Zobrazeno 1 - 10 of 13 pro vyhledávání: '"Bardh Prenkaj"'
1
Hidden space deep sequential risk prediction on student trajectories
Autor: Damiano Distante, Bardh Prenkaj, Paola Velardi, Stefano Faralli
Publikováno v: Future Generation Computer Systems. 125:532-543
Online learning environments (OLEs) have seen a continuous increase over the past decade and a sudden surge in the last year, due to the coronavirus outbreak. The widespread use of OLEs has led to an increasing number of enrolments, even from student
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c9df4e7de7d891ddde8276ea3404ea5c
https://doi.org/10.1016/j.future.2021.07.002
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3
Latent and sequential prediction of the novel coronavirus epidemiological spread
Autor: Dario Aragona, Paola Velardi, Bardh Prenkaj, Luca Podo
Publikováno v: ACM SIGAPP Applied Computing Review. 21:5-18
In this paper we present C o R o NN a a deep sequential framework for epidemic prediction that leverages a flexible combination of sequential and convolutional components to analyse the transmission of COVID-19 and, perhaps, other undiscovered viruse
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::749551286e24c55d652b546e6f2ee9c6
https://doi.org/10.1145/3493499.3493500
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7
C o R o NN a
Autor: Luca Podo, Dario Aragona, Bardh Prenkaj, Paola Velardi
Publikováno v: SAC
We propose CoRoNNa, a deep framework for epidemic prediction to analyse the spread of COVID-19 and, potentially, of other unknown viruses, based on a flexible integration of sequential and convolutional components. Importantly, our framework is gener
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::ea36ac38ea2ab3608f41ee59908c61d5
https://doi.org/10.1145/3412841.3441883
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8
A Reproducibility Study of Deep and Surface Machine Learning Methods for Human-related Trajectory Prediction
Autor: Paola Velardi, Stefano Faralli, Damiano Distante, Bardh Prenkaj
Publikováno v: CIKM
In this paper, we compare several deep and surface state-of-the-art machine learning methods for risk prediction in problems that can be modelled as a trajectory of events separated by irregular time intervals. Trajectories are the abstract represent
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::80c3193fc27e4772c145e4e0846b3578
https://doi.org/10.1145/3340531.3412088
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9
Challenges and Solutions to the Student Dropout Prediction Problem in Online Courses
Autor: Bardh Prenkaj, Lorenzo Madeddu, Giovanni Stilo
Publikováno v: CIKM
Proceedings of the 29th ACM International Conference on Information & Knowledge Management
Online courses and e-degrees, although present since the mid-1990, have received enormous attention only in the last decade. Moreover, the new Coronavirus disease (COVID-19) outbreak forced many nations (e.g. Italy, the US, and other countries) to ma
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::44da87fcc294ef293c66082ddb4a452a
http://hdl.handle.net/11697/160379
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10
Unsupervised Boosting-based Autoencoder Ensembles for Outlier Detection
Autor: Hamed Sarvari, Carlotta Domeniconi, Bardh Prenkaj, Giovanni Stilo
Publikováno v: Advances in Knowledge Discovery and Data Mining ISBN: 9783030757618
PAKDD (1)
Autoencoders, as a dimensionality reduction technique, have been recently applied to outlier detection. However, neural networks are known to be vulnerable to overfitting, and therefore have limited potential in the unsupervised outlier detection set
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f03cb04007c511c13658c3ab983ae042
http://arxiv.org/abs/1910.09754
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