Scopus İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/395
Browse
16 results
Search Results
Article Citation - Scopus: 1Machine Learning and Scenario-Based Forecasting of Türkiye’s Renewable Energy Transition toward Net-Zero 2053(Elsevier Ltd, 2026-05) Sutcu, Muhammed; Yildiz, Baris; Sahin, Nurettin; Almomany, Abedalmuhdi; Gulbahar, Ibrahim TumayThe issue of global warming has been identified as one of the most critical challenges of the 21st century, with the consumption of fossil fuels being identified as a major contributor to greenhouse gas emissions. In response to these challenges, countries worldwide are expediting their transition towards renewable energy sources to meet international climate commitments, such as the Paris Agreement, and to achieve long-term sustainability goals. Türkiye has established a target to achieve net-zero emissions by 2053. This objective is consistent with both the nation's domestic energy strategy and its international commitments. Nevertheless, the transition from fossil fuels to renewable energy sources is impeded by geographical, economic, and technological constraints. The present study aims to assess the capacity and efficiency of renewable energy in Türkiye with environmental protocols and future electricity demand projections. Electricity generation, transmission data, and national energy plans are used to identify future electricity generation and capacity trends. In the context of this study, a range of machine learning models is executed across diverse scenarios, yielding a series of outcomes. Consequently, the repercussions of regulatory measures and financial investments were examined, and prospective inferences were derived. The findings underscore the pivotal role of scenario-based modeling in formulating sustainable energy policies and directing investment decisions within the context of climate change mitigation.Article Forecasting the Consumer Price Index in Türkiye Using Machine Learning Models: A Comparative Analysis(Gazi Univ, 2025-09-01) 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.Conference Object Citation - WoS: 1Citation - Scopus: 8Short Term Electricity Load Forecasting: A Case Study of Electric Utility Market in Turkey(Institute of Electrical and Electronics Engineers Inc., 2015-04) Ishik, Muhammed Yasin; Göze, Tolga; Ozcan, Ihsan; Güngör, Vehbi Çağrı; Aydin, Zafer; Yasin, MuhammedWith the recent developments in energy sector, the pricing of electricity is now governed by the spot market where a variety of market mechanisms are effective. After the new legislation of market liberalization in Turkey, competition-based on hourly price has received a growing interest in the energy market, which necessitated generators and electric utility companies to add new dimensions to their scope of operation: short-term load and price forecasting. The field has several opportunities though not free from challenges. The dynamic behavior of the market price has caused the electric load to become variable and non-stationary. Furthermore, the number of nodes, in which the load must be predicted, is not constant anymore and can no longer be estimated by experts alone. In this competitive scenario, statistical forecasting methods that can automatically and accurately process thousands of data samples are essential. The purpose of this study is to demonstrate the importance of short-term load forecasting, how it has received a growing interest in Turkey and to propose an artificial neural network that can forecast the short term electricity load. Through detailed performance evaluations, we demonstrate that our forecasting method is capable of predicting the hourly load accurately. © 2017 Elsevier B.V., All rights reserved.Article Citation - Scopus: 1Shear Strength Prediction for Fiber Reinforced Concrete Beams(Taylor and Francis Ltd., 2025-08-17) Burak Bakir, Burcu; Yagmur, ErenDiscrete fibers are often used to increase the tensile and shear strengths of reinforced concrete. Influence of fibers on the behavior of shear critical members is quite significant, therefore, it is crucial to accurately estimate the fiber contribution to ultimate strength. In this study, first a comprehensive database of 446 FRC shear critical beams from 51 different experimental studies is compiled and nonlinear correlation analyses are utilized to identify the key parameters affecting the shear strength. Then, parametric equations are developed to obtain interfacial bond strength of fibers and shear strength of beams with different fiber types, volume fractions, aspect ratios and reinforcement detailing. Shear strengths corresponding to both shear and flexural failures are computed to verify the failure mode. Comparison of the predicted and experimental load carrying capacities indicates the improved accuracy of the prediction equation when compared to the code requirements and existing equations. Due to its applicability to FRC beams with different configurations, reinforcement detailing, fiber types and failure modes, the proposed method is feasible for integration into structural codes as a conservative and practical design approach. © 2025 Elsevier B.V., All rights reserved.Book Part Citation - Scopus: 3ROSE: A Novel Approach for Protein Secondary Structure Prediction(Springer Science and Business Media Deutschland GmbH, 2021) Görmez, Yasin; Aydin, ZaferThree-dimensional structure of protein gives important information about protein’s function. Since it is time-consuming and costly to find the structure of protein by experimental methods, estimation of three-dimensional structures of proteins through computational methods has been an efficient alternative. One of the most important steps for the 3-D protein structure prediction is protein secondary structure prediction. Proteins which contain different number and sequences of amino acids may have similar structures. Thus, extracting appropriate input features has crucial importance for secondary structure prediction. In this study, a novel model, ROSE, is proposed for secondary structure prediction that obtains probability distributions as a feature vector by using two position specific scoring matrices obtained by PSIBLAST and HHblits. ROSE is a two-stage hybrid classifier that uses a one-dimensional bi-directional recurrent neural network at the first stage and a support vector machine at the second stage. It is also combined with DSPRED method, which employs dynamic Bayesian networks and a support vector machine. ROSE obtained comparable results to DSPRED in cross-validation experiments performed on a difficult benchmark and can be used as an alternative to protein secondary structure prediction. © 2021 