Compare and evaluate AI models for automatically classifying and categorizing URIs

Authors

  • Pooja Tiwari Department of Computer Science and Engineering, Jharkhand University of Technology, Ranchi, Jharkhand, India 834010
  • Ravi Kumar Burman Department of Computer Science and Engineering, Jharkhand University of Technology, Ranchi, Jharkhand, India 834010
  • Abhishek Kumar Department of Computer Science and Engineering, Jharkhand University of Technology, Ranchi, Jharkhand, India 834010

DOI:

https://doi.org/10.57159/jcmm.4.2.25196

Keywords:

Upper Respiratory Infections, Artificial Intelligence, Convolutional Neural Networks, DenseNet, ResNet-50

Abstract

URIs, or upper respiratory infections, are among the most prevalent illnesses. Nevertheless, a thorough assessment of the associated burden has not been conducted.Thus, this study's goal is to outline the global and regional burden of URIs. In environments with limited resources, artificial intelligence (AI) systems that use symptoms and signals to identify URTI (upper respiratory tract infection), Pneumonia Bronchiectasis, Bronchiolitis with the help of Such AI systems heterogeneity makes performance analysis necessary to guide future research. Strong evidence exists to encourage more research into machine learning's ability to automatically identify pneumonia based on symptoms and indicators that are easily recognized. Based on the results of this study, suggestions are given for developing and utilizing AI tools, which should enhance the effectiveness of subsequent research.

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Published

30-04-2025

How to Cite

Tiwari, P., Burman, R. K., & Kumar, A. (2025). Compare and evaluate AI models for automatically classifying and categorizing URIs. Journal of Computers, Mechanical and Management, 4(2), 24–29. https://doi.org/10.57159/jcmm.4.2.25196

Issue

Vol. 4 No. 2 (2025)

Section

Original Articles

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