Suchergebnisse - Cascade stacked autoencoder neural network

Cascade stacked autoencoder neural network for intrusion detection in CAN-FD vehicular network

Autoren: V. Anjana Devi P.V. Bhaskar Reddy Sreenu Ponnada et al.

Quelle: Knowledge-Based Systems. 329:114301

Stacked encoded cascade error feedback deep extreme learning machine network for manufacturing order completion time.

Autoren: Khan, Waqar Ahmed Masoud, Mahmoud Eltoukhy, Abdelrahman E. E. et al.

Quelle: Journal of Intelligent Manufacturing. Feb2025, Vol. 36 Issue 2, p1313-1339. 27p.

Schlagwörter: *PRODUCTION planning, EXTREME learning machines, AUTOENCODERS, ERROR functions, NETWORK performance

A hierarchical integration deep flexible neural forest framework for cancer subtype classification by integrating multi-omics data

Autoren: Jing Xu Peng Wu Yuehui Chen et al.

Quelle: BMC Bioinformatics
BMC Bioinformatics, Vol 20, Iss 1, Pp 1-11 (2019)

Schlagwörter: Artificial neural network, FOS: Computer and information sciences, 0301 basic medicine, Cancer Research, Artificial intelligence, QH301-705.5, Bioinformatics, Computer applications to medicine. Medical informatics, 0206 medical engineering, R858-859.7, Gene Expression, Omics, 02 engineering and technology, Gene, Biochemistry, Genomic Data Integration, Computational biology, 03 medical and health sciences, Neoplasms, Biochemistry, Genetics and Molecular Biology, Microarray Data Analysis and Gene Expression Profiling, Machine learning, Genetics, Humans, Biology (General), Molecular Biology, Data mining, Biology, Cancer, Genome, Cancer subtype classification, Methodology Article, High-Throughput Nucleotide Sequencing, Life Sciences, Deep learning, Autoencoder, Genomics, DNA Methylation, Computer science, Analysis of Gene Interaction Networks, Genomic Landscape of Cancer and Mutational Signatures, 3. Good health, MicroRNAs, Biological Network Integration, FOS: Biological sciences, Data integration, Cascade forest

Zugangs-URL: https://bmcbioinformatics.biomedcentral.com/track/pdf/10.1186/s12859-019-3116-7
https://pubmed.ncbi.nlm.nih.gov/31660856
https://doaj.org/article/339d1cf8431845b79254277493a322b4
https://link.springer.com/article/10.1186%2Fs12859-019-3116-7
https://dblp.uni-trier.de/db/journals/bmcbi/bmcbi20.html#XuWCMDD19
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6819613
https://link.springer.com/content/pdf/10.1186/s12859-019-3116-7.pdf
https://europepmc.org/article/MED/31660856
https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-019-3116-7

Target Detection in Clutter/Interference Regions Based on Deep Feature Fusion for HFSWR

Autoren: Maokai Wu Ling Zhang Jiong Niu et al.

Quelle: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 14, Pp 5581-5595 (2021)

Schlagwörter: Convolutional neural network (CNN), extremum detection, feature fusion, high-frequency surface wave radar (HFSWR), stacked autoencoder (SAE), target detection, Ocean engineering, TC1501-1800, Geophysics. Cosmic physics, QC801-809

Dateibeschreibung: electronic resource

Relation: https://ieeexplore.ieee.org/document/9436001/; https://doaj.org/toc/2151-1535

Zugangs-URL: https://doaj.org/article/fdddac9749c14e7eaa07c932fa7e035b

JSRNN: Joint Sampling and Reconstruction Neural Networks for High Quality Image Compressed Sensing

Autoren: Zeng, Chunyan Ye, Jiaxiang Wang, Zhifeng et al.

Index Begriffe: Computer Science - Computer Vision and Pattern Recognition, Electrical Engineering and Systems Science - Image and Video Processing, text

URL: http://arxiv.org/abs/2211.05963

Data-Driven Method for Real-Time Reconstruction of the Structural Displacement Field.

