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In this study, it is aimed to examine the theses written in the field of machine learning and decision trees with the bibliometric analysis method by evaluating them within the scope of various parameters. For this purpose, 368 theses were reached as a result of the search performed in May 2020 by using the keywords "machine learning", "decision trees" in the database of the Council of Higher Education's National Thesis Center. Theses reached; It was examined in detail in terms of bibliography taking into account the year, thesis type, language, university, institute, department, the number of pages, the software used, the methods used, the keywords. In view of the research questions defined for the theses, the data were obtained and transferred to the Excel program. In this software, all calculations, graphs and tables were made. As a result of the examinations, important findings were obtained regarding the theses written in the fields of machine learning and decision tree based on the supervised learning strategy. These include important findings such as the distribution of theses by years, the university, institute, department that contributed the most to the field, the most commonly used machine learning method, the feature selection methods used, the parameter optimization methods used, the hybrid approaches developed, the ensemble learning methods developed, the most popular programming language / software, etc.

Decision tree algorithm is an important classification method in data mining techniques. A decision tree creates classification and regression models like a tree that has a root node, branches, and leaf nodes. Logistic regression which is an alternative method to regression analysis when the dependent variable is a dichotomy, is another technique used for classification purposes. Within the scope of this research, logistic regression, linear regression, classification tree, and regression tree were applied on the same data set. This study explores the most important variables determining the house price by using these four methods. Models’ performances and predictive powers were compared and the best model is determined. This comparison was performed using 414 real estate data on 5 independent variables and the dependent variable is house price. The findings showed that the classification tree model for real estate valuation data performs better than standard approaches. [ABSTRACT FROM AUTHOR]

It is important to predict the sales price of second-hand car for both persons and institutions who are operating in second-hand market. The sales price of cars are affected by many factors which makes predicting difficult. Especially there is no readily available method to determine which factors are affecting the sales price most. The purpose of this study is to predict the sales price of second-hand cars with decision trees. Genetic Algortihm is used to select the most relevant features. For this purpose, 252645 advertisements are scanned fort his study. For each advertisement there are 139 features available. Different models are examined using genetic algorithms with selecting 5, 10, 15 and 20 features. The best predicting performance in the out-of-sample experiment is 65.67%. Proposed model can be used as a decision support system for those operating in second-hand car market

It is thought that data mining can solve many problems in visible light communication. In the VLC systems, different receiver and transmitter units are usually designed for each modulation technique. Here, the proposed systems are designed for shortened pulse position modulation. In the paper, K-Nearest Neighbor algorithm and Decision Trees based demodulators that can be applied to synchronous modulation techniques are proposed. Afterwards, a generalized entropy expression is obtained for M-SPPM signals. Additionally, demodulators that can decode signals according to the Manhattan and Minkowski distance and other demodulators are compared in terms of accuracy and bit error rate. From the numerical results, it can be seen that the proposed KNN and Decision Trees based demodulators can be used as traditional systems for synchronous modulation techniques. Furthermore, it is seen that bit error rate performances and accuracy rates of the proposed KNN based demodulator and decision trees based demodulator that is optimally designed are completely the same. In 4-SPPM signals, it is seen that the accuracy of KNN (Demodulator-1) and Decision Trees (Demodulator-3) is 98.78% for SNR=10 dB. Here, the accuracy of the KNN (Demodulator-2) and Decision Trees (Demodulator-5) is 99.07% for 8-SPPM signals. [ABSTRACT FROM AUTHOR]

