Research on Optimization Methods of ELM Classification Algorithm for Hyperspectral Remote Sensing Images

The study was intended to eliminate the noise in three-dimensional transvaginal ultrasound (3D-TVS) images and improve the diagnostic accuracy in intrauterine adhesion (IUA). The extreme learning machine (ELM) algorithm was introduced first for statement. One hundred and thirty cases of suspected IUA patients were taken as the research subjects. The denoising effects of ELM algorithm were evaluated in terms of mean square errors (MSE), peak signal-to-noise ratio (PSNR), and running time, and its diagnostic efficiency of IUA was identified from precise, specificity, and sensitivity. Furthermore, the support vector machine (SVM) algorithm was introduced for comparison. It was found that the MSE and PSNR of the ELM algorithm were 0.0021 and 64.5, respectively, and its average operation time was 11.22 ± 0.89s, that the MSE values of SVM algorithm and ELM algorithm were 0.0045 and 0.0021 and the PSNR values were 52.3 and 64.5, respectively, and that the average running time of SVM algorithm was 16.35 ± 1.33s, and the average running time of ELM algorithm was 11.22 ± 0.89s, superior to the SVM algorithm in denoising effects. Moreover, the ELM algorithm showed excellent diagnostic efficiency for patients with various degrees of IUA. In conclusion, ELM can effectively eliminate noise in 3D-TVS images and demonstrates excellent diagnostic efficiency on IUA, which is worthy of clinical application.

Among other machine learning techniques, the extreme learning machine has evidently proved its diagnostic accuracy on many cases in medical domain. Its accuracy mainly depends on the optimal parameters that are used in training. The proposed work is based on optimizing the extreme learning machine using the recently proposed meta-heuristic optimization technique named artificial algae algorithm with multi-light source. In this work, two experiments are conducted using four binary classification datasets related to medical domain. The feasible number of hidden neurons is found from the first experiment using relevant performance parameters. In the second experiment, the classifier with feasible number of hidden neurons is further evaluated with the ten-fold cross-validation method based on its computation time and classification accuracy. In both the experiments, the proposed classifier performance compared with that of other four similar hybrid approaches. It is also statistically compared using Friedman test and Wilcoxon signed rank test based on the area under curve and accuracy values respectively. It is found that the proposed classifier produces better results than the other classifiers.

Extreme Learning Machine (ELM) algorithm assigns the input weights and biases in a “one-time stamp” fashion, this method makes the algorithm to be ill-conditioned and reduces its classification accuracy. The contribution of this work is the enhancement of the performance of ELM with the Moth-Flame Optimization (MFO) algorithm to improve classification accuracy. A hybrid of the Moth-Flame Optimization and Extreme Learning Machine (MFO-ELM) algorithm is implemented in MATLAB. MFO ensures a concurrent simulation of exploration and exploitation of the search space to select an optimum candidate solution. The candidate solution is reshaped into input weights and biases for ELM classification. The hybrid algorithm is validated on five life-selected datasets. The performance improvement of MFO-ELM is compared with ELM-optimized Particle Swarm Optimization (PSO-ELM) and Competitive Swarm Optimization (CSO-ELM) algorithms. The improvement rates are qualitatively and quantitatively evaluated to show the improvement of MFO-ELM on ELM and the other meta-heuristic algorithms. MFO-ELM improved the accuracies of the basic ELM in all 100% of the simulations and performed better than the other meta-heuristic algorithms in 80% of the simulations. The performance of MFO-ELM is more competitive, and it is recommended for solving classification problems.

In this paper, after analyzing the second-order power chaotic oscillation system, the factors of electromagnetic interference and charge interference are analyzed by using bifurcation diagram, Lyapunov exponent and 0-1 test theory. The sensitivity of electromagnetic interference parameters in power system is studied. At the same time, the extreme learning machine algorithm is improved and optimized into the deep extreme learning machine(DELM) algorithm, which is applied to the speed or load prediction of the power system. The experimental results show that the deep learning machine algorithm can achieve the prediction well. It provides reference for power system operation.

The purpose of this study was to predict students dropping out of the Education Management Doctoral Program of FKIP Mulawarman University and to evaluate the Extreme Learning Machine in predicting student dropouts. This research uses the Extreme Learning Machine algorithm, the feedforward neural network learning method and the Support Vector Machine algorithm for comparison of the level of accuracy using the same data. The data used is as much as 110 data according to the number of students from the class of 2012 to 2018, the data is taken from the SIA Education Management Study Program of the Mulawarman University Doctoral Program and then processed. In this case, how to predict student dropouts using the variable Gender, Semester 3 IP Value, Working Status, Family Status, Age, and using two DO and NON DO Classes? And calculating the accuracy value using a confusion matrix ?. From the results of this study, it can be concluded that students drop out in the Educational Management of the FKIP Mulawarman University Doctoral Program can be predicted by Extreme Learning Machine using the training value obtained from semester 3 of the 2012-2018 class. From the results of testing the predictive accuracy of the Extreme Learning Machine is 72 %.

