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Nondestructive, rapid, and accurate detection of nutritional compositions in sorghum is crucial for agricultural and food industries. In our study, the crude protein, tannin, and crude fat contents of sorghum variety samples were taken as the research object. The visible near-infrared (VIS-NIR) hyperspectral of sorghum were measured by the indoor mobile scanning platform. The nutritional components were determined using chemical methods to analyze the differences in nutritional composition among different varieties. After preprocessing the original spectral, the competitive adaptive reweighted sampling (CARS) and bootstrapping soft shrinkage (BOSS) algorithms were used to coarsely extract the key variables. Subsequently, the iteratively retains informative variables (IRIV) was employed to assess the importance of these key variables, resulting in explanatory wavelength sets for crude protein, tannin, and crude fat. Finally, the partial least squares (PLS), back propagation (BP) and extreme learning machine (ELM) were utilized to establish detection models. The results indicated that the optimal wavelength variable sets for crude protein, tannin, and crude fat contained 41, 38, and 22 wavelength variables, respectively. The CARS-IRIV-PLS, BOSS-IRIV-PLS and BOSS-IRIV-ELM were suitable for detecting crude protein, tannin and crude fat, respectively. Meanwhile, the Rp2, RMSEp and RPDp values of the model were 0.69, 0.80% and 1.80, 0.88, 0.22% and 2.84, 0.61, 0.32% and 1.61, respectively. These detection models can be used for the effective estimation of the nutritional compositions in sorghum with VIS-NIR spectral data, and can provide an important basis for the application of food nutrition assessment. [ABSTRACT FROM AUTHOR]

Agriculture is crucial to the global economy, particularly in ensuring food security. Recent trends indicate that various plant diseases are causing substantial financial losses in the agricultural sector worldwide. Traditional manual inspection methods for detecting fruit and plant diseases are labor-intensive and inefficient. Adopting automated disease detection technologies could significantly enhance early diagnosis and reduce the economic impact of these diseases on agriculture. This study introduces an advanced model for classifying apple diseases by integrating a pre-trained convolutional neural network (PCNN), such as VGG16, VGG19, or ResNet50, with an incremental extreme learning machine (I-ELM) for efficient feature extraction and classification. A key innovation of this model is replacing the PCNN's fully connected layer with the I-ELM, which eliminates the lengthy back-propagation process and significantly reduces training time. Integrating I-ELM with PCNN harnesses the rapid learning capabilities and robust generalization of I-ELM with the superior feature extraction abilities of CNNs. I-ELM simplifies the network architecture by avoiding the complex neural networks commonly used in other methods. The model's effectiveness is rigorously evaluated on the well-known Plant Village dataset, demonstrating its ability to identify various apple diseases through performance metrics such as precision, sensitivity, specificity, accuracy, and the F1-score. Comparing existing deep learning models using these metrics highlights its superior performance. This innovation is up-and-coming for intelligent agricultural systems, offering an effective solution for classifying apple diseases and enabling timely and innovative farming practices. [ABSTRACT FROM AUTHOR]

Marine Predator Algorithm (MPA) is a recently proposed population-based metaheuristic algorithm (MA), and its effectiveness has been proven in many stochastic optimization challenges. However, like most MAs, MPA suffers from shortcomings such as imbalanced search preferences and stagnation in the late phase of optimization. Therefore, this paper presents a Multi-strategy Enhanced Marine predator algorithm (MEMPA), where (1) a low-discrepancy Sobol sequence is introduced to generate promising initial solutions in the high-dimensional search domain, (2) the mutualism mechanism is integrated to enhance the ability to escape from local optima, and (3) a distance-based selection scheme is embedded to enhance the diversity of the population during optimization. We conducted comprehensive numerical experiments and rigorous statistical analysis to evaluate the performance of MEMPA against eleven well-known MAs in the CEC2020 benchmark and six classic engineering problems. The experimental results and statistical analysis confirm the efficiency and effectiveness of our proposed MEMPA comprehensively. Finally, we extend the proposed MEMPA to optimize the hyper-parameters of the Extreme Learning Machine (ELM) for intrusion detection, and the performance of MEMPA-ELM increases the average accuracy by 0.79% than the second-best model. The source code of this research can be downloaded at https://github.com/RuiZhong961230/MEMPA. [ABSTRACT FROM AUTHOR]

Lumpy Skin Disease (LSD) presents a global threat to cattle populations, with over 110,000 cattle deaths reported in India alone. Lumpy skin disease presents a significant economic challenge to India's cattle industry, impacting farmers and agricultural productivity. A timely and accurate diagnosis is essential for effective disease management. A novel methodology has been developed to address this problem by using stacking ensemble approaches. This innovative approach combines various foundational models, including capsule networks and extreme learning machines. Capsule networks represent spatial hierarchies, while extreme learning machines handle feature spaces with high dimensions. During the ultimate forecasting stage, a level 2 model employing Random Forest is utilized. This complete approach has an impressive accuracy rate of 98.30%, significantly enhancing the efficacy of identifying lumpy skin disease in cattle. By integrating advanced machine learning techniques into diagnostic procedures, stakeholders may minimize human errors, reduce misdiagnosis, minimize economic losses, and safeguard the health and productivity of cattle, thereby improving agricultural resilience and sustainability in the face of this disease threat. [ABSTRACT FROM AUTHOR]

An origin discrimination model for rice from five production regions in Heilongjiang Province was constructed based on the combination of confocal microscopy Raman spectroscopy and chemometrics. A total of 150 field rice samples were collected from the Fangzheng, Chahayang, Jiansanjiang, Xiangshui, and Wuchang production areas. The optimal sample processing conditions, instrument parameter settings, and spectrum acquisition techniques were identified by investigating the influencing factor. The Raman spectra of milled rice within the range of 100–3200 cm−1 were selected as the raw data, and the optimal preprocessing method combination consisting of normalization, Savitzky–Golay smoothing, and multivariate scatter correction was identified. Subsequently, the competitive adaptive reweighted sampling and discrete binary particle swarm optimization algorithms were employed to optimize the feature wavelength selection, resulting in the screening of 226 and 1899 feature wavelength variables, respectively. Using the full Raman spectrum data and feature wavelength data as inputs, partial least squares discriminant analysis, support vector machine and extreme learning machine origin discrimination models were constructed. The results indicated that the BPSO-GA-SVM model exhibited the best predictive ability, achieving a testing set accuracy of 86.67%. [ABSTRACT FROM AUTHOR]

Concrete-filled steel tube (CFST) as the high-tech composite members utilized as a main load-carrying element in high-rise buildings' construction. CFST element load capacity is considered one of the most crucial and challenging engineering parameters for designing columns structurally and economically for steel–concrete composite. Because of the designing complexity of theoretically simulation and serviceability limits, this paper attempted to overcome the engineering problem using a machine learning (ML) methods. To do so, numerous efficient ML modeling called multivariate adaptive regression spline (MARS), M5p model tree (M5p), extreme learning machine (ELM), and random forest (RF) are implemented to propose a new auto-estimated and interpretable model. Through extensive literature, including 1305 (circular column) and 1003 (rectangular column) subjected to concentric axial force, data-intelligence models are developed. The developed models were compared with corresponding values computed by design code provisions, including Eurocode 4, LRFD, AISC 360–16, AS5100, ACI 318–14, and empirical equations extracted. The statistical metrics present that the proposed MARS models (r = 0.990, RMSE = 361.32 KN, WI = 0.995, and PMARE = 14.078% (circular column)) and (r = 0.974, RMSE = 494.94 KN, WI = 0.984, and PMARE = 11.238% (rectangular column)) boosted the performance of the simulation of the CFTS column compare to other models and design codes. In addition, global sensitivity analysis was performed using SOBOL methods to evaluate effective parameters. The explicit simulation model of the CFST columns is satisfied with the parametric study and shows the ability to perform the modeling and the cost-effective benefits of the information approach. [ABSTRACT FROM AUTHOR]

