Extreme Learning Machine Research Articles

Pair-Soil-Spectra: An Approach for NIRS-Based Soil Total Nitrogen Content Detection with Feature Metrics in Cases of Small Sample Sizes.

Soil total nitrogen (STN) plays an important role in plant growth, and rapid and nondestructive detection of STN content is essential for agricultural production. Near-infrared spectroscopy (NIRS) takes advantage of the fast detection speed, low cost, and nondestructiveness, and it can be used for STN content detection. Typically, NIRS-based approaches require a large number of samples for detection model training. However, it is difficult to collect sufficient samples due to various causes (e.g., time-varying state, high assay costs, etc.) in practical application. To tackle this problem, a feature metric approach is introduced to detect the STN content based on NIRS in this work, and a new approach (named Pair-Soil-Spectra) is proposed to mine fine-grained features by contrasting different soil sample pairs, which takes full advantage of soil particle heterogeneity and NIRS penetration. For the validation of this study, three different soil datasets with various collection sources are selected as research subjects, and the performance of Pair-Soil-Spectra is analyzed from different perspectives. According to the results, Pair-Soil-Spectra has significantly improved the performance of STN content detection models (e.g., partial least-squares (PLS), Cubist, extreme learning machine (ELM), and random forest (RF)) in small sample cases. Of these, the coefficient of determination of RF has improved by 0.13, 0.42, and 0.10, and the root-mean-square of prediction has decreased by 0.15, 0.52, and 0.01 g/kg with different datasets, which has gained the greatest improvement. Meanwhile, this approach can be easily expanded to cover other domains.

Optimizing quality of service forecasting in mobile networks through modified walrus optimization and multivariate approaches

This paper presents Ensemble-based Service Quality Prediction (EAQP), an automated method for predicting service quality under changing mobile network conditions. EAQP incorporates data preparation methods such as transformation, purification, & imputation, and then performs feature extraction utilizing statistical, geographical, as well as temporal approaches. An improved feature selection method, using a unique weighting approach and optimized by a modified Walrus Optimization Algorithm, improves the accuracy of predictions. EAQP utilizes a variety of prediction models such as support vector regression, recurrent neural network models, bi-directional short-term long-term memory networks, extreme learning machines, along with multi-layer perceptron neural networks to enhance predictive accuracy. EAQP uses complex optimization algorithms and ensemble learning approaches to provide precise and dependable predictions about service quality in real-time. This helps in proactive network management as well as improvement. This comprehensive approach shows potential for boosting network efficiency, optimizing the distribution of resources, and enhancing the end-user experience when using mobile communications systems.

Machine learning in predicting heart failure survival: a review of current models and future prospects.

Heart failure is a complex and prevalent condition with significant implications for patient management and survival prediction. Traditional predictive models often fall short in accuracy due to their reliance on pre-specified predictors and assumptions of variable independence. This review aims to assess the role of machine learning (ML) algorithms in predicting heart failure survival, comparing their performance with traditional statistical methods and identifying key predictive features. We conducted a review of studies utilizing ML algorithms for heart failure survival prediction. Data were sourced from PubMed/MEDLINE, Google Scholar, ScienceDirect, Embase, DOAJ, and the Cochrane Library, covering studies published until July 2024. A total of 10 studies were reviewed, encompassing 468,171 patients with heart failure. ML algorithms, particularly random forests and gradient boosting methods, demonstrated superior performance compared to traditional statistical models. These algorithms effectively identified key risk factors and stratified patients into risk categories with high accuracy. Notably, extreme learning machine (ELM) and CatBoost models showed exceptional predictive capabilities, as indicated by metrics such as Harrell's concordance index (C-index) and area under the curve (AUC). Key predictive features included ejection fraction (EF), serum creatinine (S Cr), and blood urea nitrogen (BUN). ML algorithms offer significant advantages in predicting heart failure survival by uncovering complex patterns and improving risk stratification. Their integration into clinical practice could lead to more personalized treatment strategies and enhanced patient outcomes. However, challenges such as data quality, model interpretability, and integration into clinical workflows need to be addressed.

