Neural Network Methodology Research Articles

Facial Skin Type Detection for Race Classification using Convolutional Neural Network and Haar Cascade Method

Every organism possesses a unique structural makeup, extending from molecules to organ systems. One such organ, the skin, is our body's largest and the most complex. Its diversity in color corresponds with various human races, although the facial skin type, an often-overlooked factor, also plays a significant role in race identification. In this research, a system was developed to recognize racial classifications from facial skin types by focusing on features in the facial T and U areas, using the Convolutional Neural Network (CNN) and Haar cascade methodologies. CNN was employed due to its ability to use the convolution process, moving a kernel across an image, multiplying it with the applied filter, and thereby generating new representative information. It is especially effective in image recognition and processing. The Haar cascade method, on the other hand, was used to outline the T and U areas on the face for the skin type detection system. The T area, known for oil detection, identifies skin types, while the U area identifies race types by forming facial patterns. This system, trained on 1670 race and 60 skin type datasets and optimized using the Adam optimizer, exhibited high accuracy levels. Upon testing with five new samples, it demonstrated an average accuracy of 98% in race detection and 97% in skin type detection.

Global attention-enabled texture enhancement network for MR image reconstruction.

Although recent convolutional neural network (CNN) methodologies have shown promising results in fast MR imaging, there is still a desire to explore how they can be used to learn the frequency characteristics of multicontrast images and reconstruct texture details. A global attention-enabled texture enhancement network (GATE-Net) with a frequency-dependent feature extraction module (FDFEM) and convolution-based global attention module (GAM) is proposed to address the highly under-sampling MR image reconstruction problem. First, FDFEM enables GATE-Net to effectively extract high-frequency features from shareable information of multicontrast images to improve the texture details of reconstructed images. Second, GAM with less computation complexity has the receptive field of the entire image, which can fully explore useful shareable information of multi-contrast images and suppress less beneficial shareable information. The ablation studies are conducted to evaluate the effectiveness of the proposed FDFEM and GAM. Experimental results under various acceleration rates and datasets consistently demonstrate the superiority of GATE-Net, in terms of peak signal-to-noise ratio, structural similarity and normalized mean square error. A global attention-enabled texture enhancement network is proposed. it can be applied to multicontrast MR image reconstruction tasks with different acceleration rates and datasets and achieves superior performance in comparison with state-of-the-art methods.

A novel artificial neural network methodology to produce high-resolution bioclimatic maps using Earth Observation data: A case study for Cyprus

The aim of this research is to propose a novel methodology that exploits Earth Observation (EO) data to accurately produce high-resolution bioclimatic maps at large spatiotemporal scales. This method directly links EO products (i.e., land surface temperature - LST and Normalized Difference Vegetation Index - NDVI) to air temperature (Tair) and such thermal indices as the Universal Thermal Climate Index (UTCI), and the Physiologically Equivalent Temperature (PET) to produce large-scale high-quality bioclimatic maps at a spatial resolution of 100 m. The proposed methodology is based on Artificial Neural Networks (ANNs), and the bioclimatic maps are developed with the use of Geographical Information Systems. High-resolution LST maps are produced from the spatial downscaling of EO images and the application of the methodology in the case of the island of Cyprus highlights the ability of EO parameters to estimate accurately Tair as well as the above mentioned thermal indices. The results are validated for different conditions and the overall Mean Absolute Error for each case ranges from 1.9 °C for Tair to 2.8 °C for PET and UTCI. The trained ANNs could be used in near real-time for estimating the spatial distribution of outdoor thermal conditions and for assessing the relationship between human health and the outdoor thermal environment. On the basis of the developed bioclimatic maps, high-risk areas were identified. Furthermore, the study examines the relationship between land cover and Tair, UTCI, and PET, and the results provide evidence of the suitability of the method to monitor the dynamics of the urban environment and the effectiveness of urban nature-based solutions. Studies on bioclimate analysis monitor thermal environment, raise awareness and enhance the capacity of national public health systems to respond to thermally-induced health risks.

