Results 51 to 60 of about 14,251,445 (242)

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

SemanticST: A Scalable Multi‐Contextual Graph Learning Framework for Uncovering Spatial Niches and Robust Multi‐Sample Integration in Spatial Transcriptomics

Advanced Science, EarlyView.
Technical limitations often let dominant signals overshadow rare cell types and fine‐grained heterogeneity in spatial transcriptomics. SemanticST, a graph neural network using multi‐semantic graph fusion and a novel min‐cut loss, recovers these subtle patterns.
Roxana Zahedi, Ahmadreza Argha, Nona Farbehi, Ivan Bakhshayeshi, Thantrira Porntaveetus, Youqiong Ye, Nigel H. Lovell, Hamid Alinejad‐Rokny   +7 more
wiley   +1 more source

Deep‐Learning‐Based Denoising for Improved Phase Precision in Electron Holography of Electromagnetic Fields in Nanoscale Materials

Advanced Science, EarlyView.
Low‐dose electron holography is limited by shot noise, which buries weak phase signals. HoloDenoiser, a physics‐informed network that works simultaneously in the spatial and frequency domains, locates and protects the holographic sideband while suppressing noise in the hologram.
Ye Luo, Luyang Wang, Zhiling Yu, Xiaowen Chen, Xiaoyu Wang, Ya‐Hui Jia, Thibaud Denneulin, Yu Han, Rafal E. Dunin‐Borkowski, Fengshan Zheng, Qi Liu   +10 more
wiley   +1 more source

An ATP‐Driven N Protein–DDX21 Molecular Switch Dynamically Controls SARS‐CoV‐2 RNA G‐Quadruplex Heterogeneity

Advanced Science, EarlyView.
An ATP‐driven molecular switch, comprising viral nucleocapsid (N) protein and host helicase DDX21, dynamically modulates SARS‐CoV‐2 RNA G‐quadruplex (G4) heterogeneity. These viral G4s feature non‐canonical ion‐dependence and act as energy‐sensitive structural checkpoints.
Ya‐Ting Zheng, Li‐Yan Zhai, Jie Jin, Qiu‐Ying Chen, Hang Fu, Xue‐Qian Sun, Ying Lu, Hui Li, Xi‐Miao Hou   +8 more
wiley   +1 more source

Advances in Thermal Modeling and Simulation of Lithium‐Ion Batteries with Machine Learning Approaches

Advanced Intelligent Discovery, EarlyView.
Heat generation in lithium‐ion batteries affects performance, aging, and safety, requiring accurate thermal modeling. Traditional methods face efficiency and adaptability challenges. This article reviews machine learning‐based and hybrid modeling approaches, integrating data and physics to improve parameter estimation and temperature prediction ...
Qi Lin, Kang Fu, Junjie Ding, Kai Sun, Peng Tan   +4 more
wiley   +1 more source

Large Language Model‐Based Chatbots in Higher Education

Advanced Intelligent Systems, Volume 7, Issue 3, March 2025.
The use of large language models (LLMs) in higher education can facilitate personalized learning experiences, advance asynchronized learning, and support instructors, students, and researchers across diverse fields. The development of regulations and guidelines that address ethical and legal issues is essential to ensure safe and responsible adaptation
Defne Yigci, Merve Eryilmaz, Ail K. Yetisen, Savas Tasoglu, Aydogan Ozcan   +4 more
wiley   +1 more source

Using Hidden Markov Chains in Recognition of Vowel Letters in English Language [PDF]

المجلة العراقية للعلوم الاحصائية, 2006
This study deals with hidden Markov models . These models consist of sets of finite states , each one of them is associated with a probability distribution .
doaj   +1 more source

Toward Complex In‐Car Environment Human–Vehicle Interactions Through Smart Glasses and sEMG‐Based Gesture Recognition

Advanced Intelligent Systems, EarlyView.
This study proposes a novel weighted random forest multimodal fusion method that combines smart glasses and sEMG data for in‐vehicle gesture interaction. It realizes stable performance in dim, occluded, and other constrained scenarios, providing feasible solutions and laying a foundation for universal human–machine interaction.
Wenbo Zhang, Wenting Wei, Kuang Li, Chuyuan Bian, Junkai Xu, Jianing Qiu, Yuxi Lu, Bin He, Shuo Jiang   +8 more
wiley   +1 more source

Theory and Inference for a Markov-Switching GARCH Model [PDF]

We develop a Markov-switching GARCH model (MS-GARCH) wherein the conditional mean and variance switch in time from one GARCH process to another. The switching is governed by a hidden Markov chain. We provide sufficient conditions for geometric ergodicity
Jeroen V.K. Rombouts, Arie Preminger, Luc Bauwens   +2 more
core   +6 more sources

DifferentiableHMM: Neural Differentiable Hidden Markov Model

ITEGAM-JETIA
Modeling clinical time series demands interpretable patient states alongside the ability to capture long-range dependencies. Classical HMMs provide discrete, clinically meaningful states but ignore distant history; RNNs capture rich temporal patterns ...
Arbia Boudaoud, Salheddine Kabou
doaj   +1 more source