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13-19 of 19

Cheetah

Fast and differentiable particle accelerator optics simulation for reinforcement learning and optimisation applications.

Automatic Differentiation
Differentiable Programming
Differentiable Simulation
+ 7

Python

BioAutoML - Democratizing Machine Learning in Life Sciences

BioAutoML automatically runs an end-to-end ML pipeline that can be effectively employed by non-experts. BioAutoML does not require specialized human assistance, i.e., it only needs a training dataset of biological sequences (FASTA files) to perform an end-to-end ML experiment.

Artificial Intelligence
Big data
BioAutoML
+ 3

Jupyter Notebook
Python

AutoGaitA

AutoGaitA simplifies, accelerates, and standardises gait analyses after body posture tracking with deep learning algorithms in Python.

biology
Gait
Locomotion
+ 3

Python

sdaas

Simple machine learning tool in Python computing an anomaly score of seismic waveform amplitudes. By using a pre-trained Isolation forest model, the program can be used for identification of outliers in semismic data, assign robustness weights, or check instruments and metadata errors

Machine learning
outlier detection
seismic metadata
+ 2

Python

DeepLOKI

DeepLOKI is an AI-based software that uses deep learning to classify zooplankton species from high-resolution images, improving research efficiency by enabling faster and more accurate onboard image analysis in marine science.

Data analysis
Earth & Environment
Machine learning
+ 2

Python

simona

This package implements infrastructures for ontology analysis by offering efficient data structures, fast ontology traversal methods, and elegant visualizations. It provides a robust toolbox supporting over 70 methods for semantic similarity analysis.

Data analysis
Data Visualization
FAIR Data
+ 3

R
C++
JavaScript
+ 3

UTILE-Oxy

Automated workflow using deep learning for the analysis of videos containing oxygen bubbles in PEM electrolyzers: 1. preparing annotated dataset and training models to conduct semantic seg- mentation of bubbles and 2. automating the extraction of bubble properties for further distribution analysis.

bubbles
Data analysis
Data Visualization
+ 7

Python
Jupyter Notebook