UTILIZING DEEP LEARNING TECHNIQUES FOR DETECTING AND ANALYZING FOOD ALLERGIES

Authors

  • Junaid Ahmad Department of Computer Science, NFCIET, Multan, Pakistan. Author
  • Naeem Aslam Department of Computer Science, NFCIET, Multan, Pakistan. Author
  • Mian Abdul Qadeer Ahmad Khan Department of Computer Science, NCBA&E, Multan, Pakistan. Author
  • Muhammad Kamran Abid Department of Computer Science, NFCIET, Multan, Pakistan. Author
  • Muhammad Fuzail Department of Computer Science, NFCIET, Multan, Pakistan. Author
  • Nasir Umar Department of Computer Science, NFCIET, Multan, Pakistan. Author
  • Talha Farooq Khan Department of Computer Science, USP, Multan, Pakistan. Author

DOI:

https://doi.org/10.71146/kjmr332

Keywords:

Deep Learning, Food Allergens, Hybrid Ensemble Model, Data Normalization

Abstract

There are promising trends in using deep learning approaches for identifying and studying food allergies which are increasingly becoming a major health concern in developed countries where millions of people suffer from some form of food allergy. Currently, ELISA and PCR are often used for the identification of food allergens, but both the methods are cumbersome and lack specificity and sensitivity, and therefore unsuitable for the largescale food allergen monitoring. This research aims at assessing the effectiveness of deep learning models such as CNN and RNN in enhancing the classification and identification of food allergens. Stellar such problems as data imbalance, the presence of low-quality datasets, and the interpretability of the generated models are solved by employing ensemble learning methods that can include Boosting and Bagging. These techniques take best characteristic from individual models to improve the prediction, and the hybrid models demonstrated higher ability in identifying allergens. Different techniques such as data cleaning and data normalization were applied on a data set obtained from Kaggle in order to build a better dataset for model building. The Hybrid Ensemble Model clearly yields higher accuracy, precision and F1 score than the other benchmark models like Logistic Regression and SVM. This paper reveals the viability of enumerating allergens using deep learning techniques with high accuracy, that can be implemented in industries and mark enhance consumer safety.

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Published

2025-03-09

Issue

Section

Engineering and Technology

How to Cite

UTILIZING DEEP LEARNING TECHNIQUES FOR DETECTING AND ANALYZING FOOD ALLERGIES. (2025). Kashf Journal of Multidisciplinary Research, 2(03), 46-60. https://doi.org/10.71146/kjmr332

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