The recent increase in the deployment of machine learning models critical domains such as healthcare, criminal justice, and finance has highlighted need for trustworthy methods that can explain these to stakeholders. Feature importance (e.g. gain SHAP) are among most popular explainability used address this need. For any technique be meaningful, it provide an explanation is accurate stable. Alt...

The spread of hoaxes in Indonesia has become a big concern for the public, especially now that COVID-19 virus pandemic is hitting whole world. Due to large number people who believe hoax news regarding vaccination on social media, many refuse carry out as form government effort dealing with this pandemic. Therefore, need be wiser when reading networks. To help public not read hoaxes, it necessa...

Ensemble weather forecasts enable a measure of uncertainty to be attached each forecast, by computing the ensemble's spread. However, generating an ensemble with good spread-error relationship is far from trivial, and wide range approaches achieve this have been explored—chiefly in context numerical prediction models. Here, we aim transform deterministic neural network forecasting system into s...

Changes in human lifestyle have led to an increase the number of people suffering from depression over past century. Although recent years, rates diagnosing mental illness improved, many cases remain undetected. Automated detection methods can help identify depressed or individuals at risk. An understanding requires effective feature representation and analysis language use. In this article, te...

Convolutional Neural Networks (CNNs) are used in many domains but the requirement of large datasets for robust training sessions and no overfitting makes them hard to apply medical fields similar fields. However, when quantities samples cannot be easily collected, various methods can still applied stem problem depending on sample type. Data augmentation, rather than other methods, has recently ...

brain mr images tissue segmentation is one of the most important parts of the clinical diagnostic tools. pixel classification methods have been frequently used in the image segmentation with two supervised and unsupervised approaches up to now. supervised segmentation methods lead to high accuracy but they need a large amount of labeled data, which is hard, expensive and slow to obtain. moreove...