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In the ever-evolving field of public health, statistical analysis serves as a cornerstone for understanding and managing disease outbreaks. By leveraging various statistical tools, health professionals can predict potential outbreaks, analyze ongoing situations, and devise effective responses to mitigate impact. For that to happen, there are a few possible stages of the analysis:
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Statistical Analysis System (SAS)01:14

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SAS, short for Statistical Analysis System, is a powerful data analysis, management, and visualization tool. Developed by the SAS Institute in the early 1970s, SAS has evolved into a comprehensive software suite used across various industries for statistical analysis, business intelligence, and predictive modeling.
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A Secure Artificial Intelligence-Enabled Critical Sars Crisis Management Using Random Sigmoidal Artificial Neural

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  • 1School of Politics and Public Administration, Zhenghzhou University, Zhengzhou, China.

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This study introduces an AI-driven framework for rapid COVID-19 diagnosis using CT scans. The novel system aims to improve upon slow RT-PCR testing for faster patient identification.

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Infectious Disease Diagnostics

Background:

  • COVID-19, caused by SARS-CoV-2, presents diagnostic challenges due to symptom overlap with common illnesses.
  • Current RT-PCR testing is time-consuming and faces availability issues, necessitating faster diagnostic methods.
  • Artificial intelligence (AI) offers potential for efficient large-scale healthcare diagnostics.

Purpose of the Study:

  • To propose a novel AI-enabled framework for the rapid detection of SARS-CoV-2 infections.
  • To evaluate the efficacy of the proposed AI model in diagnosing COVID-19 from CT images.
  • To compare the performance of the AI system against existing diagnostic approaches.

Main Methods:

  • CT images were preprocessed using block-matching filters and histogram equalization.
  • Image segmentation was performed using the Compact Entropy Rate Superpixel (CERS) technique.
  • Features were extracted using Histogram of Gradient (HOG), selected via Principal Component Analysis (PCA), and classified using Random Sigmoidal Artificial Neural Networks (RS-ANN).

Main Results:

  • The proposed AI framework successfully diagnosed the presence of COVID-19.
  • The AI model demonstrated potential for faster identification of COVID-19 patients compared to conventional methods.
  • Performance analysis indicated the effectiveness of the RS-ANN classification approach.

Conclusions:

  • The developed AI system offers a promising, rapid alternative for COVID-19 diagnosis.
  • This AI-enabled framework can aid healthcare professionals in quicker patient identification and management.
  • Further research and validation of AI in infectious disease diagnostics are warranted.