In this study, an artificial neural network (ANN) has been developed to predict the boundary layer flow of a single‐walled carbon nanotubes nanofluid toward three different nonlinear thin isothermal ...needles of paraboloid, cone, and cylinder shapes with convective boundary conditions. Different effects of particle diameter and solid–fluid interface coating have been taken into account in the thermal conductivity model of nanofluid in which ethylene glycol has been used as the base fluid. Single and dual phase approach is used to establish the management model under the phenomenon of zero heat and mass flux. A dataset has been developed for different scenarios of the fluid model by changing the relevant parameters with the Runge–Kutta based shooting technique. Two different ANN models have been developed to predict Nusselt number and skin friction coefficient (SFC) values. The values obtained from ANN models have been compared with the numerical data, which are the target values. In addition, mean square error and R values have also been examined in order to analyze the prediction performance of ANN models more comprehensively. The calculated R values for Nusselt number and SFC were obtained as 0.9999. The results obtained showed that ANN can predict Nusselt number and SFC values with high accuracy.
An artificial neural network (ANN) has been developed to predict the boundary layer flow of a single‐walled carbon nanotubes nanofluid toward three different nonlinear thin isothermal needles of paraboloid, cone, and cylinder shapes with convective boundary conditions. The results obtained showed that ANN can predict Nusselt number and skin friction coefficient values with high accuracy.
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BFBNIB, FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SAZU, SBCE, SBMB, UL, UM, UPUK
Higher heating value (HHV) is an important parameter for design and operation of biomass-fueled energy systems. Experimental approach is always time-consuming and expensive for determinating this ...property compared with mathematical models. In this paper, three machine learning approaches, including artificial neural network (ANN), support vector machine (SVM) and random forest (RF), are employed for accurately estimating biomass HHV from ultimate or proximate analysis. The linear and nonlinear empirical correlations are also carried out for comparison. The results show machine learning approaches give better predictions (R2 > 0.90) compared with those of empirical correlations (R2 < 0.70), especially for the extreme values. The RF model shows the best performances for both the ultimate and proximate analysis, with the determination coefficient R2>0.94. The SVM and ANN approaches show similar performances with R2∼ 0.90. Ultimate-based models show better performances compared with those of the proximate-based models even with much less samples. Relative importance analysis shows for the proximate analysis, the ash, volatile matter and fixed carbon fractions show the maximum, medium and minimum effects, respectively. For the ultimate analysis, carbon and hydrogen fractions hold the first two significant places with carbon fraction having the most significant influence, while the oxygen and nitrogen fractions have limited effects.
•Estimating biomass HHV from biomass property via machine learning approaches.•Machine learning models give better predictions compared with empirical correlations.•Random forest model shows the best performance with R2 > 0.94.•Artificial neural network and support vector machine models show similar predictions.•Relative importance of each input on biomass HHV is explored.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Machine learning is shaping up our lives in many ways. In analytical sciences, machine learning provides an unprecedented opportunity to extract information from complex or big datasets in ...chromatography, mass spectrometry, NMR, and spectroscopy, among others. This is especially the case in Raman and surface-enhanced Raman scattering (SERS) techniques where vibrational spectra of complex chemical mixtures are acquired as large datasets for the analysis or imaging of chemical systems. The classical linear methods of processing the information no longer suffice and thus machine learning methods for extracting the chemical information from Raman and SERS experiments have been implemented recently. In this review, we will provide a brief overview of the most common machine learning techniques employed in Raman, a guideline for new users to implement machine learning in their data analysis process, and an overview of modern applications of machine learning in Raman and SERS.
