Machine learning in materials science
Traditional methods of discovering new materials, such as the empirical trial and error
method and the density functional theory (DFT)‐based method, are unable to keep pace …
method and the density functional theory (DFT)‐based method, are unable to keep pace …
A state-of-the-art review on machine learning-based multiscale modeling, simulation, homogenization and design of materials
Multiscale simulation and homogenization of materials have become the major
computational technology as well as engineering tools in material modeling and material …
computational technology as well as engineering tools in material modeling and material …
Visual recognition with deep nearest centroids
We devise deep nearest centroids (DNC), a conceptually elegant yet surprisingly effective
network for large-scale visual recognition, by revisiting Nearest Centroids, one of the most …
network for large-scale visual recognition, by revisiting Nearest Centroids, one of the most …
Machine learning for molecular and materials science
Here we summarize recent progress in machine learning for the chemical sciences. We
outline machine-learning techniques that are suitable for addressing research questions in …
outline machine-learning techniques that are suitable for addressing research questions in …
Artificial intelligence and machine learning in pathology: the present landscape of supervised methods
HH Rashidi, NK Tran, EV Betts… - Academic …, 2019 - journals.sagepub.com
Increased interest in the opportunities provided by artificial intelligence and machine
learning has spawned a new field of health-care research. The new tools under …
learning has spawned a new field of health-care research. The new tools under …
Machine learning for medical imaging
Machine learning is a technique for recognizing patterns that can be applied to medical
images. Although it is a powerful tool that can help in rendering medical diagnoses, it can be …
images. Although it is a powerful tool that can help in rendering medical diagnoses, it can be …
Machine learning: accelerating materials development for energy storage and conversion
With the development of modern society, the requirement for energy has become
increasingly important on a global scale. Therefore, the exploration of novel materials for …
increasingly important on a global scale. Therefore, the exploration of novel materials for …
Naive Bayes: applications, variations and vulnerabilities: a review of literature with code snippets for implementation
I Wickramasinghe, H Kalutarage - Soft Computing, 2021 - Springer
Naïve Bayes (NB) is a well-known probabilistic classification algorithm. It is a simple but
efficient algorithm with a wide variety of real-world applications, ranging from product …
efficient algorithm with a wide variety of real-world applications, ranging from product …
Deep Learning over Multi-field Categorical Data: –A Case Study on User Response Prediction
Predicting user responses, such as click-through rate and conversion rate, are critical in
many web applications including web search, personalised recommendation, and online …
many web applications including web search, personalised recommendation, and online …
Bayesian Naïve Bayes classifiers to text classification
S Xu - Journal of Information Science, 2018 - journals.sagepub.com
Text classification is the task of assigning predefined categories to natural language
documents, and it can provide conceptual views of document collections. The Naïve Bayes …
documents, and it can provide conceptual views of document collections. The Naïve Bayes …