Application Of Extreme Learning Machine Research Articles

Investigation of the thermal analysis of a wavy fin with radiation impact: an application of extreme learning machine

The combined impact of radiation and convection on the heat transfer of a wavy fin is scrutinized in the present analysis. The novelty of this research work is that it proposes a deterministic machine learning model known as an extreme learning machine to address the heat transfer problem of a wavy fin. The effect of radiation on convective heat transfer and the Rosseland approximation for the radiation heat exchange have been considered in the investigation. The nonlinear ordinary differential equation (ODE) is converted to its nondimensional form using the appropriate dimensionless variables. Runge-Kutta-Fehlberg's fourth-fifth order technique (RKF 45) is used to solve the nondimensional ODE numerically. The roles of convection-conduction, radiation-conduction, thermal conductivity, and radiation parameters have been discussed for satisfying a prescribed temperature distribution in rectangular and wavy fins with graphical visualization. A rise in convection-conduction and radiation-conduction variables decreased the thermal distribution of both the wavy fin and rectangular fin. Further, ANSYS simulation analyzes the variation of temperature and total heat flux in both rectangular and wavy fins. The study demonstrates the effectiveness of the model selected through the obtained results, which indicate the potential of the regression model for providing an accurate prediction.

Application of Extreme Learning Machine to Reservoir Proxy Modeling

Application of Extreme Learning Machine to Reservoir Proxy Modeling

Application of Extreme Learning Machine in the Survival Analysis of Chronic Heart Failure Patients With High Percentage of Censored Survival Time.

Objective: To explore the application of the Cox model based on extreme learning machine in the survival analysis of patients with chronic heart failure.Methods: The medical records of 5,279 inpatients diagnosed with chronic heart failure in two grade 3 and first-class hospitals in Taiyuan from 2014 to 2019 were collected; with death as the outcome and after the feature selection, the Lasso Cox, random survival forest (RSF), and the Cox model based on extreme learning machine (ELM Cox) were constructed for survival analysis and prediction; the prediction performance of the three models was explored based on simulated data with three censoring ratios of 25, 50, and 75%.Results: Simulation results showed that the prediction performance of the three models decreased with increasing censoring proportion, and the ELM Cox model performed best overall; the ELM Cox model constructed with 21 highly influential survival predictors screened from actual chronic heart failure data showed the best performance with C-index and Integrated Brier Score (IBS) of 0.775(0.755, 0.802) and 0.166(0.150, 0.182), respectively.Conclusion: The ELM Cox model showed good discrimination performance in the survival analysis of patients with chronic heart failure; it performs consistently for data with a high proportion of censored survival time; therefore, the model could help physicians identify patients at high risk of poor prognosis and target therapeutic measures to patients as early as possible.

Plant Diseases Classification using Machine Learning

Plant diseases are one of source of obstruction in the quality and productivity of plants which can lead to the shortage of food supply. Therefore, plant disease classification is essential to the agriculture industry. The objective of this research is to classify the plant diseases by assessing the images of the leaves with the application of Extreme Learning Machine (ELM), a Machine Learning classification algorithm with a single layer feed-forward neural network. This work proposed image features as input where the image is pre-processed via HSV colour space and features extraction via Haralick textures. The features are then fitted in the ELM classifier to perform the model training and testing. The accuracy of ELM is then calculated after the testing has been done. The dataset used comprises of tomato plant leaves which is a subset of the Plant-Village dataset. The results produced from the ELM shows a better accuracy that is 84.94% when compared to other models such as the Support Vector Machine and Decision Tree.

Application of extreme learning machine in plant disease prediction for highly imbalanced dataset

Plant diseases are responsible for global economic losses due to degradation in the quality and productivity of plants. Therefore, plant disease prediction has become an essential area of research for agricultural scientists. The current study implements Extreme Learning Machine (ELM) algorithm for plant disease prediction based on a dataset collected in real time scenario namely Tomato Powdery Mildew Disease (TPMD) dataset. Since, the collected TPMD dataset was imbalanced thus; various resampling techniques namely Importance Sampling (IMPS), Synthetic Minority Over-sampling Technique (SMOTE), Random under Sampling (RUS), and Random over Sampling (ROS) have been used here for balancing the dataset before using it in the specified prediction model. ELM models have been developed for each of the balanced TPMD datasets obtained from these resampling techniques as well as for the imbalanced TPMD dataset. Area under Curve (AUC) and Classification Accuracy (CA) are considered as performance measures to analyze the performance of ELM model. Results show that ELM performed better for TPMD dataset using resampling techniques. Best results are obtained with IMPS technique having maximum values for CA and AUC i.e. 89.19% and 88.57% respectively.

