Search Results - Deep learning and distributed stacked denoising sparse autoencoder

Unsupervised Feature Learning With Distributed Stacked Denoising Sparse Autoencoder for Abnormal Behavior Detection Using Apache Spark

Authors: Naila Marir Huiqiang Wang Guangsheng Feng

Source: 2019 IEEE 2nd International Conference on Knowledge Innovation and Invention (ICKII). :473-476

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

Access URL: https://ieeexplore.ieee.org/document/9042645/
https://dblp.uni-trier.de/db/conf/ickii/ickii2019.html#MarirWF19
http://dblp.uni-trier.de/db/conf/ickii/ickii2019.html#MarirWF19

M-ary Phase Position Shift Keying Demodulation Using Stacked Denoising Sparse Autoencoders.

Authors: Lu, Conghui Chen, Peng Zhong, Hua et al.

Source: Electronics (2079-9292); Apr2022, Vol. 11 Issue 8, p1233-1233, 12p

Subject Terms: PHASE shift keying, DEMODULATION, PHASE detectors, RANDOM noise theory, IMAGE denoising

Deep Learning-Based Machinery Fault Diagnostics

Contributors: Chen, Hongtian Zhong, Kai Ran, Guangtao et al.

Subject Terms: process monitoring, dynamics, variable time lag, dynamic autoregressive latent variables model, sintering process, hammerstein output-error systems, auxiliary model, multi-innovation identification theory, fractional-order calculus theory, canonical variate analysis, disturbance detection, power transmission system, k-nearest neighbor analysis, statistical local analysis, intelligent fault diagnosis, stacked pruning sparse denoising autoencoder, convolutional neural network, anti-noise, flywheel fault diagnosis, belief rule base, fuzzy fault tree analysis, Bayesian network, evidential reasoning, aluminum reduction process, alumina concentration, subspace identification, distributed predictive control, spatiotemporal feature fusion, gated recurrent unit, attention mechanism

File Description: application/octet-stream

Relation: ONIX_20221025_9783036551739_23; https://mdpi.com/books/pdfview/book/6065

Availability: https://directory.doabooks.org/handle/20.500.12854/93169
https://hdl.handle.net/20.500.12854/93169
https://mdpi.com/books/pdfview/book/6065

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.

Application of Artificial Intelligence Technologies as an Intervention for Promoting Healthy Eating and Nutrition in Older Adults: A Systematic Literature Review.

Authors: Kalu, Kingsley Ataguba, Grace Onifade, Oyepeju et al.

Source: Nutrients. Oct2025, Vol. 17 Issue 20, p3223. 33p.

Evaluating water resource carrying capacity using the deep learning method: a case study of Yunnan, Southwest China.

Authors: Zhang H Huang C Hu X et al.

Source: Environmental science and pollution research international [Environ Sci Pollut Res Int] 2022 Jul; Vol. 29 (32), pp. 48812-48826. Date of Electronic Publication: 2022 Feb 24.

Publication Type: Journal Article

Journal Info: Publisher: Springer Country of Publication: Germany NLM ID: 9441769 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1614-7499 (Electronic) Linking ISSN: 09441344 NLM ISO Abbreviation: Environ Sci Pollut Res Int Subsets: MEDLINE

MeSH Terms: Deep Learning* , Water Resources*, China ; Conservation of Natural Resources ; Economic Development

Deep Learning: Theoretical and Practical Approach

Authors: Farhadi, Sina Ranjbar Kooh

Subject Terms: Machine Learning, Artificial Neural Network, Deep Learning, Artificial Intelligence, Data Science

Hydroelectric Unit Vibration Signal Feature Extraction Based on IMF Energy Moment and SDAE.

Authors: Liu, Dong Kong, Lijun Yao, Bing et al.

Source: Water (20734441); Jul2024, Vol. 16 Issue 14, p1956, 15p

Subject Terms: FEATURE extraction, FOURIER analysis, MOMENTS method (Statistics), FOURIER transforms, COUPLINGS (Gearing), HILBERT-Huang transform

A Hybrid CMOS-Memristive Approach to Designing Deep Generative Models.

