Traditional Artificial Neural Network Models Research Articles

Kriging-based surrogate data-enriching artificial neural network prediction of strength and permeability of permeable cement-stabilized base

Limited test data hinder the accurate prediction of mechanical strength and permeability of permeable cement-stabilized base materials (PCBM). Here we show a kriging-based surrogate model assisted artificial neural network (KS-ANN) framework that integrates laboratory testing, mathematical modeling, and machine learning. A statistical distribution model was established from limited test data to enrich the dataset through the combination of markov chain monte carlo simulation and kriging-based surrogate modeling. Subsequently, an artificial neural network (ANN) model was trained using the enriched dataset. The results demonstrate that the well-trained KS-ANN model effectively captures the actual data distribution characteristics. The accurate prediction of the mechanical strength and permeability of PCBM under the constraint of limited data validates the effectiveness of the proposed framework. As compared to traditional ANN models, the KS-ANN model improves the prediction accuracy of PCBM’s mechanical strength by 21%. Based on the accurate prediction of PCBM’s mechanical strength and permeability by the KS-ANN model, an optimization function was developed to determine the optimal cement content and compaction force range of PCBM, enabling it to concurrently satisfy the requirements of mechanical strength and permeability. This study provides a cost-effective and rapid solution for evaluating the performance and optimizing the design of PCBM and similar materials.

ANN-LSTM-A Water Consumption Prediction Based on Attention Mechanism Enhancement

To reduce the energy consumption of domestic hot water (DHW) production, it is necessary to reasonably select a water supply plan through early predictions of DHW consumption to optimize energy consumption. However, the fluctuations and intermittence of DHW consumption bring great challenges to the prediction of water consumption. In this paper, an ANN-LSTM-A water quantity prediction model based on attention mechanism (AM) enhancement is improved. The model includes an input layer, an AM layer, a hidden layer, and an output layer. Based on the combination of artificial neural network (ANN) and long short-term memory (LSTM) models, an AM is incorporated to address the issue of the traditional ANN model having difficulty capturing the long-term dependencies, such as lags and trends in time series, to improve the accuracy of the DHW consumption prediction. Through comparative experiments, it was found that the root mean square error of the ANN-LSTM-A model was 15.4%, 13.2%, and 13.2% lower than those of the ANN, LSTM, and ANN-LSTM models, respectively. The corresponding mean absolute error was 17.9%, 11.5%, and 8% lower than those of the ANN, LSTM, and ANN-LSTM models, respectively. The results showed that the proposed ANN-LSTM-A model yielded better performances in predicting DHW consumption than the ANN, LSTM, and ANN-LSTM models. This work provides an effective reference for the reasonable selection of the water supply plan and optimization of energy consumption.

Compressive strength prediction of sustainable concrete containing waste foundry sand using metaheuristic optimization‐based hybrid artificial neural network

AbstractThis study seeks to present a sophisticated artificial intelligence (AI) framework to model the compressive strength () of concrete containing waste foundry sand (WFS), with the aim of minimizing the need for time‐consuming laboratory tests and skilled technicians. For this purpose, artificial neural network (ANN) is hybridized with two metaheuristic algorithms—particle swarm optimization (PSO) and ant colony optimization (ACO) to predict the of 340 samples containing WFS collected from the literature. Results indicated that the ACO + ANN model showed the best performance with the Pearson coefficient of 0.9971, mean absolute error of 0.0221 MPa, and root mean squared error of 0.7473 MPa. The values of prediction errors exhibited that more than 90% of them in the ACO + ANN model fall within the range of (−1.5 MPa, 1.5 MPa), while this range for the PSO + ANN and traditional ANN models was obtained as (−3 MPa, 3 MPa) and (−4 MPa, 4 MPa), respectively. Furthermore, the proposed ACO + ANN model predicted the in the range of 5.24–54.48 MPa. Besides, the results indicated that the water‐to‐cement ratio, cement strength class, and cement content had the most significant impact on the of WFS‐containing concrete. Finally, a comparison was made between the proposed ACO + ANN model and four other AI models recently proposed in the literature, in which the performance criteria demonstrated that the proposed ACO + ANN model outperformed the models in the literature.

