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The extreme learning machine (ELM) stands out as a contemporary neural network learning model designed for neural networks, specifically emphasizing those with a single hidden layer. This model has gained significant importance in recent years and is frequently employed in research projects due to its reputation as one of the swiftest and most robust methods. ELM is distinguished by its ability to obtain accurate results without the need for prolonged training, setting it apart from other classifiers. Additionally, its reduced reliance on human intervention significantly diminishes the likelihood of errors. Despite their considerable potential, ELMs are not extensively employed. One contributing factor could be the ongoing challenges that ELM is yet to overcome, requiring successful resolution. A prevalent issue is the model's performance being notably dependent on the weights, biases within the hidden layer, and the quantity of neurons in that layer. Optimizing the number of neurons, referred to as hyperparameter optimization, falls under the category of NP-hard optimization problems. The second challenge lies in training the ELM, which involves establishing the weights and biases tailored for a specific task, presenting another NP-hard challenge. The research presented in this manuscript concentrates on addressing both aspects: optimizing hyperparameters, specifically the number of neurons in the hidden layer, and training the network to fine-tune the weights and biases. The main goal of this research is to effectively resolve both optimization and training by utilizing an improved swarm intelligence algorithm. As a result, both issues were addressed using an adapted version of the firefly algorithm. The proposed approach was tested and validated on twelve authentic datasets and four synthetic datasets designed for classification purposes. One of the forefront tasks among them involves the fetal nonstress test, commonly known as the cardiotocography problem, requiring the interpretation of data from two wearable sensors to discriminate between 3 and 10 imbalanced classes. The obtained outcomes are compared with the results reached by similar state of the art approaches, and the simulations show that the firefly algorithm improved by the group search operator can lead to superior performance. Additionally, enhancements of proposed method are confirmed by rigid statistical tests and results of best generated model for significant heart disease dataset are interpreted by valuable Shapley Additive Explanations (SHAP) tool. [ABSTRACT FROM AUTHOR]

Extreme learning machines (ELMs), single hidden-layer feedforward neural networks, are renowned for their speed and efficiency in classification and regression tasks. However, their generalization ability is often undermined by the random generation of hidden layer weights and biases. To address this issue, this paper introduces a Hierarchical Learning-based Chaotic Crayfish Optimization Algorithm (HLCCOA) aimed at enhancing the generalization ability of ELMs. Initially, to resolve the problems of slow search speed and premature convergence typical of traditional crayfish optimization algorithms (COAs), the HLCCOA utilizes chaotic sequences for population position initialization. The ergodicity of chaos is leveraged to boost population diversity, laying the groundwork for effective global search efforts. Additionally, a hierarchical learning mechanism encourages under-performing individuals to engage in extensive cross-layer learning for enhanced global exploration, while top performers directly learn from elite individuals at the highest layer to improve their local exploitation abilities. Rigorous testing with CEC2019 and CEC2022 suites shows the HLCCOA's superiority over both the original COA and nine renowned heuristic algorithms. Ultimately, the HLCCOA-optimized extreme learning machine model, the HLCCOA-ELM, exhibits superior performance over reported benchmark models in terms of accuracy, sensitivity, and specificity for UCI breast cancer diagnosis, underscoring the HLCCOA's practicality and robustness, as well as the HLCCOA-ELM's commendable generalization performance. [ABSTRACT FROM AUTHOR]

The Extreme Learning Machine (ELM) has sparked a lot of attention since it can learn fast and be applied to various problems. In this study, a convolutional layer-based extreme learning machine (CELM) architecture has been designed and implemented to recognize handwritten characters and reduce execution time. Furthermore, to validate the robustness of the approach, the characters are chosen from four different languages, mainly Indian languages, including English. The recognition has been performed on both numerals and alphabets. The experimental results regarding accuracy and execution time have been presented in a tabular fashion to discriminate the slight differences between the different datasets chosen. On three different datasets, including chars74kFnt, cMaterDB 3.1.2, and IIITBOdiaV1, the proposed method achieves an accuracy of 91.76%, 94.12%, and 91.43%, respectively. This effectiveness is shown to be higher than other feature extraction method-based recognition methods found in recent experiments. [ABSTRACT FROM AUTHOR]

