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Hybrid Machine Learning Techniques Applied in Real Engineering Applications

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A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Computational and Applied Mathematics".

Deadline for manuscript submissions: closed (31 January 2020) | Viewed by 39926

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Dr. Mohammad Shojafar

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Department of Computer Science, Ryerson University, 350 Victoria Street, Toronto, ON, M5B 2K3, Canada
Interests: network security; cloud/fog data centers; mathematical optimization; artificial intelligence; machine learning algorithms; wireless sensor network
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Dear Colleagues,

With the increasing rate of information stored in databases, the development of efficient and effective tools for extracting knowledge from these data has become a vital task for researchers in the areas of databases, statistics, machine learning, and data visualization. Furthermore, prediction methods combining clustering and classification techniques have the potential for creating more accurate results than the individual methods, particularly for large datasets. One of the challenges that every machine learning algorithm is faced with is scalability and validity to large datasets. In the literature, research devoted to applying hybrid data mining methods, hybrid machine learning, and corporation deep learning technologies, which combine clustering and classification techniques, can improve the key performance metric of different real engineering applications. In the context of these trends, the forthcoming Special Issue will address significant issues in the field of applied machine learning, and mathematical techniques, such as nonlinear analysis, variational analysis, convex analysis, optimization techniques, and soft computing strategies, to deal with the large/big datasets in real engineering problems.

Dr. Mohammad Shojafar
Guest Editor

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Keywords

Hybrid machine learning
Artificial intelligence
Convex analysis
(Non)linear optimization
Engineering application
Big data

Published Papers (5 papers)

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Research

22 pages, 4403 KiB

Open AccessArticle

Comparative Analysis of Machine Learning Models for Prediction of Remaining Service Life of Flexible Pavement

by Narjes Nabipour, Nader Karballaeezadeh, Adrienn Dineva, Amir Mosavi, Danial Mohammadzadeh S. and Shahaboddin Shamshirband

Mathematics 2019, 7(12), 1198; https://doi.org/10.3390/math7121198 - 06 Dec 2019

Cited by 38 | Viewed by 4424

Abstract

Prediction of the remaining service life (RSL) of pavement is a challenging task for road maintenance and transportation engineering. The prediction of the RSL estimates the time that a major repair or reconstruction becomes essential. The conventional approach to predict RSL involves using non-destructive tests. These tests, in addition to being costly, interfere with traffic flow and compromise operational safety. In this paper, surface distresses of pavement are used to estimate the RSL to address the aforementioned challenges. To implement the proposed theory, 105 flexible pavement segments are considered. For each pavement segment, the type, severity, and extent of surface damage and the pavement condition index (PCI) were determined. The pavement RSL was then estimated using non-destructive tests include falling weight deflectometer (FWD) and ground-penetrating radar (GPR). After completing the dataset, the modeling was conducted to predict RSL using three techniques include support vector regression (SVR), support vector regression optimized by the fruit fly optimization algorithm (SVR-FOA), and gene expression programming (GEP). All three techniques estimated the RSL of the pavement by selecting the PCI as input. The correlation coefficient (CC), Nash–Sutcliffe efficiency (NSE), scattered index (SI), and Willmott’s index of agreement (WI) criteria were used to examine the performance of the three techniques adopted in this study. In the end, it was found that GEP with values of 0.874, 0.598, 0.601, and 0.807 for CC, SI, NSE, and WI criteria, respectively, had the highest accuracy in predicting the RSL of pavement. Full article

(This article belongs to the Special Issue Hybrid Machine Learning Techniques Applied in Real Engineering Applications)

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12 pages, 1682 KiB

Open AccessArticle

Moisture Estimation in Cabinet Dryers with Thin-Layer Relationships Using a Genetic Algorithm and Neural Network

by Behzad Maleki, Mahyar Ghazvini, Mohammad Hossein Ahmadi, Heydar Maddah and Shahaboddin Shamshirband

Mathematics 2019, 7(11), 1042; https://doi.org/10.3390/math7111042 - 03 Nov 2019

Cited by 7 | Viewed by 2362

Abstract

Nowadays, industrial dryers are used instead of traditional methods for drying. When designing dryers suitable for controlling the process of drying and reaching a high-quality product, it is necessary to predict the gradual moisture loss during drying. Few studies have been conducted to compare thin-layer models and artificial neural network models on the kinetics of pistachio drying in a cabinet dryer. For this purpose, ten mathematical-experimental models with a neural network model based on the kinetic data of pistachio drying were studied. The data obtained was from a cabinet dryer evaluated at four temperatures of inlet air and different air velocities. The pistachio seeds were placed in a thin layer on an aluminum sheet on a drying tray and weighed by a scale attached to the computer at different times. In the neural network, data was divided into three parts: Educational (60%), validation (20%) and testing (20%). Finally, the best mathematical-experimental model using a genetic algorithm and the best neural network structure for predicting instantaneous moisture were selected based on the least squared error and the highest correlation coefficient. Full article

