Welcome to The Athletic’s Premier League predictions challenge, which I’m horrified to have to say has been won by the subscribers. Yes, Arsenal fan Neel from New Delhi held his nerve on the final day ...

Abstract: Timely and accurate wildfire detection is critical for ecological monitoring and disaster response. Using a model to handle both long-term time series and multiscale spatial features is ...

Abstract: The problem of developing forecasting models based on multidimensional time series, which are a kind of features and are used in the formation of the datasets, dividing further into the ...

Machine learning and deep learning have been widely embraced, and even more widely misunderstood. In this article, I’ll step back and explain both machine learning and deep learning in basic terms, ...

Large language models have captured the news cycle, but there are many other kinds of machine learning and deep learning with many different use cases. Amid all the hype and hysteria about ChatGPT, ...

The ever-increasing problem of forest fire risks has intrigued researchers globally to find a mechanism that can provide an early warning of forest fire incidents as well as assess the extent of ...

Considering the strong non-linear time-varying behavior of dam deformation, a novel prediction model, called Levy flight-based grey wolf optimizer optimized support vector regression (LGWO-SVR), is ...

Random forest is also one of the popularly used machine learning models which have a very good performance in the classification and regression tasks. A random forest regression model can also be used ...

Change point detection aims to identify structural breaks in the probability distribution of a time series. Existing methods either assume a parametric model for within-segment distributions or are ...

1 China National Tobacco Quality Supervision and Test Center, Zhengzhou, China. 2 Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, China. 3 University of Science and ...