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All Journal Journal of Information Systems and Informatics Journal of Technology and Informatics (JoTI) Internet of Things and Artificial Intelligence Journal International Journal of Artificial Intelligence and Science
Inyang, Saviour
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering Mechanical Engineering

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Realtime-Based System for Facemask Detection Using PCA, with CNN and COCO Model Etuk, Ubong; Umoren, Imeh; Umoren, Odudu; Inyang, Saviour
Journal of Information System and Informatics Vol 6 No 2 (2024): June
Publisher : Universitas Bina Darma

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.
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.
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 E. Etuk, Ubong Edet, Anthony Etuk, Ubong James, Gabriel Umoren, Imeh Umoren, Odudu