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All Journal International Journal of Informatics and Communication Technology (IJ-ICT) Bulletin of Electrical Engineering and Informatics Knowledge Engineering and Data Science Applied Engineering and Technology Journal of Computing Theories and Applications Journal of Fuzzy Systems and Control (JFSC) Journal of Future Artificial Intelligence and Technologies
Ako, Rita Erhovwo
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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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BloFoPASS: A blockchain food palliatives tracer support system for resolving welfare distribution crisis in Nigeria Aghware, Fidelis Obukohwo; Adigwe, Wilfred; Okpor, Margareth Dumebi; Odiakaose, Christopher Chukwufunaya; Ojugo, Arnold Adimabua; Eboka, Andrew Okonji; Ejeh, Patrick Ogholorunwalomi; Taylor, Onate Egerton; Ako, Rita Erhovwo; Geteloma, Victor Ochuko
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 13, No 2: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v13i2.pp178-187

Abstract

With population rising to approximately 200 million Nigerians – fast-paced, urbanization has continued to advent food insecurity with maladministration, corruption, internal rife, and starvation. These, threatened the nation's unity with the lockdown of 2020; and consequently, have now become the trend. Nigeria must as a nation, re-examine her methods in the administration of palliatives (in lieu of food and relief) distribution – as the above-listed issues have become of critical need in the equitable distribution of reliefs, both from the humanitarian agency view, and the Government (State and Federal). They have noticed non-transparency, corruption, and data inadequacies, as major drawbacks in its management. Our study presents a blockchain ensemble for the administration of food palliatives distribution in Nigeria that first ensures, that all beneficiaries be registered, and the food palliatives are sensor-tagged and recorded on the blockchain. Results show the number of transactions per second and page retrieval abilities for the proposed chain were quite low with 30-TPS and 0.38seconds respectively – as compared to public blockchain. Proposed ensemble eliminates fraud that is herein rippled across the existing system, minimizes corrupt practices via sensor-based model, provides insight for stakeholders, and minimize the error in reported data on the supply chain.
FiMoDeAL: pilot study on shortest path heuristics in wireless sensor network for fire detection and alert ensemble Ifeanyi Akazue, Maureen; Efetobore Edje, Abel; Okpor, Margaret Dumebi; Adigwe, Wilfred; Ejeh, Patrick Ogholuwarami; Odiakaose, Christopher Chukwufunaya; Ojugo, Arnold Adimabua; Edim, Edim Bassey; Ako, Rita Erhovwo; Geteloma, Victor Ochuko
Bulletin of Electrical Engineering and Informatics Vol 13, No 5: October 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i5.8084

Abstract

With the incessant outbreak of fire, the heavy loss to both lives and properties in the society fire has since become a critical issue and challenge that needs our daily attention to be resolved. Loss of lives and properties to fire outbreak in 2021 alone as occurring in major Nigerian markets and residential homes was estimated at over 3 trillion Naira. Our study proposes a wireless sensor network internet of things (IoT) based ensemble to aid the effective monitoring, detection and alerting of residents and fire service departments. With cost as a major issue and the requisite installation of fire and smoke detectors in many houses our ensemble can efficiently integrate into the existing system using the ESP8285-controller to create a comprehensive access control system. The system provides real time monitor and control capabilities that will allow administrators to track and manage fire monitor and detection within a facility. Thus, enhances system's efficiency and performance.
Forging a User-Trust Memetic Modular Neural Network Card Fraud Detection Ensemble: A Pilot Study Ojugo, Arnold Adimabua; Akazue, Maureen Ifeanyi; Ejeh, Patrick Ogholuwarami; Ashioba, Nwanze Chukwudi; Odiakaose, Christopher Chukwufunaya; Ako, Rita Erhovwo; Emordi, Frances Uche
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.v1i2.9259

