Nalici, Mehmet Eren

Profile Picture
Profile URL
https://hdl.handle.net/20.500.12573/2686
Name Variants
ME Nalici
Nalici, Mehmet Eren
Job Title
Arş. Gör.
Email Address
mehmeteren.nalici@agu.edu.tr
Main Affiliation
02.02. Endüstri Mühendisliği
Status
Current Staff
Website
ORCID ID
0000-0002-7954-6916
Scopus Author ID
Turkish CoHE Profile ID
382126
Google Scholar ID
HL0JLS8AAAAJ
WoS Researcher ID
HTR-2909-2023

Sustainable Development Goals

13

CLIMATE ACTION
CLIMATE ACTION Logo

0

Research Products

15

LIFE ON LAND
LIFE ON LAND Logo

0

Research Products

8

DECENT WORK AND ECONOMIC GROWTH
DECENT WORK AND ECONOMIC GROWTH Logo

1

Research Products

10

REDUCED INEQUALITIES
REDUCED INEQUALITIES Logo

0

Research Products

2

ZERO HUNGER
ZERO HUNGER Logo

0

Research Products

6

CLEAN WATER AND SANITATION
CLEAN WATER AND SANITATION Logo

0

Research Products

14

LIFE BELOW WATER
LIFE BELOW WATER Logo

0

Research Products

11

SUSTAINABLE CITIES AND COMMUNITIES
SUSTAINABLE CITIES AND COMMUNITIES Logo

1

Research Products

16

PEACE, JUSTICE AND STRONG INSTITUTIONS
PEACE, JUSTICE AND STRONG INSTITUTIONS Logo

0

Research Products

5

GENDER EQUALITY
GENDER EQUALITY Logo

0

Research Products

9

INDUSTRY, INNOVATION AND INFRASTRUCTURE
INDUSTRY, INNOVATION AND INFRASTRUCTURE Logo

0

Research Products

7

AFFORDABLE AND CLEAN ENERGY
AFFORDABLE AND CLEAN ENERGY Logo

1

Research Products

4

QUALITY EDUCATION
QUALITY EDUCATION Logo

0

Research Products

1

NO POVERTY
NO POVERTY Logo

0

Research Products

17

PARTNERSHIPS FOR THE GOALS
PARTNERSHIPS FOR THE GOALS Logo

1

Research Products

3

GOOD HEALTH AND WELL-BEING
GOOD HEALTH AND WELL-BEING Logo

0

Research Products

12

RESPONSIBLE CONSUMPTION AND PRODUCTION
RESPONSIBLE CONSUMPTION AND PRODUCTION Logo

0

Research Products
This researcher does not have a Scopus ID.
Documents

4

Citations

1

Go to WoS profile
Scholarly Output

7

Articles

6

Views / Downloads

1/0

Supervised MSc Theses

1

Supervised PhD Theses

0

WoS Citation Count

1

Scopus Citation Count

1

WoS h-index

1

Scopus h-index

1

Patents

0

Projects

0

WoS Citations per Publication

0.14

Scopus Citations per Publication

0.14

Open Access Source

4

Supervised Theses

1

JournalCount
Applied Fruit Science1
Bitlis Eren Üniversitesi Fen Bilimleri Dergisi1
Erciyes Üniversitesi Fen Bilimleri Enstitüsü Dergisi1
Gazi University Journal of Science1
International Journal of Industrial Engineering-Theory Applications and Practice1
Current Page: 1 / 2

Scopus Quartile Distribution

Competency Cloud

GCRIS Competency Cloud

Filters

2024 - 20257
Reset filters

Settings

Scholarly Output Search Results

Now showing 1 - 7 of 7
  • Thumbnail Image
    Article
    Citation - WoS: 1
    Citation - Scopus: 1
    Strategic Investment in BIST100: A Machine Learning Approach Using Symbolic Aggregate Approximation Clustering
    (Univ Cincinnati industrial Engineering, 2025) Nalici, Mehmet Eren; Soylemez, Ismet; Unlu, Ramazan
    This 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.
  • Thumbnail Image
    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, İsmet
    Market 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.
  • Thumbnail Image
    Article
    Forecasting the Consumer Price Index in Türkiye Using Machine Learning Models: A Comparative Analysis
    (Gazi Univ, 2025) Söylemez, İsmet; Ünlü, Ramazan; Nalici, Mehmet Eren
    This 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.
  • Thumbnail Image
    Article
    Türkiye'de Tahıl Üretiminin Tahminlemesi: Karşılaştırmalı Analiz
    (2024) Nalici, Mehmet Eren; Ünlü, Ramazan; Soylemez, İsmet
    Tarım, Türkiye'de hayati bir sektör olmuş ve ülkenin ekonomik ve sosyal yapısına önemli katkılarda bulunmuştur. Bu çalışma, çeşitli tahmin modelleri kullanarak 2023-2030 yılları arasında Türkiye'de dokuz farklı tahıl ürününün üretim miktarlarını tahmin etmeyi amaçlamaktadır. Kullanılan modeller arasında Üstel Düzeltme, Holt Doğrusal Yöntemi, Holt-Winters Sönümlü Trend, Hareketli Ortalama ve ARIMA yer almaktadır. Bu modellerin performansı Ortalama Karesel Hata (MSE) değerleri kullanılarak değerlendirilmiştir. Bu analiz için kullanılan veriler 1990-2022 yıllarını kapsamaktadır ve Türkiye İstatistik Kurumu'ndan (TÜİK) alınmıştır. Sonuçlar, buğday, arpa, mısır ve yulafın artan bir üretim eğilimi yaşayacağını, çeltik, çavdar, darı ve kaplıca ise azalan bir eğilim göstereceğini göstermektedir. Bu çalışma, iklim değişikliği ve nüfus artışı gibi küresel zorluklar karşısında sürdürülebilir tarımsal üretim ve istikrarı sağlayarak etkili ulusal gıda güvenliği politikaları ve stratejileri geliştirmede doğru tahmin modellerinin önemini vurgulamaktadır.
  • Thumbnail Image
    Article
    Symbolic Aggregate Approximation-Based Clustering of Monthly Natural Gas Consumption
    (2024) Söylemez, İsmet; Ünlü, Ramazan; Nalici, Mehmet Eren
    Natural 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.
  • Thumbnail Image
    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, Ramazan
    This 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.
  • Thumbnail Image
    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, Ramazan
    To 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.