Autoren: Yan, Jun Du, Hongze Bu, Yufeng et al.

Quelle: Journal of Aerospace Engineering; May2024, Vol. 37 Issue 3, p1-11, 11p

Schlagwörter: CONVOLUTIONAL neural networks, DEEP learning, KALMAN filtering

CSO-CNN: circulatory system optimization-based cascade region CNN for fault estimation and driver behavior detection.

Autoren: Priyadharshini, G. Ukrit, M. Ferni

Quelle: Signal, Image & Video Processing; Sep2023, Vol. 17 Issue 6, p3063-3071, 9p

CDAE: A Cascade of Denoising Autoencoders for Noise Reduction in the Clustering of Single-Particle Cryo-EM Images

Autoren: Houchao Lei Yang Yang

Quelle: Frontiers in Genetics, Vol 11 (2021)

Schlagwörter: cryo-EM, autoencoder, image denoising, clustering, deep learning, Genetics, QH426-470

Dateibeschreibung: electronic resource

Relation: https://www.frontiersin.org/articles/10.3389/fgene.2020.627746/full; https://doaj.org/toc/1664-8021

Zugangs-URL: https://doaj.org/article/b6c4add6a8b2425a989cefc397914165

Australia's long-term electricity demand forecasting using deep neural networks

Autoren: Hamedmoghadam, Homayoun Joorabloo, Nima Jalili, Mahdi

Index Begriffe: Computer Science - Neural and Evolutionary Computing, text

URL: http://arxiv.org/abs/1801.02148

Sparse stacked autoencoder network for complex system monitoring with industrial applications

Autoren: Keke Huang Ziwei Deng Zhaohui Tang et al.

Quelle: Chaos, Solitons & Fractals. 137:109838

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

Zugangs-URL: http://ui.adsabs.harvard.edu/abs/2020CSF...13709838D/abstract
https://ideas.repec.org/a/eee/chsofr/v137y2020ics0960077920302381.html
https://ui.adsabs.harvard.edu/abs/2020CSF...137j9838D/abstract
https://www.sciencedirect.com/science/article/pii/S0960077920302381

LPI-SKMSC: Predicting LncRNA-Protein Interactions with Segmented k-mer Frequencies and Multi-space Clustering.

Autoren: Sun DZ Sun ZL Liu M et al.

Quelle: Interdisciplinary sciences, computational life sciences [Interdiscip Sci] 2024 Jun; Vol. 16 (2), pp. 378-391. Date of Electronic Publication: 2024 Jan 11.

Publikationsart: Journal Article

Info zur Zeitschrift: Publisher: Springer-Verlag Country of Publication: Germany NLM ID: 101515919 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1867-1462 (Electronic) Linking ISSN: 18671462 NLM ISO Abbreviation: Interdiscip Sci Subsets: MEDLINE

MeSH-Schlagworte: RNA, Long Noncoding*/genetics , RNA, Long Noncoding*/metabolism , Neural Networks, Computer*, Cluster Analysis ; Computational Biology/methods ; Algorithms ; Proteins/metabolism ; Proteins/genetics ; Humans

Cascade neural network-based joint sampling and reconstruction for image compressed sensing

Autoren: Chunyan Zeng Jiaxiang Ye Zhifeng Wang et al.

Quelle: Signal, Image and Video Processing. 16:47-54

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

Zugangs-URL: https://link.springer.com/article/10.1007/s11760-021-01955-w

Multi-label classification using a cascade of stacked autoencoder and extreme learning machines

Autoren: Anwesha Law Ashish Ghosh

Quelle: Neurocomputing. 358:222-234

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

Zugangs-URL: https://www.sciencedirect.com/science/article/pii/S092523121930757X
https://dblp.uni-trier.de/db/journals/ijon/ijon358.html#LawG19

Parallel Multistage Wide Neural Network

Autoren: Jiangbo Xi Okan K. Ersoy Jianwu Fang et al.

