No abstract

The Global Innovation Index (GII) aims to rank countries using different innovation factors. This ranking list enables countries to observe their potential status according to the rankings of other countries. The countries are classified under four groups according to the World Bank Income Group Classification on the GII list. The groups are named as; low income (LI), lower-middle income (LM), upper-middle income (UM) and high income (HI). Also, every country has a score in this ranking list. In this study, the ranking scores of 128 countries are estimated using the artificial neural network (ANN). We chose the relevant 27 features on GII 2016 Report, as input data. The significance of this paper is that; it is the first curve fitting and estimation of the score processes on GII 2016 dataset. The low root mean square error (RMSE) value which is obtained in an experimental study shows that the fitting structure is good enough to determine the approximate score of the countries in GII list. The results also show that the selected 27 features are sufficient for obtaining the income score of the countries. Increasing the number of features would lower the RMSE value and enable better approximation in the curve fitting process. The final results can assist the countries in achieving long-term output growth and improving their innovation capabilities.

Purpose The current Classification of Periodontal and Peri-Implant Diseases and Conditions, published and disseminated in 2018, involves some difficulties and causes diagnostic conflicts due to its criteria, especially for inexperienced clinicians. The aim of this study was to design a decision system based on machine learning algorithms by using clinical measurements and radiographic images in order to determine and facilitate the staging and grading of periodontitis. Methods In the first part of this study, machine learning models were created using the Python programming language based on clinical data from 144 individuals who presented to the Department of Periodontology, Faculty of Dentistry, Süleyman Demirel University. In the second part, panoramic radiographic images were processed and classification was carried out with deep learning algorithms. Results Using clinical data, the accuracy of staging with the tree algorithm reached 97.2%, while the random forest and k-nearest neighbor algorithms reached 98.6% accuracy. The best staging accuracy for processing panoramic radiographic images was provided by a hybrid network model algorithm combining the proposed ResNet50 architecture and the support vector machine algorithm. For this, the images were preprocessed, and high success was obtained, with a classification accuracy of 88.2% for staging. However, in general, it was observed that the radiographic images provided a low level of success, in terms of accuracy, for modeling the grading of periodontitis. Conclusions The machine learning-based decision system presented herein can facilitate periodontal diagnoses despite its current limitations. Further studies are planned to optimize the algorithm and improve the results.

Ülkelerin gelişmesinde sanayinin büyük bir rolü olup geçmişten günümüze kadar sanayi faaliyetleri hız kesmeden ilerlemiştir. Bu gelişime ayak uyduran ülkeler ucuz hammaddeleri işleyip yüksek ücretlere satarak hazinelerini genişletmişlerdir. Endüstri 4.0 devriminin şafağında bu gelişimden geri kalınmaması gerekmekte olup gerek sanayi gerekse teknoloji birlikte geliştirilmelidir. Sanayileşmedeki en büyük ihtiyaçlardan biri elektrik enerjisi olup Türkiye'de elektrik enerjisi tüketiminin sanayi için oranları yıllara göre %40 ile %60 arasında değişmektedir. Bu oranlar düşünüldüğünde elektrik tüketiminin büyük bir payı sanayiye ait olup ileriye yönelik planlamaların yapılmasına kesinlikle ihtiyaç duyulmaktadır. Türkiye'nin Endüstri 4.0 ile birlikte gelecek planlarında elektrik enerjisi sıkıntısına düşmemesi için ileriye yönelik tahminleme ve buna uygun yeni tesislerin kurulumlarının planlanması gerekmektedir. Bu çalışmada, Türkiye'de 1970-2016 yıllarına ait sanayi için elektrik tüketimleri yapay sinir ağları ile modellenmiş olup elde edilen model daha sonra 2017-2023 yıllarındaki tüketimi tahmin etmek için kullanılmıştır. Yapay sinir ağı birisi-dışarıda çapraz doğrulama yöntemi ile test edilmiş olup elde edilen sonuçlara göre; ortalama karesel hataların karekökü değeri 8.99, ortalama mutlak yüzde hata %31.6 ve belirleme katsayısı ise 0.94 olarak elde edilmiş olup bu sonuçlar modelin iyi kurulduğunu ortaya koymaktadır. Ayrıca 2023 yılına kadar olan tahmin 1 Bu çalışma, Uluslararası EMI Sosyal Bilimler Kongresinde (EMISSC 2018) sözlü olarak sunulan çalışmanın genişletilmiş halidir.

