Securing transactions: a hybrid dependable ensemble machine learning model using IHT-LR and grid search

Cybersecurity, Год журнала: 2024, Номер 7(1)

Опубликована: Ноя. 2, 2024

Abstract Financial institutions and businesses face an ongoing challenge from fraudulent transactions, prompting the need for effective detection methods. Detecting credit card fraud is crucial identifying preventing unauthorized transactions. While incidents are relatively rare, they can result in substantial financial losses, particularly due to high monetary value associated with Timely of enables investigators take swift actions mitigate further losses. However, investigation process often time-consuming, limiting number alerts that be thoroughly examined each day. Therefore, primary objective a model provide accurate while minimizing false alarms missed cases. In this paper, we introduce state-of-the-art hybrid ensemble (ENS) dependable machine learning (ML) intelligently combines multiple algorithms proper weighted optimization using grid search, including decision tree (DT), random forest (RF), K-nearest neighbor (KNN), multilayer perceptron (MLP), enhance identification. To address data imbalance issue, employ instant hardness threshold (IHT) technique conjunction logistic regression (LR), surpassing conventional approaches. Our experiments conducted on publicly available dataset comprising 284,807 The proposed achieves impressive accuracy rates 99.66%, 99.73%, 98.56%, 99.79%, perfect 100% DT, RF, KNN, MLP ENS models, respectively. outperforms existing works, establishing new benchmark detecting transactions high-frequency scenarios. results highlight effectiveness reliability our approach, demonstrating superior performance metrics showcasing its exceptional potential real-world applications.

Язык: Английский

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A systematic review of AI-enhanced techniques in credit card fraud detection

Journal Of Big Data, Год журнала: 2025, Номер 12(1)

Опубликована: Янв. 14, 2025

Abstract The rapid increase of fraud attacks on banking systems, financial institutions, and even credit card holders demonstrate the high demand for enhanced detection (FD) systems these attacks. This paper provides a systematic review techniques using Artificial Intelligence (AI), machine learning (ML), deep (DL), meta-heuristic optimization (MHO) algorithms (CCFD). Carefully selected recent research papers have been investigated to examine effectiveness AI-integrated approaches in recognizing wide range These AI were evaluated compared discover advantages disadvantages each one, leading exploration existing limitations ML or DL-enhanced models. Discovering limitation is crucial future work robustness various key finding from this study demonstrates need continuous development models that could be alert latest fraudulent activities.

Язык: Английский

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Cloud computing load prediction by decomposition reinforced attention long short-term memory network optimized by modified particle swarm optimization algorithm

Annals of Operations Research, Год журнала: 2023, Номер unknown

Опубликована: Дек. 15, 2023

Язык: Английский

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Potential of Coupling Metaheuristics-Optimized-XGBoost and SHAP in Revealing PAHs Environmental Fate

Toxics, Год журнала: 2023, Номер 11(4), С. 394 - 394

Опубликована: Апрель 21, 2023

Polycyclic aromatic hydrocarbons (PAHs) refer to a group of several hundred compounds, among which 16 are identified as priority pollutants, due their adverse health effects, frequency occurrence, and potential for human exposure. This study is focused on benzo(a)pyrene, being considered an indicator exposure PAH carcinogenic mixture. For this purpose, we have applied the XGBoost model two-year database pollutant concentrations meteorological parameters, with aim identify factors were mostly associated observed benzo(a)pyrene describe types environments that supported interactions between other polluting species. The data collected at energy industry center in Serbia, vicinity coal mining areas power stations, where maximum concentration period reached 43.7 ngm-3. metaheuristics algorithm has been used optimize hyperparameters, results compared models tuned by eight cutting-edge algorithms. best-produced was later interpreted applying Shapley Additive exPlanations (SHAP). As indicated mean absolute SHAP values, temperature surface, arsenic, PM10, total nitrogen oxide (NOx) appear be major affecting its environmental fate.

Язык: Английский

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Supervised contrastive representation learning with tree-structured parzen estimator Bayesian optimization for imbalanced tabular data

Expert Systems with Applications, Год журнала: 2023, Номер 237, С. 121294 - 121294

Опубликована: Сен. 2, 2023

Язык: Английский

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