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Journal articles on the topic 'Deep extreme learning machine'

Author: Grafiati

Published: 5 June 2025

Last updated: 2 August 2025

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1

Wang, Tianlei, Jiuwen Cao, Xiaoping Lai, and Badong Chen. "Deep Weighted Extreme Learning Machine." Cognitive Computation 10, no. 6 (2018): 890–907. http://dx.doi.org/10.1007/s12559-018-9602-9.

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Mercaldo, Francesco, Luca Brunese, Fabio Martinelli, Antonella Santone, and Mario Cesarelli. "Experimenting with Extreme Learning Machine for Biomedical Image Classification." Applied Sciences 13, no. 14 (2023): 8558. http://dx.doi.org/10.3390/app13148558.

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Currently, deep learning networks, with particular regard to convolutional neural network models, are typically exploited for biomedical image classification. One of the disadvantages of deep learning is that is extremely expensive to train due to complex data models. Extreme learning machine has recently emerged which, as shown in experimental studies, can produce an acceptable predictive performance in several classification tasks, and at a much lower training cost compared to deep learning networks that are trained by backpropagation. We propose a method devoted to exploring the possibility

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Jiang, X. W., T. H. Yan, J. J. Zhu, et al. "Densely Connected Deep Extreme Learning Machine Algorithm." Cognitive Computation 12, no. 5 (2020): 979–90. http://dx.doi.org/10.1007/s12559-020-09752-2.

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Ding, Shifei, Nan Zhang, Xinzheng Xu, Lili Guo, and Jian Zhang. "Deep Extreme Learning Machine and Its Application in EEG Classification." Mathematical Problems in Engineering 2015 (2015): 1–11. http://dx.doi.org/10.1155/2015/129021.

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Recently, deep learning has aroused wide interest in machine learning fields. Deep learning is a multilayer perceptron artificial neural network algorithm. Deep learning has the advantage of approximating the complicated function and alleviating the optimization difficulty associated with deep models. Multilayer extreme learning machine (MLELM) is a learning algorithm of an artificial neural network which takes advantages of deep learning and extreme learning machine. Not only does MLELM approximate the complicated function but it also does not need to iterate during the training process. We c

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Cheng, Xiangyi, Huaping Liu, Xinying Xu, and Fuchun Sun. "Denoising deep extreme learning machine for sparse representation." Memetic Computing 9, no. 3 (2016): 199–212. http://dx.doi.org/10.1007/s12293-016-0185-2.

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Chu, Yunfei, Chunyan Feng, Caili Guo, and Yaqing Wang. "Network embedding based on deep extreme learning machine." International Journal of Machine Learning and Cybernetics 10, no. 10 (2018): 2709–24. http://dx.doi.org/10.1007/s13042-018-0895-5.

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Sasou, Akira. "Deep Residual Learning With Dilated Causal Convolution Extreme Learning Machine." IEEE Access 9 (2021): 165708–18. http://dx.doi.org/10.1109/access.2021.3134700.

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Man, Zhihong, and Guang-Bin Huang. "Special issue on extreme learning machine and deep learning networks." Neural Computing and Applications 32, no. 18 (2020): 14241–45. http://dx.doi.org/10.1007/s00521-020-05175-0.

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Ding, Shifei, Lili Guo, and Yanlu Hou. "Extreme learning machine with kernel model based on deep learning." Neural Computing and Applications 28, no. 8 (2016): 1975–84. http://dx.doi.org/10.1007/s00521-015-2170-y.

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Phumrattanaprapin, Khanittha, and Punyaphol Horata. "Extended Hierarchical Extreme Learning Machine with Multilayer Perceptron." ECTI Transactions on Computer and Information Technology (ECTI-CIT) 10, no. 2 (2017): 196–204. http://dx.doi.org/10.37936/ecti-cit.2016102.68266.

