Results 31 to 40 of about 8,821,946 (173)

Protein secondary structure prediction for a single-sequence using hidden semi-Markov models

BMC Bioinformatics, 2006
Background The accuracy of protein secondary structure prediction has been improving steadily towards the 88% estimated theoretical limit. There are two types of prediction algorithms: Single-sequence prediction algorithms imply that information about ...
Borodovsky Mark, Altunbasak Yucel, Aydin Zafer   +2 more
doaj   +1 more source

On Regime Switching Models

Mathematics
Regime switching models have been widely studied for their ability to capture the dynamic behavior of time series data and are widely used in economic and financial data analysis.
Zhenni Tan, Yuehua Wu
doaj   +1 more source

Log-Viterbi algorithm applied on second-order hidden Markov model for human activity recognition

International Journal of Distributed Sensor Networks, 2018
Recognition of human activities is getting into the limelight among researchers in the field of pervasive computing, ambient intelligence, robotic, and monitoring such as assistive living, elderly care, and health care.
Yang Sung-Hyun, Keshav Thapa, M Humayun Kabir, Lee Hee-Chan   +3 more
doaj   +1 more source

SMCis: An Effective Algorithm for Discovery of Cis-Regulatory Modules. [PDF]

PLoS ONE, 2016
The discovery of cis-regulatory modules (CRMs) is a challenging problem in computational biology. Limited by the difficulty of using an HMM to model dependent features in transcriptional regulatory sequences (TRSs), the probabilistic modeling methods ...
Haitao Guo, Hongwei Huo, Qiang Yu
doaj   +1 more source

Spatio-temporal categorization for first-person-view videos using a convolutional variational autoencoder and Gaussian processes

Frontiers in Robotics and AI, 2022
In this study, HcVGH, a method that learns spatio-temporal categories by segmenting first-person-view (FPV) videos captured by mobile robots, is proposed.
Masatoshi Nagano, Tomoaki Nakamura, Takayuki Nagai, Takayuki Nagai, Daichi Mochihashi, Ichiro Kobayashi   +5 more
doaj   +1 more source

Accelerating Materials Discovery: A Review of Machine Learning in X‐Ray Absorption Spectroscopy

Advanced Intelligent Systems, EarlyView.
This review systematically details how machine learning transforms X‐ray absorption spectroscopy (XAS) analysis. It covers advanced deep learning architectures for structure‐spectra mapping and inverse tasks, while discussing key challenges like the simulation‐to‐reality gap.
Melaku Lake Tegegne, Haodong Yao, Liyuan Wu, Yihe Pang, Fazhi Qi, Lina Zhao   +5 more
wiley   +1 more source

Proper account of auto-correlations improves decoding performances of state-space (semi) Markov models

Peer Community Journal
State-space models are widely used in ecology to infer hidden behaviors. This study develops an extensive numerical simulation-estimation experiment to evaluate the state decoding accuracy of four simple state-space models.
Bez, Nicolas, Gloaguen, Pierre, Etienne, Marie-Pierre, Lanco, Sophie, Joo, Rocío, Rivot, Etienne, Walker, Emily, Woillez, Mathieu, Mahévas, Stéphanie   +8 more
doaj   +1 more source

Extending the hyper‐logistic model to the random setting: New theoretical results with real‐world applications

Mathematical Methods in the Applied Sciences, EarlyView.
We develop a full randomization of the classical hyper‐logistic growth model by obtaining closed‐form expressions for relevant quantities of interest, such as the first probability density function of its solution, the time until a given fixed population is reached, and the population at the inflection point.
Juan Carlos Cortés, Ana Navarro‐Quiles, Sorina Madalina Sferle   +2 more
wiley   +1 more source

Computational Tonality Estimation: Signal Processing and Hidden Markov Models [PDF]

, 2009
PhDThis thesis investigates computational musical tonality estimation from an audio signal. We present a hidden Markov model (HMM) in which relationships between chords and keys are expressed as probabilities of emitting observable chords from a hidden
Noland, Katy C
core   +4 more sources

Global discriminative learning for higher-accuracy computational gene prediction. [PDF]

PLoS Computational Biology, 2007
Most ab initio gene predictors use a probabilistic sequence model, typically a hidden Markov model, to combine separately trained models of genomic signals and content.
Axel Bernal, Koby Crammer, Artemis Hatzigeorgiou, Fernando Pereira   +3 more
doaj   +1 more source