Thapar University

About: Thapar University is a education organization based out in Patiāla, Punjab, India. It is known for research contribution in the topics: Cloud computing & Fuzzy logic. The organization has 2944 authors who have published 8558 publications receiving 130392 citations.

Hatred and trolling detection transliteration framework using hierarchical LSTM in code-mixed social media text

Abstract: The paper describes the usage of self-learning Hierarchical LSTM technique for classifying hatred and trolling contents in social media code-mixed data. The Hierarchical LSTM-based learning is a novel learning architecture inspired from the neural learning models. The proposed HLSTM model is trained to identify the hatred and trolling words available in social media contents. The proposed HLSTM systems model is equipped with self-learning and predicting mechanism for annotating hatred words in transliteration domain. The Hindi–English data are ordered into Hindi, English, and hatred labels for classification. The mechanism of word embedding and character-embedding features are used here for word representation in the sentence to detect hatred words. The method developed based on HLSTM model helps in recognizing the hatred word context by mining the intention of the user for using that word in the sentence. Wide experiments suggests that the HLSTM-based classification model gives the accuracy of 97.49% when evaluated against the standard parameters like BLSTM, CRF, LR, SVM, Random Forest and Decision Tree models especially when there are some hatred and trolling words in the social media data.

Data Offloading in 5G-Enabled Software-Defined Vehicular Networks: A Stackelberg-Game-Based Approach

Abstract: Data offloading using vehicles is one of the most challenging tasks to perform due to the high mobility of vehicles. There are many solutions available for this purpose, but due to the inefficient management of data along with the control decisions, these solutions are not adequate to provide data offloading by making use of the available networks. Moreover, with the advent of 5G and related technologies, there is a need to cope with high speed and traffic congestion in the existing infrastructure used for data offloading. Hence, to make intelligent decisions for data offloading, an SDN-based scheme is presented in this article. In the proposed scheme, an SDNbased controller is designed that makes decisions for data offloading by using the priority manager and load balancer. Using these two managers in SDN-based controllers, traffic routing is managed efficiently even with an increase in the size of the network. Moreover, a single-leader multi-follower Stackelberg game for network selection is also used for data offloading. The proposed scheme is evaluated with respect to several parameters where its performance was found to be superior in comparison to the existing schemes.

Synergistic Role of Bacterial Urease and Carbonic Anhydrase in Carbonate Mineralization

Abstract: The investigation on the synergistic role of urease (UA) and carbonic anhydrase (CA) in biomineralization of calcium carbonate in Bacillus megaterium suggested that the precipitation of CaCO3 is significantly faster in bacterial culture than in crude enzyme solutions. Calcite precipitation is significantly reduced when both the enzymes are inhibited in comparison with those of the individual enzyme inhibitions indicating that both UA and CA are crucial for efficient mineralization. Carbonic anhydrase plays a role in hydrating carbon dioxide to bicarbonate, while UA aids in maintaining the alkaline pH that promotes calcification process.