Paper Info
Title
MATLAB-based framework for data analytics applied to Hajj dataset: Hajj health meter.
Abstract
The total number of pilgrims for the Hajj Season of 1438H reached 2,352, 122 - according to the General Authority for statistics Kingdom of Saudi Arabia. Pilgrims data analysis and prediction help concerned entities of the country in the future planning programs for the purpose of ensuring the necessary services - social, health, security, food and transportation services to name a few. Predictive analytics is the process of using data along with analysis, statistics, and machine learning techniques to create a predictive model for forecasting future events. Predictive analytics is often discussed in the context of big data as businesses apply algorithms to derive insights from large datasets using a framework like Hadoop, HDFS, and Spark. Building MATLAB-based framework for data analytics applied to Hajj dataset is the main aim of this research paper. The proposed framework is mainly relying on four main concepts; namely the cloud-based Internet of things (IoT), fog, Edge-of-Things (EoT), and predictive analytics. This proposed framework helps in reducing the amount of data sent, lowering network traffic, increasing bandwidth, and reducing power energy consumption. On top that, the framework including regression has the potential to predict how likely Hajj is susceptible to illness or even death.
Year
DOI
Venue
2020
10.3233/JIFS-179536
JOURNAL OF INTELLIGENT & FUZZY SYSTEMS
Keywords
DocType
Volume
Big data,cloud,Edge-of-Things (EoT),health,Internet of Things (IoT),pilgrim,prediction
Journal
38
Issue
ISSN
Citations 
SP3.0
1064-1246
0
PageRank 
References 
Authors
0.34
0
4
Name
Order
Citations
PageRank
Mohammed Farsi100.34
Zohair Malki200.34
Mostafa A. El-Hosseini3386.13
Mahmoud Mohammed Badawy4357.44