Suchergebnisse - stacked sparse autoencoder(sage)

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

Autoren: He, Zhaopeng Shi, Tielin Chen, Xu

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

Schlagwörter: 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

Autoren: Ziyang Kang Chi Feng Xiaoqing Wan et al.

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

Schlagwörter: 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

Zugangs-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

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

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

Schlagwörter: 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

Zugangs-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.

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

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

Unifying attentive sparse autoencoder with neural collaborative filtering for recommendation

Autoren: Yihao Zhang Chu Zhao Meng Yuan et al.

Quelle: Intelligent Data Analysis. 26:841-857

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

Hybrid graph-based models in medical decision support.

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

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

Graph Regularized Autoencoder and its Application in Unsupervised Anomaly Detection.

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

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

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

Cognitive workload estimation using physiological measures: a review.

Autoren: Das Chakladar, Debashis Roy, Partha Pratim

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

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

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

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

Schlagwörter: FAULT diagnosis, ROTATING machinery, DEEP learning, ALGORITHMS, DIAGNOSIS

Unsupervised feature selection and class labeling for credit card fraud.

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

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

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

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

Autoren: Jian Wang Ying Huang Liguo Wei et al.

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

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

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

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

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

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

Autoren: Kaya, Esra Saritas, Ismail

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

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

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

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

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

Boosting sparsity-induced autoencoder: A novel sparse feature ensemble learning for image classification.

Autoren: Rui Shi Jian Ji Chunhui Zhang et al.

Quelle: International Journal of Advanced Robotic Systems; May/Jun2019, Vol. 16 Issue 3, p1-9, 9p

Schlagwörter: IMAGE reconstruction algorithms, COMPUTER vision, BACK propagation, IMAGE processing, IMAGE denoising, CLASSIFICATION, DEEP learning

Graph neural network-based drug-drug interaction prediction.

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

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

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

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

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

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

A Survey on Variational Autoencoders in Recommender Systems.

Autoren: Liang, Shangsong Pan, Zhou liu, wei et al.

Quelle: ACM Computing Surveys; Oct2024, Vol. 56 Issue 10, p1-40, 40p

Schlagwörter: ARTIFICIAL neural networks, REINFORCEMENT learning, DEEP reinforcement learning, GRAPH neural networks, FEEDFORWARD neural networks, DEEP learning, LATENT variables

Heterogeneous Graph Auto-Encoder for Credit Card Fraud Detection.

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

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

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

A review of hyperspectral image classification based on graph neural networks.

Autoren: Zhao, Xiaofeng Ma, Junyi Wang, Lei et al.

Quelle: Artificial Intelligence Review; Jun2025, Vol. 58 Issue 6, p1-56, 56p