Artificial neural network modeling on the polymer-electrolyte aqueous two-phase systems involving biomolecules

Aqueous solutions of different polymers can separate and form aqueous two-phase systems (ATPS). ATPS provide an aqueous, biocompatible, and mild environment for separation and fractionation of biomolecules. The interfacial tension between the two aqueous phases plays a major role in ATPS-mediated partition of biomolecules. Because of the structure of the two aqueous phases, the interfacial tensions between the phases can be 3-4 orders of magnitude smaller than conventional fluid-liquid systems: ∼1-100 μJ/m(2) for ATPS compared to ∼72 mJ/m(2) for the water-vapor interface. This poses a major challenge for the experimental measurements of reproducible interfacial tension data for these systems. We address the need for precise determination of ultralow interfacial tensions by systematically studying a series of polymeric ATPS comprising of polyethylene glycol (PEG) and dextran (DEX) as the phase-forming polymers. Sessile and pendant drops of the denser DEX phase are formed within the immersion PEG phase. An axisymmetric drop shape analysis (ADSA) is used to determine interfacial tensions of eight different ATPS. Specific criteria are used to reproducibly determine ultralow interfacial tensions of the ATPS from pendant and sessile drops. Importantly, for a given ATPS, pendant drop and sessile drop experiments return values within 0.001 mJ/m(2) indicating reliability of our measurements. Then, the pendant drop technique is used to measure interfacial tensions of all eight ATPS. Our measured values range from 0.012 ± 0.001 mJ/m(2) to 0.381 ± 0.006 mJ/m(2) and vary with the concentration of polymers in equilibrated phases of ATPS. Measurements of ultralow interfacial tensions with such reproducibility will broadly benefit studies involving partition of different biomolecules in ATPS and elucidate the critical effect of interfacial tension.

Field-scale investigation for a period of more than four months was conducted to evaluate the performance of a landfill for biogas extraction upon the injection of food waste leachate (FWL), a liquid organic waste generated from the food waste recycling facilities in Korea. The target was set at recovering about 50–60 % methane from the landfill gas (LFG) at extraction rates varying between 10 and 30 m3/h. An application of the artificial neural network (ANN) was presented in this paper to predict the performance parameter namely methane percentage (%). The input parameters to the network were LFG extraction rate (m3/h) and landfill leachate: FWL ratio, respectively, which were obtained from the field-scale investigation. Four different back error propagation learning algorithms were used to train the ANN for a comparative analysis, and the best among them was selected. To substantiate our claim, performance of the network was analyzed for different set of training and test data points. Predictions were attained by appropriately selecting the network parameters and, adequately training the network with 130 set of data points. The accuracy of back propagation neural network (BPNN)-based model predictions was evaluated by calculating the correlation coefficient (R) and mean absolute percentage error values. The results from this predictive modeling work showed that BPNNs were able to predict the methane percentage of the LFG in an acceptable range.

Nowadays , nonlinear time series and artificial neural networks (ANN) models are used for forecasting in the field of business, agriculture and soon. Recent studies have shown, ANN have been successfully used for forecasting of financial and agriculture data series The classical methods used for time series prediction like Box-Jenkins or ARIMA assumes that there is a linear relationship between inputs and outputs. ANN have more advantages that can approximate to model both linear and nonlinear structures in time series, they are not able to handling both structures equally well. The autoregressive integrated moving average (ARIMA) model and two ANN models namely, Radial basis function neural networks (RBFNN), and Elman recurrent neural networks (ERNN) methods were applied to Hyderabad airport traffic data. The data obtained for 15 years from 2002–2003 to 2016–2017 about domestic and international passenger of International Airport of Hyderabad, India. In this research paper, we compared the performances of ARIMA, RBFNN and ERNN were based on three measures: mean absolute error (MAE), mean absolute percentage error (MAPE), and root mean square error (RMSE). The results showed that RBFNN obtained the smallest MAE, MAPE and RMSE in both the modeling and forecasting processes. The performances of the three models ranked in ascending order were: ARIMA, ERNN and the RBFNN model. Keywords: T ime series, forecasting, artificial neural networks, ARIMA models, radial basis function neural networks, and Elman recurrent neural networks Cite this Article R. Ramakrishna, Berhe Aregay, Tewodros Gebregergs. The Comparison in Time Series Forecasting of Air Traffic Data by Autoregressive Integrated Moving Average Model, Radial Basis Function and Elman Recurrent Neural Networks. Research & Reviews: Journal of Statistics . 2018; 7(3): 75–90p.

