Yazar "Yücebaş, Sait Can" seçeneğine göre listele
Listeleniyor 1 - 11 / 11
Sayfa Başına Sonuç
Sıralama seçenekleri
Öğe A C4.5 - CART DECISION TREE MODEL FOR REAL ESTATE PRICE PREDICTION AND THE ANALYSIS OF THE UNDERLYING FEATURES(Konya Teknik Univ, 2022) Yücebaş, Sait Can; Doğan, Melike; Genç, LeventThe machine learning approaches are used in different domains for price prediction. Real estate price prediction comes to fore in recent years. However, most of the studies focus on the prediction performance and the factors affecting the price are often ignored. In this study, a C4.5 - CART model to predict the residential real estate prices is developed. This model is capable of predicting both numeric and categorical price for real estate properties. In addition, the factors affecting the price are reveled and analyzed in detail. The performance of the developed model is compared to Direct Capitalization model, which is used as a gold standard in the domain. Both models are tested on a dataset that includes updated real time data that is gathered by a web scraper. For numeric prediction, RMSE of the developed model is 13.169 and 358.69 for the Direct Capitalization model. KAPPA and accuracy is used for the categorical prediction. The model has 81% KAPPA and 88% accuracy.Öğe A novel approach to distinguish complicated and non-complicated acute cholecystitis: Decision tree method(Lippincott Williams and Wilkins, 2023) Gojayev, Afig; Karakaya, Emre; Erkent, Murathan; Yücebaş, Sait Can; Aydın, Hüseyin Onur; Kavasoğlu, Lara; Aydoğan, Cem; Yıldırım, SedatIt is difficult to differentiate between non-complicated acute cholecystitis (NCAC) and complicated acute cholecystitis (CAC) preoperatively, which are two separate pathologies with different management. The aim of this study was to create an algorithm that distinguishes between CAC and NCAC using the decision tree method, which includes simple examinations. In this retrospective study, the patients were divided into 2 groups: CAC (149 patients) and NCAC (885 patients). Parameters such as patient demographic data, American Society of Anesthesiologists (ASA) score, Tokyo grade, comorbidity findings, white blood cell (WBC) count, neutrophil/lymphocyte ratio, C-reactive protein (CRP) level, albumin level, CRP/albumin ratio (CAR), and gallbladder wall thickness (GBWT) were evaluated. In this algorithm, the CRP value became a very important parameter in the distinction between NCAC and CAC. Age was an important predictive factor in patients with CRP levels >57 mg/L, and the critical value for age was 42. After the age factor, the important parameters in the decision tree were WBC and GBWT. In patients with a CRP value of ≤57 mg/L, GBWT is decisive and the critical value is 4.85 mm. Age, neutrophil/lymphocyte ratio, and WBC count were among the other important factors after GBWT. Sex, ASA score, Tokyo grade, comorbidity, CAR, and albumin value did not have an effect on the distinction between NCAC and CAC. In statistical analysis, significant differences were found groups in terms of gender (34.8% vs 51.7% male), ASA score (P < .001), Tokyo grade (P < .001), comorbidity (P < .001), albumin (4 vs 3.4 g/dL), and CAR (2.4 vs 38.4). By means of this algorithm, which includes low-cost examinations, NCAC and CAC distinction can be made easily and quickly within limited possibilities. Preoperative prediction of pathologies that are difficult to manage, such as CAC, can minimize patient morbidity and mortality.Öğe An Ontology based product recommendation system for next generation e-retail(Taylor and Francis Ltd., 2023) Tiryaki, Ali Murat; Yücebaş, Sait CanThe number of e-commerce resources has increased considerably. Thus, it has become important for sellers to be able to quickly recommend products to potential buyers. Some product recommendation systems developed for this purpose. However, due to the lack of semantics, the systems’ success in recommending accurate products according to user preferences is low. In this study carried out within the scope of a state-funded R&D project, an ontology-based personalized product recommendation system named E-Prod was developed. E-Prod tracks various