Neural Network Analysis as a Base of the Future System of Water–Environmental Regulation

The article considers neural-network methods and technologies, which are relatively new even for many researchers and experts, as applied to water–environmental regulation. The efficiency of neural networks in this line of studies is due to their self-training, and the ensuing ability to reveal complex nonlinear relationships between the characteristics under control by data processing instruments, consisting of interrelated neurons. The methodology of artificial neural networks and the features of their functioning are described. Training and methodological examples are given to illustrate their potential use. A practical problem, considered as an example of ANN application, is the potential for improving the efficiency of identification of large enterprises polluting natural water among many water users in an industrial region. This is made with the use of data on the concentrations of some priority water-polluting metals at the hydrochemical gages in the Iset river near Ekaterinburg City. The neural-network analysis is shown to detect relationships between individual water quality characteristics at nearby gages. This allowed the conclusion that there exist close logistic economic relationships between water users, which help revealing water pollutants by the water footprint produced by plants working in the same branch. It is also shown that the use of ANN opens new ways for determining the contribution of industrial waste discharges to the level of water pollution by substances of dual genesis (natural and technogenic). The reliability of the conclusions is confirmed by the possibility to use the data on a given hydrochemical gage to satisfactorily predict water quality at a gage further downstream.

Enhancing the efficiency of polytetrafluoroethylene-modified silica hydrosols coated solar panels by using artificial neural network and response surface methodology

Abstract In this article, an attempt was made to improve the efficiency of coated solar panels by using artificial neural networks (ANNs) and response surface methodology (RSM). Using the spray coating technique, the glass surface of the photovoltaic solar panel was coated with silicon dioxide nanoparticles incorporated with polytetrafluoroethylene-modified silica sols. Multilayer perceptron with feed-forward back-propagation algorithm was used to develop ANN models for improving the efficiency of the coated solar panels. Out of the 200 sets of data collected, 75% were used for training and 25% were used for testing. On evaluating the models using performance indicators, a four-input technological parameter model (silicon dioxide nanoparticle quantity, coating thickness, surface temperature and solar insolation) with eight neurons in a single hidden layer combination was observed to be the best. The prediction accuracy indicator values of the ANN model were 0.9612 for the coefficient of determination, 0.1971 for the mean absolute percentage error, 0.2317 for the relative root mean square error and 0.00741 for the mean bias error. Using a central composite design model, empirical relationships were developed between input and output responses. The significance of the developed model was ascertained by using analysis of variance, up to a 95% confidence level. For optimization, the RSM was used, and a high efficiency of 17.1% was predicted for the coated solar panel with optimized factors; it was validated to a very high level of predictability. Using interaction and perturbation plots, a ranking of the parameters was done.

PAHs remediation from hazardous waste landfill leachate using fenton, photo – fenton and electro - fenton oxidation processes – performance evaluation under optimized conditions using RSM and ANN

Polycyclic aromatic hydrocharbons (PAHs) are a class of highly toxic pollutants that are highly detrimental to the ecosystem. Landfill leechate emanated from municipal solid waste are reported to constitute significant PAHs. In the present investigation, three Fenton proceses, namely conventional Fenton, photo-fenton and electro-fenton methods have been employed to treat landfill leehcate for removing PAHs from a waste dumpig yard. Response surface methodology (RSM) and artificial neural network (ANN) methodologies were adopted to optimize and validate the conditions for optimum oxidative removal of COD and PAHs. The statistical analysis results showed that all independent variables chosen in the study are reported to have significant influence of the removal effects with P-values <0.05. Sensitivity analysis by the developed ANN model showed that the pH had the highest significance of 1.89 in PAH removal when compared to the other parameters. However for COD removal, H2O2 had the highest relative importance of 1.15, followed by Fe2+ and pH. Under optimal treatment conditions, the photo-fenton and electro-fenton processes showed better removal of COD and PAH compared to the Fenton process. The photo-fenton and electro-fenton treatment processes removed 85.32% and 74.64% of COD and 93.25% and 81.65% of PAHs, respectively. Also the investigations revelaed the presence of 16 distinct PAH compunds and the removal percentage of each of these PAHs are also reported. The PAH treatment research studies are generally limited to the assay of removal of PAH and COD levels. In the present investigation, in addition to the treatment of landfill leachate, particle size distribution analysis and elemental characterization of the resultant iron sludge by FESEM and EDX are reported. It was revealed that elemental oxygen is present in highest percentage, followed by iron, sulphur, sodium, chlorine, carbon and potassium. However, iron percentage can be reduced by treating the Fenton-treated sample with NaOH.

