Shiv Nadar University

About: Shiv Nadar University is a education organization based out in Dadri, Uttar Pradesh, India. It is known for research contribution in the topics: Population & Graphene. The organization has 1015 authors who have published 1924 publications receiving 18420 citations.

Hydrogel of gelatin in the presence of graphite for the adsorption of dye: Towards the concept for water purification

Abstract: Gelatin grafted environment-friendly hydrogel composite was utilized to perform the effective adsorption of methyl violet (MV) dye. Due to non-toxic, high surface zone and biodegradability, gelatin has received considerable attention for sustainable applications. Gelatin grafted methyl methacrylate (GE-g-MMA) hydrogels were prepared through the microwave-assisted green method. The GE-g-MMA hydrogel showed the highest swelling of 1039.4 %. Graphite (Gph) was introduced in GE-g-MMA hydrogel matrix to construct gelatin grafted methyl methacrylate/graphite (GE-g-MMA/Gph) hydrogel composite for adsorptive removal of poisonous methyl violet (MV) dye from water. The GE-g-MMA hydrogel and GE-g-MMA/Gph hydrogel composite were described through FTIR, XPS, SEM, Raman, XRD, TGA and BET techniques. Various adsorption factors like adsorbent dose, % removal of dye, contact period and solution pH were examined to develop ideal adsorption conditions. The GE-g-MMA/Gph hydrogel composite exhibited high adsorption efficiency of 99.9 % in 40 min (pH 9, MV: 50 mg L−1, adsorbent dose: 20 mg, MV volume: 20 ml). Experimental analysis results revealed that the synthesized GE-g-MMA/Gph hydrogel composite has a high capacity to remove MV molecules from aqueous solution at 25 °C. The values of the regression coefficient (R2) in case of Frendluich (0.999), Langmuir (0.978), Dubinin-Radushkevitch (0.936) and Temkin (0.931) clearly showed that the Freundlich isotherm fitted best to the adsorption process and pseudo-first-order kinetics demonstrated the better fit of adsorption with highest R2 (0.935). The GE-g-MMA/Gph hydrogel composite is economical, after six regeneration cycles, the adsorption efficiency of 95.8 % has been maintained.

A paraphrase and semantic similarity detection system for user generated short-text content on microblogs

Abstract: Existing systems deliver high accuracy and F1-scores for detecting paraphrase and semantic similarity on traditional clean-text corpus. For instance, on the clean-text Microsoft Paraphrase benchmark database, the existing systems attain an accuracy as high as 0:8596. However, existing systems for detecting paraphrases and semantic similarity on user-generated short-text content on microblogs such as Twitter, comprising of noisy and ad hoc short-text, needs significant research attention. In this paper, we propose a machine learning based approach towards this. We propose a set of features that, although well-known in the NLP literature for solving other problems, have not been explored for detecting paraphrase or semantic similarity, on noisy user-generated short-text data such as Twitter. We apply support vector machine (SVM) based learning. We use the benchmark Twitter paraphrase data, released as a part of SemEval 2015, for experiments. Our system delivers a paraphrase detection F1-score of 0.717 and semantic similarity detection F1-score of 0.741, thereby significantly outperforming the existing systems, that deliver F1-scores of 0.696 and 0.724 for the two problems respectively. Our features also allow us to obtain a rank among the top-10, when trained on the Microsoft Paraphrase corpus and tested on the corresponding test data, thereby empirically establishing our approach as ubiquitous across the different paraphrase detection databases.