Zobrazeno 1 - 10
of 1 295
pro vyhledávání: '"H.2.8"'
Co-clustering simultaneously clusters rows and columns, revealing more fine-grained groups. However, existing co-clustering methods suffer from poor scalability and cannot handle large-scale data. This paper presents a novel and scalable co-clusterin
Externí odkaz:
http://arxiv.org/abs/2410.18113
Autor:
Thakur, Nirmalya
The work presented in this paper makes three scientific contributions with a specific focus on mining and analysis of COVID-19-related posts on Instagram. First, it presents a multilingual dataset of 500,153 Instagram posts about COVID-19 published b
Externí odkaz:
http://arxiv.org/abs/2410.03293
Autor:
Osorio-Marulanda, Pablo A., Ramirez, John Esteban Castro, Jiménez, Mikel Hernández, Reyes, Nicolas Moreno, Unanue, Gorka Epelde
Creation of synthetic data models has represented a significant advancement across diverse scientific fields, but this technology also brings important privacy considerations for users. This work focuses on enhancing a non-parametric copula-based syn
Externí odkaz:
http://arxiv.org/abs/2409.18611
Autor:
Maturo, Fabrizio, Porreca, Annamaria
The positioning of this research falls within the scalar-on-function classification literature, a field of significant interest across various domains, particularly in statistics, mathematics, and computer science. This study introduces an advanced m
Externí odkaz:
http://arxiv.org/abs/2409.17804
Autor:
Sarr, Djibril
In the era of big data, ensuring the quality of datasets has become increasingly crucial across various domains. We propose a comprehensive framework designed to automatically assess and rectify data quality issues in any given dataset, regardless of
Externí odkaz:
http://arxiv.org/abs/2409.10139
Autor:
Himpe, Christian
Metadata management for distributed data sources is a long-standing but ever-growing problem. To counter this challenge in a research-data and library-oriented setting, this work constructs a data architecture, derived from the data-lake: the metadat
Externí odkaz:
http://arxiv.org/abs/2409.05512
Autor:
Thakur, Nirmalya
The world is currently experiencing an outbreak of mpox, which has been declared a Public Health Emergency of International Concern by WHO. No prior work related to social media mining has focused on the development of a dataset of Instagram posts ab
Externí odkaz:
http://arxiv.org/abs/2409.05292
Autor:
Salvagnin, Cristiano
This study conducts a bibliometric review of scientific research on the European Union Emissions Trading System (EU ETS) from 2004 to 2024, using research articles from the Scopus database. Using the Bibliometrix package in R, we analyze publication
Externí odkaz:
http://arxiv.org/abs/2409.01739
We introduce the Robustness of Hierarchically Organized Time Series (RHiOTS) framework, designed to assess the robustness of hierarchical time series forecasting models and algorithms on real-world datasets. Hierarchical time series, where lower-leve
Externí odkaz:
http://arxiv.org/abs/2408.03399
Autor:
Benitez, Edgar, Balaguer, Alvaro
In this study, the combined use of structural equation modeling (SEM) and Bayesian network modeling (BNM) in causal inference analysis is revisited. The perspective highlights the debate between proponents of using BNM as either an exploratory phase
Externí odkaz:
http://arxiv.org/abs/2407.18612