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The Deep Learning approach provides a high performance of classification, especially when invoking image classification problems. However, a shortcoming of the traditional Deep Learning method is the large time scale of training. The hierarchical extreme learning machine (H-ELM) framework was based on the hierarchical learning architecture of multilayer perceptron to address the problem. H-ELM is composed of two parts; the first entails unsupervised multilayer encoding, and the second is the supervised feature classification. H-ELM can give a higher accuracy rate than the traditional ELM. Howe

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Zhang, Jian, Shifei Ding, Nan Zhang, and Zhongzhi Shi. "Incremental extreme learning machine based on deep feature embedded." International Journal of Machine Learning and Cybernetics 7, no. 1 (2015): 111–20. http://dx.doi.org/10.1007/s13042-015-0419-5.

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Zhao, Jin, and Licheng Jiao. "Sparse Deep Tensor Extreme Learning Machine for Pattern Classification." IEEE Access 7 (2019): 119181–91. http://dx.doi.org/10.1109/access.2019.2924647.

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Chen, Yinghao, Xiaoliang Xie, Tianle Zhang, Jiaxian Bai, and Muzhou Hou. "A deep residual compensation extreme learning machine and applications." Journal of Forecasting 39, no. 6 (2020): 986–99. http://dx.doi.org/10.1002/for.2663.

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Lavanya, Maloth. "Random Resistive Memory-Based Deep Extreme Point Learning Machine." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 06 (2025): 1–9. https://doi.org/10.55041/ijsrem49321.

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Abstract: The rapid advancement of artificial intelligence has driven the development of more efficient and scalable machine learning models. In this paper, we propose a novel approach combining random resistive memory with a deep extreme point learning machine for unified visual processing. Resistive memory, such as memristors, offers non-volatile, low-power memory storage that is well-suited for neuromorphic computing, enabling enhanced energy efficiency and parallel processing capabilities. By integrating this memory into a deep learning framework, we introduce a new model architecture that

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Grace, Odette Boussi, Gupta Himanshu, and Akhter Hossain Syed. "Enhancing financial cybersecurity via advanced machine learning: analysis, comparison." IAES International Journal of Artificial Intelligence (IJ-AI) 14, no. 2 (2025): 1281–89. https://doi.org/10.11591/ijai.v14.i2.pp1281-1289.

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The financial sector is a prime target for cyber-attacks due to the sensitive nature of the data it handles. As the frequency and sophistication of cyber threats continue to rise, implementing effective security measures becomes paramount. In this paper we provide a comprehensive comparison of six prominent machine learning techniques utilized in the financial industry for cyber-attack prevention. The study aims to identify the best-performing model and subsequently compares its performance with a proposed model tailored to the specific challenges faced by financial institutions. This paper lo

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Li, Dongping. "AUTOMATIC DETECTION OF CARDIOVASCULAR DISEASE USING DEEP KERNEL EXTREME LEARNING MACHINE." Biomedical Engineering: Applications, Basis and Communications 30, no. 06 (2018): 1850038. http://dx.doi.org/10.4015/s1016237218500382.

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The electrocardiogram (ECG) is a principal signal employed to automatically diagnose cardiovascular disease in shallow and deep learning models. However, ECG feature extraction is required and this may reduce diagnosis accuracy in traditional shallow learning models, while backward propagation (BP) algorithm used by the traditional deep learning models has the disadvantages of local minimization and slow convergence rate. To solve these problems, a new deep learning algorithm called deep kernel extreme learning machine (DKELM) is proposed by combining the extreme learning machine auto-encoder

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Kiranpure, Ayush. "Cyclone Intensity Prediction Using Deep Learning on INSAT-3D IR Imagery: A Comparative Analysis." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 04 (2025): 1–9. https://doi.org/10.55041/ijsrem45392.

