Article Per Year (5 Year)
p-Index From 2021 - 2026
0.882
P-Index
This Author published in this journals
All Journal Innovation in Research of Informatics (INNOVATICS) Journal of Technology and Informatics (JoTI) Internet of Things and Artificial Intelligence Journal Journal of Technology Informatics and Engineering International Journal of Artificial Intelligence and Science
Umoren, Imeh
Unknown Affiliation
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering Mechanical Engineering

Published : 5 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 5 Documents
Search

 1 
Machine Learning Approach for Classification of Cyber Threats Actors in Web Region Edet, Anthony; Inyang, Saviour; Umoren, Imeh; E. Etuk, Ubong
Journal of Technology and Informatics (JoTI) Vol. 6 No. 1 (2024): Vol. 6 No.1 (2024)
Publisher : Universitas Dinamika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37802/joti.v6i1.679

Abstract

In the interconnected scape of today's internet, the dark web emerges as a concealed point, covering a myriad of illicit activities that pose substantial cybersecurity risks. This study investigates the attribution of threats within the dark web environment, leveraging on a machine learning approach to bridge the gap between technical indicators and linguistic and behavioral insights. Through a comprehensive methodology involving web crawling and data gathering, a dataset encompassing key variables such as attack motivation, method, web part, and threat actor was gathered. Principal Component Analysis was employed for feature selection, followed by the application of Multinomial Naive Bayes (MNB), Support Vector Machine (SVM), Random Forest (RF), and CatBoost algorithms for classification. Performance evaluation metrics including precision, recall, and F1-score were utilized to assess the efficacy of each algorithm. Results indicate a notable prevalence of cybercrimes within the dark web, underscoring the necessity for enhanced cybersecurity strategies tailored to address its unique challenges. Furthermore, the comparative analysis demonstrates varying performance levels among the machine learning algorithms, with Multinomial Naive Bayes exhibiting the highest accuracy. This research contributes to advancing threat attribution techniques in the dark web, ultimately aiming to bolster cybersecurity defenses and mitigate future cyber threats.
NLP-Semantic Machine Learning-Based System for Intelligent Classification of Professional Skill-Sets for Efficient Human Resource Management Process Umoren, Imeh; Akwang, Nse; Inyang, Saviour; Afolorunso, Adenrele; James, Gabriel
Internet of Things and Artificial Intelligence Journal Vol. 5 No. 1 (2025): Volume 5 Issue 1, 2025 [February]
Publisher : Association for Scientific Computing, Electronics, and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/iota.v5i1.882

Abstract

Skill sets can improve individual professional proficiency and enable individuals to perform better at work. Professional skill sets create opportunities to aid in advancement in job classification of individual skill advantage resulting in good human resource management to efficiently present employers with adequate and qualified candidates for a given job offer. Classifying the right people for the right skills is a common task in human resource management. This research work presents a mechanism for classifying individual extracted Summary page texts of Curriculum Vitae (CV) through the application of the Semantic Machine Learning Model. First, data was gathered by mining different summary page curriculum vitae both online and offline. Second, preprocessing of datasets, by undergoing data cleaning, text normalization, and feature extraction and splitting data sets into training and test sets in the ratio of 80:20% for train and test set. Thirdly, exploratory data analysis was carried out to visualize different variables to determine how each metrics (parameter) interact with each other regarding Skill Sets classification based on the five topics concerns (Goal Oriented, Emotional Intelligence, Good Communication Skills, Problem Solving, and Leadership skills). Fourthly, Using an Artificial Neural Network for the classification of the text vectors, ANN gave an accuracy of 94% on the 10-epoch used in the model. Performance evaluation on the model was carried out and results show a precision of 82%, 76%, 40%, 66%, and 57 % respectively for Goal Oriented, Emotional Intelligence, Good Communication Skills, Problem Solving, and Leadership skills classifications. The proposed system served as an efficient Human resource management process.
A Clustering-Based Artificial Intelligence Approach for Minimizing of Ionizing Radiation Exposure in Uyo Metropolis Nigeria Umoren, Imeh; Inyang, Saviour Joshua; Etuk, Ubong E.; Essien, Daniel
Innovation in Research of Informatics (Innovatics) Vol 7, No 1 (2025): March 2025
Publisher : Department of Informatics, Siliwangi University, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37058/innovatics.v7i1.13024