Amaç: Üretim sektöründeki bir firmanın 2018-2019 yılı orjinal verilerinden türetilmiş sıralı ölçekteki Toplam Ekipman Etkinliği (TEE) puanı üzerinde etkili olan değişkenlerin makine öğrenim algoritmaları ile modellenmesi, yorumlanması ve model performanslarının karşılaştırılması çalışmanın temel amacıdır. Yöntem: TEE puanının modellemesinde karar ağaçları (CART, CHAID), lojistik regresyon (LogR) ve yapay sinir ağları (YSA) kullanılmıştır. Kurulan modellerin performans değerleri “duyarlılık”, “seçicilik”, “kesinlik” ve “doğruluk” kriterlerine göre hesaplanmıştır. Modelleri yorumlarken karar ağaçları ve YSA sonuçları için yüzdelerden, LogR için odds oranından yararlanılmıştır. Bulgular: Modellerde TEE puanı üzerinde “saat”, “üretim”, “tecrübe” ve “kayıp metre” değişkenleri incelenmiştir. Performans karşılaştırmasında en iyi sonuç veren algoritmanın sıralı LogR olduğu ve bu modele göre üretimin düşük ve çalışanlarının daha az tecrübeli olduğu firmalarda daha “düşük” TEE puanı elde edilirken, kayıp metresi daha az olan firmalarda daha “yüksek” TEE” puanı alma şanslarının olduğu saptanmıştır. Özgünlük: Literatürde sürekli olarak modellenen TEE puanının kategorik hale getirilerek sınıflar arasındaki farklılığın belirlenmesiyle firmaların kendi konumlarını belirlemesi sağlanmıştır. Böylece firmalar kategorisini belirleyip seçilen modeldeki önemlilik sırasındaki faktörlerini değiştirerek bir üst kategoriye daha hızlı çıkabilecektir. Literatürde kategorik olanTEE puanını makine öğrenim algoritmaları ile çözümleyen modellerin olmaması bu çalışmanın özgünlüğü olarak belirlenmiştir.

McCabe and Halstead method-level metrics are among the well-known and widely used quantitative software metrics are used to measure software quality in a concrete way. Software defect prediction can guess which or which of the sub-modules in the software to be developed may be more prone to defect. Thus, loss of labor and time can be avoided. The datasets which are used for software defect prediction, usually have an unbalanced class distribution, since the number of records with defective class can be fewer than the number of records with not defective class and this situation adversely affect the results of the machine learning methods. Information gain is employed in decision trees and decision tree based rule classifier and attribute selection methods. In this study, software metrics that provide important information for software defect prediction have been investigated and CM1, JM1, KC1 and PC1 datasets of NASA's PROMISE software repository have been balanced with the synthetic data over-sampling Smote algorithm and improved in terms of information gain. As a result, the software defect prediction datasets with higher classification success performance and the software metrics with increased information gain ratio are obtained in the decision trees. [ABSTRACT FROM AUTHOR]

Copyright of Celal Bayar University Journal of Social Sciences / Celal Bayar Üniversitesi Sosyal Bilimler Dergisi is the property of Celal Bayar University Journal of Social Sciences and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The aim of the study is describing Turkish household's olive oil consumption levels and the profiles by using decision tree data mining method. The most effective factor is income level and while income level increases, the ratio of the olive oil consuming households increased. The other factors are household head's age and education level and household size. Household size is distinctive among low income households. While household size increases, the ratio of olive oil consuming households decreased. Amongst lower-middle and upper-middle income level households, while the household head's age increases, the ratio of olive oil consuming households increased. [ABSTRACT FROM AUTHOR]

The traditional statistics method is frequently used in classification problems. However data mining techniques draw more attention day by day because they do not have any assumption and their classification performance is determined to be stronger via many researches. In this study the Decision Trees and Artificial Neural Networks data mining techniques will be introduced and the classification performances of these methods will be compared with application sample. In the application 9 macroeconomic variable data of member and candidate states of the European Union (EU) have been used. The aim of the application is to determine whether the factor of 'increasing the economical development level' is effective or not and if it is which variables are more important. The data have been analyzed via machine training package program of WEKA. C4.5 algorithm has been used for KA analysis whereas Multilayer Perceptrons have been used for YSA analysis. [ABSTRACT FROM AUTHOR]

