Extreme Learning Machine Technique Research Articles

Adaptive command filtered control for a class of nonlinear time delay systems with extreme learning machine

AbstractIn this paper, an extreme learning machine (ELM)‐based adaptive command filtered control method is investigated for a class of strict feedback nonlinear systems with unknown external disturbances and time delay. To begin with, the original system state equation is transformed into a new form on account of coordinate transformation. Subsequently, an ELM is adopted to approximate unknown functions which exist in the whole system states, without any prior knowledges of the ideal weight vectors and approximation errors. Secondly, a command filter is developed for the system under consideration, which can avoid the probem of “explosion of complexity” caused by repeated derivation of virtual control signals in traditional backstepping control. Meanwhile, error compensation signals are designed to conquer the shortcoming of a dynamic surface control (DSC) method. The combination of ELM and command filter technique has been used to construct corresponding controllers and adaptive laws. Effective handling of the impact of time delay terms is ensured through designing a novel Lyapunov–Krasovskii functional. The proposed strategy guarantees boundedness of all signals in the closed‐loop system and the tracking error asymptotically converges to a compact set around the origin. In the end, two continuous stirred tank reactors (CSTRs) are taken as example to further verify the efficiency of the put forward control method.

Hybrid machine learning models for groundwater level prediction in a snow‐dominated region: An evaluation of EEMD, VMD and EWT decomposition techniques

Hybrid machine learning models for groundwater level prediction in a <scp>snow‐dominated</scp> region: An evaluation of <scp>EEMD</scp>, <scp>VMD</scp> and <scp>EWT</scp> decomposition techniques

Various optimized machine learning techniques to predict agricultural commodity prices

AbstractRecent 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.

Betel Leaf Diseases Classification using Machine Learning Algorithm: A Feasible Approach

Even though the development in science and technology in various domains, agriculture is essential for humans to survive. It is mandatory to implement the development of science in the agriculture sector. There is a huge demand for agricultural workers because of lots of risk in production behind productivity. One of the major risks is diseases caused in plants. Our approach uses region of interest algorithms for image segmentation and feature extractions are done by GLCM and finally the diseases are classified with the help of ELM algorithm. These improvements in the classification of plant leaf diseases not only increase the precision and effectiveness of disease diagnosis, but also make it easier to accurately monitor vast fields of crops quickly. Due to its ability to precisely diagnose and categorize plant leaf diseases, the extreme learning machine technique is a novel strategy that has attracted a lot of attention. Additionally, this method has benefits over conventional image processing methods, which could find it difficult to categorize various plant leaf diseases. Research on betel leaf has received a lot of attention because of its possible medical benefits. Studies on its impact on dental health have been done because betel leaf extracts have antibacterial and anti-inflammatory properties. Approximately 1047 betel leaf images are captured to test our approach by taking five classes of diseases like Anthracnose, Phytophthora Foot Rot, Fusarium wilt, Bacterial Leaf Rot, Powdery Mildew. The model reports accuracy of 97%.

Enhanced Epileptic Seizure Identification using Sparse ELM-ABO Fusion with Feature Reduction and Multi-class Classification

The interpretation of the electroencephalogram (EEG) signal is one method that can be utilized to diagnose epilepsy, which is one of the most prevalent brain illnesses. The length of an EEG signal is typically quite long, making it difficult to interpret manually. Extreme Learning Machine (ELM) is used to detection of Epilepsy and Seizure. But in ELM Storage space and training time is high. In order to reduce training time and storage space African Buffalo Optimization (ABO) algorithm is used. ABO is combined with Sparse ELM to improve the speed, accuracy of detection and reduce the storage space. First, Wavelet transform is used to extract relevant features. Due to their high dimensionality, these features are then reduced by using linear discriminant analysis (LDA). The proposed Hybrid Sparse ELM technique is successfully implemented for diagnosing epileptic seizure disease. For classification, the Sparse ELM-ABO classifier is applied to the UCI Epileptic Seizure Recognition Data Set training dataset, and the experimental findings are compared to those of the SVM, Sparse ELM, and ELM classifiers applied to the same database. The proposed model was tested in two scenarios: binary classification and multi-label classification. Seizure identification is the only factor in binary classification. Seizure and epilepsy identification are part of multi-label classification. It is observed that the proposed method obtained high accuracy in classification with less execution time along with performance evaluation of parameters such as prediction accuracy, specificity, precision, recall and F-score. Binary classification scores 96.08%, while multi-label classification achieves 90.89%.