ELM (Extreme learning machine) has drawn great attention due its high training speed and outstanding generalization performance. To solve the problem that the long training time of kernel ELM auto-encoder and the difficult setting of the weight of kernel function in the existing multi-kernel models, a multi-kernel cost-sensitive ELM method based on expectation kernel auto-encoder is proposed. Firstly, from the view of similarity, the reduced kernel auto-encoder is defined by randomly selecting the reference points from the input data; then, the reduced expectation kernel auto-encoder is designed according to the expectation kernel ELM, and the combination of random mapping and similarity mapping is realized. On this basis, two multi-kernel ELM models are designed, and the output of the classifier is converted into posterior probability. Finally, the cost-sensitive decision is realized based on the minimum risk criterion. The experimental results on the public and realistic datasets verify the effectiveness of the method. [ABSTRACT FROM AUTHOR]

Financial distress (FD) occurs when external economic factors or internal financial challenges threaten the stability of a business, often leading to financial difficulties or bankruptcy. Predicting FD is critical, but existing methods suffer from limitations such as a narrow focus on input variables, inadequate emphasis on key financial indicators, and challenges in handling large, imbalanced datasets. Besides, most of the existing techniques often fail to minimize computational complexity while enhancing forecast accuracy, necessitating the development of more effective models. Recognizing these challenges, this work proposes the APCIELM model, a multistage approach specifically designed for FDP with imbalanced datasets. Davies–Bouldin index-based hierarchical K-means clustering approach is devised to group data samples, followed by a new strategic differentiation between minority and majority classes. The proposed Rotation Affinity Propagation Cluster-based hypothesis determines the necessity for oversampling within specific clusters based on data distribution characteristics. Finally, an incremental extreme learning machine (IELM) model is employed for FDP which optimizes computational efficiency by eliminating ineffective calculations while maintaining high prediction performance. The results demonstrate that the proposed multistage prediction model outperforms single-stage models when dealing with imbalanced data. The efficiency of APCIELM model is evaluated using different metrics, including accuracy, precision, recall, F-score, and time complexity. The comprehensive analysis reveals the superior performance of the APCIELM model over the existing methods. [ABSTRACT FROM AUTHOR]

As a major burden of blast furnace, sinter mineral with desired quality performance needs to be produced in sinter plants. The tumbler index (TI) is one of the most important indices to characterize the quality of sinter, which depends on the raw materials proportion, operating system parameters and the chemical compositions. To accurately predict TI, an integrate model is proposed in this study. First, to decrease the data dimensionality, the sintering production data is addressed through principal component analysis (PCA) and the principal components with the accumulated contribution rate no more than 95% are extracted as the inputs of the predictive model based on Extreme Learning Machine (ELM). Second, the genetic algorithm (GA) has been applied to promote the improvement of the robustness and generalization performance of the original ELM. Finally, the model is examined using actual production data of a year from a sinter plant, and is compared with the algorithms of single ELM, GA-BP and deep learning method. A comparison is conducted to confirm the superiority of the proposed model with two traditional models. The results showed that an improvement in predictive accuracy can be obtained by the GA-ELM approach, and the accuracy of TI prediction is 81.85% for absolute error under 0.7%. [ABSTRACT FROM AUTHOR]

A timely and accurate cancer diagnosis is essential for improving treatment outcomes. This study presents a hybrid model integrating Extreme Learning Machine (ELM) with FuNet transfer learning, applied on a multi-cancer dataset and optimized using the Quantum-Genetic Binary Grey Wolf Optimizer (Q-GBGWO). This model leverages a diverse feature fusion strategy, enhancing the extraction of critical imaging features, while Q-GBGWO optimizes ELM parameters to achieve superior classification performance. Results demonstrate that Q-GBGWO-ELM improves diagnostic accuracy by an average of 6.5% compared to traditional methods, with notable accuracy rates across various cancers: 98.80% for breast cancer, 92.30% for brain tumors, 97.00% for skin cancer, and 96.98% for lung cancer. The model integrates advanced feature extraction and optimization techniques, indicating significant potential for early cancer detection. The proposed Q-GBGWO-ELM model contributes to a more innovative diagnostic approach in clinical practice by offering enhanced precision, efficiency, and adaptability across multiple cancer types. This advancement supports a shift toward more personalized and rapid diagnostic procedures, aiming to improve patient outcomes and reshape current cancer care practices with AI-driven accuracy and efficiency. [ABSTRACT FROM AUTHOR]

Quantum extreme learning machines (QELMs) leverage untrained quantum dynamics to efficiently process information encoded in input quantum states, avoiding the high computational cost of training more complicated nonlinear models. On the other hand, quantum information scrambling (QIS) quantifies how the spread of quantum information into correlations makes it irretrievable from local measurements. Here, we explore the tight relation between QIS and the predictive power of QELMs. In particular, we show efficient state estimation is possible even beyond the scrambling time, for many different types of dynamics — in fact, we show that in all the cases we studied, the reconstruction efficiency at long interaction times matches the optimal one offered by random global unitary dynamics. These results offer promising venues for robust experimental QELM-based state estimation protocols, as well as providing novel insights into the nature of QIS from a state estimation perspective. [ABSTRACT FROM AUTHOR]

Accurate predictive daily global horizontal irradiation models are essential for diverse solar energy applications. Their long-term performances can be assessed using average years. This study scrutinized 70 machine learning and 44 empirical models using two disjoint 5-year average daily training and validation datasets, each comprising 365 records and ten features. The features included day number, minimum and maximum air temperature, air temperature amplitude, theoretical and observed sunshine hours, theoretical extraterrestrial horizontal irradiation, relative sunshine, cloud cover, and relative humidity. Fourteen machine learning algorithms, namely, multiple linear regression, ridge regression, Lasso regression, elastic net regression, Huber regression, k-nearest neighbors, decision tree, support vector machine, multilayer perceptron, extreme learning machine, generalized regression neural network, extreme gradient boosting, gradient boosting machine, and light gradient boosting machine were trained, validated, and instantiated as base learners in four strategically designed homogeneous parallel ensembles--variants of pasting, random subspace, bagging, and random patches--which also were scrutinized, producing 70 models. Specific hyperparameters from the algorithms were optimized. Validation showed that at least two ensembles outperformed its individual model. Huber-subspace ranked first with a root mean square error of 1.495 MJ/m2/day. The multilayer perceptron was most robust to the random perturbations of the ensembles which extrapolate to good tolerance to ground-truth data noise. The best empirical model returned a validation root mean square error of 1.595 MJ/m2/day but was outperformed by 93% of the machine learning models with the homogeneous parallel ensembles producing superior predictive accuracies. [ABSTRACT FROM AUTHOR]