The secondhand vehicle market in Türkiye has always been active. In the secondhand vehicle market, information such as brand, model, and fuel type can be analyzed, and this information can be made useful. Prices vary according to the various features of the vehicles. Machine learning techniques can predict prices and help users set prices when selling or buying vehicles. Price prediction falls under regression. Since the number of secondhand vehicles is quite high, big data systems are used. Apache Spark and its machine learning library are quite useful for this. Linear regression, decision tree regression, random forest regression, GBT regression, and isotonic regression algorithms are used for price prediction. The random forest algorithm achieved the highest success for the price prediction with 21435.09 RMSE and 0.887 R2 values. As a result of the statistical tests performed to check the significant difference between the RMSE and R2 values obtained with the random forest algorithm and other algorithms, it is concluded that the results obtained with the random forest algorithm are statistically better than other algorithms. The random forest algorithm gives better results because the algorithm performs training over multiple decision trees, its flexibility, and strong hyperparameters. [ABSTRACT FROM AUTHOR]

Copyright of Gümüshane Üniversitesi Fen Bilimleri Enstitüsü Dergisi is the property of Gumushane University, Faculty of Science & Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of the Institute of Science & Technology / Iğdır Üniversitesi Fen Bilimleri Enstitüsü Dergisi is the property of Igdir University, Institute of Science & Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Online Journal of Art & Design is the property of Online Journal of Art & Design and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Credit risk is one of the major risks faced by commercial banks and has an important effect on profitability ratios. Consequently, the credit scoring system in decision making for banks and other financial institutions lending to consumers is very important. Financial institutions constitute various internal credit assessment models reveal relationships between variables affecting credit scoring. In the literature, statistics and machine learning techniques for credit ratings have been widely studied. In this study, particularly for banks, financial institutions and customers of these institutions are discussed, including issues that are important to determine customer's credit score. Customer's credit score is a term used to allocate credit using statistical techniques and methods of machine learning techniques. Such methods have become increasingly more important in recent years with the growth in commercial loans. Although a wide range of applied statistical methods included in the literature, they are limited because of commercial confidentiality. In this study, for issuing the decision on the assessment of customers' demand for credit, acceptance of loan requests or refusal, a software model based on the artificial neural network (ANN) methodology is recommended. A real data set belongs to a deposit bank was used for the application. In addition to this, this study benchmarks the performance of ANN model with decision trees (DT) model. Based on the findings, ANN model outperforms the DT model in terms of estimating credit risk and customer's credit score. It is also considered that the model would be helpful in increasing the profitability of lenders gained from credits. [ABSTRACT FROM AUTHOR]

In this study, a data mining application was conducted using the data from students of Faculty of Economics and Administrative Sciences in Cumhuriyet University. Both decision tree algorithm which classifies the data best and classes produced by this algorithm were tried to determine by benefiting from the students' data. As a result of the study, it is explored that LADTree algorithm was the best algorithm which classifies the students' data and nineteen classes were produced by this algorithm. [ABSTRACT FROM AUTHOR]

Copyright of Journal of the Institute of Science & Technology / Iğdır Üniversitesi Fen Bilimleri Enstitüsü Dergisi is the property of Igdir University, Institute of Science & Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Pamukkale University Journal of Social Sciences Institute / Pamukkale Üniversitesi Sosyal Bilimler Enstitüsü Dergisi is the property of Pamukkale University, Social Sciences Institute and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In this study, a decision support system is designed which helps the physicians and facilitates their works for detection of various diseases from histopathological images. The developed decision support system is based on pattern recognition. The feature extraction stage of the pattern recognition system is realized by using fractal analysis and a decision tree classifier is used for classification stage. The performance of the proposed pattern recognition system is evaluated with the histopathological images which are taken from hepatic patients. The correct classification rate is 92 % for 50 patient's data. [ABSTRACT FROM AUTHOR]

