Results 141 to 150 of about 13,513 (170)

Enhancing image compression through a novel Structural Fidelity Weighted Ensemble (SFWE) model. [PDF]

MethodsX
I PSM, Siddalingappa R, M V, S D, P MS, M K.   +5 more
europepmc   +1 more source

Autoencoder-Transformed Transcriptome Improves Genotype-Phenotype Association Studies. [PDF]

IEEE Trans Comput Biol Bioinform
Li Q, Bian J, Weeraman J, Zhang Z, Leung A, Ding QX, Chekouo T, Wu L, Yan J, Wu J, Long Q.   +10 more
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Detecting Perceived Unfair Treatment Among US College Students Using Mobile Sensing: Pilot Machine Learning Study. [PDF]

JMIR Form Res
Ren Y, Mulukutla R, Mankoff J, Dey AK.
europepmc   +1 more source

Restoration of multi-channel signal loss using autoencoder with recursive input strategy. [PDF]

Sci Rep
Lee J, Yu Y, Seo H.
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AI post-intervention operational and functional outcomes prediction in ischemic stroke patients using MRIs. [PDF]

BMC Med Imaging
Wittrup E, Reavey-Cantwell J, Pandey AS, Rivet Ii DJ, Najarian K.   +4 more
europepmc   +1 more source

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AE-DCNN: Autoencoder Enhanced Deep Convolutional Neural Network For Malware Classification

2021 International Conference on Intelligent Technologies (CONIT), 2021
Malware classification is a problem of great significance in the domain of information security. This is because the classification of malware into respective families helps in determining their intent, activity, and level of threat. In this paper, we propose a novel deep learning approach to malware classification. The proposed method converts malware
Shashank Kumar, Shivangi Meena, Savya Khosla, Anil Singh Parihar   +3 more
openaire   +1 more source

PM-AE: Pyramid Memory Autoencoder for Unsupervised Textured Surface Defect Detection

2020 5th International Conference on Mechanical, Control and Computer Engineering (ICMCCE), 2020
Anomaly detection for textured surface is a key task in product quality control. In recent years, supervised deep learning approaches have begun to be applied in this field, whereas most of the approaches are usually impracticable in collecting a large scale of defect samples. To this end, this paper proposes an efficient pyramid memory autoencoder.
Haiming Yao, Dan Li, Yiwen Zhu, Wenyong Yu   +3 more
openaire   +1 more source

AE-MCCF: An Autoencoder-Based Multi-criteria Recommendation Algorithm

Arabian Journal for Science and Engineering, 2019
Recommender systems enable users to deal with the information overload problem by serving personalized predictions. Traditional recommendation techniques produce referrals for users by considering their overall opinions over items. On the other hand, users may consider several criteria while evaluating an item.
Zeynep Batmaz, Cihan Kaleli
openaire   +1 more source

SF-AE: Split Federated Autoencoder for Unsupervised IoT Intrusion Detection

Smart systems have become increasingly popular in recent years, widening the attack surface of cyber threats. Machine learning algorithms have been successfully integrated into modern security mechanisms to detect such attacks. Internet of Things (IoT) systems often have limited computational resources and are unable to execute entire machine learning ...
Augello, Andrea, De Paola, Alessandra, Giosuè, Domenico, Lo Re, Giuseppe   +3 more
openaire   +1 more source