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All Journal Journal of Fuzzy Systems and Control (JFSC) Journal of Future Artificial Intelligence and Technologies
Ugbotu, Eferhire Valentine
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Energy Engineering

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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.
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.
EcoSMEAL: Energy Consumption with Optimization Strategy via a Secured Smart Monitor-Alert Ensemble Aghaunor, Tabitha Chukwudi; Agboi, Joy; Ugbotu, Eferhire Valentine; Onoma, Paul Avweresuoghene; Ojugo, Arnold Adimabua; Odiakaose, Christopher Chukwufunaya; Eboka, Andrew Okonji; Ezzeh, Peace Oguguo; Geteloma, Victor Ochuko; Binitie, Amaka Patience; Orobor, Anderson Ise; Nwozor, Blessing Uche; Ejeh, Patrick Ogholuwarami; Onochie, Christopher Chukwudi
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.v3i3.319

Abstract

The global demand for automation that seeks the efficient consumption and usage of energy via the adoption of embedded-fit management solutions that yield improved performance with reduced consumption has become the new norm. These explore sensor-based units in their own right with eco-friendly platforms that raise germane environmental, health, and consumption regulation(s) concerns that have today become a global issue, even when they proffer improved life standards that replace traditional solutions. Our study posits an embedded sensor design to observe environmental conditions associated with energy consumption by residential or home appliances. It utilizes a machine learning scheme and algorithm to analyze the total energy consumed by each appliance and delivers optimal consumption that reduces energy waste. The system was tested across multiple parameters and found to yield desired effectiveness, reliability, and efficiency. Our utilization of the ESP8266 and ThingSpeak is able to handle extensive inputs without significant delays or data losses. Results affirms the system ability to maintain stable performance even with more devices connected to the unit.
Co-Authors Abdullahi, Mustapha Barau Agboi, Joy Aghaunor, Tabitha Chukwudi Aghware, Fidelis Obukohwo Ako, Rita Erhovwo Binitie, Amaka Patience De Rosal Ignatius Moses Setiadi Eboka, Andrew Okonji Ejeh, Patrick Ogholuwarami Emordi, Frances Uche Ezzeh, Peace Oguguo Geteloma, Victor Ochuko Ibor, Ayei Egu Idama, Rebecca Okeoghene Jumbo, Evans Fubara Max-Egba, Asuobite ThankGod Niemogha, Star Umiyemeromesu Nwozor, Blessing Uche Odiakaoase, Christopher Chukwufunaya Odiakaose, Christopher Chukwufunaya Odoh, Anne Ojugo, Arnold Adimabua Okpor, Margaret Dumebi Onochie, Christopher Chukwudi Onoma, Paul Avweresuo Onoma, Paul Avweresuoghene Onoma, Paul Avwerosuoghene Orobor, Anderson Ise Oweimieotu, Amanda Enaodona