SRTM DEM Correction Based on PSO-DBN Model in Vegetated Mountain Areas

The Shuttle Radar Topography Mission (SRTM) digital elevation model (DEM) is extensively utilized in various fields, such as forestry, oceanography, geology, and hydrology. However, due to limitations in radar side-view imaging, the SRTM DEM still contains gaps and anomalies, particularly in areas with an intricate topography, like forests. To enhance the accuracy of the SRTM DEM in forested regions, commonly employed approaches include regression analysis and artificial neural networks (ANN). Nevertheless, existing regression methods struggle to accurately capture the intricate nonlinear relationship between the error and influencing factors. Additionally, traditional ANN models are susceptible to overfitting, resulting in subpar accuracy. Deep Belief Network (DBN) is a highly precise algorithm in deep learning. However, the intricate combination of hyperparameters often leads to limited generalization ability and model robustness when correcting DEM. The present study proposes an error prediction model based on the DBN optimized by Particle Swarm Optimization (PSO) for SRTM DEM correction. By utilizing the PSO algorithm, we aim to identify the optimal combination of hyperparameters of DBN, including the number of neurons in the hidden layer and the learning rates. The experiment focuses on two regions in Hunan Province, China, characterized by abundant vegetation cover. The reference data utilized for comparison is ICESat/GLAS data. The experimental results demonstrate that the mean error (ME) and root mean square error (RMSE) of the SRTM DEM corrected by the proposed algorithm in these two regions are significantly reduced by 93.5%–96.0% and 21.5%–23.5%, respectively. Moreover, there is an improvement of over 26.1% in accuracy within complex terrain areas. Specifically, in broadleaf forest, the PSO-DBN method exhibits a remarkable accuracy improvement of 26.2%, while the DBN-corrected SRTM DEM shows an improvement of 15.3%. In coniferous forest, the PSO-DBN method achieves an accuracy improvement of 14.8%, whereas the DBN-corrected SRTM DEM demonstrates a gain of 5.8%. The approach provides a more effective and robust tool for correcting SRTM DEM or other similar DEMs over vegetated mountain areas.

Sequence-to-sequence digital twin model in chemical plants with internal rolling training algorithm

A data-driven digital twin based on the sequence-to-sequence (StS) model is introduced in this study. The rolling training algorithm is presented as a plug-in in StS training to achieve long-term rolling predictions. The model performs long-term horizontal predictions over 8-days based on initial observed data and manipulation sequence conditions without any new historical inputs. The digital twin model is validated using the dynamic simulation of the vapor-recompression C3 process. When the rolling training algorithm is used, the StS model demonstrates the best predictions than the traditional artificial neural network model and simple StS model for 1- and 8-days tests. Moreover, noise added cannot affect the prediction performance of StS with rolling training, and the step-change test validated the StS with rolling training containing physical meaning. Hence, the proposed StS model is a data-driven digital twin having physical interpretability. The StS model can implement long-term rolling predictions for days.

An efficient hybrid LSTM-ANN joint classification-regression model for PPG based blood pressure monitoring

