Söylemez, İsmet
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Soylemez, Ismet
Stylemez, Ismet
Söylemez, İsmet
Stylemez, Ismet
Söylemez, İsmet
Job Title
Arş. Gör.
Email Address
ismet.soylemez@agu.edu.tr
Main Affiliation
07.03. Endüstri Mühendisliği Anabilim Dalı
Status
Current Staff
Website
ORCID ID
Scopus Author ID
Turkish CoHE Profile ID
Google Scholar ID
WoS Researcher ID
Sustainable Development Goals
3
GOOD HEALTH AND WELL-BEING
1
Research Products
7
AFFORDABLE AND CLEAN ENERGY
1
Research Products
8
DECENT WORK AND ECONOMIC GROWTH
4
Research Products
9
INDUSTRY, INNOVATION AND INFRASTRUCTURE
2
Research Products
11
SUSTAINABLE CITIES AND COMMUNITIES
1
Research Products
12
RESPONSIBLE CONSUMPTION AND PRODUCTION
3
Research Products
15
LIFE ON LAND
2
Research Products
17
PARTNERSHIPS FOR THE GOALS
4
Research Products
Documents
6
Citations
16
h-index
2
Documents
10
Citations
22
Scholarly Output
13
Articles
8
Views / Downloads
277/179
Supervised MSc Theses
1
Supervised PhD Theses
0
WoS Citation Count
18
Scopus Citation Count
16
WoS h-index
3
Scopus h-index
2
Patents
0
Projects
2
WoS Citations per Publication
1.38
Scopus Citations per Publication
1.23
Open Access Source
4
Supervised Theses
1
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| Journal | Count |
|---|---|
| -- Uncertainty Modelling in Knowledge Engineering and Decision Making - 12th International Fuzzy Logic and Intelligent Technologies in Nuclear Science Conference, FLINS 2016 -- Roubaix -- 131626 | 2 |
| 5th World Conference on Business, Economics and Management (BEM) -- MAY 12-14, 2016 -- Antalya, TURKEY | 1 |
| Applied Fruit Science | 1 |
| Bitlis Eren Üniversitesi Fen Bilimleri Dergisi | 1 |
| Ege Academic Review | 1 |
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Now showing 1 - 10 of 13
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.Article Çok işçili montaj hatlarında istasyon ve kaynak yatırımı maliyetinin enküçüklenmesine yönelik tavlama benzetimi ve tam sayılı doğrusal programlamaya dayalı yeni bir algoritma(Dicle Üniversitesi, 2018) Şahin, Murat; Kellegöz, Talip; Söylemez, İsmetStandardize edilmiş ürünlerin üretiminde yaygın olarak kullanılan montaj hatları önemli bir akış tipi üretim şeklidir. Buhatların dengelenme problemleri üretim ve kaynak yatırımı maliyetlerinin enküçüklenmesi açısından büyük önem arzetmektedir. NP-zor bir yapıya sahip olan probleme endüstriyel ortamlarda yaygın olarak karşılaşılmasına karşın makulsüreler içerisinde kesin çözüm yöntemleri ile çözümü mümkün olmayabilmektedir. Bu çalışmada yenilenebilir kaynakyatırımı maliyetini de dikkate alan çok işçili montaj hattı dengeleme problemine yönelik yeni bir algoritma sunulmuştur.Önerilen algoritmada tamsayılı doğrusal programlama ile çözülecek olan alt problemler tavlama benzetimi yöntemi ilebelirlenmiştir. Literatürde montaj hattı dengeleme problemlerinde sıklıkla tercih edilen rassal sayılar dizisi kullanılarakgörevlerin hangi önceliklerle atanacağı belirlenmiştir. Tavlama benzetimi ve tamsayılı doğrusal programlamanın birliktekullanımına dayanan algoritmanın etkinliği test problemleri üzerinde ölçülmüştür. Tavlama benzetimi sezgiseli C#programlama dilinde kodlanmış ve oluşturulan her bir alt problemin tamsayılı doğrusal programlama modeli CPLEX10.2 çözücü kullanılarak 3.2 GHZ /4 GB Ram’a sahip bilgisayarda koşturulmuştur. Tavlama benzetiminde aynı altproblemler oluşturulması durumunda hafızada kaydedilen çözüm ve atamalar kullanmıştır. Bunun temel nedenimatematiksel model ile çözülen alt problemlerde aynı modelin oluşturulması durumunda elde edilecek sonuçlara dahaönceden ulaşılmış olmasıdır. Bu sayede algoritmanın daha hızlı bir şekilde çalışması gerçekleştirilmiş olup, çözülen vetekrarlanan matematiksel model sayıları özetlenerek sunulmuştur. Geliştirilen algoritmanın orta ve büyük boyutluproblem örneklerinde kabul edilebilir kalitede çözümler üretebildiği gözlemlenmiştir. Montaj hattı problemlerine ilişkinliteratürde bulunan çalışmalar dikkate alındığında, ilgili problem üzerindeki çalışmaların eksikliğine vurgu yapılmıştır.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 - WoS: 7Citation - Scopus: 11An Ant Colony Optimisation Algorithm for Balancing Two-Sided U-Type Assembly Lines With Sequence-Dependent Set-Up Times(Springer India, 2018) Delice, Yilmaz; Aydogan, Emel Kizilkaya; Soylemez, Ismet; Ozcan, UgurSome practical arrangements in assembly lines necessitate set-up times between consecutive tasks. To create more realistic models of operations, set-up times must be considered. In this study, a sequence-dependent set-up times approach for two-sided u-type assembly line (TUAL) structures is proposed for the first time. Previous studies on TUAL have not included set-up times in their analyses. Furthermore, an algorithm based on the Ant Colony