Secure Privacy Preserving across Personal Health Data and Single Cell Genomics Research INSPIRE Academic Pedagogy - Merging Big Data Multiplatform with Deep Learning

Mary Qu Yang, Shucheng Yu, Carolina Cruz-Neira, William Yang, M. Eduard Tudoreanu, Dan Li, Yifan Zhang, Qingfang He, Renchu Guan, Richard Y. Wang, Wenbing Zhao

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

Enhancing student academic performance and transdisciplinary ability is challenging, but the time and effort put into accomplishing this ambitious feat is priceless. We develop secure privacy preserving across Personal Health Data (PHD) repository and single-cell genomics research for building an Innovative Systematic Pedagogy for Integrated Research - Education (INSPIRE) (http://americancse.org/events/csce2017/csce17-awards). In this paper we further build a novel, eclectic, and insightful framework based on classical and popular machine learning approaches to help us meet the educational challenge. Our framework focuses on using integrative research technologies to help solve 'Education's Performance Prediction Data Mining Crisis' (EPPDMC), by putting to rest issues associated with mining and making best use of big data for educational enhancement, such as multi-source education acquisition, data fusion, and unstructured data analysis. We exploit the uses of deep learning, text classification, and semi-supervised learning approaches to solve challenging problems that educators face when analyzing multiplatform big data involved in education, research and training students. Based on new machine learning approached we developed for genomic big-data research and in combination with machine learning methods (http://americancse.org/events/csce2017/keynotes-lectures/yang-talk) and the vast availability of education data available to us, not only can we utilize structured, unstructured, and even multi-media data, but while engaging in leaning intelligent thinking along the way, we can also maximize the utilization of big data by studying the motion and performance of these data. Hence we build the INSPIRE model that can further incorporate Student Face Expression in Class (SFEiC) to help educators and managers make further improvements as they become involved in the teaching-learning process. This research further facilitates the effectiveness of the INSPIRE model.

Original languageEnglish
Title of host publicationProceedings - 2017 International Conference on Computational Science and Computational Intelligence, CSCI 2017
EditorsFernando G. Tinetti, Quoc-Nam Tran, Leonidas Deligiannidis, Mary Qu Yang, Mary Qu Yang, Hamid R. Arabnia
Pages1244-1251
Number of pages8
ISBN (Electronic)9781538626528
DOIs
StatePublished - 4 Dec 2018
Event2017 International Conference on Computational Science and Computational Intelligence, CSCI 2017 - Las Vegas, United States
Duration: 14 Dec 201716 Dec 2017

Publication series

NameProceedings - 2017 International Conference on Computational Science and Computational Intelligence, CSCI 2017

Conference

Conference2017 International Conference on Computational Science and Computational Intelligence, CSCI 2017
Country/TerritoryUnited States
CityLas Vegas
Period14/12/1716/12/17

Keywords

  • Deep Learning
  • Innovative Systematic Pedagogy for Integrated Research and Education (INSPIRE)
  • Secure privacy preserving across Personal Health Data (PHD)
  • Single-cell genomics

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