Jaypee Institute of Information Technology

About: Jaypee Institute of Information Technology is a education organization based out in Noida, Uttar Pradesh, India. It is known for research contribution in the topics: Cluster analysis & Wireless sensor network. The organization has 2136 authors who have published 3435 publications receiving 31458 citations. The organization is also known as: JIIT Noida.

Performance Analysis of Decode-and-Forward-Based Mixed MIMO-RF/FSO Cooperative Systems With Source Mobility and Imperfect CSI

Abstract: This paper investigates a decode-and-forward-based asymmetric dual-hop mixed radio-frequency/free-space optical (RF/FSO) communication link. We consider a moving source user equipment (UE) that communicates with the relay over an RF link and the relay communicates with the serving base station (eNodeB) over an FSO link, assuming there is no direct link between the UE and eNodeB. It is assumed that the source-to-relay link experiences time-selective Rayleigh fading, that arises due to the UE mobility and the relay-destination link is affected by the optical channel impairments, including path loss, atmospheric turbulence, and pointing errors. As the FSO link has a higher data rate in comparison to the RF link, we employ multiple-input multiple-output with zero-forcing (ZF) based linear receiver in the source-to-relay link, in order to enhance the RF link data rate without increasing the transmit power and bandwidth requirements. Moreover, we assume that the relay node has imperfect channel state information (CSI) for the ZF decoding. For the considered system and channel models, novel closed-form expressions for the per-frame average outage probability, bit error rate, and ergodic capacity are derived and verified using Monte Carlo simulations. Additionally, asymptotic floors for the system outage and error probabilities are derived. It is observed that the time-varying nature of the RF link and imperfect CSI at the relay significantly degrades the end-to-end performance of the system.

Stylometric Analysis for Authorship Attribution on Twitter

Abstract: Authorship Attribution (AA), the science of inferring an author for a given piece of text based on its characteristics is a problem with a long history. In this paper, we study the problem of authorship attribution for forensic purposes and present machine learning techniques and stylometric features of the authors that enable authorship to be determined at rates significantly better than chance for texts of 140 characters or less. This analysis targets the micro-blogging site Twitter, where people share their interests and thoughts in form of short messages called "tweets". Millions of "tweets" are posted daily via this service and the possibility of sharing sensitive and illegitimate text cannot be ruled out. The technique discussed in this paper is a two stage process, where in the first stage, stylometric information is extracted from the collected dataset and in the second stage different classification algorithms are trained to predict authors of unseen text. The effort is towards maximizing the accuracy of predictions with optimum amount of data and users under consideration.

The quantum cryptographic switch

Abstract: We illustrate the principle of a cryptographic switch for a quantum scenario, in which a third party (Charlie) can control to a continuously varying degree the amount of information the receiver (Bob) receives, after the sender (Alice) has sent her information through a quantum channel. Suppose Charlie transmits a Bell state to Alice and Bob. Alice uses dense coding to transmit two bits to Bob. Only if the 2-bit information corresponding to the choice of the Bell state is made available by Charlie to Bob can the latter recover Alice's information. By varying the amount of information Charlie gives, he can continuously alter the information recovered by Bob. The performance of the protocol as subjected to the squeezed generalized amplitude damping channel is considered. We also present a number of practical situations where a cryptographic switch would be of use.

Forecasting spread of COVID-19 using Google Trends: A hybrid GWO-Deep learning approach.

Abstract: The recent outbreak of COVID-19 has brought the entire world to a standstill. The rapid pace at which the virus has spread across the world is unprecedented. The sheer number of infected cases and fatalities in such a short period of time has overwhelmed medical facilities across the globe. The rapid pace of the spread of the novel coronavirus makes it imperative that its' spread be forecasted well in advance in order to plan for eventualities. An accurate early forecasting of the number of cases would certainly assist governments and various other organizations to strategize and prepare for the newly infected cases, well in advance. In this work, a novel method of forecasting the future cases of infection, based on the study of data mined from the internet search terms of people in the affected region, is proposed. The study utilizes relevant Google Trends of specific search terms related to COVID-19 pandemic along with European Centre for Disease prevention and Control (ECDC) data on COVID-19 spread, to forecast the future trends of daily new cases, cumulative cases and deaths for India, USA and UK. For this purpose, a hybrid GWO-LSTM model is developed, where the network parameters of Long Short Term Memory (LSTM) network are optimized using Grey Wolf Optimizer (GWO). The results of the proposed model are compared with the baseline models including Auto Regressive Integrated Moving Average (ARIMA), and it is observed that the proposed model achieves much better results in forecasting the future trends of the spread of infection. Using the proposed hybrid GWO-LSTM model incorporating online big data from Google Trends, a reduction in Mean Absolute Percentage Error (MAPE) values for forecasting results to the extent of about 98% have been observed. Further, reduction in MAPE by 74% for models incorporating Google Trends was observed, thus, confirming the efficacy of utilizing public sentiments in terms of search frequencies of relevant terms online, in forecasting pandemic numbers.