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All Journal Journal of Information Systems and Informatics 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

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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
Towards Sustainable Smart Living: Cloud-Based IoT Solutions for Home Automation Ubong E Etuk; Gabriel Omenaru; Saviour Joshua Inyang; Imeh Umoren
Journal of Information System and Informatics Vol 5 No 4 (2023): Journal of Information Systems and Informatics
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v5i4.621

Abstract

In recent times, the realm of home automation systems has garnered significant attention, thanks to the ever-evolving landscape of communication technology. The concept of a smart home, essentially an application of the Internet of Things (IoT), leverages the power of the internet to oversee and employ household appliances through a sophisticated automation infrastructure. Nevertheless, challenges persist within the existing home automation systems, such as constrained wireless transmission reach, a deficiency in backup power management, and the substantial financial outlay involved. Addressing these limitations, our study introduces an economical and resilient solution that combines cloud based IoT with an uninterrupted power management system, making a cutting-edge home automation prototype. This system relies on a microcontroller unit, specifically the ESP-32, which functions as a Wi-Fi-enabled gateway for connecting a variety of sensors and transmitting their data to the Blynk IoT cloud server. The data assembled from a multitude of sensors, including vibration sensors and voltage detectors, becomes readily accessible on users' devices, be it smartphones or laptops, irrespective of their geographical location. The system is further strengthened by a set of relays that link the ESP-32 with household appliances, allowing for centralized control. Structurally, the design uses a control box that can be seamlessly integrated into a real home environment, offering the means to both monitor and govern an array of household devices. This IoT-based home automation solution not only efficiently manages internet-connected appliances but also provides an effective emergency power management system, enabling remote initiation and deactivation of backup generators. It represents a innovative leap in the evolution of home automation systems, steering in convenience, efficiency, and cost-effectiveness.
Realtime-Based System for Facemask Detection Using PCA, with CNN and COCO Model Ubong Etuk; Imeh Umoren; Odudu Umoren; Saviour Inyang
Journal of Information System and Informatics Vol 6 No 2 (2024): June
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v6i2.759

Abstract

The instant spread of COVID-19 has underscored the need for effective measures such as wearing face masks to control transmission. As a response, facemask detection systems using advanced machine learning techniques have become essential for ensuring compliance and public safety. This research focused on developing a system for detecting facemask usage using a hybridized approach comprising of Convolutional Neural Networks (CNN), Principal Component Analysis (PCA), and the Common Objects in Context (COCO) model. A hybridized detection model is often explored to enhance the precision and efficiency of previous methods that leveraged traditional machine learning or deep learning for the same task. Hence, this system effectively identifies whether individuals are properly wearing masks, not wearing masks at all, or wearing masks improperly from images and real-time video streams using bounding boxes. The results demonstrate that the hybrid approach achieves high accuracy in detecting various facemask conditions across different scenarios. Evaluation metrics such as Average Precision (AP) and Average Recall (AR) indicate the model's robustness, with a reported AP value of 70% and an AR value of 81%, primarily evaluated on larger objects within images. Further evaluations involving different individuals and types of facemasks revealed variability in detection accuracy, highlighting the model's effectiveness and areas for improvement. Nevertheless, the development and deployment of facemask detection systems are crucial for managing public health and ensuring safety in the face of ongoing and future pandemics.
Co-Authors Ekemini Okpongkpong Gabriel Omenaru Ifreke Udoeka Odudu Umoren Saviour Inyang Saviour Joshua Inyang Ubong E Etuk Ubong Etuk