e-commerce systems in real time and transfers the product information to the ontology model. E-Prod uses a novel recommendation approach that combines machine learning and semantic matching to provide personalized recommendations. The system learns user’s preferences based on semantic relationships between products by monitoring their behaviors. In this way, accurate recommendations are made by semantic matching between products and user preferences. E-Prod has been tested with over 250 registered users and compared to traditional collaborative recommendations in terms of accuracy, precision, and recall. As a result, E-Prod outperformed traditional methods by 92.79% accuracy, 92.93% precision, and 90.58% recall. Within the scope of this study, E-Prod covers the clothing, shoes, and bag retail sectors. However, it provides a generic infrastructure for new generation e-commerce systems. Its reusable modules can be adapted to any domain.Öğe Determination of the relationship between housing characteristics and housing prices before and after the Kahramanmaraş earthquake using machine learning: A case study of Adana, Turkiye(Yildiz Technical Univ, Fac Architecture, 2024) Doğan, Simge; Genç, Levent; Yücebaş, Sait Can; Uşaklı, Metin; Dumlu, CengizhanEarthquakes have a significant impact on the real estate sector. Damage caused by earthquakes leads to an imbalance in the supply and demand for housing, thus temporarily causing stagnation in the real estate sector. Two earthquakes occurred in the Pazarc & imath;k and Elbistan districts of Kahramanmara & scedil; on February 6, 2023, at 04:17 am with a magnitude of 7.7 and at 13:24 pm with a magnitude of 7.6. A machine learning-based model was created to analyze the change in house prices and the variables affecting the price during the earthquake, which is called the Disaster of the Century. After the earthquake, the prices of houses for sale in the central districts of Adana province (Seyhan, Y & uuml;re & gbreve;ir, Sar & imath;& ccedil;am, and & Ccedil;ukurova), where there was the least damage, were collected from the relevant website with a web scraper. These data were classified as categorical and numerical datasets, and the necessary pre-processing stage for machine learning algorithms was performed. The characteristics that change and are effective in housing preferences before the earthquake (February 2022) and after the earthquake (February 2023) were determined by the decision tree method, which is one of the machine learning algorithms. In this context, it is aimed to determine the housing variables that are effective in before- and after-earthquake pricing in the central districts of Adana province. In the study, while 'Building Age and Number of Rooms' are effective in determining the price in 2022, 'Housing Shape and Facade' features come to the fore in 2023. The housing characteristics that affect the price change in two years. The change in housing preference criteria after the earthquake shows that the lifestyle in cities has also changed. According to this change, it requires the development of new approaches in urban design and planning approaches and is expected to be a reference for future studies.Öğe Gövde-Türk: A Turkish stemming method(Institute of Electrical and Electronics Engineers Inc., 2017) Yücebaş, Sait Can; Tintin, RabiaThis study presents a stemming method for Turkish Language that searches inflectional suffixes at the end of the words and eliminate them according to the rules provided by finite state machines and longest match manner. © 2017 IEEE.Öğe Karmaşık Hastalıkların Teşhisinde Veri Madenciliği Yöntemlerinin Başarım Karşılaştırması(Çanakkale Onsekiz Mart Üniversitesi, 2018-05) Yücebaş, Sait CanBütünsel genom ilişkilendirme çalışmalarında (BGİÇ) ortaya çıkan verilerin yüksek miktarda ve çok boyutlu olması, profillerin hastalıklarla ilişkilendirilmesi ve buradan teşhise gidilmesi sırasında farklı veri madenciliği yöntemlerinin kullanılması ile mümkün olmaktadır. Yapılan çalışmada 1025 vaka ve 531 kontrolden oluşan melonom veri kümesi ile farklı etnik kökenli 2325 vaka ve 2350 kontrolden oluşan ve prostat kanseri veri kümesi kullanılmıştır. Bu hastalıklarla ilgili profiller Karar Ağacı, Naive Bayes, Destek Vektör Makinası gibi farklı veri madenciliği yöntemleri ile incelenmiştir. Her iki hastalık için de destek vektör makinası kullanılan yöntemler arasında en iyi başarımı sağlamıştır. İlgili yöntem prostat kanseri veri kümesinde %75.68’lık bir kesinlik değeri sunarken, melonom veri kümesi için %78,6’lik bir kesinlik değeri yakalamıştır.