Breaking Into The Black Box: Unlocking Meta’s Product-Level Ad Data

Brands can challenge Meta’s automation by surfacing product-level trends, aligning them with purchase behavior, and ...
Proceedings of the National Academy of Sciences (PNAS)

Identifying variation in dinosaur footprints and classifying problematic specimens via unbiased unsupervised machine learning

Machine learning holds great promise for classifying and identifying fossils, and has recently been marshaled to identify trackmakers of dinosaur ...
IEEE

AdaBoost-LSTM: A Hybrid Ensemble Framework for Sequential Credit Card Fraud Detection

Abstract: Credit card fraud detection is a critical task in financial systems, requiring effective algorithms to accurately classify transactions as fraudulent or non-fraudulent. This paper proposes a ...
Frontiers

Machine learning prediction of feeding intolerance in preterm infants: a pre-feeding risk stratification model

1 Department of Traditional Chinese Medicine, Capital Center for Children's Health, Capital Medical University, Capital Institute of Pediatrics, Beijing, China 2 Department of Neonatology, Capital ...
GitHub

TreeSHAP-compatible AdaBoost and RUSBoost classifiers for explainable AI on imbalanced datasets.

This package provides custom implementations of AdaBoost and RUSBoost classifiers that are compatible with SHAP's TreeExplainer, enabling fast local explanations for boosting algorithms on imbalanced ...
Scientific Research Publishing

A Comparative Study of Ensemble Learning Techniques and Classification Models to Identify Phishing Websites ()

The advent of the internet, as we all know, has brought about a significant change in human interaction and business operations around the world; yet, this evolution has also been marked by security ...
Scientific Research Publishing

Adane, K., Beyene, B. and Abebe, M. (2023) Single and Hybrid-Ensemble Learning-Based Phishing Website Detection: Examining Impacts of Varied Nature Datasets and Informative ...

ABSTRACT: The advent of the internet, as we all know, has brought about a significant change in human interaction and business operations around the world; yet, this evolution has also been marked by ...
Frontiers

ADHD detection from EEG signals using GCN based on multi-domain features

Introduction: Attention deficit hyperactivity disorder (ADHD) is a common psychiatric disorder in children during their early school years. While many researchers have explored automated ADHD ...
IEEE

SMOTEHashBoost: Ensemble Algorithm for Imbalanced Dataset Pattern Classification

Abstract: Class imbalance occurs frequently in machine learning, particularly in binary classification tasks where the majority class has a significantly larger number of samples than the minority ...