Abstract

The advent of the Internet as an effective means for resource sharing has consequently, led to proliferation of adversaries, with unauthorized access to network resources. Adversaries achieved fraudulent activities via carefully crafted attacks of large magnitude targeted at personal gains and rewards. With the cost of over $1.3Trillion lost globally to financial crimes and the rise in such fraudulent activities vis the use of credit-cards, financial institutions and major stakeholders must begin to explore and exploit better and improved means to secure client data and funds. Banks and financial services must harness the creative mode rendered by machine learning schemes to help effectively manage such fraud attacks and threats. We propose HyGAMoNNE – a hybrid modular genetic algorithm trained neural network ensemble to detect fraud activities. The hybrid, equipped with knowledge to altruistically detect fraud on credit card transactions. Results show that the hybrid effectively differentiates, the benign class attacks/threats from genuine credit card transaction(s) with model accuracy of 92%.
CoSoGMIR: A Social Graph Contagion Diffusion Framework using the Movement-Interaction-Return Technique Ojugo, Arnold Adimabua; Ejeh, Patrick Ogholuwarami; Akazue, Maureen Ifeanyi; Ashioba, Nwanze Chukwudi; Odiakaose, Christopher Chukwufunaya; Ako, Rita Erhovwo; Nwozor, Blessing; Emordi, Frances Uche
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 Ako, Rita Erhovwo; Aghware, Fidelis Obukohwo; Okpor, Margaret Dumebi; Akazue, Maureen Ifeanyi; Yoro, Rume Elizabeth; Ojugo, Arnold Adimabua; Setiadi, De Rosal Ignatius Moses; Odiakaose, Chris Chukwufunaya; Abere, Reuben Akporube; Emordi, Frances Uche; Geteloma, Victor Ochuko; Ejeh, Patrick Ogholuwarami
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.
AQuamoAS: unmasking a wireless sensor-based ensemble for air quality monitor and alert system Geteloma, Victor Ochuko; Aghware, Fidelis Obukohwo; Adigwe, Wilfred; Odiakaose, Chukwufunaya Chris; Ashioba, Nwanze Chukwudi; Okpor, Margareth Dumebi; Ojugo, Arnold Adimabua; Ejeh, Patrick Ogholuwarami; Ako, Rita Erhovwo; Ojei, Emmanuel Obiajulu
Applied Engineering and Technology Vol 3, No 2 (2024): August 2024
Publisher : ASCEE

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/aet.v3i2.1409

Abstract

The increased awareness by residents of their environment to maintain safe health states has consequently, birthed the integration of info tech to help resolve societal issues. These, and its adopted approaches have become critical and imperative in virtualization to help bridge the lapses in human mundane tasks and endeavors. Its positive impacts on society cannot be underestimated. Study advances a low-cost wireless sensor-based ensemble to effectively manage air quality tasks. Thus, we integrate an IoT framework to effectively monitors environment changes via microcontrollers, sensors, and blynk to assist users to monitor temperature, humidity, detect the presence of harmful gases in/out door environs. The blynk provides vital knowledge to the user. Our AQuaMoAS algorithm makes for an accurate and user-friendly mode using cloud services to ease monitor and data visualization. The system was tested at 3 different stages of rainy, sunny and heat with pollutant via alpha est method. For all functions at varying conditions, result revealed 70.7% humidity, 29.5OC, and 206 ppm on a sunny day. 51.5% humidity, 20.4OC and 198ppm on a rainy, and 43.1 humidity, 45.6OC, 199ppm air quality on heat and 66.5% humidity, 30.2 OC and 363 ppm air quality on application of air pollutant were observed
Enhanced data augmentation for predicting consumer churn rate with monetization and retention strategies: a pilot study Geteloma, Victor Ochuko; Aghware, Fidelis Obukohwo; Adigwe, Wilfred; Odiakaose, Chukwufunaya Chris; Ashioba, Nwanze Chukwudi; Okpor, Margareth Dumebi; Ojugo, Arnold Adimabua; Ejeh, Patrick Ogholuwarami; Ako, Rita Erhovwo; Ojei, Emmanuel Obiajulu
Applied Engineering and Technology Vol 3, No 1 (2024): April 2024
Publisher : ASCEE

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/aet.v3i1.1408

Abstract

Customer retention and monetization have since been the pillar of many successful firms and businesses as keeping an old customer is far more economical than gaining a new one – which, in turn, reduce customer churn rate. Previous studies have focused on the use of single heuristics as well as provisioned no retention strategy. To curb this, our study posits the use of the recen-cy-frequency-monetization framework as strategy for customer retention and monetization impacts. With dataset retrieved from Kaggle, and partitioned into train and test dataset/folds to ease model construction and training. Study adopt a tree-based Random Forest ensemble with synthetic minority oversampling technique edited nearest neighbor (SMOTEEN). Various benchmark models were trained to asssess how well each performs against our proposed ensemble. The application was tested using an application programming interface Flask and integrated using streamlit into a device. Our RF-ensemble resulted in a 0.9902 accuracy prior to applying SMOTEENN; while, LR, KNN, Naïve Bayes and SVM yielded an accuracy of 0.9219, 0.9435, 0.9508 and 0.9008 respectively. With SMOTEENN applied, our ensemble had an accuracy of 0.9919; while LR, KNN, Naïve Bayes, and SVM yielded an accuracy of 0.9805, 0.921, 0.9125, and 0.8145 respectively. RF has shown it can be implemented with SMOTEENN to yield enhanced prediction for customer churn prediction using Python
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.
Evidence of Students’ Academic Performance at the Federal College of Education Asaba Nigeria: Mining Education Data Ojugoa, Arnold Adimabua; Odiakaose, Christopher Chukwufunaya; Emordi, Frances; Ako, Rita Erhovwo; Adigwe, Winifred; Anazia, Kizito Eluemonor; Geteloma, Victor
Knowledge Engineering and Data Science Vol 6, No 2 (2023)
Publisher : Universitas Negeri Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17977/um018v6i22023p145-156

