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Copyright of Afyon Kocatepe University Journal of Science & Engineering / Afyon Kocatepe Üniversitesi Fen Ve Mühendislik Bilimleri Dergisi is the property of Afyon Kocatepe University, Faculty of Science & Literature 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 International Journal of InformaticsTechnologies is the property of Institute of Informatics, Gazi 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.)

Occupational accidents occur in many sectors in Türkiye every year. In this study, which aims to evaluate occupational accidents occurring in the oil sector, accident estimation was made using artificial neural networks. Data on 2210 work accidents that occurred between 2020 and 2023 in a company operating in the oil sector were used. In this study; Artificial neural network modeling was done with monthly accident data. In the study, Multilayer Perceptron Artificial Neural Networks (MLPANN) and Radial Basis Function Artificial Neural Networks (RBFANN), which are feed-forward networks, were used. 70% of the data is divided as training data and 30% as test data. As a result of the analysis; An 84.1% correct classification rate was obtained with the MLPANN method, and an 86.4% correct classification rate was obtained with the RBFANN method. It can be said that the RBFANN method performs more successfully than the MLPANN method. lt is suggested to use the methods in order to estimate the occupational accidents . [ABSTRACT FROM AUTHOR]

Copyright of International Journal of InformaticsTechnologies is the property of Institute of Informatics, Gazi 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.)

Copyright of Izmir Journal of Economics is the property of Dokuz Eylul University, Faculty of Economics & Administrative 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.)

Copyright of Duzce University Journal of Science & Technology is the property of Duzce University Journal 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 Artvin Çoruh Üniversitesi Orman Fakültesi Dergisi is the property of Artvin Coruh 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.)

Copyright of SDU Journal of Engineering Sciences & Design / Mühendislik Bilimleri ve Tasarım Dergisi is the property of Journal of Engineering Sciences & 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.)

In today's competitive conditions, it has become important for investors to make accurate predictions from data. The diversity of data because of developments in information and technology has led to the need for modern statistical techniques. With these techniques, identifying and estimating unknown hidden relationships in the data is increasing day by day. Data mining is used in many fields, as well as in the field of finance. The data set used in this study consists of 1.423 trading days published between January 2, 2018 and June 27, 2023. The Fp Growth Algorithm was used to determine the co-change between the BIST30 Index, BIST100 Index, Dollar Rate, Euro Rate, Gold, and Brent Oil variables published in the daily bulletins, through association rule analysis, which is a descriptive model in data mining. After conducting the association analysis, a total of 20 association rules were generated, and the top 10 association rules were selected. OIL, GOLD, BIST30, BIST100 variables at 0.99 confidence criteria, and 0.98 confidence criteria; OIL, EURO, BIST30, BIST100 variables, in 0.97 confidence criterion, there is a significant association between USD, EUR, BIST30, BIST 100 and USD, GOLD, BIST30, BIST 100, as well as EUR, BIST30, BIST 100 and USD, BIST30, BIST 100 seen. [ABSTRACT FROM AUTHOR]

Copyright of Bulletin of Legal Medicine / Adli Tıp Bülteni is the property of Galenos Yayinevi Tic. LTD. STI 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 Gazi Üniversitesi Fen Bilimleri Dergisi Part C: Tasarım ve Teknoloji is the property of Gazi 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.)

Emtialar, yatırımları çeşitlendirmek ve enflasyona karşı korunmak için alternatif bir yol olarak görülmüştür. Bu nedenle yatırımcıların bir piyasanın düşüşü veya yükselişi sonrasında diğer piyasaların veya finansal varlıkların hangi yöne doğru hareket edeceğini öngörmesi, hızlı ve etkili kararlar almasında kritik öneme sahiptir. Bu çalışmada, emtia piyasalarının birlikte hareketi veri madenciliğinde yer alan birliktelik kuralı ile analiz edilmiştir. Bu doğrultuda çalışmada 20 adet emtianın 01.01.2010-01.08.2023 tarihleri arasındaki 3216 işlem günündeki birlikte hareketleri analiz edilmiştir. Çalışmada birliktelik kuralı analizleri, Apriori ve FP-Growth algoritmaları kullanılarak gerçekleştirilmiştir. Hem Apriori hem de FP-Growth algoritmaları ile üretilen birliktelik kurallarının tümünde Brent petrolün diğer emtialara eşlik ettiği gözlemlenmiştir. Bu sonuç, Brent petrol fiyatlarının yukarı veya aşağı yönde hareketinin, Brent petrol fiyatlarını yakından takip eden yatırımcılara, karar vericilere ve politika yapıcılara, diğer emtiaların hareketi ile ilgili yol gösterici olabileceğini göstermektedir. Petrolün ekonomik sistemi etkileyen stratejik bir enerji kaynağı olduğu gerçeği göz önüne alındığında, bu sonucun şaşırtıcı olmadığı ifade edilebilir.