This research utilizes experimental data derived from various Venturi flume setups to assess the Venturi flume aeration efficiency (VFAE20). This evaluation encompasses dimensional factors (such as discharge per unit width denoted as q , throat width as W , throat length as F , flume length as L , flume height as H , and submergence ratio as S) as well as nondimensional parameters including the ratio of throat length to width denoted as F/W , the ratio between flume length and throat width as L/W , the ratio of flume height to throat width as H/W , the reciprocal of the submergence ratio as 1/ S , and functions incorporating the Reynolds number R , Hb gauge, and hydraulic radius R represented as RHb/R , and another function incorporates the Froude number F , Hb gauge, and throat width W represented as [(F23/W)Hb]3. The study compares empirical relations from multiple nonlinear regression (MNLR) and multiple linear regression (MLR) with proposed artificial intelligence (AI) models, including neural networks (NN), deep neural networks (DNN), extreme learning machines (ELM), gradient-boosting machines (GBMs), and neuro-fuzzy systems (NFS). Using performance metrics and graphical evaluators, GBM consistently outperforms all models (dimensional and nondimensional data), followed by NFS_Tri. Despite minor variances, all the suggested machine learning (ML) models exhibit commendable performance. Uncertainty analysis identifies GBM as the top performer, closely followed by NFS_Tri while existing relations perform poorly. Analysis of variance (ANOVA) results indicate insignificant disparities between experimental and predicted VFAE20 values across all proposed AI models but notable differences within existing relations in dimensional and nondimensional data sets. Sensitivity analysis highlights (q) and (RHb/R) as the most influential factors affecting VFAE20 in dimensional and nondimensional data sets, respectively, supported by correlation diagrams and Shapley values. Further, we also investigated how the flumes performed in terms of aeration discharge and water flow characteristics. Practical Applications: Venturi flumes find diverse applications in environmental engineering, aquaculture, hydroelectric power generation, and irrigation systems, providing precise measurement and regulation of water flow rate, oxygen levels, and air infusion. Their effective aeration capabilities bolster treatment efficacy in wastewater facilities, foster the growth of aquatic organisms in aquaculture settings, optimize turbine functionality in hydroelectric plants, and mitigate clogging in irrigation setups. This versatility renders Venturi flumes indispensable across multiple domains where precise water flow and oxygen control are essential. [ABSTRACT FROM AUTHOR]

Recognizing hand gestures poses a formidable challenge, particularly when dealing with semantic gestures that require disentanglement prior to recognition. This paper addresses the intricate issue of an additional stroke, commonly referred to as 'movement epenthesis stroke,' which emerges between continuous gestures. Our proposed system employs a multifaceted approach to tackle this challenge. Initially, the system extracts color-motion information to facilitate hand detection, subsequently employing a fusion of shape information and a modified Kanade–Lucas–Tomasi (KLT) feature tracker. This integration significantly mitigates the issue of occlusions. The identification of movement epenthesis is accomplished by analyzing the gesture trajectory using a speed profile. Furthermore, self-co-articulation strokes are discerned by leveraging slope-angle information. To enhance the recognition process, a carefully selected set of 40 features is extracted, which are then employed for recognizing the resulting meaningful gestures. These features serve as inputs to various classification models, including support vector machines (SVM), k-nearest neighbors (kNN), and extreme learning machines (ELM). Deep learning algorithms are judiciously deployed to recognize gesture trajectories, thus streamlining the time-consuming feature extraction process. The outcomes of individual classifiers are amalgamated, resulting in a classifier fusion model. This model is enhanced through majority voting and is used in conjunction with cross-validation results. The experimental analysis culminates in an impressive accuracy rate of 98.88% achieved by the classifier fusion model. This achievement surpasses the performance of individual classifiers, underscoring the effectiveness of our proposed methodology. [ABSTRACT FROM AUTHOR]

Modeling distributed parameter systems (DPSs) is challenging due to their strong nonlinearity and spatiotemporal coupling. In this study, a three-dimensional fuzzy modeling method combining genetic algorithm (GA)-based automatic clustering and hierarchical extreme learning machine (HELM) is proposed for DPS modeling. The method utilizes GA-based automatic clustering to learn the premise part of 3D fuzzy rules, while HELM is employed to learn spatial basis functions and construct a complete fuzzy rule base. This approach effectively captures the spatiotemporal coupling characteristics of the system and mitigates the information loss commonly observed in dimensionality reduction in traditional fuzzy modeling methods. Through experimental verification, the proposed method is successfully applied to a rapid thermal chemical vapor deposition system. The experimental results demonstrate that the method can accurately predict temperature distribution and maintain good robustness under noise and disturbances. [ABSTRACT FROM AUTHOR]

Papermaking wastewater consists of a sizable amount of industrial wastewater; hence, real-time access to precise and trustworthy effluent indices is crucial. Because wastewater treatment processes are complicated, nonlinear, and time-varying, it is essential to adequately monitor critical quality indices, especially chemical oxygen demand (COD). Traditional models for predicting COD often struggle with sensitivity to parameter tuning and lack interpretability, underscoring the need for improvement in industrial wastewater treatment. In this manuscript, an optimized papermaking wastewater treatment method is proposed that predicts effluent quality using node-level capsule graph neural networks (PWWT-PEQ-NLCGNN). To improve the accuracy of predicting important effluent COD quality indices, the NLCGNN weight parameters are optimized using the hermit crab optimization (HCO) algorithm. The performance of the proposed PWWT-PEQ-NLCGNN technique demonstrated improvements over existing techniques. Specifically, the proposed strategy achieved 30.53%, 23.34%, and 32.64% higher accuracy; 20.53%, 25.34%, and 29.64% higher precision; and 20.53%, 25.34%, and 29.64% higher sensitivity compared to the water quality prediction model using Gaussian process regression based on deep learning for carbon neutrality in papermaking wastewater treatment system (WQP-GPR-DL-CLPWWTS), the prediction of effluent quality in papermaking wastewater treatment processes using dynamic kernel-based extreme learning machine (POEQ-PWWTP-DKBELM), and the quality-related monitoring of papermaking wastewater treatment processes using dynamic multi-block partial least squares (QRM-PWWTP-DMPLS). These results highlight the potential of the PWWT-PEQ-NLCGNN method for enabling timely and accurate monitoring of wastewater treatment processes. [ABSTRACT FROM AUTHOR]

The utilisation of rotating machinery across diverse industrial applications underscores the critical importance of evaluating its reliability to ensure the safe operation of these systems. Bearings, as fundamental components within engineering facilities, hold particular significance; their malfunction can result in severe safety incidents, heightened maintenance expenditures, and considerable economic consequences. Extreme learning machine (ELM) represents a machine learning approach that proficiently addresses numerous challenges inherent in conventional machine learning algorithms. Nonetheless, the efficacy of the ELM may deteriorate and yield inaccurate results due to an inappropriate use of its parameters, which include input weights, biases, and the number of hidden neurons. This paper proposes an improved ELM (IELM) model that incorporates the Hippopotamus optimization algorithm (HOA) to optimise the parameters and enhance the performance of the ELM in rotating machinery reliability assessment. Initially, the HOA method is employed to identify optimised parameter values for the ELM. Subsequently, these optimised values are integrated into the proposed IELM-HOA framework for the purpose of fault classification. This study utilises time-domain statistical features to extract significant information from the vibration signals. The dataset comprises vibration signals derived from both online bearing datasets and experimental bearing datasets. The findings indicate that the proposed IELM-HOA method enhances the performance of the ELM technique. Furthermore, it demonstrates the capability to exceed and compete with recently introduced fault diagnosis methodologies. [ABSTRACT FROM AUTHOR]

In this paper, a novel stacked encoded cascade error feedback deep extreme learning machine (SEC-E-DELM) network is proposed to predict order completion time (OCT) considering the historical production planning and control data. Usually, the actual OCT significantly deviates from the planned because of recessive disturbances. The disturbances do not shut down production but slow down the production that accumulates over time, causing significant deviation of actual time from planned. The generation of weight parameters in neural networks using a randomization approach has a significant effect on generalization performance. To predict the OCT, firstly, the stacked autoencoder is used to generate input connection weights for the network by learning a deep representation of the real data. Secondly, the learned distribution of the encoder is connected to the network output through output connection weights incrementally learned by the Moore–Penrose inverse. Thirdly, the new hidden unit is added one by one to the network, which receives input connections from the input units and the last layer of the encoder to avoid overfitting and improve model generalization. The input connection weights for the newly added hidden unit are analytically calculated by the error feedback function to enhance the convergence rate by further learning deep features. Lastly, the hidden unit keeps on adding one by one by receiving connections from input units and some of the existing hidden units to make a deep cascade architecture. An extensive comparative study demonstrates that calculating connection weights by the proposed method helps to significantly improve the generalization performance and robustness of the network. [ABSTRACT FROM AUTHOR]

