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23 results on '"Malik, Parveen"'

1. Classification of Insect Pest Using Transfer Learning Mechanism

Author

Malik, Parveen, Parida, Manoj Kumar, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Kaur, Harkeerat, editor, Jakhetiya, Vinit, editor, Goyal, Puneet, editor, Khanna, Pritee, editor, Raman, Balasubramanian, editor, and Kumar, Sanjeev, editor

Published
2024
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3. Blind Quality Assessment of PFA-Affected Images Based on Chromatic Eigenvalue Ratio

Author

Karthik, Kannan, Malik, Parveen, Kacprzyk, Janusz, Series Editor, Pal, Nikhil R., Advisory Editor, Bello Perez, Rafael, Advisory Editor, Corchado, Emilio S., Advisory Editor, Hagras, Hani, Advisory Editor, Kóczy, László T., Advisory Editor, Kreinovich, Vladik, Advisory Editor, Lin, Chin-Teng, Advisory Editor, Lu, Jie, Advisory Editor, Melin, Patricia, Advisory Editor, Nedjah, Nadia, Advisory Editor, Nguyen, Ngoc Thanh, Advisory Editor, Wang, Jun, Advisory Editor, Chaudhuri, Bidyut B., editor, Nakagawa, Masaki, editor, Khanna, Pritee, editor, and Kumar, Sanjeev, editor

Published
2020
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4. WheatQTLdb: a QTL database for wheat

Author

Singh, Kalpana, Batra, Ritu, Sharma, Shiveta, Saripalli, Gautam, Gautam, Tinku, Singh, Rakhi, Pal, Sunita, Malik, Parveen, Kumar, Manoj, Jan, Irfat, Singh, Sahadev, Kumar, Deepak, Pundir, Saksham, Chaturvedi, Deepti, Verma, Anjali, Rani, Anshu, Kumar, Anuj, Sharma, Hemant, Chaudhary, Jyoti, Kumar, Kuldeep, Kumar, Sourabh, Singh, Vikas Kumar, Singh, Vivudh Pratap, Kumar, Sachin, Kumar, Rahul, Gaurav, Shailendra Singh, Sharma, Shailendra, Sharma, Pradeep Kumar, Balyan, Harindra Singh, and Gupta, Pushpendra Kumar

Published
2021
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10. Correction to: WheatQTLdb: a QTL database for wheat

Author

Singh, Kalpana, Batra, Ritu, Sharma, Shiveta, Saripalli, Gautam, Gautam, Tinku, Singh, Rakhi, Pal, Sunita, Malik, Parveen, Kumar, Manoj, Jan, Irfat, Singh, Sahadev, Kumar, Deepak, Pundir, Saksham, Chaturvedi, Deepti, Verma, Anjali, Rani, Anshu, Kumar, Anuj, Sharma, Hemant, Chaudhary, Jyoti, Kumar, Kuldeep, Kumar, Sourabh, Singh, Vikas Kumar, Singh, Vivudh Pratap, Kumar, Sachin, Kumar, Rahul, Gaurav, Shailendra Singh, Sharma, Shailendra, Sharma, Pradeep Kumar, Balyan, Harindra Singh, and Gupta, Pushpendra Kumar

Published
2021
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17. Additional file 1 of Multi-locus genome-wide association mapping for spike-related traits in bread wheat (Triticum aestivum L.)

Author

Malik, Parveen, Kumar, Jitendra, Sharma, Shiveta, Sharma, Rajiv, and Sharma, Shailendra