To obtain a precise finite-element model (FEM) for analyzing the dynamic response of corroded beams at high temperatures, a stepwise FEM modification strategy is proposed based on the improved extreme learning machine. Three concrete beams were designed and cast, and the dynamic response characteristics of corroded concrete beams at room temperature and high temperature are discussed. Firstly, electrical accelerated corrosion tests and vibration tests were conducted on simply supported beams at room temperature. The fundamental frequencies of concrete beams under different corrosion ratios were measured. The attenuation law of fundamental frequency with corrosion ratio also was studied. Subsequently, the FEM under different corrosion ratios was modified. The bond-slip between steel bars and concrete under different degrees of corrosion was considered during the correction process. Finally, a vibration test at high temperature was performed. The modal attenuation law of corroded beams at high temperatures was analyzed. Based on the modified FEM, numerical analysis at high temperature was performed. The proposed FEM modification strategy and the study of the attenuation regularities of modal information under fire exposure provide a foundation for further research on the damage development of corroded reinforced concrete (RC) beams under fire exposure. [ABSTRACT FROM AUTHOR]

This paper introduces a novel hybrid optimization technique aimed at improving the prediction accuracy of solar photovoltaic (PV) outputs using an Improved Hippopotamus Optimization Algorithm (IHO). The IHO enhances the traditional Hippopotamus Optimization (HO) algorithm by addressing its limitations in search efficiency, convergence speed, and global exploration. The IHO algorithm used Latin hypercube sampling (LHS) for population initialization, significantly enhancing the diversity and global search potential of the optimization process. The integration of the Jaya algorithm further refines solution quality and accelerates convergence. Additionally, a combination of unordered dimensional sampling, random crossover, and sequential mutation is employed to enhance the optimization process. The effectiveness of the proposed IHO is demonstrated through the optimization of weights and neuron thresholds in the extreme learning machine (ELM), a model known for its rapid learning capabilities but often affected by the randomness of initial parameters. The IHO-optimized ELM (IHO-ELM) is tested against benchmark algorithms, including BP, the traditional ELM, the HO-ELM, LCN, and LSTM, showing significant improvements in prediction accuracy and stability. Moreover, the IHO-ELM model is validated in a different region to assess its generalization ability for solar PV output prediction. The results confirm that the proposed hybrid approach not only improves prediction accuracy but also demonstrates robust generalization capabilities, making it a promising tool for predictive modeling in solar energy systems. [ABSTRACT FROM AUTHOR]

Copyright of Electric Power Automation Equipment / Dianli Zidonghua Shebei is the property of Electric Power Automation Equipment Press and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Power load data frequently display outliers and an uneven distribution of noise. To tackle this issue, we present a forecasting model based on an improved extreme learning machine (ELM). Specifically, we introduce the novel Pinball-Huber robust loss function as the objective function in training. The loss function enhances the precision by assigning distinct penalties to errors based on their directions. We employ a genetic algorithm, combined with a swift nondominated sorting technique, for multiobjective optimization in the ELM-Pinball-Huber context. This method simultaneously reduces training errors while streamlining model structure. We practically apply the integrated model to forecast power load data in Taixing City, which is situated in the southern part of Jiangsu Province. The empirical findings confirm the method's effectiveness. [ABSTRACT FROM AUTHOR]

The fast growth of internet of vehicles (IoV) has created a new area of connectedness, with promising safety and efficiency in transportation. However, this advancement in vehicle technology has come with significant cybersecurity risks, specifically through control area network (CAN) protocol and other communication techniques within vehicles. This experimental study suggests a machine learning (ML) based security approach based on the extreme learning machine (ELM) algorithm to address these challenges. Unlike customary neural networks, ELM is known for its fast processing, minimal training time, and high accuracy, making it preferably suitable for dynamic IoV environments. The methodology involves data preprocessing, feature selection, and employing ELM for attack classification; the algorithm’s performance is evaluated using CARHacking, NSL-KDD, and EdgeIIoT datasets. We also examine the significance of distributed processing to enhance the computational efficiency of the model, obtaining 89% accuracy in 3 ms run-time for external networks, and 83% accuracy with 9 ms run-time for intra-vehical networks. This newly proposed security mechanism using ELM shows very accurate results in detecting intrusions with a high recall rate and reduced computation time through distributed processing. [ABSTRACT FROM AUTHOR]

This paper proposes a learning-based model for the resource-constrained edge nodes in the blockchain-enabled Internet of Medical Things (IoMT) systems to realize efficient querying. Three layers are designed in the new model: data evaluation layer, data storage layer and data distribution layer. The data evaluation layer extracts the features from medical data and evaluates their values based on the Extreme Learning Machine (ELM) method. Then, in the data storage layer, according to the value of medical data, a novelty data structure called Merkle–Huffman tree (M-H tree) is established. Compared with the Merkle tree, high-value data (frequently accessed data) in M-H tree is saved closer to the root node and can be found faster. In the data distribution layer, the sharding-based blockchain model is adopted to increase the storage scalability of the IoMT system. Finally, the experimental results show that the new learning-based model can effectively improve the query speed of the blockchain-enabled medical system by about 3.5% and free up large amounts of storage space on IoMT devices. [ABSTRACT FROM AUTHOR]