Comments on Das et al., “Optimization of enzymatic saccharification of water hyacinth biomass for bio-ethanol: Comparison between artificial neural network and response surface methodology” [Sustainable Materials and Technologies 3 (2015) 17–28, DOI: 10.1016/j.susmat.2015.01.001

Comments on Das et al., “Optimization of enzymatic saccharification of water hyacinth biomass for bio-ethanol: Comparison between artificial neural network and response surface methodology” [Sustainable Materials and Technologies 3 (2015) 17–28, DOI: 10.1016/j.susmat.2015.01.001

Evaluation of least square support vector machine, generalized regression neural network and response surface methodology in modeling the removal of Levofloxacin and Ciprofloxacin from aqueous solutions using ionic liquid @Graphene oxide@ ionic liquid NC

This study focused on the preparation, characterization of a supported dicationic ionic liquid nanocomposite (IL@GO@IL NC) and employing of it in the adsorption of Levofloxacin (LVX) and Ciprofloxacin (CPF) antibiotic. Also, the nonlinear least square support vector machine (N-LSSVM), generalized regression neural network (GRNN), along with response surface methodology used for modeling the removal of both antibiotics (i.e. LVX and CPF) from aqueous solution. In this work, the experiments designed based on critical parameters such as adsorbent dose, antibiotic concentration, sonication time and temperature. The nonlinear adsorption isotherms and kinetics models as well as thermodynamic used for fitting the experimental data. The statistical results demonstrated that the LSSVM and GRNN model efficiently predicted the antibiotics removal percentage with very high accuracy, respectively. Furthermore, for an optimal adsorption of LVX and CPF by IL@GO@IL NC, the adsorbent dose, antibiotic concentration, temperature and sonication time should be set to 0.23 and 0.35 g, 95.20 and 100.00 mg L-1, 2.10 and 4.60 min, and 45.00 ℃, for LVX and CPF, respectively. Under these optimal conditions, the removal percentages and adsorption capacity of LVX and CPF will be 98.20 and 97.50%, as well as 730 and 630 mg g−1, respectively. Besides, the IL@GO@IL NC efficiency under optimal conditions drops up by 15% after seven cycles for both antibiotics. Therefore, the IL@GO@IL NC may be considered as a promising option in removing fluoroquinolones antibiotics from water.

Neural Network Analysis as a Base of the Future System of Water–Environmental Regulation

The article considers neural-network methods and technologies, which are relatively new even for many researchers and experts, as applied to water–environmental regulation. The efficiency of neural networks in this line of studies is due to their self-training, and the ensuing ability to reveal complex nonlinear relationships between the characteristics under control by data processing instruments, consisting of interrelated neurons. The methodology of artificial neural networks and the features of their functioning are described. Training and methodological examples are given to illustrate their potential use. A practical problem, considered as an example of ANN application, is the potential for improving the efficiency of identification of large enterprises polluting natural water among many water users in an industrial region. This is made with the use of data on the concentrations of some priority water-polluting metals at the hydrochemical gages in the Iset river near Ekaterinburg City. The neural-network analysis is shown to detect relationships between individual water quality characteristics at nearby gages. This allowed the conclusion that there exist close logistic economic relationships between water users, which help revealing water pollutants by the water footprint produced by plants working in the same branch. It is also shown that the use of ANN opens new ways for determining the contribution of industrial waste discharges to the level of water pollution by substances of dual genesis (natural and technogenic). The reliability of the conclusions is confirmed by the possibility to use the data on a given hydrochemical gage to satisfactorily predict water quality at a gage further downstream.