Abstract

One main objective of higher education is to provide quality education to its students. One way to achieve the highest level of quality in the higher education system is by discovering knowledge for prediction regarding enrolment of students in a particular course, alienation of traditional classroom teaching model, detection of unfair means used in online examination, detection of abnormal values in the result sheets of the students, and prediction about students’ performance. The knowledge is hidden among the educational data set and is extractable through data mining techniques. The present paper is designed to justify the capabilities of data mining techniques in the context of higher education by offering a data mining model for the higher education system in the university. In this research, the classification task is used to evaluate student’s performance, and as many approaches are used for data classification, the decision tree method is used here. By this, we extract data that describes students’ summative performance at semester’s end, helps to identify the dropouts and students who need special attention, and allows the teacher to provide appropriate advising/counseling.
Pilot Study on Enhanced Detection of Cues over Malicious Sites Using Data Balancing on the Random Forest Ensemble Okpor, Margaret Dumebi; Aghware, Fidelis Obukohwo; Akazue, Maureen Ifeanyi; Eboka, Andrew Okonji; Ako, Rita Erhovwo; Ojugo, Arnold Adimabua; Odiakaose, Christopher Chukwufunaya; Binitie, Amaka Patience; Geteloma, Victor Ochuko; Ejeh, Patrick Ogholuwarami
Journal of Future Artificial Intelligence and Technologies Vol. 1 No. 2 (2024): September 2024
Publisher : Future Techno Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/faith.2024-14

Abstract

The digital revolution frontiers have rippled across society today – with various web content shared online for users as they seek to promote monetization and asset exchange, with clients constantly seeking improved alternatives at lowered costs 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. The birth of smartphones has proliferated feats such as mobility, ease of accessibility, and portability – which, in turn, have continued to ease their rise in adoption, exposing user device vulnerability as they are quite susceptible to phishing. With users classified as more susceptible than others due to online presence and personality traits, studies have sought to reveal lures/cues as exploited by adversaries to enhance phishing success and classify web content as genuine and malicious. Our study explores the tree-based Random Forest to effectively identify phishing cues via sentiment analysis on phishing website datasets as scrapped from user accounts on social network sites. The dataset is scrapped via Python Google Scrapper and divided into train/test subsets to effectively classify contents as genuine or malicious with data balancing and feature selection techniques. With Random Forest as the machine learning of choice, the result shows the ensemble yields a prediction accuracy of 97 percent with an F1-score of 98.19% that effectively correctly classified 2089 instances with 85 incorrectly classified instances for the test-dataset.
Co-Authors Abere, Reuben Akporube Adigwe, Wilfred Adigwe, Winifred Afotanwo, Anderson Aghaunor, Tabitha Chukwudi Aghware, Fidelis Obukohwo Akazue, Maureen Ifeanyi Anazia, Kizito Eluemonor 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 Emordi, Frances Uche Geteloma, Victor Geteloma, Victor Ochuko Ibor, Ayei Egu Ifeanyi Akazue, Maureen Nwozor, Blessing Odiakaose , Christopher Chukwufunaya Odiakaose, Chris Chukwufunaya Odiakaose, Christopher Chukufunaya Odiakaose, Christopher Chukwufunaya Odiakaose, Chukwufunaya Chris Ojei, Emmanuel Obiajulu Ojugo, Arnold Adimabua Ojugoa, Arnold Adimabua Okpako, Ejaita Abugor Okpor, Margaret Dumebi Okpor, Margareth Dumebi Onyemenem, Innocent Sunny Taylor, Onate Egerton Ugbotu, Eferhire Valentine Yoro, Rume Elizabeth