Title: A novel blockchain approach to identify child malnutrition using residual pyramid forward fractional network

Authors: Prateeksha Chouksey; Prasadu Peddi; Sandeep Kadam

Addresses: Department of Computer Engineering, Shri Jagdishprasad Jhabarmal Tibrewala University, Jhunjhunu, Rajasthan, 333010, India ' Department of CSE and IT, Shri Jagdishprasad Jhabarmal Tibrewala University, Jhunjhunu, Rajasthan, 333010, India ' Keystone School of Engineering, Pune, Maharashtra, 412308, India

Abstract: Malnutrition occurs due to a lack of nutrients, which is often curable with early detection. However, detecting malnutrition at an early stage is challenging and ineffective in various techniques. Overfitting also affects the previous method's performance. Therefore, here the residual pyramid forward fractional network (RPFF-Net) is developed for child malnutrition. Initially, the child's health is recorded in the blocks of the blockchain. Then, the recorded data is saved in the cloud server, which holds the tracking architecture. Next, the data is normalised by Z-score normalisation. After that, the features are selected by employing an ensemble-based model with mutual information, information gain (IG), and recursive feature elimination (RFE). After that, nutrition status is tracked by the RPFF-Net approach, which is created by fusing PyramidNet, deep residual network (DRN), and fractional calculus (FC). The RPFF-Net attained a true positive rate (TPR), accuracy, and true negative rate (TNR) of 91.87%, 90.59%, and 90.98%.

Keywords: malnutrition; Z-score normalisation; deep residual network; DNR; pyramid network; fractional calculus; FC.

DOI: 10.1504/IJAMECHS.2026.150485

International Journal of Advanced Mechatronic Systems, 2026 Vol.13 No.1, pp.46 - 59

Received: 03 Aug 2024
Accepted: 09 Jun 2025
Published online: 15 Dec 2025 *

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