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All Journal Indonesian Journal of Electrical Engineering and Computer Science
Md. Masum Billah
Daffodil International University

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Bangla numerical sign language recognition using convolutional neural networks (CNNs) F. M. Javed Mehedi Shamrat; Sovon Chakraborty; Md. Masum Billah; Moumita Kabir; Nazmus Shakib Shadin; Silvia Sanjana
Indonesian Journal of Electrical Engineering and Computer Science Vol 23, No 1: July 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v23.i1.pp405-413

Abstract

The amount of deaf and mute individuals on the earth is rising at an alarmingrate. Bangladesh has about 2.6 million people who are unable to interact with the community using language. Hearing-impaired citizens in Bangladesh use Bangladeshi sign language (BSL) as a means of communication. In this article,we propose a new method for Bengali sign language recognition based on deep convolutional neural networks. Our framework employs convolutional neural networks (CNN) to learn from the images in our dataset and interpret hand signs from input images. Checking their collections of ten indications (we usedten sets of images with 31 distinct signs) for a total of 310 images. The proposed system takes snap shots from a video by using a webcam with applying a computer vision-based approach. After that, it compares those photos to a previously trained dataset generated with CNN and displays the Bengali numbers (০-৯). After estimating the model on our dataset, weobtained an overall accuracy of 99.8%. We want to streng then things as far aswe can to make silent contact with the majority of society as simple asprobable.
Sentiment analysis on twitter tweets about COVID-19 vaccines usi ng NLP and supervised KNN classification algorithm F. M. Javed Mehedi Shamrat; Sovon Chakraborty; M. M. Imran; Jannatun Naeem Muna; Md. Masum Billah; Protiva Das; Md. Obaidur Rahman
Indonesian Journal of Electrical Engineering and Computer Science Vol 23, No 1: July 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v23.i1.pp463-470

Abstract

The pandemic has taken the world by storm. Almost the entire world went into lockdown to save the people from the deadly COVID-19. Scientists around the around have come up with several vaccines for the virus. Amongthem, Pfizer, Moderna, and AstraZeneca have become quite famous. General people however have been expressing their feelings about the safety and effectiveness of the vaccines on social media like Twitter. In this study, such tweets are being extracted from Twitter using a Twitter API authentication token. The raw tweets are stored and processed using NLP. The processed data is then classified using a supervised KNN classification algorithm. The algorithm classifies the data into three classes, positive, negative, and neutral. These classes refer to the sentiment of the general people whose Tweets are extracted for analysis. From the analysis it is seen that Pfizer shows 47.29%positive, 37.5% negative and 15.21% neutral, Moderna shows 46.16%positive, 40.71% negative, and 13.13% neutral, AstraZeneca shows 40.08%positive, 40.06% negative and 13.86% neutral sentiment.
Co-Authors F. M. Javed Mehedi Shamrat Jannatun Naeem Muna M. M. Imran Md. Obaidur Rahman Moumita Kabir Nazmus Shakib Shadin Protiva Das Silvia Sanjana Sovon Chakraborty