Makine Öğrenmesi Algoritmaları Kullanılarak İtfaiye İstasyonu İhtiyacının Sınıflandırılması

Can, Aydin

doi:10.31590/ejosat.458613

Cited by 25 publications

(27 citation statements)

References 16 publications

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“…Estimates are based on the majority vote of neighboring samples. Care should be taken as it tends to over-conform to higher k values [14]. While applying the k-NN algorithm, the optimal k value was accepted as 2.…”

Section: Methodsmentioning

confidence: 99%

Makine Öğrenmesi Algoritmalarıyla Kalabalık Ortamlarda Anomali Tespiti

BOYRAZLI¹

2021

DÜMF Mühendislik Dergisi

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Camera systems have a very important place as they are widely used in providing security in crowded environments. The video images recorded by cameras are examined to check whether there is dangerous or unusual behavior. It is tried to develop appropriate measures according to the result of this control. Modeling human behaviors for the definition and detection of abnormal behavior has become a popular research area in recent years. This study was carried out by applying supervised learning algorithms, one of the machine learning methods, on five different scenes that in two open data sets. Normal and abnormal motion scenes were detected on the videos in the data sets. In these two data sets, abnormal motion was detected in a total of five different locations. Random Forest, Support Vector Machines and k Nearest Neighbor algorithms, which are among the supervised learning algorithms, were used in this process. The algorithms used were compared with performance criteria such as accuracy, sensitivity, precision and F1 score.

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Section: Methodsmentioning

confidence: 99%

Makine Öğrenmesi Algoritmalarıyla Kalabalık Ortamlarda Anomali Tespiti

BOYRAZLI¹

2021

DÜMF Mühendislik Dergisi

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“…Bu içeriklerin çok hızlı bir şekilde geniş kitlelere yayılabilmesi, sosyal ağların kurbanlar için endişe verici alanlar haline gelmesine neden olmuştur [13]. [18].…”

Section: Akran Siber Takipçiliğiunclassified

“…NB, birbirleri ile ilişkili girdiler içermedikçe diğer algoritmalara karşı iyi performans göstermektedir. Bu algoritma diğer sınıflandırma algoritmalarına göre daha sade olması ve daha iyi sonuçlar elde etmesi yönüyle siber zorbalık tespitlerinde sıkça kullanılmaktadır [18].…”

Section: Naive Bayes Sınıflandırma Algoritmasıunclassified

Siber Zorbalık Tespit Yöntemleri Potansiyel Uygulama Alanları ve Zorluklar

Baykara¹

2021

DÜMF Mühendislik Dergisi

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Cyberbullying, which is one of the problems faced by social media users worldwide, is increasing day by day. It puts psychological pressure on its victims, causing them to be excluded from the society and even to the brink of suicide. In order to prevent these and similar problems, cyberbullying must be detected. Although cyberbullying detection is very difficult, many methods have been developed for its detection. The widespread use of the internet among people caused cyber bullying the application areas to increase. Cyber bullies; They bully more than traditional bullying with the thought that their bullying on the internet cannot be detected or even with the thought that there will be no legal sanction. Various studies have been carried out in the literature to identify this problem. In this study first of all cyberbullying definition, types, legal regulations in the international arena and in turkey, used in detection methods machine learning methods and algorithms, studies in the literature and generally presented implementation steps used in the detection method.

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“…Naive Bayesian algorithm is a basic statistical classifier. For given dataset, this classifier calculates a probability set by counting combinations of values and frequency (Aydın, 2018). This algorithm supposes that all variables are independent.…”

Section: Naive Bayesmentioning

confidence: 99%

Prostat kanseri tahmini için çeşitli denetimli makine öğrenimi tekniklerinin karşılaştırılması

Erdem¹,

Bozkurt²

2021

European Journal of Science and Technology

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Prostate cancer is a kind of cancer that is seen worldwide and causes death of many people. Early diagnosis of cancer helps patients during the treatment phase. For this reason, cancer prediction is very crucial, according to the symptoms seen in the patient. One of the biggest problems in medicine is diagnosing diseases. The absence of certain definitive rules for the evaluation of symptoms of prostate cancer and the low rate of prediction of the diagnostic methods currently in effect made this study essential. It is thought that machine learning methods can be effective for the solution of the problems where there are no specific and definite rules and the factors affecting the event can be predicted. With this awareness, various solutions are developed by computer-aided systems. In this paper, we compare and discuss the performance of different supervised machine learning algorithms (i.e., k-nearest neighbor, support vector machines, random forest, logistic regression, linear regression, Naive Bayes, linear discrimination analysis, linear classification, multi-layer perceptron and deep neural network) for prostate cancer prediction. In this study, an open-access online prostate cancer data which consists of observations of 100 patients is used. The main intention is to evaluate the correctness in classifying data with respect to effectiveness and efficiency of each algorithm in terms of precision, recall, AUC, F1-Score, accuracy. The accuracy of the methods may vary according to the training and test data. In order to obtain more stable results, each algorithm was run more than ten times and their five best performances were recorded. The results show that multi-layer perceptron (MLP) can result in high prediction accuracy that is better compared to other approaches. Experimental results show that MLP gives the highest accuracy (97%) with the lowest error rate (0.03). The MLP classifier outperformed the other algorithms used in this study and is one of the best studies ever reported in the literature in terms of accuracy, AUC and F1 score performance criteria. As a result, we can say that if the computer is trained with machine learning methods based on patient information, it can be clinically useful with high accuracy in predicting cancer. In this way, an unnecessary biopsy of the patient can be prevented.

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Makine Öğrenmesi Algoritmaları Kullanılarak İtfaiye İstasyonu İhtiyacının Sınıflandırılması

Cited by 25 publications

References 16 publications

Makine Öğrenmesi Algoritmalarıyla Kalabalık Ortamlarda Anomali Tespiti

Makine Öğrenmesi Algoritmalarıyla Kalabalık Ortamlarda Anomali Tespiti

Siber Zorbalık Tespit Yöntemleri Potansiyel Uygulama Alanları ve Zorluklar

Prostat kanseri tahmini için çeşitli denetimli makine öğrenimi tekniklerinin karşılaştırılması

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