Results 61 to 70 of about 73,803 (311)

Prediction of annual rainfall pattern using Hidden Markov Model (HMM) in Jos, Plateau State, Nigeria

Journal of Applied Sciences and Environmental Management, 2016
A Hidden Markov Model (HMM) is a double stochastic process in which one of the stochastic processes is an underlying Markov chain, the other stochastic process is an observable stochastic process.
A Lawal, U.Y. Abubakar, H Danladi, A.S. Gana   +3 more
doaj   +1 more source

Multiple-regression hidden Markov model [PDF]

2001 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings (Cat. No.01CH37221), 2002
Proposes a class of hidden Markov model (HMM) called multiple-regression HMM (MR-HMM) that utilizes auxiliary features such as fundamental frequency (F/sub 0/) and speaking styles that affect spectral parameters to better model the acoustic features of phonemes.
Fujinaga, Katsuhisa, Nakai, Mitsuru, Shimodaira, Hiroshi, Sagayama, Shigeki   +3 more
openaire   +2 more sources

Hidden Markov Modeling Over Graphs

2022 IEEE Data Science and Learning Workshop (DSLW), 2022
This work proposes a multi-agent filtering algorithm over graphs for finite-state hidden Markov models (HMMs), which can be used for sequential state estimation or for tracking opinion formation over dynamic social networks. We show that the difference from the optimal centralized Bayesian solution is asymptotically bounded for geometrically ergodic ...
Mert Kayaalp, Virginia Bordignon, Stefan Vlaski, Ali H. Sayed   +3 more
openaire   +2 more sources

AI‐Physics‐Experiment Trinity for Integrated Protein Dynamics Modeling

Advanced Science, EarlyView.
This review unites experiments, physics‐based simulations, and AI as a synergistic triad for protein dynamics modeling. It highlights integrative strategies, resolves sampling and forcefield bottlenecks, and outlines challenges and future directions for accurate, interpretable conformational ensemble prediction.
Chen Shi, Minying Low, Peng Xiu, Kresten Lindorff‐Larsen, Yong Wang   +4 more
wiley   +1 more source

Allosteric Regulation of Photophysics and Binding in Oxazine‐macrocycle Complexes at Single‐molecule Resolution

Angewandte Chemie, EarlyView.
This work explores the interactions between the oxazine dye ATTO655 and two macrocyclic hosts using optical (single‑molecule) spectroscopy. Although ATTO655 forms a classical inclusion complex with cucurbit[8]uril (CB8), its interaction with p‑sulfonatocalix[4]arene (sCX4) leads to the formation of dim exclusion complexes.
Siyu Lu, Thomas‐Otavio Peulen, Hongbin Wu, Florian Lindemann, Despoina Kapiki, Rasmus Linser, Andreas Hennig, Thorben Cordes   +7 more
wiley   +2 more sources

A Phase‐Resolved Geometric Deep Learning Framework Maps Structural Determinants of Disease‐Associated Protein Aggregation and Guides Suppressor Design

Advanced Science, EarlyView.
SKALE 2.0 maps disease‐associated protein aggregation as a phase‐resolved structural process, linking mutation‐induced geometric perturbations to nucleation, elongation, and suppressor design. Across neurodegenerative proteins, the framework reveals cryptic aggregation vulnerabilities, separates phase‐concordant and phase‐switching mutations, and ...
Jia Shen Sio, Wei Xuan Wilson Loo, Yan Shan Loo, Wen Xin Tan, Hui Xuan Lim, Huitao Liu, Chen Seng Ng   +6 more
wiley   +1 more source

Hidden semi-Markov models

Artificial Intelligence, 2010
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
openaire   +1 more source

Utile distinction hidden Markov models [PDF]

Twenty-first international conference on Machine learning - ICML '04, 2004
This paper addresses the problem of constructing good action selection policies for agents acting in partially observable environments, a class of problems generally known as Partially Observable Markov Decision Processes. We present a novel approach that uses a modification of the well-known Baum-Welch algorithm for learning a Hidden Markov Model (HMM)
Wierstra, D., Wiering, M.A.
openaire   +3 more sources

AI‐Assisted Digital Single‐Molecule Activity Tracker for Decoupling Intrinsic Heterogeneity from Photo‐Oxidative Damage in High‐Photon‐Flux Enzymology

Advanced Science, EarlyView.
Employing a digital single‐molecule activity tracker (dSMAT), this research demonstrates that high‐photon‐flux irradiation drives progressive oxidative scarring in polymerases. Unlike simple thermal denaturation, real‐time kinetic tracking dynamically visualizes enzymes degrading into multiple impaired subpopulations.
Anran Zheng, Qi Yang, Jinze Li, Fuqiang Ma, Xuefeng Wang, Zhen Guo, Chuanyu Li, Dongshu Li, Jia Yao, Zhiqi Zhang, Wei Zhang, Lianqun Zhou   +11 more
wiley   +1 more source

Advanced Experiment Design Strategies for Drug Development

Advanced Intelligent Discovery, EarlyView.
Wang et al. analyze 592 drug development studies published between 2020 and 2024 that applied design of experiments methodologies. The review surveys both classical and emerging approaches—including Bayesian optimization and active learning—and identifies a critical gap between advanced experimental strategies and their practical adoption in ...
Fanjin Wang, Anthony Harker, Mohan Edirisinghe, Maryam Parhizkar   +3 more
wiley   +1 more source