Search Results - stacked sparse autoencoder((sage OR ssad))

An Innovative Study for Tool Wear Prediction Based on Stacked Sparse Autoencoder and Ensemble Learning Strategy.

Authors: He, Zhaopeng Shi, Tielin Chen, Xu

Source: Sensors (14248220); Apr2025, Vol. 25 Issue 8, p2391, 14p

Subject Terms: ARTIFICIAL neural networks, MACHINE learning, ENSEMBLE learning, NUMERICAL control of machine tools, AUTOENCODERS, DEEP learning

Stacked sparse autoencoder in cavitation noise signal data classification of hydro turbine based on power spectrum

Authors: Ziyang Kang Chi Feng Xiaoqing Wan et al.

Source: Journal of Low Frequency Noise, Vibration and Active Control, Vol 39 (2020)

Subject Terms: Control engineering systems. Automatic machinery (General), TJ212-225, Acoustics. Sound, QC221-246, 0211 other engineering and technologies, 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology

Access URL: https://journals.sagepub.com/doi/pdf/10.1177/1461348419830815
https://doaj.org/article/f03aa17f19474a70bea79c414ca9c293
https://journals.sagepub.com/doi/full/10.1177/1461348419830815
https://journals.sagepub.com/doi/pdf/10.1177/1461348419830815

Image fusion based on shift invariant shearlet transform and stacked sparse autoencoder

Authors: Peng-Fei Wang Xiao-Qing Luo Xin-Yi Li et al.

Source: Journal of Algorithms & Computational Technology, Vol 12 (2018)

Subject Terms: T57-57.97, Applied mathematics. Quantitative methods, QA1-939, 0211 other engineering and technologies, 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology, Mathematics

Access URL: https://journals.sagepub.com/doi/pdf/10.1177/1748301817741001
https://doaj.org/article/825d7b3b76554f269536a51d36acc093
http://journals.sagepub.com/doi/pdf/10.1177/1748301817741001
http://journals.sagepub.com/doi/abs/10.1177/1748301817741001
https://journals.sagepub.com/doi/pdf/10.1177/1748301817741001
https://journals.sagepub.com/doi/abs/10.1177/1748301817741001

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.

Unifying attentive sparse autoencoder with neural collaborative filtering for recommendation

Authors: Yihao Zhang Chu Zhao Meng Yuan et al.

Source: Intelligent Data Analysis. 26:841-857

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

Hybrid graph-based models in medical decision support.

Authors: Bouslimi, Riadh Zitouni, Fatma Ben Smida, Takwa et al.

Source: Network Modeling & Analysis in Health Informatics & Bioinformatics; 12/4/2025, Vol. 14 Issue 1, p1-21, 21p

基于深度堆栈网络的心电信号识别. (Chinese)

Alternate Title: ECG Signal Recognition Based on Deep Stacked Network. (English)

Authors: 张锐 王茹 黄俊 et al.

Source: Journal of Harbin University of Science & Technology; Jun2021, Vol. 26 Issue 3, p108-114, 7p

Subject Terms: PROBLEM solving, ELECTROCARDIOGRAPHY, ALGORITHMS, DATABASES

Graph Regularized Autoencoder and its Application in Unsupervised Anomaly Detection.

Authors: Ahmed, Imtiaz Galoppo, Travis Hu, Xia et al.

Source: IEEE Transactions on Pattern Analysis & Machine Intelligence; Aug2022, Vol. 44 Issue 8, p4110-4124, 15p

Subject Terms: ANOMALY detection (Computer security), GENERATIVE adversarial networks, EUCLIDEAN metric, GEODESIC distance, EUCLIDEAN distance, SPANNING trees, GEODESICS

Cognitive workload estimation using physiological measures: a review.

Authors: Das Chakladar, Debashis Roy, Partha Pratim

Source: Cognitive Neurodynamics; Aug2024, Vol. 18 Issue 4, p1445-1465, 21p

Identifying optimal channels and features for multi-participant motor imagery experiments across a participant's multi-day multi-class EEG data.

