A. H. Bhat

About

Dr. Ashiq Hussain Bhat (A H Bhat) is a researcher in the field of Statistics\Biostatistics, Medical Statistics, (Information Theory, Coding theory, Fuzzy measures) and presently acting as a Scientist-C in the ICMR-RMRIMS, Patna, (Bihar), India. He has completed his Ph.D. from Department of Statistics, University of Kashmir, Srinagar (J&K), India on topic entitled “Generalization of Fuzzy Divergence Measures and Their Applications”. He has done three online courses regarding Probability and statistics from Swayam. He has published several research articles in reputed international journals of statistical and mathematical sciences with good indexing like Sci, Scopus, Copernicus, Zentralblatt and so on. He has attended more than five training programs and workshops regarding Statistical Software learning for biological and other applied fields of data analysis which include R Studio, SPSS, Minitab, Advanced Excel, Latex software learning for different practical fields. He has attended and presented in more than ten national and international conferences.

Employment

• Rajendra Memorial Research Institute of Medical Sciences Scientist-C
  2023 - Present
• University of Kashmir Research Fellow-II
  2022 - 2023
• Cluster University Srinagar Statistics Lecturer on Academic arrangement Basis
  2022 - 2022
• Cluster University Srinagar Statistics Lecturer on Academic arrangement Basis
  2021 - 2021
• Government of Jammu and Kashmir Statistics Lecturer on Academic arrangement Basis
  2020 - 2020
• Government of Jammu and Kashmir Statistics Lecturer on Academic arrangement Basis
  2019 - 2020
• Government of Jammu and Kashmir Statistics Lecturer on Academic arrangement Basis
  2018 - 2019

Education

• University of Kashmir Ph.D in statistics
  2014 - 2018
• University of Kashmir Masters in Statistics
  2011 - 2013
• University of Kashmir Bachelors in Arts (Mathematics, Statistics, Economics)
  2008 - 2011

Projects & Funding

Projects & funding information is unavailable.

Publications (24)

• Count data statistical models for predicting visceral leishmaniasis (VL)
  Leishmaniasis 2026 DOI: 10.1201/9781003596455-12
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• Systematic Evaluation of Handheld Chest X-ray Image Quality and Diagnostic Adequacy for Tuberculosis Screening: A Generic Protocol (Preprint)
  2026 DOI: 10.2196/preprints.100797
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• Luliconazole Potentiates Efficacy of Miltefosine Against Leishmania donovani
  Drug Development Research 2026 DOI: 10.1002/ddr.70337
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• Sustainable reduction of Cr (VI) by green synthesized chitosan coated copper (II) nanoparticles in batch reactor system: Isotherm, thermodynamics and kinetic study
  International Journal of Biological Macromolecules 2025 DOI: 10.1016/j.ijbiomac.2025.146868
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• Rotavirus Infection in Children Suffering from Diarrhea in Patna, Bihar Region, India: A Hospital-Based Study
  The American Journal of Tropical Medicine and Hygiene 2025 DOI: 10.4269/ajtmh.24-0543
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• Fractal Dimension of Generalized Rényian Entropy With Potential Fractal Dimension Applications to Smart Cities
  2024 DOI: 10.20944/preprints202404.0353.v1
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• Shallow Learning vs. Deep Learning in Anomaly Detection Applications
  The Springer Series in Applied Machine Learning 2024 DOI: 10.1007/978-3-031-69499-8_7
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• Shallow Learning vs. Deep Learning in Finance, Marketing, and e-Commerce
  The Springer Series in Applied Machine Learning 2024 DOI: 10.1007/978-3-031-69499-8_3
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• Shallow Learning vs. Deep Learning in Social Applications
  The Springer Series in Applied Machine Learning 2024 DOI: 10.1007/978-3-031-69499-8_4
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• Generalization of Renyi’s Entropy and its Application in Source Coding
  Applied Mathematics & Information Sciences 2023 DOI: 10.18576/amis/170521
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