Python
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.
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.
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
LE-YOLO: Lightweight and Efficient Detection Model for Wind Turbine Blade Defects Based on Improved YOLO
Lightweight Detection Algorithm for Breast-Mass Features in Ultrasound Images
LMD_YOLO: A Lightweight and Efficient Model for Pavement Defects Detection
MAD-CTI: Cyber Threat Intelligence Analysis of the Dark Web Using a Multi-Agent Framework
Obfuscated Privacy Malware Classification Using Machine Learning and Deep Learning Techniques
Python, Cybersecurity, Deep Learning, Machine Learning, Artificial Intelligence, Cyber Security, Deep Learning, Machine Learning
Aim
The aim of this research is to develop an intelligent system capable of detecting and classifying obfuscated privacy malware into various categories and families. This system leverages machine learning and deep learning models trained on the CIC-MalMem-2022 dataset to improve accuracy and address the challenges posed by data imbalance and complex malware behaviour.
Object Detection Method Using Image and Number of Objects on Image as Label
To develop an object detection model using YOLOv8 to address the limitations of existing methods and improve detection accuracy, robustness, and efficiency. The aim is to design a system that reduces the dependency on extensive labelling while ensuring adaptability to unseen environments. The model will utilize YOLOv8’s capabilities to process data efficiently and deliver high-performance results for diverse applications in object detection.
Octascope: A Lightweight Pre-Trained Model for Optical Coherence Tomography
Aim:
Aim to build a reliable system that can identify different retinal diseases from OCT images. To create a practical workflow that can analyze images, compare predictions, and flag mistakes for improvement. It combine the strengths of multiple models so the final decision is more accurate and stable.
