tag:joss.theoj.org,2005:/papers/tagged/artificial%20neural%20networkJournal of Open Source Software2022-07-05T15:52:45ZJournal of Open Source Softwarehttps://joss.theoj.orgtag:joss.theoj.org,2005:Paper/29842022-07-05T15:52:45Z2022-07-06T00:01:06ZBCImat: a Matlab-based framework for Intracortical Brain-Computer Interfaces and their simulation with an artificial spiking neural networkacceptedv.1.0.02021-08-20 13:08:03 UTC752022-07-05 15:52:45 UTC720223956EnricoFerreaGerman Primate Center, Sensorimotor Group, Goettingen, Germany, Institute for Neuromodulation and Neurotechnology, University Hospital and University of Tuebingen, Tuebingen, GermanyPierreMorelGerman Primate Center, Sensorimotor Group, Goettingen, Germany, Univ. Littoral Côte d’Opale, Univ. Artois, Univ. Lille, ULR 7369 - URePSSS - Unité de Recherche Pluridisciplinaire Sport Santé Société, F-62100 Calais, FranceAlexanderGailGerman Primate Center, Sensorimotor Group, Goettingen, Germany, University of Goettingen, Georg-Elias-Mueller Institute of Psychology, Goettingen, Germany, Bernstein Center for Computational Neuroscience, Goettingen, Germany10.21105/joss.03956https://doi.org/10.5281/zenodo.6759182MATLAB, C++https://joss.theoj.org/papers/10.21105/joss.03956.pdfMatlab, Brain-Computer Interface, Closed-Loop, Motor Controltag:joss.theoj.org,2005:Paper/11272020-02-06T16:49:11Z2021-02-15T11:31:54ZASCENDS: Advanced data SCiENce toolkit for Non-Data Scientistsacceptedv0.4.02019-07-24 15:01:28 UTC462020-02-06 16:49:11 UTC520201656SangkeunLeeOak Ridge National Laboratory0000-0002-1317-5112JianPengOak Ridge National Laboratory0000-0002-9763-4741AndrewWilliamsCornell UniversityDongwonShinOak Ridge National Laboratory0000-0002-5797-342310.21105/joss.01656https://doi.org/10.5281/zenodo.3635782Python, Jupyter Notebook, JavaScripthttps://joss.theoj.org/papers/10.21105/joss.01656.pdfmachine learning, artificial intelligence, neural networktag:joss.theoj.org,2005:Paper/13202020-02-05T15:14:48Z2021-02-15T11:31:24Zpasst: An R implementation of the Probability Associator Time (PASS-T) modelacceptedv0.1.02019-11-05 16:03:32 UTC462020-02-05 15:14:48 UTC520201900JohannesTitzDepartment of Psychology, TU Chemnitz, Germany0000-0002-1102-571910.21105/joss.01900https://doi.org/10.5281/zenodo.3638130Rhttps://joss.theoj.org/papers/10.21105/joss.01900.pdfjudgments of frequency, judgments of duration, PASS-T, artificial neural networktag:joss.theoj.org,2005:Paper/9612019-07-11T22:16:07Z2021-02-15T11:32:18ZECabc: A feature tuning program focused on Artificial Neural Network hyperparametersacceptedv2.2.22019-04-12 22:05:32 UTC392019-07-11 22:16:07 UTC420191420SanskritiSharmaEnergy and Combustion Research Laboratory, University of Massachusetts Lowell, Lowell, MA 01854, U.S.A.0000-0002-9884-7351HernanGelaf-RomerEnergy and Combustion Research Laboratory, University of Massachusetts Lowell, Lowell, MA 01854, U.S.A.0000-0003-0690-576XTravisKesslerEnergy and Combustion Research Laboratory, University of Massachusetts Lowell, Lowell, MA 01854, U.S.A.0000-0002-7363-4050JohnHunterMackEnergy and Combustion Research Laboratory, University of Massachusetts Lowell, Lowell, MA 01854, U.S.A.0000-0002-5455-861110.21105/joss.01420https://doi.org/10.5281/zenodo.3256403Pythonhttps://joss.theoj.org/papers/10.21105/joss.01420.pdfartificial bee colony, hyperparameter optimization, machine learning, artificial neural networkstag:joss.theoj.org,2005:Paper/8102019-02-15T10:05:23Z2021-02-15T11:32:39ZBoltzMM: an R package for maximum pseudolikelihood estimation of fully-visible Boltzmann machinesaccepted0.1.32019-01-14 07:17:37 UTC342019-02-15 10:05:23 UTC420191193AndrewT.JonesSchool of Mathematics and Physics, University of Queensland, St. Lucia 4072, Queensland AustraliaJessicaJ.BagnallDepartment of Mathematics and Statistics, La Trobe University, Bundoora 3086, Victoria AustraliaHienD.NguyenDepartment of Mathematics and Statistics, La Trobe University, Bundoora 3086, Victoria Australia0000-0002-9958-432X10.21105/joss.01193https://doi.org/10.5281/zenodo.2563411R, C++https://joss.theoj.org/papers/10.21105/joss.01193.pdfartificial neural network, graphical model, maximum pseudolikelihood estimation, multivariate binary data, probability mass functiontag:joss.theoj.org,2005:Paper/2722017-09-21T00:00:00Z2021-02-15T11:33:55ZECNet: Large scale machine learning projects for fuel property predictionacceptedv1.2.4.dev12017-09-04 22:40:24 UTC172017-09-21 00:00:00 UTC22017401TravisKesslerUMass Lowell Energy and Combustion Research Laboratory0000-0002-7363-4050JohnHunterMackUMass Lowell Energy and Combustion Research Laboratory10.21105/joss.00401https://doi.org/10.5281/zenodo.910866Pythonhttps://joss.theoj.org/papers/10.21105/joss.00401.pdfmachine learning, artificial neural networks, fuel property prediction, cetane number, QSPR