Authors: Parmar, Vivek Suri, Manan

Source: IEEE Transactions on Neural Networks & Learning Systems; Jun2021, Vol. 32 Issue 6, p2790-2796, 7p

Subject Terms: DEEP learning, BOLTZMANN machine, HANDWRITING recognition (Computer science), NONVOLATILE random-access memory, COMPUTER architecture, SYNAPSES

A Stacked Denoising Sparse Autoencoder Based Fault Early Warning Method for Feedwater Heater Performance Degradation.

Authors: Li, Xingshuo Liu, Jinfu Li, Jiajia et al.

Source: Energies (19961073); Nov2020, Vol. 13 Issue 22, p6061, 1p

Subject Terms: HEATING, STEAM-turbines, ELECTRIC transients, KEY performance indicators (Management)

Deep Denoising Autoencoding Method for Feature Extraction and Recognition of Vehicle Adhesion Status.

Authors: He, Jing Liu, Linfan Zhang, Changfan et al.

Source: Journal of Sensors; 9/16/2018, p1-8, 8p

Subject Terms: SIGNAL denoising, DEEP learning, FEATURE extraction, SUPPORT vector machines, ELECTROSTATIC adhesion

Majorization Minimization Technique for Optimally Solving Deep Dictionary Learning.

Authors: Singhal, Vanika Majumdar, Angshul

Source: Neural Processing Letters; Jun2018, Vol. 47 Issue 3, p799-814, 16p

Subject Terms: DEEP learning, HYPERSPECTRAL imaging systems, ARTIFICIAL neural networks, BOLTZMANN machine, ELECTRONIC dictionaries

An Enhanced Intrusion Detection System Using Attention-Based Stacked Sparse Autoencoder Feature Extraction.

Authors: Varanasi, Venkata Ramani Razia, Shaik

Source: Engineering, Technology & Applied Science Research; Aug2025, Vol. 15 Issue 4, p24436-24441, 6p

Subject Terms: INTRUSION detection systems (Computer security), FEATURE extraction, SIGNAL denoising, LONG short-term memory, ARTIFICIAL neural networks, BIG data, MACHINE learning

A hybrid stacked sparse autoencoder and LightGBM framework for high-performance intrusion detection in IoT networks.

Authors: Madhavan Nair Vasanthamma, Hari Vinayak Thankaswamy, Jarin

Source: Iran Journal of Computer Science; Dec2025, Vol. 8 Issue 4, p2175-2198, 24p

Stacked denoising autoencoder for missing traffic data reconstruction via mobile edge computing.

Authors: Dai, Penglin Luo, Jingtao Zhao, Kangli et al.

Source: Neural Computing & Applications; Jul2023, Vol. 35 Issue 19, p14259-14274, 16p

Subject Terms: MOBILE computing, EDGE computing, DEEP learning, CLOUD computing, ALGORITHMS, ORTHOGONAL matching pursuit

Deep learning for electrolysis process anode effect prediction based on long short-term memory network and stacked denoising autoencoder.

Authors: Yin, Gang Li, Yi-Hui Yan, Fei-Ya et al.

Source: Rare Metals; Dec2024, Vol. 43 Issue 12, p6730-6741, 12p

Latent Feature‐Based Type 2 Diabetes Prediction Using a Hybrid Stacked Sparse Autoencoder and Machine Learning Models.

Authors: Abdussamad Daud, Hanita Sokkalingam, Rajalingam et al.

Source: Engineering Reports; Sep2025, Vol. 7 Issue 9, p1-17, 17p

Subject Terms: TYPE 2 diabetes, MACHINE learning, CLINICAL decision support systems, METADATA, FORECASTING, FEATURE extraction

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

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

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

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

DC Disturbance Classification Method Based on Compressed Sensing and Encoder.

Authors: Yu, Huanan Zhang, Xiang Wang, Jian

Source: Energy Engineering; 2025, Vol. 122 Issue 12, p5055-5071, 17p

Subject Terms: COMPRESSED sensing, POWER quality disturbances, DIMENSIONAL reduction algorithms, SIGNAL reconstruction, SIGNAL processing, HYBRID power, FEATURE extraction

Physics-Informed SDAE-Based Denoising Model for High-Impedance Fault Detection.

Authors: Lin, Jianxin Wang, Xuchang Wang, Huaiyuan

Source: Processes; Nov2025, Vol. 13 Issue 11, p3673, 23p

Subject Terms: FAULT diagnosis, SIGNAL denoising, SIGNAL integrity (Electronics), ELECTRIC power distribution, SIGNAL processing, DISCRETE wavelet transforms