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This paper aims to propose a new neural-based enhanced extreme learning machine (EELM) algorithm, used as an online adaptive estimation model, regarding undetermined system dynamics and containing internal/external perturbations.

The EELM structure bases on the single layer feed-forward neural (SLFN) model in which the hidden weighting coefficients are initiated in random and the weighting outputs of the SLFN are online modified using an online adaptive rule implemented from Lyapunov stability concept.

Four different benchmark uncertain chaotic system tests have been satisfactorily investigated for demonstrating the superiority of proposed EELM technique.

Authors confirm that this manuscript is original.

This paper aims to deal with the optimal choice of a novel extreme learning machine (ELM) architecture based on an ensemble of classic ELM called Meta-ELM structural parameters by using a forecasting process.

The modelling performance of the Meta-ELM architecture varies depending on the network parameters it contains. The choice of Meta-ELM parameters is important for the accuracy of the models. For this reason, the optimal choice of Meta-ELM parameters is investigated on the problem of wind speed forecasting in this paper. The hourly wind-speed data obtained from Bilecik and Bozcaada stations in Turkey are used. The different number of ELM groups (M) and nodes (N_h) are analysed for determining the best modelling performance of Meta-ELM. Also, the optimal Meta-ELM architecture forecasting results are compared with four different learning algorithms and a hybrid meta-heuristic approach. Finally, the linear model based on correlation between the parameters was given as three dimensions (3D) and calculated.

It is observed that the analysis has better performance for parameters of Meta-ELM, M = 15 − 20 and N_h = 5 − 10. Also considering the performance metric, the Meta-ELM model provides the best results in all regions and the Levenberg–Marquardt algorithm -feed forward neural network and adaptive neuro fuzzy inference system -particle swarm optimization show competitive results for forecasting process. In addition, the Meta-ELM provides much better results in terms of elapsed time.

The original contribution of the study is to investigate of determination Meta-ELM parameters based on forecasting process.

This paper aims to introduce a novel algorithm to solve initial/boundary value problems of high-order ordinary differential equations (ODEs) and high-order system of ordinary differential equations (SODEs).

The proposed method is based on Hermite polynomials and extreme learning machine (ELM) algorithm. The Hermite polynomials are chosen as basis function of hidden neurons. The approximate solution and its derivatives are expressed by utilizing Hermite network. The model function is designed to automatically meet the initial or boundary conditions. The network parameters are obtained by solving a system of linear equations using the ELM algorithm.

To demonstrate the effectiveness of the proposed method, a variety of differential equations are selected and their numerical solutions are obtained by utilizing the Hermite extreme learning machine (H-ELM) algorithm. Experiments on the common and random data sets indicate that the H-ELM model achieves much higher accuracy, lower complexity but stronger generalization ability than existed methods. The proposed H-ELM algorithm could be a good tool to solve higher order linear ODEs and higher order linear SODEs.

The H-ELM algorithm is developed for solving higher order linear ODEs and higher order linear SODEs; this method has higher numerical accuracy and stronger superiority compared with other existing methods.

The purpose of this paper is to investigate the relationship between the keyhole geometry and acoustic signatures from the backside of a workpiece. It lays a solid foundation for monitoring the penetration state in variable polarity keyhole plasma arc welding.

The experiment system is conducted on 6-mm-thick aluminum alloy plates based on a dual-sensor system including a sound sensor and a charge coupled device (CCD) camera. The first step is to extract the keyhole boundary from the acquired keyhole images based on median filtering and edge extraction. The second step is to process the acquired acoustic signal to obtain some typical time domain features. Finally, a prediction model based on the extreme learning machine (ELM) technique is built to recognize different keyhole geometries through the acoustic signatures and then identify the welding penetration status according to the recognition results.

The keyhole geometry and acoustic features after processing can be closely related to dynamic change information of keyhole. These acoustic features can predict the keyhole geometry accurately based on the ELM model. Meanwhile, the predict results also can identify different welding penetration status.

This paper tries to make a foundation work to achieve the monitoring of keyhole condition and penetration status through image and acoustic signals. A useful model, ELM, is built based on these features for predicting the keyhole geometry. Compared with back-propagating neural network and support vector machine, this proposed model is faster and has better generalization performance in the case studied in this paper.

The purpose of this study is to establish a vibration prediction of pellet mills power transmission by artificial neural network. Vibration monitoring is an important task for any system to ensure safe operations. Improvement of control strategies is crucial for the vibration monitoring.

As predictive control is one of the options for the vibration monitoring in this paper, the predictive model for vibration monitoring was created.

Although the achieved prediction results were acceptable, there is need for more work to apply and test these results in real environment.

Artificial neural network (ANN) was implemented as the predictive model while extreme learning machine (ELM) and back propagation (BP) learning schemes were used as training algorithms for the ANN. BP learning algorithm minimizes the error function by using the gradient descent method. ELM training algorithm is based on selecting of the input weights randomly of the ANN network and the output weight of the network are determined analytically.

In this research, the authors apply artificial neural networks (ANNs) to uncover non-linear relationships among factors that influence the productivity of ragpickers in the Indian context.

A broad long-term action research program provides a means to shape the research question and posit relevant factors, whereas ANNs capture the true underlying non-linear relationships. ANN models the relationships between four independent variables and three forms of waste value chains without assuming any distributional forms. The authors apply bootstrapping in conjunction with ANNs.

