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Anil Rajput

Bio: Anil Rajput is an academic researcher from Indian Institute of Information Technology, Design and Manufacturing, Jabalpur. The author has contributed to research in topics: Microstrip & Static random-access memory. The author has an hindex of 6, co-authored 49 publications receiving 101 citations. Previous affiliations of Anil Rajput include P.G. College & Indian Institute of Information Technology and Management, Gwalior.


Papers
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Journal ArticleDOI
05 Jan 2021-BMJ Open
TL;DR: In this article, the authors have studied the percentage seropositivity for SARS-CoV-2 to understand the pandemic status and predict the future situations in Ahmedabad.
Abstract: Objectives To study the percentage seropositivity for SARS-CoV-2 to understand the pandemic status and predict the future situations in Ahmedabad. Study design Cross-sectional study. Settings Field area of Ahmedabad Municipal Corporation. Participants More than 30 000 individuals irrespective of their age, sex, acute/past COVID-19 infection participated in the serosurvey which covered all the 75 Urban Primary Health Centres (UPHCs) across 48 wards and 7 zones of the city. Study also involved healthcare workers (HCWs) from COVID-19/non-COVID-19 hospitals. Interventions Seropositivity of IgG antibodies against SARS-CoV-2 was measured as a mark of COVID-19 infection. Primary and secondary outcomes Seropositivity was used to calculate cumulative incidence. Correlation of seropositivity with available demographic detail was used for valid and precise assessment of the pandemic situation. Results From 30 054 samples, the results were available for 29 891 samples and the crude seropositivity is 17.61%. For all the various age groups, the seropositivity calculated between 15% and 20%. The difference in seropositivity for both the sex group is statistically not significant. The seropositivity is significantly lower (13.64%) for HCWs as compared with non-HCWs (18.71%). Seropositivity shows increasing trend with time. Zone with maximum initial cases has high positivity as compared with other zones. UPHCs with recent rise in cases are leading in seropositivity as compared with earlier and widely affected UPHCs. Conclusions The results of serosurveillance suggest that the population of Ahmedabad is still largely susceptible. People still need to follow preventive measures to protect themselves till an effective vaccine is available to the people at large. The data indicate the possibility of vanishing immunity over time and need further research to cross verify with scientific evidences.

20 citations

DOI
01 Jan 2014
TL;DR: This paper presents a Classification data model using decision tree for the purpose of analyzing water quality data of MAA Narmada River at Harda district and observed in the analysis that the Nitrogen, pH, Temp, BOD, COD, other parameter relevant to water processes play an important role to assess the quality of river water.
Abstract: This paper presents a Classification data model using decision tree for the purpose of analyzing water quality data of MAA Narmada River at Harda district. The data model was implemented in WEKA software. Classification using decision tree was applied to classify /predict the pollutant class of water. It is observed in the analysis that the Nitrogen (NH3_N ,NO3_N), pH ,Temp _C, BOD, COD, other parameter relevant to water processes play an important role to assess the quality of river water. In this experiment we have used five attribute of water quality data which can affect accuracy of water.

18 citations

Proceedings ArticleDOI
05 Jun 2020
TL;DR: This paper presents an In memory computational methodology and arithmetic circuit co- designs using 8T SRAM cell and proposed sensing scheme and mapped half adder and half subs tractor NOR net-list into SRAM memory array to verify their functionality.
Abstract: The current computing system based on von-Neumann architecture is facing a memory wall, power wall, instruction parallelism wall. These walls of the current computing system have been a significant impact on computing efficiency of computing systems in the present time due to high prominence on Data insensitive applications. Computation In Memory (CIM) architecture is one of the emerging architecture for computation to break these three walls. In this paper, we present an In memory computational methodology and arithmetic circuit co- designs using 8T SRAM cell. The boolean logic operation and arithmetic functions are demonstrated with 8T SRAM cell in 180 nm CMOS technology. The NAND, AND, NOR, OR boolean logics are demonstrate using 8T SRAM cells with the proposed sensing scheme to verifying the In-Memory computations ability of 8T SRAM cells. This proposed sensing scheme with 8T SRAM cell provides an energy improvement of 26.4% over 8+T SRAM based IMC and also offer a high sense margin of NAND operation. Finally, for implementation of the arithmetic circuit, SRAM memory array has to be designed using the 8T SRAM cell and proposed sensing scheme and mapped half adder and half subs tractor NOR net-list into SRAM memory array and verify their functionality.