Fixed-time Sliding Mode-based Adaptive Path Tracking Control of Maize Plant Protection Robot via Extreme Learning Machine

During the maize middle and late periods, the soil between rows is soft and also involved with weeds and straw. When the plant protection robot (PPR) moves on the soil, there exists uncertain shear perturbation because of the shear action and pressure subsidence, leading to the difficulty of the controller design. In this work, we propose an adaptive path tracking control (PTC) considering disturbances for the PPR. The disturbance of PRR in contact with soil is first revealed according to Bekker pressure subsidence and Janosi shear models, through which the plant model of PPR system is established. Then, we propose an adaptive fixed-time sliding mode (AFTSM)-based PTC to achieve excellent path tracking performance, where an extreme learning machine (ELM) estimator is developed, releasing the requirement for bound derivations in the control design. Using the fixed-time control and the ELM techniques in the proposed control, a remarkable control performance is well ensured, i.e., high-accuracy tracking, fast convergence, and excellent robustness. Experimental studies on a PPR are executed for demonstrating the validity and good performance of the designed controller.

An Analysis of Image Classification Using Wavelet‐Based Autoencoder Architecture and Extreme Learning Machine

In recent machine learning applications, promising outcomes have emerged through the integration of Deep Learning (DL) and Extreme Learning Machine (ELM) techniques with wavelet networks (WN), leading to high classification accuracy. Researchers have explored various strategies to enhance classifier performance. Among these methods, Deep Wavelet Autoencoders (AE) and the Deep Wavelet ELM algorithm have been extensively applied in diverse image classification domains. This research paper conducts a comparative analysis between these two wavelet AE‐based techniques. The first approach involves the development of a Deep Stacked Sparse Wavelet Autoencoder (DSSWAE), while the second method focuses on creating a Deep Wavelet ELM Autoencoder (DW‐ELM‐AE). To enable a comprehensive comparison, experiments were conducted using different datasets, specifically MNIST and COIL‐20. The results clearly demonstrate that the Deep Stacked Wavelet AE (DSWAE) and DSSWAE algorithms outperform the State‐of‐the‐Art (SOTA) techniques in terms of accuracy efficiency, emphasizing their significant potential for a wide range of image classification applications.

WITHDRAWN: Utilization of waste coffee ground ash as fine aggregates in concrete: Enhancing shear strength through activated carbon reinforcement

This study addresses burgeoning environmental concerns amid rising global coffee consumption amid rising global coffee consumption by exploring the viability of utilizing waste coffee ground ash (SCG) as a sustainable fine aggregate in concrete production. Significant improvements are noted by investigating the impact of SCG on the shear strength of standard concrete and enhancing properties through activated carbon reinforcement derived from SCG via physical activation. Experimental results reveal that incorporating up to 1.5% activated carbon by weight markedly boosts early shear strength. Moreover, composites with a small inclusion of activated carbon demonstrate superior shear power during curing compared to conventional mixes. The study employs Teaching-Learning Optimization (TLBO) and Extreme Learning Machine (ELM) techniques to forecast shear strength accurately. ELM emerges superior, showcasing heightened accuracy, lower RMSE, and superior r and R2 values compared to TLBO. Conclusively, leveraging waste coffee ground ash as a fine aggregate in concrete not only presents an effective method for enhancing shear strength but also underscores a promising pathway toward sustainable waste management and reduced environmental impact, reflecting a pivotal stride in harmonizing concrete performance enhancement with eco-friendly practices.

Breast cancer diagnosis using evolving deep convolutional neural network based on hybrid extreme learning machine technique and improved chimp optimization algorithm

Today, diagnostic systems based on artificial intelligence play a significant role in confirming doctors' recommendations. These systems are becoming effective tools in clinical treatment. In this paper, we propose a new method for identifying atypical breast cancer based on the ZFNet network for breast mammography images. Initially, the Wiener and CALHE filters are used in order to evaluate the effectiveness of the preprocessing step. Following this, a pre-trained ZFNet was modified and trained on the CBIS-DDSM dataset. Furthermore, an extreme learning machine (ELM) was used to replace the remaining few layers at the very end. Moreover, a method was presented for estimating the optimum number of layers that should be replaced in the structure. In the end, ELM was developed in order to enhance the classification performance. This was accomplished by utilizing an improved version of the Chimp Optimization Algorithm (called SWChOA), along with four other benchmark meta-heuristic optimization algorithms. These algorithms were WSO, COA, AVOA, and AHA. Accuracy, precision, specificity, sensitivity, Matthew's correlation coefficient (MCC), and F1-score were the six popular metrics that were used to investigate the diagnostic performance of our method and to compare it to the performance of other well-known methods. Precision-recall curves and area of interest (ROI) curves were also utilized in this investigation. The approach known as ZFNet-SWChOA-ELM was shown to have the highest level of performance based on the findings of investigations that made use of a 10 hold-out validation.

