Article Per Year (5 Year)
p-Index From 2021 - 2026
3.788
P-Index
This Author published in this journals
All Journal IAES International Journal of Artificial Intelligence (IJ-AI) International Journal of Informatics and Communication Technology (IJ-ICT) Bulletin of Electrical Engineering and Informatics Journal of Applied Science, Engineering, Technology, and Education Jurnal Teknik Informatika (JUTIF) JINAV: Journal of Information and Visualization Quantitative Economics and Management Studies ARRUS Journal of Mathematics and Applied Science ARRUS Journal of Engineering and Technology Applied Engineering and Technology Journal of Fuzzy Systems and Control (JFSC) Journal of Future Artificial Intelligence and Technologies
Ojugo, Arnold Adimabua
Unknown Affiliation
Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Library & Information Science Materials Science & Nanotechnology Mathematics Physics Other

Published : 33 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 33 Documents
Search

 1   2   3   4 
Integrating SMOTE-Tomek and Fusion Learning with XGBoost Meta-Learner for Robust Diabetes Recognition Setiadi, De Rosal Ignatius Moses; Nugroho, Kristiawan; Muslikh, Ahmad Rofiqul; Iriananda, Syahroni Wahyu; Ojugo, Arnold Adimabua
Journal of Future Artificial Intelligence and Technologies Vol. 1 No. 1 (2024): June 2024
Publisher : Future Techno Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/faith.2024-11

Abstract

This research aims to develop a robust diabetes classification method by integrating the Synthetic Minority Over-sampling Technique (SMOTE)-Tomek technique for data balancing and using a machine learning ensemble led by eXtreme Gradient Boosting (XGB) as a meta-learner. We propose an ensemble model that combines deep learning techniques such as Bidirectional Long Short-Term Memory (BiLSTM) and Bidirectional Gated Recurrent Units (BiGRU) with XGB classifier as the base learner. The data used included the Pima Indians Diabetes and Iraqi Society Diabetes datasets, which were processed by missing value handling, duplication, normalization, and the application of SMOTE-Tomek to resolve data imbalances. XGB, as a meta-learner, successfully improves the model's predictive ability by reducing bias and variance, resulting in more accurate and robust classification. The proposed ensemble model achieves perfect accuracy, precision, recall, specificity, and F1 score of 100% on all tested datasets. This method shows that combining ensemble learning techniques with a rigorous preprocessing approach can significantly improve diabetes classification performance.
Analyzing Quantum Feature Engineering and Balancing Strategies Effect on Liver Disease Classification Safriandono, Achmad Nuruddin; Setiadi, De Rosal Ignatius Moses; Dahlan, Akhmad; Rahmanti, Farah Zakiyah; Wibisono, Iwan Setiawan; Ojugo, Arnold Adimabua
Journal of Future Artificial Intelligence and Technologies Vol. 1 No. 1 (2024): June 2024
Publisher : Future Techno Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/faith.2024-12

Abstract

This research aims to improve the accuracy of liver disease classification using Quantum Feature Engineering (QFE) and the Synthetic Minority Over-sampling Tech-nique and Tomek Links (SMOTE-Tomek) data balancing technique. Four machine learning models were compared in this research, namely eXtreme Gradient Boosting (XGB), Random Forest (RF), Support Vector Machine (SVM), and Logistic Regression (LR) on the Indian Liver Patient Dataset (ILPD) dataset. QFE is applied to capture correlations and complex patterns in the data, while SMOTE-Tomek is used to address data imbalances. The results showed that QFE significantly improved LR performance in terms of recall and specificity up to 99%, which is very important in medical diagnosis. The combination of QFE and SMOTE-Tomek gives the best results for the XGB method with an accuracy of 81%, recall of 90%, and f1-score of 83%. This study concludes that the use of QFE and data balancing techniques can improve liver disease classification performance in general.
Pilot Study on Enhanced Detection of Cues over Malicious Sites Using Data Balancing on the Random Forest Ensemble Okpor, Margaret Dumebi; Aghware, Fidelis Obukohwo; Akazue, Maureen Ifeanyi; Eboka, Andrew Okonji; Ako, Rita Erhovwo; Ojugo, Arnold Adimabua; Odiakaose, Christopher Chukwufunaya; Binitie, Amaka Patience; Geteloma, Victor Ochuko; Ejeh, Patrick Ogholuwarami
Journal of Future Artificial Intelligence and Technologies Vol. 1 No. 2 (2024): September 2024
Publisher : Future Techno Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/faith.2024-14

