Suchergebnisse - Deep learning kernel ELM Autoencoder

Adaptive VMD based optimized deep learning mixed kernel ELM autoencoder for single and multistep wind power forecasting von Krishna Rayi, Vijaya, Mishra, S.P., Naik, Jyotirmayee, Dash, P.K.

“… ) and Deep learning mixed Kernel ELM (MKELM) Autoencoder (AE) has been presented for precise prediction of wind power …”
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Hessenberg Elm Autoencoder Kernel For Deep Learning von ALTAN, Gokhan, KUTLU, Yakup

“… Deep Learning (DL) is an effective way that reveals on computation capability and advantage of the hidden layer in the network models …”
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Adaptive VMD based optimized deep learning mixed kernel ELM autoencoder for single and multistep wind power forecasting

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Detecting Fraudulent Transactions Using Stacked Autoencoder Kernel ELM Optimized by the Dandelion Algorithm von El Hlouli, Fatima Zohra, Riffi, Jamal, Sayyouri, Mhamed, Mahraz, Mohamed Adnane, Yahyaouy, Ali, El Fazazy, Khalid, Tairi, Hamid

“… To resolve this issue there is a need for reliable real-time fraud detection technologies. This research introduced an innovative method called stacked autoencoder kernel extreme learning machine optimized by the dandelion algorithm (S-AEKELM-DA …”
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A novel deep output kernel learning method for bearing fault structural diagnosis von Mao, Wentao, Feng, Wushi, Liang, Xihui

“… •We propose a new deep learning method to conduct structural diagnosis of multiple bearing faults …”
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Multi‐objective auto‐encoder deep learning‐based stack switching scheme for improved battery life using error prediction of wind‐battery storage microgrid von Mishra, Sthita Prajna, Krishna Rayi, Vijaya, Dash, Pradipta Kishore, Bisoi, Ranjeeta

“… Summary For any wind power generation system, battery energy storage is a suitable backup power unit for ensuring greater functionality by compensating the …”
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Processes soft modeling based on stacked autoencoders and wavelet extreme learning machine for aluminum plant-wide application von Lei, Yongxiang, Karimi, Hamid Reza, Cen, Lihui, Chen, Xiaofang, Xie, Yongfang

“… First, a stacked autoencoder (SAE) is used to extract the deep features. Then, a top-layer extreme learning machine (ELM …”
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An adaptive hierarchical hybrid kernel ELM optimized by aquila optimizer algorithm for bearing fault diagnosis von Yan, Hao, Shang, Liangliang, Chen, Wan, Jiang, Mengyao, lu, Tianqi, Li, Fei

“… ) and adaptive hierarchical hybrid kernel extreme learning machine (AHHKELM). First, a hybrid kernel extreme learning machine (HKELM …”
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Stacked autoencoder based deep random vector functional link neural network for classification von Katuwal, Rakesh, Suganthan, P.N.

“… Inspired by the better performance of RVFL over ELM, in this paper, we propose several deep RVFL variants by utilizing the framework of stacked autoencoders …”
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Generative Autoencoder Kernels on Deep Learning for Brain Activity Analysis von ALTAN, Gokhan, KUTLU, Yakup

“… Deep Learning (DL) is a two-step classification model that consists feature learning, generating feature representations using unsupervised ways and the supervised learning stage at the last step of model using …”
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Chapter three - Generalization performance of deep autoencoder kernels for identification of abnormalities on electrocardiograms von Altan, Gokhan, Kutlu, Yakup

“… depending on the increase in the number of optimization parameters. This chapter addresses the problem of how to reduce the training time required for DL algorithms by combining theories in deep autoencoder kernels …”
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Stock market prediction under a deep learning approach using Variational Autoencoder, and kernel extreme learning machine von Hemant, Parida, Ajaya Kumar, Kumari, Rina, Singh, Aru Ranjan, Bandyopadhyay, Anjan, Swain, Sujata

“… In this work, the convolutional neural network (CNN) technique was implemented to extract features and a variational autoencoder (VAE) for predictions …”
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Software defect prediction based on stacked sparse denoising autoencoders and enhanced extreme learning machine von Zhang, Nana, Ying, Shi, Zhu, Kun, Zhu, Dandan

“… ) and Extreme Learning Maching (ELM) optimised by Particle Swarm Optimisation (PSO) and another complementary Gravitational Search Algorithm (GSA …”
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Generative Autoencoder Kernels on Deep Learning for Brain Activity Analysis von Altan, Gokhan, Kutlu, Yakup

“… Deep Learning (DL) is a two-step classification model that consists feature learning, generating feature representations using unsupervised ways and the supervised learning stage at the last step of model using …”
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Multiple power quality disturbances analysis in photovoltaic integrated direct current microgrid using adaptive morphological filter with deep learning algorithm von Dash, P.K., Prasad, Eluri N.V.D.V., Jalli, Ravi Kumar, Mishra, S.P.

“… •A deep stacked autoencoder is used for extracting unsupervised features.•Kernel random vector functional link network is used for disturbance classification …”
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Dynamic identification of coupler yaw angle of heavy haul locomotive: An optimal multi-task ELM-based method von Xie, Bo, Chen, Shiqian, Song, Peize, Ran, Xiangrui, Wang, Kaiyun

“… To address these issues, within this paper a multi-task deep multiple kernel extreme learning machine (MT-DMKELM …”
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Deep Layer Kernel Sparse Representation Network for the Detection of Heart Valve Ailments from the Time-Frequency Representation of PCG Recordings von Acharya, U. Rajendra, Tripathy, R. K., Ponnalagu, R. N., Ghosh, Samit Kumar

“… In this paper, a time-frequency based deep layer kernel sparse representation network (DLKSRN) is proposed for the detection of various HVAs using PCG signals …”
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A novel machine learning framework: cross transformer based optimization model for the detection and classification of brain tumor using clinical decision analysis von Mayuri, A. V. R., Maniraj, S. P., Duraisamy, M., Murthy, G. L. N., Garg, Kanika, Sangeetha, M.

“… In order to rectify these problems, this work proposes a Cross Attention Transformer based Dragonfly Optimized Kernel Extreme Learning Machine method for accurate brain tumor detection and classification …”
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MSCNE:Predict miRNA-Disease Associations Using Neural Network Based on Multi-Source Biological Information von Han, Genwei, Kuang, Zhufang, Deng, Lei

“… ) feature extractor and an extreme learning machine (ELM) classifier is proposed. Specifically, the semantic similarity of diseases, the gaussian interaction profile …”
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Decision-making method for residual support force of hydraulic supports during pressurized moving under fragmented roof conditions in ultra-thin coal seams von ZHANG Chuanwei, ZHANG Gangqiang, LU Zhengxiong, LI Linyue, HE Zhengwei, GONG Lingxiao, HUANG Junfeng

“… -thin coal seams and ensuring operational safety. To address this challenge, this study proposed a novel decision-making method based on a Deep Hybrid Kernel Extreme Learning Machine (DHKELM …”
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