Python
Heterogeneous Data Storage Management with Deduplication in Cloud Computing
House Price Prediction Using Machine Learning And Neural Networks
Human Activity Recognition Through Ensemble Learning of Multiple Convolutional Neural Networks
Hydroponic Power plant Monitoring Robot With Obstacles Detection
Aim:
The aim of this project is to develop an autonomous robot for monitoring hydroponic power plants using line-following navigation and environmental sensors. The robot detects obstacles, identifies objects with an ESP32-CAM, and updates data to Firebase for real-time monitoring. It provides an efficient and sustainable solution for automating hydroponic farming operations
Hyphatia: A Card-Not-Present Fraud Detection System Based on Self-Supervised Tabular Learning
Aim:
To develop a fraud-detection system that learns patterns directly from transaction data without relying on labels. Captures differences between legitimate and fraudulent activity using an unsupervised representation-learning approach. It improve fraud-spotting accuracy by training simple classifiers on these learned representations instead of raw features.
i-AVR: IoT-Based Ambulatory Vitals Monitoring and Recommender System
IBGS A Wearable Smart System to Assist Visually Challenged
Embedded, Artificial Intelligence(AI), Internet of Things(IoT), Social Cause, Artificial Intelligence, IoT
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
Integrated Digital Twins System for Oil Temperature Prediction of Power Transformer Based on Internet of Things
