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All Journal International Journal of Informatics and Communication Technology (IJ-ICT) Bulletin of Electrical Engineering and Informatics Applied Engineering and Technology Journal of Computing Theories and Applications Journal of Fuzzy Systems and Control (JFSC) Journal of Future Artificial Intelligence and Technologies
Ejeh, Patrick Ogholuwarami
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Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Materials Science & Nanotechnology

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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.
Anomaly-based Detection of Denial of Service via Deep Learning Memetic Trained Modular Network Ejeh, Patrick Ogholuwarami; Adjogbe, Fidelis Oghenevweta; Nwanze, David; 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.v3i1.274

Abstract

Internet’s popularity for dissemination of data – has birthed the proliferation of attacks that exploit networks for personal gain. Attackers via social-engineering attacks, gain unauthorized access to a compromised device via subterfuge mode and deny users of network resources. Denial of service (DoS) attack is carefully crafted to exploit high levels of network infrastructures. Our study presents a deep learning scheme to effectively classify between genuine and malicious packets. With benchmark XGBoost, Random Forest, and Decision Tree – our resultant model yields an accuracy 0.9984 and F1 0.9945 to outperform the benchmark XGBoost, RF and DT (with F1 of 0.9925, 0.9881 and 0.9805 – and Accuracy of 0.9981, 0.9964 and 0.9815) respectively. Proposed model correctly classified 13,418 cases with a 0.9984 accuracy and has only 283 cases incorrectly classified. Proposed memetic ensemble effectively differentiates malicious from genuine packets using anomaly-based detection.
Investigating a SMOTE-Tomek Boosted Stacked Learning Scheme for Phishing Website Detection: A Pilot Study Ugbotu, Eferhire Valentine; Emordi, Frances Uchechukwu; Ugboh, Emeke; Anazia, Kizito Eluemunor; Odiakaose, Christopher Chukwufunaya; Onoma, Paul Avwerosuoghene; Idama, Rebecca Okeoghene; Ojugo, Arnold Adimabua; Geteloma, Victor Ochuko; Oweimieotu, Amanda Enaodona; Aghaunor, Tabitha Chukwudi; Binitie, Amaka Patience; Odoh, Anne; Onochie, Chris Chukwudi; Ezzeh, Peace Oguguo; Eboka, Andrew Okonji; Agboi, Joy; Ejeh, Patrick Ogholuwarami
Journal of Computing Theories and Applications Vol. 3 No. 2 (2025): JCTA 3(2) 2025
Publisher : Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/jcta.14472

Abstract

The daily exchange of informatics over the Internet has both eased the widespread proliferation of resources to ease accessibility, availability and interoperability of accompanying devices. In addition, the recent widespread proliferation of smartphones alongside other computing devices has continued to advance features such as miniaturization, portability, data access ease, mobility, and other merits. It has also birthed adversarial attacks targeted at network infrastructures and aimed at exploiting interconnected cum shared resources. These exploits seek to compromise an unsuspecting user device cum unit. Increased susceptibility and success rate of these attacks have been traced to user's personality traits and behaviours, which renders them repeatedly vulnerable to such exploits especially those rippled across spoofed websites as malicious contents. Our study posits a stacked, transfer learning approach that seeks to classify malicious contents as explored by adversaries over a spoofed, phishing websites. Our stacked approach explores 3-base classifiers namely Cultural Genetic Algorithm, Random Forest, and Korhonen Modular Neural Network – whose output is utilized as input for XGBoost meta-learner. A major challenge with learning scheme(s) is the flexibility with the selection of appropriate features for estimation, and the imbalanced nature of the explored dataset for which the target class often lags behind. Our study resolved dataset imbalance challenge using the SMOTE-Tomek mode; while, the selected predictors was resolved using the relief rank feature selection. Results shows that our hybrid yields F1 0.995, Accuracy 0.997, Recall 0.998, Precision 1.000, AUC-ROC 0.997, and Specificity 1.000 – to accurately classify all 2,764 cases of its held-out test dataset. Results affirm that it outperformed bench-mark ensembles. Result shows the proposed model explored UCI Phishing Website dataset, and effectively classified phishing (cues and lures) contents on websites.
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 Abere, Reuben Akporube Adigwe, Wilfred Adjogbe, Fidelis Oghenevweta Afotanwo, Anderson Agboi, Joy Aghaunor, Tabitha Chukwudi Aghware, Fidelis Obukohwo Akazue, Maureen Ifeanyi Ako, Rita Erhovwo Anazia, Kizito Eluemunor Ashioba, Nwanze Chukwudi Binitie, Amaka Patience De Rosal Ignatius Moses Setiadi Eboka, Andrew Okonji Edim, Edim Bassey Efetobore Edje, Abel Emordi, Frances Uche Emordi, Frances Uchechukwu Ezzeh, Peace Oguguo Geteloma, Victor Ochuko Idama, Rebecca Okeoghene Ifeanyi Akazue, Maureen Jumbo, Evans Fubara Nwanze, David Nwozor, Blessing Nwozor, Blessing Uche Odiakaose , Christopher Chukwufunaya Odiakaose, Chris Chukwufunaya Odiakaose, Christopher Chukufunaya Odiakaose, Christopher Chukwufunaya Odiakaose, Chukwufunaya Chris Odoh, Anne Ojei, Emmanuel Obiajulu Ojugo, Arnold Adimabua Okpako, Ejaita Abugor Okpor, Margaret Dumebi Okpor, Margareth Dumebi Oladele, James Kolapo Onochie, Chris Chukwudi Onochie, Christopher Chukwudi Onoma, Paul Avweresuoghene Onoma, Paul Avwerosuoghene Onyemenem, Innocent Sunny Orobor, Anderson Ise Oweimieotu, Amanda Enaodona Ugboh, Emeke Ugbotu, Eferhire Valentine Yoro, Rume Elizabeth