Wooden materials used in the building sector are exposed to different loading types and different strength depending on the place of use. The use of materials suitable for the type of loading affects important factors such as safety, performance and cost. Another important issue in wooden materials used in the building sector is wood-water relations. Moisture causes significant changes on the physical, mechanical and technological (hardness, wear) properties of wood. In this study, 5-layer LVL (Laminated Veneer Lumber) was produced from 2 mm beech (Fagus orientalis L.) veneer obtained by peeling process. Produced LVLs were subjected to four different moisture (0%, 12%, 18% and 25%) compressio strength in two different directions, perpendicular and parallel to the fibers. Using the data obtained from the specified moisture values, the pressure resistance values in other moisture amounts were estimated by artificial intelligence. Artificial Neural Networks (ANN), Decision Trees (DT) and Random Forest (RF) algorithms are used in the predictions. According to the mechanical test results, the highest compression strength value (51.96 N/mm²) was obtained in the loading parallel to the fibers of the samples with 0% moisture (oven dry). The lowest compression strength value (13.57 N/mm²) was determined in the loading vertical direction to the fibers of 25% moisture samples. The highest prediction success was obtained from the Random Forest algorithm with a value of R2 = 0.984. As a result, it has been determined that artificial intelligence techniques can be used successfully as a solution to predict the pressure resistance of LVLs at different humidity.

Copyright of Celal Bayar University Journal of Social Sciences / Celal Bayar Üniversitesi Sosyal Bilimler Dergisi is the property of Celal Bayar University Journal of Social Sciences and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

To find out which period the historical coins belong to requires a number of scientific procedures that archaeologists or experts can do. These operations can often be time-consuming and demanding operations. From this point on, in this study, the automatically classification of historical coins by using machine learning methods is discussed. Being able to use machine learning methods to classify historical coins can help experts and can become an analysis tool without the need for scientific tests for non-experts. For this purpose, some physical properties of different coins used in Anatolian geography were collected and classified by various machine learning methods named SVM, Random Forest, Bagging, and Decision Trees. Also, two different missing values strategies are deployed in conjunction with each chosen method. Based on our findings, random forest method together with imputing missing values with mean gives an acceptable results with the accuracy rate of %71, although there are some limitations such as high rate of missing values and working with a small dataset.

Copyright of Ege Üniversitesi Ziraat Fakültesi Dergisi is the property of Ege Universitesi, Ziraat Fakultesi and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Turkiye Klinikleri Journal of Biostatistics is the property of Turkiye Klinikleri and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper proposes a novel approach for intrusion detection using a metaheuristic-based feature selection method combined with convolutional neural networks (CNNs). The feature selection method employs a decision tree and a metaheuristic algorithm to select the most important features from different datasets. The selected features are then feed into CNNs, including ResNet50, VGG16, and EfficientNet, to improve the accuracy of intrusion detection. Experimental results on several benchmark datasets show that the proposed method can be promising in terms of different criteria. The final results prove that EfficientNet and ResNet50 perform much better than VGG16. When EfficientNet and ResNet50 algorithms are applied to NSL-KDD, DEFCON and CDX datasets, the best accuracy rates are 96.2% and 81.3% correspondingly. In addition, while EfficientNet has the highest rate of 98.6% according to the specificity criterion, ResNet50 stands out with a recall rate of 95.1% and a rate of 95.2% for F1score. [ABSTRACT FROM AUTHOR]

