The hidden Markov model (HMM), a statistical model widely applied in machine learning, has proven effective in addressing various problems in bioinformatics. Once primarily regarded as a mathematical ...

The amino acid sequence of the transmembrane protein and its corresponding positions on the cell membrane are transformed into a hidden Markov process. After evaluating the parameters, the Viterbi ...

School of Sustainable Engineering and the Built Environment, Arizona State University, Tempe, AZ, United States Understanding the nature of climatic change impacts on spatial and temporal ...

Abstract: The Hidden Markov Model is widely used in weather forecasting, Bioinformatics, disease diagnosis, signal processing, stock market, interpretation of clinical results, etc. The model provides ...

A new process—the factorial hidden Markov volatility (FHMV) model—is proposed to model financial returns or realized variances. Its dynamics are driven by a latent volatility process specified as a ...

Several prominent foundation models are employed to enhance our understanding of high-throughput biological data, followed by a discussion on the application of prediction and generation models across ...

An extensive model of the Antarctic ice sheet is helping researchers peer deep beneath the ice to reveal the continent's hidden plumbing. Scientists used computer models to predict how water flows ...

1 Department of Plant Biology, University of Dschang, Dschang, Cameroon. 2 Department of Forestry and Wildlife, Faculty of Agriculture and Veterinary Medicine, University of Buea, Buea, Cameroon. 3 ...

Background: Prevention of (suicidal) crisis starts with appreciating its dynamics. However, crisis is a complex multidimensional phenomenon and how it evolves over time is still poorly understood.

In the rapidly evolving landscape of machine learning and artificial intelligence, understanding the fundamental representations within transformer models has emerged as a critical research challenge.