Optimization (ACO) algorithm, which is using a heuristic priority rule based procedure has been proposed in order to solve this new approach. In this paper, we look at the sequence-dependent set-up times between consecutive tasks and consecutive cycles, called the "forward set-up time'' and the "backward set-up time'', respectively. Additionally, we examine the "crossover set-up time'', which arises from a new sequence of tasks in a crossover station. In order to model more realistic assembly line configurations, it is necessary to include sequence-dependent set-up times when computing all of the operational times such as task starting times and finishing times as well as the total workstation time. In this study, the proposed approach aims to minimize the number of mated-stations as the primary objective and to minimize the number of total workstations as a secondary objective. In order to evaluate the efficiency of the proposed algorithm, a computational study is performed. As can be seen from the experimental results the proposed approach finds promising results for all literature-test problems.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.Conference Object Citation - Scopus: 1Sustainable Economic Development Indicators: the Case of Turkey(World Scientific Publishing Co. Pte Ltd wspc@wspc.com.sg, 2016) Söylemez, İsmet; Dogan, Ahmet; Özcan, UǧurSustainable development indicators are a good road map for financial, social and economic targets of countries. This paper aims to show which indicators are affect sustainable development of Turkey for last twelve years. 132 sustainable development indicators determined by European Union Statistical Office (Eurostat). Sustainable development indicators are calculated by related unit, institution or establishment in the direction of definitions determined by Eurostat. These indicators are calculated by TUIK (Turkish Statistical Institute) for Turkey. Some indicators as follows: socio-economic development, sustainable consumption and production, climate change and energy, sustainable transport, financing for sustainable development. However, only economic indicators are presented and analyzed in the case study. Official development assistance has tenfold rise in the last 12 years. These indicators will show which areas at economic changes should be considered to the sustainable development of country. © 2017 Elsevier B.V., All rights reserved.Conference Object Citation - WoS: 4Citation - Scopus: 3Green Supplier Selection by Using Fuzzy TOPSIS Method(World Scientific Publishing Co. Pte Ltd wspc@wspc.com.sg, 2016) Dogan, Ahmet; Söylemez, İsmet; Özcan, UǧurWith the increased environmental consciousness in customers, organizations took upon the task of redesigning their strategic goals in a more environment-sensitive way in order to fulfill their social obligations, to enable sustainability, to gain competitive advantage and to make the world more habitable. Because, the emerging conditions in the 21st century indicate that the traditional criteria -such as price, cost so on for supply chain management, supplier selection and performance measurement of suppliers are no more sufficient and there is the necessity of adding new criteria such as environmental matters. This paper deals with the problem of selecting green suppliers in an organization in Turkey that has operations in the field of accumulator. The aim is to select the greenest of 3 suppliers in Turkey, France and Bulgaria which supply the organization with the plastic material used in the production of accumulator. The problem is solved via fuzzy TOPSIS, which is a multi-criteria decision making method (MCDM), and the results are used to select the greenest supplier. © 2017 Elsevier B.V., All rights reserved.Article Citation - WoS: 3Association Rules on Traffic Accident: Case of Ankara(Ege Univ, Fac Economics & Admin Sciences, 2016) Soylemez, Ismet; Dogan, Ahmet; Ozcan, UgurIn this study, association rules analysis of the data mining techniques are used for data of traffic accidents in 2010 and some rules are obtained. With this rules, what is the possibility of accident which resulted anybody injured for "different weather conditions (snowy, rainy etc.)", "where the accidents occurred (street, road etc.)" and "way situations (separated road or not)". Different algorithms are used to analyze the association rules. Apriori algorithm is selected for this study and SPSS Clementine 12.0 is used for this algorithm. Firstly, frequency of items are found. Then, items are grouped. In this study, data preprocessing is done and missing values are filled or rejected. In the second phase, outliers are rejected and data type is converted type of 1-0 (binary). In the third phase, Apriori algorithm is applied and results are evaluated.
