TR-Dizin İndeksli Yayınlar Koleksiyonu

Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/396

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Language: search.filters.language.enSubject: search.filters.subject.Bilgisayar Bilimleri, Yazılım Mühendisliği

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  • Article
    Makine Öğrenmesi ve Derin Öğrenme Yöntemleri ile Hidroponik Tarım
    (2023) Bulut, Nurten; Hacıbeyoglu, Mehmet
    In the face of the rapidly increasing population of our world today, researchers have turned to studies that use existing resources more effectively and efficiently in addition to searching for new resources in order to meet the rapidly decreasing needs such as raw materials and nutrients. The use of hydroponic agriculture, which is one of the alternative methods that can be used to meet the need for nutrients, which is one of the greatest needs of humanity, has become more popular day by day. The use of nutrient solution water instead of soil, the fact that it is not affected by weather conditions, that it can be applied indoors and that it can be vertically oriented are the characteristics that make hydroponic agriculture different from other agricultural methods. In addition, the lack of soil in this agricultural method brings with it the need for more observation and supervision. The aim of this study is to show that the observation and surveillance needs necessary to increase yield in hydroponic agriculture can be achieved using machine learning and deep learning methods. For this purpose, it has been observed that the efficiency of hydroponic agriculture has been increased in experimental studies conducted using five machine learning and deep learning methods. The deep learning method has achieved better results with 99.7% success compared to other methods.
  • Article
    İşbirlikçi Filtreleme temelinde Film Öneri Sistemleri: Netflix üzerinde bir VakaÇalışması
    (2021) Sütçü, Muhammed; Kaya, Ecem; Erdem, Oğuzkan
    Filmler, şarkılar ve alışveriş ürünleri gibi ögelerin kullanıcı değerlendirmeleriÖneri Sistemleri (ÖS) tarafından henüz değerlendirilmemiş ürünleri tahmin etmekiçin kullanılır. ÖS kullanıcılara çeşitli alanlarda öneri vermek için geliştirilmiştir veÖS uygulama alanlarından birisi de film önerisidir. Bu alanda üç genel algoritmakullanılmaktadır; kullanıcılar arası benzerliğe dayanarak tavsiye veren İşbirlikçiFiltreleme, kullanıcı-eşya eşleştirilmesindeki ilişkiden beslenen İçerik TabanlıFiltreleme ve bu iki algoritmayı birleştiren Hibrit Filtreleme. Bu çalışmamızdaİşbirlikçi Filtreleme çerçevesinde hangi metotların daha etkili çalıştığı incelenmiştir.Analizimizde Netflix Ödül veri seti kullanılmış ve iyi bilinen İşbirlikçi Filtrelememetotları olan Tekil Değer Ayrışımı, Tekil Değer Ayrışımı++, K En Yakın Komşu veEş Kümeleme kıyaslanmıştır. Her metodun hatası Ortalama Hata Kare Kökükullanılarak ölçülmüştür. Son olarak, K En Yakın Komşu metodunun veri setimizdedaha başarılı olduğu sonuçlanmıştır.
  • Article
    Fine-Tuning Large Language Models for Turkish Flutter Code Generation
    (Sakarya University, 2025-12-29) Uluırmak, Buğra Alperen; Kurban, Rifat
    The rapid advancement of large language models (LLMs) for code generation has largely centered on English programming queries. This paper focuses on a low-resource language scenario, specifically Turkish, in the context of Flutter mobile app development. Two representative LLMs (a 4B-parameter multilingual model and a 3B code-specialized model) on a new Turkish question-and-answer dataset for Flutter/Dart are fine-tuned in this study. Fine-tuning with parameter-efficient techniques yields dramatic improvements in code generation quality: Bilingual Evaluation Understudy (BLEU), Recall-Oriented Understudy for Gisting Evaluation (ROUGE-L), Metric for Evaluation of Translation with Explicit Ordering (METEOR), Bidirectional Encoder Representations from Transformers Score (BERTScore), and CodeBLEU scores show significant increases. The rate of correct solutions increased from ~30–70% (for base models) to 80–90% after fine-tuning. The performance trade-offs between models are analyzed, revealing that the multilingual model slightly outperforms the code-focused model in accuracy after fine-tuning. However, the code-focused model demonstrates faster inference speeds. These results demonstrate that even with very limited non-English training data, customizing LLMs can bridge the gap in code generation, enabling high-quality assistance for Turkish developers comparable to that for English. The dataset was released on GitHub to facilitate further research in multilingual code generation.