A cascade neural network methodology for fault detection and diagnosis in solar thermal plants

Detecting and isolating faults in collector fields of solar thermal power plants is a crucial and challenging task. The system variables in the collector area are highly coupled, which can lead to a high misclassification rate. For this reason, it becomes necessary to combine knowledge of systems engineering with machine learning techniques that unravel the complex dynamics that govern the systems using historical data. Furthermore, the performance of a solar thermal plant is highly dependent on solar irradiance which changes during the day and is subject to perturbations caused by clouds and other atmospheric conditions. Detecting the fault requires using techniques that cope with the disturbances in solar irradiance.In this work, real irradiance profiles with many types of clouds are used. First, a model-based fault detector is applied, obtaining an accuracy of over 89% for all test irradiances. Then, different machine learning techniques are compared: static neural networks with and without decoupling strategy, dynamic neural networks, dynamic neural networks in cascade, classification trees, random forests, radial basis function networks, and self-organizing maps. The combination of neural networks was the only method that obtained a total accuracy of over 73% and F1-scores over 50% for all the test irradiance profiles.

Application of a parallel physics-informed neural network to solve the multi-body dynamic equations for full-scale train collisions

The prohibitive cost of acquiring data from full-scale train collision experiments limits the applicability of data-driven machine learning methods in train collision simulation. Physics-informed neural networks (PINN) attempt to address this challenge by incorporating physics equations as part of the loss function construction. However, the PINN approach is relatively time-consuming when it comes to solving a large number of physical equations. In this paper, a parallel physics-informed neural network (PPINN) methodology is developed for the solution of multibody dynamics equations to further reduce the computational cost. As well, a PPINN-based framework for engineering applications is proposed, investigating the dynamic responses, absorbed energy, collision forces, and wheel vertical rises of the full-scale train collision. Automatic differentiation and parallelization algorithms are applied to the multi-body dynamic equations including mass, damping, and stiffness matrices, as well as the. The residuals and the initial conditions are included in the loss function. The dynamic responses, absorbed energy, collision forces, and wheel vertical rise of the full-scale train collision are simulated and studied in detail. The results obtained from the PPINN method are in excellent agreement with those obtained from the finite element method (FEM), Newmark-β, and fourth-order Runge–Kutta methods for all four train collision scenarios. Additionally, the PPINN methodology keeps better stability even under large time steps which implies a big potential for computational cost reduction.

Synergistic effects of ternary mixture formulation and process parameters optimization in a sequential approach for enhanced L-asparaginase production using agro-industrial wastes.

A novel ternary mixture of inexpensive and nutrient-rich agro-substrates comprising groundnut de-oiled cake, corn gluten meal, and soybean meal has been explored to enhance the L-asparaginase production in solid-state fermentation. To achieve the aim, a hybrid strategy was implemented by utilizing a combination of a mixture design and artificial neural networks. The study initiated with the judicious selection of the agro-substrates based on their low C/N content in comparison to the control using the CHNS elemental analysis. The mixture composition of soybean meal (49.0%), groundnut de-oiled cake (31.5%), and corn gluten meal (19.5%) were found optimum using the simplex lattice mixture design. The agro-industrial substrates mix revealed synergistic effects on the L-asparaginase production than either of the substrates alone. The maximum L-asparaginase activity of 141.45 ± 5.24IU/gds was observed under the physical process conditions of 70% moisture content, autoclaving period of 30min and 6.0 pH by adopting the machine learning-derived artificial neural network (ANN) methodology. The ANN modeling showed excellent prediction ability with a low mean squared error of 0.7, a low root mean squared error of 0.84, and a high value of 0.99 for regression coefficient. Moisture content (%) was assessed to be the most sensitive process parameter in the global sensitivity analysis. The net outcome from the two sequential optimization designs is the selection of the ideal mixture composition followed by the optimum physical process parameters. The application of the enzyme demonstrated significant cytotoxicity against leukemia cell line and therefore exhibited an anti-cancer effect. The present study reports a novel mixture combination and methodology that can be used to lower the cost and enhance the production of L-asparaginase using an agro-industrial substrate mixture.

