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Ojugo, Arnold Adimabua
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Journal : Journal of Computing Theories and Applications

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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.
BEHeDaS: A Blockchain Electronic Health Data System for Secure Medical Records Exchange Oladele, James Kolapo; Ojugo, Arnold Adimabua; Odiakaose, Christopher Chukwufunaya; Emordi, Frances Uchechukwu; Abere, Reuben Akporube; Nwozor, Blessing; Ejeh, Patrick Ogholuwarami; Geteloma, Victor Ochuko
Journal of Computing Theories and Applications Vol. 1 No. 3 (2024): JCTA 1(3) 2024
Publisher : Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/jcta.9509

Abstract

Blockchain platforms propagate into every facet, including managing medical services with professional and patient-centered applications. With its sensitive nature, record privacy has become imminent with medical services for patient diagnosis and treatments. The nature of medical records has continued to necessitate their availability, reachability, accessibility, security, mobility, and confidentiality. Challenges to these include authorized transfer of patient records on referral, security across platforms, content diversity, platform interoperability, etc. These, are today – demystified with blockchain-based apps, which proffers platform/application services to achieve data features associated with the nature of the records. We use a permissioned-blockchain for healthcare record management. Our choice of permission mode with a hyper-fabric ledger that uses a world-state on a peer-to-peer chain – is that its smart contracts do not require a complex algorithm to yield controlled transparency for users. Its actors include patients, practitioners, and health-related officers as users to create, retrieve, and store patient medical records and aid interoperability. With a population of 500, the system yields a transaction (query and https) response time of 0.56 seconds and 0.42 seconds, respectively. To cater to platform scalability and accessibility, the system yielded 0.78 seconds and 063 seconds, respectively, for 2500 users.
Strategic Feature Selection for Enhanced Scorch Prediction in Flexible Polyurethane Form Manufacturing Omoruwou, Felix; Ojugo, Arnold Adimabua; Ilodigwe, Solomon Ebuka
Journal of Computing Theories and Applications Vol. 1 No. 3 (2024): JCTA 1(3) 2024
Publisher : Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/jcta.9539

Abstract

The occurrence of scorch during the production of flexible polyurethane is a significant issue that negatively impacts foam products' resilience and generally jeopardizes their integrity. The likelihood of foam product failure can be decreased by optimizing production variables based on machine learning algorithms used to predict the occurrence of scorch. Investigating technology is required because prevention is the best approach to dealing with this problem. Hence, machine learning algorithms were trained to predict the occurrence of scorch using the thermodynamic profile of polyurethane foam, which is made up of recorded production variables. A variety of heuristics algorithms were trained and assessed for how well they performed, namely XGBoost, Decision trees, Random Forest, K-nearest neighbors, Naive Bayes, Support Vector Machines, and Logistic Regression. The XGboost ensemble was found to perform best. It outperformed others with an accuracy of 98.3% (i.e., 0.983), followed by logistic regression, decision tree, random forest, K-nearest neighbors, and naïve Bayes, yielding a training accuracy of 88.1%, 66.7%, 84.2%, 87.5%, and 67.5% respectively. The XGBoost was finally used, yielding 2-distinct cases of non(occurrence) of scorch. Ensemble demonstrates that it is quite capable and is an effective way to predict the occurrence of scorch.
IMANoBAS: An Improved Multi-Mode Alert Notification IoT-based Anti-Burglar Defense System Omede, Edith Ugochi; Edje, Abel E; Akazue, Maureen Ifeanyi; Utomwen, Henry; Ojugo, Arnold Adimabua
Journal of Computing Theories and Applications Vol. 1 No. 3 (2024): JCTA 1(3) 2024
Publisher : Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/jcta.9541

Abstract

Burglary involves forced or unauthorized entry, which leads to damage or loss of property having monetary or emotional value and, more severely, puts lives at risk. The dire need for the safety of lives and properties has attracted so much research on burglary alert system using Internet of Things (IoT) technology. Most of the research focused on alerting the users of burglary attempts using any or a combination of two notification methods: SMS, call, and email. This study emphasizes three-mode notification that combines SMS, call, and email using the application of IoT technology in a burglary alert system, which uses a Passive Infrared (PIR) sensor for burglar detection to ensure that Homeowners or authorized personnel get alerts in events of imminent attempt to break-ins. The study also details the sensor integration with its supporting components, such as the central hub or microcontroller, buzzer, LED, and network interface in the development of the system. The software was developed to facilitate seamless integration with the hardware, ensuring timely and accurate event detection and subsequent alert generation using Arduino IDE programming language, a framework based on the C++ language. The system effected the 3-mode notification to ensure that users get notification in case of an imminent break-in since the failure of the three modes simultaneously is extremely rare. The system’s performance based on its responsiveness on the 3-mode notifications was evaluated, and an average of 83.56% responsiveness was obtained, indicating an acceptable response time.
Enhancing the Random Forest Model via Synthetic Minority Oversampling Technique for Credit-Card Fraud Detection Aghware, Fidelis Obukohwo; Ojugo, Arnold Adimabua; Adigwe, Wilfred; Odiakaose, Christopher Chukwufumaya; Ojei, Emma Obiajulu; Ashioba, Nwanze Chukwudi; Okpor, Margareth Dumebi; Geteloma, Victor Ochuko
Journal of Computing Theories and Applications Vol. 1 No. 4 (2024): JCTA 1(4) 2024
Publisher : Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/jcta.10323

