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    Comparison of Classification Success Rates of Different Machine Learning Algorithms in the Diagnosis of Breast Cancer
    (Asian Pacific Organization for Cancer Prevention, 2022) Ozcan, Irem; Aydin, Hakan; Cetinkaya, Ali
    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 dataset was preprocessed. Sensitivity, accuracy, and definiteness metrics were used to measure the success of the methods. Result: Compared to other ML algorithms used in the study, the GBC ML algorithm was found to be the most successful method in the classification of tumors with an accuracy of 99.12%. The XGB ML algorithm was found to be the lowest method with an accuracy rate of 88.10%. In addition, it was determined that the general accuracy rates of the 11 ML algorithms used in the study varied between 88-95%.Conclusion: When the results obtained from the ML classifiers used in the study are evaluated, the efficiency of the GBC algorithm in the classification of tumors is obvious. It can be said that the success rates obtained from 11 different ML algorithms used in the study are valuable in terms of being used to predict different cancer types. © This work is licensed under a Creative Commons Attribution-Non Commercial 4.0 International License.
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    Monitoring of Miner by RF Signal
    (IEEE, 2017) Saray, Tugba; Cetinkaya, Ali; Mendi, Sekip Engin
    Wireless communication technology is spreading rapidly to all areas of our lives. The technology, which has a wide range of applications from radios, intelligent home systems, automation applications to GPS units, was used in monitoring the workers working in mines in this study. Most of the mining area underground mining is risky and the possibility of accident (gas jams, the explosion and dents, etc.) is the area of high. Locating is vital in this line of work when a sudden accident or dent occurred in which the worker will be known and the position of the recovery efforts can be intensified in that area. It was developed using a wireless receiver, two radio transmitters, and a reference receiver, which are used in the Internet of thinks. CP2102 V2 module was used as the center receiver, D1 mini module was used as the reference receiver, ESP8266-01 was used as the transmitter card and it was used to carry the miner.