Introduction: Attention classification based on EEG signals is crucial for brain-computer interface (BCI) applications. However, noise interference and real-time signal fluctuations hinder accuracy, especially in portable single-channel devices. This study proposes a robust Kalman filtering method combined with a norm-constrained extreme learning machine (ELM) to address these challenges. Methods: The proposed method integrates Discrete Wavelet Transformation (DWT) and Independent Component Analysis (ICA) for noise removal, followed by a robust Kalman filter enhanced with convex optimization to preserve critical EEG components. The norm-constrained ELM employs L1/L2 regularization to improve generalization and classification performance. Experimental data were collected using a Schulte Grid paradigm and TGAM sensors, along with publicly available datasets for validation. Results: The robust Kalman filter demonstrated superior denoising performance, achieving an average AUC of 0.8167 and a maximum AUC of 0.8678 on self-collected datasets, and an average AUC of 0.8344 with a maximum of 0.8950 on public datasets. The method outperformed traditional Kalman filtering, LMS adaptive filtering, and TGAM's eSense algorithm in both noise reduction and attention classification accuracy. Discussion: The study highlights the effectiveness of combining advanced signal processing and machine learning techniques to improve the robustness and generalization of EEG-based attention classification. Limitations include the small sample size and limited demographic diversity, suggesting future research should expand participant groups and explore broader applications, such as mental health monitoring and neurofeedback. [ABSTRACT FROM AUTHOR]

Background: Cancer rates are rising rapidly, causing global mortality. According to the World Health Organization (WHO), 9.9 million people died from cancer in 2020. Machine learning (ML) helps identify cancer early, reducing deaths. An ML-based cancer diagnostic model can use the patient's genetic information, such as microarray data. Microarray data are high dimensional, which can degrade the performance of the ML-based models. For this, feature selection becomes essential. Methods: Swarm Optimization Algorithm (SSA), Improved Maximum Relevance and Minimum Redundancy (IMRMR), and Boruta form the basis of this work's ML-based model BIMSSA. The BIMSSA model implements a pipelined feature selection method to effectively handle high-dimensional microarray data. Initially, Boruta and IMRMR were applied to extract relevant gene expression aspects. Then, SSA was implemented to optimize feature size. To optimize feature space, five separate machine learning classifiers, Support Vector Machine (SVM), Random Forest (RF), Extreme Learning Machine (ELM), AdaBoost, and XGBoost, were applied as the base learners. Then, majority voting was used to build an ensemble of the top three algorithms. The ensemble ML-based model BIMSSA was evaluated using microarray data from four different cancer types: Adult acute lymphoblastic leukemia and Acute myelogenous leukemia (ALL-AML), Lymphoma, Mixed-lineage leukemia (MLL), and Small round blue cell tumors (SRBCT). Results: In terms of accuracy, the proposed BIMSSA (Boruta + IMRMR + SSA) achieved 96.7% for ALL-AML, 96.2% for Lymphoma, 95.1% for MLL, and 97.1% for the SRBCT cancer datasets, according to the empirical evaluations. Conclusion: The results show that the proposed approach can accurately predict different forms of cancer, which is useful for both physicians and researchers. [ABSTRACT FROM AUTHOR]

Lung cancer is an abnormal development of cells that are uncontrollably proliferating. When using a system for medical diagnostics, the precise identification of lung cancer is crucial. Magnetic Resonance Imaging (MRI) and Positron Emission Tomography (PET) are the most common methods for diagnoses. Due to the limited sensitivity of the border pixels in PET and MRI imaging, finding lung cancer might be difficult. As a result, image fusion was created, which successfully combines several modalities to identify the disease and cure it. But merging images from multiple modalities has always been troublesome in medicine because the final image includes distorted spectral information. To avoid the problems, in this paper,pixel level image fusion for lung cancer detection is proposed. Pre-processing, multi-modality image fusion, feature extraction, and classification are the four phases of the suggested methodology. Images from the PET and MRI scanners are initially gathered and pre-processed. The best pixel-level fusion method is then used to merge the PET and MRI images. Here, the adaptive tee seed optimization (ATSO) method is used to ideally choose the fusion parameter contained in the approach to improve the fusion model. The texture characteristics are taken from the fused image after the image fusion. The deep extreme learning machine (DELM) classifier will then identify animage as normal or abnormal using the retrieved features.Utilizing a variety of criteria, the effectiveness of the suggested methodology is assessed and compared to previous state-of-art studies. The experimental results shows proposed approach attained the maximum accuracy of 97.23%. [ABSTRACT FROM AUTHOR]

The article discusses the integration of remote sensing and soil metagenomics data to advance precision agriculture. Precision agriculture aims to increase yields, reduce resource waste, and minimize environmental impacts by utilizing advanced technologies. Soil metagenomics provides insights into microbial communities, while remote sensing technologies like UAVs offer real-time data collection for soil and crop monitoring. The integration of these technologies enhances farming efficiency, reduces environmental impact, and promotes sustainable agriculture. The article highlights the importance of integrating diverse datasets within an interdisciplinary framework to optimize precision farming practices. [Extracted from the article]

The transient electromagnetic method (TEM) is widely used in geophysical exploration. In TEM data interpretation, nonlinear inversion plays an important role. However, traditional TEM nonlinear inversion adopts the OCCAM imaging method, which merely presents the approximate shape of the stratum model, with poor inversion accuracy and much iteration time. To solve the above problems, a novel nonlinear inversion approach based on extreme learning machine (ELM) is proposed in this paper. The ELM is required to establish the input–output mapping relationship of the inversion network only once through the analytical method, which is different from the traditional neural network method that demands iterative gradient learning and is prone to fall into the local optimum. Moreover, the ELM inversion network by randomly assigning the hidden layer parameters is capable of mapping the observed TEM data and quickly producing resistivity images, which avoids time-consuming iteration and inversion calculations. The presented approach is applied to both synthetic and field examples. The results show that compared with the traditional nonlinear inversion algorithms (BP and OCCAM), the proposed method achieves better inversion accuracy and significantly reduces the calculation time, which verifies the effectiveness of the ELM algorithm for TEM data interpretation. Additionally, the research provides a new method and technology for TEM data inversion. [ABSTRACT FROM AUTHOR]

Simulated Moving Bed (SMB) chromatography separation is a novel absorptive separation technique with high separation capacity, and it is challenging to make the process run stably at the desired operating point for a long time. Therefore, powerful, and adaptive soft sensor models are important for the overall stability, efficiency, and optimization of the SMB chromatographic separation process. Extreme Learning Machine (ELM), known for its robust generalization capability, can serve as a valuable soft-sensor model for predicting economic and technical indicators such as purity and yield in SMB chromatography separation processes. To improve the prediction accuracy of ELM, avoid stochasticity and learn incrementally, a bidirectional online sequential ELM (BOSELM) is proposed. BOSELM learns data with fixed or varying block sizes on a one-by-one or block-by-block basis (data chunks) without the need to define the size of the network beforehand and determines the output weights based on the analysis of the sequentially arriving data. On the other hand, BOSELM also makes use of the bidirectional ELM's ability to explore the number of hidden nodes to reduce the number of hidden nodes without affecting the learning efficiency, improving the speed and adaptability of model training. The moving window (MW) strategy was adopted to adaptively correct the BOSELM (MW-BOSELM) to address the issue of decreasing model prediction accuracy caused by changes in process conditions. Additionally, the MW strategy kernel-extreme learning machine (MW-KELM) exhibits higher prediction accuracy than MW-BOSELM at certain instances. To enhance the adaptability of the model, an adaptive hybrid soft-sensor model is proposed to intelligently switch between BOSELM and KELM. Comparative analysis with previous models like BELM, OSELM and BOSELM highlights the superiority of the proposed hybrid adaptive soft-sensor model. Thus, these models help to improve the operational efficiency, product quality and economics of the SMB chromatographic separation process. [ABSTRACT FROM AUTHOR]