15 citations

Journal ArticleDOI
TL;DR: In this paper, a low profile circularly polarized high directive dielectric Resonator Antenna (DRA) is presented for X-band wireless applications, which is excited by microstrip aperture slot coupling which is employed on the bottom side of the substrate.
Abstract: A low profile circularly polarized high directive Dielectric Resonator Antenna (DRA) is presented for X - band wireless applications. DRA is excited by microstrip aperture slot coupling which is employed on the bottom side of the substrate. Two asymmetric rectangular split rings are created adjacent to the feed line on the substrate to enhance the 3-dB axial ratio bandwidth and impedance bandwidth. Corrugated circular ring shaped single layer double sided meta superstrate is loaded on the DRA to enhance the peak gain to 11.9 dBi. The extracted lumped element model of the Superstrate unit is found to be in concurrence with Electromagnetic (EM) simulations. The proposed geometry offers a 1.1 GHz axial ratio bandwidth with 2.6 GHz impedance bandwidth. A prototype is fabricated and experimentally verified.

11 citations


Cited by
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01 Jan 2002

9,314 citations

Journal ArticleDOI
TL;DR: In this paper, a coupled CNN-LSTM model was proposed to predict water quality variables, namely dissolved oxygen (DO; mg/L) and chlorophyll-a (Chl-a; µg/L), in the Small Prespa Lake in Greece.
Abstract: Water quality monitoring is an important component of water resources management. In order to predict two water quality variables, namely dissolved oxygen (DO; mg/L) and chlorophyll-a (Chl-a; µg/L) in the Small Prespa Lake in Greece, two standalone deep learning (DL) models, the long short-term memory (LSTM) and convolutional neural network (CNN) models, along with their hybrid, the CNN–LSTM model, were developed. The main novelty of this study was to build a coupled CNN–LSTM model to predict water quality variables. Two traditional machine learning models, support-vector regression (SVR) and decision tree (DT), were also developed to compare with the DL models. Time series of the physicochemical water quality variables, specifically pH, oxidation–reduction potential (ORP; mV), water temperature (°C), electrical conductivity (EC; µS/cm), DO and Chl-a, were obtained using a sensor at 15-min intervals from June 1, 2012 to May 31, 2013 for model development. Lag times of up to one (t − 1) and two (t − 2) for input variables pH, ORP, water temperature, and EC were used to predict DO and Chl-a concentrations, respectively. Each model’s performance in both training and testing phases was assessed using statistical metrics including the correlation coefficient (r), root mean square error (RMSE), mean absolute error (MAE), their normalized equivalents (RRMSE, RMAE; %), percentage of bias (PBIAS), Nash–Sutcliffe coefficient ($$E_{NS}$$), Willmott’s Index, and graphical plots (Taylor diagram, box plot and spider diagram). Results showed that LSTM outperformed the CNN model for DO prediction, but the standalone DL models yielded similar performances for Chl-a prediction. Generally, the hybrid CNN–LSTM models outperformed the standalone models (LSTM, CNN, SVR and DT models) in predicting both DO and Chl-a. By integrating the LSTM and CNN models, the hybrid model successfully captured both the low and high levels of the water quality variables, particularly for the DO concentrations.

182 citations

01 Apr 2004
TL;DR: The idea of intuitionistic fuzzy set due to Atanassov is defined as a natural generalization of fuzzy metric spaces due to George and Veeramani and some known results of metric spaces including Baire's theorem and the Uniform limit theorem are proved.
Abstract: Using the idea of intuitionistic fuzzy set due to Atanassov, we define the notion of intuitionistic fuzzy metric spaces as a natural generalization of fuzzy metric spaces due to George and Veeramani and prove some known results of metric spaces including Baire's theorem and the Uniform limit theorem for intuitionistic fuzzy metric spaces.

139 citations