This paper investigates the importance of classification in optimizing the estimation accuracy of blood pressure (BP) using photoplethysmography (PPG) signal features, with the aim of balancing efficiency and performance. Accordingly, the experimental design of this study was employed by constructing various artificial neural network (ANN) and long short-term memory (LSTM) topologies such as conventional single-stage models and classification-regression schemes including two-stage models, joint learning models, and the hybrid LSTM-ANN model. All models were tested on 40 subjects from the Physionet’s MIMIC II database. With only 4 features, the proposed hybrid LSTM-ANN model achieved the best MAE ± SD of 3.39 ± 5.47 and 1.79 ± 3.72 mmHg for SBP and DBP, respectively. Furthermore, the single-stage and the two-stage ANN-based models were selected to be embedded in STM32 Microcontroller Unit (MCU) for real-time predictions of BP. The two-stage classification-regression ANN model showed superior performance and robustness in real-time testing on six subjects, achieved an MAE ± SD of 1.41 ± 1.29 mmHg, resulting in an 83.5% reduction of MAE compared with the traditional single-stage ANN model. Overall, the data presented in this work substantiate the significant role of classification in improving regression accuracy in both ANN and LSTM-based models. Compared to other state-of-the-art models, the hybrid LSTM-ANN with a joint classification-regression approach was found to be the optimal architecture to achieve the best performance and a trade-off between memory usage, complexity, and latency with acceptable BP prediction accuracy.

Predicting mechanical behaviors of rubber materials with artificial neural networks

Rubber is considered as a new material for making landing gear shock absorbers for new energy electric aircraft. This investigation tested nitrile butadiene rubber blocks with different hardness at different loading rates to determine their stress-strain relationships. The mechanical behaviors of rubbers are predicted via an artificial neural network model for the first time. A self-adjusting particle swarm optimization was proposed to optimize the artificial neural network's weights and thresholds with the inertia factors and learning factors automatically adjusted. The number of neurons was optimized. The results revealed that the Young's modulus of the rubber increases with the increasing strain. With larger hardness and a larger strain rate, both the Young's modulus and its increasing rate become larger. The proposed self-adjusting particle swarm optimization improves the accuracy and the convergence speed. With the optimal number of neurons, the proposed artificial neural network has a mean square error of 1.611 × 10−4 in predicting the stress-strain relationships of the rubber. Compared with traditional artificial neural network models and an artificial neural network optimized by a traditional particle swarm optimization, the proposed model increases the accuracy by 56.5% and 26.5%, respectively. It is an initial attempt to account the coupling effect of the hardness and the strain rate in the rubber constitutive model.

The use of multilayer perceptron and radial basis function: an artificial intelligence model to predict progression of oral cancer.

The use of multilayer perceptron and radial basis function: an artificial intelligence model to predict progression of oral cancer.

A Comparative Study of a Fully-Connected Artificial Neural Network and a Convolutional Neural Network in Predicting Bridge Maintenance Costs

The cost assessment of bridge maintenance is a difficult topic to study, but it is critical for a bridge life cycle cost analysis. The maintenance costs sample database was established in this study according to actual engineering data, and a bridge maintenance cost prediction model was developed using a fully-connected artificial neural network (ANN) and convolutional neural network (CNN), respectively. First, eight main factors affecting maintenance costs were evaluated based on the random forest method, and the evaluation results were verified by an exploratory data analysis. The original data were then screened based on the isolation forest principle, and the recent gross domestic product (GDP) growth rate was used to illustrate the relationship between economic development and bridge maintenance costs. Finally, these two neural networks were used to establish maintenance cost prediction models, respectively. The results from the two models were compared and their prediction accuracies were analyzed. The prediction performance of the CNN model for bridge maintenance costs was found to be better than that of the traditional fully-connected ANN model. The results of this study will enhance the opportunity for bridge managers to balance lifecycle maintenance costs.

A New Hybrid SARFIMA-ANN Model for Tourism Forecasting

Many countries developed and increased greenery in their country sights to attract international tourists. This planning is now significantly contributing to their economy. The next task is to facilitate the tourists by sufficient arrangements and providing a green and clean environment; it is only possible if an upcoming number of tourists’ arrivals are accurately predicted. But accurate prediction is not easy as empirical evidence shows that the tourists’ arrival data often contains linear, nonlinear, and seasonal patterns. The traditional model, like the seasonal autoregressive fractional integrated moving average (SARFIMA), handles seasonal trends with seasonality. In contrast, the artificial neural network (ANN) model deals better with nonlinear time series. To get a better forecasting result, this study combines the merits of the SARFIMA and the ANN models and the purpose of the hybrid SARFIMA-ANN model. Then, we have used the proposed model to predict the tourists’ arrival in New Zealand, Australia, and London. Empirical results showed that the proposed hybrid model outperforms in predicting tourists’ arrival compared to the traditional SARFIMA and ANN models. Moreover, these results can be generalized to predict tourists’ arrival in any country or region with a complicated data pattern.

