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4 days ago · Bayesian Neural Networks (BNNs) offer probability distributions for model parameters, enabling uncertainty quantification in predictions. However, they often underperform compared to deterministic neural networks. Utilizing mutual learning can effectively enhance the performance of peer BNNs. In this paper, we propose a novel approach to improve BNNs performance through deep mutual learning. The proposed approaches aim to increase diversity in both network parameter distributions and feature ...
5 days ago · Let’s say the system identifies 8 emails as spam out of a dataset of 12 emails. Of the 8 classified as spam, only 5 are truly spam. Precision = (Correctly Identified Spam) / (Total Emails Identified as Spam) = 5 / 8. The system has a precision of 62.5%, meaning 62.5% of the emails it flagged as spam were actual spam.
3 days ago · Neural networks, also known as artificial neural networks (ANNs) or simulated neural networks (SNNs), are a subset of machine learning and are at the heart of deep learning algorithms. Their name and structure are inspired by the human brain, mimicking the way that biological neurons signal to one another. Artificial neural networks (ANNs) are ...
4 days ago · Image classification using Convolutional Neural Networks (CNN) has revolutionized computer vision tasks by enabling automated and accurate recognition of objects within images. Consequently, this technology has significantly advanced fields such as medical imaging, autonomous driving, and industrial automation.
2 days ago · Moreover, artificial neural networks (ANN), decision trees, logistic regression, random forest (RF), regression trees, and support vector machines (SVM) are among the extensively employed machine learning models for conducting flood risk assessments (Roozbeh Hasanzadeh and Tuan 2018; Kourgialas and Karatzas 2017; Gotham et al. 2018; Mojaddadi et al. 2017). Although numerous studies have focused on evaluating flood damage prediction, none of them have specifically investigated the impact of ...
