Comparison of different deep learning algorithms in pavement crack detectionAlireza NaseriAmirreza Parsaeihttps://orcid.org/0000-0002-4222-9861agolroo@aut.ac.irAmir Golroo
6a). After training, we calculated the permutation importance of each variable. The top 5 variables were year of diagnosis, age, grade, tumor size, and T stage (Fig. 6b). Fig. 6 Establishing a surgical decision model. (a) Accuracy score of different deep learning algorithms in test set....
Deep learningis an advanced form of machine learning that uses multilayered algorithms called neural networks which simultaneously receive input, process data, and provide output. While deep learning mimics certain processes in the human brain, it doesn’t result in consciousness or reasoning. Deep le...
Deep learning dramatically improved AI's image recognition capabilities, and soon other kinds of AI algorithms were born, such as deepreinforcement learning. These AI models were much better at absorbing the characteristics of their training data, but more importantly, they were able to improve over...
the authors address common barriers in PAP smear analysis such as scant data and poor image quality. This framework featured ConvNeXtv2 and GRN-based MLP blocks and achieved 99.02% accuracy using the SIPaKMeD dataset. Another study21elucidates that an ensemble of machine learning algorithms such as...
— Page 105,Deep Learning, 2016. Some algorithms may be specifically designed for classification (such aslogistic regression) or regression (such as linear regression) and some may be used for both types of problems with minor modifications (such as artificial neural networks). ...
Deep learning Machine learning models Machine learning is a subset of AI. While all machine learning is AI, not all AI is machine learning. To create a machine learning model, data scientists train algorithms with labeled, unlabeled, or mixed data. There are different types of machine learning...
AI models work by processing data through mathematical formulas known as algorithms to learn patterns and relationships, enabling them to make predictions or decisions without explicit programming. These models typically function as artificial neural networks. They consist of layers of interconnected nodes ...
a higher classification accuracy on HR and hail than support vector machine, random forests, and other traditional machine learning algorithms. The objective forecasts by use of the deep learning algorithm also showed better forecasting skills than the subjective forecasts by the forecasters. The threat...
Deep learning has been applied to image recognition, speech recognition, video synthesis, and drug discoveries. In addition, it has been applied to complex creations, like self-driving cars, which use deep learning algorithms to identify obstacles and perfectly navigate around them. ...