Improved Cattle Disease Diagnosis Based on Fuzzy Logic Algorithms

Mustapoevich, Dilmurod Turimov; Tulkunovna, Dilnoz Muhamediyeva; Safarova, Lola; Primova, Holida; Kim, Wooseong

doi:10.3390/s23042107

Cited by 14 publications

(8 citation statements)

References 37 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…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: 91%

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)

View full text Add to dashboard Cite

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

show abstract

Section: Resultsmentioning

confidence: 91%

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)

View full text Add to dashboard Cite

show abstract

“…The application of fuzzy logic approaches may improve the accuracy of sarcopenia detection by accounting for the inherent imprecision in defining this syndrome. The fuzzy set theory provides mathematical formalism to handle such linguistic uncertainty [ 115 ]. Additionally, deep learning techniques like convolutional neural networks (CNNs) offer powerful pattern recognition capabilities that are well-suited for medical imaging applications.…”

Section: Discussionmentioning

confidence: 99%

Machine Learning Applications in Sarcopenia Detection and Management: A Comprehensive Survey

Turimov Mustapoevich,

Kim

2023

Healthcare

Self Cite

View full text Add to dashboard Cite

This extensive review examines sarcopenia, a condition characterized by a loss of muscle mass, stamina, and physical performance, with a particular emphasis on its detection and management using contemporary technologies. It highlights the lack of global agreement or standardization regarding the definition of sarcopenia and the various techniques used to measure muscle mass, stamina, and physical performance. The distinctive criteria employed by the European Working Group on Sarcopenia in Older People (EWGSOP) and the Asian Working Group for Sarcopenia (AWGSOP) for diagnosing sarcopenia are examined, emphasizing potential obstacles in comparing research results across studies. The paper delves into the use of machine learning techniques in sarcopenia detection and diagnosis, noting challenges such as data accessibility, data imbalance, and feature selection. It suggests that wearable devices, like activity trackers and smartwatches, could offer valuable insights into sarcopenia progression and aid individuals in monitoring and managing their condition. Additionally, the paper investigates the potential of blockchain technology and edge computing in healthcare data storage, discussing models and systems that leverage these technologies to secure patient data privacy and enhance personal health information management. However, it acknowledges the limitations of these models and systems, including inefficiencies in handling large volumes of medical data and the lack of dynamic selection capability. In conclusion, the paper provides a comprehensive summary of current sarcopenia research, emphasizing the potential of modern technologies in enhancing the detection and management of the condition while also highlighting the need for further research to address challenges in standardization, data management, and effective technology use.

show abstract

“…This may result in the creation of more intricate models, making interpretation more challenging. [7][8][9][10][11][12].…”

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)

View full text Add to dashboard Cite

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.

show abstract

Improved Cattle Disease Diagnosis Based on Fuzzy Logic Algorithms

Cited by 14 publications

References 37 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

Machine Learning Applications in Sarcopenia Detection and Management: A Comprehensive Survey

Utilizing ensemble learning methods for the classification of forest cover types

Contact Info

Product

Resources

About