Öğe MovieANN: A Hybrid Approach to Movie Recommender Systems Using Multi Layer Artificial Neural Networks(2019) Yücebaş, Sait CanThe amount of data in World Wide Web is growing exponentially. Users are often lost inthis vast ocean of data. In order to filter the valuable information from vast amount of data,recommendation systems are used. These systems are based on collaborative filtering,content based filtering and hybrid approaches. We combined collaborative and contentbased filtering to build a hybrid movie recommendation system, MovieANN, based onneural network model. To make better recommendations in a collaborative approach, bothuser and movie clusters are formed. In addition to rating information, content informationwas also considered in the formation of the clusters. Clusters are formed according to KMeans and X-Means algorithms. Final clusters are chosen according to Davies-BouldinIndex and intra cluster distance. Homogeneity and density of the clusters are alsoconsidered. Movie and user clusters are mapped in the recommendation phase. The modelis tested on a MoiveLens 1M dataset that consists of six thousand users, four thousandmovies and one million ratings. Four clusters are formed to represent movie – usermappings and for each cluster, a recommendation model based on multi-layer neuralnetwork is constructed. The recommendation performance in terms of accuracy is 84.52%,84.54% in terms of precision and 99.98% in terms of recall.Öğe Öğrenci ve Akademisyenlerin E-Öğrenmeye Hazır Bulunuşlarının Daha Az Soru ile Sınıflandırılması(2023) Karacan, Merter Hami; Yücebaş, Sait CanKüresel boyuttaki KOVİD-19 pandemisinin etkisiyle birlikte tüm dünyada alışveriş, çalışma ve eğitim gibi konular “uzaktan” ve “elektronik” olarak daha fazla değerlendirilmeye başlandı. Mart 2020’deki Yüksek Öğretim Kurumu kararının ardından Türkiye’deki tüm üniversiteler eğitimlerine uzaktan devam etme kararı almıştır. Bu karar sonucunda akademisyenlerin ve öğrencilerin e-öğrenme sürecine ne kadar hazır olduklarını değerlendiren çalışmalar da hızla artmıştır. Bu çalışmada iki farklı üniversitedeki akademisyen ve öğrencilerin e-öğrenmeye ne kadar hazır olduklarının incelendiği bir anket çalışmasına makine öğrenmesi teknikleri uygulanmış, daha az soru ile aynı sonuçların elde edilmesi hedeflenmiştir. Soruların azaltılmasında özyinelemeli öznitelik eleme yöntemi kullanılmış, azaltılan sorular ile en yüksek Cronbach Alpha değerini CatBoost ve XGBoost yöntemleri sağlamıştır. Ek olarak, en yüksek sonuç tahmin performansını destek vektör makineleri sağlamıştır. Destek vektör makineleri, daha az soru ile akademisyen yanıtlarını %100, öğrencilerin yanıtlarını %97.48 doğrulukla tahmin etmiştir. Önerilen yaklaşım, anket sonuçlarında en az kayıpla uzun süren anket verisi toplama süresini azaltmada yardımcı olacaktır.Öğe Optimized Decision Tree and Black Box Learners for Revealing Genetic Causes of Bladder Cancer(Tech Science Press, 2023) Yücebaş, Sait CanThe number of studies in the literature that diagnose cancer with machine learning using genome data is quite limited. These studies focus on the prediction performance, and the extraction of genomic factors that cause disease is often overlooked. However, finding underlying genetic causes is very important in terms of early diagnosis, development of diagnostic kits, preventive medicine, etc. The motivation of our study was to diagnose bladder cancer (BCa) based on genetic data and to reveal underlying genetic factors by using machine-learning models. In addition, conducting hyperparameter optimization to get the best performance from different models, which is overlooked in most studies, was another objective of the study.Within the framework of these motivations, C4.5, random forest (RF), artificial neural networks (ANN), and