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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI) International Journal of Informatics and Communication Technology (IJ-ICT) Indonesian Journal of Electrical Engineering and Computer Science Journal of Applied Science, Engineering, Technology, and Education JINAV: Journal of Information and Visualization Quantitative Economics and Management Studies ARRUS Journal of Mathematics and Applied Science ARRUS Journal of Engineering and Technology

Arnold Adimabua Ojugo

Federal University of Petroleum Resources Effurun

Author-ID : 2393818

Biochemistry, Genetics & Molecular Biology Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Library & Information Science Mathematics Physics Other

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Investigating The Unexpected Price Plummet And Volatility Rise In Energy Market: A Comparative Study of Machine Learning Approaches Arnold Adimabua Ojugo; Oghenevwede Debby Otakore
Quantitative Economics and Management Studies Vol. 1 No. 3 (2020)
Publisher : Yayasan Ahmar Cendekia Indonesia

The energy market aims to manage risks associated with prices and volatility of oil asset. It is a capital-intensive market that is rippled with chaos and complex interactions among its demand-supply derivatives. Models help users forecast such interactions, to provide investors with empirical evidence of price direction. Our study sought to investigate the reasons for the unexpected plummet in price of the energy market using evolutionary modeling – which seeks to analyze input data and yield an optimal, complete solution that are tractable, robust and low-cost with tolerance of ambiguity, uncertainty and noise. We adopt the Gabillon’s model to: (a) predict spots/futures prices, (b) investigate why previous predictions failed as to why price plummet, and (c) seek to critically evaluate values reached by both proposed deep learning model and the memetic algorithm by Ojugo and Allenotor (2017).

Predicting Futures Price And Contract Portfolios Using The ARIMA Model: A Case of Nigeria’s Bonny Light and Forcados Arnold Adimabua Ojugo; Rume Elizabeth Yoro
Quantitative Economics and Management Studies Vol. 1 No. 4 (2020)
Publisher : Yayasan Ahmar Cendekia Indonesia

Market prediction has been the goal of many study as investors sought traded assets since the inception of the capital market. With each asset exchanged for money, investors seek to stay ahead the market trend in the hope of amassing profits. Businesses’ growth (rise/fall) is evident upon their response to market behaviour. Thus, accurate prediction of the market often offers as its reward, enlarged financial portfolio. Market participants thus, seek to manage the risks associated with asset prices and its volatility, which can be rippled with chaos and complex tasks arising from a demand-supply curve. We seek to model the Oil market and forecast its price direction supported with empirical evidence using ARIMA model to analyze inputs in search of an optimal solution. We adopt the OPEC model to: (a) predict spot/futures-prices, (b) investigate why previous prediction was poor and price plummeted, and (c) compares value(s) from Ojugo and Yoro (2020) and Ojugo and Allenotor (2017). Results shows demand-supply curve rise (and a price rise) even though the policies and trend in real life scenario is currently experiencing a price plummet.

Quest For Convergence Solution Using Hybrid Genetic Algorithm Trained Neural Network Model For Metamorphic Malware Detection Arnold Adimabua Ojugo; Chris Obaro Obruche; Andrew Okonji Eboka
ARRUS Journal of Engineering and Technology Vol. 2 No. 1 (2022)
Publisher : PT ARRUS Intelektual Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35877/jetech613

An unstable economy is rife with fraud. Perpetrated on customers, it ranges from employees’ internal abuse to large fraud via high-value contracts cum control breaches that impose serious consequences to biz. Loyal employees may not perpetrate fraud if not for societal pressures and economic recession with its rationalization that they have bills to pay and children to feed. Thus, the need for financial institutions to embark on effective measures via schemes that will aids both fraud prevention and detection. Study proposes genetic algorithm trained neural net model to accurately classify credit card transactions. Compared, model used a rule-based system to provide it with start-up solution and it has a fraud catching rate of 91% with a consequent, false alarm rate of 9%. Its convergence time is found to depend on how close the initial solution space is to the fitness function, and for recombination and mutation rates applied.

Modeling Behavioural Evolution as Social Predictor for the Coronavirus Contagion and Immunization in Nigeria Arnold Adimabua Ojugo; Andrew Okonji Eboka
Journal of Applied Science, Engineering, Technology, and Education Vol. 3 No. 2 (2021)
Publisher : PT Mattawang Mediatama Solution

Since the outbreak of the novel coronavirus (covid-19) pandemic from China in 2019, it has left the world leaders in great confusing due to its fast-paced propagation and spread that has left infected a world population of over Eleven Million persons with over five hundred and thirty four thousand deaths and counting with the United States of America, Brazil, Russia, India and Peru in the lead on these death toll. The pandemic whose increased mortality rate is targeted at ‘aged’ citizens, patients with low immunology as well as patients with chronic diseases and underlying health conditions. Study models covid-19 pandemic via a susceptible-infect-remove actor-based graph, with covid-19 virus as the innovation diffused within the social graph. We measure the rich connective patterns of the actor-based graph, and explore personal feats as they influence other nodes to adopt or reject an innovation. Results shows current triggers (lifting of inter-intra state migration bans) and shocks (exposure to covid-19 by migrants) will lead to late widespread majority adoption of 23.8-percent. At this, the death toll will climb from between 4.43-to-5.61-percent to over 12%.

