Suchergebnisse - "deep autoencoder"

Multi-path long-term vessel trajectories forecasting with probabilistic feature fusion for problem shifting

Autoren: Spadon, Gabriel Kumar, Jay Eden, Derek et al.

Quelle: Ocean Engineering. 312(Part 1)

Schlagwörter: Feature fusion Probabilistic modeling Deep autoencoder Spatiotemporal forecasting Phased framework approach Trajectory reconstruction, Computer Science, Datavetenskap

Dateibeschreibung: electronic

Zugangs-URL: https://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-132248
https://doi.org/10.1016/j.oceaneng.2024.119138

Optimisation of sparse deep autoencoders for dynamic network embedding

Autoren: Huimei Tang Yutao Zhang Lijia Ma et al.

Quelle: CAAI Transactions on Intelligence Technology, Vol 9, Iss 6, Pp 1361-1376 (2024)

Schlagwörter: QA76.75-76.765, network embedding, Computational linguistics. Natural language processing, 0202 electrical engineering, electronic engineering, information engineering, dynamic networks, low‐dimensional feature space, deep autoencoder, Computer software, 02 engineering and technology, P98-98.5, sparse structure

Zugangs-URL: https://doaj.org/article/f943469b92564a6dbb34831ccf9d8f76

A Two-Stage Neural Network for Speech Signal Reconstruction from Mel Spectrograms

Autoren: Villani, Filippo Scarpiniti, Michele Uncini, Aurelio

Quelle: Smart Innovation, Systems and Technologies ISBN: 9789819609932
Villani, F, Scarpiniti, M & Uncini, A 2025, A Two-Stage Neural Network for Speech Signal Reconstruction from Mel Spectrograms. in Smart Innovation, Systems and Technologies. https://doi.org/10.1007/978-981-96-0994-9_25

Schlagwörter: Mel spectrogram inversion, phase reconstruction, speech enhancement, time-frequency representation, deep autoencoder

Dateibeschreibung: application/pdf

Zugangs-URL: https://hdl.handle.net/11573/1740169
https://doi.org/10.1007/978-981-96-0994-9_25

Detecting network intrusions in cyber-physical systems using deep autoencoder-based dimensionality reduction approach anddeep neural networks

Autoren: A. E. Ibor D. O. Egete A. O. Otiko et al.

Quelle: Journal of Nigerian Society of Physical Sciences, Vol 7, Iss 3 (2025)

Schlagwörter: Adversarial attacks, Deep autoencoder, Deep learning, Intrusion detection, Cyber-physical systems, Physics, QC1-999

Dateibeschreibung: electronic resource

Relation: https://journal.nsps.org.ng/index.php/jnsps/article/view/2689; https://doaj.org/toc/2714-2817; https://doaj.org/toc/2714-4704

Zugangs-URL: https://doaj.org/article/776211c46eca45988f2d0f196bd4892f

PMiner: Process Mining using Deep Autoencoder for Anomaly Detection and Reconstruction of Business Processes

Autoren: Chinnaiah, Veluru Veerabhadram, Vadlamani Aavula, Ravi et al.

Quelle: International journal of electrical and computer engineering systems
Volume 15
Issue 6

Schlagwörter: Process Mining, Deep Learning, Artificial Intelligence, Deep Autoencoder, Long Short Term Memory

Dateibeschreibung: application/pdf

Zugangs-URL: https://hrcak.srce.hr/317691
https://doi.org/10.32985/ijeces.15.6.7
https://hrcak.srce.hr/317691
https://doi.org/10.32985/ijeces.15.6.7
https://hrcak.srce.hr/file/459047

A Deep Semi-Supervised Community Detection Based on Point-Wise Mutual Information

Autoren: Kamal Berahmand Yuefeng Li Yue Xu

Quelle: IEEE Transactions on Computational Social Systems. 11:3444-3456

Schlagwörter: deep autoencoder (AE), semi-supervised clustering, Complex networks, deep clustering, pair-wise constraints

Konuşma İşaretlerinin Derin Evrişimsel Oto Kodlayıcı ve Artık Vektör Nicemleme Tabanlı Sıkıştırılması

Autoren: Tahir Bekiryazıcı Gürkan Aydemir Hakan Gürkan

Quelle: Volume: 6, Issue: 1113-124
Mühendislik Bilimleri ve Araştırmaları Dergisi

Schlagwörter: Deep Learning, Coding, Information Theory and Compression, Derin Öğrenme, Speech Compression, Causal Convolutional Neural Network, Residual Vector Quantization, Deep Autoencoder, Konuşma Sıkıştırma, Nedensel Evrişimsel Sinir Ağları, Artık Vektör Nicemlemesi, Derin Oto kodlayıcı, Kodlama, Bilgi Teorisi ve Sıkıştırma

Dateibeschreibung: application/pdf

Zugangs-URL: https://dergipark.org.tr/tr/pub/bjesr/issue/84275/1452937

Hybrid Deep Learning Framework for Anomaly Detection in Power Plant Systems

Autoren: Shuchong Wang Changxiang Zhao Xingchen Liu et al.