Evolutionary extreme learning machine based on an improved MOPSO algorithm

Evolutionary extreme learning machine based on an improved MOPSO algorithm

Security Situation Assessment for Terminal Area Control System Operation Based on BN-ISSA-ELM

Performing an operational safety situation evaluation of the terminal area control system is crucial for enhancing safety management and ensuring operational safety in the terminal area. We use a combination active–passive risk source identification method to thoroughly identify the safety features of the terminal area control system, and then establish a related indicator system. After that, the system’s secondary and primary indicators are used as input and output vectors for an Extreme Learning Machine (ELM). This creates an ISSA-ELM-based risk probability prediction model for the primary indicators, which gives us the predicted values of the primary indicator risks. Bayesian theory merges the output results to determine the overall safety status of the terminal area control system. The evaluation results indicate a distinct correlation between the secondary and primary indicators in a terminal area of North China. The ELM, which has been improved by the Sparrow Search Algorithm (ISSA), correctly displays this mapping and predicts the operational risks. The improved model is able to make predictions that are more than 80% accurate. A comparison of the model’s evaluation results with the actual operational conditions during the same period demonstrates consistency, suggesting that this method may consistently evaluate the operational safety status of the terminal area control system.

Prediction of Anthocyanin Content in Purple-Leaf Lettuce Based on Spectral Features and Optimized Extreme Learning Machine Algorithm

Prediction of Anthocyanin Content in Purple-Leaf Lettuce Based on Spectral Features and Optimized Extreme Learning Machine Algorithm

Advancements in evaporation prediction: introducing the Gated Recurrent Unit–Multi-Kernel Extreme Learning Machine (MKELM)–Gaussian Process Regression (GPR) model

Predicting evaporation is an essential topic in water resources management. It is critical to plan irrigation schedules, optimize hydropower production, and accurately calculate the overall water balance. Thus, researchers have developed many prediction models for predicting evaporation. Despite the development of these models, there are still unresolved challenges. These challenges include selecting the most important input parameters, handling nonstationary data, extracting critical information from data, and quantifying the uncertainty of predicted values. Thus, the main aim of this study is to address these challenges by developing a new prediction model. The new prediction model, named Gated Recurrent Unit–Multi-Kernel Extreme Learning Machine (MKELM)–Gaussian Process Regression (GPR), was used to predict one-month ahead evaporation in the Kashafrood basin, Iran. This model was executed in multiple stages. First, a feature selection algorithm was used to determine the most critical input parameters. A data processing technique was then employed to decompose nonstationary data into stationary intrinsic mode functions (IMFs). The GRU model then processed these components to extract their essential information. In the following step, the extracted information was inserted into the MKELM model to predict evaporation. Finally, the GPR model quantified the uncertainty of predicted values. Our research also introduces a new optimizer called the Salp Swarm Optimization Algorithm–Sine Cosine Optimization Algorithm. This algorithm was used to tune the model parameters. This algorithm's performance and the prediction models’ accuracy were evaluated using several error indices. According to the study results, the GRU–MKELM–GPR model performed better than other models in predicting monthly evaporation. It improved the training and testing mean absolute error values of the other models by 21%-43% and 8.2–33%, respectively. Moreover, the new model improved the R2 (R-squared or coefficient of determination) values of other models by 5–12%. Generally, the main findings of this paper included the superior performance of the new model in predicting evaporation data and the superior performance of a new optimizer in adjusting model parameters. These findings highlighted the effectiveness of the suggested model in addressing the challenges associated with evaporation prediction.

