Empirical evidence of phishing menace among undergraduate smartphone users in selected universities in Nigeria

Maureen, Akazue; Ojugo, Arnold Adimabua; Yoro, Rume Elizabeth; Malasowe, Bridget Ogheneovo; Nwankwo, Obinna

doi:10.11591/ijeecs.v28.i3.pp1756-1765

Cited by 14 publications

(15 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…All negative values on the correlation also yield a corresponding negative relationship between both variables and vice versa. And this can be visually confirmed from the pair plots as supported by [87]- [89].…”

Section: Discussion Of Findingssupporting

confidence: 59%

Strategic Feature Selection for Enhanced Scorch Prediction in Flexible Polyurethane Form Manufacturing

Omoruwou,

Ojugo,

Ilodigwe

2024

J. Comput. Theor. Appl.

View full text Add to dashboard Cite

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 were trained to predict the occurrence of scorch using the thermodynamic profile of polyurethane foam, which is made up of recorded production variables. A variety of heuristics algorithms were trained and assessed for how well they performed, namely XGBoost, Decision trees, Random Forest, K-nearest neighbors, Naive Bayes, Support Vector Machines, and Logistic Regression. The XGboost ensemble was found to perform best. It outperformed others with an accuracy of 98.3% (i.e., 0.983), followed by logistic regression, decision tree, random forest, K-nearest neighbors, and naïve Bayes, yielding a training accuracy of 88.1%, 66.7%, 84.2%, 87.5%, and 67.5% respectively. The XGBoost was finally used, yielding 2-distinct cases of non(occurrence) of scorch. Ensemble demonstrates that it is quite capable and is an effective way to predict the occurrence of scorch.

show abstract

Section: Discussion Of Findingssupporting

confidence: 59%

Strategic Feature Selection for Enhanced Scorch Prediction in Flexible Polyurethane Form Manufacturing

Omoruwou,

Ojugo,

Ilodigwe

2024

J. Comput. Theor. Appl.

View full text Add to dashboard Cite

show abstract

“…Its input feats are numerically pre-processed with PCA transformation. Due to confidentiality constraints -the original characteristics and additional context for the dataset are not provided [86]- [88]. A description of the table 1 is thus:…”

Section: Data Gatheringmentioning

confidence: 99%

Enhancing the Random Forest Model via Synthetic Minority Oversampling Technique for Credit-Card Fraud Detection

Aghware,

Ojugo,

Adigwe

et al. 2024

J. Comput. Theor. Appl.

View full text Add to dashboard Cite

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 application of the Synthetic Minority Over-sampling Technique (SMOTE) to assess their performance. These algorithms included Random Forest (RF), K-Nearest Neighbors (KNN), Naïve Bayes (NB), Support Vector Machines (SVM), and Logistic Regression (LR). The methodology was implemented and tested through an API using Flask and Streamlit in Python. Before applying SMOTE, the RF classifier outperformed the others with an accuracy of 0.9802, while the accuracies for LR, KNN, NB, and SVM were 0.9219, 0.9435, 0.9508, and 0.9008, respectively. Conversely, after the application of SMOTE, RF achieved a prediction accuracy of 0.9919, whereas LR, KNN, NB, and SVM attained accuracies of 0.9805, 0.9210, 0.9125, and 0.8145, respectively. These results highlight the effectiveness of combining RF with SMOTE to enhance prediction accuracy in credit card fraud detection.

show abstract

“…But, as study did not use purchase data -it failed to resolve cross-selling effect [52]. Also, [58], [59] in a bid to resolve this via shelf management, grouped the task into 2-parts: (a) a heuristic model to value shelf-layout impact on transaction, and (b) used simulated annealing to maximize expected total profit. This agrees with [60]- [62].…”

Section: (Source: Authors' Processing and Translation)mentioning

confidence: 99%

FePARM: The Frequency-Patterned Associative Rule Mining Framework on Consumer Purchasing-Pattern for Online Shops

Malasowe,

Okpako,

Ashioba

et al. 2024

AIMS Research Journal

View full text Add to dashboard Cite

Transaction data often is a true presentation of consumers’ buying behavior, stored as a set of relational records, which properly harnessed via mining – can help businesses improve their sales volume as a decision support system. Managing such a system can pose many issues to biz such as feature evolution, concept evolution, concept drift, and infinite data length – and often makes it impractical to effectively store such big-data. To curb this, previous studies have assumed data to be stationary in using associative rule mining. This has deprived such systems of the flexibility and adaptiveness required to handle the dynamics of concept drift that characterizes transaction datasets. Our study thus proposes a basket frequent pattern growth trained associative rule mining model to handle large data. The dataset was retrieved from the Delta-Mall Asaba and consists of 556,000 transaction consumer records. The model consists of six-layers, and yields the best result with a 0.1 value for both the confidence and support level(s) at a 94% accuracy, sensitivity of 87%, and a specificity of 32% with a 20-second convergence and processing time. Keywords: Big-Data Analysis, Feature Evolution, Inventory Model, Market Basket Analysis, Businesses, Transactional Data Streams, Concept Drift CISDI Journal Reference Format Malasowe, B.O., Okpako, E.A., Ashioba, N.O., Ejeh, P.O., Ojugo, A.A. & Ako, R.E. (2024): FePARM: The Frequency-Patterned Associative Rule Mining Framework on Consumer Purchasing-Pattern for Online Shops. Computing, Information Systems, Development Informatics & Allied Research Journal. Vol 15 No 2, Pp 1-14. dx.doi.org/10.22624/AIMS/CISDI/V15N2P2. Available online at www.isteams.net/cisdijournal

show abstract

Empirical evidence of phishing menace among undergraduate smartphone users in selected universities in Nigeria

Cited by 14 publications

References 29 publications

Strategic Feature Selection for Enhanced Scorch Prediction in Flexible Polyurethane Form Manufacturing

Strategic Feature Selection for Enhanced Scorch Prediction in Flexible Polyurethane Form Manufacturing

Enhancing the Random Forest Model via Synthetic Minority Oversampling Technique for Credit-Card Fraud Detection

FePARM: The Frequency-Patterned Associative Rule Mining Framework on Consumer Purchasing-Pattern for Online Shops

Contact Info

Product

Resources

About