AIMC Topic: Algorithms

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Validation of wastewater data using artificial intelligence tools and the evaluation of their performance regarding annotator agreement.

Water science and technology : a journal of the International Association on Water Pollution Research
To prevent the pollution of water resources, the measurement and the limitation of wastewater discharges are required. Despite the progress in the field of data acquisition systems, sensors are subject to malfunctions that can bias the evaluation of ...

A data-driven framework for learning hybrid dynamical systems.

Chaos (Woodbury, N.Y.)
The existing data-driven identification methods for hybrid dynamical systems such as sparse optimization are usually limited to parameter identification for coefficients of pre-defined candidate functions or composition of prescribed function forms, ...

Comparison of hybrid machine learning models to predict short-term meteorological drought in Guanzhong region, China.

Water science and technology : a journal of the International Association on Water Pollution Research
Reliable drought prediction plays a significant role in drought management. Applying machine learning models in drought prediction is getting popular in recent years, but applying the stand-alone models to capture the feature information is not suffi...

Ensemble deep learning of embeddings for clustering multimodal single-cell omics data.

Bioinformatics (Oxford, England)
MOTIVATION: Recent advances in multimodal single-cell omics technologies enable multiple modalities of molecular attributes, such as gene expression, chromatin accessibility, and protein abundance, to be profiled simultaneously at a global level in i...

Quantification of motion during microvascular anastomosis simulation using machine learning hand detection.

Neurosurgical focus
OBJECTIVE: Microanastomosis is one of the most technically demanding and important microsurgical skills for a neurosurgeon. A hand motion detector based on machine learning tracking technology was developed and implemented for performance assessment ...

Automatic Differentiation is no Panacea for Phylogenetic Gradient Computation.

Genome biology and evolution
Gradients of probabilistic model likelihoods with respect to their parameters are essential for modern computational statistics and machine learning. These calculations are readily available for arbitrary models via "automatic differentiation" implem...