A Novel Method for Parameter Identification of Renewable Energy Resources based on Quantum Particle Swarm–Extreme Learning Machine

Accurately determining load model parameters is of the utmost importance for conducting power system simulation analysis and designing effective control strategies. Measurement-based approaches are commonly employed to identify load model parameters that closely reflect the actual operating conditions. However, these methods typically rely on iterative parameter search processes, which can be time-consuming, particularly when dealing with complex models. To address this challenge, this paper introduces a parameter identification method for the generalized synthetic load model (SLM) using the Extreme Learning Machine (ELM) technique, with the aim of enhancing computational efficiency. Furthermore, to achieve better alignment with load response curves, a Quantum Particle Swarm Optimization (QPSO) algorithm is adopted to train the ELM model. The proposed QPSO-ELM-based SLM parameter identification method is subsequently evaluated using a standard test system. To assess its effectiveness, parameter sensitivity analysis is performed, and simulation results are analyzed. The findings demonstrate that the proposed method yields favorable outcomes, offering improved computation efficiency in load model parameter identification tasks.

Adaptive ELM-Based Security Control for a Class of Nonlinear-Interconnected Systems With DoS Attacks

This article is concerned with the output feedback security control of a class of high-order nonlinear-interconnected systems with denial-of-service (DoS) attacks, nonlinear dynamics, and exogenous disturbances. First, extreme learning machine (ELM) and adaptive techniques are adopted to approximate the unknown nonlinearities. Then, novel adaptive ELM-based nonlinear state observers with adaptive compensation functions are developed to estimate the unmeasurable states during DoS attacks under the influence of the disturbances. Further, by combining with the backstepping control and filtering techniques, adaptive ELM-based controllers are proposed to achieve uniformly ultimately bounded results based on the observation and adaption control signals under the influence of DoS attacks, nonlinear dynamics, and exogenous disturbances. Comparative studies are carried out to validate the effectiveness of the developed ELM-based adaptive observation and control strategies for two interconnected power systems.

Novel DV-hop algorithm-based machines learning technics for node localization in rang-free wireless sensor networks

Localization is a critical concern in many wireless sensor network (WSN) applications. Furthermore, correct information regarding the geographic placements of nodes (sensors) is critical for making the collected data valuable and relevant. Because of their benefits, such as simplicity and acceptable accuracy, the based connectivity algorithms attempt to localize multi-hop WSN. However, due to environmental factors, the precision of localisation may be rather low. This publication describes an Extreme Learning Machine (ELM) technique for minimizing localization error in range-free WSN. In this paper, we propose a Cascade Extreme Learning Machine (Cascade-ELM) to increase localization accuracy in Range-Free WSNs. We tested the proposed approaches in a variety of multi-hop WSN scenarios. Our research focused on an isotropic and irregular environment. The simulation results show that the proposed Cascade-ELM algorithm considerably improves localization accuracy when compared to previous algorithms derived from smart computing approaches. When compared to previous work, isotropic environments show improved localization results.

A method for computing inverse parametric PDE problems with random-weight neural networks

We present a method for computing the inverse parameters and the solution field to inverse parametric partial differential equations (PDE) based on randomized neural networks. This extends the local extreme learning machine technique originally developed for forward PDEs to inverse problems. We develop three algorithms for training the neural network to solve the inverse PDE problem. The first algorithm (termed NLLSQ) determines the inverse parameters and the trainable network parameters all together by the nonlinear least squares method with perturbations (NLLSQ-perturb). The second algorithm (termed VarPro-F1) eliminates the inverse parameters from the overall problem by variable projection to attain a reduced problem about the trainable network parameters only. It solves the reduced problem first by the NLLSQ-perturb algorithm for the trainable network parameters, and then computes the inverse parameters by the linear least squares method. The third algorithm (termed VarPro-F2) eliminates the trainable network parameters from the overall problem by variable projection to attain a reduced problem about the inverse parameters only. It solves the reduced problem for the inverse parameters first, and then computes the trainable network parameters afterwards. VarPro-F1 and VarPro-F2 are reciprocal to each other in some sense. The presented method produces accurate results for inverse PDE problems, as shown by the numerical examples herein. For noise-free data, the errors of the inverse parameters and the solution field decrease exponentially as the number of collocation points or the number of trainable network parameters increases, and can reach a level close to the machine accuracy. For noisy data, the accuracy degrades compared with the case of noise-free data, but the method remains quite accurate. The presented method has been compared with the physics-informed neural network method.

