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Alzakari SA, Hassairi S, Ali Alhussan A, Ejbali R. A mobile Deep Sparse Wavelet autoencoder for Arabic acoustic unit modeling and recognition. Heliyon 2024; 10:e26583. [PMID: 38434048 PMCID: PMC10906401 DOI: 10.1016/j.heliyon.2024.e26583] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2024] [Revised: 02/03/2024] [Accepted: 02/15/2024] [Indexed: 03/05/2024] Open
Abstract
In this manuscript, we introduce a novel methodology for modeling acoustic units within a mobile architecture, employing a synergistic combination of various motivating techniques, including deep learning, sparse coding, and wavelet networks. The core concept involves constructing a Deep Sparse Wavelet Network (DSWN) through the integration of stacked wavelet autoencoders. The DSWN is designed to classify a specific class and discern it from other classes within a dataset of acoustic units. Mel-frequency cepstral coefficients (MFCC) and perceptual linear predictive (PLP) features are utilized for encoding speech units. This approach is tailored to leverage the computational capabilities of mobile devices by establishing deep networks with minimal connections, thereby immediately reducing computational overhead. The experimental findings demonstrate the efficacy of our system when applied to a segmented corpus of Arabic words. Notwithstanding promising results, we will explore the limitations of our methodology. One limitation concerns the use of a specific dataset of Arabic words. The generalizability of the sparse deep wavelet network (DSWN) to various contexts requires further investigation "We will evaluate the impact of speech variations, such as accents, on the performance of our model, for a nuanced understanding.
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Affiliation(s)
- Sarah A. Alzakari
- Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, P.O. Box 84428, Riyadh, 11671, Saudi Arabia
| | - Salima Hassairi
- Research Team in Intelligent Machines, National School of Engineers of Gabes, B.P. W 6072, Gabes, Tunisia
| | - Amel Ali Alhussan
- Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, P.O. Box 84428, Riyadh, 11671, Saudi Arabia
| | - Ridha Ejbali
- Research Team in Intelligent Machines, National School of Engineers of Gabes, B.P. W 6072, Gabes, Tunisia
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Yue Y, Cao L, Chen H, Chen Y, Su Z. Towards an Optimal KELM Using the PSO-BOA Optimization Strategy with Applications in Data Classification. Biomimetics (Basel) 2023; 8:306. [PMID: 37504194 PMCID: PMC10807650 DOI: 10.3390/biomimetics8030306] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2023] [Revised: 07/09/2023] [Accepted: 07/09/2023] [Indexed: 07/29/2023] Open
Abstract
The features of the kernel extreme learning machine-efficient processing, improved performance, and less human parameter setting-have allowed it to be effectively used to batch multi-label classification tasks. These classic classification algorithms must at present contend with accuracy and space-time issues as a result of the vast and quick, multi-label, and concept drift features of the developing data streams in the practical application sector. The KELM training procedure still has a difficulty in that it has to be repeated numerous times independently in order to maximize the model's generalization performance or the number of nodes in the hidden layer. In this paper, a kernel extreme learning machine multi-label data classification method based on the butterfly algorithm optimized by particle swarm optimization is proposed. The proposed algorithm, which fully accounts for the optimization of the model generalization ability and the number of hidden layer nodes, can train multiple KELM hidden layer networks at once while maintaining the algorithm's current time complexity and avoiding a significant number of repeated calculations. The simulation results demonstrate that, in comparison to the PSO-KELM, BBA-KELM, and BOA-KELM algorithms, the PSOBOA-KELM algorithm proposed in this paper can more effectively search the kernel extreme learning machine parameters and more effectively balance the global and local performance, resulting in a KELM prediction model with a higher prediction accuracy.
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Affiliation(s)
- Yinggao Yue
- School of Intelligent Manufacturing and Electronic Engineering, Wenzhou University of Technology, Wenzhou 325035, China; (Y.Y.); (L.C.); (H.C.); (Y.C.)
- Intelligent Information Systems Institute, Wenzhou University, Wenzhou 325035, China
| | - Li Cao
- School of Intelligent Manufacturing and Electronic Engineering, Wenzhou University of Technology, Wenzhou 325035, China; (Y.Y.); (L.C.); (H.C.); (Y.C.)
| | - Haishao Chen
- School of Intelligent Manufacturing and Electronic Engineering, Wenzhou University of Technology, Wenzhou 325035, China; (Y.Y.); (L.C.); (H.C.); (Y.C.)
| | - Yaodan Chen
- School of Intelligent Manufacturing and Electronic Engineering, Wenzhou University of Technology, Wenzhou 325035, China; (Y.Y.); (L.C.); (H.C.); (Y.C.)