A short-term photovoltaic power forecasting method is proposed, integrating variational mode decomposition (VMD), an improved dung beetle algorithm (IDBO), and a deep hybrid kernel extreme learning machine (DHKELM). First, the weather factors less relevant to photovoltaic (PV) power generation are filtered using the Spearman correlation coefficient. Historical data are then clustered into three categories—sunny, cloudy, and rainy days—using the K-means algorithm. Next, the original PV power data are decomposed through VMD. A DHKELM-based combined prediction model is developed for each component of the decomposition, tailored to different weather types. The model's hyperparameters are optimized using the IDBO. The final power forecast is determined by combining the outcomes of each individual component. Validation is performed using actual data from a PV power plant in Australia and a PV power station in Kashgar, China demonstrates. Numerical evaluation results show that the proposed method improves the Mean Absolute Error (MAE) by 3.84% and the Root-Mean-Squared Error (RMSE) by 3.38%, confirming its accuracy. [ABSTRACT FROM AUTHOR]

Background: In this study, the unconfined compressive strength (qu) of a mixture consisting of clay reinforced with 24 mm-long basalt fiber was estimated using extreme learning machine (ELM). The aim of this study is to estimate the results closest to the data obtained through experimental studies without the need for experimental studies. The literature review reveals that the ELM technique has not been applied to predict the compressive strength of basalt fiber-reinforced clay, and this study aims to provide a novel contribution in this area. Methods: The experimental studies included data derived from a series of mixtures where water contents of 20%, 25%, 30%, and 35% were combined with kaolin clay reinforced with 24 mm-long basalt fiber at reinforcement rates of 0%, 1%, 2%, and 3%. Based on the experimental results obtained for these mixtures, an ELM model was developed to predict the qu. Results: ELM, recognized for its computational efficiency and high predictive accuracy, demonstrated exceptional performance in this application, achieving an R value of 0.9976 and an RMSE of 0.0001. Furthermore, this study includes a figure representation illustrating that the ELM-based predictions align closely with the experimental results, underscoring its reliability. Conclusions: To further validate its performance, ELM was compared with other artificial intelligence models through a 5-fold cross-validation approach. The analysis revealed that ELM outperformed its counterparts, achieving a remarkable RMSE value of 0.000174, thereby solidifying its capability to accurately estimate the compressive strength of the soil under varying reinforcement and water content conditions. Thus, it is aimed to save labor, material, and time. [ABSTRACT FROM AUTHOR]

Wind-structures interaction has been extensively examined in the last few decades using field measurements, full scale measurements and wind tunnel testing. These experimental approaches are considered costly and time consuming. The need for a reliable analytical approach that can be used for examining wind-effects on buildings is clear. Although Computational Fluid Dynamics (CFD) is one of the other alternative numerical options yet might not reached the level of confidence to be reliably used to finalize the structural design. On the other hand, a limited number of studies have been carried out using soft computing methods to examine wind-induced loads on structures. However, its promising results, more work is still required towards achieving the full analytical prediction of wind effects on structures. This study investigates the use of different soft-computing techniques in predicting wind pressures on tall buildings. Two deep learning methods viz deep belief network (DBN) and deep neural network (DNN), and five machine learning methods namely feedforward neural network, extreme learning machine, weighted extreme learning machine, random forest, and gradient boosting machine were evaluated, and compared in predicting the design wind pressures on tall buildings. Wind tunnel datasets, used in the current study to develop the proposed computing models, were collected from testing three tall buildings having the same full-scale horizontal dimensions of (40 m and 80 m) and different heights of (80 m, 120 m and 160 m). The buildings were tested at a scale of 1:400 in urban terrain exposure. Mean and fluctuating wind pressure coefficients on the building with the height of 120 m are herein predicted using the seven computing methods and the results were compared to the corresponding measured pressures. Overall, the examined methods performed well in the wind pressure prediction process. Furthermore, the employed DNN was found to have the best performance in predicting mean and fluctuating wind pressures with the highest correlation coefficients. Hence, the DNN was also used in predicting the mean and fluctuating wind pressures on the two other buildings with heights of 80 m and 160 m. Experimental results indicate that the employed DNN model can be effectively used in predicting wind-induced pressures on tall buildings. [ABSTRACT FROM AUTHOR]

As one of the main energy sources in human production and life, the accurate and rapid classification of coal is of great significance to industrial production and the control of pollution emissions. However, the complex composition and highly similar elemental composition of coal with different physical properties and chemical composition lead to a high degree of similarity in coal spectral data measured by laser-induced breakdown spectroscopy (LIBS), which poses a great challenge to accurate classification and identification work. In this paper, based on LIBS technology, we integrate the chi-square test (CST) and principal component analysis (PCA) to construct a quadratic dimensionality reduction network (CST-PCA), and for the first time, we propose a new improved sparrow search algorithm (ISSA) by introducing spatial pyramid matching (SPM) chaotic mapping, adaptive inertia weights (w) and Gaussian mutation, and combine it with kernel based extreme learning machine (KELM) to construct an ISSA-KELM data classification model to classify and identify seven types of coal samples. Firstly, 2520 12248-dimensional coal spectral data were preprocessed using a combination of the chi-square test (CST) and principal component analysis (PCA). The KELM was hyper-parameter optimised using ISSA. By comparing with the unoptimized model, the accuracy of coal classification reaches 99.773%. The experimental results show that the CST-PCA-based ISSA-KELM algorithm effectively optimizes the parameters, improves the classification accuracy of coal, and provides a new data processing scheme for accurate qualitative analysis of coal. [ABSTRACT FROM AUTHOR]

Efficient nitrogen management is crucial for improving corn productivity while minimizing environmental impacts. This study evaluates the response of corn to nitrogen fertilization using three key metrics: yield; nitrogen harvest index (NHI); and agronomic nitrogen use efficiency (ANUE). This experiment was conducted over three years (2021–2023) across 84 sites in Quebec, Canada, with five nitrogen treatments applied post-emergence (0, 50, 100, 150, 200 kg N/ha) and initial nitrogen applied at seeding (30 to 60 kg/ha). In addition, various soil health indicators, including physical, chemical, and biochemical properties, were monitored to understand their interaction with nitrogen use efficiency. Machine learning techniques, such as augmented extreme learning machine (AELM) and particle swarm optimization (PSO), were employed to optimize nitrogen recommendations by identifying the most relevant features for predicting yield and nitrogen use efficiency (NUE). The results highlight that integrating soil health indicators such as enzyme activities (β-glucosidase [BG] and N-acetyl-β-D-glucosaminidase [NAG]) and soil proteins into nitrogen management models improves prediction accuracy, leading to enhanced productivity and environmental sustainability. These findings suggest that advanced data-driven approaches can significantly contribute to more precise and sustainable nitrogen fertilization strategies. [ABSTRACT FROM AUTHOR]

Ergonomics in product design enhance user comfort, correlating with increased efficiency and usability between the user and the product. Anthropometry is an important part of creating ergonomic design that correlates with human body size. By utilising anthropometry data, this research aims to analyse the cabin's comfort in drilling rig machine by creating 3D simulations for human-machine interaction. The comfort values will be assessed using Jack software. An experimental orthogonal design is applied to generate 81 optimal combination samples. The sample with the optimal comfort value can also be obtained. Following that, the extreme learning machine and mean impact value (ELM-MIV) algorithm is employed to evaluate the comfort weight analysis of each influencing factor. The accuracy value of the prediction model assessed using mean squared error (MSE) is 0.004, and the squared correlation coefficient (R2) value is 0.996. It can be inferred that the prediction error is apparently minimal, indicating the reliability of ELM implementation in data training. It will optimise the redesign of a drilling rig machine cabin to solve work-related health issues and improve operator comfort and efficiency. [ABSTRACT FROM AUTHOR]

