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All Journal International Journal of Electrical and Computer Engineering Bulletin of Electrical Engineering and Informatics
Etinosa Noma Osaghae
Covenant University
Computer Science & IT Electrical & Electronics Engineering

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Epidemic Alert System: A Web-based Grassroots Model Etinosa Noma Osaghae; Kennedy Okokpujie; Charles Ndujiuba; Olatunji Okesola; Imhade P. Okokpujie
International Journal of Electrical and Computer Engineering (IJECE) Vol 8, No 5: October 2018
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (816.656 KB) | DOI: 10.11591/ijece.v8i5.pp3809-3828

Abstract

Most web-based disease surveillance systems that give epidemic alerts are based on very large and unstructured data from various news sources, social media and online queries that are parsed by complex algorithms. This has the tendency to generate results that are so diverse and non-specific. When considered along with the fact that there are no existing standards for mining and analyzing data from the internet, the results or decisions reached based on internet sources have been classified as low-quality. This paper proposes a web-based grassroots epidemic alert system that is based on data collected specifically from primary health centers, hospitals and registered laboratories. It takes a more traditional approach to indicator-based disease surveillance as a step towards standardizing web-based disease surveillance. It makes use of a threshold value that is based on the third quartile (75th percentile) to determine the need to trigger the alarm for the onset of an epidemic. It also includes, for deeper analysis, demographic information.
Cellular network bandwidth improvement using subscribers’ classification and Wi-Fi offloading Adewale Adeyinka Ajao; Ben Obaje Abraham; Etinosa Noma Osaghae; Okesola Olatunji; Edikan Ekong; Abdulkareem Ademola
Bulletin of Electrical Engineering and Informatics Vol 11, No 2: April 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i2.2575

Abstract

Cellular networks are highly prone to congestion especially at peak traffic periods. This is compounded by the fact that the blocking probability increases. In this study, a machine learning based subscriber classification along with an adaptive Wi-Fi offloading scheme is proposed to improve the throughput and lower the blocking probability of the network. The proposed subscriber classification was implemented using a back propagation based artificial neural network. The result of the subscriber classification was used to develop an adaptive Wi-Fi offloading algorithm based on bandwidth utilization and system throughput. The developed neural network models are shown to be effective, with 94.6% in one experiment, in classifying a user into user classes or levels based on previous data usage. The levenberg–marquardt (LM) algorithm gave the highest accuracy in categorizing the four classes. A relatively large sample size was used for the neural network training cycle and the resulting neural network was then made to use many neurons in its hidden layer. The implementation of the proposed subscriber classification and adaptive Wi-Fi offloading scheme led to a 20% drop in blocking probability and a 50.53% increase in the system throughput.
Co-Authors Abdulkareem Ademola Adewale Adeyinka Ajao Ben Obaje Abraham Charles Ndujiuba Edikan Ekong Imhade P. Okokpujie Kennedy Okokpujie Okesola Olatunji Olatunji Okesola