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All Journal International Journal of Electrical and Computer Engineering Indonesian Journal of Electrical Engineering and Computer Science Journal of Computing Theories and Applications
Maureen Ifeanyi Akazue
Delta State University Abraka
Computer Science & IT Decision Sciences, Operations Research & Management Electrical & Electronics Engineering

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Improved services traceability and management of a food value chain using block-chain network: a case of Nigeria Maureen Ifeanyi Akazue; Rume Elizabeth Yoro; Bridget Ogheneovo Malasowe; Obinna Nwankwo; Arnold Arnold Ojugo
Indonesian Journal of Electrical Engineering and Computer Science Vol 29, No 3: March 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v29.i3.pp1623-1633

Abstract

Competitive asset markets and increased globalization have continued to ripple the food value chain with complex dynamics, which has led to a range of challenges such as food safety and quality, traceability, and overall supply chain inefficiency. These have further continued to endanger the general well-being of society. With rice as a staple food in Nigeria, the rice food supply value chain consists of a series of tasks, processes, and activities that are linked together from freshly harvested products to consumer demand and supply. Study advances the SmartRice, a sensor-based block-chain framework that decentralizes as well as provides a decision-support for the food supply value chain process by first ensuring that accurate data of harvested goods are reported, and passed on to a chain. The study advances a decentralized framework to eliminate various forms of fraud rippled across the existing centralized system, minimize corruption through its sensor-based layered model as well as minimize the error in reported data along the value chain.
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

Abstract

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.
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

Abstract

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.
Forging a User-Trust Memetic Modular Neural Network Card Fraud Detection Ensemble: A Pilot Study Arnold Adimabua Ojugo; Maureen Ifeanyi Akazue; Patrick Ogholuwarami Ejeh; Nwanze Chukwudi Ashioba; Christopher Chukwufunaya Odiakaose; Rita Erhovwo Ako; Frances Uche Emordi
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 Arnold Adimabua Ojugo; Patrick Ogholuwarami Ejeh; Maureen Ifeanyi Akazue; Nwanze Chukwudi Ashioba; Christopher Chukwufunaya Odiakaose; Rita Erhovwo Ako; Blessing Nwozor; Frances Uche Emordi
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.
Effects of Data Resampling on Predicting Customer Churn via a Comparative Tree-based Random Forest and XGBoost Rita Erhovwo Ako; Fidelis Obukohwo Aghware; Margaret Dumebi Okpor; Maureen Ifeanyi Akazue; Rume Elizabeth Yoro; Arnold Adimabua Ojugo; De Rosal Ignatius Moses Setiadi; Chris Chukwufunaya Odiakaose; Reuben Akporube Abere; Frances Uche Emordi; Victor Ochuko Geteloma; Patrick Ogholuwarami Ejeh
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.
Co-Authors Arnold Adimabua Ojugo Arnold Adimabua Ojugo Arnold Arnold Ojugo Ayei Egu Ibor Blessing Nwozor Bridget Ogheneovo Malasowe Bridget Ogheneovo Malasowe Chris Chukwufunaya Odiakaose Christopher Chukwufunaya Odiakaose De Rosal Ignatius Moses Setiadi Fidelis Obukohwo Aghware Fidelis Obukohwo Aghware Frances Uche Emordi Frances Uche Emordi Margaret Dumebi Okpor Nwanze Chukwudi Ashioba Obinna Nwankwo Patrick Ogholuwarami Ejeh Patrick Ogholuwarami Ejeh Reuben Akporube Abere Rita Erhovwo Ako Rita Erhovwo Ako Rita Erhovwo Ako Rume Elizabeth Yoro Rume Elizabeth Yoro Victor Ochuko Geteloma