Expression of Concern: Application of extreme learning machine for prediction of aqueous solubility of carbon dioxide

The Editors-in-Chief of Environmental Earth Sciences are issuing an editorial expression of concern to alert readers that this article [1] shows evidence of substantial text overlap (most notably with the article cited [2]) and authorship manipulation. None of the authors responded to correspondence about this editorial expression of concern.

Application of extreme learning machine in behavior of beam to column connections

The effectiveness and performance of beam to column connections have been highly affected by the type and properties of structures. An effective and stable design of beam to column connections is critical because in many cases, a single connection must be able to carry several types of loads at the same time. When designing these connections, it is important to consider erection, serviceability, strength, and cost-effectiveness as an efficient design that must be economic in practice. In this study, experimental results for the designed beam to column connection in concrete frames have been applied while evaluating the outcomes. Development and applying of Extreme Learning Machine (ELM) for moment prediction of beam to column connections are the objectives of this study. By understanding and integrating the relevant simulation outcomes and other statistical indicators, the reliability of the computational models has been analyzed.

Wind Power Prediction of Kernel Extreme Learning Machine Based on Differential Evolution Algorithm and Cross Validation Algorithm

As fossil fuel is being depleted, the percentage of wind power capacity in total electricity generation is increasing. In order to improve the absorption capacity of wind power, wind power prediction has been introduced. Aiming at the disadvantage of low prediction accuracy and unstable model of traditional extreme learning machine (ELM), a kernel extreme learning machine based on differential evolution (DE) and cross validation optimization method is proposed to predict short-term wind power generation. Firstly, the average mean square error (MSE) verified by k folding and cross validation is adopted as the error function of the model to improve the stability and generalization performance of the model. Secondly, differential evolution algorithm is used to optimize the regularization coefficient and kernel width of the kernel extreme learning machine with cross validation and improve the precision of model is 8.34%. Finally, compared with the application of extreme learning machine with genetic algorithm and cross validation to a wind farm prediction case in northwest China, the experimental results show that the convergence rate of this method is twice that of genetic algorithm (GA) optimization algorithm, and the accuracy is higher.

Segmented analysis of time-of-flight diffraction ultrasound for flaw detection in welded steel plates using extreme learning machines

This work investigates the application of extreme learning machine, a fast training neural network model, for an ultrasound nondestructive evaluation decision support system. A novel segmented analysis of time-of-flight diffraction ultrasound signals is proposed in order to produce high flaw detection efficiency and low computational requirements, making it possible to be used in embedded applications. The frequency contents of TOFD signals temporal segments, estimated using the discrete Fourier transform, were used to feed the classification system. The test objects consisted of a set of SAE 1020 welded carbon steel plates, in which occur four types of defects. The obtained experimental results indicate that the proposed method is able to combine high accuracy, fast training and full exploration of the TOFD signal information.

Application of Extreme Learning Machine for Predicting Chlorophyll-a Concentration Inartificial Upwelling Processes

