Tootfinder

Evaluation of Machine Learning Models in Student Academic Performance Prediction
A. G. R. Sandeepa, Sanka Mohottala
https://arxiv.org/abs/2506.08047 https:…

Evaluation of Machine Learning Models in Student Academic Performance Prediction
This research investigates the use of machine learning methods to forecast students' academic performance in a school setting. Students' data with behavioral, academic, and demographic details were used in implementations with standard classical machine learning models including multi-layer perceptron classifier (MLPC). MLPC obtained 86.46% maximum accuracy for test set across all implementations. Under 10-fold cross validation, MLPC obtained 79.58% average accuracy for test set while for train…

An amazing example of how human testimonies of suffering might fail, and why - as in any other context - validation through fact-checking and triangulation are crucial in order to understand history.
https://forward.com/news/761437/holocaust-survivor-ari-schnei…

His grandfather’s Holocaust stories didn’t add up — so he set out to tell the fact-checked version
In "Stolen Headstones," Ari Schneider tells the fact-checked version of his family's Holocaust survival story.

Numerical Validation of the Yarkovsky Effect in Super-Fast Rotating Asteroids
Dusan Marceta, Bojan Novakovic, Marko Gavrilovic
https://arxiv.org/abs/2507.05789

Numerical Validation of the Yarkovsky Effect in Super-Fast Rotating Asteroids
Recent discoveries show that asteroids spinning in less than a few minutes undergo sizeable semi-major-axis drifts, possibly driven by the Yarkovsky effect. Analytical formulas can match these drifts only if very low thermal inertia is assumed, implying a dust-fine regolith or a highly porous interior that is difficult to retain under such extreme centrifugal forces. With analytical theories of the Yarkovsky effect resting on a set of assumptions, their applicability to cases of super-fast rota…

Case Studies of Generative Machine Learning Models for Dynamical Systems
Nachiket U. Bapat, Randy C. Paffenroth, Raghvendra V. Cowlagi
https://arxiv.org/abs/2508.04459 https://

Case Studies of Generative Machine Learning Models for Dynamical Systems
Systems like aircraft and spacecraft are expensive to operate in the real world. The design, validation, and testing for such systems therefore relies on a combination of mathematical modeling, abundant numerical simulations, and a relatively small set of real-world experiments. Due to modeling errors, simplifications, and uncertainties, the data synthesized by simulation models often does not match data from the system's real-world operation. We consider the broad research question of whether …

Multivariate Spatio-temporal Modelling for Completing Cancer Registries and Forecasting Incidence
Garazi Retegui, Jaione Etxeberria, Mar\'ia Dolores Ugarte
https://arxiv.org/abs/2507.21714

Multivariate Spatio-temporal Modelling for Completing Cancer Registries and Forecasting Incidence
Cancer data, particularly cancer incidence and mortality, are fundamental to understand the cancer burden, to set targets for cancer control and to evaluate the evolution of the implementation of a cancer control policy. However, the complexity of data collection, classification, validation and processing result in cancer incidence figures often lagging two to three years behind the calendar year. In response, national or regional population-based cancer registries (PBCRs) are increasingly inte…