Cihan Çilgin

1990’lı yıllarda ortaya çıkan ERP yaklaşımı, günümüzde yerini kendi verisini üreten ve bu verileri yapay zekâ yaklaşımını kullanarak analiz edebilen bir yapıya bırakmıştır. Bu durum da ERP sistemlerinin akıllılık boyutunda incelenmesi gerekliliğini ortaya çıkarmıştır. Bir başka ifadeyle, akıllı ERP; içerdiği veriyi yönetirken kendi verisini de üretmeye başlayan ve bunları yapay zekâ teknikleriyle birleştiren bir karar mekanizmasını oluşturmuştur. Akıllı ERP’nin ortaya koyduğu kayıtların/verilerin yönetilmesi, karara döndürülebilmesi ve rasyonel anlamda modernize edilmesi boyutunda istatistiksel analiz, iş zekâsı ve yapay zekâ bu aşamadaki temel bakış açılarıdır. Bu çalışmanın amacı, mevcut literatür kapsamında, ERP yolculuğunu ele alıp, yapay zekânın ERP sistemlerine entegrasyonu sonucu doğan akıllı ERP kavramını incelemektir. Yeni nesil ERP olarak da adlandırılan bu yaklaşım; makine öğrenmesi algoritmaları ve analitik araçlar tarafından yönlendirildiğinden dolayı, otomasyon sürecini kolay ve hızlı hale getirerek işletmelerin performans ve rekabet gücünü artırma potansiyeli taşımaktadır.

Purpose: Vitamin D level is emphasized as an important biomarker in determining risk factors for different diseases. Vitamin D is an important vitamin for human health and its deficiency is associated with serious health problems. Therefore, it is of great importance to detect vitamin D deficiency, which can be easily prevented and treated. The possible relationship between vitamin D deficiency and musculoskeletal pain, osteoporosis, diabetes mellitus, hypertension is frequently discussed in researches. In this research, it is aimed to analyze the factors in determining the vitamin D level and the decision rules related to it. Methods: A descriptive framework based on one of the machine learning techniques, that is decision tree is followed. The data used to create the decision rules were obtained from volunteers between the ages of 18-85 who applied to Izmir Katip Çelebi University Atatürk Training and Research Hospital Infectious Diseases and Family Medicine Polyclinics and agreed to participate in the study between 01.03.2017 and 01.09.2017. Results: It was observed that age, gender and laboratory test values are strong predictors for vitamin D level. As a result of two CART (Classification and Regression Trees) models, %90.47 and %95 predictive accuracies were observed respectively. In the first model, uric acid, age and creatine; in the second model TSH, ALP and smoking(yes) were the most important three biomarkers affecting vitamin D level. Discussion: The collected features give a comprehensive list of variables that have an effect on vitamin D in the dataset under consideration. Important findings of the study include not only the identification of these variables, but also the effective categorization determination procedures. In contrast to previous research, the Age variable is the most influential factor within the scope of this dataset, which includes demographic information on patients and their existing disorders.

The 13th Presidential election has created a wide agenda in many countries as well as in Turkey. In this election period, along with traditional media tools, social media tools were also used frequently in the execution of election campaigns. Interactions received through social media platforms once again proved the effective power of social media tools to reach large masses of all parties and party leaders. For this reason, the Open Microphone program organized by Oğuzhan Uğur, in which many politicians participated, was followed with interest not only in Turkey's agenda, but also in the world's agenda. In this context, this study aims to reveal various analysis findings with Emotion Analysis methods, especially from the comments made within the scope of this program. For this purpose, in this study, a total of 261.728 user comments, specific to 7 different politicians, were analyzed using the NRC emotion dictionary. With the NRC emotion dictionary, a broader emotional polarity was obtained, including the emotions of anger, fear, trust, anticipation, surprise, sadness, joy, and disgust, in addition to positive or negative emotion polarity. As a result of the findings, this study reveals that the emotion analysis of the masses through Youtube comments or different platforms can be a critical source of information for political campaigns.

Since COVID-19 has spread almost across any country and is a serious threat to mankind, it was declared to be a pandemic by WHO. Forecasting the results of a pandemic is a quite important and difficult task for policy makers and decision makers. The aim of this study is to forecast the daily case numbers in Turkey by using various time series modeling approaches. In this context, positive case numbers between March 11, 2020, and December 24, 2021, were taken into account in this study. This study, with the number of observations it covers, differentiates from other studies which have been conducted with few number of observations. In this study, all the waves during the COVID 19 pandemic were included in the analysis by studying a more extensive time period. Moreover, in our study, along with a comparison of machine learning algorithms by making case forecasting with these algorithms, increasing the forecasting performance was aimed by combining the predictions of all models used with the stacking approach under a single model. By taking all the related studies analyzed into account, our study, as far as we know, is the first one to assess this many model performances together and make a stacking model on COVID-19 case numbers. The findings obtained from the study prove that forecasting of the cases validated via the developed stacking model were made with high accuracy, and all ensemble learning approaches produce better results than individual methods.

