Machine failure prediction using machine learning can enhance operational dependability, making the fundamental purposes of predictive maintenance and the usefulness of incorporating machine learning into collapse forecasting come true. ML specialists can also examine the most influential algorithms shaping workfl...
Student Future Prediction Using Machine LearningSelecting an appropriate career is one of the most important decisions and with the increase in the number of career paths and opportunities, making this decision have become quite difficult for the students. According to the survey conducted by the ...
Occupancy prediction models are developed using lots of data collected from various sources. Regarding the amount of data, here is the rule “the more the better.” Of course, you can’t neglect the quality, but the quantity is important. “To achieve high accuracy of predictions, it’s bet...
BEIJING, March 26 (Xinhua) -- Chinese scientists made accurate predictions regarding Antarctic sea ice for December 2023 to February 2024 using deep learning methods. The research team utilized a Convolutional Long Short-Term Memory (ConvLSTM) neural network to construct a seasonal-scale Antarctic sea...
Machine learning (ML) methods has recently contributed very well in the advancement of the prediction models used for energy consumption. Such models highly improve the accuracy, robustness, and precision and the generalization ability of the conventional time series forecasting tools. This article revie...
A Novel transfer learning based approach for pneumonia detection in chest X-ray images Appl. Sci., 10 (2020), p. 559 CrossrefView in ScopusGoogle Scholar [25] S. Sreeja, L. Bhavya, S. Swamynath, R. Dhanuja Chest x-ray pneumonia prediction using machine learning algorithms Int. J. ...
With the development of machine learning (ML) and deep learning (DL), many computational methods using ML or DL have been developed for predicting mutation disruption or pathogenicity. Some methods were developed based on specific biological mechanisms or data types. For example, SpliceAI employs a...
We build a highly accurate model for predicting formation energy of materials from their compositions; using an experimental data set of 1,643 observations, the proposed approach yields a mean absolute error (MAE) of 0.07 eV/atom, which is significantly better than existing machine learning (ML)...
Using a lab-based system that mimics real earthquakes, the researchers trained a machine learning algorithm to predict future earthquakes by analyzing the acoustic signals coming from the fault as it moved and search for patterns. The characteristics of this sound pattern can be used to give a pr...
Machine learning-based predictions and analyses of the creep rupture life of the Ni-based single crystal superalloy ArticleOpen access05 September 2024 Introduction All materials break under sufficiently high stress. However, even when the system can support a load at the instance of its application...