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Tech for good: Class12 students develop an app to help the deaf, blind and mute communicate with ease


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, ....

New Delhi , Rohan Velanki , Keshav Maheshwari , Aryaman Agrawal , Padam Chopra , Alok Bansal , National Science Fair , Convolutional Neural Network , Amity International , World Health Organisation , Morse Code , Health Organisation , Good Vibes , Microsoft Imagine Cup Indian Nationals , Best Project , Science Fair , Developmental Issues , புதியது டெல்ஹி , கேசவ் மகேஸ்வரி , அலோக் பன்சால் , தேசிய அறிவியல் நியாயமான , நட்பு சர்வதேச , உலகம் ஆரோக்கியம் ஆர்கநைஸேஶந் , மோர்ஸ் குறியீடு , ஆரோக்கியம் ஆர்கநைஸேஶந் , நல்ல அதிர்வுகள் ,

AI cracks identifying ancient pottery


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United States , Convolutional Neural Network , ஒன்றுபட்டது மாநிலங்களில் ,

"Sarcasm Detection using Deep Learning with Contextual Features" by Md Saifullah Razali, Alfian Abdul Halin et al.


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 ....

Convolutional Neural Network , Deep Learning , Deep Learning , Feature Extraction , Natural Language Processing , Sarcasm Detection , Ocial Networking Online , Task Analysis , ஆழமான கற்றல் , கேளுங்கள் பகுப்பாய்வு ,