Christopher Chukwudi Onochie
Federal College of Education (Technical)

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EcoSMEAL: Energy Consumption with Optimization Strategy via a Secured Smart Monitor-Alert Ensemble Tabitha Chukwudi Aghaunor; Joy Agboi; Eferhire Valentine Ugbotu; Paul Avweresuoghene Onoma; Arnold Adimabua Ojugo; Christopher Chukwufunaya Odiakaose; Andrew Okonji Eboka; Peace Oguguo Ezzeh; Victor Ochuko Geteloma; Amaka Patience Binitie; Anderson Ise Orobor; Blessing Uche Nwozor; Patrick Ogholuwarami Ejeh; Christopher Chukwudi Onochie
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.
Phishing Website Detection via a Transfer Learning based XGBoost Meta-learner with SMOTE-Tomek Joy Agboi; Frances Uche Emordi; Christopher Chukwufunaya Odiakaose; Rebecca Okeoghene Idama; Evans Fubara Jumbo; Amanda Enaodona Oweimieotu; Peace Oguguo Ezzeh; Andrew Okonji Eboka; Anne Odoh; Eferhire Valentine Ugbotu; Paul Avwerosuoghene Onoma; Arnold Adimabua Ojugo; Tabitha Chukwudi Aghaunor; Amaka Patience Binitie; Christopher Chukwudi Onochie; Patrick Ogholuwarami Ejeh; Blessing Uche Nwozor
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.