Application of Algorithm of Fuzzy Rule Conclusions in Determination of Animal’s Diseases

Primova, Holida; Mukhamedieva, D T; Safarova, Lola

doi:10.1088/1742-6596/2224/1/012007

Cited by 7 publications

(5 citation statements)

References 12 publications

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“…Average values for all classes ( weighted avg ) are also provided. These metrics indicate that the decision tree model performs the classification task well for a given dataset, and the results represent high precision and recall for most classes [12][13][14][15][16].…”

Section: Resultsmentioning

confidence: 92%

Solving the problem of classifying forest cover types based on soil characteristics

Muhamediyeva,

Safarova,

Nabiyeva

2024

Third International Conference on Optics, Computer Applications, and Materials Science (CMSD-III 2023)

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The " covtype " dataset in scikit-learn represents forest cover information and includes a variety of soil characteristics for seven different forest cover types. The proposed work solves the classification problem, where the goal is to accurately determine the type of forest cover based on given soil characteristics. The study uses various machine learning methods such as decision trees and naive Bayes classifier. Models are trained on an extensive training set and then evaluated on test data to determine their ability to accurately predict forest cover types. The classification results are analyzed, including metrics of accuracy, recall, F1-measures, as well as ROC curves are constructed and the areas under them (AUC) are calculated. The results and metrics obtained allow us to compare the effectiveness of different models in solving a given classification problem. The knowledge gained can be useful for the application of machine learning algorithms in ecology and forest resource management

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Section: Resultsmentioning

confidence: 92%

Solving the problem of classifying forest cover types based on soil characteristics

Muhamediyeva,

Safarova,

Nabiyeva

2024

Third International Conference on Optics, Computer Applications, and Materials Science (CMSD-III 2023)

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“…An algorithm for fuzzy inference is developed and the dependence of parameters is considered. To fully introduce fuzzy information, we developed a fuzzy algorithm that uses fuzzy arithmetic in fuzzy inference, which will yield the least loss of information comprising uncertainties in a computational experiment [ 33 , 34 , 35 , 36 ].…”

Section: Resultsmentioning

confidence: 99%

Improved Cattle Disease Diagnosis Based on Fuzzy Logic Algorithms

Mustapoevich

Tulkunovna

Safarova

et al. 2023

Sensors

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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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“…The use of the following notation will help us shorten the record of the fuzzy knowledge base, make it more compact and easy to understand [12][13]:…”

Section: Methodsmentioning

confidence: 99%

Expert systems for diagnostics of infectious diseases in cattle

Muhamediyeva,

Safarova,

Ruzikulov

2023

BIO Web Conf.

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Expert systems designed to identify diseases in cattle may use a variety of sources of information such as clinical data, signs of disease, medical history, laboratory results, environmental exposure and other relevant factors. They can be developed based on rules, heuristics, or machine learning, using a large amount of data to train the model. The expert system may provide the veterinarian with a list of possible diagnoses, ranked by probability, based on the data provided. She may also recommend further investigations or tests for a more accurate diagnosis. The developed expert system for veterinary medicine provides a wide range of functions and capabilities to support the work of veterinarians and processing data on the incidence of cattle.

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Application of Algorithm of Fuzzy Rule Conclusions in Determination of Animal’s Diseases

Cited by 7 publications

References 12 publications

Solving the problem of classifying forest cover types based on soil characteristics

Solving the problem of classifying forest cover types based on soil characteristics

Improved Cattle Disease Diagnosis Based on Fuzzy Logic Algorithms

Expert systems for diagnostics of infectious diseases in cattle

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