Ünlü, Ramazan
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Ünlü, Ramazan
Unlu, Ramazan
Unlu, Ramazan
Job Title
Doç. Dr.
Email Address
ramazan.unlu@agu.edu.tr
Main Affiliation
02.02. Endüstri Mühendisliği
02. Mühendislik Fakültesi
01. Abdullah Gül University
02. Mühendislik Fakültesi
01. Abdullah Gül University
Status
Current Staff
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ORCID ID
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Turkish CoHE Profile ID
Google Scholar ID
WoS Researcher ID
Sustainable Development Goals
4
QUALITY EDUCATION
1
Research Products
7
AFFORDABLE AND CLEAN ENERGY
2
Research Products
8
DECENT WORK AND ECONOMIC GROWTH
2
Research Products
11
SUSTAINABLE CITIES AND COMMUNITIES
1
Research Products
13
CLIMATE ACTION
2
Research Products
14
LIFE BELOW WATER
1
Research Products
17
PARTNERSHIPS FOR THE GOALS
2
Research Products
Documents
20
Citations
301
h-index
10
Documents
14
Citations
205
Scholarly Output
10
Articles
9
Views / Downloads
261/179
Supervised MSc Theses
1
Supervised PhD Theses
0
WoS Citation Count
2
Scopus Citation Count
10
WoS h-index
1
Scopus h-index
2
Patents
0
Projects
0
WoS Citations per Publication
0.20
Scopus Citations per Publication
1.00
Open Access Source
6
Supervised Theses
1
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Now showing 1 - 10 of 10
Article Citation - WoS: 1Citation - Scopus: 2Prediction of Biomechanical Properties of Ex Vivo Human Femoral Cortical Bone Using Raman Spectroscopy and Machine Learning Algorithms(Elsevier, 2025) Unal, Mustafa; Unlu, Ramazan; Uppuganti, Sasidhar; Nyman, Jeffry S.This study applied Raman spectroscopy (RS) to ex vivo human cadaveric femoral mid-diaphysis cortical bone specimens (n = 118 donors; age range 21-101 years) to predict fracture toughness properties via machine learning (ML) models. Spectral features, together with demographic variables (age, sex) and structural parameters (cortical porosity, volumetric bone mineral density), were fed into support vector regression (SVR), extreme tree regression (ETR), extreme gradient boosting (XGB), and ensemble models to predict fracture-toughness metrics such as crack-initiation toughness (Kinit) and energy-to-fracture (J-integral). Feature selection was based on Raman-derived mineral and organic matrix parameters, such as nu 1Phosphate (PO4)/CH2-wag, nu 1PO4/ Amide I, and others, to capture the complex composition of bone. Our results indicate that ensemble models consistently outperformed individual models, with the best performance for crack initiation toughness (Kinit) prediction being achieved using the ensemble approach. This yielded a coefficient of determination (R2) of 0.623, root-mean squared error (RMSE) of 1.320, mean absolute error (MAE) of 1.015, and mean percentage absolute error (MAPE) of 0.134. For prediction of the overall energy to propagate a crack (J-integral), the XGB model achieved an R2 of 0.737, RMSE of 2.634, MAE of 2.283, and MAPE of 0.240. This study highlights the importance of incorporating mineral quality properties (MP) and organic matrix properties (OMP) for enhanced prediction accuracy. This work represents the first-ever study combining Raman spectroscopy with other clinical and structural features to predict fracture toughness of human cortical bone, demonstrating the potential of artificial intelligence (AI) and ML in advancing bone research. Future studies could focus on larger datasets and more advanced modeling techniques to further improve predictive capabilities.Article Forecasting the Consumer Price Index in Türkiye Using Machine Learning Models: A Comparative Analysis(Gazi Univ, 2025) Söylemez, İsmet; Ünlü, Ramazan; Nalici, Mehmet ErenThis study utilizes machine learning models to forecast Türkiye's Consumer Price Index (CPI), thereby addressing a critical gap in inflation prediction methodologies. The central research problem involves the forecasting of CPI in a volatile economic environment, which is essential for informed policymaking. The primary objective of this study is to evaluate the performance of three machine learning models, such as Decision Tree (DT), Random Forest (RF), and Support Vector Machine (SVM), in forecasting CPI over periods ranging from one to six months, utilizing data from 2012 to 2024. The study's unique contribution lies in the application of the \"SelectKBest\" method, which identifies the most relevant indices, thereby enhancing the efficiency of the models. An ensemble method, Averaging Voting, is also employed to combine the strengths of these models, producing more accurate and robust predictions. The findings indicate that while the RF model consistently generates the most accurate forecasts across all shifts, the SVM model demonstrates a particular strength in the domain of short-term predictions. The ensemble model demonstrates a substantial performance improvement, with a R2 value of 0.962 for one-month ahead of estimates and 0.956 for five-month forecasts. This combined approach has been shown to outperform individual models, offering a more reliable framework for CPI forecasting. The findings offer valuable insights for economic policymakers, enabling more precise and stable inflation predictions in Türkiye.Master Thesis Sembolik Toplam Yaklaşım Kümelemesi Yoluyla BIST100 Yatırımlarında Yön Bulma: Yatırımcılara Yönelik Bilgiler(Abdullah Gül