Computational intelligence and neuroscience
Jun 17, 2022
The study examines the prospects and challenges of machine learning (ML) applications in academic forecasting. Predicting academic activities through machine learning algorithms presents an enhanced means to accurately forecast academic events, inclu...
This paper presents a Long Short Term Memory Recurrent Neural Network and Hidden Markov Model (LSTM-HMM) to predict China's Gross Domestic Product (GDP) fluctuation state within a rolling time window. We compare the predictive power of LSTM-HMM with ...
Forecasting road flow has strong importance for both allowing authorities to guarantee safety conditions and traffic efficiency, as well as for road users to be able to plan their trips according to space and road occupation. In a summer resort, such...
BACKGROUND: Dengue fever (DF) represents a significant health burden in Vietnam, which is forecast to worsen under climate change. The development of an early-warning system for DF has been selected as a prioritised health adaptation measure to clima...
This paper proposes a new hybrid framework for short-term load forecasting (STLF) by combining the Feature Engineering (FE) and Bayesian Optimization (BO) algorithms with a Bayesian Neural Network (BNN). The FE module comprises feature selection and ...
Advances in artificial intelligence (AI) enable new ways of exercising and experiencing power by automating interpersonal tasks such as interviewing and hiring workers, managing and evaluating work, setting compensation, and negotiating deals. As the...
Computational and mathematical methods in medicine
Jun 8, 2022
Unused hotel rooms, unused event tickets, and unsold items are all examples of wasted expenses and earnings. Governments require accurate tourism demand forecasting in order to make informed decisions on topics such as infrastructure development and ...
Environmental science and pollution research international
Jun 6, 2022
The improvement of groundwater burial depth prediction accuracy is an important guiding significance for the development and management of groundwater resources. Groundwater burial depth sequence has the characteristics of uncertainty and nonlinearit...
Oklahoma earthquakes in the past decade have been mostly associated with wastewater injection. Here we use a machine learning technique-the Random Forest to forecast induced seismicity rate in Oklahoma based on injection-related parameters. We split ...