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Improved Cattle Disease Diagnosis Based on Fuzzy Logic Algorithms.

Dilmurod Turimov Mustapoevich¹, Dilnoz Muhamediyeva Tulkunovna², Lola Safarova Ulmasovna³

¹Department of IT Convergence Engineering, Gachon University, Sujeong-Gu, Seongnam-Si 461-701, Gyeonggi-Do, Republic of Korea.

Sensors (Basel, Switzerland)

|February 28, 2023

Summary

This study introduces a novel algorithm for building knowledge bases and enhancing Sugeno fuzzy logic models for diagnosing cattle diseases. The developed program aids in prompt diagnosis, reducing data analysis time and improving veterinary decision-making.

Keywords:

decision making expert systems fuzzy sets hypomicroselementosis ketosis osteodystrophy secondary osteodystrophy

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Area of Science:

Veterinary Medicine
Artificial Intelligence
Soft Computing

Background:

Cattle diseases significantly impact animal health, productivity, and farmer economics.
Accurate and timely diagnosis is crucial for effective disease management and control in livestock.
Existing diagnostic methods require enhancement for improved efficiency and reliability.

Conclusions:

The study successfully developed an improved algorithm for constructing Sugeno fuzzy logic models for cattle disease diagnosis.
The findings offer a valuable tool for veterinary medicine, supporting prompt diagnosis and informed decision-making in intelligent systems.
The developed system enhances the efficiency and accuracy of diagnosing cattle diseases.