Assessing contributor features to phishing susceptibility amongst students of petroleum resources varsity in Nigeria

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

doi:10.11591/ijece.v13i2.pp1922-1931

Cited by 14 publications

(15 citation statements)

References 36 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…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)supporting

confidence: 87%

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

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

confidence: 87%

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

show abstract

“…Thus, for true-positive cases, the model predicted positive (correctly) and also predicted negative (correctly) for true-negative cases. Conversely, sensitivity and specificity rates were computed with standardized tests for our test data [78], [79]. These proved, to be more efficient.…”

Section: B Discussion Of Findingsmentioning

confidence: 99%

DeLClustE: Protecting Users from Credit-Card Fraud Transaction via the Deep-Learning Cluster Ensemble

Aghware,

Yoro,

Ejeh

et al. 2023

IJACSA

View full text Add to dashboard Cite

Fraud is the unlawful acquisition of valuable assets gained via intended misrepresentation. It is a crime committed by either an internal/external user, and associated with acts of theft, embezzlement, and larceny. The proliferation of credit cards to aid financial inclusiveness has its usefulness alongside it attracting malicious attacks for gains. Attempts to classify fraudulent credit card transactions have yielded formal taxonomies as these attacks seek to evade detection. We propose a deep learning ensemble via a profile hidden Markov model with a deep neural network, which is poised to effectively classify credit-card fraud with a high degree of accuracy, reduce errors, and timely fashion. The result shows the ensemble effectively classified benign transactions with a precision of 97 percent. Thus, we posit a new scheme that is more logical, intuitive, reusable, exhaustive, and robust in classifying such fraudulent transactions based on the attack source, cause(s), and attack time gap.

show abstract

“…And thus, adopt a uniform standard for data exchange -poised to interface and integrate a variety of data structures to help achieve interoperability [76]. Interoperability is a secure access, integration, and timely adaptation of clinical data explored to help optimize health outcomes in all scenarios [77]- [79]. Its goal is to promote retrieval/access ease of clinical records to yield timely, equitable, and efficient patientcentric care [80], [81].…”

Section: The Electronic Health Information Systemmentioning

confidence: 99%

BEHeDaS: A Blockchain Electronic Health Data System for Secure Medical Records Exchange

Oladele,

Ojugo,

Odiakaose

et al. 2024

J. Comput. Theor. Appl.

View full text Add to dashboard Cite

Blockchain platforms propagate into every facet, including managing medical services with professional and patient-centered applications. With its sensitive nature, record privacy has become imminent with medical services for patient diagnosis and treatments. The nature of medical records has continued to necessitate their availability, reachability, accessibility, security, mobility, and confidentiality. Challenges to these include authorized transfer of patient records on referral, security across platforms, content diversity, platform interoperability, etc. These, are today – demystified with blockchain-based apps, which proffers platform/application services to achieve data features associated with the nature of the records. We use a permissioned-blockchain for healthcare record management. Our choice of permission mode with a hyper-fabric ledger that uses a world-state on a peer-to-peer chain – is that its smart contracts do not require a complex algorithm to yield controlled transparency for users. Its actors include patients, practitioners, and health-related officers as users to create, retrieve, and store patient medical records and aid interoperability. With a population of 500, the system yields a transaction (query and https) response time of 0.56 seconds and 0.42 seconds, respectively. To cater to platform scalability and accessibility, the system yielded 0.78 seconds and 063 seconds, respectively, for 2500 users.

show abstract

Assessing contributor features to phishing susceptibility amongst students of petroleum resources varsity in Nigeria

Cited by 14 publications

References 36 publications

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

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

DeLClustE: Protecting Users from Credit-Card Fraud Transaction via the Deep-Learning Cluster Ensemble

BEHeDaS: A Blockchain Electronic Health Data System for Secure Medical Records Exchange

Contact Info

Product

Resources

About