Results 41 to 50 of about 50,721 (144)
Sensors, 2022 In industrial processes, the composition of raw material and the production environment are complex and changeable, which makes the production process have multiple steady states.
Ning Chen +5 more
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International Journal of Interactive Multimedia and Artificial Intelligence, 2016 This paper presents the results and conclusions found when predicting the behavior of gamers in commercial videogames datasets. In particular, it uses Variable-Order Markov (VOM) to build a probabilistic model that is able to use the historic behavior of
Yago Saez +3 more
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Entropy, 2021 The Variational AutoEncoder (VAE) has made significant progress in text generation, but it focused on short text (always a sentence). Long texts consist of multiple sentences.
Kun Zhao +3 more
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Modeling the exchange rate using price levels and country risk
Cogent Economics & Finance, 2015 This paper builds two factor discrete time models in order to investigate the effect of sovereign risk on the nominal exchange rates in a Markov switching framework. The empirical section of the paper uses seven currencies from Chile, the Czech Republic,
Gábor Regős
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PPM-Decay: A computational model of auditory prediction with memory decay.
PLoS Computational Biology, 2020 Statistical learning and probabilistic prediction are fundamental processes in auditory cognition. A prominent computational model of these processes is Prediction by Partial Matching (PPM), a variable-order Markov model that learns by internalizing n ...
Peter M C Harrison +3 more
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Variable-order sequence modeling improves bacterial strain discrimination for Ion Torrent DNA reads
BMC Bioinformatics, 2017 Background Genome sequencing provides a powerful tool for pathogen detection and can help resolve outbreaks that pose public safety and health risks. Mapping of DNA reads to genomes plays a fundamental role in this approach, where accurate alignment and ...
Thomas M. Poulsen, Martin Frith
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Avoiding Overfitting in Variable-Order Markov Models: a Cross-Validation Approach
Higher$\text{-}$order Markov chain models are widely used to represent agent transitions in dynamic systems, such as passengers in transport networks. They capture transitions in complex systems by considering not only the current state but also the path of previously visited states. For example, the likelihood of train passengers traveling from Paris (
Secchini, Valeria +2 more
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An Alternating Variable Step-Size Adaptive Long-Range Prediction of LMS Fading Signals
International Journal of Distributed Sensor Networks, 2015 We propose a linear alternating variable step-size adaptive long-range prediction (AVSS-ALRP) scheme to predict fading signals which is especially suitable for a versatile two-state land mobile satellite (LMS) channel model at S-band. A three-step design
Xi Liao +3 more
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Markov chain modeling of daily rainfall in Lay Gaint Woreda, South Gonder Zone, Ethiopia
Journal of Degraded and Mining Lands Management, 2018 Information on seasonal Kiremet and seasonal Belg rainfall amount is important in the rain fed agriculture of Ethiopia since more than 85% of the population is dependent on agriculture particularly on rain fed farming practices.
Birhan Getachew, Menberu Teshome
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Variable Scheduling to Mitigate Channel Losses in Energy-Efficient Body Area Networks
Sensors, 2012 We consider a typical body area network (BAN) setting in which sensor nodes send data to a common hub regularly on a TDMA basis, as defined by the emerging IEEE 802.15.6 BAN standard.
Lavy Libman +2 more
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