Grasping is a fundamental action in daily life and particularly evident during mealtime situations where various grasping actions occur with tableware such as chopsticks, spoons, forks, bowls, and cups, each serving specific purposes. While tableware usage varies across regions and cultures, recognizing grasping actions is crucial for assessing performance in daily activities. In this study, we focus on assessing grasping functionality in terms of tableware usage during meals and propose a method for identifying hand movements. In recent years, there has been a surge in developing approaches for hand pose estimation and gesture recognition using deep learning. However, these approaches encounter common challenges, including the need for large-scale datasets, hyperparameter tuning, significant time and computational costs, and limited applicability to incremental learning. To address these challenges, we propose an ensemble approach employing extreme learning machines to recognize grasp postures. In addition, we apply spatiotemporal modeling to extract the relationship between grasp postures and the surrounding tools during mealtimes. [ABSTRACT FROM AUTHOR]

To address the difficulty in identifying human lower-limb movement intentions, low accuracy of classification models, and weak generalization ability, this study proposes a motion intention recognition method that combines an improved pelican optimization algorithm (IPOA) and a hybrid kernel extreme learning machine (HKELM). First, we collect the surface electromyography (sEMG) signals of subjects in six motion modes and perform feature parameter extraction under non-ideal conditions. On this basis, we establish a dataset of the relationships between the feature parameters and gait movements. Second, we build a motion intention classification model based on relational data using the HKELM to solve the problems of low modeling accuracy and weak generalizability. Third, the IPOA is used to optimize the parameters related to the HKELM, and a differential evolution algorithm is introduced to improve the population quality and prevent the algorithm from falling into a local optimal solution. The experimental results show that the IPOA exhibits better optimization accuracy and convergence speed for four classical benchmark functions. Its average classification accuracy, average classification recall, and average F-value are 94.45%, 94.47%, and 94.46%, respectively, which are significantly higher than those of other intention recognition algorithms. Therefore, the proposed method has high classification accuracy and generalization performance. [ABSTRACT FROM AUTHOR]

As the proportion of clean energy continues to increase, low carbon energy systems will be a significant way to achieve the goal of carbon neutrality. Therefore, the reliability of modular multilevel converters (MMCs) is particularly significant. However, conventional open-circuit fault diagnosis (OCFD) methods usually have a limited localization speed or are difficult to achieve in practical engineering. Therefore, a fast and simpled OCFD approach for MMC SMs based on an optimized deep learning is proposed in this article. In this approach, data on the of submodule capacitance voltages are input into a trained WOA-DKELM model without the manually settings. The problems of randomness in the regularization coefficient C and the kernel parameters K can be solved by DKELM with WOA optimization, which has a strong generalization capability and higher prognostic accuracy. The effectiveness of the proposed approach is verified by experiment results. This approach achieves an average identification probability of 0.96 within 20 ms of the fault. [ABSTRACT FROM AUTHOR]

Regular monitoring of crop moisture conditions has the potential to enhance both crop production efficiency and water utilization efficiency. To generate a moisture status map using Unmanned Aerial Vehicle (UAV) visible light imagery, the focus of the present study was on developing a predictive model for cotton leaf water potential utilizing UAV visible light imagery and multi-source data. By processing the R, G, and B channel values of UAV visible light images, the grayscale histogram mean was extracted as an indicator of vegetation growth status. The Extreme Learning Machine (ELM) was employed in conjunction with the Sparrow Search Algorithm (SSA) optimization to create the SSA-ELM model. This model integrates the grayscale histogram method with soil data and meteorological data, and trains with leaf water potential as the output feature, establishing a cotton leaf water potential prediction model. The SSA-optimized model exhibited significantly improved performance on two test sets in Alaer and Tumushuke. After SSA optimization, the R2 values increased by 0.05 and 0.04 respectively, reaching 0.80 and 0.85, while the RMSE values were 0.211 MPa and 0.215 MPa, indicating the accuracy and stability of the model predictions. By utilizing data partitioned into three distinct cotton growth stages, the model was remodeled, followed by assessment using test sets from the same stages in the cities of Alaer and Tumushuke in 2023. On the Flowering test set, the R2 values were 0.70 and 0.71, with corresponding RMSE values of 0.272 MPa and 0.269 MPa, respectively. For the Full boll test set, the R2 values were 0.78 and 0.76, with corresponding RMSE values of 0.247 MPa and 0.245 MPa. Lastly, within the Open boll stage test group, the R2 values reached 0.75 and 0.80, with corresponding RMSE values of 0.240 MPa and 0.245 MPa. These outcomes further affirm the robustness, adaptability, and generalizability of the method for predicting leaf water potential based on RGB imagery combined with multi-source data across different geographical settings. [ABSTRACT FROM AUTHOR]

Purpose: Rotating functionally graded (FG) disks of variable thickness generates vibration. This study aims to analyze the vibration generated by the rotating disks using a finite element program and compare the results obtained with the regression methods. Design/methodology/approach: Transverse vibration values of rotating FG disks with variable thickness were modeled using different regression methods. The accuracies of the obtained models are compared. In the context of comparing regression methods, multiple linear regression (MLR), extreme learning machine (ELM), artificial neural networks (ANNs) and radial basis function (RBF) were used in this study. The error graph between the observed value and the predicted value of each regression method was obtained. The error values of the regression methods used with scientific error measures were calculated. Findings: The analysis of the transverse vibration of rotating FG disks with the finite element program is consistent with the studies in the literature. When the variables and vibration value determined on the disk are modeled with ELM, MLR, ANN and RBF regression methods, it is concluded that the most accurate model order is RBF, ANN, MLR and ELM. Originality/value: There are studies on the vibration value of rotating discs in the literature, but there are very few studies on modeling. This study shows that ELM, MLR, ANN and RBF, which are machine learning methods, can be used in modeling the vibration value of rotating discs. [ABSTRACT FROM AUTHOR]

Atherosclerosis causes heart disease by forming plaques in arterial walls. IVUS imaging provides a high-resolution cross-sectional view of coronary arteries and plaque morphology. Healthcare professionals diagnose and quantify atherosclerosis physically or using VH-IVUS software. Since manual or VH-IVUS software-based diagnosis is time-consuming, automated plaque characterization tools are essential for accurate atherosclerosis detection and classification. Recently, deep learning (DL) and computer vision (CV) approaches are promising tools for automatically classifying plaques on IVUS images. With this motivation, this manuscript proposes an automated atherosclerotic plaque classification method using a hybrid Ant Lion Optimizer with Deep Learning (AAPC-HALODL) technique on IVUS images. The AAPC-HALODL technique uses the faster regional convolutional neural network (Faster RCNN)-based segmentation approach to identify diseased regions in the IVUS images. Next, the ShuffleNet-v2 model generates a useful set of feature vectors from the segmented IVUS images, and its hyperparameters can be optimally selected by using the HALO technique. Finally, an average ensemble classification process comprising a stacked autoencoder (SAE) and deep extreme learning machine (DELM) model can be utilized. The MICCAI Challenge 2011 dataset was used for AAPC-HALODL simulation analysis. A detailed comparative study showed that the AAPC-HALODL approach outperformed other DL models with a maximum accuracy of 98.33%, precision of 97.87%, sensitivity of 98.33%, and F score of 98.10%. [ABSTRACT FROM AUTHOR]

