Suchergebnisse - Cascade stacked autoencoder*

Cascade stacked autoencoder neural network for intrusion detection in CAN-FD vehicular network

Autoren: V. Anjana Devi P.V. Bhaskar Reddy Sreenu Ponnada et al.

Quelle: Knowledge-Based Systems. 329:114301

Prediction of miRNA–Disease Associations by Cascade Forest Model Based on Stacked Autoencoder

Autoren: Xiang Hu Zhixiang Yin Zhiliang Zeng et al.

Quelle: Molecules, Vol 28, Iss 13, p 5013 (2023)

Schlagwörter: miRNA–disease association, multi-source information, stacked autoencoder, cascade forest, Organic chemistry, QD241-441

Dateibeschreibung: electronic resource

Relation: https://www.mdpi.com/1420-3049/28/13/5013; https://doaj.org/toc/1420-3049

Zugangs-URL: https://doaj.org/article/01a5bdab91a149018b0b974a5a968814

Multi-label classification using a cascade of stacked autoencoder and extreme learning machines

Autoren: Anwesha Law Ashish Ghosh

Quelle: Neurocomputing. 358:222-234

Schlagwörter: 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology

Zugangs-URL: https://www.sciencedirect.com/science/article/pii/S092523121930757X
https://dblp.uni-trier.de/db/journals/ijon/ijon358.html#LawG19

Stacked encoded cascade error feedback deep extreme learning machine network for manufacturing order completion time.

Autoren: Khan, Waqar Ahmed Masoud, Mahmoud Eltoukhy, Abdelrahman E. E. et al.

Quelle: Journal of Intelligent Manufacturing. Feb2025, Vol. 36 Issue 2, p1313-1339. 27p.

Schlagwörter: *PRODUCTION planning, EXTREME learning machines, AUTOENCODERS, ERROR functions, NETWORK performance

Adaptive cascade enhancement broad learning system combined with stacked correlation information autoencoder for soft sensor modeling of industrial process

Autoren: Mingming Ni Shaojun Li

Quelle: Computers & Chemical Engineering. 177:108324

Deep Cascade Gradient RBF Networks With Output-Relevant Feature Extraction and Adaptation for Nonlinear and Nonstationary Processes

Autoren: Tong Liu Zeyue Tian Sheng Chen et al.

Quelle: IEEE Transactions on Cybernetics. 53:4908-4922

Schlagwörter: Output-relevant features, Gradient radial basis function (GRBF) network, Data models, Adaptation models, Computational modeling, Deep learning, 02 engineering and technology, Radial basis function networks, Adaptive systems, Predictive models, Online adaptation, High-dimensional and nonstationary processes, Stacked autoencoder (SAE), 0202 electrical engineering, electronic engineering, information engineering, Feature extraction

Dateibeschreibung: text

Zugangs-URL: https://eprints.soton.ac.uk/454982/1/DGRBFlv_re3.pdf
https://pubmed.ncbi.nlm.nih.gov/35239499
https://eprints.soton.ac.uk/454982/

Novel PV Fault Diagnoses via SAE and Improved Multi-Grained Cascade Forest With String Voltage and Currents Measures

Autoren: Wei Gao Rong-Jong Wai Shi-Qun Chen

Quelle: IEEE Access, Vol 8, Pp 133144-133160 (2020)

Schlagwörter: improved multi-grained cascade forest, 0202 electrical engineering, electronic engineering, information engineering, stacked autoencoder, Electrical engineering. Electronics. Nuclear engineering, 02 engineering and technology, fault diagnosis, Photovoltaic, 7. Clean energy, TK1-9971

Dateibeschreibung: 109 bytes; text/html

Zugangs-URL: https://ieeexplore.ieee.org/ielx7/6287639/8948470/09144195.pdf
https://doaj.org/article/401fc3b6560c48fea42f112d505f54aa

Prediction of miRNA–Disease Associations by Cascade Forest Model Based on Stacked Autoencoder.

Autoren: Hu, Xiang Yin, Zhixiang Zeng, Zhiliang et al.

Quelle: Molecules; Jul2023, Vol. 28 Issue 13, p5013, 14p

Schlagwörter: BREAST tumors, HEPATOCELLULAR carcinoma, FORECASTING, BREAST, MICRORNA

HD-CAE: hybrid encoding and deformable decoding autoencoder with cascade attention for visual anomaly detection.

Autoren: Chouhan, Santosh Prakash Bhattacharya, Mahua Sharma, Gopal Krishna

Quelle: Signal, Image & Video Processing; Jul2025, Vol. 19 Issue 7, p1-16, 16p

MiRNA-Disease Association Prediction Based on Stacked Autoencoders and Variant Triplet Networks.

Autoren: Quynh, Nguyen-Phuc-Xuan Tran, Hoai-Nhan Yan, Cheng et al.