Quelle: Department of Electrical and Computer Engineering Technical Reports

Schlagwörter: incremental learning, Ensemble learning, parallel testing, 0202 electrical engineering, electronic engineering, information engineering, Parallel testing, 02 engineering and technology, Multistage wide learning, multistage wide learning, Incremental learning

Dateibeschreibung: application/pdf

Zugangs-URL: https://docs.lib.purdue.edu/cgi/viewcontent.cgi?article=1761&context=ecetr
https://pubmed.ncbi.nlm.nih.gov/34699372
https://pubmed.ncbi.nlm.nih.gov/34699372/
https://docs.lib.purdue.edu/ecetr/757/
https://ieeexplore.ieee.org/document/9586062/media
https://docs.lib.purdue.edu/ecetr/757
https://docs.lib.purdue.edu/cgi/viewcontent.cgi?article=1761&context=ecetr

DeepStack-DTIs: Predicting Drug-Target Interactions Using LightGBM Feature Selection and Deep-Stacked Ensemble Classifier.

Autoren: Zhang Y Jiang Z Chen C et al.

Quelle: Interdisciplinary sciences, computational life sciences [Interdiscip Sci] 2022 Jun; Vol. 14 (2), pp. 311-330. Date of Electronic Publication: 2021 Nov 03.

Publikationsart: Journal Article

Info zur Zeitschrift: Publisher: Springer-Verlag Country of Publication: Germany NLM ID: 101515919 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1867-1462 (Electronic) Linking ISSN: 18671462 NLM ISO Abbreviation: Interdiscip Sci Subsets: MEDLINE

MeSH-Schlagworte: Algorithms* , Proteins*/chemistry, Amino Acid Sequence ; Neural Networks, Computer ; Support Vector Machine

Energy Efficiency Modeling for Configuration-Dependent Machining via Machine Learning: A Comparative Study.

Autoren: Xiao, Qinge Li, Congbo Tang, Ying et al.

Quelle: IEEE Transactions on Automation Science & Engineering; Apr2021, Vol. 18 Issue 2, p717-730, 14p

Schlagwörter: ENERGY consumption, MACHINE learning, ARTIFICIAL neural networks, DEEP learning, SPINDLES (Machine tools), CONVOLUTIONAL neural networks, KRIGING

Gravitational wave isolation with autoencoder neural network cascade.

Autoren: Sengupta, Mayank

Quelle: Neural Computing & Applications; May2024, Vol. 36 Issue 13, p6993-7006, 14p

Schlagwörter: GRAVITATIONAL waves, GRAVITATIONAL wave detectors, CASCADE connections, GRAVITATIONAL wave astronomy, GRAVITATIONAL fields, BINARY black holes, GRAPHICS processing units

Segmentation of MR images for brain tumor detection using autoencoder neural network.

Autoren: Hoseini, Farnaz Shamlou, Shohreh Ahmadi-Gharehtoragh, Milad

Quelle: Discover Artificial Intelligence; 10/26/2024, Vol. 4 Issue 1, p1-20, 20p

Schlagwörter: MAGNETIC resonance imaging, TUMOR classification, IMAGE segmentation, BRAIN imaging, BRAIN tumors

Detecting subtle transcriptomic perturbations induced by lncRNAs knock-down in single-cell CRISPRi screening using a new sparse supervised autoencoder neural network.

Autoren: Truchi, Marin Lacoux, Caroline Gille, Cyprien et al.

Quelle: Frontiers in Bioinformatics; 2024, p1-17, 17p

Inverse mechano-electrical reconstruction of cardiac excitation wave patterns from mechanical deformation using deep learning.

Autoren: Christoph, Jan Lebert, Jan

Quelle: Chaos; Dec2020, Vol. 30 Issue 12, p1-21, 21p

Schlagwörter: DEFORMATIONS (Mechanics), DEEP learning, SIGNAL convolution, MECHANICAL hearts, MYOCARDIUM, CONVOLUTIONAL neural networks