Python
A Machine Learning-Based Classification and Prediction Technique for DDoS Attacks
Advanced Heart Attack Risk Prediction Using Stacked Hybrid Machine Learning
Android Malware Detection Using Informative Syscall Subsequences
BMNet-5: A Novel Approach of Neural Network to Classify the Genre of Bengali Music Based on Audio Features
Credit Scoring Prediction Using Deep Learning Models in the Financial Sector
Data-Driven Early Diagnosis of Chronic Kidney Disease: Development and Evaluation of an Explainable AI Model
Detecting Spam Email with Machine Learning Optimized with Harris Hawks’s optimizer (HHO) Algorithm
DroneGuard: An Explainable and Efficient Machine Learning Framework for Intrusion Detection in Drone Networks
Evolving Malware and DDoS Attacks: Decadal Longitudinal Study
Identifying Fraudulent Credit Card Transactions Using Ensemble Learning
Aim:
People can use credit cards for online transactions as it provides an efficient and easy-to-use facility. With the increase in usage of credit cards, the capacity of credit card misuse has also enhanced. Credit card frauds cause significant financial losses for both credit card holders and financial companies. Fraudulent activities often go unnoticed due to the complexity of transaction behaviors and the adaptability of fraudsters. The main aim of this study is to detect fraudulent transactions using credit cards with the help of ML algorithms and deep learning algorithms. By implementing advanced techniques such as CatBoost and CNN, we aim to improve fraud detection accuracy and minimize false positives. The research also focuses on dataset balancing, feature extraction, and performance evaluation to ensure the model's robustness. By integrating these methods, we seek to enhance security and provide an efficient solution for real-world credit card fraud detection.
Neural-XGBoost A Hybrid Approach for Disaster Prediction and Management Using Machine Learning