Papüloskuamöz deri hastalıkları halk arasında oldukça sık rastlanan ve kendine has morfolojik özellikleri olan deri hastalıkları grubudur. Papüloskuamöz deri hastalıklarının alt gruplarının belirtileri birbirine çok yakın olduğu için teşhis süreci bazı durumlarda zahmetlidir. Hastalığın teşhisi klinik muayenede konulabilir. Klinik muayenenin yetersiz olduğu durumlarda, tanı deri biyopsisi ile histopatolojik değerlendirme ile konulmaktadır. Bu süreçte dermatolog ve patoloğun uyumlu bir şekilde çalışması ve her iki hekimin de teşhis süreci ile ilgili bilgi birikiminin iyi olması gerekir. Bu yüzden Papüloskuamöz deri hastalıklarının tanısı deri biyopsisine ihtiyaç duyulmadan sadece klinik muayene ile dematolog tarafından konulabilmesi için daha basit, yüksek başarı oranına sahip ve klinikte kullanılabilir yöntemlere ihtiyaç duyulmaktadır. Bu çalışmanın amacı Papüloskuamöz deri hastalıklarının yüksek başarı oranı ile tespit edebilecek, klinikte dermatolog tarafından kullanılabilecek, yapay zeka yöntemleriyle geliştirilmiş kural tabanlı algoritma geliştirmektir. Çalışma kapsamında daha önce toplanmış veri seti kullanılmıştır. Veri setinde Papüloskuamöz deri hastalıklarının altı farklı alt grubu için klinik ve histopatolojik bulgular bulunmaktadır. Öncelikle veri seti ikişer sınıflı olacak şekilde gruplandırılmıştır. Daha sonra özellik seçme algoritmalarıyla klinik ve histopatolojik bulgular seçilmiştir. Daha sonra karar ağaçları yardımıyla kural tabanlı teşhis algoritmaları oluşturulmuştur. Çalışma sonucunda, sadece seçilmiş klinik bulgular kullanılarak ortalama %82.98 doğruluk oranı, 0.89 duyarlılık, 0.76 özgüllük oranıyla Papüloskuamöz deri hastalıkları kural tabanlı algoritmalar geliştirilmiştir. Sonuç olarak, bu çalışmada elde edilen sonuçlara göre, çalışma kapsamında geliştirilen algoritmalar, Papüloskuamöz deri hastalıklarının teşhisi için yapay zeka yöntemleriyle geliştirilen yüksek doğruluk oranına sahip kural tabanlı algoritmalar klinikte kullanılabilir.

Copyright of Yuzuncu Yil Universitesi Journal of Agricultural Sciences (YYU J Agr Sci) is the property of Yuzuncu Yil University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Differential diagnosis of erythematous squamous diseases is one of the important problems in dermatology. They all share the clinical picture of erythema and scaling among each other, with little difference. The diseases included in this group can be classified as psoriasis, seborrheic dermatitis, lichen planus, pityriasis rosea, chronic dermatitis and pityriasis rubra pilaris. Biopsy for diagnosis, but unfortunately these diseases also share many histopathological features. Technologies related to some other technologies have found wide application in the biomedical field. With the use of computer technologies in medical devices, more sensitive, faster, faster, cutting devices are developed. Therefore, it has been investigated how effective machine learning algorithms are in classifying and predicting skin diseases. In this study, skin tissue samples consisting of 33 attributes belonging to 366 patients, Support Vector Machines (SVM), Ensemble Learning Algorithms (ELA), Decision Trees (DT), k-Nearest Neighborhood (k-NN) were classified with algorithms and the highest knowledge information was recorded. Accordingly, the effects related to the separation and classification of skin diseases have been investigated. SVM has achieved an accuracy of 99.73% which is higher than all the previous studies on this dataset. [ABSTRACT FROM AUTHOR]

Nowadays, with the widespread use of e-commerce, online sales are becoming more and more important for businesses. This increase in the use of e-commerce brings with it the question of what factors may affect the use of e-commerce by consumers. In this study, the features that affect the e-commerce usage frequency of individuals in Turkey are analyzed with decision trees and Support Vector Machine (SVM) algorithms and interpreted. In addition, a classification model has been designed to predict e-commerce usage frequency of individuals by using variables that affect e-commerce usage. According to the results of the study, the most important variable which affects the e-commerce usage frequency in CHAID, C&R Tree and QUEST algorithms has been determined as the number of purchases of goods or services over the internet for private use in the last three months. While variable of age has been the second most important variable in CHAID and QUEST algorithms, the variable of sending/receiving e-mails for special purposes (including mobile applications) in the last three months has been the second most important variable in the C&R Tree. For e-commerce usage frequency estimation, the CHAID algorithm has given the best classification result with an accuracy rate of 86.19%. [ABSTRACT FROM AUTHOR]

