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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
Geteloma, Victor Ochuko
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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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Journal : Journal of Fuzzy Systems and Control (JFSC)

 1 
Comparative Data Resample to Predict Subscription Services Attrition Using Tree-based Ensembles Okpor, Margaret Dumebi; Aghware, Fidelis Obukohwo; Akazue, Maureen Ifeanyi; Ojugo, Arnold Adimabua; Emordi, Frances Uche; Odiakaose, Christopher Chukwufunaya; Ako, Rita Erhovwo; Geteloma, Victor Ochuko; Binitie, Amaka Patience; Ejeh, Patrick Ogholuwarami
Journal of Fuzzy Systems and Control Vol. 2 No. 2 (2024): Vol. 2, No. 2, 2024
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/jfsc.v2i2.213

Abstract

The digital market today, is rippled with a variety of goods/services that promote monetization and asset exchange with clients constantly seeking improved alternatives at lowered cost 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. Such strategies include the upgrade of goods and services at lesser cost and targeted improved value chains to meet client needs. These are found to improve client retention and better monetization. The study predicts customer churn via tree-based ensembles with data resampling such as the random-under-sample, synthetic minority oversample (SMOTE), and SMOTE-edited nearest neighbor (SMOTEEN). We chose three (3) tree-based ensembles namely: (a) decision tree, (b) random forest, and (c) extreme gradient boosting – to ensure we have single and ensemble classifier(s) to assess how well bagging and boosting modes perform on consumer churn prediction. With chi-square feature selection mode, the Decision tree model yields an accuracy of 0.9973, F1 of 0.9898, a precision of 0.9457, and a recall of 0.9698 respectively; while Random Forest yields an accuracy of 0.9973, F1 of 0.9898, precision 0.9457, and recall 0.9698 respectively. The XGBoost outperformed both Decision tree and Random Forest classifiers with an accuracy of 0.9984, F1 of 0.9945, Precision of 0.9616, and recall of 0.9890 respectively – which is attributed to its use of hyper-parameter tuning on its trees. We also note that SMOTEEN data balancing outperforms other data augment schemes with retention of a 30-day moratorium period for our adoption of the recency-frequency-monetization to improve monetization and keep business managers ahead of the consumer attrition curve.
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.
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.
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 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 Ejeh, Patrick Ogholorunwalomi Ejeh, Patrick Ogholuwarami Emordi, Frances Uche Emordi, Frances Uchechukwu Ezzeh, Peace Oguguo Ibor, Ayei Egu Idama, Rebecca Okeoghene Ifeanyi Akazue, Maureen Max-Egba, Asuobite ThankGod Nwozor, Blessing Nwozor, Blessing Uche Odiakaoase, Christopher Chukwufunaya Odiakaose , Christopher Chukwufunaya Odiakaose, Chris Chukwufunaya Odiakaose, Christopher Chukufunaya Odiakaose, Christopher Chukwufumaya Odiakaose, Christopher Chukwufunaya Odiakaose, Chukwufunaya Chris Odoh, Anne Ojei, Emma Obiajulu 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 Avweresuo Onoma, Paul Avweresuoghene Onoma, Paul Avwerosuoghene Onyemenem, Innocent Sunny Orobor, Anderson Ise Oweimieotu, Amanda Enaodona Taylor, Onate Egerton Ugboh, Emeke Ugbotu, Eferhire Valentine Yoro, Rume Elizabeth