Amitava Choudhury

Employment

• University of Petroleum and Energy Studies Assistant Professor
  2016 - 2021
• IMS Unison University Assistant Professor
  2015 - 2016
• Indian Institute of Engineering Science and Technology, Shibpur PhD Scholar
  2013 - 2020
• Nimas College Lecturer
  2008 - 2011
• Pandit Deendayal Energy University

Education

• Jadavpur University Faculty of Engineering and Technology M.Tech
  2011 - 2013

Projects & Funding

Projects & funding information is unavailable.

Publications (20)

• Severity-aware multi-stage fusion framework for leaf disease segmentation, classification, and captioning
  Results in Engineering 2026 DOI: 10.1016/j.rineng.2026.112456
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• Breaking data barriers: advancing phase prediction in high entropy alloys through a new machine learning framework
  Canadian Metallurgical Quarterly 2025 DOI: 10.1080/00084433.2024.2395674
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• Internet of Things (IoT)-Based Smart Cities and the Challenges: A Review Study Using Bibliometric Analysis
  2025 DOI: 10.1007/978-981-96-6303-3_17
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• Unveiling the potential of a discrete titania chemiresistor: broad-spectrum sensing of C1–C4 alcohols and precise C3 isomer discrimination in binary mixtures
  Nanoscale 2025 DOI: 10.1039/D5NR02854J
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• Vulnerabilities and Security Challenges on the Internet of Things Devices
  2025 DOI: 10.1007/978-981-96-6297-5_37
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• In pursuit of a suitable machine learning algorithm for hardness prediction of aluminium alloy
  Engineering Computations 2023 DOI: 10.1108/EC-04-2022-0221
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• Prediction and Analysis of Mechanical Properties of Low Carbon Steels Using Machine Learning
  Journal of The Institution of Engineers (India): Series D 2022 DOI: 10.1007/s40033-022-00328-y
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• The Components of Big Data and Knowledge Management Will Change Radically How People Collaborate and Develop Complex Research
  2022 DOI: 10.4018/978-1-6684-3662-2.ch077
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• Random Forest Regression-Based Machine Learning Model for Accurate Estimation of Fluid Flow in Curved Pipes
  Processes 2021 DOI: 10.3390/pr9112095
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• Processing Technique Selection for Steels Based on Mechanical Properties Using Machine Learning Framework
  2021 DOI: 10.21203/rs.3.rs-336843/v1
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