A new optimized artificial neural network model to predict thermal efficiency and water yield of tubular solar still

Tubular solar still is a simple light-weight desalination unit with a large condensing surface compared with other types of solar stills. Regrettably, it suffers from the low water yield like other types of solar stills. In this work, two main research themes are studied. The first is enhancing the water yield and thermal efficiency of tubular solar still by providing the absorber plate with an electrical heater powered by a solar photovoltaic panel. The performance of the modified solar still is evaluated based on its water yield as well as energy and exergy efficiencies. The second is developing a fine-tuned artificial intelligent model to predict the thermal efficiency and water yield of the solar still. The fine-tuned model consists of a traditional artificial neural network model optimized by a meta-heuristic optimizer called humpback whale optimizer. The prediction accuracy of the developed model is compared with that of the standalone artificial neural network model and an optimized model using a traditional particle swarm optimizer. The results showed that the conventional tubular solar still produces an average accumulated water yield of 2.58 L/m2/day, while the modified tubular solar still produces an average accumulated water yield of 3.41 L/m2/day with 31.85% improvement. The daytime energy efficiency of the modified tubular solar still is 38.61%, but for the conventional one is only 30.67%. Moreover, the new developed model has the highest prediction accuracy among other investigated models. The optimized model using humpback whale optimizer has the highest correlation coefficient ranges between 0.983 and 0.999, the optimized model using particle swarm optimizer has a moderate correlation coefficient between 0.969 and 0.987, and standalone model has the lowest ranges between 0.594 and 0.937. These results revealed the vital role of the electrical heater in enhancing the thermal performance of the solar still and the important role of humpback whale optimizer for improving the prediction accuracy of the traditional neural network models.

Predictive Analysis of Municipal Solid Waste Generation Using an Optimized Neural Network Model

Developing successful municipal waste management planning strategies is crucial for implementing sustainable development. The research proposed the application of an optimized artificial neural network (ANN) to forecast quantities of waste in Poland. The neural network coupled with particle swarm optimization (PSO) algorithm is compared to the conventional neural network using five assessment metrics. The metrics are coefficient of efficiency (CE), Pearson correlation coefficient (R), Willmott’s index of agreement (WI), root mean squared error (RMSE), and mean bias error (MBE). Selected explanatory factors are incorporated in the developed models to reflect the influence of economic, demographic, and social aspects on the rate of waste generation. These factors are population, employment to population ratio, revenue per capita, number of entities by type of business activity, and number of entities enlisted in REGON per 10,000 population. According to the findings, the ANN–PSO model (CE = 0.92, R = 0.96, WI = 0.98, RMSE = 11,342.74, and MBE = 6548.55) significantly outperforms the traditional ANN model (CE = 0.11, R = 0.68, WI = 0.78, RMSE = 38,571.68, and MBE = 30,652.04). The significant level of the reported outputs is evaluated using the Wilcoxon–Mann–Whitney U-test, with a significance level of 0.05. The p-values of the pairings (ANN, observed) and (ANN, ANN–PSO) are all less than 0.05, suggesting that the models are statistically different. On the other hand, the P-value of (ANN–PSO, observed) is more than 0.05, suggesting that the difference between the models is statistically insignificant. Therefore, the proposed ANN–PSO model proves its efficiency at estimating municipal solid waste quantities and may be regarded as a cost-efficient method of developing integrated waste management systems.