Abstract

The digital revolution frontiers have rippled across society today – with various web content shared online for users as they seek to promote monetization and asset exchange, with clients constantly seeking improved alternatives at lowered costs to meet their value demands. From item upgrades to their replacement, businesses are poised with retention strategies to help curb the challenge of customer attrition. The birth of smartphones has proliferated feats such as mobility, ease of accessibility, and portability – which, in turn, have continued to ease their rise in adoption, exposing user device vulnerability as they are quite susceptible to phishing. With users classified as more susceptible than others due to online presence and personality traits, studies have sought to reveal lures/cues as exploited by adversaries to enhance phishing success and classify web content as genuine and malicious. Our study explores the tree-based Random Forest to effectively identify phishing cues via sentiment analysis on phishing website datasets as scrapped from user accounts on social network sites. The dataset is scrapped via Python Google Scrapper and divided into train/test subsets to effectively classify contents as genuine or malicious with data balancing and feature selection techniques. With Random Forest as the machine learning of choice, the result shows the ensemble yields a prediction accuracy of 97 percent with an F1-score of 98.19% that effectively correctly classified 2089 instances with 85 incorrectly classified instances for the test-dataset.
Hypertension Detection via Tree-Based Stack Ensemble with SMOTE-Tomek Data Balance and XGBoost Meta-Learner Odiakaose, Christopher Chukwufunaya; Aghware, Fidelis Obukohwo; Okpor, Margaret Dumebi; Eboka, Andrew Okonji; Binitie, Amaka Patience; Ojugo, Arnold Adimabua; Setiadi, De Rosal Ignatius Moses; Ibor, Ayei Egu; Ako, Rita Erhovwo; Geteloma, Victor Ochuko; Ugbotu, Eferhire Valentine; Aghaunor, Tabitha Chukwudi
Journal of Future Artificial Intelligence and Technologies Vol. 1 No. 3 (2024): December 2024
Publisher : Future Techno Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/faith.3048-3719-43

Abstract

High blood pressure (or hypertension) is a causative disorder to a plethora of other ailments – as it succinctly masks other ailments, making them difficult to diagnose and manage with a targeted treatment plan effectively. While some patients living with elevated high blood pressure can effectively manage their condition via adjusted lifestyle and monitoring with follow-up treatments, Others in self-denial leads to unreported instances, mishandled cases, and in now rampant cases – result in death. Even with the usage of machine learning schemes in medicine, two (2) significant issues abound, namely: (a) utilization of dataset in the construction of the model, which often yields non-perfect scores, and (b) the exploration of complex deep learning models have yielded improved accuracy, which often requires large dataset. To curb these issues, our study explores the tree-based stacking ensemble with Decision tree, Adaptive Boosting, and Random Forest (base learners) while we explore the XGBoost as a meta-learner. With the Kaggle dataset as retrieved, our stacking ensemble yields a prediction accuracy of 1.00 and an F1-score of 1.00 that effectively correctly classified all instances of the test dataset.
Stacked Learning Anomaly Detection Scheme with Data Augmentation for Spatiotemporal Traffic Flow Binitie, Amaka Patience; Odiakaose , Christopher Chukwufunaya; Okpor, Margaret Dumebi; Ejeh, Patrick Ogholuwarami; Eboka, Andrew Okonji; Ojugo, Arnold Adimabua; Setiadi, De Rosal Ignatius Moses; Ako, Rita Erhovwo; Aghaunor, Tabitha Chukwudi; Geteloma, Victor Ochuko; Afotanwo, Anderson
Journal of Fuzzy Systems and Control Vol. 2 No. 3 (2024): Vol. 2, No. 3, 2024
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/jfsc.v2i3.267

Abstract

The digital revolution births transformation in many facets of today’s society. Its adoption in transportation to curb traffic congestion in major cities globally advances smart-city initiatives. Challenges of population growth, lack of datasets, and aging infrastructure have necessitated the need for traffic analytics. Studies have estimated an associated global annual loss of $583 billion to traffic congestion for 2023. This, caused fuel wastage, loss of time, and increased costs across congested areas. With the cost of building more road networks, cities must advance new ways to improve traffic flow via anomaly detection as an early warning in the flow pattern. Our study posits stacked learning with extreme gradient boost as a meta-learner to help address imbalanced datasets, yield faster model construction, and ensure improved performance via enhanced anomalous data detection.
Pilot study on deploying a wireless sensor-based virtual-key access and lock system for home and industrial frontiers Eboka, Andrew Okonji; Aghware, Fidelis Obukohwo; Okpor, Margaret Dumebi; Odiakaose, Christopher Chukufunaya; Okpako, Ejaita Abugor; Ojugo, Arnold Adimabua; Ako, Rita Erhovwo; Binitie, Amaka Patience; Onyemenem, Innocent Sunny; Ejeh, Patrick Ogholuwarami; Geteloma, Victor Ochuko
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 14, No 1: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v14i1.pp287-297

