Fischer Linear Discrimination and Quadratic Discrimination Analysis-Based Data Mining Technique for Internet of Things Framework for Healthcare.

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

doi:10.3389/fpubh.2021.737149

Abstract

The internet of reality or augmented reality has been considered a breakthrough and an outstanding critical mutation with an emphasis on data mining leading to dismantling of some of its assumptions among several of its stakeholders. In this work, we study the pillars of these technologies connected to web usage as the Internet of things (IoT) system's healthcare infrastructure. We used several data mining techniques to evaluate the online advertisement data set, which can be categorized as high dimensional with 1,553 attributes, and the imbalanced data set, which automatically simulates an IoT discrimination problem. The proposed methodology applies 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 Multilayer perceptron (MLP) in IoT-based systems. Finally, the impact on number of projections was practically experimented, and the sensitivity of both FLDA and QDA with regard to precision and runtime was found to be challenging. The modeling results show 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 data set simulated for a healthcare framework. The RP filtering in the preprocessing stage of the attribute selection, fulfilling the model's runtime, is a standpoint in the IoT industry.Index Terms: Data Mining, Random Projection, Fischer Linear Discriminant Analysis, Online Advertisement Dataset, Quadratic Discriminant Analysis, Feature Selection, Internet of Things.

Highlights

The importance of information technology in the age of communication is not hidden from anybody
The results show that the principal component analysis (PCA) is expensive computationally but gives more accurate results compared with random projection (RP), which is the cheapest computationally but has some good characteristics
As this research concern was partly on the resources relevant to the Internet of things (IoT), it is favorable to represent which part of computational resources of their data mining activities are impacted by the drift concept: algorithm, runtime, accuracy, data warehouses, services, etc., or is there any model framework for ad selector application available to practically align data mining with the needs of all their stakeholders

Summary

Introduction

The importance of information technology in the age of communication is not hidden from anybody. It is not unrealistic to conceive that in the near future, artificial intelligence can substitute the service-based evaluators and manipulate IoTs that are much more accurate, professional, and appealing instead of ones with various privacy gaps. They can be accessed through a form of robots that hijack their information, resulting in incorrect information circulation and increasing the riskiness of information of healthcare services. The third wave Web x.0 is evolving by enhancing the Internet capacity in which companies today use the fourth generation of the IoT for their enterprise resource planning (ERP) applications. The best learned lessons on services such as Uber—which already managed to attract millions of visitors—was the reason that motivated us to perform a classification algorithm on this data set of web usage in which many points of view, such as the ethical, professional, civilian’s rights, and media laws, are likewise healthcare data sets and should be taken into account

Methods

Results

Conclusion