A novel artificial intelligent model for predicting water treatment efficiency of various biochar systems based on artificial neural network and queuing search algorithm

This study aims at providing a robust artificial intelligent model for predicting the efficiency of heavy metal removal from aqueous solutions of biochar systems with high accuracy and reliability. Not only is it environmentally significant, but it is also a powerful tool for improving biochar adsorption efficiency, reducing the risk of a global water shortage. Accordingly, 22 types of biomass feedstock with a total of 44 biochar systems and 353 experiments, aiming to remove six heavy metal ions (i.e., Cu2+, Pb2+, Zn2+, As3+, Cd2+, and Ni2+) from water were considered and evaluated. Subsequently, an artificial neural network (ANN) model was designed for predicting the heavy metal adsorption efficiency onto these biochar systems. To improve the accuracy of the ANN model, the queuing search algorithm (QSA), a human activities-based algorithm, was applied, aiming to optimize the parameters of the developed ANN model, called the QSA-ANN model. The results showed that the proposed optimization QSA-ANN model provided high accuracy with a root-mean-squared error (RMSE) of 0.051 and 0.074; determination coefficient (R2) of 0.978 and 0.960; variance accounted for (VAF) of 97.707 and 95.882, for the training and testing phases, respectively. Compared to the traditional ANN model, the accuracy of the proposed optimization QSA-ANN model was improved 2.7% on the training dataset and 2.9% on the testing dataset. With an accuracy of 96% in practice, the proposed optimization QSA-ANN model was recommended for practical engineering to predict and improve heavy metal adsorption efficiency onto biochar systems.

Quantum superposition inspired spiking neural network

SummaryDespite advances in artificial intelligence models, neural networks still cannot achieve human performance, partly due to differences in how information is encoded and processed compared with human brain. Information in an artificial neural network (ANN) is represented using a statistical method and processed as a fitting function, enabling handling of structural patterns in image, text, and speech processing. However, substantial changes to the statistical characteristics of the data, for example, reversing the background of an image, dramatically reduce the performance. Here, we propose a quantum superposition spiking neural network (QS-SNN) inspired by quantum mechanisms and phenomena in the brain, which can handle reversal of image background color. The QS-SNN incorporates quantum theory with brain-inspired spiking neural network models from a computational perspective, resulting in more robust performance compared with traditional ANN models, especially when processing noisy inputs. The results presented here will inform future efforts to develop brain-inspired artificial intelligence.

A Sustainable Early Warning System Using Rolling Forecasts Based on ANN and Golden Ratio Optimization Methods to Accurately Predict Real-Time Water Levels and Flash Flood.

Remote monitoring sensor systems play a significant role in the evaluation and minimization of natural disasters and risk. This article presents a sustainable and real-time early warning system of sensors employed in flash flood prediction by using a rolling forecast model based on Artificial Neural Network (ANN) and Golden Ratio Optimization (GROM) methods. This Early Flood Warning System (EFWS) aims to support decision makers by providing reliable and accurate information and warning about any possible flood events within an efficient lead-time to reduce any damages due to flash floods. In this work, to improve the performance of the EFWS, an ANN forecast model based on a new optimization method, GROM, is developed and compared to the traditional ANN model. Furthermore, due to the lack of literature regarding the optimal ANN structural model for forecasting the flash flood, this paper is one of the first extensive investigations into the impact of using different exogenous variables and parameters on the ANN structure. The effect of using a rolling forecast model compared to fixed model on the accuracy of the forecasts is investigated as well. The results indicate that the rolling ANN forecast model based on GROM successfully improved the model accuracy by 40% compared to the traditional ANN model and by 93.5% compared to the fixed forecast model.