This paper develops a state-of-charge (SOC) estimation model for a lithium-ion battery using an improved extreme learning machine (ELM) algorithm. ELM is suitable for an SOC estimation since the ELM algorithm has fast estimation speed, good generalization performance, and high accuracy. However, the performance of ELM is highly dependent on training accuracy and the number of neurons in a hidden layer. Hence, a gravitational search algorithm (GSA) is applied to improve the ELM computational intelligence by searching for the optimal value hidden layer neurons. The optimal ELM-based GSA model does not require internal battery knowledge and mathematical model for an SOC estimation. The model robustness is validated at different temperatures using different electric vehicle drive cycles. The performance of the ELM-GSA model is verified with two popular neural network methods: back-propagation neural network (BPNN) and radial basis function neural network (RBFNN). The results are evaluated using different error rates and computation costs. The results demonstrate that the ELM-based GSA model offers a higher accuracy and lower SOC error rate than those of BPNN-based GSA and RBFNN-based GSA models. Furthermore, a detailed comparative study between the proposed model and existing SOC strategies is conducted, which also demonstrates the superiority of the proposed model.

A fast training algorithm for extreme learning machine based on matrix decomposition

In this paper, a classification mode for commercial bank clients' classification using the extreme learning machine (ELM) algorithm is proposed to study the commercial banks VIP loss. Firstly, we adopt the existing data sets of banks to train the ELM model; then, customer classification algorithm and its parameters are selected for classification purpose. Lastly, comparative analysis with existed methods are also compared, which showed that its advantages with the traditional gradient algorithm and other classification algorithm, which further indicate that ELM algorithm can not only overcome their drawbacks but also has faster learning rate, higher rate of accuracy, and better generalization.

This work presents an application of the extreme learning machine (ELM) algorithm based on a single‐hidden layer feedforward neural network for no‐reference video quality assessment. The present research introduces an augmented version of ELM through simple stop criteria, which proved the effectiveness of the video quality assessment method. The authors present empirical studies using LIVE video data base show that the proposed method delivers accuracy (Pearson's correlation coefficient) and monotonicity (Spearman's correlation coefficient) with subjective scores against no‐reference, Joint Photographic Experts Group No‐Reference, metric and full‐reference metrics, for instance, peak signal‐to‐noise ratio, structural similarity (SSIM) and multi‐scale‐SSIM indexes, and the proposed method is suitable for quality monitoring of video transmission and reception system.

With the rapid advancement of technology, online transportation has become the main solution for many people in Indonesia to travel easily and efficiently. Companies such as GOJEK are constantly innovating to improve their services, resulting in many responses and reviews from users. This research aims to analyze customer satisfaction with these online transportation services by analyzing the sentiment of user opinions on the Twitter platform. Sentiment analysis plays a very important role in decision making by classifying user reviews. Data was retrieved through a crawling process using specific keywords related to each service. The data preprocessing process includes case folding, tokenizing, normalization, stemming, filtering, and convert negation. This aims to clean and prepare the data so that it can be processed using the algorithm better. This process includes removing irrelevant elements from the text data, converting the text into a consistent or more standardized form, reducing the number of features in the data by stemming, and converting the text into numbers or vectors so that it can be processed by the algorithm. Feature extraction is performed using the Word2Vec model to convert text into a numerical vector representation that can later be processed by ELM. Converts words into numeric vectors in a high-dimensional space, where words that have the same context in the text are close to each other in that space. The ELM (Extreme Learning Machine) algorithm is used as a classification model due to its high training speed and good generalization ability. Model evaluation is done using confusion matrix which measures classification performance through accuracy, precision, recall matrix. The results of this study show that the ELM algorithm with Word2Vec feature extraction is able to classify user sentiment with a high level of accuracy. This research provides insight into user satisfaction with online transportation services and can be a reference for companies to improve their service quality

The human vision observes an image by making a series of fixations. In fixation, our eyes continually tremble, which is called the microsaccades that may reflect an optimal sampling strategy and spatiotemporal characteristics. Although the decrease in microsaccade magnitude leads to visual fading in our brain that may provide a mechanism to shift fixation. This paper proposes an iterative framework for figure-ground segmentation by sampling-learning via simulating human vision. First, fixation-based sampling is utilized to get a few positive and negative samples. A pixels classifier based on the RGB color could be trained by ELM (extreme learning machine) algorithm, which not only extracts object regions, but also provides a reference boundary of objects. Then, the boundary of object region could be refined by minimizing graph cut. The region of refined object can be re-sampled to provide more accurate samples/pixels involved object and background for the next training. The iteration would convergence when the pixel classifier gets stable segmentation result continually. Based on the ELM algorithm, the proposed method run faster than state-of-the-art method, and can cope with the complexity and uncertainty of the scene. Experimental results demonstrate the learning-based method could reliably segment multiple-color objects from complex scenes.

