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1

Detection of various lung diseases including COVID-19 using extreme learning machine algorithm based on the features extracted from a lightweight CNN architecture

Nahiduzzaman Md., Faruq Goni Md Omaer, Robiul Islam Md., Sayeed Abu, Shamim Anower Md., Ahsan Mominul, Haider Julfikar, Kowalski Marcin

Biocybernetics and Biomedical Engineering

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2023

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Vol. 43, no. 3

528--550

EN

Around the world, several lung diseases such as pneumonia, cardiomegaly, and tuberculosis (TB) contribute to severe illness, hospitalization or even death, particularly for elderly and medically vulnerable patients. In the last few decades, several new types of lungrelated diseases have taken the lives of millions of people, and COVID-19 has taken almost 6.27 million lives. To fight against lung diseases, timely and correct diagnosis with appropriate treatment is crucial in the current COVID-19 pandemic. In this study, an intelligent recognition system for seven lung diseases has been proposed based on machine learning (ML) techniques to aid the medical experts. Chest X-ray (CXR) images of lung diseases were collected from several publicly available databases. A lightweight convolutional neural network (CNN) has been used to extract characteristic features from the raw pixel values of the CXR images. The best feature subset has been identified using the Pearson Correlation Coefficient (PCC). Finally, the extreme learning machine (ELM) has been used to perform the classification task to assist faster learning and reduced computational complexity. The proposed CNN-PCC-ELM model achieved an accuracy of 96.22% with an Area Under Curve (AUC) of 99.48% for eight class classification. The outcomes from the proposed model demonstrated better performance than the existing state-of-the-art (SOTA) models in the case of COVID-19, pneumonia, and tuberculosis detection in both binary and multiclass classifications. For eight class classification, the proposed model achieved precision, recall and fi-score and ROC are 100%, 99%, 100% and 99.99% respectively for COVID-19 detection demonstrating its robustness. Therefore, the proposed model has overshadowed the existing pioneering models to accurately differentiate COVID-19 from the other lung diseases that can assist the medical physicians in treating the patient effectively.

2

Forecasting voltage collapse when large-scale wind turbines penetrated to power systems using optimally pruned extreme learning machines (OPELM) - case study: electric power system South Sulawesi-Indonesia

Chaerah Gunadin Indar, Siswanto Agus, Safrizal Safrizal, Syukriyadin Syukriyadin, Rosyadi Marwan, Muslimin Zaenab, Gassing, Rasyid Ramly

Przegląd Elektrotechniczny

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2022

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R. 98, nr 5

80--84

EN

The problem of voltage collapse is a major issue in the operation of the current power system, especially when the penetration of wind turbines into the system continues to increase. The intermittency of the wind turbine has an impact on the stability of the system voltage. Fast Voltage Stability Index (FVSI) is used as a parameter for the condition of the system with the phenomenon of voltage collapse. This study aims to observe and predict the value of the Line stability index using Optimally Pruned Extreme Learning Machine (OP-ELM). The test case in this study is the South Sulawesi-Indonesia Electric Power System, with a total wind turbine penetration of 142 MW. From the simulation, it can be seen that OPELM can do forecasting very well with an error rate of 0.0886%.

PL

Problem załamania napięcia jest poważnym problemem w funkcjonowaniu obecnego systemu elektroenergetycznego, zwłaszcza gdy penetracja turbin wiatrowych do systemu nadal wzrasta. Przerywalność turbiny wiatrowej ma wpływ na stabilność napięcia systemu. Wskaźnik stabilności szybkiego napięcia (FVSI) jest używany jako parametr stanu systemu ze zjawiskiem załamania napięcia. Niniejsze badanie ma na celu obserwowanie i przewidywanie wartości wskaźnika stabilności linii przy użyciu maszyny OP-ELM (ang. Optimally Pruned Extreme Learning Machine). Przykładem testowym w tym badaniu jest system elektroenergetyczny South Sulawesi-Indonesia, z całkowitą penetracją turbin wiatrowych 142 MW. Z symulacji widać, że OP-ELM może bardzo dobrze wykonywać prognozy ze wskaźnikiem błędu 0,0886%.

