OSSMM is a low-cost open-source wearable headband using CTPU electrodes and machine learning that achieves four-stage sleep classification with 0.77 accuracy in a 15-night single-participant test.
Scikit-learn: Machine learning in python
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BatteryPass-12K is the first public benchmark dataset for digital battery passport conformance classification, with evaluations of 22 language models showing thinking models achieve the highest F1 scores.
MDL-GBC constructs class-conditional granular balls by comparing single-ball, two-ball, and core-boundary models under a unified MDL criterion and aggregates them for prediction, achieving the best average accuracy and Macro-F1 on 18 benchmarks.
MDL-GBG selects the shortest-description-length model among single-ball, two-ball, and core-ball-plus-residual options to generate stable granular balls that improve downstream clustering on UCI datasets.
citing papers explorer
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OSSMM: An Open-Source Sleep Monitor and Modulator
OSSMM is a low-cost open-source wearable headband using CTPU electrodes and machine learning that achieves four-stage sleep classification with 0.77 accuracy in a 15-night single-participant test.
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BatteryPass-12K: The First Dataset for the Novel Digital Battery Passport Conformance Task
BatteryPass-12K is the first public benchmark dataset for digital battery passport conformance classification, with evaluations of 22 language models showing thinking models achieve the highest F1 scores.
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A Boundary-Aware Non-parametric Granular-Ball Classifier Based on Minimum Description Length
MDL-GBC constructs class-conditional granular balls by comparing single-ball, two-ball, and core-boundary models under a unified MDL criterion and aggregates them for prediction, achieving the best average accuracy and Macro-F1 on 18 benchmarks.
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MDL-GBG: A Non-parametric and Interpretable Granular-Ball Generation Method for Clustering
MDL-GBG selects the shortest-description-length model among single-ball, two-ball, and core-ball-plus-residual options to generate stable granular balls that improve downstream clustering on UCI datasets.