Article Per Year (5 Year)
p-Index From 2021 - 2026
5.329
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 : 46 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 3 Documents
Search
Journal : JINAV: Journal of Information and Visualization

 1 
Spectral-Cluster Solution For Credit-Card Fraud Detection Using A Genetic Algorithm Trained Modular Deep Learning Neural Network Ojugo, Arnold Adimabua; Nwankwo, Obinna
JINAV: Journal of Information and Visualization Vol. 2 No. 1 (2021)
Publisher : Yayasan Ahmar Cendekia Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35877/454RI.jinav274

Abstract

Adversaries achieved such intrusion via carefully crafted attacks of large magnitude that seek to wreak havoc on network infrastructures with a focus on personal gains and rewards. Study proposes a spectral-clustering hybrid of genetic algorithm trained modular neural network to detect fraud in credit card transactions. The hybrid ensemble seeks to equip credit-card users with a system and algorithm whose knowledge will altruistically detect fraud on credit cards. Results show that the hybrid model effectively differentiates between benign and genuine credit card transactions with a model accuracy of 74%.
Multi-Agent Bayesian Framework For Parametric Selection In The Detection And Diagnosis of Tuberculosis Contagion In Nigeria Ojugo, Arnold Adimabua; Nwankwo, Obinna
JINAV: Journal of Information and Visualization Vol. 2 No. 2 (2021)
Publisher : Yayasan Ahmar Cendekia Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35877/454RI.jinav375

Abstract

Decision making has become quite a critical factor in our everyday living. The provision of data alongside its consequent processing has further sought to extend and expand our reasoning faculties as well as effectively aid proper decision making. But data is daily, produced at an exponential rapid rate and the volume in amount of data churned out to be processed even more so that we now require data storage optimization techniques to process such humongous volume of data. These have today, necessitated the need for advancement in data mining process. With the tremendous advances made in data mining, machine learning, storage virtualization and optimization – amongst other fields of computing – researchers now seek a new paradigm and platform called data science. This field today has become quite imperative as it seeks to provide beneficial support in constructing models and algorithms that can effectively assist domain experts and practitioners to make comprehensive and sound decisions regarding potential problematic cases. We focus on modeling social graph using implicit suggest algorithm in medical diagnosis to effectively respond to problematic cases of Tuberculosis (TB) in Nigeria. We introduce spectral clustering and Bayesian Network, construct algorithms cum models for predicting potential problematic cases in Tuberculosis as well as compare the algorithms based on data samples collected from an Epidemiology laboratory at the Federal Medical Center Asaba in Delta State of Nigeria. The volume of data was collated and divided into two data sets which are the training dataset and the investigation dataset. The model constructed by this study has shown a high predictive capability strength compared to other models presented on similar studies.
Migration Pattern As Threshold Parameter In The Propagation of The Covid-19 Epidemic Using An Actor-Based Model for SI-Social Graph Ojugo, Arnold Adimabua; Yoro, Rume Elizabeth
JINAV: Journal of Information and Visualization Vol. 2 No. 2 (2021)
Publisher : Yayasan Ahmar Cendekia Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35877/454RI.jinav379

Abstract

Despite the benefits inherent with social interactions, the case of epidemics cum pandemic outbreaks especially the case of the novel corona virus (covid-19) alongside its set protocols employed to contain the spread therein - has continually left the world puzzled as the disease itself has come to stay. The nature of its rapid propagation on exposure alongside its migration spread pattern of this contagion (with retrospect of other epidemics) on daily basis, has also left experts rethinking the set protocols. Our study involved modelling the covid-19 contagion on a social graph, so as to ascertain if its propagation using migration pattern as a threshold parameter can be minimized via the employment of set protocols. We also employed a design that sought to block or minimize targeted spread of the contagion with the introduction of seedset node(s) using the susceptible-infect framework on a time-varying social graph. Study results showed that migration or mobility pattern has become an imperative factors that must be added when modelling the propagation of contagion or epidemics.
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 Anujeonye, Nneamaka Christiana 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 Egbokhare, Francesca Avwuru 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 Onyemenem, Sunny Innocent 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