For those who invest in real estate as an investment tool, as well as those who buy and sell real estate, the price of real estate should be predicted realistically and with the highest accuracy. It should be noted that the predict model should be the most appropriate representation of the underlying fundamentals of the market. Otherwise, the mistake to be made in the real estate valuation will cause some undesirable results such as inconsistent and unhealthy increase or decrease of the property tax, excessive gains or losses in favor of some groups, and adverse effects on investors and potential real estate owners. At this point, data-driven real estate valuation approaches are preferred more frequently to create highly accurate and unbiased estimates. However, the consistency, precision and accuracy of the models realized with machine learning approaches are directly related to the data quality. At this point, the effects of outlier detection on prediction performance in real estate valuation are investigated with a large data set obtained in this study. For this purpose, a heterogeneous data set with 70.771 real estate data and 283 variables, 4 different outlier detection methods were tested with 3 different machine learning approaches. The empirical findings reveal that the use of different outlier detection approaches increases the prediction performance in different ranges. With the best outlier detection approach, this performance increase was at a high 21,6% for Random Forest, with a 6,97% increase in average model performance.

Owning a house is one of the most important decisions that low and middle income people make in their lives. The real estate market is a significant factor of the national economy as much as it is important for individuals. Therefore, predicting real estate values or real estate valuation is beneficial and necessary not only for buyers, but also for real estate agents, economists and policy makers. This issue represents an active area of research, as individuals, companies and governments hold considerable assets in real estate. In this context, the aim of the study is to predict real estate prices with Machine Learning methods using the real estate sales data set in June and July 2021 belonging to the province of Ankara. In particular, it is to perform a comprehensive comparison on Machine Learning regression types methods that give successful prediction results in various but similar tasks, which are not included in the real estate literature. Real estate data obtained over the Internet was first included in a detailed data preprocessing process, and then Linear, Lasso and Ridge Regression, XGBoost and Artificial Neural Networks (ANN) methods were used on this dataset. According to empirical findings, XGBoost and ANNs appear as very important alternatives in predicting real estate sales prices.

Dünyada milyarlarca kullanıcısı bulunan sosyal medya platformlarının yükselişiyle birlikte bilginin yayılması her zamankinden daha kolay hale gelmiştir. COVID-19 pandemisi aşılar da dâhil olmak üzere birçok konunun tartışılmasında sosyal medya kullanımını artırmıştır. Bu çalışmanın amacı, Türkiye’de, özellikle sosyal medya kullanıcılarının COVID-19 aşılarına ilişkin tutumunu ve endişelerini daha iyi anlamak adına Twitter üzerinde elde edilen aşıyla ilgili tweetlerin makine öğrenmesi ile kamu duyarlılığını analiz etmektir. Bu amaç doğrultusunda çalışma altı farklı sınıflandırma görevinde kullanılan makine öğrenmesi algoritması karşılaştırılarak en yüksek doğruluk oranına sahip Destek Vektör Makinesi, XGBoost ve Rastgele Orman ile bir kolektif öğrenme yöntemi olan çoğunluk oylama yöntemi geliştirilmiştir. Çoğunluk oylama yöntemlerinde birisi olan Yumuşak Oylama yöntemi hem Sert Oylama yaklaşımdan hem de bireysel diğer altı makine öğrenmesi yaklaşımlarından daha yüksek başarı oranı ile %90,5 başarı oranına ulaşmıştır. En yüksek doğruluk oranına sahip olan Yumuşak Oylama yöntemi ile Twitter’dan elde edilen 153 güne ait 412.588 adet günlük tweet analiz edilerek sonuçlar raporlanmıştır. Çalışmanın bulguları son derece çarpıcı olup, diğer ülkeler üzerine yapılan çalışmalardan da farklılık göstermektedir. Bu çalışma bildiğimiz kadarıyla Türkiye’de COVID-19 aşılarına yönelik duygu analizi gerçekleştiren ilk çalışma olmakla birlikte sosyal medya üzerinden duygu analizi yaklaşımıyla COVID-19 aşılarına ilişkin duyarlılığı izlemek için değerli ve kolayca uygulanan bir araç olduğunu göstermektedir.