Abstract

Electromagnetic Field (EMF) radio frequency exposure is a growing concern due to its impacts on public health and the environment. This study aims to develop a data-driven framework for clustering and analyzing long-term far-field EMF exposure in Uyo Metropolis, Nigeria, with a focus on identifying exposure patterns and assessing their implications. Data were measured at multiple locations using smart meter strategically deployed across three major roads in uyo metropolis to capture variations in exposure levels. The preprocessing steps involved data cleaning and normalization to enhance data quality and reliability for meaningful analysis.  Four clustering algorithms, namely, K-Means, Hierarchical Clustering, DBSCAN, and Gaussian Mixture Model (GMM), were employed to analyze the distribution of radiation levels. The Silhouette score was used to evaluate the different clustering methods with respect to cohesion within clusters and separation from other clusters. The best results were obtained by Hierarchical Clustering and GMM, each achieving a mean Silhouette score of 0.81, indicating well-defined and highly contrasting clusters. K-Means performed moderately well, with an average Silhouette score of 0.73, while DBSCAN, due to its sensitivity to noise and parameter settings, achieved a lower score of 0.62. These findings highlight significant spatial variability in EMF exposure across different urban zones, emphasizing the need for targeted regulatory measures. The study underscores effectiveness of machine learning and offers a scalable approach for characterizing EMF exposure. Results reported offer scalable and data-driven framework for characterizing exposure patterns, with important implications for public health policies, urban planning strategies, and regulatory interventions.
Contextual Framework for Remote Intelligent Monitoring and Detection System for Prediction of Pregnancy Complications Udonna, Uduakobong; Umoren, Imeh; Ansa, Godwin
Journal of Technology Informatics and Engineering Vol. 4 No. 2 (2025): AUGUST | JTIE : Journal of Technology Informatics and Engineering
Publisher : University of Science and Computer Technology

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/jtie.v4i2.244

Abstract

Maternal health disorders can cause complications and harmful incidents in women during pregnancy. To minimize risks, this research developed a platform powered by a supervised machine learning model (SMLM) to support remote intelligent monitoring and prediction of pregnancy complications caused by hypertensive disorder, gestational diabetes, and related indicators. The study used real-world datasets with six UCI Machine Learning Repository features to identify and predict Maternal Health Risk (MHR) factors. A Support Vector Machine (SVM) algorithm was applied to construct the classifier model, which was trained and evaluated using StratifiedKFold cross-validation (k=10). The model achieved 80% accuracy with a precision, recall, and F1-score of 77%. The outcome of this work is the P-Health mobile application, designed to record and track blood pressure, blood sugar, heart rate, body temperature, and weight, while predicting the risk level of pregnancy complications through inference from the integrated machine learning model. Developed with Kotlin and Android Studio, the application enables healthcare practitioners to remotely monitor patients’ vitals in real time. This innovation addresses the challenge of early detection of pregnancy complications and provides continuous monitoring and assessment. The findings suggest that P-Health can improve early detection and timely intervention, helping medical specialists minimize maternal health risks. The system also has the potential to raise public awareness of maternal health issues, thereby contributing to the prevention of complications during pregnancy.
Artificial Neural Network for Investigating the Impact of EMF on Ignition of Flammable Vapors in Gas Stations Umoren, Imeh; Inyang, Saviour; Etuk, Ubong; Akpanobong , Aloysius; James, Gabriel
International Journal of Artificial Intelligence and Science Vol. 2 No. 1 (2025): March
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/IJAIS.v2.i1.19

Abstract

The inadvertent ignition of flammable vapors by radio frequency (RF) radiation poses a significant safety risk in mega gas stations, necessitating the development of an intelligent predictive model for hazard prevention. This study proposes Artificial Neural Networks (ANN) Model to classify and predict ignition risks based on structured datasets obtained from smart sensing devices. The model formulation is based on the perceptron architecture, incorporating threshold logic units (TLUs) and multi-layer perceptron’s (MLPs) with backpropagation learning for enhanced predictive accuracy. The dataset, preprocessed to remove noise and redundancy, was divided into an 80:20 training-to-testing ratio and evaluated using cross-validation techniques. The experimental results show that the ANN-based model achieved an accuracy of 86%, demonstrating its effectiveness in identifying the impact of hazardous conditions. These findings underscore the robustness of the proposed approach, offering a reliable solution for mitigating ignition hazards in industrial environments. This research contributes to advancing safety protocols by leveraging on machine learning for predictive hazard assessment in flammable vapor-prone areas.
Co-Authors Afolorunso, Adenrele Akpanobong , Aloysius Akwang, Nse Ansa, Godwin E. Etuk, Ubong Edet, Anthony Essien, Daniel Etuk, Ubong Etuk, Ubong E. Inyang, Saviour Inyang, Saviour Joshua James, Gabriel Udonna, Uduakobong