability is possible with the ELM approach. Moreover, the results are indicated that the ELM model developed can be used confidently in further work on formulating novel models of skeletal age assessment strategies. According to the experimental results, the new presented method has the capacity to learn many hundreds of times faster than traditional learning methods and it has sufficient overall performance in many aspects. It has conclusively been found that applying ELM is particularly promising as an alternative method for evaluating skeletal age.

Extreme Learning Machine

10.1371/journal.pone.0138493

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Citations (21)

A Hybrid clustering and classification technique for forecasting short‐term energy consumption

Environmental Progress & Sustainable Energy (2018)

Mehrnoosh Torabi Sattar Hashemi Mahmoud Reza Saybani Shahaboddin Shamshirband Amir Mosavi

This paper presents a hybrid approach to predict the electric energy usage of weather‐sensitive loads. The presented method utilizes the clustering paradigm along with ANN and SVM approaches for accurate short‐term prediction of electric energy usage, using weather data. Since the methodology being invoked in this research is based on CRISP data mining, data preparation has received a great deal of attention in this research. Once data pre‐processing was done, the underlying pattern of electric energy consumption was extracted by the means of machine learning methods to precisely forecast short‐term energy consumption. The proposed approach (CBA‐ANN‐SVM) was applied to real load data and resulting higher accuracy comparing to the existing models. © 2018 American Institute of Chemical Engineers Environ Prog, 38: 66–76, 2019

Electric energy consumption

10.1002/ep.12934

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Citations (144)

Estimation of Flexible Pavement Structural Capacity Using Machine Learning Techniques

Nader Karballaeezadeh Hosein Ghasemzadeh Tehrani Danial Mohammadzadeh S.Shahaboddin Shamshirband

The most common index for representing structural condition of the pavement is the structural number. The current procedure for determining structural numbers involves utilizing falling weight deflectometer and ground-penetrating radar tests, recording pavement surface deflections, and analyzing recorded deflections by back-calculation manners. This procedure has two drawbacks: 1. falling weight deflectometer and ground-penetrating radar are expensive tests, 2. back-calculation ways has some inherent shortcomings compared to exact methods as they adopt a trial and error approach. In this study, three machine learning methods entitled Gaussian process regression, m5p model tree, and random forest used for the prediction of structural numbers in flexible pavements. Dataset of this paper is related to 759 flexible pavement sections at Semnan and Khuzestan provinces in Iran and includes “structural number” as output and “surface deflections and surface temperature” as inputs. The accuracy of results was examined based on three criteria of R, MAE, and RMSE. Among the methods employed in this paper, random forest is the most accurate as it yields the best values for above criteria (R=0.841, MAE=0.592, and RMSE=0.760). The proposed method does not require to use ground penetrating radar test, which in turn reduce costs and work difficulty. Using machine learning methods instead of back-calculation improves the calculation process quality and accuracy.

Falling weight deflectometer

Ground-Penetrating Radar

Pavement Engineering

Root mean square

10.20944/preprints202002.0411.v1

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Citations (4)

Intelligent Road Inspection with Advanced Machine Learning; Hybrid Prediction Models for Smart Mobility and Transportation Maintenance Systems

Energies (2020)

Nader Karballaeezadeh Farah Zaremotekhases Shahaboddin Shamshirband Amir Mosavi Narjes Nabipour

Prediction models in mobility and transportation maintenance systems have been dramatically improved by using machine learning methods. This paper proposes novel machine learning models for an intelligent road inspection. The traditional road inspection systems based on the pavement condition index (PCI) are often associated with the critical safety, energy and cost issues. Alternatively, the proposed models utilize surface deflection data from falling weight deflectometer (FWD) tests to predict the PCI. Machine learning methods are the single multi-layer perceptron (MLP) and radial basis function (RBF) neural networks as well as their hybrids, i.e., Levenberg–Marquardt (MLP-LM), scaled conjugate gradient (MLP-SCG), imperialist competitive (RBF-ICA), and genetic algorithms (RBF-GA). Furthermore, the committee machine intelligent systems (CMIS) method was adopted to combine the results and improve the accuracy of the modeling. The results of the analysis have been verified through using four criteria of average percent relative error (APRE), average absolute percent relative error (AAPRE), root mean square error (RMSE) and standard error (SE). The CMIS model outperforms other models with the promising results of APRE = 2.3303, AAPRE = 11.6768, RMSE = 12.0056 and SD = 0.0210.

Falling weight deflectometer

Multilayer perceptron

Extreme Learning Machine

Perceptron

10.3390/en13071718

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Citations (52)

An Overview of Audio Event Detection Methods from Feature Extraction to Classification

Applied Artificial Intelligence (2017)

Elham Babaee Nor Badrul Anuar Ainuddin Wahid Abdul Wahab Shahaboddin Shamshirband Anthony T. Chronopoulos

Audio streams, such as news broadcasting, meeting rooms, and special video comprise sound from an extensive variety of sources. The detection of audio events including speech, coughing, gunshots, etc. leads to intelligent audio event detection (AED). With substantial attention geared to AED for various types of applications, such as security, speech recognition, speaker recognition, home care, and health monitoring, scientists are now more motivated to perform extensive research on AED. The deployment of AED is actually a more complicated task when going beyond exclusively highlighting audio events in terms of feature extraction and classification in order to select the best features with high detection accuracy. To date, a wide range of different detection systems based on intelligent techniques have been utilized to create machine learning-based audio event detection schemes. Nevertheless, the preview study does not encompass any state-of-the-art reviews of the proficiency and significances of such methods for resolving audio event detection matters. The major contribution of this work entails reviewing and categorizing existing AED schemes into preprocessing, feature extraction, and classification methods. The importance of the algorithms and methodologies and their proficiency and restriction are additionally analyzed in this study. This research is expanded by critically comparing audio detection methods and algorithms according to accuracy and false alarms using different types of datasets.

Audio mining

Audio analyzer

Feature (linguistics)

10.1080/08839514.2018.1430469

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Citations (39)