Klaim Artikel Anda
Verifikasi kepemilikan artikel akademik
Apakah artikel-artikel ini milik Anda?
Daftarkan diri Anda sebagai author untuk mengklaim artikel dan dapatkan profil akademik terverifikasi dengan fitur lengkap.
Badge Verifikasi
Profil terverifikasi resmi
Statistik Lengkap
H-index, sitasi, dan metrik
Visibilitas Tinggi
Tampil di direktori author
Kelola Publikasi
Dashboard artikel terpadu
Langkah-langkah Klaim Artikel:
- 1. Daftar akun author dengan email akademik Anda
- 2. Verifikasi email dan lengkapi profil
- 3. Login dan buka menu "Klaim Artikel"
- 4. Cari dan klaim artikel Anda
- 5. Tunggu verifikasi dari admin (1-3 hari kerja)
Menampilkan 1–10 dari 13 artikel
Investigating Security Enhancement in Hybrid Clouds via a Blockchain-Fused Privacy Preservation Strategy: Pilot Study
Tabitha Chukwudi Aghaunor
; Eferhire Valentine Ugbotu
; Emeke Ugboh
; Paul Avwerosuoghene Onoma
; Frances Uchechukwu Emordi
; Arnold Adimabua Ojugo
; Victor Ochuko Geteloma
; Rebecca Okeoghene Idama
; Peace Oguguo Ezzeh
Journal of Computing Theories and Applications
Vol 3
, No 4
(2026)
The proliferation of cloud infrastructures has intensified concerns regarding data security, integrity, identity and access management, and user privacy. Despite recent advances, existing solutions often lack comprehensive integration of privacy-preserving mechanisms, dynamic trust management, and cross-provider interoperability. This study proposes an AI-enabled, zero-trust, blockchain-fused identity management framework for secure, privacy-preserving multi-cloud environments. The framework int...
Sumber Asli
Google Scholar
DOI
A Graph-Augmented Isolation Forest Using Node2Vec and GraphSAGE for Mobile User Behavior Anomaly Detection
Amaka Patience Binitie
; Sunny Innocent Onyemenem
; Nneamaka Christiana Anujeonye
; Arnold Adimabua Ojugo
; Francesca Avwuru Egbokhare
; Tabitha Chukwudi Aghaunor
Journal of Computing Theories and Applications
Vol 3
, No 3
(2026)
This study presents a Graph-Augmented Isolation Forest (GAIF), an unsupervised anomaly-detection framework for analyzing mobile user behavior. The proposed framework represents users and behavioral attributes as a user–feature bipartite graph, enabling the capture of relational dependencies that are not explicitly modeled in conventional vector-based approaches. Low-dimensional user representations are learned through Node2Vec and Graph Sample and Aggregate (GraphSAGE), and the resulting embeddi...
Sumber Asli
Google Scholar
DOI
1 Sitasi
Investigating a SMOTE-Tomek Boosted Stacked Learning Scheme for Phishing Website Detection: A Pilot Study
Ugbotu, Eferhire Valentine
; Emordi, Frances Uchechukwu
; Ugboh, Emeke
; Anazia, Kizito Eluemunor
; Odiakaose, Christopher Chukwufunaya
; Onoma, Paul Avwerosuoghene
; Idama, Rebecca Okeoghene
; Ojugo, Arnold Adimabua
; Geteloma, Victor Ochuko
; Oweimieotu, Amanda Enaodona
; Aghaunor, Tabitha Chukwudi
; Binitie, Amaka Patience
; Odoh, Anne
; Onochie, Chris Chukwudi
; Ezzeh, Peace Oguguo
; Eboka, Andrew Okonji
; Agboi, Joy
; Ejeh, Patrick Ogholuwarami
Journal of Computing Theories and Applications
Vol 3
, No 2
(2025)
The daily exchange of informatics over the Internet has both eased the widespread proliferation of resources to ease accessibility, availability and interoperability of accompanying devices. In addition, the recent widespread proliferation of smartphones alongside other computing devices has continued to advance features such as miniaturization, portability, data access ease, mobility, and other merits. It has also birthed adversarial attacks targeted at network infrastructures and aimed at expl...
Sumber Asli
Google Scholar
DOI
1 Sitasi
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)
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 addres...
Sumber Asli
Google Scholar
DOI
3 Sitasi
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)
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. Giv...
Sumber Asli
Google Scholar
DOI
15 Sitasi
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)
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 densit...
Sumber Asli
Google Scholar
DOI
23 Sitasi
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)
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 ense...
Sumber Asli
Google Scholar
DOI
22 Sitasi
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)
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 app...
Sumber Asli
Google Scholar
DOI
41 Sitasi
Strategic Feature Selection for Enhanced Scorch Prediction in Flexible Polyurethane Form Manufacturing
Journal of Computing Theories and Applications
Vol 1
, No 3
(2024)
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...
Sumber Asli
Google Scholar
DOI
14 Sitasi
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)
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...
Sumber Asli
Google Scholar
DOI
23 Sitasi