Günümüzde üniversite öğrencilerinin eğitime ve eğitim materyallerine internetten erişim oranları oldukça artmıştır. Eğitimde internetin kullanılması ve ders materyallerine erişimin artmasına bağlı olarak analiz edilebilecek veri setinde artış meydana gelmiştir. Bu veri setlerinden bir tanesi de (planlı veya acilen) uzaktan eğitim sürecine geçen üniversitelerin, uzaktan eğitim sistemlerinde biriken ödev, sınav, proje, performans, devam notları ve benzeridir. Yeni Korona Virüs (Covid-19) pandemisinde Yüksek Öğretim Kurumu’nun (YÖK) tavsiyesi ile üniversiteler eğitimlerine uzaktan Asenkron, Senkron ve Hibrit yöntemlerini kullanarak devam etmiş, hatta sınavları uzaktan eğitim sisteminde yapmak zorunda kalmışlardır. Bu araştırmada, Kayseri Üniversitesinin Uzaktan Eğitim Uygulama ve Araştırma Merkezi (KAYUZEM) sisteminden alınmış veriler kullanılmıştır. Araştırma kapsamında 8319 işlenmiş veri bulunmaktadır. Bu veriler üzerinde Veri Madenciliği (VM) alanında kullanılan RapidMiner programının otomatik modelleme özelliği kullanılarak varsayılan algoritmalarla geleceğe yönelik tahminleme işlemi yapılmıştır. Varsayılan algoritmalar arasından en iyi sonucu veren Derin Öğrenme, Naive Bayes, Gradient Boosted Trees, Lojistik Regresyon kullanılmış ve bu otomatik modelleme de yer almayan k-En Yakın Komşu (k-NN) algoritması da çalışmaya dahil edilmiştir. Bu 5 algoritmanın parametreleri üzerinde değişiklikler yapılarak daha iyi sonuçlar elde edilmeye çalışılmıştır. Öğrenci başarısına göre en iyi tahminleme sonucunu, Lojistik Regresyon ile kurulan model vermiştir. Derse katılma yöntemlerinin tümünün (Senkron, Asenkron ve Hibrit) öğrenci başarısına etkisi Karışıklık Matrisi yöntemiyle karşılaştırılmıştır ve en güvenilir yöntemin Hibrit olduğu görülmüştür. Bu çalışma ile üniversitelerde derse katılma yöntemlerinden hangisinin, öğrenciler açısından daha güvenilir olacağına yönelik çıkarımlarda bulunulmuştur. Dolayısıyla yapılan çıkarımlar ile birlikte bir dahaki akademik dönem için öğrenci başarı düzeyinin artması yönünde, derse katılma yöntemlerinden hangisinin daha güvenilir olduğu konusunda tahminleme mümkün olmuştur.

Globally, the impact of the automotive industry on the economy and our daily existence is enormous, in both directions. Not only do they function as transportation, but far beyond that, vehicles have also become data generating machines. Manufacturers or dealers keep various records and collect statistics, including categories such as model types, production rates, etc.; odometer readings of the distance driven, purchase costs or repair/maintenance expenses, cost factors related to initial or long-term vehicle ownership, engine characteristics such as fuel type/horsepower/overall volume, transmission characteristics at downshift points and more. In this study, association analysis was performed with the Apriori algorithm, one of the data mining methods, using vehicle data from the automobile sector. A total of 634 cars were analyzed based on nine different categories: model, production category, mileage category, price category, engine size category, horsepower category, fuel type, cost category, and transmission type. The data was collected from a publicly accessible source. The Apriori algorithm was used to identify the strongest relationships between these attributes. The results show that vehicles in the high-cost category generally have gasoline fuel type, while vehicles in the new production category are generally in the low range category. In addition, the vehicles in the manual transmission type were found to be mostly gasoline. Vehicles in the low-cost category were generally found to have diesel fuel type. The 634 vehicles analyzed cover a wide range of models and categories. In particular, the strong relationship between fuel type and cost category suggests that gasoline vehicles are usually in the high-cost category. In addition, vehicles in the new production category are in the low-walk category, indicating that these vehicles are generally less used or newer. These findings offer new insights on the automotive industry and consumer behavior and can help vehicle manufacturers and dealers to better understand potential customer preferences. This study highlights the importance of extracting information from large data sets and supporting decision-making processes. [ABSTRACT FROM AUTHOR]