Experimental study, prediction modeling, sensitivity analysis, and optimization of rheological behavior and dynamic viscosity of 5W30 engine oil based SiO2/MWCNT hybrid nanofluid

In this paper, the rheological performance and dynamic viscosity of hybrid nanofluid containing SiO2 and multi-walled carbon nanotubes (MWCNTs) nanoparticles (90:10) with 5W30 engine oil as base fluid is experimentally evaluated under different shear rates (SRs) in the range of 50–1000 rpm. The hybrid nanofluid volume fractions (VFs) and temperatures are considered in the ranges of 0.05–1.00 vol% and 5–65 °C, respectively. It was found that the hybrid nanofluid under study behaves as a non-Newtonian fluid. In addition, the calculated power law index was lower than unity, resulting in pseudoplastic features of hybrid nanofluid in all VFs and temperatures. It was observed that the rise of nanofluid temperature from 5 to 65 °C leads to the dynamic viscosity reduction (a 93% decrease in viscosity was observed in a VF of 0.2%), while the increase of nanofluid VF brings about the dynamic viscosity elevation (By increasing VF from 0.05% to 1% at SR of 800 rpm and temperature of 25 °C, the viscosity increases by 29.21%). Based on measured data, an innovative three-variable correlation was established that can more accurately estimate the experimental data than published correlations in the literature. Moreover, the capabilities of GMDH-type neural network (NN) and response surface methodology (RSM) to predict the relative viscosity of the hybrid nanofluid were evaluated. It was concluded that both NN and RSM approaches have a superior ability to forecast the dynamic viscosity behavior of the corresponding hybrid nanofluid, having R2 values of 0.999656 and 0.9955. Furthermore, the optimization was performed and the best solution for achieving the minimum dynamic viscosity with the maximum desirability (1.00) was obtained. Eventually, the dynamic viscosity sensitivity to changes in VF, temperature, and SR was evaluated. It was observed that the dynamic viscosity sensitivity increases as the nanofluid temperature and concentration increase considering a constant SR of 800 rpm.

Optimization of Ultrasonic-Assisted Extraction of α-Glucosidase Inhibitors from Dryopteris crassirhizoma Using Artificial Neural Network and Response Surface Methodology.

Dryopteris crassirhizoma Nakai is a plant with significant medicinal properties, such as anticancer, antioxidant, and anti-inflammatory activities, making it an attractive research target. Our study describes the isolation of major metabolites from D. crassirhizoma, and their inhibitory activities on α-glucosidase were evaluated for the first time. The results revealed that nortrisflavaspidic acid ABB (2) is the most potent α-glucosidase inhibitor, with an IC50 of 34.0 ± 0.14 μM. In addition, artificial neural network (ANN) and response surface methodology (RSM) were used in this study to optimize the extraction conditions and evaluate the independent and interactive effects of ultrasonic-assisted extraction parameters. The optimal extraction conditions are extraction time of 103.03 min, sonication power of 342.69 W, and solvent-to-material ratio of 94.00 mL/g. The agreement between the predicted models of ANN and RSM and the experimental values was notably high, with a percentage of 97.51% and 97.15%, respectively, indicating that both models have the potential to be utilized for optimizing the industrial extraction process of active metabolites from D. crassirhizoma. Our results could provide relevant information for producing high-quality extracts from D. crassirhizoma for functional foods, nutraceuticals, and pharmaceutical industries.