3

Forecasting short-term electric load using extreme learning machine with improved tree seed algorithm based on Lévy flight

Chen Xuan, Przystupa Krzysztof, Ye Zhiwei, Chen Feng, Wang Chunzhi, Liu Jinhang, Gao Rong, Wei Ming, Kochan Orest

Eksploatacja i Niezawodność

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2022

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Vol. 24, no. 1

153--162

EN

In recent years, forecasting has received increasing attention since it provides an important basis for the effective operation of power systems. In this paper, a hybrid method, composed of kernel principal component analysis (KPCA), tree seed algorithm based on Lévy flight (LTSA) and extreme learning machine (ELM), is proposed for short-term load forecasting. Specifically, the randomly generated weights and biases of ELM have a significant impact on the stability of prediction results. Therefore, in order to solve this problem, LTSA is utilized to obtain the optimal parameters before the prediction process is executed by ELM, which is called LTSA-ELM. Meanwhile, the input data is extracted by KPCA considering the sparseness of the electric load data and used as the input of LTSA-ELM model. The proposed method is tested on the data from European network on intelligent technologies (EUNITE) and experimental results demonstrate the superiority of the proposed approaches compared to the other methods involved in the paper.

4

Multitemporal meteorological drought forecasting using Bat-ELM

Gholizadeh Reza, Yılmaz Hasan, Danandeh Mehr Ali

Acta Geophysica

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2022

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Vol. 70, no 2

917--927

EN

The advancement of the machine learning (ML) models has demonstrated notable progress in geosciences. They can identify the underlying process or causality of natural hazards. This article introduces the development and verification procedures of a new hybrid ML model, namely Bat-ELM for predictive drought modelling. The multi-temporal standardized precipitation evapotranspiration index (SPEI-3 and SPEI-6) is computed as the meteorological drought index at two study regions (Beypazari and Nallihan), located in Ankara province, Turkey. The proposed hybrid model is obtained by integrating the Bat optimization algorithm as the parameter optimizer with an extreme learning machine (ELM) as the regressor engine. The efficiency of the intended model was evaluated against the classic artificial neural network (ANN) and standalone ELM models. The evaluation and assessment are conducted using statistical metrics and graphical diagrams. The forecasting results showed that the accuracy of the proposed model outperformed the benchmark models. In a quantitative assessment, the Bat-ELM model attained minimal root mean square error for the SPEI-3 and SPEI-6 (RMSE=0.58 and 0.43 at Beypazari station and RMSE=0.53 and 0.37 at Nallihan station) over the testing phase. This indicates the new model approximately 20 and 15% improves the forecasting accuracy of traditional ANN and classic ELM techniques, respectively.

5

Evaluating the performance of Extreme Learning Machine technique for ore grade estimation

Abuntori Clara Akalanya, Al-Hassan Sulemana, Mireku-Gyimah Daniel, Ziggah Yao Yevenyo

Journal of Sustainable Mining

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2021

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Vol. 20, iss. 2

56--71

EN

Due to the complex geology of vein deposits and their erratic grade distributions, there is the tendency of overestimating or underestimating the ore grade. These estimated grade results determine the profitability of mining the ore deposit or otherwise. In this study, five Extreme Learning Machine (ELM) variants based on hard limit, sigmoid, triangular basis, sine and radial basis activation functions were applied to predict ore grade. The motive is that the activation function has been identified to play a key role in achieving optimum ELM performance. Therefore, assessing the extent of influence the activation functions will have on the final outputs from the ELM has some scientific value worth investigating. This study therefore applied ELMas ore grade estimator which is yet to be explored in the literature. The obtained results from the five ELM variants were analysed and compared with the state-of-the-art benchmark methods of Backpropagation Neural Network (BPNN) and Ordinary Kriging (OK). The statistical test results revealed that the ELM with sigmoid activation function (ELM-Sigmoid) was the best among all the other investigated methods (ELM-Hard limit, ELM-Triangular basis, ELM-Sine, ELM-Radial Basis, BPNN and OK). This is because the ELM-sigmoid produced the lowest MAE (0.0175), MSE (0.0005) and RMSE (0.0229) with highest R2 (91.93%) and R (95.88%) respectively. It was concluded that ELM-Sigmoid can be used by field practitioners as a reliable alternative ore grade estimation technique.