Data classification is an important research direction in machine learning. In order to effectively handle extensive datasets, researchers have introduced diverse classification algorithms. Notably, Kernel Extreme Learning Machine (KELM), as a fast and effective classification method, has received widespread attention. However, traditional KELM algorithms have some problems when dealing with large-scale data, such as the need to adjust hyperparameters, poor interpretability, and low classification accuracy. To address these problems, this paper proposes an Enhanced Adaptive Whale Optimization Algorithm to optimize Kernel Extreme Learning Machine (EAWOA-KELM). Various methods were used to improve WOA. As a first step, a novel adaptive perturbation technique employing T-distribution is proposed to perturb the optimal position and avoid being trapped in a local maximum. Secondly, the WOA's position update formula was modified by incorporating inertia weight ω and enhancing convergence factor α, thus improving its capability for local search. Furthermore, inspired by the grey wolf optimization algorithm, use 3 excellent particle surround strategies instead of the original random selecting particles. Finally, a novel Levy flight was implemented to promote the diversity of whale distribution. Results from experiments confirm that the enhanced WOA algorithm outperforms the standard WOA algorithm in terms of both fitness value and convergence speed. EAWOA demonstrates superior optimization accuracy compared to WOA across 21 test functions, with a notable edge on certain functions. The application of the upgraded WOA algorithm in KELM significantly improves the accuracy and efficiency of data classification by optimizing hyperparameters. This paper selects 7 datasets for classification experiments. Compared with the KELM optimized by WOA, the EAWOA optimized KELM in this paper has a significant improvement in performance, with a 5%-6% lead on some datasets, indicating the effectiveness of EAWOA-KELM in classification tasks. [ABSTRACT FROM AUTHOR]

Cooperative jamming effectiveness evaluation is a key component in completing the cooperative jamming OODA loop. For the problem of evaluating the effectiveness of cooperative jamming to group network radar by formation aircraft, a cooperative jamming effectiveness evaluation method based on Improved Particle Swarm Optimization–Extreme Learning Machine (IPSO-ELM) is proposed. First, based on the working parameters of the group network radar and the information fusion rules, the cooperative jamming effectiveness evaluation function is established. On this basis, the cooperative jamming decision schemes and their corresponding cooperative jamming effectiveness values are solved at different locations in the target space, and the results are detected as outliers using box plots, thus constructing sample data for cooperative jamming effectiveness evaluation. Subsequently, a neural network based on the extreme learning machine methodology is developed, with its initial weights and biases fine-tuned through an improved particle swarm optimization, which is termed IPSO-ELM. This optimization aims to boost the model's predictive precision. Finally, the IPSO-ELM algorithm is subjected to rigorous assessment via simulation, confirming its performance of accuracy and efficiency. From the simulation results, the advanced performance of the IPSO-ELM algorithm, specifically in the context of assessing the effectiveness of cooperative jamming, is verified. [ABSTRACT FROM AUTHOR]

Image transmission refers to sending or transferring digital images from one location to another, typically over a network or communication channel across various domains, including telecommunications, multimedia messaging, surveillance systems, medical imaging, remote sensing, etc. However, with growing popularity of digital skills, ensuring safety and integrity of transmitted images has become a significant concern. For increasing security, Machine learning and cryptographic techniques have been discussed. Nevertheless, confidentiality during image transmission faces major challenges. Proposed Lamport Blum ShubSigncryptive Extreme Learning (LBSSEL) Method is introduced for secured image transmission with minimal time consumption. The Extreme Learning machine comprises different layers. Several natural images gathered as of dataset. The input layer receives these images for secure transmission. The proposed cryptographic method performs key generation, signcryption, as well as unsigncryption. Lamport One-Time Digital signature method applied in first hidden layer to generate key pairs. Signcryption carried out in second hidden layer which includes encryption and digital signature. For secured transmission, an encrypted image (i.e., cipher image) as well as signature broadcast to receiver to preserve input image. In third hidden layer, unsigncryption process carried out for receiving original image by authorized users through signature verification and decryption. Finally, confidentiality is improved during image transmission at the output layer. Simulation estimated with dissimilar factors. Outcomes of LBSSSEL model in terms of achieving maximum PSNR, confidentiality during transmission, with minimal time consumption when compared with existing approaches. [ABSTRACT FROM AUTHOR]

In order to effectively model data online, a learning model must not only have the high adaptability of dynamic data but also keep the low complexity to meet the online computing requirements. In this paper, a novel multi-innovation online sequential extreme learning machine (MIOSELM) and its kernel version called multi-innovation kernel online sequential extreme learning machine (MIKOSELM) are proposed to establish the online estimation models based on p latest samples using the multi-innovation theory. Besides, a modified whale optimization algorithm (MWOA) is introduced to optimize the execution parameters of our algorithms and is capable of automatically searching a proper p as the practical need, which can further improve the adaptability performance of the online learning models. Finally, two different datasets (the UCI dataset and KDD99 dataset) are used to evaluate the superiority of our methods. Experimental results show that the accuracy, F-score, and G-mean of MIKOSELM are 98.25%, 98.11% and 98.63% on WDBC from the UCI dataset, and are 83.61%, 75.96% and 70.97% on the KDD99 dataset respectively. Besides, our MIKOSELM based on MWOA achieves F-score of 94.28% and 76.73% on Musk from the UCI dataset and the KDD99 dataset. These results validate the effectiveness of our proposed methods. • A multi-innovation OSELM and its kernel version are proposed. • A modified whale optimization algorithm is developed for MIOSELM and MIKOSELM. • Experiments show the effectiveness of the proposed algorithms. [ABSTRACT FROM AUTHOR]

This paper introduces a hierarchical RIME algorithm with multiple search preferences (HRIME-MSP) to tackle complex optimization problems. Although the original RIME algorithm is recognized as an efficient metaheuristic algorithm (MA), its reliance on a single, simplistic search operator poses limitations in maintaining population diversity and avoiding premature convergence. To address these challenges, we propose a hierarchical partition strategy that categorizes the population into superior, borderline, and inferior layers based on their fitness values. Individuals in the superior layer utilize an exploitative local search operator, individuals in the borderline layer inherit the expert-designed soft- and hard-rime search operators from the original RIME algorithm, and individuals in the inferior layer employ the explorative OBL method. We conduct comprehensive numerical experiments on the CEC2017 and CEC2022 benchmarks, six engineering problems, and extreme learning machine (ELM) training tasks to evaluate the performance of HRIME-MSP. Twelve popular and high-performance MA approaches are used as competitor algorithms. The experimental results and statistical analyses confirm the effectiveness and efficiency of HRIME-MSP across various optimization tasks. These findings practically support the scalability and applicability of HRIME-MSP as an advanced optimization technique for diverse real-world applications. • We propose a hierarchical RIME algorithm with multiple search preferences (HRIME-MSP) for solving complex optimization problems. • The proposed HRIME-MSP partitions the swarm population into the superior, the borderline, and the inferior layer. • Individuals in the different layers have distinctive search preferences. • Comprehensive experiments on CEC benchmarks, engineering problems, and extreme learning machine (ELM) training tasks are conducted. • The experimental results and statistical analyses confirm the efficiency and effectiveness of our proposed HRIME-MSP. [ABSTRACT FROM AUTHOR]

The rapid development of artificial intelligence has brought a profound impact on college education, and is triggering a profound change in educational concepts and educational methods. This project starts from the two dimensions of teaching assessment and resource recommendation of Marxist education, and centers on the application of machine learning technology in it. Based on the evaluation of Marxist education integrating Chinese traditional culture, an improved cuckoo search algorithm is used to optimize the number of hidden layer nodes of the extreme learning machine, and the ICS-ELM model is proposed. Combining similarity calculation and the k-means clustering algorithm, a recommendation model for Marxist education resources is constructed using data mining. Through experiments, it has been found that the ICS-ELM model performs better in terms of RMSE value and R value, and can better evaluate the effects of Marxist education. The response time of the Marxist education resource recommendation model is within 210ms, and the MAE and RMSE are within 0.677 and 0.823, respectively, which shows fast response speed and high accuracy of resource recommendation, which can improve the cognitive level of teachers and students and students' learning interest. The application of machine learning technology provides a more intelligent, personalized, and efficient teaching mode for Marxist education. [ABSTRACT FROM AUTHOR]

