ITM University, Gurgaon, Haryana

About: ITM University, Gurgaon, Haryana is a education organization based out in Gurgaon, India. It is known for research contribution in the topics: Encryption & Cryptosystem. The organization has 749 authors who have published 1159 publications receiving 12997 citations.

Specific heat and magnetocaloric effect studies in multiferroic YMnO3

Abstract: Multiferroic bulk YMnO3 sample was prepared through the solid state reaction method. After characterizing the sample structurally, a systematic investigation of magnetization and specific heat has been undertaken over a temperature range 2–300 K under different magnetic fields. Based on these studies, it has been found that the sample exhibited a paramagnetic to ferrimagnetic phase transition of spin glass type at ~42 K that could be attributed to spin cantering. The magnetic transition peak seen in the magnetic entropy change versus temperature curves became broader with increasing magnetic field. A large magnetic entropy change of ~1 J mol−1 K−1 was obtained under a magnetic field change of 0–10 T.

Hybrid attribute based sentiment classification of online reviews for consumer intelligence

Abstract: Rich online consumer reviews (OCR) can be mined to gain valuable insights, beneficial for both brands and future buyers Recently, aspect based sentiment classification have shown excellent results for fine grained sentiment analysis of OCR However, there are only few studies so far that rely on both explicitly deriving sentiment using syntactic features, and capturing implicit contextual word relations for the task of aspect based sentiment classification In this paper, we propose a novel method: Hybrid Attribute Based Sentiment Classification (HABSC) with the aim to derive sentiment orientation of OCR by capturing implicit word relations and incorporating domain specific knowledge First, we detect the most frequent bigrams and trigrams in the corpus, followed by POS tagging to retain aspect descriptions and opinion words Then, we employ TFIDF (term frequency inverse document frequency) to represent each document, followed by automatically extracting optimal number of topics in the given corpus All the adjectives and adverbs are labelled using domain specific knowledge and pre-existing lexicons Lastly, we find sentiment orientation of each review under the assumption that each review is a mixture of weighted and sentiment labelled attributes We test the efficiency of our method using datasets from two different domains: hotel reviews from TripAdvisorcom and mobile phone reviews from Amazoncom Results show that, the classification accuracy of HABSC significantly exceeds various state-of-the-art methods including aspect-based sentiment classification and supervised classification using distributed word and paragraph vectors Our method also exhibits less computational time as compared to distributed vectorization schemes

Development of artificial neural-network-based models for the simulation of spring discharge

Abstract: The present study demonstrates the application of artificial neural networks (ANNs) in predicting the weekly spring discharge. The study was based on the weekly spring discharge from a spring located near Ranichauri in Tehri Garhwal district of Uttarakhand, India. Five models were developed for predicting the spring discharge based on a weekly interval using rainfall, evaporation, temperature with a specified lag time. All models were developed both with one and two hidden layers. Each model was developed with many trials by selecting different network architectures and different number of hidden neurons; finally a best predicting model presented against each developed model. The models were trained with three different algorithms, that is, quick-propagation algorithm, batch backpropagation algorithm, and Levenberg-Marquardt algorithm using weekly data from 1999 to 2005. A best model for the simulation was selected from the three presented algorithms using the statistical criteria such as correlation coefficient (R), determination coefficient, orNash Sutcliff's efficiency (DC). Finally, optimized number of neurons were considered for the best model. Training and testing results revealed that the models were predicting the weekly spring discharge satisfactorily. Based on these criteria, ANN-based model results in better agreement for the computation of spring discharge. LMR models were also developed in the study, and they also gave good results, but, when compared with the ANN methodology, ANN resulted in better optimized values.