Python
Hand Gesture Recognition for Multi-Culture Sign Language Using Graph and General Deep Learning Network
House Price Prediction Using Machine Learning And Neural Networks
Human Activity Recognition Through Ensemble Learning of Multiple Convolutional Neural Networks
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.
Impact of Fuzziness for Skin Lesion Classification with Transformer-Based Model
Incorporating Meteorological Data and Pesticide Information to Forecast Crop Yields Using Machine Learning
Integrating Sentiment Analysis with Machine Learning for Cyberbullying Detection on Social Media
Integration of Traditional Knowledge and Modern Science: A Holistic Approach to Identify Medicinal Leaves for Curing Diseases
Intelligent Crop Recommendation System using Machine Learning
Interpretable Deep Learning Framework for Land Use and Land Cover Classification in Remote Sensing Using SHAP
Krushi Sahyog: Plant disease identification and Crop recommendation using Artificial Intelligence
