Article Per Year (5 Year)
p-Index From 2021 - 2026
5.022
P-Index
This Author published in this journals
All Journal IAES International Journal of Artificial Intelligence (IJ-AI) International Journal of Informatics and Communication Technology (IJ-ICT) Bulletin of Electrical Engineering and Informatics Journal of Applied Science, Engineering, Technology, and Education Jurnal Teknik Informatika (JUTIF) JINAV: Journal of Information and Visualization Quantitative Economics and Management Studies ARRUS Journal of Mathematics and Applied Science ARRUS Journal of Engineering and Technology Applied Engineering and Technology Journal of Computing Theories and Applications Journal of Fuzzy Systems and Control (JFSC) Journal of Future Artificial Intelligence and Technologies
Ojugo, Arnold Adimabua
Unknown Affiliation
Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Library & Information Science Materials Science & Nanotechnology Mathematics Physics Other

Published : 44 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 44 Documents
Search

 1   2   3   4   5 
Phishing Website Detection via a Transfer Learning based XGBoost Meta-learner with SMOTE-Tomek Agboi, Joy; Emordi, Frances Uche; Odiakaose, Christopher Chukwufunaya; Idama, Rebecca Okeoghene; Jumbo, Evans Fubara; Oweimieotu, Amanda Enaodona; Ezzeh, Peace Oguguo; Eboka, Andrew Okonji; Odoh, Anne; Ugbotu, Eferhire Valentine; Onoma, Paul Avwerosuoghene; Ojugo, Arnold Adimabua; Aghaunor, Tabitha Chukwudi; Binitie, Amaka Patience; Onochie, Christopher Chukwudi; Ejeh, Patrick Ogholuwarami; Nwozor, Blessing Uche
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.v3i3.325

Abstract

The widespread proliferation of smartphones has advanced portability, data access ease, mobility, and other merits; it has also birthed adversarial targeting of network resources that seek to compromise unsuspecting user devices. Increased susceptibility was traced to user's personality, which renders them repeatedly vulnerable to exploits. Our study posits a stacked learning model to classify malicious lures used by adversaries on phishing websites. Our hybrid fuses 3-base learners (i.e. Genetic Algorithm, Random Forest, Modular Net) with its output sent as input to the XGBoost. The imbalanced dataset was resolved via SMOTE-Tomek with predictors selected using a relief rank feature selection. Our hybrid yields F1 0.995, Accuracy 1.000, Recall 0.998, Precision 1.000, MCC 1.000, and Specificity 1.000 – to accurately classify all 3,316 cases of its held-out test dataset. Results affirm that it outperformed benchmark ensembles. The study shows that our proposed model, as explored on the UCI Phishing Website dataset, effectively classified phishing (cues and lures) contents on websites.
EcoSMEAL: Energy Consumption with Optimization Strategy via a Secured Smart Monitor-Alert Ensemble Aghaunor, Tabitha Chukwudi; Agboi, Joy; Ugbotu, Eferhire Valentine; Onoma, Paul Avweresuoghene; Ojugo, Arnold Adimabua; Odiakaose, Christopher Chukwufunaya; Eboka, Andrew Okonji; Ezzeh, Peace Oguguo; Geteloma, Victor Ochuko; Binitie, Amaka Patience; Orobor, Anderson Ise; Nwozor, Blessing Uche; Ejeh, Patrick Ogholuwarami; Onochie, Christopher Chukwudi
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.
Empirical Evaluation for Intelligent Predictive Models in Prediction of Potential Cancer Problematic Cases In Nigeria Ojugo, Arnold Adimabua; Obruche, Chris Obaro; Eboka, Andrew Okonji
ARRUS Journal of Mathematics and Applied Science Vol. 1 No. 2 (2021)
Publisher : PT ARRUS Intelektual Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35877/mathscience614

Abstract

The rapid rate as well as the volume in amount of data churned out on daily basis has necessitated the need for data mining process. Advanced by the field of data science with machine learning approaches as new paradigm and platform, it has become imperative to provide beneficial support in constructing models that can effectively assist domain experts/practitioners – to make comprehensive decisions regarding potential cases. The study uses deep learning prognosis to effectively respond to problematic cases of cancer in Nigeria. We use the fuzzy rule-based memetic model to predict potential problematic cases of cancer – predicting results from data samples collected from the Epidemiology laboratory at Federal Medical Center Asaba, Nigeria. Dataset is split into training (85%) and testing (15%) to aid model validation. Results indicate that age, obesity, environmental conditions and family relations (to the first and second degree) are critical factors to be watched for benign and malignant cancer types. Constructed model result shows high predictive capability strength compared to other models presented on similar studies.
RICE DISEASE RECOGNITION USING TRANSFER LEARNING XCEPTION CONVOLUTIONAL NEURAL NETWORK Muslikh, Ahmad Rofiqul; Setiadi, De Rosal Ignatius Moses; Ojugo, Arnold Adimabua
Jurnal Teknik Informatika (Jutif) Vol. 4 No. 6 (2023): JUTIF Volume 4, Number 6, Desember 2023
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2023.4.6.1529

Abstract

As one of the major rice producers, Indonesia faces significant challenges related to plant diseases such as blast, brown spot, tugro, leaf smut, and blight. These diseases threaten food security and result in economic losses, underscoring the importance of early detection and management of rice diseases. Convolutional Neural Network (CNN) has proven effective in detecting diseases in rice plants. Specifically, transfer learning with CNN, particularly the Xception model, has the advantage of efficiently extracting automatic features and performing well even with limited datasets. This study aims to develop the Xception model for rice disease recognition based on leaf images. Through the fine-tuning process, the Xception model achieved accuracies, precisions, recalls, and F1-scores of 0.89, 0.90, 0.89, and 0.89, respectively, on a dataset with a total of 320 images. Additionally, the Xception model outperformed VGG16, MobileNetV2, and EfficientNetV2.
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