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 ...
InfoWorld

16 open source projects transforming AI and machine learning

From fine-tuning open source models to building agentic frameworks on top of them, the open source world is ripe with ...
BMJ Quality & Safety

Impact of COVID-19 on incidence and trends of adverse events among hospitalised patients in Calgary, Canada: a retrospective chart review study

Background While the incidence of hospital adverse events appeared to be declining before 2019, the COVID-19 pandemic may ...
Material Handling and Logistics

42% of Logistics Leaders Still Holding Back on Agentic AI

News, analysis and features focused on issues related to environment, health and safety in the workplace, including risk ...
Frontiers

Development of an interpretable machine learning model for predicting sarcopenia in patients undergoing maintenance hemodialysis

1 Department of Nephrology, Nanchong Central Hospital Affiliated to North Sichuan Medical College, Nanchong, China 2 Department of Nephrology, Guangyuan Central Hospital, Guangyuan, China Background: ...
Law

Training Alone Is Not Enough: Lessons From 'Recentive' and USPTO AI Examples on Patent Eligible Machine-Learning Claims

The Recentive decision exemplifies the Federal Circuit’s skepticism toward claims that dress up longstanding business problems in machine-learning garb, while the USPTO’s examples confirm that ...
IEEE

Machine Learning–Based Hypertension Risk Stratification Using Logistic Regression and XGBoost

Abstract: Hypertension is a critical global health concern, necessitating accurate prediction models and effective prescription decisions to mitigate its risks. This study proposes a hybrid machine ...
Frontiers

Development and validation of an early predictive model for hemiplegic shoulder pain: a comparative study of logistic regression, support vector machine, and random forest

Objective: In this study, we aim to identify the predictive variables for hemiplegic shoulder pain (HSP) through machine learning algorithms, select the optimal model and predict the occurrence of HSP ...
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Logistic Regression Cost Function ¦ Machine Learning ¦ Simply Explained

Learn what is Logistic Regression Cost Function in Machine Learning and the interpretation behind it. Logistic Regression Cost function is "error" representation of the model. It shows how the model ...