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In recent years, the smart electronic nose (E-nose) has witnessed the rapid applications in diverse fields. Apart from sensor arrays, recognition algorithm plays a determinant role on the performance ...of E-nose. Focusing on the signal processing of E-nose, the response signal characteristic of a sensor is introduced first in this paper. Based on the differences between the processing of features, the algorithms are subsequently divided into traditional and artificial neural networks (ANN)-based, and their respective properties are specifically analyzed through the application in reality. The evaluation metrics for these algorithms are then summarized. Finally, the challenges and prospects of the algorithm are concluded. This paper aims to help researchers in diverse fields employ and explore the appropriate gas recognition algorithms for the emerging applications of E-nose.
•Waste-derived biogas is used for powering a diesel engine in a dual-fuel model.•Experimental data used for BRT and ANN-based prognostic model development.•Statistical indices i.e., RBRT > RANN, ...R2BRT > R2ANN, and KGEBRT > KGEANN show the superiority of BRT over ANN.•Low modeling errors (RMSE less than 0.1154), Theil’s statistics (U2 less than 0.081), and Taylor’s diagram demonstrated superior prediction ability of BRT.
The current study looks at using waste-derived biodiesel as pilot fuel and waste-derived biogas as a gaseous fuel to power a diesel engine in dual-fuel mode. A new ensemble approach called Boosted Regression Trees (BRT) was utilized to model the performance and emissions of a variable compression ratio diesel engine. The model's input parameters were selected to be load, fuel injection time, and compression ratio. The BRT-based model was created based on experimental data to predict brake thermal efficiency (BTE), Biogas Fuel Ratio (BFR), NOx, CO, and HC. As indicated by correlation values ranging from 0.9947 to 0.9997, low Theil's values (0.081), and high Kling-Gupta efficiency (>98 percent), the suggested BRT model predicted performance and emission parameters with reasonable accuracy. The BRT model was compared to an ANN model under similar operating conditions. The BRT-based model outperformed the ANN-based model on all statistical metrics. The efficacy of BRT models was demonstrated further by employing a novelmethod of comparing prediction models using Taylor's diagram.
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In this work, firstly we propose an artificial neural network (ANN) based channel modeling and simulation framework to playback a measurement channel to overcome the shortcomings of traditional ...geometry based stochastic modelling (GBSM) and simulation approach which is unable to predict a time or position-varying channel to match with real environment. Secondly, we implement the framework based on channel measurements performed at 28 GHz in a large waiting hall at Qingdao high-speed railway station, China. Thirdly, we validate the proposed framework by comparisons of the large scale channel parameters (LSCPs) and small scale channel parameters (SSCPs) extracted from the measured, ANN and GBSM simulation channels. The results show that the ANN-based framework can playback the measured channels accurately, while GBSM-based simulated channels have large deviations. This work offers a solution to playback the measured channels accurately to be used in 5G and beyond radio system research and engineering applications, while it's also able to be applied in future channel predictions in case of large amount of measured data available.
Summary
In this study, the effect of the amount of data used in the design of artificial neural networks (ANNs) on the predictive accuracy of ANNs was investigated. Five different ANNs were designed ...using the experimentally measured specific heat data of the Al2O3/water nanofluid prepared at volumetric concentrations of 0.0125, 0.025, 0.05, 0.1 and 0.2 using the Al2O3 nanoparticle. The developed ANN is a multi‐layer perceptron, feedforward and backpropagation model. In each ANN with 15 neurons in the hidden layer, the volumetric concentration (φ) and temperature (T) values were nominated as input layer factors and the specific heat value was estimated as the output value. With the aim of survey the effect of the amount of data on the predicted results of the ANN, a different amount of datasets were used in each developed ANN. In this context, in total 260 data were used in the Model 1 ANN. Subsequently, the total amount of data was reduced by 20% in each developed neural network and 55 data were used in the ANN named Model#5. The results obtained show that ANNs are highly talented of predicting the specific heat values of Al2O3/water nanofluid. However, in the comparisons, it was evaluated that the amount of data used had a share on the prediction performance of the ANN and that the decrease in the amount of data with the prediction performance of the ANN decreased.
