Results 221 to 230 of about 66,463 (260)

General Intelligence Framework to Predict Virus Adaptation Based on a Genome Language Model. [PDF]

Research (Wash D C)
Jiang SY, Zhao SS, Wei JQ, Zhang S, Zhao Z, Tong Y, Liu W, Wang J, Jiang T, Li J.   +9 more
europepmc   +1 more source

Genome-wide characterization of WD40 repeat proteins in cucumber reveals their functional roles in stress response and parthenocarpy. [PDF]

Sci Rep
Zhang T, Wang X, Guo Q, Fang P, Wei J, Li C, Liu J.   +6 more
europepmc   +1 more source

Cost-effectiveness analysis of artificial intelligence (AI) for response prediction of neoadjuvant radio(chemo)therapy in locally advanced rectal cancer (LARC) in the Netherlands. [PDF]

Expert Rev Pharmacoecon Outcomes Res
Maas L, Contreras-Meca C, Ghezzo S, Belmans F, Corsi A, Cant J, Vos W, Bobowicz M, Rygusik M, Wysocki PT, Weiss MER, Neri E, Caputo FP, Franceschello R, Fanni SC, Annemans L, Hiligsmann M.   +16 more
europepmc   +1 more source

Power Control in Wireless Body Area Networks: A Review of Mechanisms, Challenges, and Future Directions. [PDF]

Sensors (Basel)
Su H, Zhao Z, Gu B, Lin S.
europepmc   +1 more source

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Machine condition recognition via hidden semi-Markov model

Computers & Industrial Engineering, 2021
Abstract In intelligent manufacturing systems, machines are subject to condition deterioration.Identifying machine condition is crucial for making practical decisions in production management. This paper studies the machine condition recognition problem in wafer fabrication.
Wenhui Yang, Lu Chen
openaire   +1 more source

Hidden semi-Markov model for anomaly detection

Applied Mathematics and Computation, 2008
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Tan, Xiaobin, Xi, Hongsheng
openaire   +1 more source

Hidden semi-Markov model based speech synthesis

Interspeech 2004, 2004
In the present paper, a hidden-semi Markov model (HSMM) based speech synthesis system is proposed. In a hidden Markov model (HMM) based speech synthesis system which we have proposed, rhythm and tempo are controlled by state duration probability distributions modeled by single Gaussian distributions.
Heiga Zen, Keiichi Tokuda, Takashi Masuko, Takao Kobayashi, Tadashi Kitamura   +4 more
openaire   +1 more source

Online identification of hidden Semi-Markov models

3rd International Symposium on Image and Signal Processing and Analysis, 2003. ISPA 2003. Proceedings of the, 2004
Hidden Markov models (HMM) are a powerful tool in signal modelling. In an HMM, the probability that signal leaves a state is constant, and hence the duration that signal stays in each state has an exponential distribution. However, this exponential density is not appropriate for a large class of physical signals.
M. Azimi, P. Nasiopoulos, R.K. Ward
openaire   +1 more source

Asynchronous Brain Computer Interface using Hidden Semi-Markov Models

2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2012
Ideal Brain Computer Interfaces need to perform asynchronously and at real time. We propose Hidden Semi-Markov Models (HSMM) to better segment and classify EEG data. The proposed HSMM method was tested against a simple windowed method on standard datasets. We found that our HSMM outperformed the simple windowed method.
Gareth, Oliver, Peter, Sunehag, Tom, Gedeon   +2 more
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ECG segmentation algorithm based on bidirectional hidden semi-Markov model

Computers in Biology and Medicine, 2022
Accurate segmentation of electrocardiogram (ECG) waves is crucial for cardiovascular diseases (CVDs). In this study, a bidirectional hidden semi-Markov model (BI-HSMM) based on the probability distributions of ECG waveform duration was proposed for ECG wave segmentation.
Rui, Huo, Liting, Zhang, Feifei, Liu, Ying, Wang, Yesong, Liang, Shoushui, Wei   +5 more
openaire   +2 more sources