Results 221 to 230 of about 39,296 (257)

A Hybrid Approach for Sports Activity Recognition Using Key Body Descriptors and Hybrid Deep Learning Classifier. [PDF]

Sensors (Basel)
Tayyab M, Alateyah SA, Alnusayri M, Alatiyyah M, AlHammadi DA, Jalal A, Liu H.   +6 more
europepmc   +1 more source

Universally Accurate or Specifically Inadequate? Stress‐Testing General Purpose Machine Learning Interatomic Potentials

Advanced Intelligent Discovery, EarlyView.
We investigate MACE‐MP‐0 and M3GNet, two general‐purpose machine learning potentials, in materials discovery and find that both generally yield reliable predictions. At the same time, both potentials show a bias towards overstabilizing high energy metastable states. We deduce a metric to quantify when these potentials are safe to use.
Konstantin S. Jakob, Karsten Reuter, Johannes T. Margraf   +2 more
wiley   +1 more source

Estimating changepoints in extremal dependence, applied to aviation stock prices during COVID-19 pandemic. [PDF]

J Appl Stat
Hazra A, Bose S.
europepmc   +1 more source

What to Make and How to Make It: Combining Machine Learning and Statistical Learning to Design New Materials

Advanced Intelligent Discovery, EarlyView.
Combining machine learning and probabilistic statistical learning is a powerful way to discover and design new materials. A variety of machine learning approaches can be used to identify promising candidates for target applications, and causal inference can help identify potential ways to make them a reality.
Jonathan Y. C. Ting, Amanda S. Barnard
wiley   +1 more source

Artificial Intelligence‐Driven Insights into Electrospinning: Machine Learning Models to Predict Cotton‐Wool‐Like Structure of Electrospun Fibers

Advanced Intelligent Discovery, EarlyView.
Electrospinning allows the fabrication of fibrous 3D cotton‐wool‐like scaffolds for tissue engineering. Optimizing this process traditionally relies on trial‐and‐error approaches, and artificial intelligence (AI)‐based tools can support it, with the prediction of fiber properties. This work uses machine learning to classify and predict the structure of
Paolo D’Elia, Giovanna Pappalardo, Aldo R. Boccaccini, Liliana Liverani   +3 more
wiley   +1 more source

A QSPR study of coronary artery disease drugs using eccentricity-based indices. [PDF]

Sci Rep
Iqbal N, Akhter S, Alraih AM, Iqbal Z, Aslam A, Tola KA.   +5 more
europepmc   +1 more source

The GFB Tree and Tree Imbalance Indices. [PDF]

Bull Math Biol
Cleary S, Fischer M, St John K.
europepmc   +1 more source

Machine-Learning-Driven Stochastic Modeling Method for 3D Asphalt Mixture Reconstruction from 2D Images. [PDF]

Materials (Basel)
Zhang J, Huang L.
europepmc   +1 more source

Comparative study of Sombor index and its various versions using regression models for top priority polycyclic aromatic hydrocarbons. [PDF]

Sci Rep
Kirana B, Shanmukha MC, Usha A.
europepmc   +1 more source

A systematic approach to classifying and evaluating heterogeneity measures. [PDF]

R Soc Open Sci
Ottow R.
europepmc   +1 more source