Quelle: Algorithms, Vol 18, Iss 11, p 704 (2025)

Schlagwörter: anomaly detection, Transformer, deep autoencoder, Gaussian mixture model, coal-fired power plant, Industrial engineering. Management engineering, T55.4-60.8, Electronic computers. Computer science, QA75.5-76.95

Dateibeschreibung: electronic resource

Relation: https://www.mdpi.com/1999-4893/18/11/704; https://doaj.org/toc/1999-4893

Zugangs-URL: https://doaj.org/article/853462f86faa49d99115503a874cca2c

A Deep Autoencoder Compression-Based Genomic Prediction Method for Whole-Genome Sequencing Data

Autoren: Hailiang Song Tian Dong Wei Wang et al.

Quelle: Biology, Vol 14, Iss 11, p 1622 (2025)

Schlagwörter: deep autoencoder, data compression, genomic prediction, whole-genome sequencing, Biology (General), QH301-705.5

Dateibeschreibung: electronic resource

Relation: https://www.mdpi.com/2079-7737/14/11/1622; https://doaj.org/toc/2079-7737

Zugangs-URL: https://doaj.org/article/818c4fa942464e45b6c4e779852b77ea

CResDAE: A Deep Autoencoder with Attention Mechanism for Hyperspectral Unmixing

Autoren: Chong Zhao Jinlin Wang Qingqing Qiao et al.

Quelle: Remote Sensing, Vol 17, Iss 21, p 3622 (2025)

Schlagwörter: deep autoencoder, attention mechanism, hyperspectral unmixing, abundance estimation, Science

Dateibeschreibung: electronic resource

Relation: https://www.mdpi.com/2072-4292/17/21/3622; https://doaj.org/toc/2072-4292

Zugangs-URL: https://doaj.org/article/705411ff57964d4bbd561a364b23c412

Deep autoencoders and multivariate analysis for enhanced hypertension detection during the COVID-19 era

Autoren: Khongorzul Dashdondov Mi-Hye Kim Mi-Hwa Song

Quelle: Electronic Research Archive, Vol 32, Iss 5, Pp 3202-3229 (2024)

Schlagwörter: T57-57.97, 03 medical and health sciences, hypertension, Applied mathematics. Quantitative methods, 0302 clinical medicine, covid-19, machine-learning, QA1-939, deep autoencoder, outlier, knhanes, Mathematics, 3. Good health

Zugangs-URL: https://doaj.org/article/d527d479d3334c3a9210f595dac6a2f5

Unsupervised Deep Clustering With Hard Balanced Constraint: Application in Disciplinary-Focused Student Section Formation

Autoren: Siripinyo Chantamunee Pornpon Thamrongrat Putthiporn Thanathamathee et al.

Quelle: IEEE Access, Vol 12, Pp 98239-98253 (2024)

Schlagwörter: group formation, Hard balanced clustering, 4. Education, 0202 electrical engineering, electronic engineering, information engineering, deep clustering, deep autoencoder, Electrical engineering. Electronics. Nuclear engineering, 02 engineering and technology, balanced clustering, TK1-9971

Zugangs-URL: https://doaj.org/article/3eea4767062b4238af743c70c6e00f21

A Comparative Study of Sentiment-Aware Collaborative Filtering Algorithms for Arabic Recommendation Systems

Autoren: Sumaia Mohammed Al-Ghuribi Shahrul Azman Mohd Noah Mawal A. Mohammed et al.

Quelle: IEEE Access, Vol 12, Pp 174441-174454 (2024)

Schlagwörter: sentiment analysis, collaborative filtering, Arabic language, user reviews, Electrical engineering. Electronics. Nuclear engineering, deep autoencoder network, TK1-9971

Zugangs-URL: https://doaj.org/article/b6affff419e448dca46a96e202c36793

Learning autoencoder ensembles for detecting malware hidden communications in IoT ecosystems

Autoren: Nunziato Cassavia Luca Caviglione Massimo Guarascio et al.

