Numerous cutting-edge machine learning algorithms have been developed in recent years for efficiently optimizing chemical processes. While many are open-source, evaluating and applying them to chemical processes remains challenging due to varying, non-standard implementations and interfaces. CyRxnOpt has been developed as a general, Python-based interface to install, use, and switch between reaction optimization algorithms under a single software interface. By integrating optimizers with the CyRxnOpt interface, many state-of-the-art reaction optimization algorithms can be more easily used in other software and workflows.
Crandall, Zachery, Kulathunga, Dulitha
Full Changelog : https://github.com/attogram/fantastic-octo-giggle/compare/0001...0002 42 and the Open Error Foundation: An Academic Interpretation Abstract The relationship between 42 and the Open Error Foundation (i.o.) can be understood as more than a recurring joke, reference, or organizational number. It can function as a compact epistemological model for the Foundation itself. The central problem of 42 is not that the answer is numerically wrong. It is that an answer can be generated before the question has been adequately established. That distinction is extraordinarily relevant to an organization concerned with artificial intelligence
David, Imgbot
Reproducibility package for the Med3DVLM (MagicXin/Med3DVLM-Qwen-2.5-7B) clinical evaluation. Contains the preprocessing scripts (NIfTI-to-PNG conversion, PNG-to-prepared-NIfTI, mediastinal windowing transformation), the HPC inference pipelines, and the 15 evaluation prompts (5 closed pathology, 5 closed technical, 5 open report generation). Images are deposited separately: ChestPathCT5-S100 (doi:10.5281/zenodo.18256797). Local paths and HPC credentials have been anonymized as XXXXX placeholders.
Fahrni, Guillaume
Software’s: MATLAB 2022b and MATLAB 2025b, and Python 3.12.2 (Anaconda distribution). The Python code depends on numpy 1.26.4, pandas 2.2.2, scikit-learn 1.8.0, imbalanced-learn 0.14.1, xgboost 2.1.3, tensorflow 2.17.0 (used with TF_USE_LEGACY_KERAS=True), hyperopt 0.2.7, matplotlib 3.10.1, openpyxl 3.1.2 and h5py 3.11.0. License: A commercial MATLAB license is required to execute the provided code. The code has not been tested with open-source alternatives (e.g., GNU Octave) and compatibility is not guaranteed. Platform: The code is platform-independent and should run on Windows, macOS, and Linux operating systems supported by MATLAB R2022b
Chandriyan, Haritha, KUMAR, SHASHI, Nasim, M Quamer, Singha Roy, Paresh
AI-Aquatica v2.3.0 AI-Aquatica is an open-source Python library for reproducible machine-learning-assisted water-quality and hydrochemical analysis. The software integrates environmental data loading and preprocessing, hydrochemical quality control, classical machine learning, visualization, validation, and automated HTML reporting within a single auditable workflow. Version 2.3.0 is a major methodological and software-quality release prepared in connection with the revised SoftwareX software article. Major changes in v2.3.0 Leakage-safe machine-learning workflows Supervised preprocessing has been redesigned to prevent information leakage bet
Miller, Tymoteusz
Pipeline for processing large-scale tray images of pinned beetles: detect specimens, optionally sort and crop with metadata matching, and produce fine-grained morphological segmentation (5-class or 9-class). It uses an open-vocabulary detector (Grounding DINO), a vision-language model (LLaVA-NeXT) for verification, and fine-tuned Mask2Former for transformer-based part segmentation and downstream morphological analysis.
Liu, Fangxun, Rayeed, S M, Stevens, Samuel, East, Alyson, Chiang, Cheng Hsuan, Lee, Colin, Yi, Daniel, Yang, Junke, Naik, Tejas, Wang, Ziyi, Kilrain, Connor, Buckwalter, Elijah H., Hou, Jiacheng, Bueno, Saul Ibaven, Wang, Shuheng, Ma, Xinyue, Liu, Yifan, Tao, Zhiyuan, Zhang, Ziheng, Sokol, Eric, Belitz, Michael, Record, Sydne, Stewart, Charles V., Chao, Wei-Lun
Now with ADAF detection and RF-DETR, training and detection. This update expands Mustatil as a GIS-level AI vision workspace with improved support for geospatial processing, model experimentation, and multi-model remote-sensing workflows. The release focuses on stability and practical usability: the existing AI Pipeline tab remains unchanged, while the surrounding detection and GIS tooling has been extended. New and improved components include additional support for Faster R-CNN, Mask R-CNN, U-Net and SAM/SAM2-based workflows, optional video detection experiments, improved GIS/console tooling for Rasterio, GDAL/OGR and GMT, and better integra
Tarek, Wasfy
About EMGFlow EMGFlow is an open-source Python package for preprocessing and extracting features from surface electromyographic (sEMG) signals. It has been designed to facilitate the analysis of large datasets through batch processing of signal files, a common requirement in machine learning. Although several packages process physiological and neurological signals, support for sEMG has remained limited. Many lack a comprehensive feature set for sEMG, forcing researchers to use a patchwork of tools. Others focus on event detection with GUI-centric workflows that suit continuous recordings of a single participant, but complicate batch feature e
Conley, D. William Lawrence, Livingstone, Steven
This repository contains Python code and documentation for an Automated Machine Learning (AutoML) workflow using the open-source framework Optuna ( https://optuna.org/ ). The AutoML studies focus on simultaneous Hyperparameter Optimization (HPO) and Neural Architecture Search (NAS) for an LSTM-based Deep Neural Network (DNN) . The resulting models have been integrated into a Deep Nonlinear Model Predictive Control (NMPC) framework, as published in [Paper Link tbd]. Please see the ReadMe in the github Repo for more information.
Winkler, Alexander
Geoff is a framework of Python packages that harmonizes access to various methods of machine learning and numerical optimization, and to ease the transition towards and between them. Its focus lies on gradient-free evaluation of black-box functions, a typical use case in particle accelerator control. It has been open-sourced to facilitate cooperation between accelerator laboratories. Developments are carried out through a collaboration between GSI and CERN under the umbrella of the EURO-LABS project. The plugin API is documented here . A mirror of the canonical repository exists on Github .
Madysa, Penny, Kain, Verena
Supplemental Software and test data sets for fully automated dSTORM acquistion using deep learning. The correspondig paper has been published .... . A. Python software for training and application of DeepLabV3 for segmentation of microscopy images (train_apply_NN.zip). Jupyter notebooks for training and applying deep neural netoworks (including explanations for input paramters) Training and Test data for nucleus segmentation B. Python software for fully automated acquisition of dSTORM images using networks trained with software A (automated_STORM_v1_0_0.zip). Jupyter notebook for acquistion of high-content images and fully automated dSTORM, w
Linke, Janis T., Appeltshauser, Luise, Doppler, Kathrin, Heinze, Katrin G.
EDDL is a free-software open-source deep-learning toolkitoriginally originally designed in the H2020 DeepHealth project. This version of the library has been adapted to leverage computation capabilities and run inference of AI models on REBECCA's RISC-V platform.
Solver Machine Learning SL
