Search Results - "stacked sparse denoising autoencoder"

Flatness pattern recognition based on stacked sparse denoising autoencoder and improved Osprey optimisation algorithm kernel-extreme learning machine

Authors: Yaluo Zhou Shaochuan Zhang Wenguang Liu et al.

Source: Ironmaking & Steelmaking: Processes, Products and Applications.

Toward Robust Fault Identification of Complex Industrial Processes Using Stacked Sparse-Denoising Autoencoder With Softmax Classifier

Authors: Jinping Liu Longcheng Xu Yongfang Xie et al.

Source: IEEE Transactions on Cybernetics. 53:428-442

Subject Terms: 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology

Access URL: https://pubmed.ncbi.nlm.nih.gov/34550897
https://ieeexplore.ieee.org/document/9546667

A novel stacked sparse denoising autoencoder for mammography restoration to visual interpretation of breast lesion

Authors: Swarup Kr Ghosh Biswajit Biswas Anupam Ghosh

Source: Evolutionary Intelligence. 14:133-149

Subject Terms: 03 medical and health sciences, 0302 clinical medicine, 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology

Low-level structure feature extraction for image processing via stacked sparse denoising autoencoder

Authors: Zunlin Fan Duyan Bi Linyuan He et al.

Source: Neurocomputing. 243:12-20

Subject Terms: 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology

Access URL: https://dl.acm.org/doi/10.1016/j.neucom.2017.02.066
https://www.sciencedirect.com/science/article/pii/S0925231217303909
http://www.sciencedirect.com/science/article/pii/S0925231217303909
https://dblp.uni-trier.de/db/journals/ijon/ijon243.html#FanBHMGL17

Fault Diagnosis of Rolling Bearing Based on Multiscale Intrinsic Mode Function Permutation Entropy and a Stacked Sparse Denoising Autoencoder

Authors: Juying Dai Jian Tang Faming Shao et al.

Source: Applied Sciences, Vol 9, Iss 13, p 2743 (2019)

Subject Terms: IMFs, multiscale permutation entropy, stacked sparse denoising autoencoder, fault diagnosis, Technology, Engineering (General). Civil engineering (General), TA1-2040, Biology (General), QH301-705.5, Physics, QC1-999, Chemistry, QD1-999

File Description: electronic resource

Relation: https://www.mdpi.com/2076-3417/9/13/2743; https://doaj.org/toc/2076-3417

Access URL: https://doaj.org/article/513f8bf811eb491dab82fad9ce1ea4c2

Modified metaheuristics with stacked sparse denoising autoencoder model for cervical cancer classification

Authors: Thavavel Vaiyapuri Haya Alaskar Liyakathunisa Syed et al.

Source: Computers and Electrical Engineering. 103:108292

Subject Terms: 03 medical and health sciences, 0302 clinical medicine, 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology

Electricity theft detection based on stacked sparse denoising autoencoder

Authors: Qifeng Xu Yifan Huang

Source: International Journal of Electrical Power & Energy Systems. 125:106448

Subject Terms: 0211 other engineering and technologies, 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology

Access URL: https://www.sciencedirect.com/science/article/pii/S014206151933666X

The optimally designed autoencoder network for compressed sensing

Authors: Zufan Zhang Yunfeng Wu Chenquan Gan et al.

Source: EURASIP Journal on Image and Video Processing, Vol 2019, Iss 1, Pp 1-12 (2019)

Subject Terms: Compressed sensing, Stacked sparse denoising autoencoder, Deep learning, Multiple nonlinear measurement, Signal reconstruction, Electronics, TK7800-8360

File Description: electronic resource

Relation: http://link.springer.com/article/10.1186/s13640-019-0460-5; https://doaj.org/toc/1687-5281

Access URL: https://doaj.org/article/8a403d77e24e4e6189172e0d05faf8f3

A novel stacked sparse denoising autoencoder for mammography restoration to visual interpretation of breast lesion.

Authors: Ghosh, Swarup Kr Biswas, Biswajit Ghosh, Anupam

Source: Evolutionary Intelligence; Mar2021, Vol. 14 Issue 1, p133-149, 17p

Fault Diagnosis of Rolling Bearing Based on Multiscale Intrinsic Mode Function Permutation Entropy and a Stacked Sparse Denoising Autoencoder.

Authors: Dai, Juying Tang, Jian Shao, Faming et al.

