Building a fuzzy sugeno model for diagnosing cattle diseases on the basis of developing a knowledge base

Muhamediyeva, Dilnoz; Safarova, Lola; Tukhtamurodov, N.

doi:10.1063/5.0148278

Cited by 9 publications

(1 citation statement)

References 7 publications

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“…Depending on the task at hand, random forest may be preferable if model simplicity and explainability are key factors. While random forest exhibits superiority in this regard, it's important to highlight that the selection between random forest and gradient boosting hinges on the particular needs of the problem and the attributes of the dataset [15][16][17][18][19][20].…”

Section: Report On the Classificationsmentioning

confidence: 99%

Utilizing ensemble learning methods for the classification of forest cover types

Muhamediyeva,

Safarova,

Eshankulov

2024

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

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This work is devoted to the study and application of ensemble methods in the problem of classifying forest cover types based on the " covtype " data set. The paper examines two popular ensemble methods: random forest and gradient boosting . First, data analysis and preprocessing is carried out, including dividing the sample into training and test sets. Then random forest and gradient boosting models are built on the training set. F1-measures, as well as ROC AUC. Results of study shows that both ensemble methods effectively cope with the task of classifying forest cover types. The resulting metrics confirm the high accuracy and ability of the models to generalize to new data. An important step in the research is to compare the performance of random forest and gradient boosting . The work also includes visualization of results such as ROC curves for further exploration and comparison of the two methods. The findings can be useful for choosing the best method in specific scenarios and understanding their applicability in natural data classification problems.

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Section: Report On the Classificationsmentioning

confidence: 99%

Utilizing ensemble learning methods for the classification of forest cover types

Muhamediyeva,

Safarova,

Eshankulov

2024

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

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Clinical and hematological indicators of cows belonging to different selections of the holshtin breed by seasons

Mirsaidova,

Narzullayeva,

Amonov

et al. 2024

BIO Web Conf.

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This paper examines the hematological (erythrocyte, leukocyte, total protein, hemoglobin, glucose) and clinical (respiration, heart rate, body temperature, belly movement) characteristics of Holstein dairy cows from various selections. These results showed that the blood of cows in our republic is saturated with morphological elements due to the same kind of feeding that they receive throughout the seasons, including concentrate feeding.

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Diagnosis of saproligniosis and protozoa of fish and veterinary and sanitary assessment of their meat (Uzbekistan)

Yunusov,

Kurbanov,

Yuldashev

et al. 2024

BIO Web Conf.

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Recent experiments have shown that the pH value of saproligniosis in muasa ryb was found to be 7.3. In our experiments, it was clear that in the study of muasa ryb, b with chylodinilliosis, this indicator was higher (7.4-7.6) than in muasa ryb, infected with Saprolignia mixta. In the prevention of saproligniosis in fish, the high effectiveness of timely use of chloramine B, malachite blue, bleach, slaked lime, sodium chloride solution, formalin-sodium alkali, potassium permanganate, malachite blue has been established. The fish disease trichodinosis is widespread in nature and affects mainly juvenile, one- and two-year-old fish. Good results in the treatment of trichodinosis are obtained by using the bath method with appropriate solutions of table salt, formalin, chlorine preparations and treatment with a mixture of FMS according to 2 schemes. In order to prevent the disease, it is effective to regularly carry out fish reclamation, veterinary, sanitary and therapeutic measures.

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Building a fuzzy sugeno model for diagnosing cattle diseases on the basis of developing a knowledge base

Cited by 9 publications

References 7 publications

Utilizing ensemble learning methods for the classification of forest cover types

Utilizing ensemble learning methods for the classification of forest cover types

Clinical and hematological indicators of cows belonging to different selections of the holshtin breed by seasons

Diagnosis of saproligniosis and protozoa of fish and veterinary and sanitary assessment of their meat (Uzbekistan)

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