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All Journal Journal of Computing Theories and Applications
Patrick Ogholuwarami Ejeh
Dennis Osadebay University Anwai-Asaba
Computer Science & IT Decision Sciences, Operations Research & Management

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CoSoGMIR: A Social Graph Contagion Diffusion Framework using the Movement-Interaction-Return Technique Arnold Adimabua Ojugo; Patrick Ogholuwarami Ejeh; Maureen Ifeanyi Akazue; Nwanze Chukwudi Ashioba; Christopher Chukwufunaya Odiakaose; Rita Erhovwo Ako; Blessing Nwozor; Frances Uche Emordi
Journal of Computing Theories and Applications Vol. 1 No. 2 (2023): JCTA 1(2) 2023
Publisher : Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33633/jcta.v1i2.9355

Abstract

Besides the inherent benefits of exchanging information and interactions between nodes on a social graph, they can also become a means for the propagation of knowledge. Social graphs have also become a veritable structure for the spread of disease outbreaks. These and its set of protocols are deployed as measures to curb its widespread effects as it has also left network experts puzzled. The recent lessons from the COVID-19 pandemic continue to reiterate that diseases will always be around. Nodal exposure, adoption/diffusion of disease(s) among interacting nodes vis-a-vis migration of nodes that cause further spread of contagion (concerning COVID-19 and other epidemics) has continued to leave experts bewildered towards rejigging set protocols. We model COVID-19 as a Markovian process with node targeting, propagation and recovery using migration-interaction as a threshold feat on a social graph. The migration-interaction design seeks to provision the graph with minimization and block of targeted diffusion of the contagion using seedset(s) nodes with a susceptible-infect policy. The study results showed that migration and interaction of nodes via the mobility approach have become an imperative factor that must be added when modeling the propagation of contagion or epidemics.
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.
Co-Authors Arnold Adimabua Ojugo Blessing Nwozor Chris Chukwufunaya Odiakaose Christopher Chukwufunaya Odiakaose De Rosal Ignatius Moses Setiadi Fidelis Obukohwo Aghware Frances Uche Emordi Frances Uche Emordi Margaret Dumebi Okpor Maureen Ifeanyi Akazue Nwanze Chukwudi Ashioba Reuben Akporube Abere Rita Erhovwo Ako Rita Erhovwo Ako Rume Elizabeth Yoro Victor Ochuko Geteloma