B
Bishwajit Purkaystha
Researcher at Shahjalal University of Science and Technology
Publications - 6
Citations - 111
Bishwajit Purkaystha is an academic researcher from Shahjalal University of Science and Technology. The author has contributed to research in topics: Bengali & Recommender system. The author has an hindex of 5, co-authored 6 publications receiving 70 citations.
Papers
More filters
Proceedings ArticleDOI
Bengali handwritten character recognition using deep convolutional neural network
TL;DR: A convolutional deep model to recognize Bengali handwritten characters is proposed that first learnt a useful set of features by using kernels and local receptive fields, and then it has employed densely connected layers for the discrimination task.
Proceedings ArticleDOI
Bengali speech recognition: A double layered LSTM-RNN approach
TL;DR: This paper has investigated long short term memory (LSTM), a recurrent neural network, approach to recognize individual Bengali words, and divided each word into a number of frames each containing 13 mel-frequency cepstral coefficients (MFCC), providing a useful set of distinctive features.
Journal ArticleDOI
Rating prediction for recommendation: Constructing user profiles and item characteristics using backpropagation
TL;DR: The aim of the study was to automate the construction of user profiles and item characteristics without using any demographic information and then use these constructed features to predict the degree of acceptability of an item to a user.
Posted Content
Comprehending Real Numbers: Development of Bengali Real Number Speech Corpus.
TL;DR: The development process of the first comprehensive Bengali speech dataset on real numbers, which comprehends all the possible words that may arise in uttering any Bengali real number, is described.
Proceedings ArticleDOI
Product recommendation: A deep learning factorization method using separate learners
TL;DR: This paper proposes a deep neural network model which does not require any information be given to it other than the rating triples, and produces an RMSE 4.1824 on Jester 4-million datasti, which shows the deep network is comparable to the state of the art models.