Abstract

Fraudsters increasingly exploit unauthorized credit card information for financial gain, targeting un-suspecting users, especially as financial institutions expand their services to semi-urban and rural areas. This, in turn, has continued to ripple across society, causing huge financial losses and lowering user trust implications for all cardholders. Thus, banks cum financial institutions are today poised to implement fraud detection schemes. Five algorithms were trained with and without the application of the Synthetic Minority Over-sampling Technique (SMOTE) to assess their performance. These algorithms included Random Forest (RF), K-Nearest Neighbors (KNN), Naïve Bayes (NB), Support Vector Machines (SVM), and Logistic Regression (LR). The methodology was implemented and tested through an API using Flask and Streamlit in Python. Before applying SMOTE, the RF classifier outperformed the others with an accuracy of 0.9802, while the accuracies for LR, KNN, NB, and SVM were 0.9219, 0.9435, 0.9508, and 0.9008, respectively. Conversely, after the application of SMOTE, RF achieved a prediction accuracy of 0.9919, whereas LR, KNN, NB, and SVM attained accuracies of 0.9805, 0.9210, 0.9125, and 0.8145, respectively. These results highlight the effectiveness of combining RF with SMOTE to enhance prediction accuracy in credit card fraud detection.
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.
Outlier Detection Using Gaussian Mixture Model Clustering to Optimize XGBoost for Credit Approval Prediction Setiadi, De Rosal Ignatius Moses; Muslikh, Ahmad Rofiqul; Iriananda, Syahroni Wahyu; Warto, Warto; Gondohanindijo, Jutono; Ojugo, Arnold Adimabua
Journal of Computing Theories and Applications Vol. 2 No. 2 (2024): JCTA 2(2) 2024
Publisher : Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/jcta.11638

Abstract

Credit approval prediction is one of the critical challenges in the financial industry, where the accuracy and efficiency of credit decision-making can significantly affect business risk. This study proposes an outlier detection method using the Gaussian Mixture Model (GMM) combined with Extreme Gradient Boosting (XGBoost) to improve prediction accuracy. GMM is used to detect outliers with a probabilistic approach, allowing for finer-grained anomaly identification compared to distance- or density-based methods. Furthermore, the data cleaned through GMM is processed using XGBoost, a decision tree-based boosting algorithm that efficiently handles complex datasets. This study compares the performance of XGBoost with various outlier detection methods, such as LOF, CBLOF, DBSCAN, IF, and K-Means, as well as various other classification algorithms based on machine learning and deep learning. Experimental results show that the combination of GMM and XGBoost provides the best performance with an accuracy of 95.493%, a recall of 91.650%, and an AUC of 95.145%, outperforming other models in the context of credit approval prediction on an imbalanced dataset. The proposed method has been proven to reduce prediction errors and improve the model's reliability in detecting eligible credit applications.
Feature Fusion with Albumentation for Enhancing Monkeypox Detection Using Deep Learning Models Pratama, Nizar Rafi; Setiadi, De Rosal Ignatius Moses; Harkespan, Imanuel; Ojugo, Arnold Adimabua
Journal of Computing Theories and Applications Vol. 2 No. 3 (2025): JCTA 2(3) 2025
Publisher : Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/jcta.12255

Abstract

Monkeypox is a zoonotic disease caused by Orthopoxvirus, presenting clinical challenges due to its visual similarity to other dermatological conditions. Early and accurate detection is crucial to prevent further transmission, yet conventional diagnostic methods are often resource-intensive and time-consuming. This study proposes a deep learning-based classification model by integrating Xception and InceptionV3 using feature fusion to enhance performance in classifying Monkeypox skin lesions. Given the limited availability of annotated medical images, data augmentation was applied using Albumentation to improve model generalization. The proposed model was trained and evaluated on the Monkeypox Skin Lesion Dataset (MSLD), achieving 85.96% accuracy, 86.47% precision, 85.25% recall, 78.43% specificity, and an AUC score of 0.8931, outperforming existing methods. Notably, data augmentation significantly improved recall from 81.23% to 85.25%, demonstrating its effectiveness in enhancing sensitivity to positive cases. Ablation studies further validated that augmentation increased overall accuracy from 82.02% to 85.96%, emphasizing its role in improving model robustness. Comparative analysis with other models confirmed the superiority of our approach. This research enhances automated Monkeypox detection, offering a robust and efficient tool for low-resource clinical settings. The findings reinforce the potential of feature fusion and augmentation in improving deep learn-ing-based medical image classification, facilitating more reliable and accessible disease identification.
Integrating Hybrid Statistical and Unsupervised LSTM-Guided Feature Extraction for Breast Cancer Detection Setiadi, De Rosal Ignatius Moses; Ojugo, Arnold Adimabua; Pribadi, Octara; Kartikadarma , Etika; Setyoko, Bimo Haryo; Widiono, Suyud; Robet, Robet; Aghaunor, Tabitha Chukwudi; Ugbotu, Eferhire Valentine
Journal of Computing Theories and Applications Vol. 2 No. 4 (2025): JCTA 2(4) 2025
Publisher : Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/jcta.12698

