Weibull Multiplicative Model and Machine Learning Models for Full-Automatic Dark-Spot Detection From Sar Images

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.

Section: Feature Selection (Fs)mentioning

confidence: 99%

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

Aghware,

Ojugo,

Adigwe

et al. 2024

“…The dataset consists of 15-features namely: polyurethane throughput, calcium throughput, TDI throughput, water throughput, polyurethane dials-set, calcium dial-set, TDI dial-set, water dial-set, quantity of polyurethane, quantity of calcium, quantity of TDI, quantity of water, polyurethane water content, production time, and scorch -all of which sums up to about 8,540 data-points as in table 1. These are associated with the formation of a scorch on the retrieved dataset spreadsheet (from its unstructured format) [51]- [54].…”

Section: Data Gatheringmentioning

confidence: 99%

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

Omoruwou,

Ojugo,

Ilodigwe

2024

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.

“…Nigeria is arguably in Africa, the most strategic and influential nation -in lieu of her being the most populous black nation, her vast natural resources of hydrocarbon(s) vis-à-vis her government's commitment to anti-corruption and to the unity of Africa. The abundance of minerals has continued to foster Nigeria's heavy dependence on her oil, which accounts for over 41% of her Gross Domestic Product and 90% of her export revenues [51], [64]- [66]. The study uses the COVID-19 contagion (as its innovation), and models Delta State as a small-world graph with diffusion time as the lockdown from April 2020 to March 2022.…”

Section: Data Gatheringmentioning

confidence: 99%

CoSoGMIR: A Social Graph Contagion Diffusion Framework using the Movement-Interaction-Return Technique

Ojugo,

Ejeh,

Akazue

et al. 2023

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.