Search Results - stack marginalized sparse denoising autoencoder publications*

Conventional to Deep Learning Methods for Hyperspectral Unmixing: A Review.

Authors: Zou, Jinlin Qu, Hongwei Zhang, Peng

Source: Remote Sensing; Sep2025, Vol. 17 Issue 17, p2968, 44p

Subject Terms: DEEP learning, REMOTE sensing, ALGORITHMS, METADATA, IMAGE processing, IMAGE reconstruction algorithms

Cross-project defect prediction based on autoencoder with dynamic adversarial adaptation: Cross-project defect prediction based on autoencoder with dynamic adversarial adaptation: W. Zhang et al.

Authors: Zhang, Wen Zhao, Jiangpeng Qin, Guangjie et al.

Source: Applied Intelligence; Apr2025, Vol. 55 Issue 5, p1-19, 19p

Subject Terms: CONSTRUCTION defects (Buildings), AUTOENCODERS, PREDICTION models, FORECASTING, COMPUTER software

Preprocessing and Denoising Techniques for Electrocardiography and Magnetocardiography: A Review.

Authors: Jia, Yifan Pei, Hongyu Liang, Jiaqi et al.

Source: Bioengineering (Basel); Nov2024, Vol. 11 Issue 11, p1109, 38p

Subject Terms: SIGNAL denoising, MAGNETOCARDIOGRAPHY, SIGNAL processing, ELECTRIC lines, MACHINE learning, DEEP learning

Fabrication-Aware Synthetic Dataset Generation and Compact Geometric Encoding for Architectural Robotic Assembly of Brick Wall Designs.

Authors: Rafizadeh, Hamidreza Fialho Leandro Alves Teixeira, Muge Donovan, Jared et al.

Source: Buildings (2075-5309); Nov2025, Vol. 15 Issue 22, p4041, 28p

Subject Terms: MACHINE learning, BRICK walls, ARTIFICIAL intelligence, ENCODING, INDUSTRIAL robots, INDUSTRIALIZED building, STRUCTURAL stability

Deep learning approaches to address cold start and long tail challenges in recommendation systems: a systematic review.

Authors: Jangid, Manisha Kumar, Rakesh

Source: Multimedia Tools & Applications; Feb2025, Vol. 84 Issue 5, p2293-2325, 33p

Subject Terms: RECOMMENDER systems, DEEP learning, PROCESS capability, INFORMATION overload, INFORMATION storage & retrieval systems

Mitigation of Through-Wall Distortions of Frontal Radar Images Using Denoising Autoencoders.

Authors: Vishwakarma, Shelly Ram, Shobha Sundar

Source: IEEE Transactions on Geoscience & Remote Sensing; Sep2020, Vol. 58 Issue 9, p6650-6663, 14p

Subject Terms: IMAGE denoising, RADAR, ARRAY processing, BISTATIC radar, MIMO radar, DOPPLER radar, FINITE differences

Stack Autoencoder Transfer Learning Algorithm for Bearing Fault Diagnosis Based on Class Separation and Domain Fusion.

Authors: Sun, Meidi Wang, Hui Liu, Ping et al.

Source: IEEE Transactions on Industrial Electronics; Mar2022, Vol. 69 Issue 3, p3047-3058, 12p

Subject Terms: FAULT diagnosis, MACHINE learning, DATA distribution, SUPPLY & demand, CLASS differences

WIRELESS ECG MONITORING SYSTEM.

Authors: Bhilare, Aditi S. Kamble, Mansi G. Shah, P. P.

Source: International Scientific Journal of Engineering & Management; Apr2025, Vol. 4 Issue 4, p1-2, 2002p

Subject Terms: PATIENT monitoring, EARLY diagnosis, ACQUISITION of data, DATA transmission systems, DIAGNOSIS

PID Controller-Guided Attention Neural Network Learning for Fast and Effective Real Photographs Denoising.

Authors: Ma, Ruijun Zhang, Bob Zhou, Yicong et al.

Source: IEEE Transactions on Neural Networks & Learning Systems; Jul2022, Vol. 33 Issue 7, p3010-3023, 14p

Subject Terms: COMPUTER vision, PID controllers, IMAGE denoising, MACHINE learning, ARTIFICIAL neural networks, NOISE measurement, PHOTOGRAPHS, SEXTING

Foundations and Innovations in Data Fusion and Ensemble Learning for Effective Consensus.

