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Found 8 papers, 3 papers with code
Range-Only Bearing Estimator for Localization and Mapping
Navigation and exploration within unknown environments are typical examples in which simultaneous localization and mapping (SLAM) algorithms are applied.
An Industry 4.0 example: real-time quality control for steel-based mass production using Machine Learning on non-invasive sensor data
The model achieves an excellent performance (F3-score of 0. 95) predicting material running out of specifications for the tensile strength.
Interpretable Models Capable of Handling Systematic Missingness in Imbalanced Classes and Heterogeneous Datasets
Medical datasets face common issues such as heterogeneous measurements, imbalanced classes with limited sample size, and missing data, which hinder the straightforward application of machine learning techniques.
Secure Formation Control via Edge Computing Enabled by Fully Homomorphic Encryption and Mixed Uniform-Logarithmic Quantization
Recent developments in communication technologies, such as 5G, together with innovative computing paradigms, such as edge computing, provide further possibilities for the implementation of real-time networked control systems.
Detection of extragalactic Ultra-Compact Dwarfs and Globular Clusters using Explainable AI techniques
Both methods are able to identify UCDs/GCs with a precision and a recall of >93 percent and provide relevances that reflect the importance of each feature dimension %(colors and angular sizes) for the classification.
LAAT: Locally Aligned Ant Technique for discovering multiple faint low dimensional structures of varying density
The algorithm performance in comparison to state-of-the-art approaches for noise reduction in manifold detection and clustering is demonstrated, on several synthetic and real datasets, including an N-body simulation of a cosmological volume.
Visualisation and knowledge discovery from interpretable models
In this contribution we introduced a few intrinsically interpretable models which are also capable of dealing with missing values, in addition to extracting knowledge from the dataset and about the problem.
Decision Making
Explainable Artificial Intelligence (XAI)
+2
Sparse group factor analysis for biclustering of multiple data sources
Motivation: Modelling methods that find structure in data are necessary with the current large volumes of genomic data, and there have been various efforts to find subsets of genes exhibiting consistent patterns over subsets of treatments.