Copyright of Duzce University Journal of Science & Technology is the property of Duzce University Journal 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 Research in Economics, Politics & Finance / Ekonomi, Politika & Finans Arastirmalari Dergisi is the property of Journal of Research in Economics, Politics & Finance 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 present study aims to investigate the impact of organizational psychological capital, organizational citizenship behaviour, individual performance and various demographic variables on school principas’ burnout in public schools. The opinions of princals working in Diyarbakır province and districts between the years of 2016- 2017 were considered. This research employed a survey model, which falls under the quantitative research paradigm. Data were collected from 254 principals through the administration of the Organizational Psychological Capital Scale, Organizational Citizenship Behaviour Scale, Individual Performance Scale, and School Principal Burnour Scale. The collected data were analyzed using the Extreme Gradient Boosting (XGBoost) mining method. The findings revealed several key predictors of principals’ burnout, including altruism, resilience, professional seniority, individual performance, civic virtue, type of school, hope, sportsmanship, school principal’s length of service, marital status, optimism and the district of the school. Based on these results, it is recommended to alleviate principals’ burnout by reducing their responsibilities and creating conducive environments that foster their leadership roles.

Copyright of Journal of Polytechnic is the property of Journal of Polytechnic 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 Acta Infologica is the property of Acta Infologica 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 SDU Journal of Engineering Sciences & Design / Mühendislik Bilimleri ve Tasarım Dergisi is the property of Journal of Engineering Sciences & 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.)

In the field of after-sales services in the furniture sector, it is aimed to increase customer satisfaction by responding to customers' calls for spare parts (repair-maintenance process using spare parts) in a shorter time, that is, by supplying spare parts in a shorter time and sending them to the relevant region, by performing repair-maintenance operations on their products in a shorter time. After all spare parts under the same call number reach the area, the customer's problematic product can only be repaired. The determination of the items that were taken together as an order under the same call number was made with the help of the Weka package program, with the help of data mining association analysis application. In order to increase customer satisfaction in the after-sales services sector, which is gaining importance day by day and is now accepted by everyone as being as important as production, the practice of keeping spare parts stock by making association analysis is the first application in this field. The association analysis study also has quite different application and usage areas. At the end of this study, an improvement of 19.3% was achieved in the spare part collection time from the warehouse. [ABSTRACT FROM AUTHOR]

Copyright of Turkish Online Journal of Design, Art & Communication is the property of Turkish Online Journal of Design, Art & Communication 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 International Anatolian Journal of Social Sciences / Uluslararasi Anadolu Sosyal Bilimler Dergisi is the property of Uluslararasi Anadolu Sosyal Bilimler 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.)

Copyright of Journal of Traffic & Transportation Research / Trafik ve Ulaşım Araştırmaları Dergisi is the property of Journal of Traffic & Transportation Research 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 SDU Journal of Engineering Sciences & Design / Mühendislik Bilimleri ve Tasarım Dergisi is the property of Journal of Engineering Sciences & 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.)

Copyright of Journal of Ahmet Kelesoglu Educational Faculty is the property of Journal of Ahmet Kelesoglu Educational Faculty 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 use of a data warehouse (DW) in e-learning applications helps us evaluate students from different perspectives. Depending on user behavior, we may change the content or appearance of the e-learning platform to achieve the best results. The aim of this study is to build a data warehouse infrastructure, which is a central information system for Anadolu University's distance education system. Thanks to the data warehouse project, all learners’ activities in the Learning Management System (ÖYS: Anadolum e-Kampüs) will be recorded regularly. Various analytical reports were obtained about students' study styles, preferred e-learning resources, and the time spent in the system. Additionally, the data warehouse system regularly pulls information from other information systems, such as the student information system related to the open education system. Data warehousing is designed to combine data from multiple sources into a single unified view, rather than performing operational operations like a database. Generally, data warehouses are used for analytical processing as opposed to transaction processing. They are used to analyze historical data and trends, as well as to identify patterns and insights that can aid in informing business decisions. When analysis and reporting is done in a different environment using data warehousing, there is no additional load on the database, which is already busy with daily operational processes. The data warehouse has made it easy to obtain student analytics for various periods, including daily, monthly, and yearly. The reports provided through this infrastructure are used in studies to increase the quality of the LMS system. [ABSTRACT FROM AUTHOR]

Copyright of International Journal of Engineering Research & Development (IJERAD) is the property of International Journal of Engineering Research & Development 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 Sosyal Güvence Dergisi is the property of Sosyal Guvenlik Uzmanlari Dernegi 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.)

