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Yayın Reviewing the effects of spatial features on price prediction for real estate market: Istanbul case(IEEE, 2022-09-16) Ecevit, Mert İlhan; Erdem, Zeki; Dağ, HasanIn the real estate market, spatial features play a crucial role in determining property appraisals and prices. When spatial features are considered, classification techniques have been rarely studied compared to regression, which is commonly used for price prediction. This study reviews spatial features' effects on predicting the house price ranges for real estate in Istanbul, Turkey, in the classification context. Spatial features are generated and extracted by geocoding the address information from the original data set. This geocoding and feature extraction is another challenge in this research. The experiments compare the performance of Decision Trees (DT), Random Forests (RF), and Logistic Regression (LR) classifier models on the data set with and without spatial features. The prediction models are evaluated based on classification metrics such as accuracy, precision, recall, and F1-Score. We additionally examine the ROC curve of each classifier. The test results show that the RF model outperforms the DT and LR models. It is observed that spatial features, when incorporated with non-spatial features, significantly improve the prediction performance of the models for the house price ranges. It is considered that the results can contribute to making decisions more accurately for the appraisal in the real estate industry.Yayın Çizge evrişim ağı kullanarak patojen-konak ağlarında protein etkileşim tahmini(IEEE, 2021-06-09) Koca, Mehmet Burak; Karadeniz, İlknur; Nourani, Esmaeil; Sevilgen, Fatih ErdoğanProteinler yaşamsal faaliyetlerin gerçekleşmesinde kritik rol oynayan biyolojik moleküllerdir. Konak canlı proteinleri ile patojen proteinleri arasındaki etkileşimler patojenkonak etkileşim (PHI) ağlarını oluşturmaktadır. Bu iki parçalı etkileşim ağları patojenin hangi yaşamsal faaliyetleri etkilediğini belirlemede ve dolayısıyla sebep olabileceği hastalıkların tespitinde büyük öneme sahiptir. Proteinler arası etkileşimlerin laboratuvar ortamında tespiti hem zaman alıcı hem de maliyetlidir. Deneysel olarak saptanabilen etkileşim sayısının kısıtlı olması ve bazı etkileşimlerin gözden kaçması hesaplamalı tahmin yöntemlerinin geliştirilmesine önayak olmaktadır. Bu çalışmada PHI ağlarında protein etkileşim tahmini yapmayı sağlayan çizge evrişim ağı (GCN) tabanlı bir yöntem sunulmaktadır. Gözetimsiz olarak eğitilen GCN modeli (GraphSAGE) topolojik bilginin yanı sıra temel öznitelik olarak amino asit dizilimlerini kullanmaktadır. Bu çalışma bildiğimiz kadarıyla PHI ağlarında GCN tabanlı etkileşim tahmini sağlayan ilk çalışmadır. Deneysel sonuçlar geliştirilen modelin kıyaslama için kullanılan PHI veri seti üzerinde yüksek performanslı algoritmalardan %10 daha iyi performans göstererek %96 oranında doğrulukla etkileşim tahmini yaptığını göstermektedir.Yayın Hotel sales forecasting with LSTM and N-BEATS(IEEE, 2023-09-15) Özçelik, Şuayb Talha; Tek, Faik Boray; Şekerci, ErdalTime series forecasting aims to model the change in data points over time. It is applicable in many areas, such as energy consumption, solid waste generation, economic indicators (inflation, currency), global warming (heat, water level), and hotel sales forecasting. This paper focuses on hotel sales forecasting with machine learning and deep learning solutions. A simple forecast solution is to repeat the last observation (Naive method) or the average of the past observations (Average method). More sophisticated solutions have been developed over the years, such as machine learning methods that have linear (Linear Regression, ARIMA) and nonlinear (Polynomial Regression and Support Vector Regression) methods. Different kinds of neural networks are developed and used in time series forecasting problems, and two of the successful ones are Recurrent Neural Networks and N-BEATS. This paper presents a forecasting analysis of hotel sales from Türkiye and Cyprus. We showed that N-BEATS is a solid choice against LSTM, especially in long sequences. Moreover, N-BEATS has slightly better inference time results in long sequences, but LSTM is faster in short sequences.Yayın Predictive vector quantization of 3-D polygonal mesh geometry by representation of vertices in local coordinate systems(IEEE, 2005) Bayazıt, Uluğ; Orcay, Özgür; Konur, Umut; Gürgen, Sadık FikretA large family of lossy 3-D mesh geometry compression schemes operate by predicting the position of each vertex from the coded neighboring vertices and encoding the prediction error vectors. In this work, we first employ entropy constrained extensions of the predictive vector quantization and asymptotically closed loop predictive vector quantization techniques that have been suggested in [3] for coding these prediction error vectors. Then we propose