Python
A System Design With Deep Learning and IoT to Ensure Education Continuity for Post-COVID
ATT Squeeze U-Net A Lightweight Network for Forest Fire Detection and Recognition
Canine Skin Disease Classification Using Convolutional Neural Networks (CNN)
CNN and Arduino based Stress Level Detection System
CNN Based Object Recognition and Tracking System to Assist Visually Impaired People
CNN-Keypoint Based Two-Stage Hybrid Approach for Copy-Move Forgery Detection
Comparative Analysis of Vehicle-Based and Driver-Based Features for Driver Drowsiness Monitoring by Support Vector Machines
Credit Card Fraud Detection Using State-of-the-Art Machine Learning and Deep Learning Algorithms
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.
LCDctCNN: Lung Cancer Diagnosis of CT scan Images Using CNN Based Model
