Article Per Year (5 Year)
p-Index From 2021 - 2026
0.444
P-Index
This Author published in this journals
All Journal Journal of Innovation Information Technology and Application (JINITA) International Journal of information Systems and Informatics
Imeh Umoren
AKWA IBOM STATE UNIVERSITY
Computer Science & IT Decision Sciences, Operations Research & Management Engineering Social Sciences

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
A Fuzzy Knowledge-Based Approach for End-2-End Behavioural Analysis of Wormhole Attacks on Mobile Ad-Hoc Networks Imeh Umoren; Ekemini Okpongkpong; Ifreke Udoeka
International Journal of Information Systems and Informatics Vol. 2 No. 4 (2021): International Journal of information Systems and Informatics
Publisher : Training & Research Institute - Jeramba Ilmu Sukses

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47747/ijisi.v2i4.581

Abstract

Mobile Ad hoc Networks (MANETs) involves series of travelling nodes communicating with each other without a fixed Set-up. Certainly, MANETs are networks that utilize communication peripatetic nodes such as; Personal Digital Assistants (PDAs), mobile phones, laptops which enable wireless transmission across an area and forwarding data packets to the other nodes resulting in frequent change in topology. MANETs are exposed to several communication assaults such as active attack and passive attack. The active attack disrupts network operations while the passive attack obtains information without upsetting normal networks operation. Wormhole is a typical case of active attack. Indeed, an attacker receives packets at one end of the network, tunnels them to another end of the network, and then replays them into the networks from that point resulting in a collapse in communication across wireless setups. This research work simulates and models a typical wormhole attack in MANET using Network Simulator (NS-2.35) and Fuzzy Inference System (FIS). The End-2-End behavioural analysis of wormhole attacks on the transmitting networks layer of MANET was realized. To detect the level of wormhole attack in the network several parameters such as Packet Delivery Ratio, Packet Forwarding Probability and Packet Dropping Probability were considered by determining the degree of severity of wormhole attack which may upset the Quality of Service (QOS) delivery. The aim of this research paper is in the direction of Network security optimization.
From Text to Insights: NLP-Driven Classification of Infectious Diseases Based on Ecological Risk Factors Saviour Inyang; Imeh Umoren
Journal of Innovation Information Technology and Application (JINITA) Vol 5 No 2 (2023): JINITA, December 2023
Publisher : Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/jinita.v5i2.2084

Abstract

Numerous factors can affect the development of infectious diseases that emerge. While many are the result of natural procedures, such as the gradual emergence of viruses over time, certain ones are the result of human activity. Human activities form an integral part of our ecosystem, and especially the ecological aspect of human activities can encourage disease transmission. Additionally, Health ecologists examine changes in the biological, physical, social, and economic settings to understand how these alterations impact the mental and physical well-being of individuals. Hence, this research adopts a Framework-Based Method (FBM) in carrying out the task of classification of infectious diseases. The Framework-Based Method outlines all phases that this research follows to carry out the infectious disease classification process, providing a structured and reproducible approach. Results show that: XGB: Confusion matrix accuracy: 76%, Kappa: 73%, RF: Confusion matrix accuracy: 65%, Kappa: 60%, SVM: Confusion matrix accuracy: 63%, Kappa: 58%, ANN: Confusion matrix accuracy: 71%, Kappa: 67%, LDA: Confusion matrix accuracy: 76%, Kappa: 73%, GBM: Confusion matrix accuracy: 60%, Kappa: 53%, KNN: Confusion matrix accuracy: 43%, Kappa: 34%, and DT: Confusion matrix accuracy: 37%, Kappa: 29%. Furthermore, a Deep Learning model BERT was integrated with the best classification model XGBoots to create an interactive interface for users to carry out infectious disease classification. This integration enhances user experience and accessibility, contributing to the practical application of machine learning and Natural language processing in ecological disease classification
Co-Authors Ekemini Okpongkpong Ifreke Udoeka Saviour Inyang