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Pedro Celard, Adrián Seara Vieira, José Manuel Sorribes-Fdez, Eva Lorenzo Iglesias and Lourdes Borrajo
In this study, we propose a novel Temporal Development Generative Adversarial Network (TD-GAN) for the generation and analysis of videos, with a particular focus on biological and medical applications. Inspired by Progressive Growing GAN (PG-GAN) and Tem...
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Wiem Chebil, Mohammad Wedyan, Moutaz Alazab, Ryan Alturki and Omar Elshaweesh
This research proposes a new approach to improve information retrieval systems based on a multinomial naive Bayes classifier (MNBC), Bayesian networks (BNs), and a multi-terminology which includes MeSH thesaurus (Medical Subject Headings) and SNOMED CT (...
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Jens Dörpinghaus, David Samray and Robert Helmrich
The German labor market relies heavily on vocational training, retraining, and continuing education. In order to match training seekers with training offers and to make the available data interoperable, we present a novel approach to automatically detect...
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Munir Ahmad, Sagheer Abbas, Areej Fatima, Ghassan F. Issa, Taher M. Ghazal and Muhammad Adnan Khan
The importance of accurate livestock identification for the success of modern livestock industries cannot be overstated as it is essential for a variety of purposes, including the traceability of animals for food safety, disease control, the prevention o...
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Antti Hynninen, Ville Viitanen, Jukka Tanttari, Rhena Klose, Claudio Testa and Jussi Martio
This work focuses on the main issues related to noise measurements in cavitation tunnels. The scope of the paper is to twofold: to obtain a better understanding on the main phenomena underlying experiments and to define consistent cavitation tunnel measu...
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