Suchergebnisse - stack marginalized sparse denoising autoencoder (application OR applications)

Deep feature fusion‐based stacked denoising autoencoder for tag recommendation systems.

Autoren: Fei, Zhengshun Wang, Jinglong Liu, Kangling et al.

Quelle: IET Cyber-systems & Robotics; Sep2023, Vol. 5 Issue 3, p1-16, 16p

Gender Identification Using Marginalised Stacked Denoising Autoencoders on Twitter Data.

Autoren: Al-onazi, Badriyya B. Nour, Mohamed K. Alshamrani, Hassan et al.

Quelle: Intelligent Automation & Soft Computing; 2023, Vol. 36 Issue 3, p2529-2544, 16p

Schlagwörter: GENDER differences (Sociology)

Firma/Körperschaft: X Corp.

Proceedings of the International Conference on Advances and Applications in Artificial Intelligence (ICAAAI 2025)

Autoren: Suman Kumar Swarnkar Yogesh Kumar Rathore

Resource Type: eBook.

Categories: COMPUTERS / Artificial Intelligence / General

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

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

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

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

Machine Learning and Its Application to Reacting Flows : ML and Combustion

Autoren: Nedunchezhian Swaminathan Alessandro Parente

Resource Type: eBook.

Schlagworte: Turbulence--Data processing, Machine learning, Combustion--Data processing

Categories: TECHNOLOGY & ENGINEERING / Power Resources / Fossil Fuels, COMPUTERS / Artificial Intelligence / General, MATHEMATICS / Probability & Statistics / General, SCIENCE / Mechanics / Thermodynamics

RACMF: robust attention convolutional matrix factorization for rating prediction.

Autoren: Zeng, Biqing Shang, Qi Han, Xuli et al.

Quelle: Pattern Analysis & Applications; Nov2019, Vol. 22 Issue 4, p1655-1666, 12p

Schlagwörter: MATRIX decomposition, RECOMMENDER systems, RATINGS of hospitals, CONVOLUTIONAL neural networks, MACHINE learning

Online transferable representation with heterogeneous sources.

Autoren: Li, Yanchao Li, Hao

Quelle: Applied Intelligence; Jun2020, Vol. 50 Issue 6, p1674-1686, 13p

Schlagwörter: KNOWLEDGE transfer, ONLINE education, MODEL railroads, LABELS

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

Autoren: Zou, Jinlin Qu, Hongwei Zhang, Peng

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

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

Autoencoders and their applications in machine learning: a survey.

Autoren: Berahmand, Kamal Daneshfar, Fatemeh Salehi, Elaheh Sadat et al.

Quelle: Artificial Intelligence Review; Feb2024, Vol. 57 Issue 2, p1-52, 52p

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

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

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

Schlagwörter: 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.

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

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

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

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.

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

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

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

Representation learning via an integrated autoencoder for unsupervised domain adaptation.

Autoren: Zhu, Yi Wu, Xindong Qiang, Jipeng et al.

Quelle: Frontiers of Computer Science; Oct2023, Vol. 17 Issue 5, p1-13, 13p

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

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

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

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

Discriminative Autoencoder for Feature Extraction: Application to Character Recognition.

Autoren: Gogna, Anupriya Majumdar, Angshul

Quelle: Neural Processing Letters; Jun2019, Vol. 49 Issue 3, p1723-1735, 13p

Schlagwörter: PATTERN recognition systems, FEATURE extraction, IMAGE recognition (Computer vision), SUPERVISED learning, ARCHITECTURE

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

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

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

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

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

Autoren: Waikhom, Lilapati Patgiri, Ripon

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

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

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

Autoren: Jangid, Manisha Kumar, Rakesh

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

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

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

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

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

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

WIRELESS ECG MONITORING SYSTEM.

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

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

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