the representation of the prediction error vectors in a local coordinate system with an axis coinciding with the surface normal vector in order to cluster the prediction error vectors around a 2-D subspace. We adopt a least squares approach to estimate the surface normal vector from the non-coplanar, previously coded neighboring vertices. Our simulation results demonstrate that the prediction error vectors can be more efficiently vector quantized by representation in local coordinate systems than in global coordinate systems.Yayın A novel regression method for software defect prediction with kernel methods(2013) Okutan, Ahmet; Yıldız, Olcay TanerIn this paper, we propose a novel method based on SVM to predict the number of defects in the files or classes of a software system. To model the relationship between source code similarity and defectiveness, we use SVM with a precomputed kernel matrix. Each value in the kernel matrix shows how much similarity exists between the files or classes of the software system tested. The experiments on 10 Promise datasets indicate that SVM with a precomputed kernel performs as good as the SVM with the usual linear or RBF kernels in terms of the root mean square error (RMSE). The method proposed is also comparable with other regression methods like linear regression and IBK. The results of this study suggest that source code similarity is a good means of predicting the number of defects in software modules. Based on the results of our analysis, the developers can focus on more defective modules rather than on less or non defective ones during testing activities.Yayın Regional analysis and forecasting of broiler and layer poultry production in Türkiye: a statistical and machine learning approach(Liberty Publishing House, 2025-10-20) Aydın, Şahin; Gül, Osman KubilayIntroduction and Purpose: As well as cattle farming and sheep & goat farming, poultry farming also has a significant place in Türkiye’s agricultural economy. There are two important branches, such as broiler and egg in this sector. There is not enough systematic research which examines the regional perspectives and provide future projections in poultry farming as in many areas of agriculture and livestock. The main purpose of this study is to analyze broiler and layer production in Türkiye, identify the main producing regions, and generate forecasts using both traditional statistical models and modern machine learning algorithms. Materials and Methods: The regional broiler and layer production datasets have been acquired from the web-based data platform of Turkish Statistical Institute (TÜİK). Top producer regions and long-term changes in broiler and layer chicken production have been identified using descriptive statistics. Two statistical techniques- Autoregressive Integrated Moving Average (ARIMA) and Exponential Smoothing (ES)- have been used to anticipate the total national production of broiler and egg chicken. Two machine learning models such as Random Forest and Gradient Boosting, nevertheless, have been created. Random Forest allows for assessing variable importance and capturing nonlinearities, and Gradient Boosting provides flexible parameterization (e.g., learning rate, tree depth) and can be tuned effectively to the dataset. The model performance has been evaluated by way of Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and R². The projections for ten years have been generated. Results: The broiler chicken production has been largely concentrated on the north-west line. The top three producer regions are TR42 (Kocaeli–Sakarya–Düzce–Bolu–Yalova), TR33 (Manisa–Afyonkarahisar–Kütahya–Uşak), and TR22 (Balıkesir–Çanakkale) respectively. The models ES and ML envisioned moderate growth in broiler chicken production, on the other hand, the suggestion of ARIMA is a flatter trend. The top three producer regions in layer chicken production are TR33 (Manisa, Afyonkarahisar, Kütahya, Uşak), TR52 (Konya–Karaman), and TR83 (Samsun–Tokat–Çorum–Amasya) respectively. A slight decline from the recent peak has been indicated by ES. On the other hand, moderate growth has been referred to by ARIMA. ML models harmonized the differences between statistical models by drawing a more balanced growth path. Discussion and Conclusion: This research shows the importance of using both statistical and machine learning approaches together with the purpose of identifying the trend dynamics and nonlinear relationships in broiler and layer chicken production. The results reveal that north-western regions are leading in the broiler chicken production. On the other hand, western-central regions are dominating the layer chicken production. The results of this study can be utilized to create critical policy deductions and decisions of targeted investments by considering these distinct geographies. The proposed methodological framework can be adapted to other livestock production data as well.
