Tags: Machine Learning

All Categories (1-20 of 24)

  1. Muhammad Bilal

    Bilal’s research focuses on data-driven solutions for the environmental and health impact assessment of engineered nanomaterials (ENMs) using advanced machine learning/data mining and simulation...

    http://nanohub.org/members/179709

  2. Applying Machine Learning to Computational Chemistry: Can We Predict Molecular Properties Faster without Compromising Accuracy?

    14 Aug 2017 | Contributor(s):: Hanjing Xu, Pradeep Kumar Gurunathan

    Non-covalent interactions are crucial in analyzing protein folding and structure, function of DNA and RNA, structures of molecular crystals and aggregates, and many other processes in the fields of biology and chemistry. However, it is time and resource consuming to calculate such interactions...

  3. Predicting Locations of Pollution Sources using Convolutional Neural Networks

    07 Aug 2017 | Contributor(s):: Yiheng Chi, Nickolas D Winovich, Guang Lin

    Pollution is a severe problem today, and the main challenge in water pollution controls and eliminations is detecting and locating pollution sources. This research project aims to predict the locations of pollution sources given diffusion information of pollution in the form of array or...

  4. S Kiran Kadam

    http://nanohub.org/members/172030

  5. IPython Notebooks for Machine Learning

    Collections | 21 May 2017 | Posted by Tanya Faltens

    http://nanohub.org/groups/ncnure2017/collections/technical-resources

  6. Dedy Farhamsa

    http://nanohub.org/members/170299

  7. Claire Battye

    "Research is creating new knowledge."Neil Armstrong

    http://nanohub.org/members/169180

  8. Marius Stan

    http://nanohub.org/members/156713

  9. Model Selection Using Gaussian Mixture Models and Parallel Computing

    20 Jul 2016 | | Contributor(s):: Tian Qiu, Yiyi Chen, Georgios Karagiannis, Guang Lin

    Model Selection Using Gaussian Mixture Models

  10. Lukasz Burzawa

    http://nanohub.org/members/147258

  11. Juan Sebastian Martinez

    I am a senior in Electronic Engineering and Systems and Computer Engineering at Universidad de los Andes in Bogotá. Throughout my learning, I have gained experience with different programming...

    http://nanohub.org/members/145729

  12. Gaussian process regression in 1D

    26 Nov 2014 | | Contributor(s):: Ilias Bilionis, Alejandro Strachan, Benjamin P Haley, Martin Hunt, Rohit Kaushal Tripathy, Sam Reeve

    Use Gaussian processes to represent x-y data

  13. Ilias Bilionis

    Dr. Ilias Bilionis is an Assistant Professor at the School of Mechanical Engineering, Purdue University. His research is motivated by energy and material science applications and it focuses on the...

    http://nanohub.org/members/107467

  14. Rohit Kaushal Tripathy

    http://nanohub.org/members/106614

  15. German Felipe Giraldo

    http://nanohub.org/members/85538

  16. Ahmed-Amine Homman

    http://nanohub.org/members/82074

  17. anupam ghosh

    M.Sc. Physics (2007), 1 year experience in neuroscience (2008-09), 1.5 yrs experience in synthesis and characterization of Nickel nano-wires (2010-11), 1 year experience in simulation of...

    http://nanohub.org/members/81944

  18. Mahika Dubey

    University of Illinois Urbana Champaign Class of 2016 (Urbana, IL) B.S. Computer Engineering (Department of ECE) Minor in Statisics, iFoundry Innovation Certificate Program Monta Vista High School...

    http://nanohub.org/members/80897

  19. Random Forest Model Objects for Pulmonary Toxicity Risk Assessment

    09 Apr 2013 | | Contributor(s):: Jeremy M Gernand

    This download contains MATLAB treebagger or Random Forest (RF) model objects created via meta-analysis of nanoparticle rodent pulmonary toxicity experiments. The ReadMe.txt file contains object descriptions including output definitions, input parameter descriptions, and applicable limits.

  20. Model Validation Document for "A Meta-Analysis of Carbon Nanotube Pulmonary Toxicity Studies – How Physical Dimensions and Impurities Affect the Toxicity of Carbon Nanotubes"

    19 Nov 2012 | | Contributor(s):: Jeremy M Gernand, Elizabeth Casman

    This document contains model learning statistics, and structure of the models utilized in the paper “A meta-analysis of carbon nanotube pulmonary toxicity studies – How physical dimensions and impurities affect the toxicity of carbon nanotubes.” This information is meant to supplement and...