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This study investigates the effectiveness of deep learning techniques in accurately estimating tropical cyclone intensity using infrared (IR) imagery from the INSAT-3D satellite. We assess the performance of three models—Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), and a hybrid CNN-RNN model—comparing them against traditional machine learning methods like Support Vector Machines (SVM) and Random Forests (RF). Results demonstrate that deep learning models significantly outperform traditional approaches, with the CNN-RNN model achieving the highest accuracy. These findings

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Sornam, M., and R. Shalini. "A Survey on Selected Algorithm Evolutionary, Deep Learning, Extreme Learning Machine." International Journal of Computing Algorithm 5, no. 1 (2016): 50–54. http://dx.doi.org/10.20894/ijcoa.101.005.001.012.

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Zheng, Wendong, Huaping Liu, Bowen Wang, and Fuchun Sun. "Cross-modal learning for material perception using deep extreme learning machine." International Journal of Machine Learning and Cybernetics 11, no. 4 (2019): 813–23. http://dx.doi.org/10.1007/s13042-019-00962-1.

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Verak, Nutveesa, Phaklen Ehkan, Ruzelita Ngadiran, et al. "Deep Learning-based Blind Image Quality Assessment using Extreme Learning Machine." Pena Journal of Computer Science and Informatics 1, no. 1 (2025): 1–12. https://doi.org/10.37934/pjcsi.1.1.112.

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In many image processing applications, Blind Image Quality Assessment (BIQA) plays a crucial role when the reference image is unavailable. However, existing BIQA methods often lack consistency with human perception and are limited to specific types of distortion. This research introduces a novel approach for image quality assessment by combining Convolutional Neural Network (CNN) feature extraction and Extreme Learning Machine (ELM) regression. By leveraging a pre-trained ResNet CNN model, features are extracted from distorted images and used to train the ELM model. The proposed method achieve

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Li, Jiaojiao, Bobo Xi, Qian Du, Rui Song, Yunsong Li, and Guangbo Ren. "Deep Kernel Extreme-Learning Machine for the Spectral–Spatial Classification of Hyperspectral Imagery." Remote Sensing 10, no. 12 (2018): 2036. http://dx.doi.org/10.3390/rs10122036.

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Extreme-learning machines (ELM) have attracted significant attention in hyperspectral image classification due to their extremely fast and simple training structure. However, their shallow architecture may not be capable of further improving classification accuracy. Recently, deep-learning-based algorithms have focused on deep feature extraction. In this paper, a deep neural network-based kernel extreme-learning machine (KELM) is proposed. Furthermore, an excellent spatial guided filter with first-principal component (GFFPC) is also proposed for spatial feature enhancement. Consequently, a new

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Yu, Wenchao, Fuzhen Zhuang, Qing He, and Zhongzhi Shi. "Learning deep representations via extreme learning machines." Neurocomputing 149 (February 2015): 308–15. http://dx.doi.org/10.1016/j.neucom.2014.03.077.

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23

Abbas, Sagheer, Ghassan F. Issa, Areej Fatima, et al. "Fused Weighted Federated Deep Extreme Machine Learning Based on Intelligent Lung Cancer Disease Prediction Model for Healthcare 5.0." International Journal of Intelligent Systems 2023 (April 17, 2023): 1–14. http://dx.doi.org/10.1155/2023/2599161.

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In the era of advancement in information technology and the smart healthcare industry 5.0, the diagnosis of human diseases is still a challenging task. The accurate prediction of human diseases, especially deadly cancer diseases in the smart healthcare industry 5.0, is of utmost importance for human wellbeing. In recent years, the global Internet of Medical Things (IoMT) industry has evolved at a dizzying pace, from a small wristwatch to a big aircraft. With this advancement in the healthcare industry, there also rises the issue of data privacy. To ensure the privacy of patients’ data and fast

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Mahmoud, Akeel, and Sahar Ahmed. "Deep Learning for Computer Vision: Innovations in Image Recognition and Processing Techniques." CyberSystem Journal 1, no. 1 (2024): 23–32. http://dx.doi.org/10.57238/n65d0p57.

