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Performance Comparison of Deep and Machine Learning Approaches Toward COVID-19 Detection

Buket İşler

The coronavirus (COVID-19) is a disease declared a global pan-demic that threatens the whole world. Since then, research has accelerated and varied to find practical solutions for the early detection and correct identification of this disease. Several researchers have focused on using the potential of Artificial Intelligence (AI) techniques in disease diagnosis to diagnose and detect the coronavirus. This paper developed deep learning (DL) and machine learning (ML)-based models using laboratory findings to diagnose COVID-19. Six different methods are used in this study: K -nearest neighbor (KN ...Daha fazlası

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Cervical cancer risk assessment using machine learning integrated fuzzy MCDM methodology

Fatih Şahin

Cervical cancer is entirely preventable if diagnosed at an early stage; however, the current rate of cervical cancer screening participation is not very adequate, and early detection approaches are still open and demanding. Evaluating the risk levels of potential patients in a practical and economic way is crucial to direct risky candidates to screening and establishing potential treatments to conquer the disease. In this study, a machine learning-integrated fuzzy multi-criteria decision-making (MCDM) methodology is proposed to assess the cervical cancer risk levels of patients. In this contex ...Daha fazlası

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Comparison of Classification Success Rates of Different Machine Learning Algorithms in the Diagnosis of Breast Cancer

Hakan Aydın

Objective: To identify which Machine Learning (ML) algorithms are the most successful in predicting and diagnosing breast cancer according to accuracy rates. Methods: The “College of Wisconsin Breast Cancer Dataset”, which consists of 569 data and 30 features, was classified using Support Vector Machine (SVM), Naive Bayes (NB), Random Forest (RF), Decision Tree (DT), K-Nearest Neighbor (KNN), Logistic Regression (LR), Multilayer Perceptron (MLP), Linear Discriminant Analysis (LDA), XgBoost (XGB), Ada-Boost (ABC) and Gradient Boosting (GBC) ML algorithms. Before the classification process, the ...Daha fazlası

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Automated evaluation of Cr-III coated parts using Mask RCNN and ML methodse

Metin Zontul

In this study, chrome coatings were carried out using a Cr-III electroplating bath. The coated parts were classified depending on their appearance. A new approach was developed to classify the coated parts automatically using artificial intelligence methods. Mask RCNN and machine learning (ML) methods such as Multilayer Perceptron (MLP), Support Vector Classifier (SVC), Gaussian Process (GP), K-nearest Neighbors (KNN), XGBoost, and Random Forest Classifier (RFC) were used together. Mask RCNN was used to clean the coated parts from the redundant data. The extracted data were flattened and conve ...Daha fazlası

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Patient privacy in smart cities by blockchain technology and feature selection with Harris Hawks Optimization (HHO) algorithm and machine learning

Haedar Al-Safi | Jorge Munilla | Cevat Rahebi

A medical center in the smart cities of the future needs data security and confidentiality to treat patients accurately. One mechanism for sending medical data is to send information to other medical centers without preserving confidentiality. This method is not impressive because in treating people, the privacy of medical information is a principle. In the proposed framework, the opinion of experts from other medical centers for the treatment of patients is received and consider the best therapy. The proposed method has two layers. In the first layer, data transmission uses blockchain. In the ...Daha fazlası

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