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

Denoising Autoencoder for Reconstructing Sensor Observation Data and Predicting Evapotranspiration: Noisy and Missing Values Repair and Uncertainty Quantification.

Authors: Johnsen, Timothy K. Bi, Xiangyu Chou, Chunwei et al.

Source: Water Resources Research; Oct2025, Vol. 61 Issue 10, p1-23, 23p

Subject Terms: EVAPOTRANSPIRATION, AUTOENCODERS, STOCHASTIC analysis, MACHINE learning, HYDROLOGICAL forecasting, NOISE control, TELEMETRY

A Comprehensive Survey of ECG Signal Denoising Techniques, Challenges and Novel Trends.

Authors: Saad, Maryam El-Rabaie, El-Sayed Mahmoud Saber, Mohamed et al.

Source: Delta University Scientific Journal; 2024 Special Issue, Vol. 7, p291-308, 18p

Subject Terms: HILBERT-Huang transform, SIGNAL denoising, WAVELET transforms, DEEP learning, ELECTRIC lines

An automated extraction of spectral-temporal and spatial-temporal features of EEG for emotion detection.

Authors: Islam, Monira Lee, Tan

Source: Brain Informatics; 8/2/2025, Vol. 12 Issue 1, p1-19, 19p

Subject Terms: EMOTION recognition, ELECTROENCEPHALOGRAPHY, MULTIVARIATE analysis, SIGNAL processing, SPECTRUM analysis, DEEP learning

Unravelling emotions: exploring deep learning approaches for EEG-based emotion recognition with current challenges and future recommendations.

Authors: Abgeena, Abgeena Garg, Shruti

Source: Cognitive Neurodynamics; 10/23/2025, Vol. 19 Issue 1, p1-32, 32p

Comprehensive review of machine learning and deep learning techniques for epileptic seizure detection and prediction based on neuroimaging modalities.

Authors: Slama, Khadija Yahyaouy, Ali Riffi, Jamal et al.

Source: Visual Computing for Industry, Biomedicine & Art; 12/11/2025, Vol. 8 Issue 1, p1-37, 37p

Deep Learning and Attention-Based Methods for Human Activity Recognition and Anticipation: A Comprehensive Review.

Authors: Shuvo, Md Rezowan Mekala, M S Elyan, Eyad

Source: Cognitive Computation; Dec2025, Vol. 17 Issue 6, p1-28, 28p

An Overview of the Latest Developments and Potential Paths for Artificial Intelligence in Wastewater Treatment Systems.

Authors: Sun, Wenquan Gao, Yun Zhou, Jun et al.

Source: Water (20734441); Aug2025, Vol. 17 Issue 16, p2432, 21p

Subject Terms: ARTIFICIAL intelligence, WASTEWATER treatment, PROCESS optimization, WASTE treatment, DEEP learning, TECHNOLOGICAL innovations, ARTIFICIAL neural networks, WATER quality monitoring

A comprehensive survey on RGB-D-based human action recognition: algorithms, datasets, and popular applications.

Authors: Zhang, Yumin Wang, Yanyong

Source: EURASIP Journal on Image & Video Processing; 8/13/2025, Vol. 2025 Issue 1, p1-65, 65p

Subject Terms: HUMAN activity recognition, COMPUTER vision, DEPTH maps (Digital image processing), ALGORITHMS, TRANSFORMER models, RECURRENT neural networks, DEEP learning, CONVOLUTIONAL neural networks

DuSAFNet: A Multi-Path Feature Fusion and Spectral–Temporal Attention-Based Model for Bird Audio Classification.

Authors: Lu, Zhengyang Li, Huan Liu, Min et al.

Source: Animals (2076-2615); Aug2025, Vol. 15 Issue 15, p2228, 40p

Subject Terms: BIODIVERSITY monitoring, BIODIVERSITY conservation, DEEP learning, TIME-frequency analysis, BIRD vocalizations, ACOUSTIC signal detection

Stain Normalization of Histopathological Images Based on Deep Learning: A Review.

Authors: Xu, Chuanyun Sun, Yisha Zhang, Yang et al.

Source: Diagnostics (2075-4418); Apr2025, Vol. 15 Issue 8, p1032, 34p

Subject Terms: DEEP learning, IMAGE analysis, DATA distribution, CANCER diagnosis, RESEARCH personnel

A Novel Hybrid Machine Learning-Based System Using Deep Learning Techniques and Meta-Heuristic Algorithms for Various Medical Datatypes Classification.

Authors: Kadhim, Yezi Ali Guzel, Mehmet Serdar Mishra, Alok

Source: Diagnostics (2075-4418); Jul2024, Vol. 14 Issue 14, p1469, 25p

Subject Terms: COMPUTER-aided diagnosis, METAHEURISTIC algorithms, CONVOLUTIONAL neural networks, DEEP learning, COMPUTER science