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Deep learning is a key area of research in the field of computer vision, image processing and bioinformatics. The techniques of deep learning generally are divided into three categories namely Convolutional Neural Networks (CNN), Restricted Boltzmann Machines (RBM), Stacked RBM and HOG (Histograms of oriented Gradient) feature extraction, Convolutional Neural Networks as a Database (CNN as D). Additionally, one in few deep learning architectures which is gaining popularity and is frequently used in the field of computer vision and image processing is Extreme Learning Machine and ensemble of Ex

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Yang, Yutu, Xiaolin Zhou, Ying Liu, Zhongkang Hu, and Fenglong Ding. "Wood Defect Detection Based on Depth Extreme Learning Machine." Applied Sciences 10, no. 21 (2020): 7488. http://dx.doi.org/10.3390/app10217488.

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The deep learning feature extraction method and extreme learning machine (ELM) classification method are combined to establish a depth extreme learning machine model for wood image defect detection. The convolution neural network (CNN) algorithm alone tends to provide inaccurate defect locations, incomplete defect contour and boundary information, and inaccurate recognition of defect types. The nonsubsampled shearlet transform (NSST) is used here to preprocess the wood images, which reduces the complexity and computation of the image processing. CNN is then applied to manage the deep algorithm

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Hwang, Jung, Jae Seo, Jeong Kim, Suyoung Park, Young Kim, and Kwang Kim. "Comparison between Deep Learning and Conventional Machine Learning in Classifying Iliofemoral Deep Venous Thrombosis upon CT Venography." Diagnostics 12, no. 2 (2022): 274. http://dx.doi.org/10.3390/diagnostics12020274.

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In this study, we aimed to investigate quantitative differences in performance in terms of comparing the automated classification of deep vein thrombosis (DVT) using two categories of artificial intelligence algorithms: deep learning based on convolutional neural networks (CNNs) and conventional machine learning. We retrospectively enrolled 659 participants (DVT patients, 282; normal controls, 377) who were evaluated using contrast-enhanced lower extremity computed tomography (CT) venography. Conventional machine learning consists of logistic regression (LR), support vector machines (SVM), ran

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Bhattacharyya, Debnath. "COMPREHENSIVE ANALYSIS ON COMPARISON OF MACHINE LEARNING AND DEEP LEARNING APPLICATIONS ON CARDIAC ARREST." Journal of Medical pharmaceutical and allied sciences 10, no. 4 (2021): 3125–31. http://dx.doi.org/10.22270/jmpas.v10i4.1395.

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Machine Learning is the technology of having machines to understand and behave as humans do. Refining their learning in supervised manner over time, by feeding them information and data in the form of experiences in the real world. Heart disease has a wide variety of consequences, varying from asymptomatically to extreme arrhythmias, and even premature cardiac failure. A comparative computational analysis was conducted on open-source datasets among the most frequently used classification algorithms in Machine Learning and Neural Networks by randomly splitting data in to test and training and a

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Wang, Hairui, Dongwen Li, Guifu Zhu, and Xiuqi Yang. "Application of Deep Wavelet Kernel Extreme Learning Machine in Fault Diagnosis of Tamping Vehicle." Journal of Physics: Conference Series 2449, no. 1 (2023): 012030. http://dx.doi.org/10.1088/1742-6596/2449/1/012030.

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Abstract Since it is easy to overfit due to the long training time of the fault diagnosis model for machinery. Introducing the idea of autoencoder (AE) into the wavelet extreme learning machine (WELM) and then stacking to form WELM-AE can convert the underlying fault features to more abstract and advanced ones. And then the adaptive boosting kernel extreme learning machine (Adaboost-KELM) is used as the top-level classifier for fault recognition. The experimental results verify the feasibility of the proposed algorithm in the fault diagnosis of tamping machine with the characteristics of the f

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Alom, Zahangir, Venkata Ramesh Bontupalli, and Tarek M. Taha. "Intrusion Detection Using Deep Belief Network and Extreme Learning Machine." International Journal of Monitoring and Surveillance Technologies Research 3, no. 2 (2015): 35–56. http://dx.doi.org/10.4018/ijmstr.2015040103.

