Abraham Yosipof

TL;DR: This work developed a data mining workflow and applied it to the analysis of two recently reported solar cell libraries based on Titanium and Copper oxides, demonstrating that QSAR models with good prediction statistics for multiple solar cells properties could be developed and that these models highlight important factors affecting these properties in accord with experimental findings.

TL;DR: The first implementation in chemoinformatics of the t-Distributed Stochastic Neighbor Embedding method for the visualization and the representation of chemical space and the use of different machine learning methods separately and together to form a new ensemble learning method called AL Boost are provided.

TL;DR: In this paper, the authors examined which factors can influence the success of crowdfunding campaigns in the gaming industry and identified several attributes, including textual variables, that influence crowdfunding campaigns, and provided valuable insights for the success surrounding such campaigns.

TL;DR: This work describes the first application of RNASAC in material informatics, focusing on the analysis of solar cells, and demonstrates that for three datasets representing different metal oxide based solar cell libraries RANSAC-derived models select descriptors previously shown to correlate with key photovoltaic properties and lead to good predictive statistics for these properties.

TL;DR: It is demonstrated for three different datasets that the removal of outliers using the new algorithm provides filtered datasets which are better than those provided by four alternative outlier removal procedures as well as by random compound removal.