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All Journal Journal of Computing Theories and Applications Journal of Fuzzy Systems and Control (JFSC)
Victor Ochuko Geteloma
Federal University of Petroleum Resources Effurun
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Energy Engineering

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Effects of Data Resampling on Predicting Customer Churn via a Comparative Tree-based Random Forest and XGBoost Rita Erhovwo Ako; Fidelis Obukohwo Aghware; Margaret Dumebi Okpor; Maureen Ifeanyi Akazue; Rume Elizabeth Yoro; Arnold Adimabua Ojugo; De Rosal Ignatius Moses Setiadi; Chris Chukwufunaya Odiakaose; Reuben Akporube Abere; Frances Uche Emordi; Victor Ochuko Geteloma; Patrick Ogholuwarami Ejeh
Journal of Computing Theories and Applications Vol. 2 No. 1 (2024): JCTA 2(1) 2024
Publisher : Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/jcta.10562

Abstract

Customer attrition has become the focus of many businesses today – since the online market space has continued to proffer customers, various choices and alternatives to goods, services, and products for their monies. Businesses must seek to improve value, meet customers' teething demands/needs, enhance their strategies toward customer retention, and better monetize. The study compares the effects of data resampling schemes on predicting customer churn for both Random Forest (RF) and XGBoost ensembles. Data resampling schemes used include: (a) default mode, (b) random-under-sampling RUS, (c) synthetic minority oversampling technique (SMOTE), and (d) SMOTE-edited nearest neighbor (SMOTEEN). Both tree-based ensembles were constructed and trained to assess how well they performed with the chi-square feature selection mode. The result shows that RF achieved F1 0.9898, Accuracy 0.9973, Precision 0.9457, and Recall 0.9698 for the default, RUS, SMOTE, and SMOTEEN resampling, respectively. Xgboost outperformed Random Forest with F1 0.9945, Accuracy 0.9984, Precision 0.9616, and Recall 0.9890 for the default, RUS, SMOTE, and SMOTEEN, respectively. Studies support that the use of SMOTEEN resampling outperforms other schemes; while, it attributed XGBoost enhanced performance to hyper-parameter tuning of its decision trees. Retention strategies of recency-frequency-monetization were used and have been found to curb churn and improve monetization policies that will place business managers ahead of the curve of churning by customers.
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.
Co-Authors Amaka Patience Binitie Anderson Ise Orobor Andrew Okonji Eboka Arnold Adimabua Ojugo Blessing Uche Nwozor Chris Chukwufunaya Odiakaose Christopher Chukwudi Onochie Christopher Chukwufunaya Odiakaose De Rosal Ignatius Moses Setiadi Eferhire Valentine Ugbotu Fidelis Obukohwo Aghware Frances Uche Emordi Joy Agboi Margaret Dumebi Okpor Maureen Ifeanyi Akazue Patrick Ogholuwarami Ejeh Patrick Ogholuwarami Ejeh Paul Avweresuoghene Onoma Peace Oguguo Ezzeh Reuben Akporube Abere Rita Erhovwo Ako Rume Elizabeth Yoro Tabitha Chukwudi Aghaunor