Quelle: Journal of intelligent information systems (2023): 1–25. doi:10.1007/s10844-023-00819-8
info:cnr-pdr/source/autori:Nunziato Cassavia, Luca Caviglione, Massimo Guarascio, Angelica Liguori, Marco Zuppelli/titolo:Learning autoencoder ensembles for detecting malware hidden communications in IoT ecosystems/doi:10.1007%2Fs10844-023-00819-8/rivista:Journal of intelligent information systems/anno:2023/pagina_da:1/pagina_a:25/intervallo_pagine:1–25/volume

Schlagwörter: Covert Channel, Intelligent cyber attack detection system, 0202 electrical engineering, electronic engineering, information engineering, Ensemble Method, 02 engineering and technology, Deep autoencoder

Dateibeschreibung: application/pdf

Operationalization of the construct “Business model of a Bank”: clustering analyses with deep neural networks

Autoren: Herdt, Manfred Schulte-Mattler, Hermann

Schlagwörter: ddc:330, G21, C45, N24, L21, E58, C38, Business model of banks, Balance sheet characteristics, Cluster analysis, Deep learning, Autoencoders, Deep autoencoder-based clustering

Relation: https://hdl.handle.net/10419/330416

Verfügbarkeit: https://hdl.handle.net/10419/330416
https://doi.org/10.1057/s41261-025-00269-y

Inclusivity-in-global-research-questionnaire.

Autoren: Catalina Saini Cristian Salazar-Vilches Caroline C.V. Blanchard et al.

Schlagwörter: Cell Biology, Neuroscience, Physiology, Biotechnology, Mental Health, novel treatment approaches, extrapyramidal movement sub, extrapyramidal movement disorders, exploratory analysis showed, approach enables identification, subtle tissue anomalies, normative deep autoencoder, influential features contributing, features &# 8217, apparent diffusion coefficient, cerebellar white matter, cerebellar gray matter, learning , +features%22">xlink "> features, telangiectasia using deep, shap scores revealed, pallidum perfusion underestimated, diffusion reconstruction errors, %22">xlink ">, diffusion features, cerebellar diffusion, subcortical anomalies, reconstruction errors, normative self, perfusion features

Verfügbarkeit: https://doi.org/10.1371/journal.pone.0328828.s003
https://figshare.com/articles/journal_contribution/Inclusivity-in-global-research-questionnaire_/30012423

Appendix A1 and Appendix A2.

Autoren: Catalina Saini Cristian Salazar-Vilches Caroline C.V. Blanchard et al.

Schlagwörter: Cell Biology, Neuroscience, Physiology, Biotechnology, Mental Health, novel treatment approaches, extrapyramidal movement sub, extrapyramidal movement disorders, exploratory analysis showed, approach enables identification, subtle tissue anomalies, normative deep autoencoder, influential features contributing, features &# 8217, apparent diffusion coefficient, cerebellar white matter, cerebellar gray matter, learning , +features%22">xlink "> features, telangiectasia using deep, shap scores revealed, pallidum perfusion underestimated, diffusion reconstruction errors, %22">xlink ">, diffusion features, cerebellar diffusion, subcortical anomalies, reconstruction errors, normative self, perfusion features

Relation: https://figshare.com/articles/journal_contribution/Appendix_A1_and_Appendix_A2_/30012420

Verfügbarkeit: https://doi.org/10.1371/journal.pone.0328828.s002
https://figshare.com/articles/journal_contribution/Appendix_A1_and_Appendix_A2_/30012420

Deep Autoencoder Framework for Classifying Damage Mechanisms in Repaired CFRP

Autoren: Barile, Claudia Casavola, Caterina Mpoyi, Dany Katamba et al.

Weitere Verfasser: Barile, Claudia Casavola, Caterina Mpoyi, Dany Katamba et al.

Schlagwörter: Acoustic emission, adhesive bonding, CFRP composite, deep autoencoder, joggled lap shear joint, k-mean, structure lightweighting

Dateibeschreibung: ELETTRONICO

Relation: info:eu-repo/semantics/altIdentifier/wos/WOS:001418398900001; volume:15; issue:3; numberofpages:27; journal:APPLIED SCIENCES; https://hdl.handle.net/11589/286625

Verfügbarkeit: https://hdl.handle.net/11589/286625
https://doi.org/10.3390/app15031209

Enhanced Deep Autoencoder-Based Reinforcement Learning Model with Improved Flamingo Search Policy Selection for Attack Classification

Autoren: Dharani Kanta Roy Hemanta Kumar Kalita

Quelle: Journal of Cybersecurity and Privacy ; Volume 5 ; Issue 1 ; Pages: 3

Schlagwörter: attack classification, reinforcement learning, deep autoencoder, support vector machine, improved flamingo search algorithm, tent chaotic map

Dateibeschreibung: application/pdf

Relation: https://dx.doi.org/10.3390/jcp5010003

Verfügbarkeit: https://doi.org/10.3390/jcp5010003

Generation and discrimination of autism MRI images based on autoencoder

Autoren: Yuxin Shi Yongli Gong Yurong Guan et al.

Quelle: Front Psychiatry
Frontiers in Psychiatry, Vol 15 (2024)

Schlagwörter: Psychiatry, RC435-571, deep autoencoder, image generation, sMRI, ASD, image classification

Zugangs-URL: https://pubmed.ncbi.nlm.nih.gov/39473912
https://doaj.org/article/6a4abcbdeeba444392246e1fa4fab22b