Suchergebnisse - sparse autoencoder - stacked contractive autoencoder*

A hybrid stacked sparse autoencoder and LightGBM framework for high-performance intrusion detection in IoT networks.

Autoren: Madhavan Nair Vasanthamma, Hari Vinayak Thankaswamy, Jarin

Quelle: Iran Journal of Computer Science; Dec2025, Vol. 8 Issue 4, p2175-2198, 24p

A COMPARATIVE STUDY OF DIVERSE AUTOENCODER MODELS IN LOCAL GEAR PITTING FAULT DIAGNOSIS.

Autoren: YURTSEVER, Mustafa ÜMÜTLÜ, Rafet Can ÖZTÜRK, Hasan

Quelle: Konya Journal of Engineering Sciences / Konya Mühendislik Bilimleri Dergisi; Mar2025, Vol. 13 Issue 1, p59-74, 16p

Schlagwörter: GEARBOXES, FAULT diagnosis, POWER transmission, DEEP learning, AUTOENCODERS

An Intelligent Intrusion Detection System Using Hybrid Deep Learning Approaches in Cloud Environment

Autoren: Sharon, Andrea Mohanraj, Prarthna Abraham, Tanya, Elizabeth et al.

Weitere Verfasser: Sharon, Andrea Mohanraj, Prarthna Abraham, Tanya, Elizabeth et al.

Quelle: IFIP Advances in Information and Communication Technology ; 6th International Conference on Computer, Communication, and Signal Processing (ICCCSP) ; https://inria.hal.science/hal-04388151 ; 6th International Conference on Computer, Communication, and Signal Processing (ICCCSP), Feb 2022, Chennai, India. pp.281-298, ⟨10.1007/978-3-031-11633-9_20⟩

Schlagwörter: Attack detection, Bi-directional LSTM with dropout and attention layer, Cloud computing, Distributed denial of service, Long short-term memory, One-hot encoding, Sparse autoencoder - stacked contractive autoencoder, [INFO]Computer Science [cs]

Geographisches Schlagwort: Chennai, India

Verfügbarkeit: https://inria.hal.science/hal-04388151
https://inria.hal.science/hal-04388151v1/document
https://inria.hal.science/hal-04388151v1/file/527508_1_En_20_Chapter.pdf
https://doi.org/10.1007/978-3-031-11633-9_20

Artificial neural network technique for improving prediction of credit card default: A stacked sparse autoencoder approach.

Autoren: Ebiaredoh-Mienye, Sarah. A. Esenogho, E. Swart, Theo G.

Quelle: International Journal of Electrical & Computer Engineering (2088-8708); Oct2021, Vol. 11 Issue 5, p4392-4402, 11p

Schlagwörter: ARTIFICIAL neural networks, DEFAULT (Finance), CREDIT cards, MACHINE learning, MACHINE performance

CoCoChain: A Concept-Aware Consensus Protocol for Secure Sensor Data Exchange in Vehicular Ad Hoc Networks.

Autoren: Juárez, Rubén Nicolas-Sans, Ruben Tamames, José Fernández

Quelle: Sensors (14248220); Oct2025, Vol. 25 Issue 19, p6226, 51p

Schlagwörter: VEHICULAR ad hoc networks, BLOCKCHAINS, MOBILE communication systems, DATA transmission systems, DATA integrity, AUTOENCODERS

Five-category classification of pathological brain images based on deep stacked sparse autoencoder.

Autoren: Jia, Wenjuan Muhammad, Khan Wang, Shui-Hua et al.

Quelle: Multimedia Tools & Applications; Feb2019, Vol. 78 Issue 4, p4045-4064, 20p

Schlagwörter: MAGNETIC resonance imaging, ENCODING, COMPUTED tomography, IMAGE processing, CONJUGATE gradient methods

Unifying attentive sparse autoencoder with neural collaborative filtering for recommendation.

Autoren: Zhang, Yihao Zhao, Chu Yuan, Meng et al.

Quelle: Intelligent Data Analysis; 2022, Vol. 26 Issue 4, p841-857, 17p

Schlagwörter: RECOMMENDER systems, MATRIX decomposition, DEEP learning, FACE

Representation learning via a semi-supervised stacked distance autoencoder for image classification.

Autoren: Hou, Liang Luo, Xiao-yi Wang, Zi-yang et al.

Quelle: Frontiers of Information Technology & Electronic Engineering; Jul2020, Vol. 21 Issue 7, p1005-1018, 14p

K-Means Clustering Optimizing Deep Stacked Sparse Autoencoder.

Autoren: Bi, Yandong Wang, Peng Guo, Xuchao et al.

