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Mohammad Junayed Bhuyan on Muck Rack

Mohammad Junayed Bhuyan

As seen in: Color Research & Application, Nature, PLOS, MDPI
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Articles

Recognition of eating episodes via commercial smartwatch sensors analysis

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By Luca Vedovelli, Mohammad Junayed Bhuyan, BIOSTAT-X Biostatistics
| PLOS
Loading metrics Open Access Peer-reviewed Research Article Citation: Vedovelli L, Bhuyan MJ, Lanera C, Baldi I, Berchialla P, Gregori D, et al. (2026) Recognition of eating episodes via commercial smartwatch sensors analysis. PLOS Digit Health 5(7): e0001539. https://doi.org/10.1371/journal.pdig.0001539 Editor: Michael Winter, ETH Zurich, SWITZERLAND Received: February 25, 2026; Accepted: June 15, 2026; Published: July 7, 2026 Copyright: © 2026 Vedovelli et al.
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Model architecture dominates nutritional estimation accuracy in vision-language systems

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By Luca Vedovelli, Sofia Pugnaloni, Corrado Lanera, Daniele Sabbatini, Mohammad Junayed Bhuyan, Giulia Lorenzoni, Dario Gregori
| Nature Verified
Abstract Vision-language models (VLMs) enable automated dietary assessment from food photographs, yet optimal deployment strategies remain unestablished. We evaluated 40 VLMs across eight providers using Nutrition5k database and original dishes pictures, testing image configurations (1–5 angles), prompt strategies (7 approaches), and image quality conditions, benchmarked against professional nutritionists. Model architecture dominated performance variance (99.6%).
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Classifying Food Items During an Eating Occasion: A Machine Learning Approach with Slope Dynamics for Windowed Kinetic Data

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By Ileana Baldi, Corrado Lanera, Mohammad Junayed Bhuyan, Paola Berchialla
| MDPI
All articles published by MDPI are made immediately available worldwide under an open access license. No special permission is required to reuse all or part of the article published by MDPI, including figures and tables. For articles published under an open access Creative Common CC BY license, any part of the article may be reused without permission provided that the original article is clearly cited. For more information, please refer to https://www.mdpi.com/openaccess.
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