A robust single hidden layer feed forward neural network (SLFN) is used in this study to model the in-flight particle characteristics of the atmospheric plasma spray (APS) process with regard to the input processing parameters. The in-flight particle characteristics influence the structure and properties of the APS coating and, thus, are considered important parameters to comprehend the manufacturing process. The training times of traditional back propagation algorithms, mostly used to model such processes, are far slower than desired for implementation of an on-line control system. Use of slow gradient based learning methods and iterative tuning of all network parameters during the learning process are the two major causes for such slower learning speed. An extreme learning machine (ELM) algorithm, which randomly selects the input weights and biases and analytically determines the output weights, is used in this work to train the SLFNs. Performance comparisons of the networks trained with ELM algorithm and standard error back propagation algorithms are presented. It is found that networks trained with ELM have good generalization performance, much shorter training times and stable performance with regard to the changes in number of hidden layer neurons. The trends represent robustness of the trained networks and enhance reliability of the application of the artificial neural network in modelling APS processes.

Extreme Learning Machine (ELM) is an extension of feed forward Neural Network model with extremely learning capacity and good generalization capabilities. Generally, the classification performance of ELM highly focused on three parameters such as the input weight matrix, the value of bias and the number of hidden neurons presented. ELM randomly chooses the input weights and bias values to minimize the training time. The arbitrary selection of biases and the input weight produce an unforeseen result which causes training error and also produces lesser prediction accuracy. For improving efficiency of ELM training, Ant Colony Optimization with continuous domain algorithm (ACO R ) used to find the optimum input weight and hidden bias values for ELM. With the unequal distribution of classes in imbalanced data sets, ELM algorithms tussle to find good accuracy. ELM algorithm doesn't acquire the mandatory information about the minority class to make an accurate classification. To deal the issues of ELM, in this paper the hybrid algorithms Weighted ELM and Weighted ELM with ACO R are proposed. The main objective of weighted ELM is that the related weight value is computed and assigned for each training sample to increase the classification rate. ACO R method is also integrated with the weighted ELM to find the optimum input weight and bias to maximize the accuracy of classification task. The comparative analysis has been accomplished over Hepatitis dataset. Further, the experimental results clearly revealed that Weighted ELM with ACO R is superior to other proposed methods.

The threat to the environment and health posed by biomedical waste requires a proper approach to its management and recycling. This study aims to develop an advanced smart waste management system that combines IoT sensors for real-time monitoring and an Extreme Learning Machine (ELM) to segregate waste. This system applies IoT sensors to get data from hospital ward dustbins that alert bins' fullness. The ELM uses the information received to categorize the waste as recyclable or non-recyclable. The sorted-out trash items are then directed either to disposal or recycling bins accordingly. Our method improves efficiency in handling biomedical wastes by utilizing state-of-the-art ELM algorithms, which exhibit superior performance over conventional methods in accuracy and computation speed, among others. Our simulation results show that our model has 95% classification accuracy compared with other AI-based approaches such as Cohort Intelligence Algorithm (CIA), Hesitant Fuzzy Weight and Rank Finding (HFWRF), and Deep Learning (DL). This new system not only contributes to efficient waste management but also supports environmental sustainability, promoting effective recycling plans.

Based on non-supervised classification methods, multi-temporal images, such as NOAA-AVHRR, SPOT-VEGETATION and MODIS series data, have been used to map regional, continental or global land cover patterns. Because of limitations of the classification method and spatial resolution of images, traditional non-supervised classification often results in many errors in the transition zones in which the spatial distribution of vegetation show segmental patterns. This problem can decrease the integral accuracy of the classification to a certain extent.  In this paper, a new method, the topographical restrictive features, is presented to classify remotely sensed images using the Jinhe watershed in the Loess plateau as a case study. First, 25 relatively independent classes were obtained using the non-supervised classification method with multi-temporal NDVI data derived from the first two bands of MODIS data. Every cell with 500 m spatial resolution of the classification determined by the non-supervised method was divided into 25 homogenous cells with 100 m spatial resolution. Then, topographical features were defined. Information about aspect, slope, elevation, river net structure, and vegetation regional characteristics, derived from 1:250 000 geographical spatial data, ETM+ image and yield data, were used to construct topographical restrictive features. Finally, the classification of every sub-cell was tested using the restrictive features and some cells were reclassified while maintaining their original classification. After the secondary classification, all cells were labeled by land cover type according to the land cover classification system, which was defined previously on the base of the IGBP land cover classification system and UMD system.  Using this new method, the accuracy of the land cover classification increased from 71.88%, in traditional non-supervised classification method, to 84.09%. Inparticular, the accuracy of cropland and urban type classification improved. The method of cell sub-division avoids the shortcomings of traditional classification methods owing to the coarse resolution of the image processing, and makes it more highly probable that land cover types are homogenous within cells. The introduction of topographical restrictive features decreases uncertainty of traditional fuzzy classification and provides more precise distinctive features to classify the fuzzy zone, and thus improves the accuracy of classification.