Artificial upwelling, artificially pumping up nutrient-rich ocean waters from deep to surface, is increasingly applied to stimulating phytoplankton activity. As a proxy for the amount of phytoplankton present in the ocean, the concentration of chlorophyll a (chl-a) may be influenced by water physical factors altered in artificial upwelling processes. However, the accuracy and convenience of measuring chl-a are limited by present technologies and equipment. Our research intends to study the correlations between chl-a concentration and five water physical factors, i.e., salinity, temperature, depth, dissolved oxygen (DO), and pH, possibly affected by artificial upwelling. In this paper, seven models are presented to predict chl-a concentration, respectively. Two of them are based on traditional regression algorithms, i.e., multiple linear regression (MLR) and multivariate quadratic regression (MQR), while five are based on intelligent algorithms, i.e., backpropagation-neural network (BP-NN), extreme learning machine (ELM), genetic algorithm-ELM (GA-ELM), particle swarm optimization-ELM (PSO-ELM), and ant colony optimization-ELM (ACO-ELM). These models provide a quick prediction to study the concentration of chl-a. With the experimental data collected from Xinanjiang Experiment Station in China, the results show that chl-a concentration has a strong correlation with salinity, temperature, DO, and pH in the process of artificial upwelling and PSO-ELM has the best overall prediction ability.

Retraction Note to: Erratum to: Application of extreme learning machine for estimation of wind speed distribution

The Editors have retracted this article because validity of the content of this article cannot be verified.

Retraction Note to: Application of extreme learning machine for estimation of wind speed distribution

Retraction Note to: Application of extreme learning machine for estimation of wind speed distribution

A fast and efficient conformal regressor with regularized extreme learning machine

Abstract A conformal regressor combines conformal prediction and a traditional regressor for point predictions. It produces a valid prediction interval for a new testing input such that the probability of the target output being not included in the prediction interval is not more than a preset significance level. Although conformal prediction is both theoretically and empirically valid, one main drawback of the existing conformal regressors is their computational inefficiency. This paper proposes a novel fast and efficient conformal regressor named LW-JP-RELM, with combination of the local-weighted jackknife prediction (LW-JP), a new variant of conformal prediction, and the regularized extreme learning machine (RELM). The development of our learning algorithm is important both for the applications of extreme learning machine and conformal prediction. On the one hand, LW-JP-RELM complements ELM with interval predictions that satisfy a given level of confidence. On the other hand, the underlying learning process and the outstanding learning ability of RELM make LW-JP-RELM a very fast and informationally efficient conformal regressor. In the experiments, the empirical validity and informational efficiency of our method were compared to those of the state-of-art on 20 public data sets and the results confirmed that LW-JP-RELM is a competitive and promising conformal regressor.

Application of Extreme Learning Machine in GPS Positioning Process

In the positioning process of GPS, the linear least squares algorithm and Kalman filtering algorithm are widely used but still have shortcomings. Application of extreme learning machine in this area is proposed in this paper, which breaks through the limitations of the traditional method of positioning based on mathematical models. Two simulation experiments of ELM in GPS positioning process are presented in this paper while the latter is a supplement to the former. Each one contains three phases, including simulation data generation, network training and network prediction, each of which is considered carefully. The feasibility of extreme learning machine is verified through experimental simulation. A more accurate positioning result can be obtained.

Sugarcane growth prediction based on meteorological parameters using extreme learning machine and artificial neural network

ABSTRACTManagement strategies for sustainable sugarcane production need to deal with the increasing complexity and variability of the whole sugar system. Moreover, they need to accommodate the multiple goals of different industry sectors and the wider community. Traditional disciplinary approaches are unable to provide integrated management solutions, and an approach based on whole systems analysis is essential to bring about beneficial change to industry and the community. The application of this approach to water management, environmental management and cane supply management is outlined, where the literature indicates that the application of extreme learning machine (ELM) has never been explored in this realm. Consequently, the leading objective of the current research was set to filling this gap by applying ELM to launch swift and accurate model for crop production data-driven. The key learning has been the need for innovation both in the technical aspects of system function underpinned by modelling of sugarcane growth. Therefore, the current study is an attempt to establish an integrate model using ELM to predict the concluding growth amount of sugarcane. Prediction results were evaluated and further compared with artificial neural network (ANN) and genetic programming models. Accuracy of the ELM model is calculated using the statistics indicators of Root Means Square Error (RMSE), Pearson Coefficient (r), and Coefficient of Determination (R2) with promising results of 0.8, 0.47, and 0.89, respectively. The results also show better generalization ability in addition to faster learning curve. Thus, proficiency of the ELM for supplementary work on advancement of prediction model for sugarcane growth was approved with promising results.