High-strength bolts play a crucial role in ultra-high-pressure equipment such as bridges and railway tracks. Effective monitoring of bolt conditions is of paramount importance for common fault repair and accident prevention. This paper aims to detect and classify bolt corrosion levels accurately. We design and implement a bolt corrosion classification system based on a Wireless Acoustic Emission Sensor Network (WASN). Initially, WASN nodes collect high-speed acoustic emission (AE) signals from bolts. Then, the ReliefF feature selection algorithm is applied to identify the optimal feature combination. Subsequently, the Extreme Learning Machine (ELM) model is utilized for bolt corrosion classification. Additionally, to achieve high prediction accuracy, an improved goose algorithm (GOOSE) is employed to ensure the most suitable parameter combination for the ELM model. Experimental measurements were conducted on five classes of bolt corrosion levels: 0%, 25%, 50%, 75%, and 100%. The classification accuracy obtained using the proposed method was at least 98.04%. Compared to state-of-the-art classification diagnostic models, our approach exhibits superior AE signal recognition performance and stronger generalization ability to adapt to variations in working conditions. [ABSTRACT FROM AUTHOR]

Early detection of breast cancer is essential for increasing survival rates, as it is one of the primary causes of death for women globally. Mammograms are extensively used by physicians for diagnosis, but selecting appropriate algorithms for image enhancement, segmentation, feature extraction, and classification remains a significant research challenge. This paper presents a computer-aided diagnosis (CAD)-based hybrid model combining convolutional neural networks (CNN) with a pruned ensembled extreme learning machine (HCPELM) to enhance breast cancer detection, segmentation, feature extraction, and classification. The model employs the rectified linear unit (ReLU) activation function to enhance data analytics after removing artifacts and pectoral muscles, and the HCPELM hybridized with the CNN model improves feature extraction. The hybrid elements are convolutional and fully connected layers. Convolutional layers extract spatial features like edges, textures, and more complex features in deeper layers. The fully connected layers take these features and combine them in a non-linear manner to perform the final classification. ELM performs classification and recognition tasks, aiming for state-of-the-art performance. This hybrid classifier is used for transfer learning by freezing certain layers and modifying the architecture to reduce parameters, easing cancer detection. The HCPELM classifier was trained using the MIAS database and evaluated against benchmark methods. It achieved a breast image recognition accuracy of 86%, outperforming benchmark deep learning models. HCPELM is demonstrating superior performance in early detection and diagnosis, thus aiding healthcare practitioners in breast cancer diagnosis. [ABSTRACT FROM AUTHOR]

Virtual metrology (VM) is crucial for improving process capability and production yield during semiconductor manufacturing processes. However, the performance of VM deteriorates owing to the variable operating regime and the nonlinear characteristics of the process. Herein, Variational inference Gaussian mixture model (VIGMM) and extreme learning machine (ELM) are combined to solve these issues. First, variational inference is conducted on a Gaussian mixture model to determine the number of Gaussian components automatically and the corresponding operating regimes are identified. Subsequently, an extreme learning machine is developed for each operating regime to investigate the nonlinear relationship between process inputs and outputs. Finally, VM is implemented using the corresponding local ELM, which is determined based on the responsibility of the Gaussian components. The feasibility and effectiveness of the proposed methods are validated based on a numerical case and the plasma sputtering process for fabricating thin-film transistor liquid-crystal displays. The proposed VIGMM-ELM can serve as a VM algorithm for manufacturing processes with multiple stages. [ABSTRACT FROM AUTHOR]

In order to improve the mechanical property prediction accuracy of AZ80 magnesium alloy, sparrow search algorithm (SSA) is optimized by the tent chaotic mapping (TCM) algorithm, yielding TCMSSA, and TCMSSA is employed to improve extreme learning machine (ELM) model for obtaining TCMSSA-ELM model. TCMSSA-ELM model, along with the traditional ELM model, are both utilized to predict the stress of AZ80 magnesium alloy in a temperature range of 523 K to 673 K and a strain rate range of 0.001s− 1 to 1s− 1. Comparative analysis is conducted to evaluate the performance differences between the two models. The results indicate that compared with ELM model, the predicted values of TCMSSA-ELM model are closer to the experimental data and the ideal 45° line, proving the higher prediction accuracy of TCMSSA-ELM model. In addition, through algorithmic improvements, the maximum reduction of MAPE is 85.422% and the maximum determination coefficient reaches 0.99956, implying that TCMSSA greatly improves the prediction precision of ELM model. The related results offer a new optimization strategy and model to predict the mechanical property of AZ80 magnesium alloy in each molding stage, and provide a technical reference for applying intelligent algorithms to alloy design. [ABSTRACT FROM AUTHOR]