Application of artificial neural networks for estimation of solubility of acid gases (H2S and CO2) in 32 commonly ionic liquid and amine solutions

Multivariate regression (MVR) and different artificial neural network (ANN) models developed for optical transparency of conductive polymer nanocomposite films

Laboratory experiments were used to estimate the hydraulic performance of emitters, i.e., the emitter flow variation (qvar) and manufacturer’s coefficient of variation (CVm), by measuring the discharge of different labyrinth-channel emitters at different operating pressures (P) and water temperatures (T). Considering the importance of the structural parameters of the labyrinth-channel emitters in drip irrigation design, which has been experimentally confirmed, artificial neural network (ANN) and gene expression programming (GEP) models were developed to predict qvar and CVm. The ANN and GEP models were trained and tested using structural parameters (including the number, height (H), and spacing of trapezoidal units and the flow path width and length) of different labyrinth-channel emitters, P and T as the input variables, and qvar and CVm as the outputs. Statistical criteria, including the coefficients of correlation (r), relative root-mean-square error (RRMSE), and mean absolute error (MAE), were used to examine the accuracy of the developed models. The ANN models exhibited good correlation with experimental values, with high r values 0.995 and 0.969 for qvar and 0.997 and 0.947 for CVm in the training and testing processes, respectively. The ANN models had lower RRMSE and MAE values than the GEP models. Furthermore, H was the dominant variable for obtaining the most accurate prediction model. The results confirm that the ANN models are superior to the GEP models for the prediction of the hydraulic performance of emitters.

The present investigation introduces a robust soft computing model by comparing twelve least square support vector machine (LSSVM), six long short-term memory (LSTM), and thirty-six artificial neural network (ANN) models to predict the unconfined compressive strength (UCS) of fine-grained soil. For that purpose, a database of fine content, dry unit weight, porosity, void ratio, degree of saturation, and specific gravity results of 85 soil specimens has been compiled from the literature. 75 and 10 soil specimens were trained and tested for each model. Six training databases have been prepared to analyze the effect of quality and quantity of training database by selecting 50%, 60%, 70%, 80%, 90%, and 100% of 75 soil specimens. The performance comparison demonstrated that the LSTM model (MD 113) requires fewer training datasets (50% of 75) than the LSSVM (MD 102 and MD 108) and ANN (MD 120, MD 127, MD 136, MD139, MD 148, and MD 150) models. Also, it was observed that the nonlinear LSSVM model (MD 108) is unaffected by multicollinearity in training datasets and predicted UCS better than the linear LSSVM model (MD 102). Furthermore, the Levenberg-Marquardt neural network model (MD 120) has outperformed the other ANN models with the root mean square error (RMSE) of 5.1214 N/cm2, the mean absolute error (MAE) of 4.1379 N/cm2, and correlation (R) of 0.9836. The overall performance comparison revealed that the LSTM model is more potent than the LSSVM and ANN models. The LSTM model predicted the UCS of fine-grained soil with the RMSE of 4.7539 N/cm2, the MAE of 4.2461 N/cm2, and R of 0.9880. Conversely, cosine amplitude sensitivity analysis demonstrated that the fine content and dry unit weight influence the prediction of virgin UCS of fine-grained soils.

Hairiness is a very important yarn quality parameter for high speed weaving. This paper presents a comparative analysis of two modeling methodologies for the prediction of ring and rotor yarn hairiness. Cotton fibre properties measured by a high volume instrument (HVI) and yarn count have been used as inputs for artificial neural network (ANN) and linear regression models. The prediction accuracy for both of the models is found to be good as the correlation coefficient is higher than 0.92 and mean absolute error is less than 4%. However, ANN models have an edge over the regression model particularly for ring yarn hairiness prediction. The importance of the cotton fibre properties on yarn hairiness has also been analysed by the developed ANN and regression models. For ring spun yarns, the ranking of cotton fibre properties given by the ANN and regression models are generally in agreement although some disparities exist in the ranking of length properties. Both models yield almost identical ranking of cotton fibre properties for rotor yarn hairiness.