Conceptual metaphor quantum correlation and radial basis extreme learning for predicting chronic kidney disease

Chronic kidney disease is a progressive condition that often remains unnoticed until substantial kidney damage has occurred, leading to severe complications like nerve damage, pregnancy issues, and potentially fatal kidney failure. Early diagnosis of chronic kidney disease is challenging due to a lack of distinct indicators. To enhance detection, a new machine learning technique called conceptual metaphor quantum mutual correlation and radial basis extreme learning is developed for chronic kidney disease diagnosis. This approach starts by extracting critical terms from unstructured datasets. It then applies quantum mutual information-based feature selection to identify highly correlated feature subsets from both unstructured and structured data. Finally, radial basis extreme learning is used for classification, overcoming imbalanced data and computational issues typical in extreme learning machines. The proposed method demonstrates remarkable accuracy (98 %), precision (88 %), and recall (92 %), surpassing traditional models, and holds significant potential for improving chronic kidney disease detection and treatment.

Power quality disturbances categorization using Identity Feature Vector and Extreme Learning Machine

Power quality disturbances are variations or anomalies in the voltage, current, or frequency of electrical power that can affect the proper operation of electrical equipment. These disturbances are usually classified into different categories based on their attributes and effects. This article presents an intelligent technique based on an Identity Feature Vector and an Extreme Learning Machine (ELM). This study first derives a constant length vector for each disturbance signal. A wavelet transform is applied to derive attributes from the input disturbance signal, and the identity vector is formed using the approximation coefficients. After the required normalization procedures, the normalized identity vector is classified using an ELM. To assess the productivity of the suggested approach, 12 types of disturbances, single and combined, are generated, and the system's efficiency is studied. The results indicate that ten out of 12 combinations, including Harmonic, Sag, and Flicker, were detected with 100% accuracy. Additionally, the combination "Harmonic + Swell" exhibited the lowest accuracy, identified with 98% accuracy. The total average accuracy of this method is 99.75%. The outcomes demonstrate the highly favorable performance of this approach. This study evaluated the analyzed algorithm under noisy conditions with three different noise levels: 30 dB, 40 dB, and 50 dB, respectively. The average prediction accuracy for these three noise levels is 99.16%, 99.25%, and 98.91%. The outcomes demonstrate that the evaluated algorithm accurately detects power quality disturbances across various noisy conditions.

Machine learning-based performance prediction for energy storage medium-deep borehole ground source heat pump systems

With the rapid development of artificial intelligence, data-driven prediction models play an important role in energy demand forecasting and performance prediction. This study established performance prediction models for medium-deep borehole ground source heat pump (MDB-GSHP) systems to predict the coefficient of performance (COP) of GSHP, utilizing back propagation neural network (BPNN), extreme learning machine (ELM), support vector machine (SVM), and particle swarm optimization-support vector machine (PSO-SVM) method that optimizes the penalty parameter (C), insensitive loss parameter (ε), and kernel parameter (γ) of SVM using PSO. Operational data were collected through field experiments, and typical parameters were selected as features to establish the feature set. The feature set was then optimized using Pearson correlation analysis and recursive feature elimination (RFE), resulting in the creation of five distinct feature sets. Error analysis and trend evaluation results indicate that the combined feature set (FS5), which incorporates the advantages of multiple feature selection methods, demonstrates the best performance. The PSO-SVM model exhibits outstanding performance under various input conditions, achieving an accuracy of over 90% within the error range. When using FS5, the PSO-SVM model achieves a mean absolute percentage error (MAPE) of 0.037, mean absolute error (MAE) of 0.103, root mean square error (RMSE) of 0.125, coefficient of multiple determinations (R2) of 0.970, and explained variance score (EVS) of 0.967, showcasing excellent predictive performance. The research results can provide a basis for the formulation of storage-related operational parameters and the optimization of system operation for energy storage in MDB-GSHP heating systems.

Improving laryngeal cancer detection using chaotic metaheuristics integration with squeeze-and-excitation resnet model.