Quelle: Journal of Computer Science & Technology (10009000); Jul2025, Vol. 40 Issue 4, p1124-1137, 14p

Schlagwörter: MICRORNA, PREDICTION algorithms, ENSEMBLE learning, BIOLOGICAL databases, ARTIFICIAL neural networks, FORECASTING

Network intrusion detection based on Contractive Sparse Stacked Denoising Autoencoder

Autoren: Yang Yang Li Wencui Meng Huiping et al.

Quelle: 2021 IEEE International Symposium on Broadband Multimedia Systems and Broadcasting (BMSB). :1-6

基于优化堆叠降噪自编码器的 水轮发电机组故障诊断. (Chinese)

Alternate Title: Fault Diagnosis of Hydro-Generator Set Based on Optimized Stacked Denoising Autoencoder. (English)

Autoren: 肖发厚 钟波 张彬桥 et al.

Quelle: China Rural Water & Hydropower; 2025, Issue 8, p119-125, 7p

Schlagwörter: FAULT diagnosis, HYDROELECTRIC generators, ALGORITHMS, OPTIMIZATION algorithms, ARTIFICIAL neural networks, MATHEMATICAL optimization

Gravitational wave isolation with autoencoder neural network cascade.

Autoren: Sengupta, Mayank

Quelle: Neural Computing & Applications; May2024, Vol. 36 Issue 13, p6993-7006, 14p

Schlagwörter: GRAVITATIONAL waves, GRAVITATIONAL wave detectors, CASCADE connections, GRAVITATIONAL wave astronomy, GRAVITATIONAL fields, BINARY black holes, GRAPHICS processing units

CDAE: A Cascade of Denoising Autoencoders for Noise Reduction in the Clustering of Single-Particle Cryo-EM Images

Autoren: Houchao Lei Yang Yang

Quelle: Frontiers in Genetics, Vol 11 (2021)

Schlagwörter: cryo-EM, autoencoder, image denoising, clustering, deep learning, Genetics, QH426-470

Dateibeschreibung: electronic resource

Relation: https://www.frontiersin.org/articles/10.3389/fgene.2020.627746/full; https://doaj.org/toc/1664-8021

Zugangs-URL: https://doaj.org/article/b6c4add6a8b2425a989cefc397914165

Face Recognition Based on Stacked Convolutional Autoencoder and Sparse Representation

Autoren: Hong Xu Kai Liu Jianjun Yang et al.

Quelle: 2018 IEEE 23rd International Conference on Digital Signal Processing (DSP). :1-4

Schlagwörter: 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology, 3. Good health

Zugangs-URL: https://dblp.uni-trier.de/db/conf/icdsp/icdsp2018.html#ChangYLXLH18

Demodulating Optical Wireless Communication of FBG Sensing with Turbulence-Caused Noise by Stacked Denoising Autoencoders and the Deep Belief Network.

Autoren: Bogale, Shegaw Demessie Yao, Cheng-Kai Manie, Yibeltal Chanie et al.

Quelle: Electronics (2079-9292); Oct2024, Vol. 13 Issue 20, p4127, 11p

Schlagwörter: FIBER Bragg gratings, FREE-space optical technology, SIGNAL denoising, LIGHT scattering, OPTICAL communications

基于多模态栈式混合自编码器的药物靶标相互作用预测. (Chinese)

Alternate Title: Prediction of Drug-Target Interactions Based on Multimodal Stacked Hybrid Autoencoder. (English)

Autoren: 张星宇 陈 卓 黄 印 et al.

Quelle: Journal of Computer Engineering & Applications; Oct2024, Vol. 60 Issue 19, p334-342, 9p

Schlagwörter: DEEP learning, RANDOM walks, DRUG target, DRUG interactions, ALGORITHMS

Groundwater Pollution Source and Aquifer Parameter Estimation Based on a Stacked Autoencoder Substitute.

Autoren: Wang, Han Zhang, Jinping Li, Hang et al.

Quelle: Water (20734441); Sep2024, Vol. 16 Issue 18, p2564, 15p

Schlagwörter: LATIN hypercube sampling, FIX-point estimation, DEEP learning, HEURISTIC, PARAMETER estimation

CDAE: A Cascade of Denoising Autoencoders for Noise Reduction in the Clustering of Single-Particle Cryo-EM Images.

Autoren: Lei, Houchao Yang, Yang

Quelle: Frontiers in Genetics; 1/20/2021, Vol. 11, pN.PAG-N.PAG, 9p

Schlagwörter: NOISE control, IMAGE denoising, SIGNAL-to-noise ratio, IMAGE processing, COMPUTER science, IMAGE

IMU-CNN: implementing remote sensing image restoration framework based on Mask-Upgraded Cascade R-CNN and deep autoencoder.

Autoren: Dewangan, Somesh Kumar Choubey, Siddharth Patra, Jyotiprakash et al.

Quelle: Multimedia Tools & Applications; Aug2024, Vol. 83 Issue 27, p69049-69081, 33p

Schlagwörter: REMOTE sensing, IMAGING systems, IMAGE reconstruction, COMPUTATIONAL complexity, RESEARCH personnel