Scientific Research Publishing

Deep Reinforcement Learning for Phishing Detection with Transformer-Based Semantic Features ()

Phishing is a form of cybercrime in which people are deceived into exposing their personal information which can result in ...
STAT

Your sepsis algorithm shouldn’t require a time machine

You’re reading the web edition of STAT’s AI Prognosis newsletter, our subscriber-exclusive guide to artificial intelligence in health care and medicine. Sign up to get it delivered in your inbox every ...
BMJ

Development of an interpretable machine learning model for frailty risk prediction in older adult care institutions: a mixed-methods, cross-sectional study in China

Objective To develop and validate an interpretable machine learning (ML)-based frailty risk prediction model that combines real-time health data with validated scale assessments for enhanced ...
Visual Studio Magazine

Random Forest Regression Using C#

Random forest regression is a tree-based machine learning technique to predict a single numeric value. A random forest is a collection (ensemble) of simple regression decision trees that are trained ...
Phys.org

Machine learning algorithm fully reconstructs LHC particle collisions

The CMS Collaboration has shown, for the first time, that machine learning can be used to fully reconstruct particle collisions at the LHC. This new approach can reconstruct collisions more quickly ...
BMJ

Development and validation of an interpretable machine learning model for predicting Philadelphia chromosome-positive acute lymphoblastic leukaemia using clinical and ...

2 State Key Laboratory of Trauma and Chemical Poisoning, Chongqing, China 3 Chongqing Key Laboratory of Hematology and Microenvironment, Chongqing, China Participants This study analysed 471 newly ...
Frontiers

Explainable machine learning for stroke risk prediction: a comparative study with SHAP-based interpretation

Stroke is one of the leading causes of death and disability worldwide, making early screening and risk prediction crucial. Traditional methods have limitations in handling nonlinear relationships ...
Nature

Robust evaluation of classical and quantum machine learning under noise, imbalance, feature reduction and explainability

Quantum machine learning (QML) has emerged as a promising paradigm for solving complex classification problems by leveraging the computational advantages of quantum systems. While most traditional ...
International Monetary Fund

GDP Nowcasting Performance of Traditional Econometric Models vs Machine-Learning Algorithms: Simulation and Case Studies

Are Machine Learning (ML) algorithms superior to traditional econometric models for GDP nowcasting in a time series setting? Based on our evaluation of all models from both classes ever used in ...
Frontiers

Evaluating machine learning models for stroke prediction based on clinical variables

Stroke remains one of the leading causes of global mortality and long-term disability, driving the urgent need for accurate and early risk prediction tools. Traditional models such as the Framingham ...
Visual Studio Magazine

Linear Regression Using JavaScript

Dr. James McCaffrey presents a complete end-to-end demonstration of linear regression using JavaScript. Linear regression is the simplest machine learning technique to predict a single numeric value, ...
IEEE

Diabetes Prediction with Random Forest - Logistic Regression algorithm using voting classifier

Abstract: A commonly recognized chronic metabolic condition known as Diabetes mellitus significantly affects the global, social and economic standing of people. Obesity, age, high blood pressure, ...