In this paper, we introduce and study the Weyl transform of functions which are integrable with respect to a vector measure on a phase space associated to a locally compact abelian group. We also study the Weyl transform of vector measures. Later, we also introduce and study the convolution of functions from L p -spaces associated to a vector measure. We also study the Weyl transform of vector-valued functions and prove a vector-valued analogue of the Hausdorff-Young inequality.
The aim of this paper is to present some results about the space L Φ (ν), where ν is a vector measure on a compact (not necessarily abelian) group and Φ is a Young function. We show that under certain conditions, the space L Φ (ν) becomes an L 1 (G)-module with respect to the usual convolution of functions. We also define one more convolution structure on L Φ (ν).
Let G be a locally compact group. In this note, we characterise nondegenerate *-representations of A Φ (G) and B Φ (G). We also study spectral subspaces associated to a non-degenerate Banach space representation of A Φ (G).
Customer conveys their opinion in natural language about an entity. Applying sentiment analysis to those reviews is a very complex task. The significance terms that are influencing the polarity of a review are not examined. The terms that are having contextual meaning are not recognized which are present across multiple sentences in a review. To address the above two issues, we have proposed an Attention-based Convolution Bi-directional Recurrent Neural Network (ACBRNN). In this model, two convolution layer captures phrase-level feature, while Self-Attention in the middle assigns high weight to the significant terms and Bi-directional GRU performs a conceptual scanning of review through forward and backward direction. We have conducted four different experiments viz., Unidirectional, Bidirectional, Hybrid and Proposed model on IMDB dataset to show the significance of the proposed model. The proposed model has obtained an F1 score of 87.94% on IMDB dataset which is 5.41% higher than CNN. Thus the proposed architecture performs well while comparing with all other baseline models.
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