Linear Algebraic Systems Neural Network Solution. Part 1

In recent years, neural networks have become increasingly popular due to their versatility in solving complex problems. One area of interest is their application in solving linear algebraic systems, especially those that are ill-conditioned. The solutions of such systems are highly sensitive to small changes in their coefficients, leading to unstable solutions. Therefore, solving these types of systems can be challenging and require specialized techniques. This article explores the use of neural network methodologies for solving linear algebraic systems, focusing on ill-conditioned systems. The primary goal is to develop a model capable of directly solving linear equations and to evaluate its performance on a range of linear equation sets, including ill-conditioned systems. To tackle this problem, neural network implementing iterative algorithm was built. Error function of linear algebraic system is minimized using stochastic gradient descent. This model doesn’t require extensive training other than tweaking learning rate for particularly large systems. The analysis shows that the suggested model can handle well-conditioned systems of varying sizes, although for systems with large coefficients some normalization is required. Improvements are necessary for effectively solving ill-conditioned systems, since researched algorithm is shown to be not numerically stable. This research contributes to the understanding and application of neural network techniques for solving linear algebraic systems. It provides a foundation for future advances in this field and opens up new possibilities for solving complex problems. With further research and development, neural network models can become a powerful tool for solving ill-conditioned linear systems and other related problems.

The effectiveness of machine learning and image processing in detecting plant leaf disease

In our daily lives, the agricultural sector is crucial. Therefore, it is crucial to be clear about the steps taken to identify any diseases on agricultural plants’ leaves. Plant leaf disease is a significant issue or contributor to crop losses in an agricultural context. Some farmers are able to know every disease name and how to prevent them as it becomes increasingly crucial to recognize the sickness. Different plant leaf diseases appear during various seasons. This problem can be resolved using a deep learning-based approach by identifying the affected regions in plant leaf images, enabling farmers to better comprehend the disease. The primary goal of this research is to survey several image-processing methods for detecting plant diseases and to compare them. India is an agricultural nation, and the majority of its people depend on agriculture for a living. Focusing on farming with modern technology is essential to ensuring their comfort and ease of living. Crop productivity may be greatly increased by introducing new technologies. An autonomous plant disease detection method using image processing and a neural network methodology can be utilized to solve issues with plant and agricultural diseases. Plants can contract a wide range of illnesses. Different patterns are needed to detect various disorders.

Prediction of the as Low as Diagnostically Acceptable CT Dose for Identification of the Inferior Alveolar Canal Using 3D Convolutional Neural Networks with Multi-Balancing Strategies

Ionizing radiation is necessary for diagnostic imaging and deciding the right radiation dose is extremely critical to obtain a decent quality image. However, increasing the dosage to improve the image quality has risks due to the potential harm from ionizing radiation. Thus, finding the optimal as low as diagnostically acceptable (ALADA) dosage is an open research problem that has yet to be tackled using artificial intelligence (AI) methods. This paper proposes a new multi-balancing 3D convolutional neural network methodology to build 3D multidetector computed tomography (MDCT) datasets and develop a 3D classifier model that can work properly with 3D CT scan images and balance itself over the heavy unbalanced multi-classes. The proposed models were exhaustively investigated through eighteen empirical experiments and three re-runs for clinical expert examination. As a result, it was possible to confirm that the proposed models improved the performance by an accuracy of 5% to 10% when compared to the baseline method. Furthermore, the resulting models were found to be consistent, and thus possibly applicable to different MDCT examinations and reconstruction techniques. The outcome of this paper can help radiologists to predict the suitability of CT dosages across different CT hardware devices and reconstruction algorithms. Moreover, the developed model is suitable for clinical application where the right dose needs to be predicted from numerous MDCT examinations using a certain MDCT device and reconstruction technique.