6

Colour Difference Classification for Dyed Fabrics Based on Differential Evolution with Dynamic Parameter Selection to Optimise the Output Regularisation Extreme Learning Machine

Zhou Zhiyu, Liu Dexin, Zhang Jianxin, Zhu Zefei, Yang Donghe, Jiang Likai

Fibres & Textiles in Eastern Europe

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2021

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Vol. 29, no. 3 (147)

97--102

EN

A novel optimisation technique based on the differential evolution (DE) algorithm with dynamic parameter selection (DPS-DE) is proposed to develop a colour difference classification model for dyed fabrics, improve the classification accuracy, and optimise the output regularisation extreme learning machine (RELM). The technique proposed is known as DPS-DE-RELM and has three major differences compared with DE-ELM: (1) Considering that the traditional ELM provides an illness solution based on the output weights, DE is proposed to optimise the output of the RELM. (2) Considering the simple parameter setting of the traditional algorithm, the DE algorithm with DPS is adopted. (3) For DPS, an optimal range of parameters is chosen, and the efficiency of the algorithm is significantly improved. This study analyses the colour difference classification of fabric images captured under standard lighting based on the DPS-DE-RELM algorithm. First, the colour difference of the fabric images is calculated and six color-difference-related features extracted, and second the features are classified into five different levels based on the perception of humans. Finally, a colour difference classification model is built based on the DPS-DERELM algorithm, and then the optimal classification model suitable for this study is selected. The experimental results show that the output method with regularisation parameters can achieve a maximum classification accuracy of 98.87%, which is higher compared with the aforementioned optimised original ELM algorithm, which can achieve a maximum accuracy of 84.67%. Therefore, the method proposed has the advantages of greater convergence speed, high classification accuracy, and robustness.

PL

W pracy zaproponowano nowatorską technikę optymalizacji opartą na algorytmie ewolucji różnicowej (DE) z doborem parametrów (DPS-DE) w celu opracowania modelu klasyfikacji różnicy kolorów dla tkanin barwionych, poprawy dokładności klasyfikacji i optymalizacji regularyzacji wyjściowej maszyny do uczącej się (RELM). Zaproponowana technika jest znana jako DPS-DE-RELM i cechuje się trzema głównymi różnicami w porównaniu do DE-ELM: (1) Biorąc pod uwagę, że tradycyjny ELM zapewnia rozwiązanie w oparciu o wagi wyjściowe, proponuje się DE w celu optymalizacji wydajności RELM. (2) Biorąc pod uwagę proste ustawienie parametrów tradycyjnego algorytmu, przyjęto algorytm DE z DPS. (3) W przypadku DPS wybierany jest optymalny zakres parametrów, a wydajność algorytmu znacznie się poprawia. Podczas badania przeanalizowano klasyfikację różnic kolorów obrazów tkanin zarejestrowanych w standardowym oświetleniu w oparciu o algorytm DPS-DE-RELM. Po pierwsze, obliczono różnicę kolorów obrazów tkanin i wyodrębniono sześć cech związanych z różnicą kolorów, a po drugie cechy te zaklasyfikowano na pięciu różnych poziomach w oparciu o percepcję ludzi. Na koniec zbudowano model klasyfikacji różnicy kolorów w oparciu o algorytm DPS-DE-RELM, a następnie wybrano optymalny model klasyfikacji odpowiedni do tego badania. Wyniki eksperymentalne pokazały, że metoda wyjściowa z parametrami regularyzacji może osiągnąć maksymalną dokładność klasyfikacji wynoszącą 98,87%, czyli wyższą w porównaniu z zoptymalizowanym oryginalnym algorytmem ELM, który może osiągnąć maksymalną dokładność na poziomie 84,67%. Stwierdzono, że zaproponowana metoda niesie ze sobą korzyści w postaci większej szybkości zbieżności, wysokiej dokładności klasyfikacji i odporności.

7

Artificial intelligence-based hybrid forecasting models for manufacturing systems

Rosienkiewicz Maria

Eksploatacja i Niezawodność

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2021

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Vol. 23, no. 2

263--277

EN

The paper addresses the problem of forecasting in manufacturing systems. The main aim of the research is to propose new hybrid forecasting models combining artificial intelligencebased methods with traditional techniques based on time series – namely: Hybrid econometric model, Hybrid artificial neural network model, Hybrid support vector machine model and Hybrid extreme learning machine model. The study focuses on solving the problem of limited access to independent variables. Empirical verification of the proposed models is built upon real data from the three manufacturing system areas – production planning, maintenance and quality control. Moreover, in the paper, an algorithm for the forecasting accuracy assessment and optimal method selection for industrial companies is introduced. It can serve not only as an efficient and costless tool for advanced manufacturing companies willing to select the right forecasting method for their particular needs but also as an approach supporting the initial steps of transformation towards smart factory and Industry 4.0 implementation.