Quelle: Sensing & Imaging; 2/10/2019, Vol. 20 Issue 1, p1-1, 1p

Decoding whole-genome mutational signatures in 37 human pan-cancers by denoising sparse autoencoder neural network.

Autoren: Pei G Hu R Dai Y et al.

Quelle: Oncogene [Oncogene] 2020 Jul; Vol. 39 (27), pp. 5031-5041. Date of Electronic Publication: 2020 Jun 11.

Publikationsart: Journal Article; Research Support, N.I.H., Extramural; Research Support, Non-U.S. Gov't

Info zur Zeitschrift: Publisher: Nature Publishing Group Country of Publication: England NLM ID: 8711562 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1476-5594 (Electronic) Linking ISSN: 09509232 NLM ISO Abbreviation: Oncogene Subsets: MEDLINE

MeSH-Schlagworte: Deep Learning*, Genome, Human/*genetics , Neoplasms/*genetics, DNA/genetics ; Humans ; INDEL Mutation/genetics ; Point Mutation/genetics

Deep Learning and Superpixel Feature Extraction Based on Contractive Autoencoder for Change Detection in SAR Images.

Autoren: Lv, Ning Chen, Chen Qiu, Tie et al.

Quelle: IEEE Transactions on Industrial Informatics; Dec2018, Vol. 14 Issue 12, p5530-5538, 9p

Machine health surveillance system by using deep learning sparse autoencoder.

Autoren: Ullah, Faizan Salam, Abdu Abrar, Muhammad et al.

Quelle: Soft Computing - A Fusion of Foundations, Methodologies & Applications; Aug2022, Vol. 26 Issue 16, p7737-7750, 14p

Schlagwörter: DEEP learning, MANUFACTURING processes, MACHINE translating, OBJECT recognition (Computer vision), SPEECH perception, FEATURE extraction

Automated Design Error Debugging of Digital VLSI Circuits.

Autoren: Moness, Mohammed Gaber, Lamya Hussein, Aziza I. et al.

Quelle: Journal of Electronic Testing; Aug2022, Vol. 38 Issue 4, p395-417, 23p

Learning Feature Representations with a Cost-Relevant Sparse Autoencoder.

Autoren: Längkvist, Martin Loutfi, Amy

Quelle: International Journal of Neural Systems; Feb2015, Vol. 25 Issue 1, p-1, 11p

Schlagwörter: MACHINE learning, FEATURE extraction, SIGNAL-to-noise ratio, SIGNAL reconstruction, INFORMATION theory

Graph Regularized Autoencoder and its Application in Unsupervised Anomaly Detection.

Autoren: Ahmed, Imtiaz Galoppo, Travis Hu, Xia et al.

Quelle: IEEE Transactions on Pattern Analysis & Machine Intelligence; Aug2022, Vol. 44 Issue 8, p4110-4124, 15p

Schlagwörter: ANOMALY detection (Computer security), GENERATIVE adversarial networks, EUCLIDEAN metric, GEODESIC distance, EUCLIDEAN distance, SPANNING trees, GEODESICS

Battle damage assessment based on an improved Kullback-Leibler divergence sparse autoencoder.

Autoren: Qi, Zong-feng Liu, Qiao-qiao Wang, Jun et al.

Quelle: Frontiers of Information Technology & Electronic Engineering; Dec2017, Vol. 18 Issue 12, p1991-2000, 10p

Opinion mining from machine translated Bangla reviews with stacked contractive auto-encoders.

Autoren: Bodini, Matteo

Quelle: Journal of Ambient Intelligence & Humanized Computing; Sep2023, Vol. 14 Issue 9, p12119-12131, 13p

Residual based temporal attention convolutional neural network for detection of distributed denial of service attacks in software defined network integrated vehicular adhoc network.

Autoren: Karthik, V. Lakshmi, R. Abraham, Salini et al.

Quelle: International Journal of Network Management; May2024, Vol. 34 Issue 3, p1-19, 19p

Schlagwörter: DENIAL of service attacks, CONVOLUTIONAL neural networks, SOFTWARE-defined networking, VEHICULAR ad hoc networks, SOFTWARE as a service

A Deep Learning Method Combined Sparse Autoencoder with SVM.

Autoren: Ju, Yao Guo, Jun Liu, Shuchun

Quelle: IEEE Std 1657a-2015 (Amendment to IEEE Std 1657-2009); 2015, p257-260, 4p

An Intelligent Fault Diagnosis Method of Multi-Scale Deep Feature Fusion Based on Information Entropy.

Autoren: Shang, Zhiwu Li, Wanxiang Gao, Maosheng et al.

Quelle: Chinese Journal of Mechanical Engineering; 6/9/2021, Vol. 34 Issue 1, p1-16, 16p