Titlebook: Computational Intelligence in Data Science; Third IFIP TC 12 Int Aravindan Chandrabose,Ulrich Furbach,Anand Kumar M Conference proceedings

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Effective Emotion Recognition from Partially Occluded Facial Images Using Deep Learningal muscles irrespective of pose, face shape, illumination, and image resolution is very much essential for serving the purpose. However, extraction and analysis of facial and appearance based features fails with improper face alignment and occlusions. Few existing works on these problems mainly dete

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Emotion Recognition in Sentences - A Recurrent Neural Network Approachmentioned data set and an accuracy of 91.6% for the prediction of degree of emotion for a sentence. Additionally, every sentence is associated with a degree of the dominant emotion. One can infer that a degree of emotion means the extent of the emphasis of an emotion. Although, more than one sentenc

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Tamil Paraphrase Detection Using Encoder-Decoder Neural Networkst systems. The system was trained and evaluated on DPIL@FIRE2016 Shared Task dataset. To our knowledge, ours is the first deep learning model which validates the training instances of both the subtask-1 and subtask-2 dataset of DPIL shared task.

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Trustworthy User Recommendation Using Boosted Vector Similarity Measureposed model in terms of accuracy measures such as precision@k and recall@k and error measures, namely, MAE, MSE and RMSE is discussed in this paper. The evaluation shows that the proposed system outperforms other recommender system with minimum MAE and RMSE.

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Sensitive Keyword Extraction Based on Cyber Keywords and LDA in Twitter to Avoid Regretshe originality of this research work lies in identifying sensitive keywords that reveal Tweeter’s Personally Identifiable Information through the novel Topic Keyword Extractor. The potential sensitive topics in which the social media users frequently exhibit personal information and unintended infor