Tech for good: Class12 students develop an app to help the deaf, blind and mute communicate with ease
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Developed by three Class 12 students from Amity International, Delhi, the Practikality app provides an interactive mechanism for the differently abled - the deaf, the blind, and the mute - and is in its final stages of testing. The co-founders of the Practikality app
For seven-year-old Rohan Velanki, a partially mute and deaf child, the
joy of social interaction was a limited experience. A
quiet and reticent child, Rohan began interacting with Sugam, a non-governmental organisation (NGO), which works towards the betterment of abandoned and differently-abled children. Over the course of the next six months,
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Abstract
Our work focuses on detecting sarcasm in tweets using deep learning extracted features combined with contextual handcrafted features. A feature set is extracted from a Convolutional Neural Network (CNN) architecture before it is combined with carefully handcrafted feature sets. These handcrafted feature sets are created based on their respective contextual explanations. Each feature sets are specifically designed for the sole task of sarcasm detection. The objective is to find the most optimal features. Some sets are good to go even when it is used in independence. Other sets are not really significant without any combination. The results of the experiments are positive in terms of Accuracy, Precision, Recall and F1-measure. The combination of features are classified using a few machine learning techniques for comparison purposes. Logistic Regression is found to be the best classification algorithm for this task. Furthermore, result comparison to recent works and the perfor