AUTHOR=Hasan Mohammad Kamrul , Ghazal Taher M. , Alkhalifah Ali , Abu Bakar Khairul Azmi , Omidvar Alireza , Nafi Nazmus S. , Agbinya Johnson I. 

TITLE=Fischer Linear Discrimination and Quadratic Discrimination Analysis–Based Data Mining Technique for Internet of Things Framework for Healthcare

JOURNAL=Frontiers in Public Health

VOLUME=Volume 9 - 2021

YEAR=2021

URL=https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2021.737149

DOI=10.3389/fpubh.2021.737149

ISSN=2296-2565

ABSTRACT=The Internet of Reality or Augmented Reality has been considered a breakthrough and an outstanding critical mutation leading to critics in the media due to its misinterpretations and illegalization among its stakeholders. In this work, we study the pillars of these technologies connected to web usage as IoT system's infrastructure. We used several data mining techniques to evaluate the online advertisement dataset, which can be categorized as the standard, high-dimensional, large, and imbalanced dataset. The proposed methodology developed apply Fischer Linear Discrimination Analysis (FLDA) and Quadratic Discrimination Analysis (QDA) within Random Projection (RP) filters to compare our runtime and accuracy with Support Vector Machine (SVM), K-Nearest Neighbor (KNN), and Multi-Layer Perceptron (MLP) in IoT based systems. The modeling results showed not only improved accuracy but also runtime improvements. When compared with SVM, KNN, and MLP in QDA and FLDA, runtime shortens by 20 times in our chosen healthcare dataset. The RP filtering in the preprocessing stage of the attribute selection, fulfilling the model’s runtime, is a standpoint in the IoT industry.