  • Article
    Comparative Assessment of Smooth and Non-Smooth Optimization Solvers in HANSO Software
    (Balikesir University, 2021-10-27) Tor, Ali Hakan
    The aim of this study is to compare the performance of smooth and nonsmooth mization) software. The smooth optimization solver is the implementation of the Broyden-Fletcher-Goldfarb-Shanno (BFGS) method and the nonsmooth optimization solver is the Hybrid Algorithm for Nonsmooth Optimization. More precisely, the nonsmooth optimization algorithm is the combination of the BFGS and the Gradient Sampling Algorithm (GSA). We use well-known collection of academic test problems for nonsmooth optimization containing both convex and nonconvex problems. The motivation for this research is the importance of the comparative assessment of smooth optimization methods for solving nonsmooth optimization problems. This assessment will demonstrate how successful is the BFGS method for solving nonsmooth optimization problems in comparison with the nonsmooth optimization solver from HANSO. Performance profiles using the number iterations, the number of function evaluations and the number of subgradient evaluations are used to compare solvers.
  • Article
    Candida Enfeksiyonlarına Karşı Toll-Benzeri Reseptörlerin ve Antimikrobiyal Peptitlerin Özelleştirilmesine Yönelik Hesaplamalı Yaklaşımlardaki Son Gelişmeler
    (2025-09-26) Bicer, Mesude; Serçinoğlu, Onur; Okur, Tuba
    Candida albicans'ın insan sağlığı üzerindeki kayda değer patojenik etkisine rağmen, hücresel tanıma mekanizmalarının ve ardından gelen konakçı savunma aktivasyonunun anlaşılmasındaki boşluk yeterince anlaşılmamıştır. Son bilgiler, Toll benzeri reseptörlerin (TLR'ler) patojenlere karşı doğuştan gelen bağışıklık tepkilerini düzenlemedeki önemli rolünün altını çiziyor. Özellikle, son yıllardaki ampirik araştırmalar, TLR'lerin memelilerde en önemli model tanıma reseptörleri olduğunun altını çizmiştir. Örneğin TLR2, peptidoglikanlar, lipoarabinomannan ve bakteriyel lipoproteinler için afinite sergilerken TLR4, lipopolisakkarit (LPS) ve lipo-teikoik asidin saptanmasında rol oynar. Benzer şekilde TLR5 flagellini tanır ve TLR9 bakteriyel DNA tanımayla ilişkilidir. Toll'un Drosophila'da antifungal mekanizmaların düzenleyicisi olarak ilk tanımlanması, TLR'lerin memeli antifungal savunmasında potansiyel olarak dahil olduğunu düşündürmektedir. Bununla birlikte, Drosophila'daki Toll ile antifungal mekanizmalar arasındaki evrimsel bağlantıya rağmen, insanlarda fungal patojenlerle mücadelede TLR'lerin rolünün tanımlanmasına çok az önem verilmiştir; bu, TLR'lerin memeli antifungal savunmasında makul bir rol oynadığını düşündürmektedir. Özellikle kanıtlar, Aspergillus fumigatus'a yanıt olarak proinflamatuar sitokinleri indüklemede TLR4'ü gösterir ancak TLR2'yi kapsamaz; bu arada rolünün, hücrelerin Cryptococcus neoformans ile uyarılmasından sonra TNF üretimi olmasa da hücre içi sinyalleşmeye aracılık ettiği iddia edilir. Bununla birlikte, TLR aktivasyon kurallarına ilişkin içgörüler, antimikrobiyal peptit (AMP) ile TLR etkileşimlerinin incelenmesini mümkün kılmaktadır ve çeşitli moleküllerin immünomodülatör kapasitelerine ilişkin tahminleri kolaylaştırmaktadırç Bu ilerlemelere rağmen, TLR'lerin önde gelen bir insan patojeni olan Candida albicans'ı tanımadaki spesifik rolü hala belirsizliğini koruyor ve daha fazla araştırma yapılmasını gerektiriyor. Bu hesaplamalı yaklaşım, AMP'ler ve TLR'ler arasındaki etkileşimleri aydınlatan, TLR aktivasyonunu yöneten yapısal belirleyicileri tanımlayan ve böylece çeşitli moleküler varlıkların immünomodülatör potansiyeline ilişkin öngörüler sağlayan son bulguları sentezlemektedir.
  • Article
    Symbolic Aggregate Approximation-Based Clustering of Monthly Natural Gas Consumption
    (2024-03-24) 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.