This paper presents an innovative adaptive fractional-order (FO) terminal sliding mode (TSM) controller designed for a 5-degree-of-freedom (5-DOF) upper-limb exoskeleton robot. The primary focus is on addressing the challenges posed by nonlinearities and uncertainties in parameter values. The proposed approach integrates several key elements, including a novel fractional-order non-singular terminal sliding mode (FONSTSM) surface, an exponential reaching law, and the utilization of the extreme learning machine (ELM). This comprehensive strategy guarantees not only finite-time convergence but also robust stability, effectively alleviating the well-known chattering phenomenon. Furthermore, it successfully overcomes the singularity issues typically observed in conventional TSM controllers. The incorporation of the ELM with rectified linear unit (ReLU) activation function enhances robustness by facilitating the estimation of parameters related to the exponential control law. Numerical simulations provide compelling evidence of improved tracking, increased robustness against uncertainties, achievement of finite-time convergence, and notable reductions in control signal oscillations and singularity problems. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Intelligent Agriculture Mechanization is the property of Nanjing Institute of Agricultural Mechanization and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Background: Soil water content is one of the critical indicators in agricultural systems. Visible/near-infrared hyperspectral remote sensing is an effective method for soil water estimation. However, noise removal from massive spectral datasets and effective feature extraction are challenges for achieving accurate soil water estimation using this technology. Methods: This study proposes a method for hyperspectral remote sensing soil water content estimation based on a combination of continuous wavelet transform (CWT) and competitive adaptive reweighted sampling (CARS). Hyperspectral data were collected from soil samples with different water contents prepared in the laboratory. CWT, with two wavelet basis functions (mexh and gaus2), was used to pre-process the hyperspectral reflectance to eliminate noise interference. The correlation analysis was conducted between soil water content and wavelet coefficients at ten scales. The feature variables were extracted from these wavelet coefficients using the CARS method and used as input variables to build linear and non-linear models, specifically partial least squares (PLSR) and extreme learning machine (ELM), to estimate soil water content. Results: The results showed that the correlation between wavelet coefficients and soil water content decreased as the decomposition scale increased. The corresponding bands of the extracted wavelet coefficients were mainly distributed in the near-infrared region. The non-linear model (ELM) was superior to the linear method (PLSR). ELM demonstrated satisfactory accuracy based on the feature wavelet coefficients of CWT with the mexh wavelet basis function at a decomposition scale of 1 (CWT(mexh_1)), with R2, RMSE, and RPD values of 0.946, 1.408%, and 3.759 in the validation dataset, respectively. Overall, the CWT(mexh_1)-CARS-ELM systematic modeling method was feasible and reliable for estimating the water content of sandy clay loam. [ABSTRACT FROM AUTHOR]

Drones are a promising technology performing various functions, ranging from aerial photography to emergency response, requiring swift fault diagnosis methods to sustain operational continuity and minimise downtime. This optimises resources, reduces maintenance costs, and boosts mission success rates. Among these methods, traditional approaches such as visual inspection or manual testing have long been utilised. However, in recent years, data representation methods, such as deep learning systems, have achieved significant success. These methods learn patterns and relationships, enhancing fault diagnosis, but also face challenges with data complexity, uncertainties, and modelling complexities. This paper tackles these specific challenges by introducing an efficient representation learning method denoted Multiverse Augmented Recurrent Expansion (MVA-REX), allowing for an iterative understanding of both learning representations and model behaviours and gaining a better understanding of data dependencies. Additionally, this approach involves Uncertainty Bayesian Optimisation (UBO) under Extreme Learning Machine (ELM), a lighter neural network training tool, to tackle both uncertainties in data and reduce modelling complexities. Three main realistic datasets recorded based on acoustic emissions are involved in tackling propeller and motor failures in drones under realistic conditions. The UBO-MVA Extreme REX (UBO-MVA-EREX) is evaluated under many, error metrics, confusion matrix metrics, computational cost metrics, and uncertainty quantification based on both confidence and prediction interval features. Application compared to the well-known long-short term memory (LSTM), under Bayesian optimisation of the approximation error, demonstrates performances, certainty, and cost efficiency of the proposed scheme. More specifically, the accuracy obtained by UBO-MVA-EREX, ~0.9960, exceeds the accuracy of LSTM, ~0.9158, by ~8.75%. Besides, the search time for UBO-MVA-EREX is ~0.0912 s, which is ~98.15% faster than LSTM, ~4.9287 s, making it highly applicable for such challenging tasks of fault diagnosis-based acoustic emission signals of drones. [ABSTRACT FROM AUTHOR]