Üniversitesi, Fen Bilimleri Enstitüsü, 2024) Nalici, Mehmet Eren; Ünlü, Ramazan; Söylemez, İsmetMarket stakeholders, including traders and investors, strive to forecast stock market returns for informed decision-making. Computational finance employs various tools such as machine learning techniques to analyse extensive financial datasets to provide predictive insights for investors. Among all those techniques, clustering is one of the most well-known and used machine learning methods to reveal hidden patterns from unlabelled data. This study aims to help investors make more robust decisions by autonomously identifying companies that may exhibit similar price movements. In our study, with the model developed based on the Symbolic Aggregate Approximation (SAX) method, BIST100 companies are divided into clusters of various numbers and various scenarios are developed for investors from different perspectives such as risk minimization and strategic investment. The SAX clustering method is employed for analysing share movements. Moreover, dendrogram tree graph is used to analyse the clustering of different SAX combinations.Article Citation - WoS: 1Citation - Scopus: 1Strategic Investment in BIST100: A Machine Learning Approach Using Symbolic Aggregate Approximation Clustering(Univ Cincinnati industrial Engineering, 2025) Nalici, Mehmet Eren; Soylemez, Ismet; Unlu, RamazanThis study employs the Symbolic Aggregate Approximation (SAX) clustering method to enhance investor decision-making on the Borsa Istanbul (BIST100) by identifying companies exhibiting analogous stock movements. The data from 81 BIST100 companies over a three-year period has been analyzed, with a focus on risk minimization and strategic investment. The SAX method, integrated with a dendrogram, categorizes stocks into sector-based and non-sector-based clusters, providing insights for portfolio optimization. The results demonstrate the effectiveness of the method in identifying relevant stock patterns across sectors, aiding in more informed investment decisions. This approach highlights the need for considering multiple factors in investment strategies, offering a new perspective on stock market analysis with advanced clustering techniques.Article Citation - Scopus: 3Future of Clean Cooking Energy Access in Emerging Economies by 2030(Springer International Publishing, 2025) Çakır, Mehmet Ali; Ünlü, Ramazan; Çakir, Sümeyra Çay; Xanthopoulos, PetrosThis 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. © 2025 Elsevier B.V., All rights reserved.Article Citation - Scopus: 4Türkiye’de Yapılan Kuraklık Analiz Çalışmaları Üzerine Bir Derleme(Ankara University, 2022) Deniz Öztürk, Yasemin; Ünlü, RamazanDrought has become one of the most studied disaster issues by scientists, especially after the 2000s, with the importance of climate change. Many scientific publications on drought have been produced, due to many different methods on drought and the study of drought by many disciplines of science. In the study, theses, national and international articles, which include drought analysis by using any statistical method over meteorological data in Turkey, were compiled. A total of 270 studies, including 73 master's and Ph.D. theses, 107 national articles, and 90 international articles, written between 1943-2021 were examined. These studies were classified according to the year of publication, the drought analysis methods used, in publication, the scientific field of the first author, and the region examined in the study, and their frequency distributions were revealed. The main conclusions of this study are as follows: Although the first published studies on drought analysis in Turkey were made in 1943, 1956, and 1965, studies on drought started to increase after 2000 and the total number of publications reached 37 in 2019, 43 in 2020, and 64 in 2021. Publications in the period of 2019-2021 correspond to 53% of all publications. This rapid increase in recent years has led to a logarithmic increase in the number of publications. Although 63 different methods are used in drought analysis in the studies, the standardized precipitation index is the dominant method with a usage rate of 56%. Most of the studies were carried out on the basins (113). In 41 studies, the whole of Turkey was examined. Other studies were carried out for geographical regions, provinces, and smaller settlements. According to the scientific fields, it is seen that the Civil Engineering (131 units) and Geography (41 units) departments are the scientific fields that carry out the most drought analysis studies. © 2025 Elsevier B.V., All rights reserved.Article Symbolic Aggregate Approximation-Based Clustering of Monthly Natural Gas Consumption(2024) Söylemez, İsmet; Ünlü, Ramazan; Nalici, Mehmet ErenNatural gas is an indispensable non-renewable energy source for many countries. It is used in many different areas such as heating and kitchen appliances in homes, and heat treatment and electricity generation in industry. Natural gas is an essential component of the transportation sector, providing a cleaner alternative to traditional fuels in vehicles and fleets. Moreover, natural gas plays a vital role in boosting energy efficiency through the development of combined heat and power systems. These systems produce electricity and useful heat concurrently. As nations move towards