XG Boost

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• Published Subscription Original Research 15 Apr 2025

  Enhancing Credit Card Fraud Detection Using Device Fingerprinting and Behavioral Biometrics

  By Sakshi Vinod Jadhav, Gayatri Balasaheb Joshi, Deesha Shashikant Pasoli, Sanika Balkrishna Kashid, Ambuj Kumar

  Abstract: Credit card fraud is a growing global concern, with financial losses projected to reach $ 43.47 billion by 2028. Credit card fraud poses a major challenge in the financial industry, resulting in substantial financial losses and security risks. This research introduces a Machine Learning-based Credit Card Fraud Detection System designed to improve the accuracy of fraud identification. Due to the imbalanced nature of fraud datasets, SMOTE (Synthetic Minority Over-sampling Technique) …

  Published in Journal Of Network security · Vol. 13, Issue 2, 2025 · pp. 40–50 Read article →

• Published Subscription Original Research 14 Aug 2024

  Early Autism Diagnosis: Machine Learning Models and Their Effectiveness

  By Poonam Chaudhary, Anmol Bhatia, Vanshika Sangwan, Riya Saxena, Rahul, Sanyam Virmani

  Abstract: Diagnosis is of utmost importance for timely intervention and support. However, traditional diagnosis methods, which are based on subjective assessment, are delayed. This project explores the role that machine learning techniques might play in enhancing the accuracy and effectiveness of ASD detection. Several state-of-the-art classification algorithms were benchmarked using a dataset from Kaggle. Logistic Regression, XG Boost, Random Forest, Decision Tree, and Gradient Boosting were taken into consideration. Other performance …

  Published in Journal of Instrumentation Technology & Innovations · Vol. 14, Issue 2, 2024 Read article →