Sürdürülebilir Kalkınma Amaçları

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• MakaleAçık Erişim2025/r/gumushane/11702
  Future of Clean Cooking Energy Access in Emerging Economies by 2030

  Cakir, Mehmet Ali; Unlu, Ramazan; Cakir, Sümeyra Cay; Xanthopoulos, Petros

  This study assesses the future of clean energy and technology access for cooking in emerging economic blocs—BRICS, MINT, ASEAN, and MENA—through 2030. Cooking contributes 3% of global greenhouse gas emissions, with over half of household emissions coming from cooking. Therefore, clean cooking energy is critical for sustainability and human health. The study aims to evaluate the likelihood of achieving the UN Sustainable Development Goal of universal clean cooking energy access by 2030 and the 2050 net-zero emissions target. Machine learning techniques, such as support vector regression, gradient boosting, and linear regression, alongside an ensemble approach, provide forecasts for these regions. The findings show a varied outlook. Within ASEAN, two countries are expected to reach 100% clean energy access for cooking by 2030, while two are likely to experience a decline. The MENA region shows stronger progress, with eight countries expected to meet the 2030 target. Among BRICS countries, only India is projected to reach full accessibility, while Russia faces a decline. The MINT countries face challenges, with none expected to meet the target, and Nigeria is projected to experience a decrease in clean energy access. The study concludes that the current trajectory makes achieving the 2030 Sustainable Development Goals and the 2050 net-zero emissions target unlikely for these regions. Policymakers must reassess their strategies and learn from successful countries to improve outcomes.

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• MakaleYalnız künye2022/r/gumushane/13035
  Detection of damaged buildings after an earthquake with convolutional neural networks in conjunction with image segmentation

  Ünlü, Ramazan; Kiriş, Recep

  Detecting damaged buildings after an earthquake as quickly as possible is important for emergency teams to reach these buildings and save the lives of many people. Today, damaged buildings after the earthquake are carried out by the survivors contacting the authorities or using some air vehicles such as helicopters. In this study, AI-based systems were tested to detect damaged or destroyed buildings by integrating into street camera systems after unexpected disasters. For this purpose, we have used VGG-16, VGG-19, and NASNet convolutional neural network models which are often used for image recognition problems in the literature to detect damaged buildings. In order to effectively implement these models, we have first segmented all the images with the K-means clustering algorithm. Thereafter, for the first phase of this study, segmented images labeled “damaged buildings” and “normal” were classified and the VGG-19 model was the most successful model with a 90% accuracy in the test set. Besides, as the second phase of the study, we have created a multiclass classification problem by labeling segmented images as “damaged buildings,” “less damaged buildings,” and “normal.” The same three architectures are used to achieve the most accurate classification results on the test set. VGG-19 and VGG-16, and NASNet have achieved considerable success in the test set with about 70%, 67%, and 62% accuracy, respectively.

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• MakaleYalnız künye2021/r/gumushane/14776
  A reduced variance unsupervised ensemble learning algorithm based on modern portfolio theory

  Ünlü, Ramazan; Xanthopoulos, Petros

  Unsupervised ensemble learning or consensus clustering has gained popularity due to its ability to combine multiple clustering solutions into a single solution that is robust and often performs better than the individual ones. There have been several approaches to consensus clustering including voting and weighted voting algorithmic schemes. Although there have been several algorithms for adjusting the weights of a consensus clustering all of them are tuned based on some performance characteristic associated with clustering accuracy. In this paper, we propose a method for incorporating weights by taking into consideration the intra algorithmic variability i.e. algorithms that provide solutions with very different performance upon multiple runs. The methodology is inspired by modern portfolio theory and more specifically from Markowitz model for asset allocation where one is trying to identify the most efficient portfolio through the solution of a convex optimization problem. Here, efficiency is defined as the minimum amount of risk for an expected return. We apply this method to different datasets and compare with respect to performance and robustness. The proposed scheme appears to achieve competitive average performance with very low variability.