Laryngeal cancer (LC) represents a substantial world health problem, with diminished survival rates attributed to late-stage diagnoses. Correct treatment for LC is complex, particularly in the final stages. This kind of cancer is a complex malignancy inside the head and neck region of patients. Recently, researchers serving medical consultants to recognize LC efficiently develop different analysis methods and tools. However, these existing tools and techniques have various problems regarding performance constraints, like lesser accuracy in detecting LC at the early stages, additional computational complexity, and colossal time utilization in patient screening. Deep learning (DL) approaches have been established that are effective in the recognition of LC. Therefore, this study develops an efficient LC Detection using the Chaotic Metaheuristics Integration with the DL (LCD-CMDL) technique. The LCD-CMDL technique mainly focuses on detecting and classifying LC utilizing throat region images. In the LCD-CMDL technique, the contrast enhancement process uses the CLAHE approach. For feature extraction, the LCD-CMDL technique applies the Squeeze-and-Excitation ResNet (SE-ResNet) model to learn the complex and intrinsic features from the image preprocessing. Moreover, the hyperparameter tuning of the SE-ResNet approach is performed using a chaotic adaptive sparrow search algorithm (CSSA). Finally, the extreme learning machine (ELM) model was applied to detect and classify the LC. The performance evaluation of the LCD-CMDL approach occurs utilizing a benchmark throat region image database. The experimental values implied the superior performance of the LCD-CMDL approach over recent state-of-the-art approaches.

Prediction model for earthquake death toll based on PCA-BAS-ELM in mainland China

In recent years, China has experienced frequent catastrophic earthquakes, causing huge casualties. If the death toll can be quickly predicted after a disaster, then relief supplies can be delivered in a timely and reasonable manner, and the death toll and property losses can be minimized. Therefore, rapid and effective prediction of earthquake deaths plays a key role in guiding post-earthquake emergency rescue. However, there are many factors affecting the number of deaths in an earthquake. Aimed at this issue, a prediction model for earthquake deaths based on extreme learning machine (ELM) optimized by principal component analysis (PCA) and beetle antennae search (BAS) algorithm has been proposed in this study. Firstly, this study selected sample data of destructive earthquakes in mainland China in the past 50 years, then PCA was used to reduce the dimensionality of the factors affecting earthquake deaths, the principal components with lower contribution rates were removed, and the principal components with higher contribution rates were used as the input variables of ELM. Meanwhile, the earthquake deaths were used as the output variable, and the connection weights and thresholds of ELM was optimized using BAS. Finally, the prediction model for earthquake deaths based on PCA-BAS-ELM was established. The established model was used to predict the test samples. The results showed that the prediction results of PCA-BAS-ELM model had a higher fit with the actual values, and its mean square error, mean absolute percentage error and root mean square error were 2.433, 2.756% and 5.443, respectively, which suggested higher prediction accuracy.

Application of Extreme Machine Learning for Smart Agricultural Robots to Reduce Manoeuvering Adaptability Errors

The agricultural robots limit complicated manoeuvring jobs, reducing the need for human intervention. Automation and control of these robots are performed by observing and practising the crop type, regulations, and external weather conditions. This article proposes and discusses a Manoeuvering Adaptable Task Processing Model (MATPM). This model is designed to improve the adaptability and precision of agricultural robots coinciding with farming and climatic conditions. For this purpose, extreme machine learning is employed to learn, align, and respond to external conditions to improve precise manoeuvring. The external impacting features and their adaptability to the crop and climatic conditions are valued using the learning process. In this process, maximum adaptability is considered to improve precision agriculture. The tasks are then classified based on adaptability and executed sequentially. If an unpredictable impacting factor hinders a task, then the adaptability is paused from training and it improves the chances of training by using different completed and paused tasks from the previous manoeuvring processes. Therefore, precision in smart farming and robot system adaptability is ensured with fewer adaptability errors. From the comparative assessment, the proposed model improves adaptability by 8.71 % and precision by 11.44 %, with 8.96 % less adaptability error for various tasks/ day.