more sustainable energy solutions, natural gas has gained prominence as a transitional fuel. This is due to its lower carbon emissions when compared to coal and oil, thus making it an essential component of the global energy framework. In this study, monthly natural gas consumption data of 28 different European countries between 2014 and 2022 are used. Symbolic Aggregate Approximation method is used to analyse the data. Analyses are made with different numbers of segments and numbers of alphabet sizes, and alphabet vectors of each country are created. These letter vectors are used in hierarchical clustering and dendrogram graphs are created. Furthermore, the elbow method is used to determine the appropriate number of clusters. Clusters of countries are created according to the determined number of clusters. In addition, it is interpreted according to the consumption trends of the countries in the determined clusters.Article Fluctuations in the European Housing Market: Forecasting the House Price Index Change with Time-Series Models(Gazi Univ, 2025) Soylemez, Ismet; Nalici, Mehmet Eren; Unlu, RamazanThis study presents a comparative analysis of a time series models for forecasting changes in the Housing Price Index (HPI) in 27 European countries. Accurate HPI forecasting is essential for the development of effective policies and investment strategies. The study uses quarterly data from Q4 2013 to Q3 2024. Methodologically, the stationarity of the data is tested using the Dickey-Fuller test and differencing is applied to non-stationary series. The ARIMA, Holt Linear Trend, Additive Damped Trend and Exponential Smoothing models are evaluated based on the lowest mean squared error (MSE) value for each country. The findings confirmed the heterogeneous structure of the European housing market, showing that no single model is suitable for all countries. The ARIMA model provided the most accurate results for nine countries, while the Holt Linear Trend and Additive Damped Trend models performed best in seven countries each. Forecasts for the period 2025-2026 are generated based on these results. This study highlights the importance of adopting country-specific and adaptable forecasting approaches to accommodate the varying dynamics of European housing markets.Article Karadeniz Bölgesi’nde Kurak ve Nemli Dönemlerin SPI Yöntemi Kullanılarak Belirlenmesi(2024) Ünlü, Ramazan; Öztürk, Yasemin DenizKaradeniz bölgesi Türkiye’nin en çok yağış alan bölgesidir. Ancak Karadeniz Bölgesi’nde yağışlar hem yıllar arasında hem de bölge içerisinde önemli farklılıklara sahiptir. Bu durum bölgede kuraklıkların yaşanabilmesine ve kurak-nemli dönemlerin birbirini takip etmesine neden olmaktadır. Bu çalışmada yıllık ve 12 aylık SPI değerlerine göre Karadeniz bölgesinde yaşanan kurak ve nemli dönemlerin belirlenmesi amaçlanmıştır. Bölge genelinden seçilen 26 istasyonun 1960-2020 yılları arasındaki ortalama yağış verilerine göre standardize yağış indeksi (SPI) değerleri hesaplanmıştır. Tespit edilen kurak ve nemli dönemlerin eğilimleri Mann-Kendall trend analizi kullanılarak tespit edilmiştir. Ayrıca ısı haritası kullanılarak Karadeniz Bölgesi kıyı ve iç kesimleri olarak ayrılıp kurak ve nemli dönemleri saptanmıştır. Analiz sonuçlarına göre 1966, 1969, 1974-1977, 1984-1986, 1993-1994, 2006-2007 ve 2019-2020 yıllarının normalden daha az yağış aldığı ve birçok istasyonun kuraklığı şiddetli şekilde olduğu saptanmıştır. 1967, 1988, 1996-1997, 1999, 2009 ve 2016 yıllarının ise normalden fazla yağış aldığını yani nemli karakterde olduğunu göstermektedir. Mann-Kendall trend analiz sonuçlarına göre Batı Karadeniz Bölgesinin kıyı kesimlerinde azalma eğilimde olduğu saptanmamıştır. Fakat azalışta anlamlılık bulunamamıştır. Orta ve Doğu Karadeniz bölgesinde ise artış eğilimi göstermekle birlikte bu eğilim bazı istasyonlarda anlamlı bulunmuştur. Bölgenin yer şekilleri dolayısıyla genel bir kurak ve genel bir nemli dönem olmadığı, doğu-batı doğrultusu ve kıyı-iç kesimlerde kurak ve nemli dönemlerin farklılık gösterdiği saptanmıştır.Article High-Accuracy Identification of Durian Leaf Diseases: A Convolutional Neural Network Approach Validated with K-Fold Cross-Validation and Bayesian Optimization(Springer, 2025) Soylemez, Ismet; Nalici, Mehmet Eren; Unlu, RamazanTo address the economic losses caused by plant diseases in durian farming, this study presents an optimized deep learning model that diagnoses diseases from leaf images with high accuracy. The model's performance is maximized through Bayesian optimization and hyperparameter tuning, while its reliability is maximized through layered five-fold cross-validation. Training the convolutional neural network model on 2595 leaf images displaying six different states (five diseased and one healthy) resulted in an average test accuracy of 91.98%. This high, consistent success rate demonstrates the model's generalizability to different datasets without overfitting. While the 'Healthy' and 'Algal' classes were successfully detected with high F1-scores, there are difficulties distinguishing between the 'Blight' and 'Colletotrichum' classes due to visual similarities. This study establishes a new reference point for durian disease classification and makes a significant contribution to the development of reliable artificial intelligence-based diagnostic tools for precision agriculture.