Elsevier B.V., All rights reserved.Conference Object Citation - WoS: 7Citation - Scopus: 10PI-Controlled ANN-Based Energy Consumption Forecasting for Smart Grids(SciTePress, 2015) Gezer, Gülsüm; Tuna, Gürkan; Κogias, DImitrios G.; Gülez, Kayhan; Güngör, Vehbi Çağrı; Kogias, DimitrisAlthough Smart Grid (SG) transformation brings many advantages to electric utilities, the longstanding challenge for all them is to supply electricity at the lowest cost. In addition, currently, the electric utilities must comply with new expectations for their operations, and address new challenges such as energy efficiency regulations and guidelines, possibility of economic recessions, volatility of fuel prices, new user profiles and demands of regulators. In order to meet all these emerging economic and regulatory realities, the electric utilities operating SGs must be able to determine and meet load, implement new technologies that can effect energy sales and interact with their customers for their purchases of electricity. In this respect, load forecasting which has traditionally been done mostly at city or country level can address such issues vital to the electric utilities. In this paper, an artificial neural network based energy consumption forecasting system is proposed and the efficiency of the proposed system is shown with the results of a set of simulation studies. The proposed system can provide valuable inputs to smart grid applications. © 2022 Elsevier B.V., All rights reserved.Conference Object Citation - WoS: 16Citation - Scopus: 20Machine Learning Analysis of Inflammatory Bowel Disease-Associated Metagenomics Dataset(Institute of Electrical and Electronics Engineers Inc., 2018-09) Hacilar, Hilal; Nalbantoĝlu, Özkan Ufuk; Bakir-Güngör, BurcuThere is an ongoing interplay between humans and our microbial communities. The microorganisms living in our gut produce energy from our food, strengthen our immune system, break down foreign products, and release metabolites and hormones, which are significant for regulating our physiology. The shifts away from this 'healthy' gut microbiome is considered to be associated with many diseases. Inflammatory bowel diseases (IBD) including Crohn's disease and ulcerative colitis, are gut related disorders affecting the intestinal tract. Although some metagenomics studies are conducted on IBD recently, our current understanding of the precise relationships between the human gut microbiome and IBD remains limited. In this regard, the use of state-of-the art machine learning approaches became popular to address a variety of questions like early diagnosis of certain diseases using human microbiota. In this study, we investigate which subset of gut microbiota are mostly associated with IBD and if disease-associated biomarkers can be detected via applying state-of-the art machine learning algorithms and proper feature selection methods. © 2019 Elsevier B.V., All rights reserved.Conference Object Linear Vs. Non-Linear Embedding Methods in Recommendation Systems(Institute of Electrical and Electronics Engineers Inc., 2022-09-07) Gurler, Kerem; Cos¸kun, Mustafa; Karagenc, Safak; Orun, Gokhan; Kuleli Pak, Burcu Kuleli; Güngör, Vehbi Çağrı; Coskun, Mustafa; Pak, Burcu KuleliPredicting customer interest in items is very crucial in direct marketing as it can potentially boost sales. Data mining techniques are developed to predict which items a particular user might be interested in based on their purchase history or explicit feedback in form of ratings or comments. Recently, non-linear and linear methods have been developed for this purpose. In this study, we applied Neighborhood based Collaborative Filtering (CF), Matrix Factorization (MF), Singular Value Decomposition (SVD), Neural Graph CF (NGCF) and Light Graph Convolutional Network (LightGCN) on explicit user product rating data which is acquired from the online gaming and mobile entertainment platform called HADI. We compared the results of node embedding methods in terms of Precision@k, Recall@k and NDCG@k values. SVD and LightGCN showed the best test performance and SVD was significantly superior to LightGCN in terms of training speed. To further increase predictive performance of SVD, we have applied classification with Logistic Regression and Deep Random Forest on user and item embeddings created by the SVD. © 2022 Elsevier B.V., All rights reserved.Article Impact of Input Sequence Types on Healthcare Intrusion Prediction Models(IEEE-Inst Electrical Electronics Engineers Inc, 2025) Yusof, Mohammad Hafiz Mohd; Balfaqih, Mohammed; Khan, Md Munir Hayet; Almohammedi, Akram A.; Balfagih, ZainPrediction models are vital for sensing zero-day and even n-day cyberattacks, particularly in healthcare infrastructure. Most existing research focuses on developing classifiers also known as IDS to enhance detection and accuracy. However, predictive intrusion models for healthcare remain underexplored, with limited studies investigating the comparative performance of univariate and multivariate inputs against single-step and multi-step outputs in time series models. This study aims to address these gaps by evaluating the accuracy and error performance of selected predictive models across various input and output configurations. The methodology involves transforming input data sequences into univariate l* n and multivariate m * n formats, establishing single-step and multi-step splitting functions, and evaluating these configurations using the benchmark CIRA-CIC-DoHBrw-2020 dataset. Algorithms including Bidirectional LSTM, Stacked LSTM, Vanilla LSTM, Transformer Encoder-Decoder, Vector Output LSTM (GRU core), and CNN were applied, with results visualized to assess performance. The findings reveal that the Multivariate LSTM model, when trained on a sequence of multivariate inputs, demonstrates superior predictive performance, achieving low MAE error rates of 0.4% for single-step predictions and 0.1% for multi-step predictions. Additionally, GRU and Transformer models exhibit heightened sensitivity to specific input sequence configurations. In conclusion, our study demonstrates that Transformer Encoder-Decoder based prediction models exhibit exceptional prediction performance. This effectiveness is attributed to their ability to capture contextual and critical information from input sequences. These findings provide valuable insights for designing advanced intrusion prediction models, paving the way for improved prediction capabilities in future systems.Article Citation - Scopus: 3Future of Clean Cooking Energy Access in Emerging Economies by 2030(Springer International Publishing, 2025-03-07) 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.