Tool condition monitoring using I-kaz enhanced kernel extreme learning machine

Abstract In order to improve the accuracy of tool condition monitoring (TCM) in milling, an I-kaz enhanced Kernel Extreme learning Machine (I-kaz_KELM) method is proposed by combining the KELM and I-kaz statistical algorithm. It uses the I-kaz angle function to replace the conventional kernel function to avoid the selection of kernel function and the pre-set of its parameters. A two-layer network model of the I-kaz_KELM is constructed to improve the KELM in feature learning of complex non-linear high-dimensional. Research and analysis of two milling TCM public benchmark datasets (PHM 2010 TCM dataset and NASA TCM dataset) confirmed that the monitoring accuracy of the proposed method is better than SVM and KELM under limited samples. The RESM of the proposed method is reduced by at least 10% compared to the other two methods, and the RESM fluctuation amplitudes of the proposed method with model parameters is reduced by 35%–95% than that of the two other methods. It can be demonstrated that the proposed method can significantly improve learning performance without significantly affecting the learning speed, and has stronger robustness due to reduced sensitivity to the model parameter to be optimized.

Temperature extraction from Brillouin sensing based on temporal convolutional networks

The Brillouin optical time-domain sensing system has become a hot spot of research due to its ability to seamlessly monitor the temperature and strain variations in optical fibers along the line. Given its current limitations of low accuracy and inadequate real-time performance in long-distance monitoring, the Brillouin gain extraction temperature method based on temporal convolutional networks is proposed. On this basis, we established a Brillouin optical time-domain experimental system where comprehensive simulations and tests were conducted to assess the temperature extraction performance under different conditions. Besides, a comparison was made between the system and traditional methods like Lorentz fitting method and extreme learning machine method. The results have suggested that the temporal convolutional network exhibits remarkable measurement accuracy, even in scenarios with low signal-to-noise ratios and large sweep frequency steps.

Research on magnetic flux leakage testing of pipelines by finite element simulation combined with artificial neural network

Magnetic flux leakage (MFL) testing technology is widely employed in non-destructive testing of pipelines, and the analysis of leakage signals plays a crucial role in assessing pipelinea safety. This paper introduces a novel approach for MFL testing, which combines finite element simulation with artificial neural networks. First, a finite element model for MFL testing of defects is established, the influence of magnetization states on MFL signals is discussed, and the variation of signal extremum with magnetization intensity is analyzed. Next, suitable MFL signal features are selected to focus on the relationship between defect types, defect sizes, and these features. Finally, a kernel extreme learning machine (KELM) predictive model is developed to classify defect types and predict defect sizes. The results indicate that as magnetization intensity increases, the magnetization process of the pipeline can be divided into a nonlinear growth phase and a linear phase, with MFL signal extremum rapidly increasing and then gradually growing linearly. Different geometric features of defects correspond to distinct distributions of MFL signals, effectively reflecting variations in defect types and sizes. Compared to traditional ELM models, the KELM model achieves higher prediction accuracy and stable performance, with the radial basis kernel function significantly enhancing the generalization and predictive capabilities of the neural network.

Retinal imaging based glaucoma detection using modified pelican optimization based extreme learning machine

Glaucoma is defined as progressive optic neuropathy that damages the structural appearance of the optic nerve head and is characterized by permanent blindness. For mass fundus image-based glaucoma classification, an improved automated computer-aided diagnosis (CAD) model performing binary classification (glaucoma or healthy), allowing ophthalmologists to detect glaucoma disease correctly in less computational time. We proposed learning technique called fast discrete curvelet transform with wrapping (FDCT-WRP) to create feature set. This method is entitled extracting curve-like features and creating a feature set. The combined feature reduction techniques named as principal component analysis and linear discriminant analysis, have been applied to generate prominent features and decrease the feature vector dimension. Lastly, a newly improved learning algorithm encompasses a modified pelican optimization algorithm (MOD-POA) and an extreme learning machine (ELM) for classification tasks. In this MOD-POA+ELM algorithm, the modified pelican optimization algorithm (MOD-POA) has been utilized to optimize the parameters of ELM’s hidden neurons. The effectiveness has been evaluated using two standard datasets called G1020 and ORIGA with the \\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$10 \ imes 5$$\\end{document}-fold stratified cross-validation technique to ensure reliable evaluation. Our employed scheme achieved the best results for both datasets obtaining accuracy of 93.25% (G1020 dataset) and 96.75% (ORIGA dataset), respectively. Furthermore, we have utilized seven Explainable AI methodologies: Vanilla Gradients (VG), Guided Backpropagation (GBP ), Integrated Gradients ( IG), Guided Integrated Gradients (GIG), SmoothGrad, Gradient-weighted Class Activation Mapping (GCAM), and Guided Grad-CAM (GGCAM) for interpretability examination, aiding in the advancement of dependable and credible automation of healthcare detection of glaucoma.