  • Article
    Space Prospect in the Flexible Era of Late Capitalism
    (Konya Technical Univ, Fac Architecture & design, 2020-12-21) Ozmen, Nihan Mus; Asiliskender, Burak
    This study is mainly influenced by the idea of Manfredo Tafuri that architecture cannot fulfil its ideological task since it started serving capitalism and there are no more utopias. In his book Architecture and Utopia: Design and Capitalist Development, Tafuri discusses the sociophilosophical tangle in which architects have been struggling since the 18th century. According to Tafuri, the drama of today's architecture is the obligation to return to pure architecture, a matter of form without utopia, supreme uselessness. Another influence on the study is Richard Sennett's book The Corrosion of Character. Sennett mentions the concept of flexible capitalism and explains that work life is not as rigid as it was before. According to Sennett, flexibility has an impact on personal character and asks questions about how to decide the lasting value of we in an impatient society, how to pursue a long-term goal in a short-term economy, how to sustain loyalties to the continually redesigning institutions. Purpose The thoughts of Tafuri and Sennett are discussed through Patrik Schumacher's Parametricism manifesto. In the manifesto, Schumacher reflects architecture's evolving patterns of communication in relation to its social task. The main objective of the study is to propose a future space based on the ideas of Tafuri, Sennett and Schumacher. Design/Methodology/Approach This paper discusses the reviews of books of Tafuri and Sennett and manifesto of Schumacher as a methodology. Findings After the reviews of The Corrosion of Character and Architecture and Utopia, there is a discussion of flexible space through parametric design approach. Finally, there is the prediction of future space based on the findings in the previous sections. Research Limitations/Implications There are no research limitations for this paper. Social/Practical Implications According to this paper, parametric design method can be used in practice to achieve the spaces that are needed by the complex society of global era. Originality/Value This paper synthesizes the ideas of two great thinkers, who have influential discourses on architecture and business world, and approaches them from the perspective of parametric design as one of today's design tools, to make predictions about the future space.
  • Article
    Citation - WoS: 1
    Citation - Scopus: 1
    Prediction of Preference and Effect of Music on Preference: A Preliminary Study on Electroencephalography from Young Women
    (Tubitak Scientific & Technological Research Council Turkey, 2019-03-01) Yilmaz, Bulent; Gazeloglu, Cengiz; Altindis, Fatih
    Neuromarketing is the application of the neuroscientific approaches to analyze and understand economically relevant behavior. In this study, the effect of loud and rhythmic music in a sample neuromarketing setup is investigated. The second aim was to develop an approach in the prediction of preference using only brain signals. In this work, 19-channel EEG signals were recorded and two experimental paradigms were implemented: no music/silence and rhythmic, loud music using a headphone, while viewing women shoes. For each 10-sec epoch, normalized power spectral density (PSD) of EEG data for six frequency bands was estimated using the Burg method. The effect of music was investigated by comparing the mean differences between music and no music groups using independent two-sample t-test. In the preference prediction part sequential forward selection, k-nearest neighbors (k-NN) and the support vector machines (SVM), and 5-fold cross-validation approaches were used. It is found that music did not affect like decision in any of the power bands, on the contrary, music affected dislike decisions for all bands with no exceptions. Furthermore, the accuracies obtained in preference prediction study were between 77.5 and 82.5% for k-NN and SVM techniques. The results of the study showed the feasibility of using EEG signals in the investigation of the music effect on purchasing behavior and the prediction of preference of an individual.
  • Article
    Performance Analysis of Machine Learning and Bioinformatics Applications on High Performance Computing Systems
    (2020-01-31) Aydin, Zafer
    Nowadays, it is becoming increasingly important to use the most efficient and most suitable computational resources for algorithmic tools that extract meaningful information from big data and make smart decisions. In this paper, a comparative analysis is provided for performance measurements of various machine learning and bioinformatics software including scikit-learn, Tensorflow, WEKA, libSVM, ThunderSVM, GMTK, PSI-BLAST, and HHblits with big data applications on different high performance computer systems and workstations. The programs are executed in a wide range of conditions such as single-core central processing unit (CPU), multi-core CPU, and graphical processing unit (GPU) depending on the availability of implementation. The optimum number of CPU cores are obtained for selected software. It is found that the running times depend on many factors including the CPU/GPU version, available RAM, the number of CPU cores allocated, and the algorithm used. If parallel implementations are available for a given software, the best running times are typically obtained by GPU, followed by multi-core CPU, and single-core CPU. Though there is no best system that performs better than others in all applications studied, it is anticipated that the results obtained will help researchers and practitioners to select the most appropriate computational resources for their machine learning and bioinformatics projects.
  • Article
    Optimal Location Determination of Electric Vehicle Charging Stations: A Case Study on Turkey's Most Preferred Highway
    (2022-06-30) Gülbahar, İbrahim Tümay; Sütçü, Muhammed
    Today, electric vehicles are seen as one of the most suitable and environmentally friendly alternatives to internal combustion engine vehicles. An important issue related to the dissemination of electric vehicles is the location of the vehicle charging network and specifically the optimum location selection of the charging stations. Generally, most of the studies focus on popular destinations such as city centers, shopping areas, bus stations, and airports. Although these places are often used in normal life, they can usually provide an adequate solution for daily charging needs due to the number of alternative charging stations. However, finding adequate charging stations is not possible in intercity travels especially in highways. In this paper, we proposed a decision model to determine the location of electric car charging stations in highways. We create an optimization model to decide the optimum locations for the charging stations that can meet the customer demands on the Istanbul-Ankara highway. The proposed model determines optimum charging stations that enable passengers traveling with their electric vehicles to travel in Istanbul-Ankara highway in the shortest time.