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Title: Optimizing infectious disease diagnostics through AI-driven hybrid decision making structures based on image analysis

Contributor:

Woźniak, Marcin - ed. ; Kumar, Yogesh - ed. ; Ijaz, Muhammad Fazal - ed.

Subtitle:

.

Group publication title:

AMCS, volume 34 (2024)

Abstract:

Infectious diseases significantly impact global mortality rates, with their complex symptoms complicating the assessment and determination of infection severity. Various countries grapple with different forms of these diseases. This research utilizes three AI-based decision-making techniques to refine diagnostic processes through the analysis of medical imagery. The goal is achieved by developing a mathematical model that identifies potential infectious diseases from medical images, adopting a multi-criteria decision-making approach. ; The avant-garde, AI-centric methodologies are introduced, harnessing an innovative amalgamation of hypersoft sets in a fuzzy context. Decision-making might include recommendations for isolation, quarantine in domestic or specialized environments, or hospital admission for treatment. Visual representations are used to enhance comprehension and underscore the importance and efficacy of the proposed method. The foundational theory and outcomes associated with this innovative approach indicate its potential for broad application in areas like machine learning, deep learning, and pattern recognition.

Publisher:

Zielona Góra: Uniwersytet Zielonogórski

Resource Identifier:

oai:zbc.uz.zgora.pl:87158

DOI:

10.61822/amcs-2024-0037

Pages:

549-563

Source:

AMCS, volume 34, number 4 (2024) ; click here to follow the link

Language:

eng

License CC BY 4.0:

click here to follow the link

Rights:

Biblioteka Uniwersytetu Zielonogórskiego

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