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Security threats for computer networks have increased dramatically over the last decade, becoming bolder and more brazen. There is a strong need for effective Intrusion Detection Systems (IDS) that are designed to interpret intrusion attempts in incoming network traffic intelligently. In this paper, the authors explored the capabilities of Deep Belief Networks (DBN) – one of the most influential deep learning approach – in performing intrusion detection after training with the NSL-KDD dataset. Additionally, they examined the impact of using Extreme Learning Machine (ELM) and Regularized ELM on

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Wei, Jie, Huaping Liu, Gaowei Yan, and Fuchun Sun. "Robotic grasping recognition using multi-modal deep extreme learning machine." Multidimensional Systems and Signal Processing 28, no. 3 (2016): 817–33. http://dx.doi.org/10.1007/s11045-016-0389-0.

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Zeng, Zhiyong, Shiqi Dai, Yunsong Li, and Dunyu Chen. "Deep hashing using an extreme learning machine with convolutional networks." Communications in Information and Systems 17, no. 3 (2017): 133–46. http://dx.doi.org/10.4310/cis.2017.v17.n3.a1.

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Rahimi, Seyyed Amirhosein, and Hedieh Sajedi. "Monitoring air pollution by deep features and extreme learning machine." Journal of Experimental & Theoretical Artificial Intelligence 31, no. 4 (2019): 517–31. http://dx.doi.org/10.1080/0952813x.2019.1572658.

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Curia, Francesco. "Breast Cancer Early Detection Comparison with Deep Learning and Machine Learning Models: A Case of Study." Journal of Quality in Health Care & Economics 5, no. 6 (2022): 1–11. http://dx.doi.org/10.23880/jqhe-16000310.

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Breast cancer is one of the most widespread in the female population, being able to predict its developments and capturing the inputs of the onset of the disease is one of the main objectives that science is pursuing. Clinical Decision Support Systems (CDSS) in recent decades are extensively using these technological tools, such as Machine Learning (ML) and Deep Learning (DL). In this paper, two of the main methods of these subset of AI are compared: an ensemble-type algorithm, XGBoost (or Extreme Gradient Boosting) and a deep neural network (DNN) are applied to the data of a study conducted o

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Journal, of Theoretical and Applied Information Technology. "AN INNOVATIVE MACHINE LEARNING FRAMEWORK FOR PHONOCARDIOGRAPHY (PCG) USING MFCC AND DEEP EXTREME LEARNING MACHINE (DELM)." Journal of Theoretical and Applied Information Technology 102, no. 22 (2024): 8375–88. https://doi.org/10.5281/zenodo.15239912.

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Cardiovascular Diseases (CVDs) are a significant global cause of mortality, necessitating effective diagnostic techniques. Phonocardiography (PCG) is among the fundamental methods used to analyze heart sounds to detect human heart-related abnormalities. However, in an environment where state-of-the-art PCG equipment is not available, a Machine Learning (ML) based solution can serve as a reliable alternative. However, the main challenges faced by ML-based PCG systems, are the unavailability of balanced and unbiased datasets, the vanishing and exploding gradient a well-known Deep Learning (DL) i

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Rahman, Senjuti, Mehedi Hasan, Ajay Krishno Sarkar, and Fayez Khan. "Classification of Parkinson’s Disease using Speech Signal with Machine Learning and Deep Learning Approaches." European Journal of Electrical Engineering and Computer Science 7, no. 2 (2023): 20–27. http://dx.doi.org/10.24018/ejece.2023.7.2.488.

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Parkinson's disease (PD) is a chronic neurological condition that is growing in prevalence and manifests both motor and non-motor symptoms. Most PD patients have trouble speaking, writing, and walking during the early stages of the disease. Analysis of speech problems has been effective in identifying Parkinson's disease. According to studies, 90% of Parkinson's disease patients experience speech problems. Even though there is no known cure for Parkinson's disease, using the right medication at an early stage can greatly reduce the symptoms. One of the key categorization issues for the diagnos

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Silva, Alex Tavares, Jader Lugon Junior, and Wagner Rambaldi Telles. "Estimating Extreme Rainfall Equation Parameter in Southeast Brazil Using Machine Learning." Revista de Gestão Social e Ambiental 18, no. 4 (2024): e05153. http://dx.doi.org/10.24857/rgsa.v18n4-097.