The authors identify four elements that influence ragpickers’ productivity: receptiveness to non-governmental organizations, literacy, the deployment of proper equipment/technology and group size.

This study provides a unique way to analyze bottom of the pyramid (BoP) operations via ANNs.

This study provides a road map to help ragpickers in India raise incomes while simultaneously improving recycling rates.

This research is grounded in the stakeholder resource-based view and the network–individual–resource model. It generalizes these theories to the informal waste value chain at BoP communities.

Parkinson's disease (PD) is a well-known complex neurodegenerative disease. Typically, its identification is based on motor disorders, while the computer estimation of its main symptoms with computational machine learning (ML) has a high exposure which is supported by researches conducted. Nevertheless, ML approaches required first to refine their parameters and then to work with the best model generated. This process often requires an expert user to oversee the performance of the algorithm. Therefore, an attention is required towards new approaches for better forecasting accuracy.

To provide an available identification model for Parkinson disease as an auxiliary function for clinicians, the authors suggest a new evolutionary classification model. The core of the prediction model is a fast learning network (FLN) optimized by a genetic algorithm (GA). To get a better subset of features and parameters, a new coding architecture is introduced to improve GA for obtaining an optimal FLN model.

The proposed model is intensively evaluated through a series of experiments based on Speech and HandPD benchmark datasets. The very popular wrappers induction models such as support vector machine (SVM), K-nearest neighbors (KNN) have been tested in the same condition. The results support that the proposed model can achieve the best performances in terms of accuracy and g-mean.

A novel efficient PD detection model is proposed, which is called A-W-FLN. The A-W-FLN utilizes FLN as the base classifier; in order to take its higher generalization ability, and identification capability is also embedded to discover the most suitable feature model in the detection process. Moreover, the proposed method automatically optimizes the FLN's architecture to a smaller number of hidden nodes and solid connecting weights. This helps the network to train on complex PD datasets with non-linear features and yields superior result.

This research presents machine learning models for predicting international tourist arrivals in Indonesia during the COVID-19 pandemic using multisource Internet data.

To develop the prediction models, this research utilizes multisource Internet data from TripAdvisor travel forum and Google Trends. Temporal factors, posts and comments, search queries index and previous tourist arrivals records are set as predictors. Four sets of predictors and three distinct data compositions were utilized for training the machine learning models, namely artificial neural networks (ANNs), support vector regression (SVR) and random forest (RF). To evaluate the models, this research uses three accuracy metrics, namely root mean square error (RMSE), mean absolute error (MAE) and mean absolute percentage error (MAPE).

Prediction models trained using multisource Internet data predictors have better accuracy than those trained using single-source Internet data or other predictors. In addition, using more training sets that cover the phenomenon of interest, such as COVID-19, will enhance the prediction model's learning process and accuracy. The experiments show that the RF models have better prediction accuracy than the ANN and SVR models.

First, this study pioneers the practice of a multisource Internet data approach in predicting tourist arrivals amid the unprecedented COVID-19 pandemic. Second, the use of multisource Internet data to improve prediction performance is validated with real empirical data. Finally, this is one of the few papers to provide perspectives on the current dynamics of Indonesia's tourism demand.

The purpose of this paper is to deal with the classification improvement of pollutant using WO3 gases sensors. To evaluate the discrimination capacity, some experiments were achieved using three gases: ozone, ethanol, acetone and a mixture of ozone and ethanol via four WO3 sensors.

To improve the classification accuracy and enhance selectivity, some combined features that were configured through the principal component analysis were used. First, evaluate the discrimination capacity; some experiments were performed using three gases: ozone, ethanol, acetone and a mixture of ozone and ethanol, via four WO3 sensors. To this end, three features that are derivate, integral and the time corresponding to the peak derivate have been extracted from each transient sensor response according to four WO3 gas sensors used. Then these extracted parameters were used in a combined array.

The results show that the proposed feature extraction method could extract robust information. The Extreme Learning Machine (ELM) was used to identify the studied gases. In addition, ELM was compared with the Support Vector Machine (SVM). The experimental results prove the superiority of the combined features method in our E-nose application, as this method achieves the highest classification rate of 90% using the ELM and 93.03% using the SVM based on Radial Basis Kernel Function SVM-RBF.

Combined features have been configured from transient response to improve the classification accuracy. The achieved results show that the proposed feature extraction method could extract robust information. The ELM and SVM were used to identify the studied gases.

The purpose of this paper is to provide reference for researchers by reviewing the research advances and trend of agricultural product price forecasting methods in recent years.

This paper reviews the main research methods and their application of forecasting of agricultural product prices, summarizes the application examples of common forecasting methods, and prospects the future research directions.

1) It is the trend to use hybrid models to predict agricultural products prices in the future research; 2) the application of the prediction model based on price influencing factors should be further expanded in the future research; 3) the performance of the model should be evaluated based on DS rather than just error-based metrics in the future research; 4) seasonal adjustment models can be applied to the difficult seasonal forecasting tasks in the agriculture product prices in the future research; 5) hybrid optimization algorithm can be used to improve the prediction performance of the model in the future research.

The methods from this paper can provide reference for researchers, and the research trends proposed at the end of this paper can provide solutions or new research directions for relevant researchers.