Titlebook: Second RILEM International Conference on Earthen Construction; ICEC 2024 Christopher Beckett,Ana Bras,Arnaud Perrot Conference proceedings

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Céline Perlot,Agostino Walter Bruno,Magda Posani,Guillaume Habert,Snežana Vučetićethod. The .-norm sparse regularization feature selection method can be used to select features that are crucial for discrimination, and then explore the abnormal functional connections of schizophrenia with fMRI data. Our results showed that the abnormal functional connections are related to superi

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Berthia Malonga,Philippe Poullain,Fateh Bendahmane,Stéphanie Bonnet,Nabil Issaadi,Louis Ahouetrest (RF) classifier for accurate classification. In the five-fold cross-validation on . and . data sets, GCNSP achieved 93.65% and 90.69% prediction accuracy with 99.64% and 99.08% specificity, respectively. In comparison with different classifier models and other existing methods, GCNSP shows stro

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Christopher T. S. Beckett,Irene Appeaning Addo,Frederick Owusu-Nimo,Ibrahim Yakubu,Yalin Gulen,Oscarrest (RF) classifier for accurate classification. In the five-fold cross-validation on . and . data sets, GCNSP achieved 93.65% and 90.69% prediction accuracy with 99.64% and 99.08% specificity, respectively. In comparison with different classifier models and other existing methods, GCNSP shows stro

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Lily Walter,Yannick Estevez,Gildas Medjigbodo,Adeline Armougom,Baptiste Roux,Laureline Lespinasse,La is adopted to predict whether the given protein pair is interacting or not. When the proposed method performed on . and . PPIs datasets, it achieved the results with an average accuracy of 92.52% and 91.46%, respectively. To further verify the performance of the proposed method, we compare the prop

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Vania Calle,Rosario Rojas,Kevin Guillen,Bartolomeo Pantò,Javier Nakamatsu,Guido Silva,César Chácaraombine the calculated scores with the potential negative interactions to get the final correlation scores. We conduct comprehensive experiments including 5-fold cross validation (5-fold CV) and leave-one-out cross validation (LOOCV) to indicate that our framework has some advantages including improv

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