In a two-class classification task, if the number of examples of one class (majority) is much greater than that of another class (minority), then the classification is said to be class imbalanced. It can occur among many real-world applications, such as intrusion detection, medical diagnosis, etc. The class imbalance issue can make learning difficult since learning opts to bias towards the majority class. Outliers are cases with anomalous behaviors and are extreme cases of class imbalance. Despite late advances in extreme learning machines (ELMs), there are not many experimental investigations in the field of ELM with outlier detection. In this survey, we provide a comprehensive overview of existing ELMs to address the problem of outlier detection under a unified perspective. Firstly, we describe the background of our work, which includes a brief overview of previous surveys and a detailed description of the enhanced ELMs. Next, available studies regarding why ELMs are used to tackle the class imbalance problem are reviewed. Furthermore, cutting-edge algorithms are surveyed for improved ELMs to detect outliers. We classify these methods under three different machine learning perspectives (i.e., supervised, unsupervised, and semi-supervised approaches). In addition, we explore the developments of existing solutions based on three standardized quality metrics (i.e., accuracy, robustness, and speed) and other performance metrics (e.g., mean absolute percentage error and mean absolute error). After that, related datasets are detailed to facilitate future studies in this field. Last but the most important, this study concludes with discussions, challenges, and suggestions to guide future research. [ABSTRACT FROM AUTHOR]

Introduction: Rising fluoride levels in groundwater resources have become a worldwide concern, presenting a significant challenge to the safe utilization of water resources and posing potential risks to human well-being. Elevated fluoride and its vast spatial variability have been documented across different districts of Punjab, India, and it is, therefore, imperative to predict the fluoride levels for efficient groundwater resources planning and management. Methods: In this study, five different models, Support Vector Machine (SVM), Random Forest (RF), Extreme Gradient Boosting (Xgboost), Extreme Learning Machine (ELM), and Multilayer Perceptron (MLP), are proposed to predict groundwater fluoride using the physicochemical parameters and sampling depth as predictor variables. The performance of these five models was evaluated using the coefficient of determination (R²), mean absolute error (MAE), and root mean square error (RMSE). Results and discussion: ELM outperformed the remaining four models, thus exhibiting a strong predictive power. The R², MAE, and RMSE values for ELM at the training and testing stages were 0.85, 0.46, 0.36 and, 0.95, 0.31, and 0.33, respectively, while other models yielded inferior results. Based on the relative importance scores, total dissolved solids (TDS), electrical conductivity (EC), sodium (Na+), chloride (Cl-), and calcium (Ca2+) contributed significantly to model performance. High variability in the target (fluoride) and predictor variables might have led to the poor performance of the models, implying the need for better data pre-processing techniques to improve data quality. Although ELM showed satisfactory results, it can be considered a promising model for predicting groundwater quality. [ABSTRACT FROM AUTHOR]

The growing demand for sustainable materials has significantly increased interest in biocomposites, which are made from renewable raw materials and have excellent mechanical properties. The use of machine learning (ML) can improve our understanding of their mechanical behavior while saving costs and time. In this study, the mechanical behavior of innovative biocomposite sandwich structures under quasi-static out-of-plane compression was investigated using ML algorithms to analyze the effects of geometric variations on load-bearing capacities. A comprehensive dataset of experimental mechanical tests focusing on compression loading was employed, evaluating three ML models—generalized regression neural networks (GRNN), extreme learning machine (ELM), and support vector regression (SVR). Performance indicators such as R-squared (R2), mean absolute error (MAE), and root mean square error (RMSE) were used to compare the models. It was shown that the GRNN model with an RMSE of 0.0301, an MAE of 0.0177, and R2 of 0.9999 in the training dataset, and an RMSE of 0.0874, MAE of 0.0489, and R2 of 0.9993 in the testing set had a higher predictive accuracy. In contrast, the ELM model showed moderate performance, while the SVR model had the lowest accuracy with RMSE, MAE, and R2 values of 0.5769, 0.3782, and 0.9700 for training, and RMSE, MAE, and R2 values of 0.5980, 0.3976 and 0.9695 for testing, suggesting that it has limited effectiveness in predicting the mechanical behavior of the biocomposite structures. The nonlinear load-displacement behavior, including critical peaks and fluctuations, was effectively captured by the GRNN model for both the training and test datasets. The progressive improvement in model performance from SVR to ELM to GRNN was illustrated, highlighting the increasing complexity and capability of machine learning models in capturing detailed nonlinear relationships. The superior performance and generalization ability of the GRNN model were confirmed by the Taylor diagram and Williams plot, with the majority of testing samples falling within the applicability domain, indicating strong generalization to new, unseen data. The results demonstrate the potential of using advanced ML models to accurately predict the mechanical behavior of biocomposites, enabling more efficient and cost-effective development and optimization processes in the field of sustainable materials. [ABSTRACT FROM AUTHOR]