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• Kitap BölümüYalnız künye2021/r/gumushane/14681
  Safer-Driving: Application of Deep Transfer Learning to Build Intelligent Transportation Systems

  Ünlü, Ramazan

  Deep learning methods are widely used in the field of transportation as well as in many areas. These days, deep learning methods produce very successful results thanks to big data, where data collection and storage are much easier than in past years. However, it can be quite difficult to collect data or find the right data in some cases. On the other hand, even if the desired data is collected enough, training deep neural networks to learn from these data may not be comfortable in terms of hardware needed for ordinary users. In these cases, transfer learning can be an exit point, which is very popular in deep understanding, especially when the amount of data is limited. In this study, we have aimed to give comprehensive guidance on using a transfer learning strategy to create a detection model to establish a safer transportation system. More specifically, we have developed a classification model to alleviate accidents due to deer on the road. We have implemented VGG-16 convolutional neural network architecture as a part of the structured transfer learning framework. Computational results show that high accuracy performance can be achieved by implementing VGG-16 with the respectively small size of a dataset.

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• Kitap BölümüYalnız künye2021/r/gumushane/13298
  An Assessment of Imbalanced Control Chart Pattern Recognition by Artificial Neural Networks

  Ünlü, Ramazan

  Manual detection of abnormality in control data is an annoying work which requires a specialized person. Automatic detection might be simpler and effective. Various methodologies such as ANN, SVM, Fuzzy Logic, etc. have been implemented into the control chart patterns to detect abnormal patterns in real time. In general, control chart data is imbalanced, meaning the rate of minority class (abnormal pattern) is much lower than the rate of normal class (normal pattern). To take this fact into consideration, authors implemented a weighting strategy in conjunction with ANN and investigated the performance of weighted ANN for several abnormal patterns, then compared its performance with regular ANN. This comparison is also made under different conditions, for example, abnormal and normal patterns are separable, partially separable, inseparable and the length of data is fixed as being 10, 20, and 30 for each. Based on numerical results, weighting policy can better predict in some of the cases in terms of classifying samples belonging to minority class to the correct class.

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• MakaleAçık Erişim2021/r/gumushane/12878
  Predicting stock prices using machine learning methods and deep learning algorithms: The sample of the istanbul stock exchange

  Demirel, Ugur; Cam, Handan; Unlu, Ramazan

  Stock market prediction in financial and commodity markets is a major challenge for speculators, investors, and companies but also profitable with an accurate prediction. Thus, obtaining accurate prediction results becomes extremely important especially while the stock market is essentially volatile, nonlinear, complicated, adaptive, nonparametric and unpredictable in nature. This study aims to forecast the opening and closing stock prices of 42 firms listed in Istanbul Stock Exchange National 100 Index (ISE-100) using well-known machine learning methods, Multilayer Perceptrons (MLP) and Support Vector Machines (SVM) models and deep learning algorithm, Long Short Term Memory (LSTM) by comparing their forecasting performances. The analysis includes 9 years of data from 01.01.2010 to 01.01.2019. For each firm 2249 data for the opening and 2249 for the closing stock prices were established as daily data sets. Forecasting performance of these methods was evaluated by applying different criteria for each model: root mean squared error (RMSE), mean squared error (MSE) and R-squared (R2). The results of this study show that MLP and LSTM models become advantageous in estimating the opening and closing stock prices comparing to SVM model.

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• MakaleYalnız künye2021/r/gumushane/12606
  A robust data simulation technique to improve early detection performance of a classifier in control chart pattern recognition systems

  Ünlü, Ramazan

  The quality control process is essential in maintaining the stability of production systems and proactively detecting abnormalities that may result in high mechanical and labor costs. In this study, a new data simulation strategy was devised to improve the early prediction performance (EPP) of an algorithm. Traditional control chart data simulation methods work based on creating abnormal patterns consisting of only abnormal signals. However, a classifier trained with data samples consisting of only abnormal data signals may fail to early detect abnormality in a real-time production line, in which abnormal signals is obscured by volume of normal signals. From this perspective, training a model by imitating real-world cases can improve the performance of an algorithm in terms of early detection of an abnormality. Normal and abnormal patterns were simulated by implementing a new approach called Mix Ratio Data Simulation (MRDS). The proposed methodology MRDS is compared with the customary data simulation method under the predefined scenarios in terms of EPP. The findings indicated that changing the way of simulating dataset increases the EEP of the machine-learning algorithm regardless of abnormality types and parameters.

