Výsledky vyhledávání - Deep learning AND distributed stacked denoising sparse autoencoder*

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

Autoři: Naila Marir Huiqiang Wang Guangsheng Feng

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

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

Přístupová URL adresa: 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.

Autoři: Lu, Conghui Chen, Peng Zhong, Hua a další

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

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

Deep Learning-Based Machinery Fault Diagnostics

Přispěvatelé: Chen, Hongtian Zhong, Kai Ran, Guangtao a další

Témata: 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

Popis souboru: application/octet-stream

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

Dostupnost: 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.

Autoři: Batchu, Raj Kumar Bikku, Thulasi Thota, Srinivasarao a další

Zdroj: 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.

Autoři: Kalu, Kingsley Ataguba, Grace Onifade, Oyepeju a další

Zdroj: 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.

Autoři: Zhang H Huang C Hu X a další

Zdroj: 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.

Způsob vydávání: Journal Article

Informace o časopise: 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

Výrazy ze slovníku MeSH: Deep Learning* , Water Resources*, China ; Conservation of Natural Resources ; Economic Development

Deep Learning: Theoretical and Practical Approach

Autoři: Farhadi, Sina Ranjbar Kooh

Témata: Machine Learning, Artificial Neural Network, Deep Learning, Artificial Intelligence, Data Science

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

Autoři: Liu, Dong Kong, Lijun Yao, Bing a další

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

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

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

Autoři: Parmar, Vivek Suri, Manan

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

Témata: 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.

Autoři: Johnsen, Timothy K. Bi, Xiangyu Chou, Chunwei a další

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

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

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

Autoři: Saad, Maryam El-Rabaie, El-Sayed Mahmoud Saber, Mohamed a další

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

Témata: 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.

Autoři: Islam, Monira Lee, Tan

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

Témata: 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.

Autoři: Abgeena, Abgeena Garg, Shruti

Zdroj: 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.

Autoři: Slama, Khadija Yahyaouy, Ali Riffi, Jamal a další

Zdroj: 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.

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

Zdroj: 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.

Autoři: Sun, Wenquan Gao, Yun Zhou, Jun a další

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

Témata: 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.

Autoři: Zhang, Yumin Wang, Yanyong

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

Témata: 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.

Autoři: Lu, Zhengyang Li, Huan Liu, Min a další

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

Témata: 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.

Autoři: Xu, Chuanyun Sun, Yisha Zhang, Yang a další

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

Témata: 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.

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

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

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