Authors: Du, Ke-Lin Zhang, Rengong Jiang, Bingchun et al.

Source: Mathematics (2227-7390); Feb2025, Vol. 13 Issue 4, p587, 49p

Subject Terms: ENSEMBLE learning, MULTISENSOR data fusion, RANDOM forest algorithms, MACHINE learning, RESEARCH personnel, DEEP learning

Unlocking the black box: an in-depth review on interpretability, explainability, and reliability in deep learning.

Authors: ŞAHiN, Emrullah Arslan, Naciye Nur Özdemir, Durmuş

Source: Neural Computing & Applications; Jan2025, Vol. 37 Issue 2, p859-965, 107p

Subject Terms: RESEARCH personnel, INSTRUCTIONAL systems, COMPARATIVE studies, FAIRNESS, TAXONOMY, DEEP learning

Intelligent Fault Diagnosis Across Varying Working Conditions Using Triplex Transfer LSTM for Enhanced Generalization.

Authors: Iqbal, Misbah Lee, Carman K. M. Keung, Kin Lok et al.

Source: Mathematics (2227-7390); Dec2024, Vol. 12 Issue 23, p3698, 29p

Subject Terms: LONG short-term memory, MACHINE learning, HILBERT-Huang transform, FAULT diagnosis, INDUSTRIAL efficiency, BOTTLENECKS (Manufacturing), DEEP learning

A survey of autoencoder-based recommender systems.

Authors: Zhang, Guijuan Liu, Yang Jin, Xiaoning

Source: Frontiers of Computer Science; Apr2020, Vol. 14 Issue 2, p430-450, 21p

Human Activity Recognition (HAR) Using Deep Learning: Review, Methodologies, Progress and Future Research Directions.

Authors: Kumar, Pranjal Chauhan, Siddhartha Awasthi, Lalit Kumar

Source: Archives of Computational Methods in Engineering; Jan2024, Vol. 31 Issue 1, p179-219, 41p

Recommendation system based on deep learning methods: a systematic review and new directions.

Authors: Da'u, Aminu Salim, Naomie

Source: Artificial Intelligence Review; Apr2020, Vol. 53 Issue 4, p2709-2748, 40p

Subject Terms: RECOMMENDER systems, ARTIFICIAL neural networks, DEEP learning, META-analysis, NATURAL language processing, STANDARD deviations

Google Earth Engine and Artificial Intelligence (AI): A Comprehensive Review.

Authors: Yang, Liping Driscol, Joshua Sarigai, Sarigai et al.

Source: Remote Sensing; Jul2022, Vol. 14 Issue 14, pN.PAG-N.PAG, 110p

Subject Terms: ARTIFICIAL intelligence, CLIMATE change, GEOSPATIAL data, WEB-based user interfaces, WEB design, HAZARD Analysis & Critical Control Point (Food safety system)

A survey of graph neural networks in various learning paradigms: methods, applications, and challenges.

Authors: Waikhom, Lilapati Patgiri, Ripon

Source: Artificial Intelligence Review; Jul2023, Vol. 56 Issue 7, p6295-6364, 70p

Subject Terms: DEEP learning, NATURAL language processing, COMPUTER vision, ARCHITECTURAL design, MACHINE learning

Deep Learning for Natural Language Processing: A Survey.

Authors: Arkhangelskaya, E. O. Nikolenko, S. I.

Source: Journal of Mathematical Sciences; Jul2023, Vol. 273 Issue 4, p533-582, 50p

Subject Terms: DEEP learning, NATURAL language processing, MACHINE translating, MACHINE learning, SENTIMENT analysis

A review on deep learning for recommender systems: challenges and remedies.

Authors: Batmaz, Zeynep Yurekli, Ali Bilge, Alper et al.

Source: Artificial Intelligence Review; Jun2019, Vol. 52 Issue 1, p1-37, 37p

Subject Terms: RECOMMENDER systems, DEEP learning, INSTRUCTIONAL systems, INTERNET access, INFORMATION filtering, MACHINE learning

Deep learning based web service recommendation methods: A survey.

Authors: Mecheri, Karima Klai, Sihem Souici-Meslati, Labiba

Source: Journal of Intelligent & Fuzzy Systems; 2023, Vol. 44 Issue 6, p9879-9899, 21p

Subject Terms: DEEP learning, WEB services, RECOMMENDER systems, CLOUD computing, WEB-based user interfaces