Delhi Technological University

About: Delhi Technological University is a education organization based out in New Delhi, India. It is known for research contribution in the topics: Computer science & Control theory. The organization has 4427 authors who have published 6761 publications receiving 71035 citations. The organization is also known as: Delhi College of Engineering & DTU.

Sarcasm Detection Using Soft Attention-Based Bidirectional Long Short-Term Memory Model With Convolution Network

Abstract: A large community of research has been developed in recent years to analyze social media and social networks, with the aim of understanding, discovering insights, and exploiting the available information. The focus has shifted from conventional polarity classification to contemporary application-oriented fine-grained aspects such as, emotions, sarcasm, stance, rumor, and hate speech detection in the user-generated content. Detecting a sarcastic tone in natural language hinders the performance of sentiment analysis tasks. The majority of the studies on automatic sarcasm detection emphasize on the use of lexical, syntactic, or pragmatic features that are often unequivocally expressed through figurative literary devices such as words, emoticons, and exclamation marks. In this paper, we propose a deep learning model called sAtt-BLSTM convNet that is based on the hybrid of soft attention-based bidirectional long short-term memory (sAtt-BLSTM) and convolution neural network (convNet) applying global vectors for word representation (GLoVe) for building semantic word embeddings. In addition to the feature maps generated by the sAtt-BLSTM, punctuation-based auxiliary features are also merged into the convNet. The robustness of the proposed model is investigated using balanced (tweets from benchmark SemEval 2015 Task 11) and unbalanced (approximately 40000 random tweets using the Sarcasm Detector tool with 15000 sarcastic and 25000 non-sarcastic messages) datasets. An experimental study using the training- and test-set accuracy metrics is performed to compare the proposed deep neural model with convNet, LSTM, and bidirectional LSTM with/without attention and it is observed that the novel sAtt-BLSTM convNet model outperforms others with a superior sarcasm-classification accuracy of 97.87% for the Twitter dataset and 93.71% for the random-tweet dataset.

Outsourcing decisions in reverse logistics: Sustainable balanced scorecard and graph theoretic approach

Abstract: Reverse logistics has become an important issue for most of the organizations due to increased flow of product returns and growing concern for the environment, legislation, and corporate social responsibility. Reverse logistics activities include product collection, inspection and sorting, disposition (reuse, repair, remanufacture or recycle), and redistribution of products. One of the important decisions is whether such activities must be outsourced partly or all must be outsourced or nothing must be outsourced. The articles on the selection of third party reverse logistics service providers are abundant but the articles on outsourcing reverse logistics fully or partly are very limited. The proposed study develops a framework for outsourcing decisions in reverse logistics by using graph theoretic approach. The ability of graph theoretic approach to consider interdependencies and maintaining hierarchical relationship among attributes and sub-attributes makes it an attractive approach. The attributes and sub-attributes were selected by combining four traditional balanced scorecard perspectives i.e. stakeholder, internal business process, learning and growth, and finance with triple bottom line aspects of sustainability known as sustainable balanced scorecard. By considering sustainable balanced scorecard based attributes and sub-attributes, organizations can ensure their contribution toward sustainability even after outsourcing the reverse logistics activities. The proposed framework is illustrated by case example of a mobile manufacturing firm. Scenario based alternatives were developed and “outsourcing index” were calculated for all the alternatives by evaluating permanent function using graph theoretic approach. The best alternative was selected based on the “outsourcing index”. The proposed framework will help managers and practitioners in outsourcing reverse logistics decisions.

Underwater Fish Species Classification using Convolutional Neural Network and Deep Learning

Abstract: The target of this paper is to recommend a way for Automated classification of Fish species. A high accuracy fish classification is required for greater understanding of fish behavior in Ichthyology and by marine biologists. Maintaining a ledger of the number of fishes per species and marking the endangered species in large and small water bodies is required by concerned institutions. Majority of available methods focus on classification of fishes outside of water because underwater classification poses challenges such as background noises, distortion of images, the presence of other water bodies in images, image quality and occlusion. This method uses a novel technique based on Convolutional Neural Networks, Deep Learning and Image Processing to achieve an accuracy of 96.29%. This method ensures considerably discrimination accuracy improvements than the previously proposed methods.