Dew point temperature is needed as an input to calculate various meteorological variables. In general, it contributes to human and animal comfort levels. The goal of this study was to develop artificial neural network (ANN) models for dew point temperature prediction to improve upon previous research. These improvements included optimizing the stopping criteria, comparing seasonal models to year-round models, and developing ensemble ANNs to blend the output of seasonal models. For an ANN trained with 100,000 patterns per epoch, the error was reduced using a 2000-pattern stopping dataset at an interval of 20 learning events to decide when to stop training. Seasonal ANN models were blended in an ensemble ANN with the weight of the member networks determined using a fuzzy membership-type function based on the day of year. These ensemble models were shown to produce lower errors than year-round, nonensemble models. The mean absolute errors (MAEs) of the final models evaluated with an independent evaluation dataset included 0.795°C for a 2-hour prediction, 1.485°C for a 6-hour prediction, and 2.146°C for a 12-hour prediction. The final model MAEs, when compared to the previous research, were reduced by 0.008°C, 0.081°C, and 0.135°C, respectively. It can be concluded that the methods used in this research were effective in more accurately predicting year-round dew point temperature. The ANN models for different prediction periods were sequenced to provide a 12-hour dew point temperature prediction system for implementation on the Georgia Automated Environmental Monitoring Network website (www.georgiaweather.net).

The purpose of this study was to validate the use of artificial neural network (ANN) models for predicting quality of life (QOL) after breast cancer surgery and to compare the predictive capability of ANNs with that of linear regression (LR) models. The European Organization for Research and Treatment of Cancer Quality of Life Questionnaire and its supplementary breast cancer measure were completed by 402 breast cancer patients at baseline and at 2 years postoperatively. The accuracy of the system models were evaluated in terms of mean square error (MSE) and mean absolute percentage error (MAPE). A global sensitivity analysis was also performed to assess the relative significance of input parameters in the system model and to rank the variables in order of importance. Compared to the LR model, the ANN model generally had smaller MSE and MAPE values in both the training and testing datasets. Most ANN models had MAPE values ranging from 4.70 to 19.96 %, and most had high prediction accuracy. The ANN model also outperformed the LR model in terms of prediction accuracy. According to global sensitivity analysis, pre-operative functional status was the best predictor of QOL after surgery. Compared with the conventional LR model, the ANN model in the study was more accurate for predicting patient-reported QOL and had higher overall performance indices. Further refinements are expected to obtain sufficient performance improvements for its routine use in clinical practice as an adjunctive decision-making tool.

این مطالعه به منظور بررسی پیش بینی شاخص های عملکرد تولیدی در مرغان تخم گذار با استفاده از شبکه های عصبی مصنوعی و رگرسیون غیرخطی چندگانه انجام شد. بررسی بر روی اطلاعات چهار دوره متوالی پرورش در یک واحد پرورش مرغ تخم گذار صورت گرفت. روش های داده-کاوی شامل رگرسیون خطی و غیر خطی، شبکه عصبی پرسپترون سه لایه، شبکه عصبی پرسپترون چهار لایه و شبکه عصبی با تابع پایه ای شعاعی بود. در این مدل ها از متغیرهای سن گله، میزان خوراک مصرفی و فصل تولید به عنوان متغیر پیشگو و شاخص های عملکرد تولیدی شامل درصد تخم-گذاری، وزن توده ای تخم مرغ تولیدی و ضریب تبدیل غذایی به عنوان متغیر پاسخ استفاده شد. نتایج نهایی رگرسیون های خطی نشان داد که برای تمامی متغیرهای وابسته مورد مطالعه متغیر مستقل سن گله معنی دار می باشد. بنابراین رگرسیون غیر خطی شاخص های عملکرد تولیدی در مقابل سن برای مقایسه با شبکه های عصبی مختلف مورد بررسی قرار گرفت و برای مقایسه کلیه مدل ها از ضریب تعیین (R2) و میانگین قدر مطلق خطا (MAE) استفاده شد. نتایج نشان داد بین شبکه های عصبی مصنوعی مختلف مورد مطالعه، شبکه با تابع پایه ای شعاعی بهتر از سایر مدل های در پیش‌بینی شاخص های عملکرد تولیدی مرغان تخم گذار عمل می کند.