Proposing two novel hybrid intelligence models for forecasting copper price based on extreme learning machine and meta-heuristic algorithms

The focus of this study aims at developing two novel hybrid intelligence models for forecasting copper prices in the future with high accuracy based on the extreme learning machine (ELM) and two meta-heuristic algorithms (i.e., particle swarm optimization (PSO) and genetic algorithm (GA)), named as PSO-ELM and GA-ELM models. Accordingly, the time series datasets of the copper price for thirty years were collected based on the influencing parameters, such as crude oil, iron ore, gold, silver, and natural gas prices. Furthermore, the exchange rate of the four largest countries in copper-producing, including Chile (USD/CLP), China (USD/CNY), Peru (USD/PEN), and Australia (USD/AUD), were also considered to evaluate the copper prices. The GA and PSO algorithms then optimized the weights and biases of the ELM model to reduce the error of the ELM model for forecasting copper price. The traditional ELM model (without optimization), and artificial neural networks (ANN) were also developed as the comparative models for resulting in convincing experimental results in the proposed PSO-ELM and GA-ELM models. The results indicated that the proposed hybrid PSO-ELM and GA-ELM models could forecast copper price with higher accuracy and reliability over the traditional ELM and ANN models. Of those, the PSO-ELM yielded the most dominant accuracy with a root-mean-squared error (RMSE) of 304.943, mean absolute error (MAE) of 241.946, mean absolute percentage error (MAPE) of 0.037, and mean absolute scaled error (MASE) of 0.933. The t-test and Wilcoxon test also demonstrated the statistical significance of the proposed models and the best 95% confident interval of the PSO-ELM model with the range of $177.046 to $67.054 with p-value = 2.589e-05. Whereas, the GA-ELM model provided the forecasted copper price higher $137.233 than the actual copper price, and the 95% confidence interval is from $189.672 to $84.793 with p-value = 1.027e-06.

Forecasting and Modelling the Uncertainty of Low Voltage Network Demand and the Effect of Renewable Energy Sources

More and more households are using renewable energy sources, and this will continue as the world moves towards a clean energy future and new patterns in demands for electricity. This creates significant novel challenges for Distribution Network Operators (DNOs) such as volatile net demand behavior and predicting Low Voltage (LV) demand. There is a lack of understanding of modern LV networks’ demand and renewable energy sources behavior. This article starts with an investigation into the unique characteristics of householder demand behavior in Jordan, connected to Photovoltaics (PV) systems. Previous studies have focused mostly on forecasting LV level demand without considering renewable energy sources, disaggregation demand and the weather conditions at the LV level. In this study, we provide detailed LV demand analysis and a variety of forecasting methods in terms of a probabilistic, new optimization learning algorithm called the Golden Ratio Optimization Method (GROM) for an Artificial Neural Network (ANN) model for rolling and point forecasting. Short-term forecasting models have been designed and developed to generate future scenarios for different disaggregation demand levels from households, small cities, net demands and PV system output. The results show that the volatile behavior of LV networks connected to the PV system creates substantial forecasting challenges. The mean absolute percentage error (MAPE) for the ANN-GROM model improved by 41.2% for household demand forecast compared to the traditional ANN model.

A Kalman filter-based hybridization model of statistical and intelligent approaches for exchange rate forecasting