With digitalization, the world of crime has also become digital and the number of crimes committed over the internet is increasing day by day. Cybercriminals and attackers use secret networks on the Internet, called the Dark Web, to hide their identities and provide encrypted communication. Darknets have different and special access methods than normal internet infrastructure. All access to these networks is suspect and needs to be investigated. Because the Darknet provides encrypted communication, it is difficult to detect and classify with today's security tools. In this study, only the statistical information of packets was analyzed using machine learning approach without deciphering encrypted network traffic. CICDarknet2020 dataset was used and a detailed experimental study including K Nearest Neighbor, Logistic Regression, Random Forest, SVM, Decision Tree, Gaussian Naive Bayes, Linear Discriminatory Analysis, Gradient Boosting, Extra Tree and XGBoost algorithms was carried out for packet analysis. In experimental studies, it has been observed that the Decision Tree algorithm has the highest classification success with an accuracy rate of 93.32%. [ABSTRACT FROM AUTHOR]

The increasing urbanization and rapid economic development have led to growing housing markets. This has resulted in a rising demand for housing due to migration towards cities and the acceptance of housing as both an investment and a wealth protection tool. On the other hand, housing prices are affected by many factors, thereby studies on housing price forecasts are carried out within the scope of different models and various methods. In line with the significant importance of housing markets as one of Turkey's economic growth tools, this study includes the forecast of the classification of 4-bedroom (3+1) housing prices in the central district of Kütahya province. The dataset used includes various criteria that affect housing prices, and different machine learning classification algorithms that are utilized. As a result, it is seen that the accuracy rates of the k-Nearest Neighbor (k-NN), Decision Trees (DT) and Random Forest (RF) algorithms were above 60%. As the main performance criterion, the AUC scores yielded that the most successful classification forecast is acquired by the RF algorithm, which is followed by the k-NN. In the context of the variables and data set included in this study, it was seen that successful outcomes in the classification forecasts of 4-bedroom apartments’ housing prices in the city center of Kütahya were obtained by these two methods. [ABSTRACT FROM AUTHOR]

Yazılım kalitesinin somut bir şekilde ölçülebilmesi için kullanılan sayısal yazılım metrikleri içinde bilinen ve yaygın şekilde kullanılanlar arasında McCabe ve Halstead yöntem-seviye metrikleri bulunmaktadır. Yazılım hata tahmini, geliştirilecek olan yazılımda bulunan alt modüllerin hangisi veya hangilerinin daha çok hataya meyilli olabileceğini konusunda öngörüde bulunabilmektedir. Böylece işgücü ve zaman konusundaki kayıpların önüne geçilebilmektedir. Yazılım hata tahmini için kullanılan veri kümelerinde, hata var sınıflı kayıt sayısı, hata yok sınıflı kayıt sayısına göre daha az sayıda olabildiğinden bu veri kümeleri genellikle dengeli olmayan bir sınıf dağılımına sahip olmakta ve makine öğrenme yöntemlerinin sonuçlarını olumsuz etkilemektedir. Bilgi kazancı, karar ağaçları ve karar ağacı temeline dayanan kural sınıflayıcı, nitelik seçimi gibi algoritma ve yöntemlerde kullanılmaktadır. Bu çalışmada, yazılım hata tahmini için önemli bilgiler sunan yazılım metrikleri incelenmiş, NASA’nın PROMISE yazılım veri deposundan CM1, JM1, KC1 ve PC1 veri kümeleri sentetik veri artırım Smote algoritması ile daha dengeli hale getirilerek bilgi kazancı yönünden iyileştirilmiştir. Sonuçta karar ağaçlarında sınıflama başarı performansı daha yüksek yazılım hata tahmini veri kümeleri ve bilgi kazanç oranı yükseltilmiş yazılım metrik değerleri elde edilmiştir.

Copyright of Academic Platform - Journal of Engineering & Science is the property of Akademik Platform and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Verimlilik Dergisi is the property of Verimlilik Dergisi and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Data mining provides discovering the required and applicable knowledge from very large amounts of information collected in one centre. Data mining has been used in the information industry and society. Although many methods of data mining has been used, these techniques has been remarkable in animal husbandry in recent years. For the solution of complex problems in animal husbandry many methods were discussed and developed. Brief information on data mining techniques such as k-means approach, k-nearest neighbor approach, multivariate adaptive regression function (MARS), naive Bayesian classifiers (NBC), artificial neural networks (ANN), support vector machines (SVM), decision trees are given in the study. Some data mining methods are presented and examples of the application of data mining in the field of animal husbandry in the world are provided with this study.