Abstract

The rise in data processing activities vis-à-vis the consequent rise in adoption and adaptation of information and communication tech related approaches to resolve societal challenges has become both critical and imperative. Virtualization have become the order of the day to bridge various lapses of human mundane tasks and endeavors. Its positive impacts on society cannot be underestimated. This study advances a virtual wireless sensor-based key-card access system with cost-effective solution to manage access to restricted areas within a facility. We seek to integrate virtual key card access, web-access control, solenoid lock integration, and ESP32- controller to create a dependable internet of things (IoT)-enabled access control system. Results show system benefit includes improved security, improved convenience, privacy, efficiency with real-time control capabilities that will allows building administrators to track and manage access to the facility remotely.
High-Performance Face Spoofing Detection using Feature Fusion of FaceNet and Tuned DenseNet201 Zuama, Leygian Reyhan; Setiadi, De Rosal Ignatius Moses; Susanto, Ajib; Santosa, Stefanus; Gan, Hong-Seng; Ojugo, Arnold Adimabua
Journal of Future Artificial Intelligence and Technologies Vol. 1 No. 4 (2025): March 2025
Publisher : Future Techno Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/faith.3048-3719-62

Abstract

Face spoofing detection is critical for biometric security systems to prevent unauthorized access. This study proposes a deep learning-based approach integrating FaceNet and DenseNet201 to enhance face spoofing detection performance. FaceNet generates identity-based embeddings, ensuring robust facial feature representation, while DenseNet201 extracts complementary texture-based features. These features are fused using the Concatenate function to form a more comprehensive representation for im-proved classification. The proposed method is evaluated on two widely used face spoofing datasets, NUAA Photograph Imposter and LCC-FASD, achieving 100% accuracy on NUAA and 99% on LCC-FASD. Ablation studies reveal that data augmentation does not always enhance performance, particularly on high-complexity datasets such as LCC-FASD, where augmentation increases the False Rejection Rate (FRR). Conversely, DenseNet201 benefits more from augmentation, while the proposed method performs best without augmentation. Comparative analysis with previous studies further confirms the superiority of the proposed approach in reducing error rates, particularly Half Total Error Rate (HTER), False Acceptance Rate (FAR), and FRR. These findings indicate that combining identity-based embeddings and texture-based feature extraction significantly improves spoofing detection and enhances model robustness across different attack scenarios. This study advances biometric security by introducing an efficient feature fusion strategy that strengthens deep learning-based spoof detection. Future research may explore further optimization strategies and evaluate the approach on more diverse datasets to enhance generalization.
Investigating an Anomaly-based Intrusion Detection via Tree-based Adaptive Boosting Ensemble Onoma, Paul Avweresuo; Agboi, Joy; Geteloma, Victor Ochuko; Max-Egba, Asuobite ThankGod; Eboka, Andrew Okonji; Ojugo, Arnold Adimabua; Odiakaoase, Christopher Chukwufunaya; Ugbotu, Eferhire Valentine; Aghaunor, Tabitha Chukwudi; Binitie, Amaka Patience
Journal of Fuzzy Systems and Control Vol. 3 No. 1 (2025): Vol. 3, No. 1, 2025
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/jfsc.v3i1.279

Abstract

The eased accessibility, mobility, and portability of smartphones have caused the consequent rise in the proliferation of users' vulnerability to a variety of phishing attacks. Some users are more vulnerable due to factors like personality behavioral traits, media presence, and other factors. Our study seeks to reveal cues utilized by successful attacks by identifying web content as genuine and malicious data. We explore a sentiment-based extreme gradient boost learner with data collected over social platforms, scraped using the Python Google Scrapper. Our results show AdaBoost yields a prediction accuracy of 0.9989 to correctly classify 2148 cases with incorrectly classified 25 cases. The result shows the tree-based AdaBoost ensemble can effectively identify phishing cues and efficiently classify phishing lures against unsuspecting users from access to malicious content.
Voice-based Dynamic Time Warping Recognition Scheme for Enhanced Database Access Security Onoma, Paul Avweresuo; Ugbotu, Eferhire Valentine; Aghaunor, Tabitha Chukwudi; Agboi, Joy; Ojugo, Arnold Adimabua; Odiakaose, Christopher Chukwufunaya; Max-Egba, Asuobite ThankGod; Niemogha, Star Umiyemeromesu; Binitie, Amaka Patience; Abdullahi, Mustapha Barau
Journal of Fuzzy Systems and Control Vol. 3 No. 1 (2025): Vol. 3, No. 1, 2025
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/jfsc.v3i1.293