Application of Soft Computing Techniques in River Flow Modeling

Modeling of data is critical in the analysis and evaluation of hydrological behavior. River flow data is one of the most important data in explaining hydrology. Management of water resources; It takes place in the literature as an area that needs to be investigated in order to provide early warning for undesirable situations such as floods and drought. For this reason, it is of important to develop different techniques for the estimation and modeling of river flow or to make comparisons between techniques. In this study, the flow data of fourteen stations located in the Euphrates-Tigris basin between 1981 and 2010 were used. Adaptive Network Based Fuzzy Inference Systems (ANFIS), Support Vector Machine (SVM) techniques that are frequently used in the literature, and newly introduced Gaussian Process Regression (GPR), Extreme Learning Machine (ELM) and Emotional Neural Network (ENN) artificial intelligence techniques are compared. In addition, considering all performance indices, it was determined which technique gave better results with rank analysis. Although all models worked well, it was seen that the methods were ranked as ELM, GPR, ENN, SVM and ANFIS starting from the best. This has shown that ELM, GPR and ENN methods, which have been used recently in flow modeling, give better results than traditional methods with complex structures. In addition, flow values were used in the whole study and these values were examined in 3 different combinations. It was seen that the model structure that gave the best performance was the model structure that used the flow data from one, two and three days ago as an estimator. The results were analyzed with a Taylor diagram and time series graphs.

Diabetic retinopathy identification using parallel convolutional neural network based feature extractor and ELM classifier

Diabetic retinopathy (DR) is an incurable retinal condition caused by excessive blood sugar that, if left untreated, can result in even blindness. A novel automated technique for DR detection has been proposed in this paper. To accentuate the lesions, the fundus images (FIs) were preprocessed using Contrast Limited Adaptive Histogram Equalization (CLAHE). A parallel convolutional neural network (PCNN) was employed for feature extraction and then the extreme learning machine (ELM) technique was utilized for the DR classification. In comparison to the similar CNN structure, the PCNN design uses fewer parameters and layers, which minimizes the time required to extract distinctive features. The effectiveness of the technique was evaluated on two datasets (Kaggle DR 2015 competition (Dataset 1; 34,984 FIs) and APTOS 2019 (3,662 FIs)), and the results are promising. For the two datasets mentioned, the proposed technique attained accuracies of 91.78 % and 97.27 % respectively. However, one of the study's subsidiary discoveries was that the proposed framework demonstrated stability for both larger and smaller datasets, as well as for balanced and imbalanced datasets. Furthermore, in terms of classifier performance metrics, model parameters and layers, and prediction time, the suggested approach outscored existing state-of-the-art models, which would add significant benefit for the medical practitioners in accurately identifying the DR.

Online sequential, outlier robust, and parallel layer perceptron extreme learning machine models for sediment transport in sewer pipes.

Sediment transport is a noteworthy task in the design and operation of sewer pipes. Decreasing sewer pipe hydraulic capacity and transport of pollution are the main consequences of continuous sedimentation. Among different design approaches, the non-deposition with deposited bed (NDB) method can be used for the design of large sewer pipes; however, existing models are established on limited data ranges and mostly applied conventional regression methods. The current study improves the NDB sediment transport modeling by utilizing wide data ranges, and furthermore, applying robust machine learning techniques. In the present study, the conventional extreme learning machine (ELM) technique and its advanced versions, namely the online sequential-extreme learning machine (OS-ELM), outlier robust-extreme learning machine (OR-ELM), and parallel layer perceptron-extreme learning machine (PLP-ELM) are used for the modeling. In the studies conducted in the literature, sediment deposited bed thickness (ts) or deposited bed width (Wb) was used in the model structure as a deposited sediment variable, and therefore, different parameters in terms of ts and Wb can be incorporated into the model structure. However, an uncertainty arises in the selection of the appropriate parameter among Wb/Y, ts/Y, Wb/D, and ts/D (Y is flow depth and D circular pipe diameter). In order to define the most appropriate parameter to best describe the impact of deposited sediment at the channel bottom in the modeling procedure, four various scenarios using four different parameters that incorporate deposited sediment variables at their structures as Wb/Y, ts/Y, W/D, and ts/D are considered for model development. It is found that models that incorporate sediment bed thickness (ts) provide better results than those whichuse deposited bed width (Wb) in their structures. Among four different scenarios, models that utilized ts/D dimensionless parameter, give superior results in contrast to their alternatives. Based on the outcomes, the OR-ELM approach outperformed ELM, OS-ELM, and PLP-ELM techniques. The results obtained from applied methods are compared to their corresponding models in the literature, indicating the superiority of the OR-ELM model. It is figured out that the thickness of the deposited bed is an effective variable in modeling NDB sediment transport in sewer pipes.