Abstract

Breast cancer is the most prevalent cancer among women worldwide, requiring early and accurate diagnosis to reduce mortality. This study proposes a hybrid classification pipeline that integrates Hybrid Statistical Feature Selection (HSFS) with unsupervised LSTM-guided feature extraction for breast cancer detection using the Wisconsin Diagnostic Breast Cancer (WDBC) dataset. Initially, 20 features were selected using HSFS based on Mutual Information, Chi-square, and Pearson Correlation. To address class imbalance, the training set was balanced using the Synthetic Minority Over-sampling Technique (SMOTE). Subsequently, an LSTM encoder extracted non-linear latent features from the selected features. A fusion strategy was applied by concatenating the statistical and latent features, followed by re-selection of the top 30 features. The final classification was performed using a Support Vector Machine (SVM) with RBF kernel and evaluated using 5-fold cross-validation and a held-out test set. Experimental results showed that the proposed method achieved an average training accuracy of 98.13%, F1-score of 98.13%, and AUC-ROC of 99.55%. On the held-out test set, the model reached an accuracy of 99.30%, precision of 100%, and F1-score of 99.05%, with an AUC-ROC of 0.9973. The proposed pipeline demonstrates improved generalization and interpretability compared to existing methods such as LightGBM-PSO, DHH-GRU, and ensemble deep networks. These results highlight the effectiveness of combining statistical selection and LSTM-based latent feature encoding in a balanced classification framework.
Co-Authors Abdullahi, Mustapha Barau Abere, Reuben Akporube Achmad Nuruddin Safriandono Adigwe, Wilfred Afotanwo, Anderson Agboi, Joy Aghaunor, Tabitha Chukwudi Aghware, Fidelis Obukohwo Ajib Susanto Akazue, Maureen Ifeanyi Akhmad Dahlan Ako, Rita Erhovwo Anazia, Kizito Eluemunor Ashioba, Nwanze Chukwudi Binitie, Amaka Patience Budi Widjajanto De Rosal Ignatius Moses Setiadi Dian Kristiawan Nugroho Eboka, Andrew Okonji Edim, Edim Bassey Edje, Abel E Efetobore Edje, Abel Ejeh, Patrick Ogholorunwalomi Ejeh, Patrick Ogholuwarami Emordi, Frances Uche Emordi, Frances Uchechukwu Ezzeh, Peace Oguguo Farah Zakiyah Rahmanti Gan, Hong-Seng Geteloma, Victor Ochuko Ibor, Ayei Egu Idama, Rebecca Okeoghene Ifeanyi Akazue, Maureen Ilodigwe, Solomon Ebuka Imanuel Harkespan Iwan Setiawan Wibisono Jumbo, Evans Fubara Jutono Gondohanindijo, Jutono Kartikadarma , Etika Max-Egba, Asuobite ThankGod Muhamada, Keny Muslikh, Ahmad Rofiqul Niemogha, Star Umiyemeromesu Nwankwo, Obinna Nwozor, Blessing Nwozor, Blessing Uche Obruche, Chris Obaro Octara Pribadi 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 Okpako, Ejaita Abugor Okpor, Margaret Dumebi Okpor, Margareth Dumebi Oladele, James Kolapo Omede, Edith Ugochi Omoruwou, Felix Onochie, Chris Chukwudi Onochie, Christopher Chukwudi Onoma, Paul Avweresuo Onoma, Paul Avweresuoghene Onoma, Paul Avwerosuoghene Onyemenem, Innocent Sunny Orobor, Anderson Ise Otakore, Oghenevwede Debby Oweimieotu, Amanda Enaodona Pratama, Nizar Rafi Robet Robet Setyoko, Bimo Haryo Stefanus Santosa Sudibyo, Usman Suyud Widiono Syahroni Wahyu Iriananda, Syahroni Wahyu Taylor, Onate Egerton Ugboh, Emeke Ugbotu, Eferhire Valentine Utomwen, Henry Warto - Yoro, Rume Elizabeth Zuama, Leygian Reyhan