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Beata Baziak, Marek Bodziony and Robert Szczepanek
Machine learning models facilitate the search for non-linear relationships when modeling hydrological processes, but they are equally effective for automation at the data preparation stage. The tasks for which automation was analyzed consisted of estimat...
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Leon Kopitar, Iztok Fister, Jr. and Gregor Stiglic
Introduction: Type 2 diabetes mellitus is a major global health concern, but interpreting machine learning models for diagnosis remains challenging. This study investigates combining association rule mining with advanced natural language processing to im...
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Frances Heredia-Negron, Natalie Alamo-Rodriguez, Lenamari Oyola-Velazquez, Brenda Nieves, Kelvin Carrasquillo, Harry Hochheiser, Brian Fristensky, Istoni Daluz-Santana, Emma Fernandez-Repollet and Abiel Roche-Lima
Artificial intelligence (AI) and machine learning (ML) facilitate the creation of revolutionary medical techniques. Unfortunately, biases in current AI and ML approaches are perpetuating minority health inequity. One of the strategies to solve this probl...
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Giampiero Giacomello and Oltion Preka
In an increasingly technology-dependent world, it is not surprising that STEM (Science, Technology, Engineering, and Mathematics) graduates are in high demand. This state of affairs, however, has made the public overlook the case that not only computing ...
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Mark Krystofik, Christopher J. Valant, Jeremy Archbold, Preston Bruessow and Nenad G. Nenadic
We developed a framework for the risk assessment of delaying the delivery of shipments to customers in the presence of incomplete information pertaining to a significant, e.g., weather-related, event that could cause substantial disruption. The approach ...
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