The aim of this study was to evaluate the factors of affecting watermelon yield in Diyarbakır province. The data was obtained from surveying of 80 watermelon farmers in Diyarbakır province, Turkey by Simple Random Sampling Method using the Chi-square automatic interaction detector (EXHAUSTIVE CHAID) algorithm of the Data Mining Regression Tree methods. In the model created, the dependent variable was WY (watermelon yield), and the independent variables were determined as R (region), AF (age of farmer), EL (education level), CA (cultivation are), FD (fertilization date), FA (amount of fertilization), DS (date of spraying), AS (amount of spraying), NI (number of irrigation), IT (irrigation time), AN (anchor number), HT (harvest time). As a result of the study, the factors that significantly affect the yield of watermelon; AN, NI, HT, CA, R has been determined. An average of 4488.9 kg watermelon yield per decare was obtained and the number of hoes was the variable that most affected the watermelon yield. As a result in order to get a higher yield per unit area, watermelon producers should anchor number more than 4 times, irrigate 5 to 6 times at less than 2 hours, and apply fertilizer in May. In addition, Çermik, Eğil, Yenişehir and Bismil were determined as more suitable regions for watermelon production. [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]

Copyright of Journal of Polytechnic is the property of Journal of Polytechnic 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 Acta Infologica is the property of Acta Infologica 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 Turkish Studies - Economics, Finance, Politics is the property of Electronic Turkish Studies 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 Turkish Studies - Economics, Finance, Politics is the property of Electronic Turkish Studies 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 Ankara Üniversitesi Dil ve Tarih-Cografya Fakültesi Dergisi DTCF Dergisi is the property of Ankara Universitesi Dil ve Tarih-Cografya Fakultesi (DTCF 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.)

Copyright of Journal of Yüzüncü Yıl University, Faculty of Education is the property of Yuzuncu Yil University, Faculty of Education 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.)

Bu çalışma perdesiz betonarme konut binalarına etkiyen yatay tasarım deprem yükünün veri madenciliği yöntemleri ile analiz edilmesi ve makine öğrenmesi yöntemleri ile tahminlenmesi konusundadır. Bu amaçla Türkiye Bina Deprem Yönetmeliği’ne (2018) göre eşdeğer deprem yükü yöntemi kullanılarak veri seti oluşturulmuştur. Oluşturulan veri seti üzerinde öznitelik seçimi, uç değerlerin tespit edilmesi ve silinmesi, boyut azaltma gibi veri madenciliği yöntemleri kullanılmış hangi yöntemlerle sonuçların nasıl değiştiği tartışılmıştır. Sonuçlar perdesiz betonarme binalara etkiyen deprem kuvvetinin makine öğrenmesi yöntemleri ile başarılı tahmini için kısa periyot harita spektral ivme katsayısının (SS) ve bina toplam yüksekliğinin (HN) gerekmediğini ortaya koymuştur.

Amaç: Bu çalışmada Dünya Bankası sitesinde yer alan açık veri kaynağında sağlık göstergeleri olarak bulunan 30 adet değişken kullanılmış ve G-20 ülkelerinin sağlık sistemlerinin performansları değerlendirilmiştir. Yöntem: Sağlık göstergeleri için 2000-2018 yılları arasında toplam 19 yıllık veri kullanılmıştır. Oluşan veri sayısının fazla olmasından dolayı kümeleme analizi ile sınıflandırma yapılabilmesi adına veri madenciliği yöntemleri kullanılmıştır. Kümelerin performanslarının değerlendirilmesinde öncelikle ağırlıkların belirlenmesi için sağlık göstergeleri önem derecesine göre uzman kişiler tarafından gruplandırılmış ve sıralanmıştır. Elde edilen değerler ile çok kriterli karar verme tekniklerinden MAUT yöntemi kullanılarak kümelerin performansları değerlendirilmiştir. Bulgular: Kümeleme analizi sonucunda ülkelerin 4 farklı kümede sınıflandıkları görülmüştür. Sağlık göstergeleri açısından “Çok Yüksek” performansa sahip olan ülkeler Çin ve Güney Kore’dir. “Yüksek” düzeyde performansa sahip olan kümede yer alan ülkeler; Almanya, Amerika, Avustralya, Fransa, İngiltere, İtalya, Japonya, Kanada ve Rusya şeklindedir. “Ortalama Üstü” düzeyde performansa sahip olan kümede yer alan ülkeler; Arjantin, Brezilya, Meksika, Suudi Arabistan ve Türkiye’dir. “Düşük” performansa sahip olan kümede yer alan ülkeler; Endonezya, Güney Afrika ve Hindistan’dır. Özgünlük: Uygulama sonucunda ülkelerin bulunduğu kümeler arasında sağlık alanındaki yetersizlikleri açık şekilde görülebilmiştir. Böylece yapılması gereken iyileştirmeler açısından karar vericiler için ortak bir planlama alanı oluşturulmuştur.

