Sentence examples for bidirectional recognition from inspiring English sources
We suggest that bidirectional recognition may, in fact, be widely present, but simply not widely observed.
Thus, bidirectional recognition is probably more prevalent in recognition of linear helical motifs than in recognition of larger structured elements.
In retrospect, bidirectional recognition of NES is not surprising if one considers that CRM1 does not make contacts with the NES main-chain, unlike importins (e.g. Importin-α, β, etc).
Greenwich, CT based Vivapop has launched a web based calendar and events management service that offers a very nice interface, cross platform bidirectional syncing and voice recognition for mobile access.
Finally, in another study [39], a bidirectional LSTM is successfully used for emotion recognition related tasks, such as keyword spotting in emotionally colored spontaneous speech.
Ahmed et al. [22] applied Bidirectional Long Short-Term Memory (BLSTM) classifier to the character recognition of Urdu.
We show a detailed statistical analysis of the handwritten text in this database and describe recognition experiments using several recent methods including the Bidirectional Long Short-Term Memory (BLSTM) network.
As to mechanisms for launching and developing collaboration at the primary service delivery levels, THPs favoured concrete and collaborative actions from the start, especially bidirectional referrals, indicating a wish for practical (rather than rhetorical) recognition.
The MCS combines several individual recognition systems based on hidden Markov models (HMMs) and bidirectional long short-term memory networks (BLSTM).
In recent years, extensive research has been done in neural networks such as Hopfield neural networks, Cohen-Grossberg neural networks, cellular neural networks, and bidirectional associative memory neural networks, because of their potential applications in pattern recognition, image processing, associative memory, and so on, see [1 28].
As bidirectional Long Short-Term Memory (BLSTM) networks are known to enable improved phoneme recognition accuracies by incorporating long-range contextual information into speech decoding, we integrate the BLSTM principle into a Tandem front-end for probabilistic feature extraction.
