ReviewSensors (Basel, Switzerland)2023
Improved Cattle Disease Diagnosis Based on Fuzzy Logic Algorithms.
Review in Sensors (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- YOLOv9-Based Detection of Diseases in Poplar Trees Using Histogram Equalization and Computer Vision.Sensors (Basel, Switzerland) · 2026Article
- Toward smart health monitoring: multimodal sensing and intelligent disease diagnosis in poultry and livestock.Animal frontiers : the review magazine of animal agriculture · 2026Article
- Adaptive neuro-fuzzy inference systems for improved mastitis classification and diagnosis.Scientific reports · 2025Article
- Developing a Robust Fuzzy Inference Algorithm in a Dog Disease Pre-Diagnosis System for Casual Owners.Animals : an open access journal from MDPI · 2024Article
- Machine Learning Applications in Sarcopenia Detection and Management: A Comprehensive Survey.Healthcare (Basel, Switzerland) · 2023Article
- New Approach for Generating Synthetic Medical Data to Predict Type 2 Diabetes.Bioengineering (Basel, Switzerland) · 2023Article
- A YOLOv6-Based Improved Fire Detection Approach for Smart City Environments.Sensors (Basel, Switzerland) · 2023Article
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Authors and funding
5 authors.
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Abstract
The health and productivity of animals, as well as farmers' financial well-being, can be significantly impacted by cattle illnesses. Accurate and timely diagnosis is therefore essential for effective disease management and control. In this study, we consider the development of models and algorithms for diagnosing diseases in cattle based on Sugeno's fuzzy inference. To achieve this goal, an analytical review of mathematical methods for diagnosing animal diseases and soft computing methods for solving classification problems was performed. Based on the clinical signs of diseases, an algorithm was proposed to build a knowledge base to diagnose diseases in cattle. This algorithm serves to increase the reliability of informative features. Based on the proposed algorithm, a program for diagnosing diseases in cattle was developed. Afterward, a computational experiment was performed. The results of the computational experiment are additional tools for decision-making on the diagnosis of a disease in cattle. Using the developed program, a Sugeno fuzzy logic model was built for diagnosing diseases in cattle. The analysis of the adequacy of the results obtained from the Sugeno fuzzy logic model was performed. The processes of solving several existing (model) classification and evaluation problems and comparing the results with several existing algorithms are considered. The results obtained enable it to be possible to promptly diagnose and perform certain therapeutic measures as well as reduce the time of data analysis and increase the efficiency of diagnosing cattle. The scientific novelty of this study is the creation of an algorithm for building a knowledge base and improving the algorithm for constructing the Sugeno fuzzy logic model for diagnosing diseases in cattle. The findings of this study can be widely used in veterinary medicine in solving the problems of diagnosing diseases in cattle and substantiating decision-making in intelligent systems.
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