Results 21 to 30 of about 63,948 (252)
Hidden Markov models: the best models for forager movements? [PDF]
PLoS ONE, 2013 One major challenge in the emerging field of movement ecology is the inference of behavioural modes from movement patterns. This has been mainly addressed through Hidden Markov models (HMMs).
Rocio Joo +3 more
doaj +1 more source
Use of Automation Technologies and Data Mining in Speech Recognition for Autism. [PDF]
Brain BehavPipeline analyzes clinical and naturalistic speech using LENA, wav2vec 2.0, and foundation‐model ASR (Whisper) to enable scalable ASD detection and severity estimation. Future work integrates benchmarking, privacy‐preserving collaboration (federated learning), and explainable, edge‐ready AI for clinically credible assessment and longitudinal monitoring.
Mao R, Zhu Y.
europepmc +2 more sources
biomvRhsmm:Genomic Segmentation with Hidden Semi-Markov Model [PDF]
BioMed Research International, 2014 High-throughput technologies like tiling array and next-generation sequencing (NGS) generate continuous homogeneous segments or signal peaks in the genome that represent transcripts and transcript variants (transcript mapping and quantification), regions of deletion and amplification (copy number variation), or regions characterized by particular ...
Yang Du +3 more
openaire +3 more sources
A Generic Clustering-Based Algorithm for Approximating IOHMM Topology and Parameters
IEEE Access, 2021 In this paper, a novel generic clustering-based algorithm for approximating the topology and the parameters of discrete state space Input/Output Hidden Markov Models (IOHMMs) with continuous observation spaces is introduced. The algorithm can accommodate
Gerald Rocher +2 more
doaj +1 more source
SOLUTION TO EVALUATION PROBLEM OF HIDDEN SEMI-MARKOV QP-MODELS
Advanced Engineering Research, 2014 A hidden semi-Markov QP-model is considered; and the way it could be embedded in a general hidden semi-Markov model is shown. The estimation problem (the first of three classical theory problems of the hidden Markov models and hidden semi-Markov models ...
V. M. Deundyak, M. A. Zhdanova
doaj +1 more source
Hidden semi-Markov Model based earthquake classification system using Weighted Finite-State Transducers [PDF]
Nonlinear Processes in Geophysics, 2011 Automatic earthquake detection and classification is required for efficient analysis of large seismic datasets. Such techniques are particularly important now because access to measures of ground motion is nearly unlimited and the target waveforms ...
M. Beyreuther, J. Wassermann
doaj +1 more source
Flexible and practical modeling of animal telemetry data: hidden Markov models and extensions [PDF]
, 2012 We discuss hidden Markov-type models for fitting a variety of multistate random walks to wildlife movement data. Discrete-time hidden Markov models (HMMs) achieve considerable computational gains by focusing on observations that are regularly spaced in ...
Langrock, R. +5 more
core +3 more sources
An Analysis of Return States in Iran Stock Market: Hidden Semi-Markov Model Approach [PDF]
تحقیقات مالی, 2020 Objective: Analyzing the behavior of Tehran Stock Market, based on the daily asset return for the duration between 1387 and 1397 has been the main aim of this research.Methods: Tehran Stock Market daily asset return can be considered as a time-series and
Maysam Rafei, Mahin Shokri
doaj +1 more source
Analysis and Comparison of Bitcoin and S and P 500 Market Features Using HMMs and HSMMs †
Information, 2019 We implement hidden Markov models (HMMs) and hidden semi-Markov models (HSMMs) on Bitcoin/US dollar (BTC/USD) with the aim of market phase detection. We make analogous comparisons to Standard and Poor’s 500 (S and P 500), a benchmark traditional ...
David Suda, Luke Spiteri
doaj +1 more source
Process Discovery Using Classification Tree Hidden Semi-Markov Model [PDF]
2016 IEEE 17th International Conference on Information Reuse and Integration (IRI), 2016 Various and ubiquitous information systems are being used in monitoring, exchanging, and collecting information. These systems are generating massive amount of event sequence logs that may help us understand underlying phenomenon. By analyzing these logs, we can learn process models that describe system procedures, predict the development of the system,
Kang, Yihuang, Zadorozhny, Vladimir
openaire +2 more sources