Source: Applied Sciences (2076-3417); Jul2019, Vol. 9 Issue 13, p2743, 28p

Subject Terms: HILBERT-Huang transform, FAULT diagnosis, ENTROPY (Information theory), PERMUTATIONS, DEEP learning

Company/Entity: INTERNATIONAL Monetary Fund, SOCIETY of Automotive Engineers

SPR: Malicious traffic detection model for CTCS-3 in railways.

Authors: Siyang Zhou Wenjiang Ji Xinhong Hei et al.

Source: High-speed Railway; Jun2025, Vol. 3 Issue 2, p105-115, 11p

Subject Terms: EXTREME learning machines, TRAFFIC monitoring, AUTOENCODERS, INDUSTRIAL controls manufacturing, MACHINE learning

An intelligent framework for skin cancer detection and classification using fusion of Squeeze-Excitation-DenseNet with Metaheuristic-driven ensemble deep learning models.

Authors: Dorathi Jayaseeli, J. D. Briskilal, J Fancy, C. et al.

Source: Scientific Reports; 3/3/2025, Vol. 15 Issue 1, p1-23, 23p

Digital Image Forgery Detection Using Cyclic Symmetry Convolutional Neural Network.

Authors: S., Shashikala G. K., Ravikumar

Source: International Journal of Intelligent Engineering & Systems; 2025, Vol. 18 Issue 1, p368-379, 12p

Subject Terms: CONVOLUTIONAL neural networks, FEATURE selection, DIGITAL images, FEATURE extraction, AUTOENCODERS

Incipient fault detection and process monitoring of thermal power plant pulverizing system based on deep representation learning.

Authors: Zeng, Lei Zhang, Xin Yu, Ke et al.

Source: Transactions of the Institute of Measurement & Control. Nov2025, Vol. 47 Issue 15, p3111-3123. 13p.

Subject Terms: *FAULT diagnosis, *DEEP learning, *STEAM power plants, *K-nearest neighbor classification, *PROCESS control systems, *FEATURE extraction

A novel optimization-driven deep learning framework for the detection of DDoS attacks.

Authors: Batchu, Raj Kumar Bikku, Thulasi Thota, Srinivasarao et al.

Source: Scientific Reports; 11/14/2024, Vol. 12 Issue 1, p1-16, 16p

Secure and Privacy-Preserving Framework for IoT-Enabled Smart Grid Environment.

Authors: Kumar, Chandan Chittora, Prakash

Source: Arabian Journal for Science & Engineering (Springer Science & Business Media B.V. ); Mar2024, Vol. 49 Issue 3, p3063-3078, 16p

Subject Terms: DEEP learning, ELECTRIC power transmission, INTELLIGENT networks, SMART meters, WIRELESS Internet, WIRELESS communications

Copy-Move Forgery Detection and Classification Using Improved Butterfly Optimization Algorithm-based Convolutional Neural Network.

Authors: S., Shashikala G. K., Ravikumar

Source: International Journal of Intelligent Engineering & Systems; 2024, Vol. 17 Issue 1, p869-879, 11p

Subject Terms: CONVOLUTIONAL neural networks, FORGERY, OPTIMIZATION algorithms, FEATURE extraction, FEATURE selection

A deep neural network with hybrid spotted hyena optimizer and grasshopper optimization algorithm for copy move forgery detection.

Authors: Gupta, Ruchi Singh, Pushpa Alam, Tanweer et al.

Source: Multimedia Tools & Applications; Jul2023, Vol. 82 Issue 16, p24547-24572, 26p

Subject Terms: OPTIMIZATION algorithms, FORGERY, GRASSHOPPERS, EVIDENCE gaps

Applications of Markov Decision Process Model and Deep Learning in Quantitative Portfolio Management during the COVID-19 Pandemic.

Authors: Yue, Han Liu, Jiapeng Zhang, Qin

Source: Systems; Oct2022, Vol. 10 Issue 5, pN.PAG-N.PAG, 20p

Subject Terms: DEEP learning, REINFORCEMENT learning, MARKOV processes, DECISION making, COVID-19 pandemic, REWARD (Psychology)

A Novel Anti-Risk Method for Portfolio Trading Using Deep Reinforcement Learning.

Authors: Yue, Han Liu, Jiapeng Tian, Dongmei et al.

Source: Electronics (2079-9292); May2022, Vol. 11 Issue 9, pN.PAG-N.PAG, 13p

Subject Terms: REINFORCEMENT learning, DOW Jones industrial average, DEEP learning, RESISTANCE training, FEATURE extraction