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Most of investment projects have managerial flexibility and this flexibility can significantly affect the value of projects. Because decision makers have right to alter investment decisions. But traditional valuation methods like discounted cash flow do not capture this flexibility. Flexibility can be in many forms, such as defer, expand, abandon and contract. There are two approaches that correctly capture flexibility: Decision tree analysis and real option valuation model. Real option valuation model includes managerial flexibility in terms of option and it is theoretically superior to decision tree analysis. Although decision tree analysis and real option model differ on some points, each approach has some advantages in certain applications. [ABSTRACT FROM AUTHOR]

Turkish manufacturing industry is in the top three in terms of occupational accident frequency among sectors. Therefore, there is a need to determine accident cause-effect relationships in order to improve occupational safety and minimize the risks that cause occupational accidents in the manufacturing industry. An integreated data driven approach is proposed to find patterns among occupational accidents in Turkish manufacturing systems. The proposed approach uses multinomial logit model (MLM) and decision tree algorithms, namely C5.0, Classification and Regression Trees (C&RT), The quaternion estimation (QUEST), Chi-square automatic interaction detector (CHAID) ve Random Trees. In this study, 307,590 occupational accidents in the Turkish manufacturing industry between 2013 and 2019 are used. It is found that there is a statistically significant relationship among division, geographical location of the accident, year, deviation, hour day, gender and age for all accidents with injury, death and loss of limb according to the absence of disability. Additionally, division, geographical location of the accident and year are among the top five predictors based on decision tree algorithms. [ABSTRACT FROM AUTHOR]

In our coun try, the number of small ruminant animals is decreasing day by day due to various reasons. In parallel with the decrease in the number of small ruminants, significant decreases are seen in animal production. One way to prevent the reduction in the number of small ruminants is to be able to make successful predictions and analysis related to the diagnosis. Thanks to computer-aided diagnostic studies performed with machine learning, the quality of health services increases while the costs of the health sector decrease. The aim of this study is to perform computer aided diagnosis in neonatal lambs using machine learning methods. Hence in study, decision tree, naive bayes, k-nearest neighbors, artificial neural networks and random forest methods were used. The performances of these classification methods were analyzed with accuracy, balanced accuracy, specifity, recall, F-measure, kappa and area under the ROC curve (A UC) criteria. As a result of the study, the Naive bayes method more successful results than other methods for computer aided diagnosis produced. It is very important tha t, the Naive bayes method is simple and easy to apply, achieves more successful results than other complex methods. [ABSTRACT FROM AUTHOR]

Computer assisted diagnosis (CAD) systems have been used frequently in recent years in order to create a doctoral assistance decision support system using various patient information. In this study, it was aimed to compare the success of the Wechsler Intelligence Scale for Children (WISC-R) profiles by the decision trees algorithm applied to the CAD system, and the classification success in the detection of the border intelligence (BI), mild and moderate intellectual disability (ID). The data set of the study was formed by using WISC-R test results of 132 patients (50 BI, 61 mild ID and 21 moderate ID) diagnosed according to DSM-V. In order to compare the effect of WISC-R scores on the outcome, 132 patients' test scores: total, verbal and performance intelligence scores, verbal and performance intelligence subscale scores and 3 separate data sets were formed. The all the score types of the WISCR test, the first two node total intelligence scores were selected, and 108 of 132 patients were classified regardless of another attribute. In the decision tree, the first and third datasets containing the total intelligence section score type achieved a high classification success rate of 0.91. The results of this study show that the total intelligence score of the WISC-R profiles is the most effective in the decision trees algorithm in BI, mild and moderate ID diagnosis, and that CAD systems are possible. [ABSTRACT FROM AUTHOR]