Advanced fraud detection system using machine learning to identify fraudulent transactions and activities. This project implements multiple machine learning algorithms including Random Forest, XGBoost ...

Abstract: This study evaluates the performance of using machine learning models; J48 and Random Forest to classify bananas quality. The existing methods of visual inspection are qualitative and take ...

Community health outcomes significantly impact older populations' wellbeing and quality of life. Traditional analytical methods often struggle to accurately predict health risks at the community level ...

Background: Machine learning (ML) algorithms offer some advantages over traditional scoring systems to assess the influence of cardiovascular risk factors (CVRFs) on the risk of major cardiovascular ...

Abstract: In recent years, there has been a significant rise in the number of credit card bank transactions as well as the frequency of fraud and card theft. However, as financial crime becomes more ...

@IvanNardi As per our initial discussion: Is your feature request related to a problem? Please describe. Detecting malware and covert communications within encrypted traffic, especially when ...

ABSTRACT: Road traffic accidents are one of the global safety and socioeconomic challenges. According to WHO (2024), it has caused over 1.19 million annual fatalities. It is also projected to cause ...

ABSTRACT: Bipolar depression with comorbid obsessive-compulsive disorder (OCD) presents a significant clinical challenge due to its complex symptomatology, unpredictable treatment responses, and high ...

The emergence of using Machine Learning Techniques in software testing started in the 2000s with the rise of Model-Based Testing and early bug prediction models trained on historical defect data. It ...

Anurag Agrawal is a Senior Tech Lead at Google LLC. With over 12 years of experience, he's an expert in Cybersecurity and Abuse prevention. As someone who's been following the intersection of ...