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Considering the nonlinear relationship between variables and fatigue life and the computational burden, a machine learning method integrating the artificial neural network (ANN) and partial least ...squares (PLS) algorithm was proposed as a framework to identify the genetic features through optimizing fatigue life prediction. Twenty‐seven specimens of 316LN stainless steel under uniaxial and multiaxial loadings were used as examples. As results, early fatigue data were proved to be informative for fatigue life prediction. Moreover, five genetic features were identified out of them, and a predicting model was developed. The predicted fatigue life of these samples using only these five genetic features were all located within the 1.5‐factor band. This framework can be easily extended to identify genetic features and to predict fatigue life of other materials under different loadings. Therefore, it provides an efficient option in this field to greatly reduce experimental time and cost.
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BFBNIB, DOBA, FZAB, GIS, IJS, IZUM, KILJ, NLZOH, NUK, OILJ, PILJ, PNG, SAZU, SBCE, SBMB, UILJ, UKNU, UL, UM, UPUK
•In this study, unlike CNN architectures, COVID-19 was determined from chest X-ray images with a smaller number of layers.•More COVID-19, pneumonia, and no-findings images were used than in previous ...studies. This increases the reliability of the system more.•As is known, reducing the size of the image may cause some information in the image to be lost. Given these facts, good classification accuracy has been achieved with capsule networks, even the image size has been reduced to 128 × 128 pixels.
Coronavirus is an epidemic that spreads very quickly. For this reason, it has very devastating effects in many areas worldwide. It is vital to detect COVID-19 diseases as quickly as possible to restrain the spread of the disease. The similarity of COVID-19 disease with other lung infections makes the diagnosis difficult. In addition, the high spreading rate of COVID-19 increased the need for a fast system for the diagnosis of cases. For this purpose, interest in various computer-aided (such as CNN, DNN, etc.) deep learning models has been increased. In these models, mostly radiology images are applied to determine the positive cases. Recent studies show that, radiological images contain important information in the detection of coronavirus. In this study, a novel artificial neural network, Convolutional CapsNet for the detection of COVID-19 disease is proposed by using chest X-ray images with capsule networks. The proposed approach is designed to provide fast and accurate diagnostics for COVID-19 diseases with binary classification (COVID-19, and No-Findings), and multi-class classification (COVID-19, and No-Findings, and Pneumonia). The proposed method achieved an accuracy of 97.24%, and 84.22% for binary class, and multi-class, respectively. It is thought that the proposed method may help physicians to diagnose COVID-19 disease and increase the diagnostic performance. In addition, we believe that the proposed method may be an alternative method to diagnose COVID-19 by providing fast screening.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
The metaverse is a kind of imagined world with immersive digital spaces that increase, allowing a more interactive environment in educational settings. The metaverse is an expansion of the ...synchronous communication that embraces an effective number of users to share different experiences. The study aims to investigate the students' perceptions towards metaverse system for educational purposes in the Gulf area. The conceptual model comprises the adoption properties, namely trialability, observability, compatibility, and complexity, users' satisfaction, personal innovativeness, and Technology Acceptance Model (TAM) constructs. The novelty of the paper lies in its conceptual model that correlates both personal-based characteristics and technology-based features. In addition, the novel approach of hybrid analysis will be used in the current study to perform deep-learning-based analysis of structural equation modelling (SEM) and artificial neural network (ANN). Moreover, the importance-performance map analysis (IPMA) is used in the current study to evaluate the involved factors for their importance and performance. The study identified Perceived Usefulness (PU) to be an essential predictor of the factor of Users’ Intention to Use the Metaverse System (MS). The fact was discovered during ANN and IPMA analysis. Furthermore, this study is practically significant, as it helped the concerned authorities in educational sector in understanding the significance of each factor and allowed them to make efforts and plans according to the order of significance of factors. Another important implication of the study is methodological in nature. It validates that deep ANN architecture can offer deep insight into non-linear relationships shared by various factors of a theoretical model.
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