This study presents forecast of highway casualties in Turkey using nonlinear multiple regression (NLMR) and artificial neural network (ANN) approaches. Also, the effect of railway development on highway safety using ANN models was evaluated. Two separate NLMR and ANN models for forecasting the number of accidents (A) and injuries (I) were developed using 27 years of historical data (1980–2006). The first 23 years data were used for training, while the remaining data were utilized for testing. The model parameters include gross national product per capita (GNP-C), numbers of vehicles per thousand people (V-TP), and percentage of highways, railways, and airways usages (TSUP-H, TSUP-R, and TSUP-A, respectively). In the ANN models development, the sigmoid and linear activation functions were employed with feed-forward back propagation algorithm. The performances of the developed NLMR and ANN models were evaluated by means of error measurements including mean absolute percentage error (MAPE), mean absolute error (MAE), root mean square error (RMSE), and coefficient of determination (R 2). ANN models were used for future estimates because NLMR models produced unreasonably decreasing projections. The number of road accidents and as well as injuries was forecasted until 2020 via different possible scenarios based on (1) taking TSUPs at their current trends with no change in the national transport policy at present, and (2) shifting passenger traffic from highway to railway at given percentages but leaving airway traffic with its current trend. The model results indicate that shifting passenger traffic from the highway system to railway system resulted in a significant decrease on highway casualties in Turkey.

The creation of transverse openings in beams triggers the shear performance. The dual impact of height and length on the overall shear performance and strain variations in reinforcements of deep concrete beams with and without fibres was assessed to investigate the effect of opening in the beam. This effect of opening was explored and modelled using finite element software Abaqus and predicted using an artificial neural network (ANN) model. The data set for ANN was 56 deep concrete beams, while for the finite element model (FEM), 12 deep concrete beams were used. The effect of input parameters in the ANN model was assessed through sensitivity analysis. Results show that with an increase in opening depth, the strain in top steel reinforcement shifted to tensile strain, resulting in premature beam failure. In addition, experimental and FEM shear resistance had a mean absolute error (MAE) of 4.1, 5.0, and 20.6% for deep beams without fibres, with fibres and fibre mesh, respectively. Compared to available analytical models, the ANN model reasonably predicts the shear resistance with an R2of 0.84 and a mean square error (MSE) of 0.01. The use of the ANN and FEM models is recommended as they save time, and the prediction does not involve degradation of the environment, hence demonstrating sustainable construction practices. Doi: 10.28991/CEJ-2024-010-08-02 Full Text: PDF

Performance of radial basis and LM-feed forward artificial neural networks for predicting daily watershed runoff

Many theoretical or experimental studies has shown that ensemble learning is an effective technique to achieve better classification accuracy and stability than individual classifiers. In this paper, we propose a novel weighted classifier ensemble method through classifiers pruning with two stages. In the first stage, we use canonical correlation analysis (CCA) to model maximum correlation relationships between training data points and base classifiers. Based on such globally multi-linear projections, a sparse regression method is proposed to prune base classifiers so that each test data point will dynamically select a subset of classifiers to form a unique classifier ensemble, to decrease effects of noisy input data and incorrect classifiers in such a global view. In the second stage, the pruned classifiers are weighted locally by a fusion method, which utilizes the generalization ability of pruned classifiers among nearest neighbors of testing data points. By this way, each test data point can build a unique locally weighted classifier ensemble. Analysis of experimental results on several UCI data sets shows that the classification results of our method are better than other ensemble methods such as Random Forests, Majority Voting, AdaBoost and DREP.