|
|
|
|
|
Hamed Taherdoost and Mitra Madanchian
In recent years, artificial intelligence (AI) has seen remarkable advancements, stretching the limits of what is possible and opening up new frontiers. This comparative review investigates the evolving landscape of AI advancements, providing a thorough e...
ver más
|
|
|
|
|
|
|
|
|
|
|
Vasileios Thomopoulos and Kostas Tsichlas
In this research, we present the first steps toward developing a data-driven agent-based model (ABM) specifically designed for simulating infectious disease dynamics in Greece. Amidst the ongoing COVID-19 pandemic caused by SARS-CoV-2, this research hold...
ver más
|
|
|
|
|
|
|
|
|
|
|
Davide Fronzi, Gagan Narang, Alessandro Galdelli, Alessandro Pepi, Adriano Mancini and Alberto Tazioli
Forecasting of water availability has become of increasing interest in recent decades, especially due to growing human pressure and climate change, affecting groundwater resources towards a perceivable depletion. Numerous research papers developed at var...
ver más
|
|
|
|
|
|
|
|
|
|
|
Sajjad E. Rasheed, Duaa Al-Jeznawi, Musab Aied Qissab Al-Janabi and Luís Filipe Almeida Bernardo
The structural stability of pipe pile foundations under seismic loading stands as a critical concern, demanding an accurate assessment of the maximum settlement. Traditionally, this task has been addressed through complex numerical modeling, accounting f...
ver más
|
|
|
|
|
|
|
|
|
|
|
Che-Hao Chang, Jason Lin, Jia-Wei Chang, Yu-Shun Huang, Ming-Hsin Lai and Yen-Jen Chang
Recently, data-driven approaches have become the dominant solution for prediction problems in agricultural industries. Several deep learning models have been applied to crop yield prediction in smart farming. In this paper, we proposed an efficient hybri...
ver más
|
|
|
|
|
|
|
|
|
|
|
Fazlul Karim, Mohammed Ali Armin, David Ahmedt-Aristizabal, Lachlan Tychsen-Smith and Lars Petersson
Machine learning (also called data-driven) methods have become popular in modeling flood inundations across river basins. Among data-driven methods, traditional machine learning (ML) approaches are widely used to model flood events, and recently deep lea...
ver más
|
|
|
|
|
|
|
|
|
|
|
Maximilian Granzner, Alfred Strauss, Michael Reiterer, Maosen Cao and Drahomír Novák
Railway noise barrier constructions are subjected to high aerodynamic loads during the train passages, and the knowledge of their actual structural condition is relevant to assure safety for railway users and to create a basis for forecasting. This paper...
ver más
|
|
|
|
|
|
|
|
|
|
|
Yixin Huang, Rishi Srivastava, Chloe Ngo, Jerry Gao, Jane Wu and Sen Chiao
Food shortage issues affect more and more of the population globally as a consequence of the climate crisis, wars, and the COVID-19 pandemic. Increasing crop output has become one of the urgent priorities for many countries. To raise the productivity of ...
ver más
|
|
|
|
|
|
|
|
|
|
|
Vaia I. Kontopoulou, Athanasios D. Panagopoulos, Ioannis Kakkos and George K. Matsopoulos
In the broad scientific field of time series forecasting, the ARIMA models and their variants have been widely applied for half a century now due to their mathematical simplicity and flexibility in application. However, with the recent advances in the de...
ver más
|
|
|
|
|
|
|
|
|
|
|
Panagiotis Skondras, Nikos Zotos, Dimitris Lagios, Panagiotis Zervas, Konstantinos C. Giotopoulos and Giannis Tzimas
This article presents a study on the multi-class classification of job postings using machine learning algorithms. With the growth of online job platforms, there has been an influx of labor market data. Machine learning, particularly NLP, is increasingly...
ver más
|
|
|
|
|
|