| | - Zhonggen Su
- Taishun Research Institute, Wenzhou University of Technology, Wenzhou 325035, China
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Rajpal A, Sehra K, Mishra A, Chetty G. A low-resolution real-time face recognition using extreme learning machine and its variants. THE IMAGING SCIENCE JOURNAL 2023. [DOI: 10.1080/13682199.2023.2183544] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/05/2023]
Affiliation(s)
- Ankit Rajpal
- Department of Computer Science, University of Delhi, Delhi, India
| | - Khushwant Sehra
- Department of Electronic Science, University of Delhi South Campus, Delhi, India
| | - Anurag Mishra
- Department of Electronics, Deen Dayal Upadhyaya College, University of Delhi, Delhi, India
| | - Girija Chetty
- Faculty of Science and Technology, University of Canberra, Bruce, ACT, Australia
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Meng S, Li C, Peng W, Tian C. Empirical mode decomposition-based multi-scale spectral graph convolution network for abnormal electricity consumption detection. Neural Comput Appl 2023. [DOI: 10.1007/s00521-023-08222-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/21/2023]
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Liu X, Zhou Y, Meng W, Luo Q. Functional extreme learning machine for regression and classification. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2023; 20:3768-3792. [PMID: 36899604 DOI: 10.3934/mbe.2023177] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/18/2023]
Abstract
Although Extreme Learning Machine (ELM) can learn thousands of times faster than traditional slow gradient algorithms for training neural networks, ELM fitting accuracy is limited. This paper develops Functional Extreme Learning Machine (FELM), which is a novel regression and classifier. It takes functional neurons as the basic computing units and uses functional equation-solving theory to guide the modeling process of functional extreme learning machines. The functional neuron function of FELM is not fixed, and its learning process refers to the process of estimating or adjusting the coefficients. It follows the spirit of extreme learning and solves the generalized inverse of the hidden layer neuron output matrix through the principle of minimum error, without iterating to obtain the optimal hidden layer coefficients. To verify the performance of the proposed FELM, it is compared with ELM, OP-ELM, SVM and LSSVM on several synthetic datasets, XOR problem, benchmark regression and classification datasets. The experimental results show that although the proposed FELM has the same learning speed as ELM, its generalization performance and stability are better than ELM.
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Affiliation(s)
- Xianli Liu
- College of Artificial Intelligence, Guangxi University for Nationalities, Nanning 530006, China
| | - Yongquan Zhou
- College of Artificial Intelligence, Guangxi University for Nationalities, Nanning 530006, China
- Xiangsihu College of Gunagxi University for Nationalities, Nanning, Guangxi 532100, China
- Guangxi Key Laboratories of Hybrid Computation and IC Design Analysis, Nanning 530006, China
| | - Weiping Meng
- College of Artificial Intelligence, Guangxi University for Nationalities, Nanning 530006, China
| | - Qifang Luo
- College of Artificial Intelligence, Guangxi University for Nationalities, Nanning 530006, China
- Guangxi Key Laboratories of Hybrid Computation and IC Design Analysis, Nanning 530006, China
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Deep hypergraph autoencoder embedding: An efficient intelligent approach for rotating machinery fault diagnosis. Knowl Based Syst 2022. [DOI: 10.1016/j.knosys.2022.110172] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/03/2022]
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Xiao D, Yan Z, Li J, Fu Y, Li Z, Li B. Coal Identification Based on Reflection Spectroscopy and Deep Learning: Paving the Way for Efficient Coal Combustion and Pyrolysis. ACS OMEGA 2022; 7:23919-23928. [PMID: 35847264 PMCID: PMC9280928 DOI: 10.1021/acsomega.2c02665] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Coal plays an indispensable role in the world's energy structure. Coal converts chemical energy into energy such as electricity, heat, and internal energy through combustion. To realize the energy conversion of coal more efficiently, coal needs to be identified during the stages of mining, combustion, and pyrolysis. On this basis, different categories of coal are used according to industrial needs, or different pyrolysis processes are selected according to the category of coal. This paper proposes an approach combining deep learning with reflection spectroscopy for rapid coal identification in mining, combustion, and pyrolysis scenarios. First, spectral data of different coal samples were collected in the field and these spectral data were preprocessed. Then, an identification model combining a multiscale convolutional neural network (CNN) and an extreme learning machine (ELM), named RS_PSOTELM, is proposed. The effective features in the spectral data are extracted by the CNN, and the feature classification is realized utilizing the ELM. To enhance the identification performance of the model, we utilize a particle swarm optimization algorithm to optimize the parameters of the ELM. Experimental results show that RS_PSOTELM achieves 98.3% accuracy on the coal identification task and is able to identify coal quickly and accurately, providing a low-cost, efficient, and reliable approach for coal identification during the mining and application phases, as well as paving the way for efficient combustion and pyrolysis of coal.
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Affiliation(s)
- Dong Xiao
- School
of Information Science and Engineering, Northeastern University, Shenyang 110819, China
| | - Zelin Yan
- School
of Information Science and Engineering, Northeastern University, Shenyang 110819, China
| | - Jian Li
- Technical
Service Parlor, Unit 31434 of the Chinese
People’s Liberation Army, Shenyang 110000, China
| | - Yanhua Fu
- School
of JangHo Architecture, Northeastern University, Shenyang 110819, China
| | - Zhenni Li
- School
of Information Science and Engineering, Northeastern University, Shenyang 110819, China
| | - Boyan Li
- School
of Information Science and Engineering, Northeastern University, Shenyang 110819, China
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Broad learning extreme learning machine for forecasting and eliminating tremors in teleoperation. Appl Soft Comput 2021. [DOI: 10.1016/j.asoc.2021.107863] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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