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• MakaleYalnız künye2021/r/gumushane/11670
  Cost-oriented LSTM methods for possible expansion of control charting signals

  Ünlü, Ramazan

  Manual quality control may result in delayed detection of a system defect, or none at all, potentially resulting in malfunctions that can lead to disruption of the system, incur extra costs, or complete downtime of the system. To mitigate such issues, more advanced statistics and machine learning-based systems such as SVM, WSVM, etc. are used to automatically detect error signals during the process. However, these methodologies are not developed for sequential dataset such as a classical CCPR dataset. As a result, in this study, we have implemented a preliminary Cost-Oriented Long-Short Term Memory (LSTM), which is designed to learn from a sequential dataset and compare it with SVM and WSVM which are traditional methods utilized in the field. Additionally, we compared the performance of methods in terms of early detection of an abnormal pattern. Based on the findings, the Cost-Oriented LSTM method outperforms SVM and WSVM in the majority of abnormal patterns in terms of both classification and early detection performance. Global accuracy score which is the average of accuracy rates in all combination of abnormal parameters and window lengths shows that LSTM gives a better accuracy score than SVM and WSVM in all seven abnormal patterns detection.

  İncele
• MakaleAçık Erişim2020/r/gumushane/16928
  Verimlilikte Yapay Zeka’nın Rolü: Şarap Kalitesinin Tahminine Yönelik Bir Vaka Çalışması

  Ünlü, Ramazan

  Yapay zeka son yıllarda birçok alanda kullanılmaya başlanmış ve oldukça başarılı sonuçlar elde edilmiştir. Sağlık sektöründensürücüsüz araçlara kadar birçok alanda kullanılan yapay zeka, üretim sektöründe de verimliliğin artırılması için sıklıkla kullanılmıştır.Bu çalışmada normal olarak dağılmamış bir veri setinde yapay zeka algoritmalarının kullanılmasına yönelik bir çerçeve çizilmeyeçalışılmıştır. Normal dağılım gösteren veri setlerinde herhangi bir yapay zeka algoritması kolaylıkla uygulanabilirken normal dağılımgöstermeyen veri setlerinde ya verinin kendisine farklı bir işlem uygulanması gerekir veya algoritmanın teorik yapısının revize edilmesigerekmektedir. Bu açıdan bu çalışmada iki farklı yöntemde uygulanmıştır. İlk olarak literatürde sıklıkla kullanılan Destek VektörMakinaları kullanılmıştır. Buna ek olarak Destek Vektör Makinalarının normal dağılmayan veri setlerinde başarılı sonuçlar vermesiiçin uyarlanmış şekli olan Ağırlıklandırılmış Destek Vektör Makineleri uygulanmıştır. Son olarak Sentetik Azınlık Aşırı ÖrneklemeTekniği (SMOTE) tekniği uygulanmış ve kullanılan veri seti yapay olarak normal dağılıma yakınsanmıştır. Kullanılan üç teknikteduyarlılık, hassaslık, özgüllük, yaygınlık, F skor ve Geometrik Ortalama (G-Mean) değerlendirme kriterleri açısından karşılaştırılmıştır.Çalışma sonucuna göre Ağırlıklandırılmış Destek Vektör Makineleri kullanılan değerlendirme kriterlerine göre en başarılı sonuçlarıvermiştir.

  İncele
• MakaleYalnız künye2020/r/gumushane/14757
  An assessment of machine learning models for slump flow and examining redundant features

  Ünlü, Ramazan

  Over the years, several machine learning approaches have been proposed and utilized to create a prediction model for the high-performance concrete (HPC) slump flow. Despite HPC is a highly complex material, predicting its pattern is a rather ambitious process. Hence, choosing and applying the correct method remain a crucial task. Like some other problems, prediction of HPC slump flow suffers from abnormal attributes which might both have an influence on prediction accuracy and increases variance. In recent years, different studies are proposed to optimize the prediction accuracy for HPC slump flow. However, more state-of-the-art regression algorithms can be implemented to create a better model. This study focuses on several methods with different mathematical backgrounds to get the best possible results. Four well-known algorithms Support Vector Regression, M5P Trees, Random Forest, and MLPReg are implemented with optimum parameters as base learners. Also, redundant features are examined to better understand both how ingredients influence on prediction models and whether possible to achieve acceptable results with a few components. Based on the findings, the MLPReg algorithm with optimum parameters gives better results than others in terms of commonly used statistical error evaluation metrics. Besides, chosen algorithms can give rather accurate results using just a few attributes of a slump flow dataset.

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