(This article belongs to the Special Issue Hybrid Machine Learning Techniques Applied in Real Engineering Applications)

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16 pages, 2867 KiB

Open AccessEditor’s ChoiceArticle

Developing an ANFIS-PSO Model to Predict Mercury Emissions in Combustion Flue Gases

by Shahaboddin Shamshirband, Masoud Hadipoor, Alireza Baghban, Amir Mosavi, Jozsef Bukor and Annamária R. Várkonyi-Kóczy

Mathematics 2019, 7(10), 965; https://doi.org/10.3390/math7100965 - 14 Oct 2019

Cited by 45 | Viewed by 4472

Abstract

Accurate prediction of mercury content emitted from fossil-fueled power stations is of the utmost importance for environmental pollution assessment and hazard mitigation. In this paper, mercury content in the output gas of power stations’ boilers was predicted using an adaptive neuro-fuzzy inference system (ANFIS) method integrated with particle swarm optimization (PSO). The input parameters of the model included coal characteristics and the operational parameters of the boilers. The dataset was collected from 82 sample points in power plants and employed to educate and examine the proposed model. To evaluate the performance of the proposed hybrid model of the ANFIS-PSO, the statistical meter of MARE% was implemented, which resulted in 0.003266 and 0.013272 for training and testing, respectively. Furthermore, relative errors between the acquired data and predicted values were between −0.25% and 0.1%, which confirm the accuracy of the model to deal non-linearity and represent the dependency of flue gas mercury content into the specifications of coal and the boiler type. Full article

(This article belongs to the Special Issue Hybrid Machine Learning Techniques Applied in Real Engineering Applications)

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20 pages, 2521 KiB

Open AccessArticle

A Comparative Study of Bitcoin Price Prediction Using Deep Learning

by Suhwan Ji, Jongmin Kim and Hyeonseung Im

Mathematics 2019, 7(10), 898; https://doi.org/10.3390/math7100898 - 25 Sep 2019

Cited by 113 | Viewed by 22165

Abstract

Bitcoin has recently received a lot of attention from the media and the public due to its recent price surge and crash. Correspondingly, many researchers have investigated various factors that affect the Bitcoin price and the patterns behind its fluctuations, in particular, using various machine learning methods. In this paper, we study and compare various state-of-the-art deep learning methods such as a deep neural network (DNN), a long short-term memory (LSTM) model, a convolutional neural network, a deep residual network, and their combinations for Bitcoin price prediction. Experimental results showed that although LSTM-based prediction models slightly outperformed the other prediction models for Bitcoin price prediction (regression), DNN-based models performed the best for price ups and downs prediction (classification). In addition, a simple profitability analysis showed that classification models were more effective than regression models for algorithmic trading. Overall, the performances of the proposed deep learning-based prediction models were comparable. Full article

(This article belongs to the Special Issue Hybrid Machine Learning Techniques Applied in Real Engineering Applications)

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17 pages, 4335 KiB

Open AccessArticle

Modeling and Efficiency Optimization of Steam Boilers by Employing Neural Networks and Response-Surface Method (RSM)

by Heydar Maddah, Milad Sadeghzadeh, Mohammad Hossein Ahmadi, Ravinder Kumar and Shahaboddin Shamshirband

Mathematics 2019, 7(7), 629; https://doi.org/10.3390/math7070629 - 15 Jul 2019

Cited by 18 | Viewed by 5707

Abstract

Boiler efficiency is called to some extent of total thermal energy which can be recovered from the fuel. Boiler efficiency losses are due to four major factors: Dry gas flux, the latent heat of steam in the flue gas, the combustion loss or the loss of unburned fuel, and radiation and convection losses. In this research, the thermal behavior of boilers in gas refinery facilities is studied and their efficiency and their losses are calculated. The main part of this research is comprised of analyzing the effect of various parameters on efficiency such as excess air, fuel moisture, air humidity, fuel and air temperature, the temperature of combustion gases, and thermal value of the fuel. Based on the obtained results, it is possible to analyze and make recommendations for optimizing boilers in the gas refinery complex using response-surface method (RSM). Full article

(This article belongs to the Special Issue Hybrid Machine Learning Techniques Applied in Real Engineering Applications)

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