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Graphical model-based latent topic frameworks for image retrieval fall into two fundamental categories such as (i) directed topic models and (ii) undirected topic models.
From the above comparative analyses, the models can be ranked as follows: (i) series hybrid models (ii) parallel hybrid models, and (ii) individual models.
The approaches can be generally classified into two main categories: (i) architecture level (or hardware level) models and (ii) instruction level models.
Within the context of HMM modeling, we consider two observation sequences similar if: (i) they fit each other's models; and (ii) they have similar Viterbi optimal paths [17].
ViMM can (i) enable insights that can inform the design of new computational models and (ii) make value judgments explicit and more inclusive of relevant values.
RSM aims to (i) facilitate the semantic interlinking between services annotated using different semantic models and (ii) accommodate the bottom-up social annotation of services.
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To keep the full system and the subsystems equivalent, we developed two models: (i) the copy number model (Model I) and (ii) the association rate model (Model II).
Two different lung models are used: (i) a basic ring-less trachea model and (ii) an advanced Human Zygote5 model.
The framework features (i) a detailed dynamic process model, and (ii) an explicit/multi-parametric model predictive controller design step.
PAG sought public input through two approaches: (i) an open model and (ii) a representative model (Einsiedel et al 2011b, 26 27).
The non-Newtonian fluid in the core region is represented by (i) Casson fluid model and (ii) Herschel-Bulkley fluid model.
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