A Transfer Learning Methodology for Recognizing Unsafe Behavior during Lifting Operations in a Chemical Plant

Large lifting equipment is used regularly in the maintenance operations of chemical plant installations, where safety controls must be carried out to ensure the safety of lifting operations. This paper presents a convolutional neural network (CNN) methodology, based on the PyTorch framework, to identify unsafe behavior among lifting operation drivers, specifically, by collecting 22,352 images of equipment lifting operations over a certain time period in a chemical plant. The lifting drivers’ behavior was divided into eight categories, and a ResNet50 network model was selected to identify the drivers’ behavior in the pictures. The results show that the proposed ResNet50 network model based on transfer learning achieves a 99.6% accuracy rate, a 99% recall rate and a 99% F1 value for the expected behaviors of eight lifting operation drivers. This knowledge regarding unsafe behavior in the chemical industry provides a new perspective for preventing safety accidents caused by the dangerous behaviors of lifting operation drivers.

Application of synthetic data in the training of artificial intelligence for automated quality assurance in magnetic resonance imaging.

Magnetic resonance imaging scanner faults can be missed during routine quality assurance (QA) if they are subtle, intermittent, or the test being performed is insensitive to the type of fault. Coil element malfunction is a common fault within MRI scanners, which may go undetected for quite some time. Consequently, this may lead to poor image quality and the potential for misdiagnoses. Daily QA typically consists of an automated signal to noise ratio test and in some instances this test is insensitive to coil element malfunction. Instead of relying on daily QA testing, it was proposed to utilize patient images in conjunction with a trained neural network to detect coil element malfunction, even when it presents as a very subtle defect. The advantage to using patient images over phantom testing is real-time monitoring can be achieved. This allows clinical staff to focus more on patient throughput without being burdened by daily testing. A neural network was trained using simulated coil failure in 3958 abdominal or pelvic images from 497 patients. The accuracy of the trained network was then tested on an unseen dataset of 109 images from which 44 patients which had coil element malfunction present. Five MRI radiographers were shown 249 images with and without real coil malfunction to assess their accuracy compared to the neural network in identifying the scanner fault. The neural network achieved an accuracy of 91.74% in identifying coil element malfunction in the unseen data. Radiographers tasked with identifying coil element malfunction had an average accuracy of 59.99%. In the same test case, the neural network outperformed all radiographers with an accuracy of 91.56%. This work demonstrates that neural networks trained with artificial data can successfully identify MRI scanner coil element malfunction in clinical images. The method provided better accuracy than MRI radiographers (technologists) at identifying coil element malfunction and highlights the potential utility of AI methods as an alternative to support traditional QA. Further, our methodology of training neural networks with simulated data could potentially identify other faults, allowing centers to produce robust fault detection systems with minimal data.

CO2 capture by benzene‐based hypercrosslinked polymer adsorbent: Artificial neural network and response surface methodology

AbstractIn this research, porous benzene‐based hypercrosslinked polymeric adsorbents with different morphological properties were synthesized through Friedel–Crafts alkylation reaction. The resulting samples were applied for CO2 capture at different operational conditions. Two modelling approaches, including artificial neural network (radial basis function [RBF] and multi layer perceptron [MLP]) and response surface methodology (RSM), were employed to investigate the effect of independent parameters on adsorption capacity. A semi‐empirical quadratic model for adsorption capacity was presented based on RSM‐central composite design technique. Additionally, the optimal structure of RBF was determined with 200 neurons, and the optimal structure of MLP was determined with three hidden layers and 10, 8, and 7 neurons. The modelling results demonstrate the better prediction of MLP and RBF approaches than the RSM method with correlation coefficient values of 0.999, 0.989, and 0.931, respectively. Finally, process optimization was carried out using RSM optimization module and the optimized values of synthesis time, crosslinker ratio (formaldehyde dimethyl acetal [FDA]/benzene), adsorption time, pressure, and temperature were obtained at 10.11 h, 1, 220 s, 9 bar, and 55°C, respectively. The optimum value of CO2 uptake capacity was obtained around 167 (mg/g).