Purpose The purpose of this paper is to propose a comprehensive version of a hybrid autoregressive integrated moving average (ARIMA), and artificial neural networks (ANNs) in order to yield a more general and more accurate hybrid model for exchange rates forecasting. For this purpose, the Kalman filter technique is used in the proposed model to preprocess and detect the trend of raw data. It is basically done to reduce the existing noise in the underlying data and better modeling, respectively. Design/methodology/approach In this paper, ARIMA models are applied to construct a new hybrid model to overcome the above-mentioned limitations of ANNs and to yield a more general and more accurate model than traditional hybrid ARIMA and ANNs models. In our proposed model, a time series is considered as a function of a linear and nonlinear component, so, in the first phase, an ARIMA model is first used to identify and magnify the existing linear structures in data. In the second phase, a multilayer perceptron is used as a nonlinear neural network to model the preprocessed data, in which the existing linear structures are identified and magnified by ARIMA and to predict the future value of time series. Findings In this paper, a new Kalman filter based hybrid artificial neural network and ARIMA model are proposed as an alternate forecasting technique to the traditional hybrid ARIMA/ANNs models. In the proposed model, similar to the traditional hybrid ARIMA/ANNs models, the unique strengths of ARIMA and ANN in linear and nonlinear modeling are jointly used, aiming to capture different forms of relationship in the data; especially, in complex problems that have both linear and nonlinear correlation structures. However, there are no aforementioned assumptions in the modeling process of the proposed model. Therefore, in the proposed model, in contrast to the traditional hybrid ARIMA/ANNs, it can be generally guaranteed that the performance of the proposed model will not be worse than either of their components used separately. In addition, empirical results in both weekly and daily exchange rate forecasting indicate that the proposed model can be an effective way to improve forecasting accuracy achieved by traditional hybrid ARIMA/ANNs models. Originality/value In the proposed model, in contrast to the traditional hybrid ARIMA/ANNs, it can be guaranteed that the performance of the proposed model will not be worse than either of the components used separately. In addition, empirical results in exchange rate forecasting indicate that the proposed model can be an effective way to improve forecasting accuracy achieved by traditional hybrid ARIMA/ANNs models. Therefore, it can be used as an appropriate alternate model for forecasting in exchange ratemarkets, especially when higher forecasting accuracy is needed.

Machine learning approaches for estimation of compressive strength of concrete

Estimation of compressive strength of rubberized concrete is important for engineering safety. In this study, measured data (the compressive strength of rubberized concrete and its impacting factors) were collected by literature review (457 samples). In order to accurately predict the compressive strength of rubberized concrete, four machine learning models [artificial neural network (ANN), k-nearest neighbor (KNN), regression trees (RT), and random forests (RF)] were developed and compared to estimate the compressive strength of rubberized concrete, and the modeling results were compared with two traditional expressions. The model performance was evaluated using three performance indicators: the Nash–Sutcliffe efficiency coefficient (NSC), the root-mean-squared error (RMSE), and the mean absolute error (MAE). The results showed that the RT model performs the best, followed by the ANN and RF in the model training phase. In the model testing phase, the ANN model performs the best, followed by the RT, RF, and KNN. The overall results indicated that the ANN model performs the best, followed by RT and RF, and the KNN model performs the worst. The ANN and RT models outperformed the two traditional expressions. The tree-based models (RT and RF) and KNN model may not be applicative to estimate the compressive strength of rubberized concrete due to the generally poor performances in the model testing phase compared with that in the model training phase. The results showed that the traditional ANN model is sufficient for the accurate estimation of the compressive strength of rubberized concrete when the model is properly trained. The results in the present research can provide reference for the prediction of the compressive strength of rubberized concrete, which will benefit engineering management and safety as well.

Performance evaluation of optimized and adaptive neuro fuzzy inference system for predictive modeling in agriculture

The Neural Network has a significant impact in developing predictive models in a wide range of applications. In this paper, a neuro-fuzzy prediction model is developed depending on improving the performance of the traditional artificial neural networks using Adaptive Momentum Optimizer. This optimizer simulates the behavior of the International Trade Analysis in the agriculture industry, and this method is used to determine the optimal parameters of artificial neural networks. The proposed model is compared with the existing models such as Support Vector Machine, Random Forest, Decision Tree and traditional Artificial Neural Network models. To examine the forecasting performance of the proposed approach, agriculture datasets is used. The performance of the models was assessed using different performance evaluation criteria and the empirical results show that the back propagation neural network with Adam optimizer attains favorable prediction accuracy of 96.78%, and a better convergence rate. Compared to other benchmark algorithms, the proposed algorithm performs better, and the result validates the effectiveness of the back propagation with Adam optimizer for Natural Language Processing.