Authors: Kaya, Esra Saritas, Ismail

Source: Cognitive Neurodynamics; Jun2024, Vol. 18 Issue 3, p987-1003, 17p

Research on engine cooling system condition monitoring based on deep digital twin

Authors: Jian Wang Ying Huang Liguo Wei et al.

Source: Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering.

Construction of a deep sparse filtering network for rotating machinery fault diagnosis.

Authors: Cheng, Chun Zou, Wei Wang, Weiping et al.

Source: Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering (Sage Publications, Ltd.); Jan2022, Vol. 236 Issue 1, p118-126, 9p

Subject Terms: FAULT diagnosis, ROTATING machinery, DEEP learning, ALGORITHMS, DIAGNOSIS

Non-endoscopic Applications of Machine Learning in Gastric Cancer: A Systematic Review.

Authors: Sy-Janairo, Marianne Linley L. Janairo, Jose Isagani B.

Source: Journal of Gastrointestinal Cancer; Mar2024, Vol. 55 Issue 1, p47-64, 18p

Unsupervised feature selection and class labeling for credit card fraud.

Authors: Kennedy, Robert K. L. Villanustre, Flavio Khoshgoftaar, Taghi M.

Source: Journal of Big Data; 5/5/2025, Vol. 12 Issue 1, p1-20, 20p

Subject Terms: MACHINE learning, CREDIT card fraud, FRAUD investigation, ARTIFICIAL intelligence, AUTOENCODERS, FEATURE selection

A sequential cross-product knowledge accumulation, extraction and transfer framework for machine learning-based production process modelling.

Authors: Xie, Jiarui Zhang, Chonghui Sage, Manuel et al.

Source: International Journal of Production Research; Jun2024, Vol. 62 Issue 12, p4181-4201, 21p

Subject Terms: MACHINE tools, MANUFACTURING processes, AUXETIC materials, FEATURE selection, GAS turbines

Graph neural network-based drug-drug interaction prediction.

Authors: Abbas, Khushnood Hao, Chen Yong, Xu et al.

Source: Scientific Reports; 8/19/2025, Vol. 15 Issue 1, p1-18, 18p

Subject Terms: GRAPH neural networks, DRUG interactions, POLYPHARMACY, TOXICITY testing, TREATMENT effectiveness, PHARMACOLOGY, MACHINE learning

A mechanical fault diagnosis model with semi-supervised variational autoencoder based on long short-term memory network.

Authors: Qu, Yuanyuan Li, Tao Fu, Shichen et al.

Source: Nonlinear Dynamics; Jan2025, Vol. 113 Issue 1, p459-478, 20p

Visible neural networks for multi-omics integration: a critical review.

Authors: Selby, David Antony Jakhmola, Rashika Sprang, Maximilian et al.

Source: Frontiers in Artificial Intelligence; 2025, p1-17, 17p

Golden eagle optimized CONV-LSTM and non-negativity-constrained autoencoder to support spatial and temporal features in cancer drug response prediction.

Authors: Hajim, Wesam Ibrahim Zainudin, Suhaila Daud, Kauthar Mohd et al.

Source: PeerJ Computer Science; Dec2024, p1-36, 36p

Subject Terms: CONVOLUTIONAL neural networks, LONG short-term memory, DEEP learning, GOLDEN eagle, AUTOENCODERS

Heterogeneous Graph Auto-Encoder for Credit Card Fraud Detection.

Authors: Majumder, Sudipta Singh, Moirangthem Tiken Prasad, Rabinder Kumar et al.

Source: International Journal for Computers & Their Applications; Jun2025, Vol. 32 Issue 2, p124-138, 15p

Subject Terms: CREDIT card fraud, GRAPH neural networks, FRAUD investigation, MACHINE learning, ANOMALY detection (Computer security)