Revolutionizing Banking Decision-Making: A Deep Learning Approach to Predicting Customer Behavior

Journal of Business and Management Studies, Journal Year: 2024, Volume and Issue: 6(3), P. 21 - 27

Published: May 7, 2024

This article explores a machine learning approach focused on predicting bank customer behavior, emphasizing deep methods. Various architectures, including CNNs like VGG16, ResNet50, and InceptionV3, are compared with traditional algorithms such as Random Forest SVM. Results show models, particularly outperform ones, an accuracy of 86.66%. A structured methodology ensures ethical data use. Investing in infrastructure expertise is crucial for successful integration, offering competitive edge banking decision-making.

Language: Английский

---

Transformative Impact of Deep Learning in Stock Market Decision-Making: A Comparative Study of Convolutional Neural Networks

Journal of Business and Management Studies, Journal Year: 2024, Volume and Issue: 6(3), P. 28 - 34

Published: May 7, 2024

This research delves into the transformative impact of deep learning, specifically Convolutional Neural Networks (CNNs) such as VGG16, ResNet50, and InceptionV3, on organizational management business intelligence. The study follows a comprehensive methodology, emphasizing importance high-quality datasets in leveraging learning for enhanced decision-making. Results demonstrate superior performance CNN models over traditional algorithms, with (VGG19) achieving an accuracy rate 89.45%. findings underscore potential extracting meaningful insights from complex data, offering paradigm shift optimizing various processes. article concludes by significance investing infrastructure expertise successful integration, ensuring ethical considerations, addressing data privacy concerns. contributes to growing discourse application management, providing valuable resource businesses navigating dynamic landscape global market.

Language: Английский

---

A novel deep learning-based 1D-CNN-optimized GRU approach for heart disease prediction

Automatika, Journal Year: 2025, Volume and Issue: 66(1), P. 79 - 90

Published: Jan. 2, 2025

Language: Английский

---

Enhancing Credit Card Fraud Detection: A Comprehensive Study of Machine Learning Algorithms and Performance Evaluation

SSRN Electronic Journal, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 1, 2025

Language: Английский

---

Enhancing Credit Card Fraud Detection: A Comprehensive Study of Machine Learning Algorithms and Performance Evaluation

Journal of Business and Management Studies, Journal Year: 2024, Volume and Issue: 6(3), P. 252 - 259

Published: June 13, 2024

Credit card fraud detection remains a significant challenge for financial institutions and consumers globally, prompting the adoption of advanced data analytics machine learning techniques. In this study, we investigate methodology performance evaluation various algorithms credit detection, emphasizing preprocessing techniques model effectiveness. Through thorough dataset analysis experimentation using cross-validation approaches, assess logistic regression, decision trees, random forest classifiers, Naïve Bayes K-nearest neighbors (KNN), artificial neural networks (ANN-DL). Key metrics such as accuracy, sensitivity, specificity, F1-score are compared to identify most effective models detecting fraudulent transactions. Additionally, explore impact different folds in on performance, providing insights into classifiers' robustness stability. Our findings contribute ongoing efforts develop efficient systems, offering valuable researchers striving combat effectively.

Language: Английский

---

A Comparative Analysis of Machine Learning-Based Prediction for Heart Disease Detection

Studies in systems, decision and control, Journal Year: 2024, Volume and Issue: unknown, P. 159 - 174

Published: Jan. 1, 2024

Language: Английский

---

SmartScanPCOS: A Feature-Driven Approach to Cutting-Edge Prediction of Polycystic Ovary Syndrome using Machine Learning and Explainable Artificial Intelligence

Heliyon, Journal Year: 2024, Volume and Issue: 10(20), P. e39205 - e39205

Published: Oct. 1, 2024

Language: Английский

---