Suchergebnisse - "stacked sparse denoising autoencoder (SSDAE)"

Digital Image Forgery Detection Using Cyclic Symmetry Convolutional Neural Network.

Autoren: S., Shashikala G. K., Ravikumar

Quelle: International Journal of Intelligent Engineering & Systems; 2025, Vol. 18 Issue 1, p368-379, 12p

Schlagwörter: CONVOLUTIONAL neural networks, FEATURE selection, DIGITAL images, FEATURE extraction, AUTOENCODERS

Incipient fault detection and process monitoring of thermal power plant pulverizing system based on deep representation learning.

Autoren: Zeng, Lei Zhang, Xin Yu, Ke et al.

Quelle: Transactions of the Institute of Measurement & Control. Nov2025, Vol. 47 Issue 15, p3111-3123. 13p.

Schlagwörter: *FAULT diagnosis, *DEEP learning, *STEAM power plants, *K-nearest neighbor classification, *PROCESS control systems, *FEATURE extraction

A novel optimization-driven deep learning framework for the detection of DDoS attacks.

Autoren: Batchu, Raj Kumar Bikku, Thulasi Thota, Srinivasarao et al.

Quelle: Scientific Reports; 11/14/2024, Vol. 12 Issue 1, p1-16, 16p

A deep neural network with hybrid spotted hyena optimizer and grasshopper optimization algorithm for copy move forgery detection.

Autoren: Gupta, Ruchi Singh, Pushpa Alam, Tanweer et al.

Quelle: Multimedia Tools & Applications; Jul2023, Vol. 82 Issue 16, p24547-24572, 26p

Schlagwörter: OPTIMIZATION algorithms, FORGERY, GRASSHOPPERS, EVIDENCE gaps

Applications of Markov Decision Process Model and Deep Learning in Quantitative Portfolio Management during the COVID-19 Pandemic.

Autoren: Yue, Han Liu, Jiapeng Zhang, Qin

Quelle: Systems; Oct2022, Vol. 10 Issue 5, pN.PAG-N.PAG, 20p

Schlagwörter: DEEP learning, REINFORCEMENT learning, MARKOV processes, DECISION making, COVID-19 pandemic, REWARD (Psychology)

A Novel Anti-Risk Method for Portfolio Trading Using Deep Reinforcement Learning.

Autoren: Yue, Han Liu, Jiapeng Tian, Dongmei et al.

Quelle: Electronics (2079-9292); May2022, Vol. 11 Issue 9, pN.PAG-N.PAG, 13p

Schlagwörter: REINFORCEMENT learning, DOW Jones industrial average, DEEP learning, RESISTANCE training, FEATURE extraction

Fault Diagnosis of Rolling Bearing Based on Multiscale Intrinsic Mode Function Permutation Entropy and a Stacked Sparse Denoising Autoencoder.

Autoren: Dai, Juying Tang, Jian Shao, Faming et al.

Quelle: Applied Sciences (2076-3417); Jul2019, Vol. 9 Issue 13, p2743, 28p

Schlagwörter: HILBERT-Huang transform, FAULT diagnosis, ENTROPY (Information theory), PERMUTATIONS, DEEP learning

Firma/Körperschaft: INTERNATIONAL Monetary Fund, SOCIETY of Automotive Engineers

Design of an iterative method for enhanced disease detection and feature selection using particle swarm optimization, genetic algorithms, and convolutional neural networks.

Autoren: Said, Archana Ganesh Jawale, Deepali

Quelle: Evolving Systems; Sep2025, Vol. 16 Issue 3, p1-16, 16p

Deep learning-based feature selection for detection of autism spectrum disorder.

Autoren: Nafisah, Ibrahim Mahmoud, Nermine Ewees, Ahmed A. et al.

Quelle: Frontiers in Artificial Intelligence; 2025, p1-15, 15p

Non-stationary signal analysis utilizing VMD and EMD algorithm for multiple fault classification for industrial applications.

Autoren: Pavithra, R. Ramachandran, Prakash

Quelle: Journal of Intelligent & Fuzzy Systems; Jul2025, Vol. 49 Issue 1, p291-309, 19p

Schlagwörter: VIBRATION (Mechanics), CONVOLUTIONAL neural networks, TRANSIENT analysis, INDUSTRIAL applications, HILBERT transform, HILBERT-Huang transform, FAULT diagnosis

SPR: Malicious traffic detection model for CTCS-3 in railways.

Autoren: Siyang Zhou Wenjiang Ji Xinhong Hei et al.

