FloraNER: A new dataset for species and morphological terms named entity recognition in French botanical text.

FloraNER is a distantly supervised named entity recognition dataset (NER). The dataset is built from botanical French literature extracted from the OCR-preprocessed flora of New Caledonia, provided by the National Museum of Natural History in France (MNHN), and distantly annotated with a botanical French corpus created by merging botanical lexicons available online. FloraNER comprises separate sub-datasets for the recognition of plant species names, as well as coarse-grained and fine-grained botanical morphological terms.

Predicting stroke events with a proactive fusion system: a comprehensive study on imbalance class handling in computational biomechanics.

Stroke, as a critical global health concern and the second leading cause of death, occurs when blood flow to the brain is interrupted. Although machine learning has advanced in medical safety, there is limited research on stroke prediction using information fusion systems. This study presents a fusion framework that combines multiple base classifiers and a Meta classifier to improve stroke prediction performance.

Conditional reduction of the loss value versus reinforcement learning for biassing a de-novo drug design generator.

Deep learning has demonstrated promising results in de novo drug design. Often, the general pipeline consists of training a generative model (G) to learn the building rules of valid molecules, then using a biassing technique such as reinforcement learning (RL) to focus G on the desired chemical space. However, this sequential training of the same model for different tasks is known to be prone to a catastrophic forgetting (CF) phenomenon.

Class-imbalanced crash prediction based on real-time traffic and weather data: A driving simulator study.

Crash occurrence prediction has been of major importance in proactively improving traffic safety and reducing potential inconveniences to road users. Conventional statistical crash prediction models frequently suffer from severe data quality issues and require a significant amount of historical data. On the other hand, even though machine learning (ML) based algorithms have proven to be powerful in predicting future outcomes in different fields of applications, they likely fail to provide satisfactory results unless a tuning parameter approach is conducted.

Comprehensive miscarriage dataset for an early miscarriage prediction.

We present risk factors for predicting miscarriage. Our data is created through an android mobile application that collects automatically real-time data about the pregnant woman. This process is done every 60 s while the mobile application is on active mode.