Abstract

Additional file 1: Table S1: List of 225 spring wheat reference set genotypes. Table S2: Comparative analysis of environmental factors in two different environments/years using t-test. Fig. S1. The biological meaning of spike layer uniformity (the consistency of the spike distribution in the vertical space) related traits (SLURTs) shown with wheat plants having similar tillers height (a) and different tiller height (b). In (a) only one spike layer can be seen from the vertical perspective (SLN = 1) as spikes per plant have consistent vertical distribution (SLU = 1) and the SLT was identical to one spike length (SL) whereas in (b) two spike layer can be seen from the vertical perspective (SLN = 2) as spikes per plant have inconsistent vertical distribution (SLU = 0.5) and the SLT was identical to two spike length. A detailed description of SLURTs is provided in Table 1. Note: In the figure, immature wheat plants are shown just to illustrate the biological meaning of SLURTs. However, data for SLURTs were recorded on mature plants. In the figure scaling is approximate. The figure is based on the publication by Zhao et al. [12]. Fig. S2. Pairwise Pearson correlation among the eight SLURTs in E1 and grain yield (GYPP). A single asterisk (*) represents 0.05 level of significance. A double asterisk (**) represents 0.01 level of significance and a triple asterisk (***) represents 0.001 level of significance. Fig. S3. Pairwise Pearson correlation among the eight SLURTs in E2 and grain yield (GYPP). A single asterisk (*) represents 0.05 level of significance. A double asterisk (**) represents 0.01 level of significance and a triple asterisk (***) represents 0.001 level of significance. Fig. S4. Pairwise Pearson correlation among the eight SLURTs and grain yield (GYPP) for the combined E1 and E2 environments. A single asterisk (*) represents 0.05 level of significance. A double asterisk (**) represents 0.01 level of significance and a triple asterisk (***) represents 0.001 level of significance. Fig. S5. Manhattan plots obtained after using FarmCPU for eight SLURTs (a) GWPS (b) LTH (c) NSPP (d) NSPS (e) SL (f) SLN (g) SLT (h) SLU under E1 (above numbers) and E2 (below numbers) conditions. Numbers correspond to wheat chromosome. Fig. S6. QQ-plots to visualize the deviation of observed p values from expected p values (based on null hypothesis) for eight SLURTs (a) GWPS (b) LTH (c) NSPP (d) NSPS (e) SL (f) SLN (g) SLT (h) SLU under E1 and E2 conditions. Fig. S7 (i-v). Comparison of MTAs or MTA groups, detected in the present study, with historical QTLs/MTAs in different chromosomes; MTAs detected in known flanking regions or locations of QTLs/MTAs for same traits or grain yield are depicted in green color and MTAs found near to flanking regions or locations (less than 50 Mb) of QTLs/MTAs depicted in red colour. Flanking markers are shown with the same colour and with the reference details of the previous study. Novel MTAs detected in the present study are presented without any colour. The corresponding physical distances (Mb) of the QTL/MTA regions on each chromosome were obtained by blasting the flanking sequences of markers, depicted on the right side of each figure, to the Chinese Spring RefSeq v1.0. GY; grain yield, TN; tiller number.

Published
2021
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20. Iterative content adaptable purple fringe detection.

Author

Malik, Parveen and Karthik, Kannan

Abstract

In some cameras, defects in the sensor grid induce fringing artifacts near high-contrast regions. This false coloration is usually purple in color and is termed as a purple fringing aberration (PFA). Since PFA effects find applications in image forensics, it becomes important to discover ways to detect these fringes reliably and then use them for further analysis. Much of the literature rely on static gradient and saturation thresholds, selected through progressive experimentation for localizing these fringes. Given the spectral diversity associated with these fringes over a wide variety of natural images, it becomes progressively difficult to find a specific choice of parameters for detecting these fringe affected pixels. Our contributions are twofold: In the first part, we propose a content adaptive relative threshold-based PFA detection procedure which is insular and does not require any form of external training or tuning. In cases, where the fringes are mixed with background texture and this mixture exhibits extreme gradient magnitude profile variations, the proposed baseline approach demands manual tuning. To overcome this problem and to ensure complete automation, an iterative extension of the same baseline algorithm based on region growing is proposed. [ABSTRACT FROM AUTHOR]

Published
2018
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23. Descriptive epidemiology of equine influenza in India (2008-2009): temporal and spatial trends.

Author

Virmani N, Bera BC, Gulati BR, Karuppusamy S, Singh BK, Kumar Vaid R, Kumar S, Kumar R, Malik P, Khurana SK, Singh J, Manuja A, Dedar R, Gupta AK, Yadav SC, Chugh PK, Narwal PS, Thankur VL, Kaul R, Kanani A, Rautmare SS, and Singh RK

Subjects
Animals, India epidemiology, Space-Time Clustering, Disease Outbreaks, Influenza A Virus, H3N8 Subtype, Orthomyxoviridae Infections epidemiology
Abstract

Equine influenza is a contagious viral disease that affects all members of the family Equidae, i.e., horses, donkeys and mules. The authors describe the pattern of equine influenza outbreaks in a number of states of India from July 2008 to June 2009. The disease was first reported in June 2008 in Katra (Jammu and Kashmir) and spread to ten other states within a year. All outbreaks of equine influenza in the various states were confirmed by laboratory investigations (virus isolation and/or serological confirmation based on haemagglutination inhibition [HI] assays of paired samples) before declaring them as equine influenza virus-affected state(s). The virus (H3N8) was reported from various locations in the country including Katra, Mysore (Karnataka), Ahmedabad (Gujarat), Gopeshwar and Uttarkashi (Uttarakhand) and was isolated in 9- to 11-day-old embryonated chicken eggs. The virus was confirmed as H3N8 by HI assays with standard serum and amplification of full-length haemagglutinin and neuraminidase genes by reverse transcriptase-polymerase chain reaction. Serum samples (n = 4 740) of equines from 13 states in India screened by HI revealed 1074 (22.65%) samples as being positive for antibodies to equine influenza virus (H3N8).

Published
2010

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