deep learning (DL) were used. In this way, the diagnostic performance of decision tree (DT)-based models and black box models on BCa was also compared. The most successful model, DL, yielded an area under the curve (AUC) of 0.985 and a mean square error (MSE) of 0.069. For each model, hyper-parameters were optimized by an evolutionary algorithm. On average, hyper-parameter optimization increased MSE, root mean square error (RMSE), LogLoss, and AUC by 30%, 17.5%, 13%, and 6.75%, respectively. The features causing BCa were extracted. For this purpose, entropy and Gini coefficients were used for DT-based methods, and the Gedeon variable importance was used for black box methods. The single nucleotide polymorphisms (SNPs) rs197412, rs2275928, rs12479919, rs798766 and rs2275928, whose BCa relations were proven in the literature, were found to be closely related to BCa. In addition, rs1994624 and rs2241766 susceptibility loci were proposed to be examined in future studies.Öğe Predictors of rapidly progressive glomerulonephritis in acute poststreptococcal glomerulonephritis(Springer Science and Business Media Deutschland GmbH, 2023) Karakaya, Deniz; Güngör, Tülin; Kargin Çakıcı, Evrim; Yazılıtaş, Fatma; Çelikkaya, Evra; Yücebaş, Sait Can; Bülbül, MehmetBackground Acute post-streptococcal glomerulonephritis (APSGN) is an immune-mediated inflammatory respsonse in the kidneys caused by nephritogenic strains of group A (3-hemolytic streptococcus (GAS). The present study aimed to present a large patient cohort of APSGN patients to determine the factors that can be used for predicting the prognosis and progression to rapidly progressive glomerulonephritis (RPGN).Methods The study included 153 children with APSGN that were seen between January 2010 and January 2022. Inclusion criteria were age 1-18 years and follow-up of >= 1 years. Patients with a diagnosis that could not be clearly proven clinically or via biopsy and with prior clinical or histological evidence of underlying kidney disease or chronic kidney disease (CKD) were excluded from the study.Results Mean age was 7.36 +/- 2.92 years, and 30.7% of the group was female. Among the 153 patients, 19 (12.4%) progressed to RPGN. The complement factor 3 and albumin levels were significantly low in the patients who had RPGN (P = 0.019). Inflammatory parameters, such as C-reactive protein (CRP), platelet-to-lymphocyte ratio, CRP/albumin ratio, and the erythrocyte sedimentation rate level at presentation were significantly higher in the patients with RPGN (P < 0.05). Additionally, there was a significant correlation between nephrotic range proteinuria and the course of RPGN (P = 0.024).Conclusions We suggest the possibility that RPGN can be predicted in APSGN with clinical and laboratory findings.Öğe Price Prediction and Determination of the Affecting Variables of the Real Estate by Using X-Means Clustering and CART Decision Trees(Graz Univ Technolgoy, Inst Information Systems Computer Media-Iicm, 2024) Yücebaş, Sait Can; Yalpir, Şükran; Genç, Levent; Doğan, MelikeThe use of machine learning in real estate is quite new. When the working area is large, the factors affecting the price may vary according to the geographical regions and socioeconomic factors. It is thought that the price prediction performance of a model that will reflect these differences will be more successful than a general model. Unsupervised learning methods can be used both to increase performance and to show the variation of different factors affecting the price according to regions. With this aim, a hybrid model of X -Means clustering and CART decision trees was established in this study. This model successfully learned the geographical and physical variables that affect the price. The prediction performance of the model was compared with the direct capitalization method, which is the gold standard in the domain. The hybrid model has a superior performance over direct capitalization in terms of mean square error, root mean square error and adjusted R -Squared metrics. The scores were 72.86, 0.0057 and 0.978, respectively. The effect of clustering was also examined. Clustering increased the prediction performance by 36%.