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Purpose: This article explores the use of advanced machine learning techniques, including Random Forests and Deep Learning, to predict parameters of the intense rainfall equation. Methods: The study applies deep neural networks and random forests to predict parameters of the intense rainfall equation. Random Forests method is employed to handle the heterogeneity of data, while Deep Learning captures non-linear relationships. The application takes place in the state of Rio de Janeiro, with a focus on predicting parameters for specific municipalities using available data from the ANA (Brazilian

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Atif, Hussain, Umar Maheri Fahad, Islam Iftekharul, et al. "The Role of Machine Learning in Climate Change Modeling and Prediction: A Comprehensive Review." Global Scientific and Academic Research Journal of Multidisciplinary Studies 4, no. 1 (2025): 42–55. https://doi.org/10.5281/zenodo.14712488.

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<em>Climate change presents a profound global challenge, demanding accurate modeling and prediction to mitigate its impacts. Traditional climate models often struggle with the complexity and non-linearity of climate systems, limiting their ability to capture extreme events and dynamic feedback loops. Machine learning (ML) has emerged as a transformative tool, leveraging vast and diverse datasets to enhance climate modeling accuracy and provide actionable insights. This review explores the role of ML in advancing climate change modeling and prediction, focusing on key techniques such as supervi

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Mustafa, Hasan Kathim, Azma Zakaria Nurul, Abidin Z.Zainal, Kamil Maseer Ziadoon, and Hasan Alzamili Ali. "Online Sequential Extreme Learning Machine (OSELM) based Q-learning(OSELM-QL)." Seybold Report V16, no. 11 (2021): 1–14. https://doi.org/10.5281/zenodo.6553518.

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ABSTRACT The usage of reinforcement learning (RL) for many type of applications is increasing. The quick development of machine learning models in the recent years has motivated researchers to integrate Q-learning with deep learning which has opened the door for many vision based applications of RL. However, using RL with shallow types of neural network has not been tackled adequately in the literature despite its need for real time types of applications such as control systems or time constraint decision based system. In this article, we propose a novel online sequential extreme learning mach

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Wang, Weiyu, Xunxin Zhao, Lijun Luo, et al. "A Fault Diagnosis Method of Rolling Bearing Based on Attention Entropy and Adaptive Deep Kernel Extreme Learning Machine." Energies 15, no. 22 (2022): 8423. http://dx.doi.org/10.3390/en15228423.

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To address the difficulty of early fault diagnosis of rolling bearings, this paper proposes a rolling bearing diagnosis method by combining the attention entropy and adaptive deep kernel extreme learning machine (ADKELM). Firstly, the wavelet threshold denoising method is employed to eliminate the noise in the vibration signal. Then, the empirical wavelet transform (EWT) is utilized to decompose the denoised signal, and extract the attention entropy of the intrinsic mode function (IMF) as the feature vector. Next, the hyperparameters of the deep kernel extreme learning machine (DKELM) are opti

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Pang, Shan, and Xinyi Yang. "Deep Convolutional Extreme Learning Machine and Its Application in Handwritten Digit Classification." Computational Intelligence and Neuroscience 2016 (2016): 1–10. http://dx.doi.org/10.1155/2016/3049632.

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In recent years, some deep learning methods have been developed and applied to image classification applications, such as convolutional neuron network (CNN) and deep belief network (DBN). However they are suffering from some problems like local minima, slow convergence rate, and intensive human intervention. In this paper, we propose a rapid learning method, namely, deep convolutional extreme learning machine (DC-ELM), which combines the power of CNN and fast training of ELM. It uses multiple alternate convolution layers and pooling layers to effectively abstract high level features from input

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Cholissodin, Imam, and Sutrisno Sutrisno. "Prediction of Rainfall using Simplified Deep Learning based Extreme Learning Machines." Journal of Information Technology and Computer Science 3, no. 2 (2018): 120. http://dx.doi.org/10.25126/jitecs.20183258.

