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Review of multimodal machine learning approaches in healthcare
Machine learning methods in healthcare have traditionally focused on using data from a single modality, limiting their ability to effectively replicate the clinical practice of integrating multiple sources of information for improved decision making.
LT-ViT: A Vision Transformer for multi-label Chest X-ray classification
Vision Transformers (ViTs) are widely adopted in medical imaging tasks, and some existing efforts have been directed towards vision-language training for Chest X-rays (CXRs).
A Sensitivity Matrix Approach Using Two-Stage Optimization for Voltage Regulation of LV Networks with High PV Penetration
The deviation of mean voltages of the proposed methodology from load flow method was; 6. 5*10^-3 p. u for reactive power control using Q-injection, 1. 02*10^-2 p. u for reactive power control using Q-absorption, and 0 p. u for active power curtailment case.
A generalized forecasting solution to enable future insights of COVID-19 at sub-national level resolutions
In this study, a forecasting solution is proposed, to predict daily new cases of COVID-19 in regions small enough where containment measures could be locally implemented, by targeting three main shortcomings that exist in literature; the unreliability of existing data caused by inconsistent testing patterns in smaller regions, weak deploy-ability of forecasting models towards predicting cases in previously unseen regions, and model training biases caused by the imbalanced nature of data in COVID-19 epi-curves.
Modified Auto Regressive Technique for Univariate Time Series Prediction of Solar Irradiance
The integration of renewable resources has increased in power generation as a means to reduce the fossil fuel usage and mitigate its adverse effects on the environment.