The large amount of smoke generated during minimum quantity lubrication (MQL) processing not only pollutes the ambient air but also directly endangers the health of operators. Establishing a smoke diffusion model is crucial for achieving precise control of MQL smoke. Currently, accurate smoke diffusion models in this field are lacking. In this study, a smoke diffusion model under MQL was established to predict the mass concentration of PM10. The cutting speed, depth of cut, feed rate, and nozzle injection rate were integrated into the model using an extreme learning machine (ELM) to improve the accuracy of predicting the spatial distribution of smoke particles. A nonlinear equation reflecting the variation in concentration over time was solved using a backpropagation (BP) neural network. Finally, a spatiotemporal prediction model for smoke concentration during MQL turning was established. Comparing the predicted values of oil mist concentration in the test set with the true values through validation experiments, the results show that the absolute error of the prediction model at the measurement point tends to decrease with the increase of time, and the prediction accuracies are all above 90%. The maximum and minimum errors between the predicted and true values at different times are 9.77% (at the 0th second) and 4.11% (at the 6th second), respectively, which are less than 10%. Thus, the establishment of a highly accurate MQL cutting oil mist diffusion prediction model was realized. [ABSTRACT FROM AUTHOR]

The prevalence of Recommender Systems (RS) has surged due to the widespread growth of the Internet and its related technologies. The efficiency of the RS is dependent on the quality of the underlying datasets used for training. Sparse datasets present a challenge for training such systems and negatively impacts their efficiency. This research paper presents novel synergistic approaches that combine Fuzzy C-Means (FCM), Restricted Boltzmann Machines (RBM), and Extreme Learning Machines (ELM), to enhance Collaborative Filtering (CF) in RS. The study introduces two clustering-based approaches, FCM-ELM and FCM-RBM-ELM, to address the issue of non-optimal hidden biases and input weights in ELM caused by random assignment in order to overcome the data sparsity effect in RS. The efficiency of proposed approaches against different clustering methods has been evaluated using two benchmark datasets and four metrics. The empirical results indicate that the proposed methods can generate better recommendation results compared to other traditional techniques. [ABSTRACT FROM AUTHOR]

Ensuring a secure online environment hinges on the timely detection of network attacks. Nevertheless, existing detection methods often grapple with the delicate balance between speed and accuracy. In this paper, we introduce a novel intrusion detection algorithm that marries quantum particle swarm optimization with an extreme learning machine (QPSO-ELM). Firstly, we present a feature selection algorithm grounded in partitioned gains to distill vital features from data samples, thereby diminishing feature count to amplify both model training speed and accuracy. Subsequently, we unveil an intrusion detection scheme underpinned by QPSO-ELM, capable of achieving exceptional levels of training and detection speed, all while maintaining high accuracy. Finally, we fine-tune the trained model using the proposed hidden layer node selection algorithm, reducing the detection model size without compromising detection accuracy, thus further elevating its speed. The experiment results indicate that compared to the current baseline, our proposed intrusion detection scheme achieves the best results in terms of accuracy, precision, recall, and detection latency. Furthermore, the ablation experiment results demonstrate the effectiveness of our proposed method in improving both detection speed and detection accuracy. [ABSTRACT FROM AUTHOR]

Recent increases in global food demand have made this research and, therefore, the prediction of agricultural commodity prices, almost imperative. The aim of this paper is to build efficient artificial intelligence methods to effectively forecast commodity prices in light of these global events. Using three separate, well-structured models, the commodity prices of eleven major agricultural commodities that have recently caused crises around the world have been predicted. In achieving its objective, this paper proposes a novel forecasting model for agricultural commodity prices using the extreme learning machine technique optimized with the genetic algorithm. In predicting the eleven commodities, the proposed model, the extreme learning machine with the genetic algorithm, outperforms the model formed by the combination of long short-term memory with the genetic algorithm and the autoregressive integrated moving average model. Despite the fluctuations and changes in agricultural commodity prices in 2022, the extreme learning machine with the genetic algorithm model described in this study successfully predicts both qualitative and quantitative behavior in such a large number of commodities and over such a long period of time for the first time. It is expected that these predictions will provide benefits for the effective management, direction and, if necessary, restructuring of agricultural policies by providing food requirements that adapt to the dynamic structure of the countries. [ABSTRACT FROM AUTHOR]