Accurate predictions of power output in Combined Cycle Power Plants (CCPPs) are crucial for improving operational efficiency and enhancing performance monitoring. This paper compares two prominent machine learning models, artificial neural networks and extreme learning machines, for the prediction of hourly electrical power output. The analysis is based on a publicly available CCPP dataset containing 9,568 instances with key parameters like ambient temperature, atmospheric pressure, relative humidity, and exhaust vacuum. The performances of the models were compared based on standard regression metrics. The result showed that the extreme learning machine (ELM) outperformed artificial neural network (ANN) with mean squared error (MSE) of 0.26, mean absolute error (MAE) of 0.41, root mean squared error (RMSE) of 0.51, and R² of 0.98 when both models yielded a good prediction result, against the ANN model with an MSE of 19.33, MAE of 3.52, RMSE of 4.40, and R² of 0.85. Overfitting when dealing with small datasets and necessity of preprocessing for fine-tuning performance of ANN were the potential drawbacks highlighted by the paper. Results indicate that the use of ELM is quite viable and capable for estimation with excellent accuracy and, as a consequence, may have pragmatic implications for performance optimization studies concerning CCPP and also find broad applicability in energy management studies. [ABSTRACT FROM AUTHOR]

The issue of power quality disturbances in modern power systems has become increasingly complex and severe, with multiple disturbances occurring simultaneously, leading to a decrease in the recognition accuracy of traditional algorithms. This paper proposes a composite power quality disturbance identification method based on the integration of improved Complementary Ensemble Empirical Mode Decomposition (CEEMDAN), Hilbert Transform (HT), and Extreme Learning Machine (ELM). Addressing the limitations of traditional signal processing techniques in handling nonlinear and non-stationary signals, this study first preprocesses the collected initial power quality signals using the improved CEEMDAN method to reduce modal aliasing and spurious components, thereby enabling a more precise decomposition of noisy signals into multiple Intrinsic Mode Functions (IMFs). Subsequently, the HT is utilized to conduct a thorough analysis of the reconstructed signals, extracting their time-amplitude information and instantaneous frequency characteristics. This feature information provides a rich data foundation for subsequent classification and identification. On this basis, an improved ELM is introduced as the classifier, leveraging its powerful nonlinear mapping capabilities and fast learning speed to perform pattern recognition on the extracted features, achieving accurate identification of composite power quality disturbances. To validate the effectiveness and practicality of the proposed method, a simulation experiment is designed. Upon examination, the approach introduced in this study retains a fault diagnosis accuracy exceeding 95%, even amidst significant noise disturbances. In contrast to conventional techniques, such as Convolutional Neural Network (CNN) and Support Vector Machine (SVM), this method achieves an accuracy enhancement of up to 5%. Following optimization via the Particle Swarm Optimization (PSO) algorithm, the model's accuracy is boosted by 3.6%, showcasing its favorable adaptability. [ABSTRACT FROM AUTHOR]

Servo systems are important actuators of aeroengines. The repetitive, reciprocating motion of the servo system leads to significant changes in its time delay and gain characteristics, and degradation increases the uncertainty of these changes. These characteristic variations may have an adverse effect on the dynamic performance of the aeroengine. Therefore, a neural network-based parameter estimation and a multi-loop neural network-based predictive control (ML-NNPC) method for aeroengine inlet guide vane (IGV) servo systems (SVS) were proposed. In this study, the time delay estimation of the servo system was treated as a classification problem, and an SE (squeeze-and-excitation)-GRU (gated recurrent unit) network was proposed to estimate the time delay by using the selected dynamic data of the servo system. The estimated delay was embedded into an online sequential extreme learning machine, and a nonlinear model predictive controller was designed to obtain an optimal control sequence. The compensation control loop was designed to reduce the impact of the model and delay mismatch problems of the control system. The proposed method was applied to the IGV SVS control of a turboshaft engine. The simulation results demonstrate that the time delay is estimated accurately and compensated effectively. Compared to the existing PI and PI with Smith predictor methods, the ML-NNPC method achieves better control performance in the control of both the SVS and the engine rotor speed system. The stability and robustness of the ML-NNPC also show superiority. The results verify the effectiveness of the proposed time delay estimation method and the ML-NNPC method. [ABSTRACT FROM AUTHOR]

Leaf chlorophyll content (LCC) and leaf area index (LAI) are crucial for rice growth and development, serving as key parameters for assessing nutritional status, growth, water management, and yield prediction. This study introduces a novel canopy radiative transfer model (RTM) by coupling the radiation transfer model for rice leaves (RPIOSL) and unified BRDF model (UBM) models, comparing its simulated canopy hyperspectra with those from the PROSAIL model. Characteristic wavelengths were extracted using Sobol sensitivity analysis and competitive adaptive reweighted sampling methods. Using these wavelengths, rice phenotype estimation models were constructed with back propagation neural network (BPNN), extreme learning machine (ELM), and broad learning system (BLS) methods. The results indicate that the RPIOSL-UBM model's hyperspectra closely match measured data in the 500–650 nm and 750–1000 nm ranges, reducing the root mean square error (RMSE) by 0.0359 compared to the PROSAIL model. The ELM-based models using the RPIOSL-UBM dataset proved most effective for estimating the LAI and LCC, with RMSE values of 0.6357 and 6.0101 μg · cm−2, respectively. These values show significant improvements over the PROSAIL dataset models, with RMSE reductions of 0.1076 and 6.3297 μg · cm−2, respectively. The findings demonstrate that the proposed model can effectively estimate rice phenotypic parameters from UAV-measured hyperspectral data, offering a new approach to assess rice nutritional status and enhance cultivation efficiency and yield. This study underscores the potential of advanced modeling techniques in precision agriculture. [ABSTRACT FROM AUTHOR]

The editorial discusses the application of Laser-Induced Breakdown Spectroscopy (LIBS) with machine learning in chemical analysis. LIBS offers real-time, multi-element, and remote analysis capabilities but faces challenges like low repeatability and matrix effects. Machine learning methods have emerged as a promising solution to address these limitations, improving the accuracy of quantitative analysis. Recent advancements in artificial intelligence and machine learning have expanded the scope and effectiveness of LIBS applications in various fields. The editorial highlights recent achievements in LIBS quantification and applications, showcasing the potential of LIBS in analyzing elemental composition in stream sediment, stainless steel, and indoor air quality. [Extracted from the article]

The gradual growth of oxides inside thermal barrier coatings is a key factor leading to the degradation of thermal barrier coating performance until its failure, and accurate monitoring of the growth stress during this process is crucial to ensure the long-term stable operation of engines. In this study, terahertz time-domain spectroscopy was introduced as a new method to characterize the growth stress in thermal barrier coatings. By combining metallographic analysis and scanning electron microscope (SEM) observation techniques, the real microstructure of the oxide layer was obtained, and an accurate simulation model of the oxide growth was constructed on this basis. The elastic solutions of the thermally grown oxide layer of thermal insulation coatings were obtained by using the controlling equations in the rate-independent theoretical model, and the influence of the thickness of the thermally grown oxide (TGO) layer on the stress distribution was explored. Based on experimental data, multidimensional 3D numerical models of thermal barrier coatings with different TGO thicknesses were constructed, and the terahertz time-domain responses of oxide coatings with different thicknesses were simulated using the time-domain finite difference method to simulate the actual inspection scenarios. During the simulation process, white noise with signal-to-noise ratios of 10 dB to 20 dB was embedded to approximate the actual detection environment. After adding the noise, wavelet transform (WT) was used to reduce the noise in the data. The results showed that the wavelet transform had excellent noise reduction performance. For the problems due to the large data volume and small sample data after noise reduction, local linear embedding (LLE) and kernel-based extreme learning machine (KELM) were used, respectively, and the kernel function was optimized using the gray wolf optimization (GWO) algorithm to improve the model's immunity to interference. Experimental validation showed that the proposed LLE-GWO-KELM hybrid model performed well in predicting the TGO growth stress of thermal insulation coatings. In this study, a novel, efficient, nondestructive, online, and high-precision measurement method for the growth in TGO stress of thermal barrier coatings was developed, which provides reliable technical support for evaluating the service life of thermal barrier coatings. [ABSTRACT FROM AUTHOR]