Abstract

Rapid transformation with database security has remained imperative as unauthorized access exposes sensitive data to adversaries. To curb this, we suggest using a secured dynamic time-warp scheme to improve access to the database schemas. The study integrates voice biometrics with two-factor authentication to yield a robust, user-friendly platform, which utilizes time-warping to authenticate voice patterns against the variability in utterance speed. Results showcase high accuracy and resiliency in its usage against spoofing attacks as compared to state-of-the-art voice recognition systems. The model ensures the minimal possibility of credential theft by binding the access of databases to the voice features of authorized users. The study shows the system's architecture, implementation, and performance evaluation, highlighting its potential to revolutionize database security in various applications. The findings underscore the importance of leveraging advanced biometric techniques to safeguard critical information systems.
Phishing Website Detection via a Transfer Learning based XGBoost Meta-learner with SMOTE-Tomek Agboi, Joy; Emordi, Frances Uche; Odiakaose, Christopher Chukwufunaya; Idama, Rebecca Okeoghene; Jumbo, Evans Fubara; Oweimieotu, Amanda Enaodona; Ezzeh, Peace Oguguo; Eboka, Andrew Okonji; Odoh, Anne; Ugbotu, Eferhire Valentine; Onoma, Paul Avwerosuoghene; Ojugo, Arnold Adimabua; Aghaunor, Tabitha Chukwudi; Binitie, Amaka Patience; Onochie, Christopher Chukwudi; Ejeh, Patrick Ogholuwarami; Nwozor, Blessing Uche
Journal of Fuzzy Systems and Control Vol. 3 No. 3 (2025): Vol. 3 No. 3 (2025)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/jfsc.v3i3.325

Abstract

The widespread proliferation of smartphones has advanced portability, data access ease, mobility, and other merits; it has also birthed adversarial targeting of network resources that seek to compromise unsuspecting user devices. Increased susceptibility was traced to user's personality, which renders them repeatedly vulnerable to exploits. Our study posits a stacked learning model to classify malicious lures used by adversaries on phishing websites. Our hybrid fuses 3-base learners (i.e. Genetic Algorithm, Random Forest, Modular Net) with its output sent as input to the XGBoost. The imbalanced dataset was resolved via SMOTE-Tomek with predictors selected using a relief rank feature selection. Our hybrid yields F1 0.995, Accuracy 1.000, Recall 0.998, Precision 1.000, MCC 1.000, and Specificity 1.000 – to accurately classify all 3,316 cases of its held-out test dataset. Results affirm that it outperformed benchmark ensembles. The study shows that our proposed model, as explored on the UCI Phishing Website dataset, effectively classified phishing (cues and lures) contents on websites.
Co-Authors Abdullahi, Mustapha Barau Achmad Nuruddin Safriandono Adigwe, Wilfred Afotanwo, Anderson Agboi, Joy Aghaunor, Tabitha Chukwudi Aghware, Fidelis Obukohwo Ajib Susanto Akazue, Maureen Ifeanyi Akhmad Dahlan Ako, Rita Erhovwo Ashioba, Nwanze Chukwudi Binitie, Amaka Patience Budi Widjajanto De Rosal Ignatius Moses Setiadi Dian Kristiawan Nugroho Eboka, Andrew Okonji Edim, Edim Bassey Efetobore Edje, Abel Ejeh, Patrick Ogholorunwalomi Ejeh, Patrick Ogholuwarami Emordi, Frances Uche Emordi, Frances Uchechukwu Ezzeh, Peace Oguguo Farah Zakiyah Rahmanti Gan, Hong-Seng Geteloma, Victor Ochuko Ibor, Ayei Egu Idama, Rebecca Okeoghene Ifeanyi Akazue, Maureen Iwan Setiawan Wibisono Jumbo, Evans Fubara Max-Egba, Asuobite ThankGod Muhamada, Keny Muslikh, Ahmad Rofiqul Niemogha, Star Umiyemeromesu Nwankwo, Obinna Nwozor, Blessing Uche Obruche, Chris Obaro Odiakaoase, Christopher Chukwufunaya Odiakaose , Christopher Chukwufunaya Odiakaose, Christopher Chukufunaya Odiakaose, Christopher Chukwufunaya Odiakaose, Chukwufunaya Chris Odoh, Anne Ojei, Emmanuel Obiajulu Okpako, Ejaita Abugor Okpor, Margaret Dumebi Okpor, Margareth Dumebi Onochie, Christopher Chukwudi Onoma, Paul Avweresuo Onoma, Paul Avweresuoghene Onoma, Paul Avwerosuoghene Onyemenem, Innocent Sunny Orobor, Anderson Ise Otakore, Oghenevwede Debby Oweimieotu, Amanda Enaodona Stefanus Santosa Sudibyo, Usman Syahroni Wahyu Iriananda, Syahroni Wahyu Taylor, Onate Egerton Ugbotu, Eferhire Valentine Yoro, Rume Elizabeth Zuama, Leygian Reyhan