8

Acoustic Source Localization Using Kernel-based Extreme Learning Machine in Distributed Microphone Array

Wang Rong, Chen Zhe, Yin Fuliang

Archives of Acoustics

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2021

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Vol. 46, No. 1

67--78

EN

Acoustic source localization using distributed microphone array is a challenging task due to the influences of noise and reverberation. In this paper, acoustic source localization using kernel-based extreme learning machine in distributed microphone array is proposed. Specifically, the space of interest is divided into some labeled positions, and the candidate generalized cross correlation function in each node is treated as the feature mapped into the hidden nodes of extreme learning machine. During the training phase, by the implementation of kernel function, the output weights of the classifier are calculated and do not need to be tuned. After the kernel-based extreme learning machine (K-ELM) is well trained, the measured generalized cross correlation data are fed into the K-ELM classifier, and the output is the estimated acoustic source position. The proposed method needs less human intervention for both training and testing and it does not need to calibrate the node in advance. Simulation and real-world experimental results reveal that the proposed method has extremely fast training and testing speeds, and can obtain better localization performance than steered response power, K-nearest neighbor, and support vector machine methods.

9

A fast neural network learning algorithm with approximate singular value decomposition

Jankowski Norbert, Linowiecki Rafał

International Journal of Applied Mathematics and Computer Science

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2019

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Vol. 29, no. 3

581--594

EN

The learning of neural networks is becoming more and more important. Researchers have constructed dozens of learning algorithms, but it is still necessary to develop faster, more flexible, or more accurate learning algorithms. With fast learning we can examine more learning scenarios for a given problem, especially in the case of meta-learning. In this article we focus on the construction of a much faster learning algorithm and its modifications, especially for nonlinear versions of neural networks. The main idea of this algorithm lies in the usage of fast approximation of the Moore–Penrose pseudo-inverse matrix. The complexity of the original singular value decomposition algorithm is O(mn2). We consider algorithms with a complexity of O(mnl), where l < n and l is often significantly smaller than n. Such learning algorithms can be applied to the learning of radial basis function networks, extreme learning machines or deep ELMs, principal component analysis or even missing data imputation.

10

Comparison of prototype selection algorithms used in construction of neural networks learned by SVD

Jankowski N.

International Journal of Applied Mathematics and Computer Science

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2018

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Vol. 28, no. 4

719--733

EN

Radial basis function networks (RBFNs) or extreme learning machines (ELMs) can be seen as linear combinations of kernel functions (hidden neurons). Kernels can be constructed in random processes like in ELMs, or the positions of kernels can be initialized by a random subset of training vectors, or kernels can be constructed in a (sub-)learning process (sometimes by k-means, for example). We found that kernels constructed using prototype selection algorithms provide very accurate and stable solutions. What is more, prototype selection algorithms automatically choose not only the placement of prototypes, but also their number. Thanks to this advantage, it is no longer necessary to estimate the number of kernels with time-consuming multiple train-test procedures. The best results of learning can be obtained by pseudo-inverse learning with a singular value decomposition (SVD) algorithm. The article presents a comparison of several prototype selection algorithms co-working with singular value decomposition-based learning. The presented comparison clearly shows that the combination of prototype selection and SVD learning of a neural network is significantly better than a random selection of kernels for the RBFN or the ELM, the support vector machine or the kNN. Moreover, the presented learning scheme requires no parameters except for the width of the Gaussian kernel.

11

2D Cadastral Coordinate Transformation using extreme learning machine technique

Ziggah Y. Y., Issaka Y., Laari P. B., Hui Z.

Geodesy and Cartography

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2018

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Vol. 67, no. 2

321--343

EN

Land surveyors, photogrammetrists, remote sensing engineers and professionals in the Earth sciences are often faced with the task of transferring coordinates from one geodetic datum into another to serve their desired purpose. The essence is to create compatibility between data related to different geodetic reference frames for geospatial applications. Strictly speaking, conventional techniques of conformal, affine and projective transformation models are mostly used to accomplish such task. With developing countries like Ghana where there is no immediate plans to establish geocentric datum and still rely on the astro-geodetic datums as it national mapping reference surface, there is the urgent need to explore the suitability of other transformation methods. In this study, an effort has been made to explore the proficiency of the Extreme Learning Machine (ELM) as a novel alternative coordinate transformation method. The proposed ELM approach was applied to data found in the Ghana geodetic reference network. The ELM transformation result has been analysed and compared with benchmark methods of backpropagation neural network (BPNN), radial basis function neural network (RBFNN), two-dimensional (2D) affine and 2D conformal. The overall study results indicate that the ELM can produce comparable transformation results to the widely used BPNN and RBFNN, but better than the 2D affine and 2D conformal. The results produced by ELM has demonstrated it as a promising tool for coordinate transformation in Ghana.