Multimodal Machine Learning Based Crop Recommendation and Yield Prediction Model

Agriculture plays a vital role in the Indian economy. Crop recommendation for a specific region is a tedious process as it can be affected by various variables such as soil type and climatic parameters. At the same time, crop yield prediction was based on several features like area, irrigation type, temperature, etc. The recent advancements of artificial intelligence (AI) and machine learning (ML) models pave the way to design effective crop recommendation and crop prediction models. In this view, this paper presents a novel Multimodal Machine Learning Based Crop Recommendation and Yield Prediction (MMML-CRYP) technique. The proposed MMML-CRYP model mainly focuses on two processes namely crop recommendation and crop prediction. At the initial stage, equilibrium optimizer (EO) with kernel extreme learning machine (KELM) technique is employed for effectual recommendation of crops. Next, random forest (RF) technique was executed for predicting the crop yield accurately. For reporting the improved performance of the MMML-CRYP system, a wide range of simulations were carried out and the results are investigated using benchmark dataset. Experimentation outcomes highlighted the significant performance of the MMML-CRYP approach on the compared approaches with maximum accuracy of 97.91%.

An Investigation on Disease Diagnosis and Prediction by Using Modified K-Mean clustering and Combined CNN and ELM Classification Techniques

Data analysis is important for managing a lot of knowledge in the healthcare industry. The older medical study favored prediction over processing and assimilating a massive volume of hospital data. The precise research of health data becomes advantageous for early disease identification and patient treatment as a result of the tremendous knowledge expansion in the biological and healthcare fields. But when there are gaps in the medical data, the accuracy suffers. The use of K-means algorithm is modest and efficient to perform. It is appropriate for processing vast quantities of continuous, high-dimensional numerical data. However, the number of clusters in the given dataset must be predetermined for this technique, and choosing the right K is frequently challenging. The cluster centers chosen in the first phase have an impact on the clustering results as well. To overcome this drawback in k-means to modify the initialization and centroid steps in classification technique with combining (Convolutional neural network) CNN and ELM (extreme learning machine) technique is used. To increase this work, disease risk prediction using repository dataset is proposed. We use different types of machine learning algorithm for predicting disease using structured data. The prediction accuracy of using proposed hybrid model is 99.8% which is more than SVM (support vector machine), KNN (k-nearest neighbors), AB (AdaBoost algorithm) and CKN-CNN (consensus K-nearest neighbor algorithm and convolution neural network).

High-impedance fault identification and location by using mode decomposition integrated adaptive multi-kernel extreme learning machine technique for distributed generator-based microgrid

High-impedance fault identification and location by using mode decomposition integrated adaptive multi-kernel extreme learning machine technique for distributed generator-based microgrid

Investigation of optimized ELM using Invasive Weed-optimization and Cuckoo-Search optimization

Abstract In order to classify data and improve extreme learning machine (ELM), this study explains how a hybrid optimization-driven ELM technique was devised. Input data are pre-processed in order to compute missing values and convert data to numerical values using the exponential kernel transform. The Jaro–Winkler distance is used to identify the relevant features. The feed-forward neural network classifier is used to categorize the data, and it is trained using a hybrid optimization technique called the modified enhanced Invasive Weed, a meta heuristic algorithm, and Cuckoo Search, a non-linear optimization algorithm ELM. The enhanced Invasive Weed optimization (IWO) algorithm and the enhanced Cuckoo Search (CS) algorithm are combined to create the modified CSIWO. The experimental findings presented in this work demonstrate the viability and efficacy of the created ELM method based on CSIWO, with good experimental result as compared to other ELM techniques.