Model Independent Dynamic Predictive Controller Design Using Differential Extreme Learning Machine for Composition Control in Binary Distillation Column

Model Independent Dynamic Predictive Controller Design Using Differential Extreme Learning Machine for Composition Control in Binary Distillation Column

A deep extreme learning machine approach optimized by sparrow search algorithm for forecasting of traffic flow

Abstract Traffic flow modeling has a pivotal role within Intelligent Transportation Systems (ITSs), holding vital importance in alleviating traffic congestion and decreasing carbon emissions. Due to the presence of variability and nonlinear attributes in traffic flow, developing an effective and resilient model for predicting traffic flow poses a significant challenge. Precisely predicting traffic flow is not merely a feasible issue; it also poses significant difficulties to the researchers involved in this field. This study proposes a hybrid predictive model to forecast traffic flow. The proposed model effectively merges the strengths of the Sparrow Search algorithm (SSA) and Multi-layer Extreme Learning Machine (ML-ELM) model, enhancing prediction accuracy. SSA optimization technique is applied to optimize the initial weights and bias parameters for ML-ELM model. ELM approach is a machine learning approach that employs a single hidden layer to address various tasks. However, in situations where more complex problems are encountered, ML-ELM extends this concept by incorporating multiple hidden layers to enhance its capabilities and address challenges more effectively. Finally, SSA technique is utilized to achieve the optimal tuning of hyperparameters in the context of ML-ELM model to improve the prediction accuracy. Compared to the other selected models, the proposed model outperforms them in terms of performance metrics, including Root Mean Square Errors (RMSE), Mean Absolute Errors (MAE), Mean Absolute Percentage Errors (MAPE) and Correlation Coefficients (r), indicating that it is appropriate for this prediction task.

Randomized radial basis function neural network for solving multiscale elliptic equations

Abstract Ordinary deep neural network-based methods frequently encounter difficulties when tackling multiscale and high-frequency partial differential equations. To overcome these obstacles and improve computational accuracy and efficiency, this paper presents the Randomized Radial Basis Function Neural Network (RRNN), an innovative approach explicitly crafted for solving multiscale elliptic equations. The RRNN method commences by decomposing the computational domain into non-overlapping subdomains. Within each subdomain, the solution to the localized subproblem is approximated by a randomized radial basis function neural network with a Gaussian kernel. This network is distinguished by the random assignment of width and center coefficients for its activation functions, thereby rendering the training process focused solely on determining the weight coefficients of the output layer. For each subproblem, similar to the Petrov-Galerkin finite element method, a linear system will be formulated on the foundation of a weak formulation. Subsequently, a selection of collocation points is stochastically sampled at the boundaries of the subdomain, ensuring the satisfaction of $C^0$ and $C^1$ continuity and boundary conditions to couple these localized solutions. The network is ultimately trained using the least squares method to ascertain the output layer weights. To validate the RRNN method's effectiveness, an extensive array of numerical experiments has been executed. 
The comparative analysis demonstrates the RRNN's superior performance with respect to computational accuracy and training time compare to deep neural network methods based on gradient descent method, and can attain enhanced accuracy at a comparable computational cost compare to Local Extreme Learning Machine method and the classical finite element method.