12

On Certain Limitations of Recursive Representation Model

Jastrzębski S., Sieradzki I.

Schedae Informaticae

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2016

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Vol. 25

37--47

EN

There is a strong research eort towards developing models that can achieve state-of-the-art results without sacricing interpretability and simplicity. One of such is recently proposed Recursive Random Support Vector Machine (R2SVM) model, which is composed of stacked linear models. R2SVM was reported to learn deep representations outperforming many strong classi-ers like Deep Convolutional Neural Network. In this paper we try to analyze it both from theoretical and empirical perspective and show its important limitations. Analysis of similar model Deep Representation Extreme Learning Machine (DrELM) is also included. It is concluded that models in its current form achieves lower accuracy scores than Support Vector Machine with Radial Basis Function kernel.

13

Extreme learning machine for the predictions of length of day

Lei Y., Zhao D., Cai H.

Artificial Satellites : Journal of Planetary Geodesy

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2015

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Vol. 50, No. 1

19--33

EN

This work presents short- and medium-term predictions of length of day (LOD) up to 500 days by means of extreme learning machine (ELM). The EOP C04 time-series with daily values from the International Earth Rotation and Reference Systems Service (IERS) serve as the data basis. The influences of the solid Earth and ocean tides and seasonal atmospheric variations are removed from the C04 series. The residuals are used for training of the ELM. The results of the prediction are compared with those from other prediction methods. The accuracy of the prediction is equal to or even better than that by other approaches. The most striking advantages of employing ELM instead of other algorithms are its noticeably reduced complexity and high computational efficiency.

14

Fast near-infrared palmprint recognition using nonnegative matrix factorization extreme learning machine

Xu X., Zhang X., Lu L., Deng W., Zuo K

Optica Applicata

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2014

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Vol. 44, nr 2

285--298

EN

Support vector machine and artificial neural network are widely used in classification applications. Extreme learning machine (ELM) is a novel and efficient learning algorithm based on the generalized single hidden layer feed forward networks, which performs well in classification applications. The research results have shown the superiority of ELM with the existing classical algorithms: support vector machine (SVM) and back propagation neural network. In this study, we firstly propose a novel nonnegative matrix factorization extreme learning machine (NMFELM) to improve the performance of standard ELM method. Then we propose a novel near-infrared palmprint recognition approach based on NMFELM classifier. As the test data, we use the near-infrared palmprint database provided by Hong Kong Polytechnic University. The experimental results demonstrate that the proposed NMFELM method outperforms the standard ELM- and SVM-based methods.

15

Application of clustering techniques for visually evoked potentials based detection of vision impairments

Vijean V., M H., Yaacob S., Sulaiman M. N. B.

Biocybernetics and Biomedical Engineering

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2014

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Vol. 34, no. 3

169--177

EN

Visually evoked potentials (VEP) are evoked responses of the brain corresponding to a specific visual stimulus. Ophthalmologists often refer their patients to VEP test if the latter suffers any vision abnormalities that cannot be diagnosed using conventional analysis. By investigating the VEP responses, medical experts can narrow down the possible cause of the defect. Although this method provides valuable information to the medical practitioner, there are several drawbacks of the analysis that can affect the diagnosis result. The conventional averaging of the signals results in inter-trial variation between the VEP responses to be lost. This method also requires large number of trials, which causes fatigue in patients and reduces the diagnostic accuracy. Therefore, we have proposed a new method of analysis using statistical features derived from time and spectral space for the discrimination of vision impairments. Feature enhancement methods such as feature weighting and dimensional reduction are used to enhance the statistical features prior to the analysis. Four clustering methods are employed to increase the interclass separability of the control and myopic features while reducing the within class variability. The dimension of the weighted features is reduced using a combination of principal component analysis (PCA) and independent component analysis (ICA) techniques prior to classification. The proposed method is able to achieve 100% accuracy using extreme learning machine (ELM) and multi layer neural network (MLNN) classifiers.