Deep learning methods have developed rapidly in the field of tool condition monitoring, but due to the complexity and diversity of working conditions, it is difficult to ensure the high accuracy, strong generalization performance, and wide applicability of monitoring models. Therefore, this article proposes a deep transfer learning method with center loss for tool condition monitoring under different processing conditions. Firstly, Deep Extreme Learning Machine (DELM) is used to extract sample features and tool condition monitoring, and deep coral is integrated into the last feature extraction layer of DELM for domain adaptation. Secondly, the center loss is introduced into the transfer learning model to improve the intra-class compactness by minimizing the center loss, thereby obtaining a broader decision boundary. A tool wear experiment was conducted on a milling machine. Research shows that the proposed method can achieve the tool condition monitoring under different processing conditions. The introduction of central loss is beneficial for promoting the separation of samples from different categories in the target domain and effectively improves the model's applicability. Compared with other domain adaptation methods, this method has better accuracy and generalization ability. [ABSTRACT FROM AUTHOR]

ABSTRACT Firefly algorithm (FA) combined with extreme learning machine (ELM) is developed for spectral interval selection and quantitative analysis of complex samples. The method firstly segments the spectra into a certain number of intervals. Vectors with 1 and 0, which represent the interval selected or not, are used as the inputs of the FA. The RMSEP value predicted by ELM model is used as the fitness function of the FA. The activation function and number of hidden layer nodes of ELM, number of spectral intervals, population number, environmental absorbance, and constant of FA are optimized. The predictive performance of FA‐ELM is compared with full‐spectrum PLS, ELM, genetic algorithm‐ELM (GA‐ELM), and particle swarm optimization‐ELM (PSO‐ELM) by one ultraviolet (UV) spectrum dataset of gasoil and three near‐infrared (NIR) spectral datasets of corn, wheat, and tablet samples. The results show that FA‐ELM has a better performance compared with its competitors in predicting monoaromatics, water, wheat kernel texture, and active pharmaceutical ingredients (APIs) in gasoil, corn, wheat, and tablet samples. [ABSTRACT FROM AUTHOR]

Accurate fatigue driving recognition is vital for road safety, preventing accidents caused by driver exhaustion, and protecting lives. To address the challenges of inadequate accuracy in driver feature extraction, limited precision in fatigue recognition, and prolonged processing time in conventional truck driver fatigue detection systems, the paper propose a novel fatigue driving recognition of truck drivers based on multi-feature fusion. Firstly, this scheme utilizes specialized acquisition equipment to accurately capture images of truck drivers, and corrects lighting inconsistencies through two-dimensional gamma functions; Then, the image is denoised using a dual neighborhood median filtering algorithm; Subsequently, the features extracted from the driver's eyes, mouth, and head were effectively integrated into an extreme learning machine framework to achieve accurate driver fatigue classification. Experimental results demonstrate the effectiveness of new approach, achieving a peak feature extraction accuracy of 98.74%, a maximum recognition accuracy of 98.56%, and a time efficiency ranging from 0 to 0.53 seconds. [ABSTRACT FROM AUTHOR]

Simulated moving bed chromatography (SMB) is a proven and efficient separation tool for the complex separation needs of a variety of industries. On the basis of analysis on the SMB chromatographic separation, the appropriate indexes were selected to optimize and control the separation process. Taking the component purity and yield of extract and residual solution as prediction objects, the soft-sensor modeling was conducted based on online sequential extreme learning machine (OS-ELM), online sequential reduced kernel extreme learning machine (OS-RKELM) and regularized online sequential extreme learning machine (ReOS-ELM). The results show that different limit learning functions can effectively realize the accurate prediction of key economic and technical indexes, and can meet the real-time, efficient and robust operation of SMB chromatographic separation process. [ABSTRACT FROM AUTHOR]

Estimation and analysis of energy consumption for machine tool is the basis of energy efficiency improvement. To improve the accuracy of ELM algorithm in CNC machine tool energy consumption prediction, a prediction method based on an improved particle swarm optimization (CAPSO) algorithm and an extreme learning machine (ELM) is proposed. The contribution of the algorithm includes the following three aspects. First, sobol sequence is used to initialize the PSO population to make distribution of initial population more even in solution space. Second, the center wanders and boundary neighborhood updates strategy are used to improve the population quality and convergence rate of PSO. Then, to avoid the optimal local solution, the adaptive inertia weight is introduced to achieve the stochastic perturbation of the population. The performance of the algorithm is tested by ten benchmark function, indicating that the CAPSO ensures the search accuracy and improves the algorithm's convergence rate. Finally, the CAPSO algorithm is used to optimize the weights and thresholds of an ELM, and the CAPSO-ELM cutting energy consumption prediction model is established. Case analysis and comparative experiments show that the stability, prediction accuracy and generalization ability of CAPSO-ELM model are better than those of other models. [ABSTRACT FROM AUTHOR]

