Institution
Thapar University
Education•Patiāla, Punjab, India•
About: Thapar University is a education organization based out in Patiāla, Punjab, India. It is known for research contribution in the topics: Computer science & Cloud computing. The organization has 2944 authors who have published 8558 publications receiving 130392 citations.
Papers published on a yearly basis
Papers
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TL;DR: In this paper, an iron molybdophosphate (FeMoPO) nanoparticles have been fabricated using simple co-precipitation method and the nanoparticles showed promising ion exchange nature with ion exchange capacity of 1 −meq −g −1 The synthesis on nanoparticles has been ascertained by characterizing the material using various techniques including Fourier transform infrared spectroscopy (FTIR) scanning electron microscopy (SEM) and transmission electron microscope (TEM) to investigate the ion exchange behavior of FeMoPO nanoparticles its physicochemical properties
87 citations
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TL;DR: This paper shall analyse the RR interval time series from selected subjects for different sampling frequencies to compare the error introduced in selected frequency-domain measures of HRV at a constant frequency resolution for a specific duration of electrocardiogram (ECG) data.
Abstract: Spectral analysis of heart rate variability (HRV) is an accepted method for assessment of cardiac autonomic function and its relationship to numerous disorders and diseases. Various non-parametric methods for HRV estimation have been developed and extensive literature on their respective properties is available. The RR interval time series can be seen as a series of non-uniformly spaced samples. To analyse the power spectra of this series using the discrete Fourier transform (DFT), we need to interpolate the series for obtaining uniformly spaced intervals. The selection of sampling period plays a critical role in obtaining the power spectra in terms of computational efficiency and accuracy. In this paper, we shall analyse the RR interval time series from selected subjects for different sampling frequencies to compare the error introduced in selected frequency-domain measures of HRV at a constant frequency resolution for a specific duration of electrocardiogram (ECG) data. It should be pointed out that, although many other error causes are possible in the frequency-domain measures, our attention will be confined only to the performance comparison due to the different sampling frequencies. While the choice of RR interval sampling frequency (f(s)) is arbitrary, the sampling rate of RR interval series must be selected with due consideration to mean and minimum RR interval; f(s = )4 Hz was proposed for a majority of cases. This is an appropriate sampling rate for the study of autonomic regulation, since it enables us to compute reliable spectral estimates between dc and 1 Hz, which represents the frequency band within which the autonomic nervous system has significant response. Furthermore, resampled RR intervals are evenly spaced in time and are synchronized with the samples of the other physiologic signals, enabling cross-spectral estimates with these signals.
86 citations
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TL;DR: This paper provides a taxonomy of various ant colony algorithms with advantages and disadvantages of each others with respect to various metrics.
86 citations
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TL;DR: In this article, a simulative analysis of 40-bit-rate DWDM system with ultra high capacity upto 1.28-Tb/s has been carried out for carrier-suppressed return-to-zero (CSRZ), duobinary return to zero (DRZ) and modified duobbinary return-of-zero modulation formats.
86 citations
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TL;DR: A stochastic neural network model based on the random walk theory is proposed for Cryptocurrency price prediction that induces layer-wise randomness into the observed feature activations of neural networks to simulate market volatility.
Abstract: Over the past few years, with the advent of blockchain technology, there has been a massive increase in the usage of Cryptocurrencies. However, Cryptocurrencies are not seen as an investment opportunity due to the market's erratic behavior and high price volatility. Most of the solutions reported in the literature for price forecasting of Cryptocurrencies may not be applicable for real-time price prediction due to their deterministic nature. Motivated by the aforementioned issues, we propose a stochastic neural network model for Cryptocurrency price prediction. The proposed approach is based on the random walk theory, which is widely used in financial markets for modeling stock prices. The proposed model induces layer-wise randomness into the observed feature activations of neural networks to simulate market volatility. Moreover, a technique to learn the pattern of the reaction of the market is also included in the prediction model. We trained the Multi-Layer Perceptron (MLP) and Long Short-Term Memory (LSTM) models for Bitcoin, Ethereum, and Litecoin. The results show that the proposed model is superior in comparison to the deterministic models.
86 citations
Authors
Showing all 3035 results
Name | H-index | Papers | Citations |
---|---|---|---|
Gaurav Sharma | 82 | 1244 | 31482 |
Vinod Kumar | 77 | 815 | 26882 |
Neeraj Kumar | 76 | 587 | 18575 |
Ashish Sharma | 75 | 909 | 20460 |
Dinesh Kumar | 69 | 1333 | 24342 |
Pawan Kumar | 64 | 547 | 15708 |
Harish Garg | 61 | 311 | 11491 |
Rafat Siddique | 58 | 183 | 11133 |
Surya Prakash Singh | 55 | 736 | 12989 |
Abhijit Mukherjee | 55 | 378 | 10196 |
Ajay Kumar | 53 | 809 | 12181 |
Soumen Basu | 45 | 247 | 7888 |
Sudeep Tanwar | 43 | 263 | 5402 |
Yosi Shacham-Diamand | 42 | 287 | 6463 |
Rupinder Singh | 42 | 458 | 7452 |