Search

In this digital age, social media is an essential part of life. People share their moments and emotions through it. Consequently, detecting emotions in their behavior can be an effective way to determine their emotional disposition, which can then be used to control their negative thinking by making them see the positive aspects of the world. This study proposes an emotion detection-based mood control framework that reorganizes social media posts to match the user’s mental state. An emotion detection model based on Attention mechanism, Bidirectional Long Short Term Memory (LSTM), and Convolutional Neural Network (CNN) has been proposed which can detect six emotions from Bangla text with 66.98% accuracy. It also demonstrates how emotion detection frameworks can be implemented in other languages as well.

Individuals with hearing and speaking impairment communicate using sign language. The movement of hand, body and expressions of face are the means by which the people, who are unable to hear and speak, can communicate. Bangla sign alphabets are formed with one or two hand movements. There are some features which differentiates the signs. To detect and recognize the signs, analyzing its shape and comparing its features is necessary. This paper aims to propose a model and build a computer systemthat can recognize Bangla Sign Lanugage alphabets and translate them to corresponding Bangla letters by means of deep convolutional neural network (CNN). CNN has been introduced in this model in form of a pre-trained model called “MobileNet” which produced an average accuracy of 95.71% in recognizing 36 Bangla Sign Language alphabets.