Artificial neural networks, is one of the most preferred artificial intelligence techniques in the modeling of complex systems today and the models are based on the working structure of the nerve cells in the human brain. Autism spectrum disorder is a complex neuro-developmental disorder that is congenital or occurs at an early age. Since early diagnosis has a very important role in the treatment, there are many studies on this subject. In this study, a subset of current autism spectrum disorder data obtained from UCI machine learning repository for adolescents has used. In order to test the success of the model, after the necessary preprocesses have performed on the data set, the data has separated into training and test set and classified with the trained network. As a result, 100% accuracy rate in the training set and 96.77% accuracy rate in the test set are achieved. Sensitivity, Specificity and F-measure values obtained in the test set are 0.94, 1.0 and 0.97, respectively and reveals the model success.

No abstract

This work is licensed under Creative Commons Attribution-NonCommercial 4.0 International License ÖZ Veri madenciliği teknikleri, veriler arasında gizli kalmış olan örüntüleri ortaya çıkarmayı amaçlamaktadır. Bu kapsamda, tıp gibi birçok alanda yaygın bir biçimde kullanılmaktadır. Teşhis ve tedavisi oldukça zor ve uzun bir süreçten oluşan otizm spektrum bozukluğu doğuştan gelen ya da yaşamın ilk yıllarında ortaya çıkan karmaşık bir nöro-gelişimsel bozukluktur. Bu çalışmada 292 çocuktan toplanan gerçek ve güncel otizm spektrum bozukluğu verileri kullanılmıştır. Veri seti 20 girdi özniteliği ve 1 çıktı özniteliğine sahiptir. Çıktı özniteliği otizmin bulunup bulunmadığını ifade etmektedir. Çalışma da öncelikle veri seti üzerinde eksik verilerin tamamlanması, kategorik verilerin sayısallaştırılması, normalizasyon gibi veri ön işleme aşamaları gerçekleştirilmiştir. Devamında ise öznitelikler yapay sinir ağları ve dilsel kuvvetli sinir-bulanık sınıflayıcı ile sınıflandırılmış, k-means ve x-means ile kümelenmiştir. Her bir yöntemin sonuçları değerlendirilmiş ve performanslar karşılaştırılmıştır.

In this paper, it has been aimed that the strategic management approach in the metropolitan municipalities in Turkey are evaluated by examining the vision, mission and core value statements on their strategic plans which have been prepared and put into effect for the period from 2015 to 2019. Firstly, strategic management is introduced, and legislative / regulatory developments related to the strategic management approach in Turkish Public Administration are summarized in the literature review. In the methodology section, the vision, mission and core value statements specified in the strategic plans of 26 metropolitan municipalities are examined by means of 12 criteria, consisting of 4 criteria for each statement type, which have been determined according to the legislations and regulations related to strategic planning in Turkey in line with the literature. In addition, the most common values in the core value statements are determined with their frequencies by means of content analysis method. As a conclusion, it has been determined that the metropolitan municipalities in Turkey have substantially met the requirements in relation to the vision, mission and core value statements although there are some missing points such as activity area, purpose of existence, services provided, future ideals and inspirations especially in mission and vision statements. Moreover, it is observed that the metropolitan municipalities have some common values such as transparency, participative management, economical use of resources, trust, justice and equality, accountability, environment-friendly practices, commitment to history and culture, and availability for change and development.