Conference Papers
Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506
Browse
2 results
Search Results
Item CNN-GRU: Transforming image into sentence using GRU and attention mechanism(Grenze Scientific Society, 2021) Saini, G.; Patil, N.Recent advancement of the deep neural network has triggered great attention in both Natural Language Processing (NLP) and Computer Vision (CV). It provides an efficient way of understanding semantic and syntactic structure which can deal with complex task such as automatic image captioning. Image captioning methodology mainly based on the encoder-decoder approach. In the present work, we developed a CNN-GRU model using Convolutional Neural Network (CNN), Gated Recurrent Unit (GRU) and attention mechanism. Here VGG16 is used as an encoder, GRU and attention mechanism are used as a decoder. Our model has shown significant improvement compared to other state-of-art encoder-decoder models on the famous MSCOCO data set. Further, the time taken to train and test our model is two-third as compared to other similar models such as CNN-CNN and CNN-RNN. © Grenze Scientific Society, 2021.Item Ensemble Neural Models for Depressive Tendency Prediction Based on Social Media Activity of Twitter Users(Springer Science and Business Media Deutschland GmbH, 2022) Saini, G.; Yadav, N.; Kamath S․, S.In view of the ongoing pandemic, Clinical Depression (CD) is a serious health challenge for a large segment of the population. According to recent public surveys, more than 30 million American citizens are the victim of depression each year and depression also causes 30 thousand suicides each year. Early detection of depression can help provide much needed medical intervention and treatment for better mental health. Toward this, the social media posts of users can be a significant source for analyzing their mental health signals, and can also serve as a measure for assessing the prevalence of clinical depression tendencies in the population. In this paper, an approach that leverages the predictive power of supervised and semi-supervised learning algorithms for detecting depressive tendencies in the population using social media activity is presented. Learning models were trained on preprocessed tweet data from the Sentiment140 dataset containing 1.6 million labeled tweets. We also designed a convolution neural network model for the prediction task that outperformed machine learning models by a significant margin with an accuracy of 97.1%. The performance of the proposed models is benchmarked using standard metrics like SMDI (Social Media Depression Index). Crowd-sourcing approaches were adopted for collecting real-time social behavior of users to train the proposed model and demonstrate its potential for real-world applications. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.