An introductory review of deep learning for prediction models with big data
Deep learning models stand for a new learning paradigm in artificial intelligence (AI) and
machine learning. Recent breakthrough results in image analysis and speech recognition …
machine learning. Recent breakthrough results in image analysis and speech recognition …
Named entity recognition and relation detection for biomedical information extraction
The number of scientific publications in the literature is steadily growing, containing our
knowledge in the biomedical, health, and clinical sciences. Since there is currently no …
knowledge in the biomedical, health, and clinical sciences. Since there is currently no …
[HTML][HTML] Assessing optimization techniques for improving water quality model
In order to keep the" good" status of coastal water quality, it is essential to monitor and
assess frequently. The Water quality index (WQI) model is one of the most widely used …
assess frequently. The Water quality index (WQI) model is one of the most widely used …
Online health diagnosis of lithium-ion batteries based on nonlinear autoregressive neural network
Battery health diagnostics is extremely crucial to guaranty the availability and reliability of
the application in which they operate. Data-driven health diagnostics methods, particularly …
the application in which they operate. Data-driven health diagnostics methods, particularly …
[HTML][HTML] A data-centric review of deep transfer learning with applications to text data
In recent years, many applications are using various forms of deep learning models. Such
methods are usually based on traditional learning paradigms requiring the consistency of …
methods are usually based on traditional learning paradigms requiring the consistency of …
Machine learning accelerates the materials discovery
J Fang, M ** field which is focused on
development of new glasses with excellent properties. Glasses are the non-crystalline …
development of new glasses with excellent properties. Glasses are the non-crystalline …
Machine learning techniques for hypoglycemia prediction: trends and challenges
(1) Background: the use of machine learning techniques for the purpose of anticipating
hypoglycemia has increased considerably in the past few years. Hypoglycemia is the drop in …
hypoglycemia has increased considerably in the past few years. Hypoglycemia is the drop in …
Building feature‐based machine learning regression to quantify urban material stocks: A Hong Kong study
Urban material stock (UMS) represents elegant thinking by perceiving cities as a repository
of construction materials that can be reused in the future, rather than a burdensome …
of construction materials that can be reused in the future, rather than a burdensome …
Combining deep learning with token selection for patient phenoty** from electronic health records
Artificial intelligence provides the opportunity to reveal important information buried in large
amounts of complex data. Electronic health records (eHRs) are a source of such big data …
amounts of complex data. Electronic health records (eHRs) are a source of such big data …