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Full-Text Articles in Engineering
Lifelong Machine Learning With Adaptive Multi-Agent Systems, Nicolas R. Verstaevel, Jeremy Boes, Julien Nigon, Dorian D'Amico, Marie-Pierre Gleizes
Lifelong Machine Learning With Adaptive Multi-Agent Systems, Nicolas R. Verstaevel, Jeremy Boes, Julien Nigon, Dorian D'Amico, Marie-Pierre Gleizes
SMART Infrastructure Facility - Papers
Sensors and actuators are progressively invading our everyday life as well as industrial processes. They form complex and pervasive systems usually called "ambient systems" or "cyber-physical systems". These systems are supposed to efficiently perform various and dynamic tasks in an ever-changing environment. They need to be able to learn and to self-adapt throughout their life, because designers cannot specify a priori all the interactions and situations they will face. These are strong requirements that push the need for lifelong machine learning, where devices can learn models and behaviours during their whole lifetime and are able to transfer them to perform …
Ontological Learner Profile Identification For Cold Start Problem In Micro Learning Resources Delivery, Geng Sun, Tingru Cui, Jun Shen, Dongming Xu, Ghassan Beydoun, Shiping Chen
Ontological Learner Profile Identification For Cold Start Problem In Micro Learning Resources Delivery, Geng Sun, Tingru Cui, Jun Shen, Dongming Xu, Ghassan Beydoun, Shiping Chen
Faculty of Engineering and Information Sciences - Papers: Part B
No abstract provided.
Vdes J2325-5229 A Z = 2.7 Gravitationally Lensed Quasar Discovered Using Morphology-Independent Supervised Machine Learning, Fernanda Ostrovski, Richard G. Mcmahon, Andrew J. Connolly, Cameron A. Lemon, Matthew W. Auger, Manda Banerji, Johnathan M. Hung, Sergey E. Koposov, Christopher E. Lidman
Vdes J2325-5229 A Z = 2.7 Gravitationally Lensed Quasar Discovered Using Morphology-Independent Supervised Machine Learning, Fernanda Ostrovski, Richard G. Mcmahon, Andrew J. Connolly, Cameron A. Lemon, Matthew W. Auger, Manda Banerji, Johnathan M. Hung, Sergey E. Koposov, Christopher E. Lidman
Faculty of Engineering and Information Sciences - Papers: Part B
We present the discovery and preliminary characterization of a gravitationally lensed quasar with a source redshift zs = 2.74 and image separation of 2.9 arcsec lensed by a foreground zl = 0.40 elliptical galaxy. Since optical observations of gravitationally lensed quasars show the lens system as a superposition of multiple point sources and a foreground lensing galaxy, we have developed a morphology-independent multi-wavelength approach to the photometric selection of lensed quasar candidates based on Gaussian Mixture Models (GMM) supervised machine learning. Using this technique and gi multicolour photometric observations from the Dark Energy Survey (DES), near-IR JK photometry …
A Framework Of Mlaas For Facilitating Adaptive Micro Learning Through Open Education Resources In Mobile Environment, Geng Sun, Tingru Cui, Wanwu Guo, Shiping Chen, Jun Shen
A Framework Of Mlaas For Facilitating Adaptive Micro Learning Through Open Education Resources In Mobile Environment, Geng Sun, Tingru Cui, Wanwu Guo, Shiping Chen, Jun Shen
Faculty of Engineering and Information Sciences - Papers: Part B
No abstract provided.
Sbar: A Conceptual Framework To Support Learning Path Adaptation In Mobile Learning, Alva Hendi Muhammad, Jun Shen, Ghassan Beydoun, Dongming Xu
Sbar: A Conceptual Framework To Support Learning Path Adaptation In Mobile Learning, Alva Hendi Muhammad, Jun Shen, Ghassan Beydoun, Dongming Xu
Faculty of Engineering and Information Sciences - Papers: Part B
No abstract provided.
A Comparison Study For Supervised Machine Learning Models In Cancer Classification, Huaming Chen, Hong Zhao, Lei Wang, Jiangning Song, Jun Shen
A Comparison Study For Supervised Machine Learning Models In Cancer Classification, Huaming Chen, Hong Zhao, Lei Wang, Jiangning Song, Jun Shen
Faculty of Engineering and Information Sciences - Papers: Part B
No abstract provided.