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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) International Journal of Informatics and Communication Technology (IJ-ICT) Journal of Computing Theories and Applications
Andrew Okonji Eboka
Federal College of Education (Technical)
Computer Science & IT Decision Sciences, Operations Research & Management Engineering

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Empirical Bayesian network to improve service delivery and performance dependability on a campus network Arnold Adimabua Ojugo; Andrew Okonji Eboka
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 10, No 3: September 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v10.i3.pp623-635

Abstract

An effective systemic approach to task will lead to efficient communication and resource sharing within a network. This has become imperative as it aids alternative delivery. With communication properly etched into the fabrics of today’s society via effective integration of informatics and communication technology, the constant upgrades to existing network infrastructure are only a start to meeting with the ever-increasing challenges. There are various criteria responsible for network performance, scalability, and resilience. To ensure best practices, we analyze the network and select parameters required to improve performance irrespective of bottlenecks, potentials, and expansion capabilities of the network infrastructure. Study compute feats via Bayesian network design alongside upgrades implementation to result in a prototype design, capable of addressing users need(s). Thus, to ensure functionality, the experimental network uses known simulation kits such as riverbed modeler edition 17.5 and cisco packet tracer 6.0.1-to conduct standardized tests such as throughput test, application response-time test, and availability test.
Inventory prediction and management in Nigeria using market basket analysis associative rule mining: memetic algorithm based approach Arnold Adimabua Ojugo; Andrew Okonji Eboka
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 8, No 3: December 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (108.674 KB) | DOI: 10.11591/ijict.v8i3.pp128-138

Abstract

A key challenge in businesses today is determining inventory level for each product (to be) sold to clients. A pre-knowledge will suppress inventory stock-up and help avert unnecessary demurrage. It will also avoid stock out and avert loss of clients to competition. Study aims to unveil customer’s behavior in purchasing goods and thus, predict a next time purchase as well as serve as decision support to determine the required amount of each goods inventory. Study is conducted for Delta Mall (Asaba and Warri branches) department store. We adapt the memetic algorithm on market basket dataset to examine buying behavior of customers, their preference and frequency at which goods are purchased in common (basket). Result shows some items placed in basket allow customers to purchase items of similar value, or best combined with the selected items due to shelf-placement via concept of feature drift. Model yields 21-rules for eight items obtained from data transaction mining dataset acquired from Delta Mall.
Investigating a SMOTE-Tomek Boosted Stacked Learning Scheme for Phishing Website Detection: A Pilot Study Eferhire Valentine Ugbotu; Frances Uchechukwu Emordi; Emeke Ugboh; Kizito Eluemunor Anazia; Christopher Chukwufunaya Odiakaose; Paul Avwerosuoghene Onoma; Rebecca Okeoghene Idama; Arnold Adimabua Ojugo; Victor Ochuko Geteloma; Amanda Enaodona Oweimieotu; Tabitha Chukwudi Aghaunor; Amaka Patience Binitie; Anne Odoh; Chris Chukwudi Onochie; Peace Oguguo Ezzeh; Andrew Okonji Eboka; Joy Agboi; Patrick Ogholuwarami Ejeh
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.
Co-Authors Amaka Patience Binitie Amanda Enaodona Oweimieotu Anne Odoh Arnold Adimabua Ojugo Arnold Adimabua Ojugo Chris Chukwudi Onochie Christopher Chukwufunaya Odiakaose Eferhire Valentine Ugbotu Emeke Ugboh Frances Uchechukwu Emordi Joy Agboi Kizito Eluemunor Anazia Patrick Ogholuwarami Ejeh Paul Avwerosuoghene Onoma Peace Oguguo Ezzeh Rebecca Okeoghene Idama Tabitha Chukwudi Aghaunor Victor Ochuko Geteloma