Quelle: High-speed Railway; Jun2025, Vol. 3 Issue 2, p105-115, 11p

Schlagwörter: EXTREME learning machines, TRAFFIC monitoring, AUTOENCODERS, INDUSTRIAL controls manufacturing, MACHINE learning

Deep learning-based feature selection for detection of autism spectrum disorder

Autoren: Ibrahim Nafisah Nermine Mahmoud Ahmed A. Ewees et al.

Quelle: Frontiers in Artificial Intelligence, Vol 8 (2025)

Schlagwörter: autism detection, deep learning, resting-state functional MRI (rs-fMRI), feature selection, Hiking Optimization Algorithm, dynamic-opposite learning, Electronic computers. Computer science, QA75.5-76.95

Dateibeschreibung: electronic resource

Relation: https://www.frontiersin.org/articles/10.3389/frai.2025.1594372/full; https://doaj.org/toc/2624-8212

Zugangs-URL: https://doaj.org/article/40a4e1c379f04e22b49c94fbfc125de3

Coupled Analysis Dictionary Learning to inductively learn inversion: Application to real-time reconstruction of Biomedical signals

Autoren: Gupta, Kavya Bhowmick, Brojeshwar Majumdar, Angshul

Index Begriffe: Computer Science - Computer Vision and Pattern Recognition, text

URL: http://arxiv.org/abs/1812.09878

Secure and Privacy-Preserving Framework for IoT-Enabled Smart Grid Environment.

Autoren: Kumar, Chandan Chittora, Prakash

Quelle: Arabian Journal for Science & Engineering (Springer Science & Business Media B.V. ); Mar2024, Vol. 49 Issue 3, p3063-3078, 16p

Schlagwörter: DEEP learning, ELECTRIC power transmission, INTELLIGENT networks, SMART meters, WIRELESS Internet, WIRELESS communications

Copy-Move Forgery Detection and Classification Using Improved Butterfly Optimization Algorithm-based Convolutional Neural Network.

Autoren: S., Shashikala G. K., Ravikumar

Quelle: International Journal of Intelligent Engineering & Systems; 2024, Vol. 17 Issue 1, p869-879, 11p

Schlagwörter: CONVOLUTIONAL neural networks, FORGERY, OPTIMIZATION algorithms, FEATURE extraction, FEATURE selection

Image Copy-move Forgery Detection and Classification Using Golden Jackal Optimization Based Multi-Support Vector Machine.

Autoren: Archana, M. R. Jamkhandikar, Dayanand Biradar, Deepak N.

Quelle: International Journal of Intelligent Engineering & Systems; 2023, Vol. 16 Issue 6, p123-133, 11p

Schlagwörter: FORGERY, SUPPORT vector machines, DIGITAL images, FEATURE selection, CLASSIFICATION

NJUST-CCTD: An Image Database for Milling Tool Wear Classification with Deep Learning.

Autoren: Pan, Yi Xu, Guoda Xiong, Zhe et al.

Quelle: International Journal of Advanced Manufacturing Technology; Aug2023, Vol. 127 Issue 7/8, p3681-3698, 18p, 2 Color Photographs, 2 Black and White Photographs, 4 Diagrams, 16 Charts

Schlagwörter: DEEP learning, IMAGE databases, CONVOLUTIONAL neural networks, CARBIDE cutting tools, IMAGE recognition (Computer vision), NETWORK performance

Deep convolution neural network for machine health monitoring using spectrograms of vibration signal and its EMD-intrinsic mode functions.

Autoren: Pavithra, R. Ramachandran, Prakash

Quelle: Journal of Intelligent & Fuzzy Systems; 2023, Vol. 44 Issue 6, p8827-8840, 14p

Schlagwörter: CONVOLUTIONAL neural networks, HILBERT-Huang transform, FEATURE selection, FOURIER transforms, VIBRATION (Mechanics), FEATURE extraction

Electricity theft detection in unbalanced sample distribution: a novel approach including a mechanism of sample augmentation.

Autoren: Yao, Ruizhe Wang, Ning Ke, Weipeng et al.

Quelle: Applied Intelligence; May2023, Vol. 53 Issue 9, p11162-11181, 20p

Schlagwörter: GENERATIVE adversarial networks, THEFT, ELECTRICITY, ELECTRIC power distribution

Firma/Körperschaft: STATE Grid Corp. of China

Photovoltaic Power-Stealing Identification Method Based on Similar-Day Clustering and QRLSTM Interval Prediction.

Autoren: Peng, Shurong Guo, Lijuan Li, Bin et al.

Quelle: Applied Sciences (2076-3417); Mar2023, Vol. 13 Issue 6, p3506, 14p

Schlagwörter: CONFIDENCE intervals, QUANTILE regression, REGRESSION analysis, FORECASTING, LOAD forecasting (Electric power systems), WEATHER

Geografische Kategorien: CHINA