Search Results - stacked sparse denoising autoencoder (SSDAE)

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

Software defect prediction based on stacked sparse denoising autoencoders and enhanced extreme learning machine.

Authors: Zhang, Nana Ying, Shi Zhu, Kun et al.

Source: IET Software (Wiley-Blackwell). Feb2022, Vol. 16 Issue 1, p29-47. 19p.

Subject Terms: *COMPUTER software quality control, *COMPUTER software industry, *ELECTRONIC data processing, FEATURE extraction, PARTICLE swarm optimization

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

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

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

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

Authors: Liu J Xu L Xie Y et al.

Source: IEEE transactions on cybernetics [IEEE Trans Cybern] 2023 Jan; Vol. 53 (1), pp. 428-442. Date of Electronic Publication: 2022 Dec 23.

Publication Type: Journal Article

Journal Info: Publisher: Institute of Electrical and Electronics Engineers Country of Publication: United States NLM ID: 101609393 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 2168-2275 (Electronic) Linking ISSN: 21682267 NLM ISO Abbreviation: IEEE Trans Cybern Subsets: PubMed not MEDLINE; MEDLINE

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

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

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

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

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

Maximal overlap discrete wavelet transform and deep learning for robust denoising and detection of power quality disturbance.

Authors: Xiao, Fei Lu, Tianguang Wu, Mingli et al.

Source: IET Generation, Transmission & Distribution (Wiley-Blackwell); Jan2020, Vol. 14 Issue 2, p140-147, 8p

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

Authors: Zhou, Yaluo Zhang, Shaochuan Liu, Wenguang et al.

Source: Ironmaking & Steelmaking. Jul2025, p1.

Subject Terms: *FLATNESS measurement, *FEATURE extraction, *OPTIMIZATION algorithms, *SIGNAL denoising, *STEEL strip, *EXTREME learning machines, *SIGNAL processing

Building feature space of extreme learning machine with sparse denoising stacked-autoencoder.

Authors: Cao, Le-le Huang, Wen-bing Sun, Fu-chun

Source: Neurocomputing. Jan2016 Part A, Vol. 174, p60-71. 12p.

Subject Terms: *FEATURE extraction, *MACHINE learning, *SIGNAL denoising, *GENERALIZATION, *MATHEMATICAL transformations, *REGRESSION analysis

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

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

Authors: Vaiyapuri, Thavavel Alaskar, Haya Syed, Liyakathunisa et al.

Source: Computers & Electrical Engineering. Oct2022, Vol. 103, pN.PAG-N.PAG. 1p.

Subject Terms: *CERVICAL cancer, *TUMOR classification, *PAP test, *METAHEURISTIC algorithms, *ARTIFICIAL neural networks, *IMAGE segmentation

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

Authors: Han Yue Jiapeng Liu Dongmei Tian et al.

Source: Electronics, Vol 11, Iss 1506, p 1506 (2022)

Subject Terms: portfolio trading, deep reinforcement learning, deep learning, stacked autoencoder, advantage actor–critic, Electronics, TK7800-8360

Relation: https://www.mdpi.com/2079-9292/11/9/1506; https://doaj.org/toc/2079-9292; https://doaj.org/article/ed6d189daf8d448ea2d5720d876865f3

Availability: https://doi.org/10.3390/electronics11091506
https://doaj.org/article/ed6d189daf8d448ea2d5720d876865f3

The optimally designed autoencoder network for compressed sensing.

Authors: Zhang, Zufan Wu, Yunfeng Gan, Chenquan et al.

Source: EURASIP Journal on Image & Video Processing; 4/29/2019, Vol. 2019 Issue 1, pN.PAG-N.PAG, 1p

Subject Terms: COMPRESSED sensing, IMAGE denoising, LENGTH measurement, SAMPLING (Process), SIGNAL processing, RANDOM sets, DEEP learning