MR spectroscopy (MRS) is a critical imaging modality in brain tumor detection, in which brain metabolites other than water and fat can be measured. Along with the advances in artificial intelligence and data mining, it is seen that computer aided diagnosis (CAD) systems are widely used in medicine. The high achievements achieved have a positive effect on medical diagnosis and treatment processes by providing time saving for CAD applications. Deep learning, based on classical classification methods, the results are more successful on the data, and with high classification ability is the artificial intelligence algorithm. In this study, it was aimed to develop a CAD system to assist physicians in tumor detection by using the ratios of Choline (Cho), creatine (Cr) and N-Acetyl Aspartate (Naa) metabolites (Cho/Cr, Cho/Naa and Naa/Cr) obtained by MRS. For classification data, four classical data classification methods were used, which are deep learning application, Long Short-Term Memory (LSTM) and comparison. While the class performance results obtained with K-Nearest Neighborhood (KEYK), Support Vector Machine (SVM) and Decision Trees (DT) were found to be around 83±6,36 % from classical methods, this value was obtained as 93% with the LSTM-based deep learning method proposed in the study. This shows that deep learning can be used in a CAD system that can assist the experts in the relevant field in the decision-making process in classifying MRS data by demonstrating high classification performance. [ABSTRACT FROM AUTHOR]

The effect of foreign exchange markets, commodity markets and stock market indices of developing countries on Bist100 is very important. The economies of the countries are strongly dependent on the economies of both their own and other countries, and therefore the markets are affected. Forecasting methods are used to follow the market economically and to make correct decisions. The dataset consists of closing data between January 2017 and October 2021. In order to ensure objectivity in experimental studies, a k=5 and 10-fold cross-validation model was applied. In comparison of models; Mean Absolute Error (MAE), Relative Absolute Error (RAE), Root Mean Square Error (RMSE) and long short-term memory (LSTM) were used. As a result of experimental studies, it has been seen that the LSTM model gives better results than machine learning models. When the test results for the LSTM model are examined, the MAE value is 10.27, the RMSE value is 14.15, and the RAE value is 6.06. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Erciyes Communication / Erciyes İletişim Dergisi is the property of Erciyes University, Faculty of Communication / Erciyes Universitesi Iletism 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 Journal of Statistical Research / İstatistik Araştırma Dergisi is the property of Turkish Statistical 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.)

One of the most important strategic decisions taken by company managers at corporate level is in which areas to invest and how to manage and organize investments. Strategic management proposes portfolio analysis techniques in this regard. It is stated that portfolio analysis techniques, which have been criticized at many points, should be considered as initial techniques and should be supported by other techniques in practice. At this point, the question of whether or not data mining techniques will be used to identify new investment areas and allocate resources has constituted the main problematic of this study. In this study, it has been investigated whether there are association rules between investment areas thus, it is tried to reach the evaluations that may affect the strategic decisions in the process of determining investment areas, by using data belonging to various investor organizations. For this purpose, Association Rules Mining was conducted using data from 102 holding companies. As a result of the study, 35 rules were produced above the 50% confidence level. It is presented as a suggestion to the enterprises in which they can benefit from these rules in their investment planning.

Port state control (PSC) inspections are one of the best ways of improving safety at sea. Therefore, it is vital to determine the parameters that cause deficiencies in the prevention of ship accidents. The main purpose of this study is to analyze the PSC inspection results of Turkish flagged ships using the data mining model. Considering a total of 209 PSC inspection reports resulting in the detention of Turkish flagged ships between 2014 and 2019, the Apriori Algorithm was applied using SPSS Modeler 18.0 software to determine the association rules of deficiencies detected. The study found that the safety of navigation, living/working conditions, and emergency systems are the main factors creating association rules in deficiencies. However, when the deficiencies causing detention were analyzed, the most frequently associated variables were safety of navigation, certificate/documentation, and emergency systems. The results of the study are supposed to be useful for the flag state control mechanism to improve the port state control performance of Turkish flagged ships. We recommend that further research collect more data on the PSC inspection of ships flying other flags to update the proposed models and improve their analysis performance. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Polytechnic is the property of Journal of Polytechnic 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 Turkish Studies - Economics, Finance, Politics is the property of Electronic Turkish Studies 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.)