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Prediction of rainfall is needed by every farmer to determine the planting period or for an institution, eg agriculture ministry in the form of plant calendars. BMKG is one of the national agency in Indonesia that doing research in the field of meteorology, climatology, and geophysics in Indonesia using several methods in predicting rainfall. However, the accuracy of predicted results from BMKG methods is still less than optimal, causing the accuracy of the planting calendar to only reach 50% for the entire territory of Indonesia. The reason is because of the dynamics of atmospheric patterns (

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Nagashree, K. T., Shristi, Firdaushi Sania, B. Patil Shweta, and Singh Shristi. "Deep-Fake Detection Using Deep Learning." International Journal of Innovative Science and Research Technology (IJISRT) 10, no. 1 (2025): 1700–1706. https://doi.org/10.5281/zenodo.14808073.

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Deep-Fake Detection is a new technology which has caught extreme fashionability in the present generation. Deep-Fake has now held serious pitfalls over spreading misinformation to the world, destroying political faces and also blackmailing individualities to prize centrals. As this technology has taken over the internet in a veritably short span of time and also numerous readily apps are also available to execute Deep-Fake contents, and numerous of the individualities has made systems grounded on detecting the deepfake contents whether it’s fake or real. From the DL(deep learning) &ndash

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Saffari, Mohsen, Mahdi Khodayar, and Mohammad E. Khodayar. "Deep recurrent extreme learning machine for behind-the-meter photovoltaic disaggregation." Electricity Journal 35, no. 5 (2022): 107137. http://dx.doi.org/10.1016/j.tej.2022.107137.

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Khan, Junaid, Muhammad Fayaz, Ayyaz Hussain, Shah Khalid, Wali Khan Mashwani, and Jeonghwan Gwak. "An Improved Alpha Beta Filter Using a Deep Extreme Learning Machine." IEEE Access 9 (2021): 61548–64. http://dx.doi.org/10.1109/access.2021.3073876.

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Inam, Aaqib, Zhuli, Ayesha Sarwar, et al. "Detection of COVID-19 Enhanced by a Deep Extreme Learning Machine." Intelligent Automation & Soft Computing 27, no. 3 (2021): 701–12. http://dx.doi.org/10.32604/iasc.2021.014235.

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Zhang, Lei, Zhenwei He, and Yan Liu. "Deep object recognition across domains based on adaptive extreme learning machine." Neurocomputing 239 (May 2017): 194–203. http://dx.doi.org/10.1016/j.neucom.2017.02.016.

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Gu, Yang, Yiqiang Chen, Junfa Liu, and Xinlong Jiang. "Semi-supervised deep extreme learning machine for Wi-Fi based localization." Neurocomputing 166 (October 2015): 282–93. http://dx.doi.org/10.1016/j.neucom.2015.04.011.

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Qing, Yuanyuan, Yijie Zeng, Yue Li, and Guang-Bin Huang. "Deep and wide feature based extreme learning machine for image classification." Neurocomputing 412 (October 2020): 426–36. http://dx.doi.org/10.1016/j.neucom.2020.06.110.

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Imran, Javed, and Balasubramanian Raman. "Deep motion templates and extreme learning machine for sign language recognition." Visual Computer 36, no. 6 (2019): 1233–46. http://dx.doi.org/10.1007/s00371-019-01725-3.

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Nuha, Hilal H., Adil Balghonaim, Bo Liu, Mohamed Mohandes, Mohamed Deriche, and Faramarz Fekri. "Deep Neural Networks with Extreme Learning Machine for Seismic Data Compression." Arabian Journal for Science and Engineering 45, no. 3 (2019): 1367–77. http://dx.doi.org/10.1007/s13369-019-03942-3.

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