Empirical evidence of phishing menace among undergraduate smartphone users in selected universities in Nigeria Maureen Ifeanyi Akazue; Arnold Adimabua Ojugo; Rume Elizabeth Yoro; Bridget Ogheneovo Malasowe; Obinna Nwankwo
Indonesian Journal of Electrical Engineering and Computer Science Vol 28, No 3: December 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v28.i3.pp1756-1765

In our exploratory quasi-experimental study, 480-student were recruited and exposed to social engineering directives during a university orientation week. The directives phishing attacks were performed for 10 months in 2021. The contents attempted to elicit personal user-data from participants, enticing them to click compromised links. The study aimed to determine cybercrime risks among undergraduates in selected universities in Nigeria, observe responses to socially-engineered attacks, and explore their attitudes to cybercrime risks before/after such attacks. The study generalized that all participants have great deal awareness of cybercrime, and also primed all throughout study to remain vigilant to scams. The study explores various types of scam and its influence on students’ gender and age on perceived safety on susceptibility to phishing scams. Results show that contrary to public beliefs, none of these factors were associated with scam susceptibility and vulnerability rates of the participants.

Assessing contributor features to phishing susceptibility amongst students of petroleum resources varsity in Nigeria Rume Elizabeth Yoro; Fidelis Obukohwo Aghware; Bridget Ogheneovo Malasowe; Obinna Nwankwo; Arnold Adimabua Ojugo
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 2: April 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i2.pp1922-1931

In this observational quasi-experimental study, we recruited 200 participants during the Federal University of Petroleum Resources Effurun’s (FUPRE) orientation, who were exposed to socially engineered (phishing) attacks over nine months. Attacks sought to extract participants’ data and/or entice them to click (compromised) links. The study aims to determine phishing exposure and risks among undergraduates in FUPRE (Nigeria) by observing their responses to socially-engineered attacks and exploring their attitudes to cybercrime risks before and after phishing attacks. The study primed all students in place of cybercrime awareness to remain vigilant to scams and explored the various scam types with their influence on gender, age, status, and their perceived safety on susceptibility to scams. Results show that contrary to public beliefs, these factors have all been found to be associated with scam susceptibility and vulnerability of the participants.

Evidence of personality traits on phishing attack menace among selected university undergraduates in Nigerian Rume Elizabeth Yoro; Fidelis Obukohwo Aghware; Maureen Ifeanyi Akazue; Ayei Egu Ibor; Arnold Adimabua Ojugo
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 2: April 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i2.pp1943-1953

Access ease, mobility, portability, and improved speed have continued to ease the adoption of computing devices; while, consequently proliferating phishing attacks. These, in turn, have created mixed feelings in increased adoption and nosedived users’ trust level of devices. The study recruited 480-students, who were exposed to socially-engineered attack directives. Attacks were designed to retrieve personal data and entice participants to access compromised links. We sought to determine the risks of cybercrimes among the undergraduates in selected Nigerian universities, observe students’ responses and explore their attitudes before/after each attack. Participants were primed to remain vigilant to all forms of scams as we sought to investigate attacks’ influence on gender, students’ status, and age to perceived safety on susceptibility to phishing. Results show that contrary to public beliefs, age, status, and gender were not among the factors associated with scam susceptibility and vulnerability rates of the participants. However, the study reports decreased user trust levels in the adoption of these new, mobile computing devices.

Empirical Evaluation for Intelligent Predictive Models in Prediction of Potential Cancer Problematic Cases In Nigeria Arnold Adimabua Ojugo; Chris Obaro Obruche; Andrew Okonji Eboka
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

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.

Co-Authors Andrew Okonji Eboka Andrew Okonji Eboka Andrew Okonji Eboka Andrew Okonji Eboka Ayei Egu Ibor Bridget Ogheneovo Malasowe Chris Obaro Obruche David Ademola Oyemade Debby Oghenevwede Otakore Fidelis Obukohwo Aghware Maureen Ifeanyi Akazue Obinna Nwankwo Obinna Nwankwo Obinna Nwankwo Oghenevwede Debby Otakore Rume Elizabeth Yoro Rume Elizabeth Yoro Rume Elizabeth Yoro Rume Elizabeth Yoro

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