The release source term of radioactivity becomes a critical foundation for emergency response and accident consequence assessment after a nuclear accident Rapidly and accurately inverting the source term remains an urgent scientific challenge. Today source term inversion based on meteorological data and gamma dose rate measurements is a common method. But gamma dose rate actually includes all nuclides information, and the composition of radioactive nuclides is generally uncertain. This paper introduces a novel nuclear accident source term inversion model, which is Improve Snow Ablation Optimizer-Sensitivity Analysis Pruning Extreme Learning Machine (ISAO-SAELM) model. The model inverts the release rates of 11 radioactive nuclides (I-131, Xe-133, Cs-137, Kr-88, Sr-91, Te-132, Mo-99, Ba-140, La-140, Ce-144, Sb-129). It does not require the use of the physical field of the reactor to obtain prior information and establish a dispersion model. And the robustness is validated through noise analysis test. The mean absolute errors of the release rates of 11 nuclides are 15.52 %, 15.28 %, 15.70 %, 14.99 %, 14.85 %, 15.61 %, 15.96 %, 15.42 %, 15.84 %, 15.13 %, 17.72 %, which show the significant superiority of ISAO-SAELM. ISAO-SAELM model not only achieves notable advancements in accuracy but also receives validation in terms of practicality and feasibility.

Abstract Skin cancer is a lethal disease, and its early detection plays a pivotal role in preventing its spread to other body organs and tissues. Artificial Intelligence (AI)-based automated methods can play a significant role in its early detection. This study presents an AI-based novel approach, termed 'DualAutoELM' for the effective identification of various types of skin cancers. The proposed method leverages a network of autoencoders, comprising two distinct autoencoders: the spatial autoencoder and the FFT (Fast Fourier Transform)-autoencoder. The spatial-autoencoder specializes in learning spatial features within input lesion images whereas the FFT-autoencoder learns to capture textural and distinguishing frequency patterns within transformed input skin lesion images through the reconstruction process. The use of attention modules at various levels within the encoder part of these autoencoders significantly improves their discriminative feature learning capabilities. An Extreme Learning Machine (ELM) with a single layer of feedforward is trained to classify skin malignancies using the characteristics that were recovered from the bottleneck layers of these autoencoders. The 'HAM10000' and 'ISIC-2017' are two publicly available datasets used to thoroughly assess the suggested approach. The experimental findings demonstrate the accuracy and robustness of the proposed technique, with AUC, precision, and accuracy values for the 'HAM10000' dataset being 0.98, 97.68% and 97.66%, and for the 'ISIC-2017' dataset being 0.95, 86.75% and 86.68%, respectively. This study highlights the possibility of the suggested approach for accurate detection of skin cancer.

Objective: To address the issues of low accuracy and poor efficiency in walnut defect detection among existing food production enterprises. Methods: Proposed a fast non-destructive detection method for walnut defects that combined improved extreme learning machines and computer vision. Collected most of the surface image information of walnuts through computer vision, preprocess the image through Gaussian filtering, optimize color and texture features through iterative and information preserving variable methods, finally, by improving the butterfly algorithm to optimize the parameters of the Extreme Learning Machine （random weights and deviations）, fast non-destructive detection of walnut defects could be achieved, and verify the performance of the proposed defect detection method. Results: The experimental method could effectively discriminate various defects in walnuts. Compared with conventional methods, the experimental method had superior detection accuracy and efficiency in walnut defect detection, with a detection accuracy rate > 98.00% and an average detection time < 9.00 ms. Conclusion: Combining intelligent algorithms with machine vision technology can achieve rapid non-destructive detection of walnut defects.

[Objective] In order to solve the issue of excessive redundant information in near-infrared spectroscopy, enhance the accuracy of wine quality evaluation models, a rapid and non-destructive method was established for wine quality evaluation. [Methods] A wine quality evaluation model was proposed based on competitive adaptive reweighting sampling method for feature wavelength screening and extreme learning machine improved by whale optimization algorithm. Various feature wavelength screening methods such as competitive adaptive reweighting sampling was used, and the most suitable method for wine spectral feature wavelength screening was determined. In response to the problem of initial value and hidden layer bias in ELM, the whale optimization method was used to optimize the initial value and hidden layer bias of ELM, and an wine quality evaluation model based on extreme learning machine improved by whale optimization algorithm was constructed. [Results] Compared with GA-ELM, PSO-ELM, and the traditional ELM model, the accuracy of WOA-ELM was the highest, reaching 0.944 5, which was better than GA-ELM (0.929 0), PSO-ELM (0.906 1) and traditional ELM (0.817 7). [Conclusion] The parameters of the ELM model optimized by intelligent algorithms can effectively improve the accuracy of wine quality evaluation.