Extreme Learning Machine With Enhanced Composite Feature for Spectral-Spatial Hyperspectral Image Classification

The applications of extreme learning machine (ELM) to the hyperspectral-image (HSI) classification have attracted a great deal of research attention because of its excellent performance and fast learning speed. However, conventional ELM is unable to achieve satisfactory accuracy since it only exploits the spectral information to conduct the HSI classification. To address the above issues, we propose a novel classification algorithm based on both spectral and multiscale spatial information, referred to as ELM with enhanced composite feature (ELM-ECF). To be specific, we adopt the original ELM, exploit a multiscale spatial weighted-mean-filtering-based approach to extract multiple spatial information, and use the majority vote method to select the final classification result. The proposed ELM-ECF significantly improves the classification accuracy of the original ELM. Experimental results on three public HSIs (i.e., Indian Pines data set, Pavia University data set, and Salinas data set) illustrate that the proposed ELM-ECF outperforms a variety of the state-of-the-art HSI classification counterparts in terms of classification accuracy.

Application of Extreme Learning Machine to Damage Detection of Plate-Like Structures

An effective method for damage detection of plate structures using the extreme learning machine (ELM) is proposed in this study. With the ELM, the mode shapes and natural frequencies of a damaged plate are treated as the input and the damage states in the plate elements as the output. The proposed method was applied to two numerical examples, namely, a cantilever and a plate with four-fixed supports containing one or several damages with and without noise in the modal data. The results obtained reveal that the methodology can be used as an effective technique for the damage identification of plate structures using the modal data and ELM.

Application of extreme learning machine for short term output power forecasting of three grid-connected PV systems

The power output (PO) of a photovoltaic (PV) system is highly variable because of its dependence on solar irradiance and other meteorological factors. Hence, accurate PO forecasting of a grid-connected PV system is essential for grid stability, optimal unit commitment, economic dispatch, market participation and regulations. In this paper, a day ahead and 1 h ahead mean PV output power forecasting model has been developed based on extreme learning machine (ELM) approach. For this purpose, the proposed forecasting model is trained and tested using PO of PV system and other meteorological parameters recorded in three grid-connected PV system installed on a roof-top of PEARL laboratory in University of Malaya, Malaysia. The results obtained from the proposed model are compared with other popular models such as support vector regression (SVR) and artificial neural network (ANN). The performance in terms of accuracy and precision of the prediction models is conducted with standard statistical error indicators including: relative root mean square error (RMSE), mean absolute percentage error (MAPE), mean absolute bias error (MABE) and coefficient of determination (R2). The comparison of results obtained from the proposed ELM model to other models showed that ELM model enjoys higher accuracy and less computational time in forecasting the daily and hourly PV output power.

The Application of Extreme Learning Machine and Support Vector Machine in Speech Endpoint Detection

In this paper, a general voice activity detection (VAD) method based on pattern recognition is proposed, and a specific algorithm of endpoint detection is researched. In this method, the Extreme Learning Machine (ELM) and Genetic Algorithm (GA) optimization Support Vector Machine (SVM) is used as the training and recognition model. The simulation results indicates that ELM and GA-SVM have the same superior endpoint detection accuracy, and recognition time were similar, but the training time of ELM only up to a 1/2000 of the GA-SVM, the robustness of ELM and GA-SVM is greatly improved in noisy environment compare with the traditional VAD that depends on time-domain energy and zero crossing rate.

Application of Extreme Learning Machines to inverse neutron kinetics

The paper presents the application of Extreme Leaning Machines (ELMs) for inverse reactor kinetic applications. ELMs were proposed by Huang and co-workers (2004, 2006a,b, 2015), which showed their enhances capabilities in terms of training speed and generalization with respect to classical Artificial Neural Networks (ANNs). ELMs are here implemented for reactivity determination as an alternative to ANNs (e.g. Picca et al. (2008)) and Gaussian Processes (Picca and Furfaro, 2012). After a review of the main features of ELMs, their application to inverse kinetic problems is proposed. The ELMs performance is tested on a typical accelerator drive system configuration (Yalina reactor) and the inversion is carried out on an accurate kinetic model (multi-group transport).