164 research outputs found
Correction to: Inflammatory bowel disease (IBD) position statement of the Italian Society of Colorectal Surgery (SICCR): general principles of IBD management (Techniques in Coloproctology, (2020), 24, 5, (397-419), 10.1007/s10151-020-02175-z)
No full textThe affiliation of the author Silvio Danese has been incorrectly published in the original publication. The complete correct affiliation should read as follows. IBD Center, Department of Gastroenterology, Humanitas Clinical and Research Center, IRCCS, via Manzoni 56, 20089 Rozzano, Milan, Italy Department of Biomedical Sciences, Humanitas University, via Rita Levi Montalcini 4, Pieve Emanuele, 20090 Milan, Italy
Enhancing RBF-DDA Algorithm’s Robustness: Neural Networks Applied to Prediction of Fault-Prone Software Modules
No full textA Constructive RBF Neural Network for Estimating the Probability of Defects in Software Modules
No full textCorrection to: Outcomes on safety and efficacy of left atrial appendage occlusion in end stage renal disease patients undergoing dialysis (Journal of Nephrology, (2021), 34, 1, (63-73), 10.1007/s40620-020-00774-5)
No full textThe article Outcomes on safety and efficacy of left atrial appendage occlusion in end stage renal disease patients undergoing dialysis, written by Simonetta Genovesi, Luca Porcu, Giorgio Slaviero, Gavino Casu, Silvio Bertoli, Antonio Sagone, Monique Buskermolen, Federico Pieruzzi, Giovanni Rovaris, Alberto Montoli, Jacopo Oreglia, Emanuela Piccaluga, Giulio Molon, Mario Gaggiotti, Federica Ettori, Achille Gaspardone, Roberto Palumbo, Francesca Viazzi, Marco Breschi, Maurizio Gallieni, Gina Contaldo, Giuseppe D’Angelo, Pierluigi Merella, Fabio Galli, Paola Rebora, Mariagrazia Valsecchi, and Patrizio Mazzone, was originally published electronically on the publisher’s internet portal on 6 June 2020 without open access. With the author(s)’ decision to opt for Open Choice the copyright of the article changed on 10 July 2020 to © The Author(s) 2020 and this article is licensed under a Creative Commons Attribution 4.0 International License (http://creat iveco mmons .org/licen ses/ by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The original article has been updated
Software Effort Estimation using Machine Learning Techniques with Robust Confidence Intervals
No full textSoftware Effort Estimation using Machine Learning Techniques with Robust Confidence Intervals
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A GA-based feature selection and parameters optimization for support vector regression applied to software effort estimation
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