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Can the attributes of an app predict its rating?

Feng et al. | Jul 03, 2024

Can the attributes of an app predict its rating?
Image credit: Mika Baumeister

In this article the authors looked at different attributes of apps within the Google Play store to determine how those may impact the overall app rating out of five stars. They found that review count, amount of storage needed and when the app was last updated to be the most influential factors on an app's rating.

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Estimation of Reproduction Number of Influenza in Greece using SIR Model

Skarpeti et al. | Nov 18, 2020

Estimation of Reproduction Number of Influenza in Greece using SIR Model

In this study, we developed an algorithm to estimate the contact rate and the average infectious period of influenza using a Susceptible, Infected, and Recovered (SIR) epidemic model. The parameters in this model were estimated using data on infected Greek individuals collected from the National Public Health Organization. Our model labeled influenza as an epidemic with a basic reproduction value greater than one.

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Part of speech distributions for Grimm versus artificially generated fairy tales

Arvind et al. | Nov 16, 2024

Part of speech distributions for Grimm versus artificially generated fairy tales
Image credit: Nayalia Y.

Here, the authors wanted to explore mathematical paradoxes in which there are multiple contradictory interpretations or analyses for a problem. They used ChatGPT to generate a novel dataset of fairy tales. They found statistical differences between the artificially generated text and human produced text based on the distribution of parts of speech elements.

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Collaboration beats heterogeneity: Improving federated learning-based waste classification

Chong et al. | Jul 18, 2023

Collaboration beats heterogeneity: Improving federated learning-based waste classification

Based on the success of deep learning, recent works have attempted to develop a waste classification model using deep neural networks. This work presents federated learning (FL) for a solution, as it allows participants to aid in training the model using their own data. Results showed that with less clients, having a higher participation ratio resulted in less accuracy degradation by the data heterogeneity.

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