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Temporal pathways to learning: How learning emerges in an open-ended collaborative activity.
Published 2022-01-01Subjects: Get full text
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622
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623
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624
Model-Based Localization and Tracking Using Bluetooth Low-Energy Beacons
Published 2017-10-01Subjects: Get full text
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625
Learning Dynamics and Control of a Stochastic System under Limited Sensing Capabilities
Published 2022-06-01Subjects: Get full text
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626
A Novel Approach to ECG Classification Based upon Two-Layered HMMs in Body Sensor Networks
Published 2014-03-01Subjects: Get full text
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627
Multimodal Feature-Assisted Continuous Driver Behavior Analysis and Solving for Edge-Enabled Internet of Connected Vehicles Using Deep Learning
Published 2021-11-01Subjects: Get full text
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628
ANALYTICAL INVESTIGATION OF CONGESTION-AVOIDANCE STRATEGIES IN CLOSED-TYPE QUEUING MODELS OF COMPUTER NETWORKS WITH PRIORITY SCHEDULING
Published 2007-07-01Subjects: Get full text
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629
Gait Phase Detection for Lower-Limb Exoskeletons using Foot Motion Data from a Single Inertial Measurement Unit in Hemiparetic Individuals
Published 2019-07-01Subjects: Get full text
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630
A Statistical Framework for Automatic Leakage Detection in Smart Water and Gas Grids
Published 2016-08-01Subjects: Get full text
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631
Deep Models for Low-Resourced Speech Recognition: Livvi-Karelian Case
Published 2023-09-01Subjects: Get full text
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632
Enhanced PDR-BLE Compensation Mechanism Based on HMM and AWCLA for Improving Indoor Localization
Published 2021-10-01Subjects: Get full text
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633
Estimation of Particulate Matter Contributions from Desert Outbreaks in Mediterranean Countries (2015–2018) Using the Time Series Clustering Method
Published 2020-12-01Subjects: Get full text
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634
Classifying the Unclassified: A Phage Classification Method
Published 2019-02-01Subjects: “…Hidden Markov Models…”
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635
Spatiotemporal capsule neural network for vehicle trajectory prediction
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Journal Article -
636
Probabilistic Independence Networks for Hidden Markov Probability Models
Published 2004Subjects: Get full text
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637
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6.895 / 6.095J Computational Biology: Genomes, Networks, Evolution, Fall 2005
Published 2005Subjects: Get full text
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639
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ToPS: a framework to manipulate probabilistic models of sequence data.
Published 2013-01-01“…We present ToPS, a computational framework that can be used to implement different applications in bioinformatics analysis by combining eight kinds of models: (i) independent and identically distributed process; (ii) variable-length Markov chain; (iii) inhomogeneous Markov chain; (iv) hidden Markov model; (v) profile hidden Markov model; (vi) pair hidden Markov model; (vii) generalized hidden Markov model; and (viii) similarity based sequence weighting. …”
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