Yazar "Bilgin, Turgay Tugay" seçeneğine göre listele
Listeleniyor 1 - 20 / 36
Sayfa Başına Sonuç
Sıralama seçenekleri
Öğe A Classification Approach with Machine Learning Methods for Technical Problems of Distance Education: Turkey Example(INT COUNCIL OPEN & DISTANCE EDUCATION, 2021) Yayla, Rıdvan; Yayla, Halime Nur; Ortaç, Gizem; Bilgin, Turgay TugayDistance education is an education model in which the lessons can be taught simultaneously using technical material without time and space restrictions. It has gained importance after the Covid-19 pandemic processes and has been implemented as a valid educational model in all educational institutions. Due to the sudden pandemic measures, distance education has brought about a lot of technical problems at unprepared educational institutions against the pandemic. In this paper, a classification approach is proposed by machine learning methods on Twitter instead of the usual structured research methods such as survey, one-on-one meeting for technical problems of distance education. The most encountered and commented distance education problem, which can be defined in different languages by the proposed method, have been analysed with Turkey example. Sentiment analysis has been made from negative and neutral tweets about distance education. The problems have been classified by natural language processing methods based on Turkish word analysis.Öğe A New Approach to Minimize Memory Requirements of Frequent Subgraph Mining Algorithms(Gazi Universitesi, 2021) Bilgin, Turgay TugayFrequent subgraph mining (FSM) is a subsection of graph mining domain which is extensively used for graph classification and graph clustering purposes. Over the past decade, many efficient FSM algorithms have been developed. The improvements generally focus on reducing time complexity by changing the algorithm structure or using parallel programming techniques. FSM algorithms have another problem to solve, which is the high memory consumption. In this study, a new approach called Predictive Dynamic Sized Structure Packing (PDSSP) have been proposed to minimize the memory requirement of FSM algorithms. Proposed approach redesigns the internal data structures of FSM algorithms without any algorithmic modifications. PDSSP has two contributions. The first one is the Dynamic Sized Integer Type (ds_Int) which is a newly designed unsigned integer data type. The second contribution is "Data Structure packaging" component that uses a data structure packing technique which changes the behaviour of the compiler. A number of experiments have been conducted to examine the effectiveness and efficiency of the PDSSP approach by embedding it into two state-of-art algorithms called gSpan and Gaston. Proposed implementation have been compared to the official one. Almost all results show that the proposed implementation consumes less memory on each support level. As a result, PDSSP extensions can save memory and the peak memory usage may decrease up to 38% depending on the dataset.Öğe Advancements in automated program repair: a comprehensive review(Springer London Ltd, 2025) Dikici, Sena; Bilgin, Turgay TugayThis review paper presents a comprehensive examination of automated program repair (APR) and its significant contribution to the field of modern software engineering. It elucidates how APR methodologies markedly mitigate manual debugging needs by automating the detection and resolution of software glitches. The study encompasses an in-depth exploration of three primary categories of APR techniques: template-based, machine learning, and deep learning approaches, drawing from an exhaustive evaluation of 41 APR tools. Each category showcases distinct strategies for managing diverse software errors, underscoring the breadth and effectiveness of current APR methodologies. Template-based APR solutions utilize pre-established patterns to efficiently tackle common coding issues, while machine learning-driven approaches dynamically devise repair strategies from historical bug-fix datasets. Deep learning methods extend error rectification boundaries by delving into the semantic context of code, yielding more precise adjustments. The ongoing advancement of APR technologies necessitates researchers to address critical challenges, including the integration of semantic-syntactic analyses, mitigation of data scarcity, optimization of cross-platform tools, development of context-aware approaches, enhancement of fault localization and patch validation processes, and establishment of standardized performance evaluation metrics. This comprehensive analysis underscores the pivotal role of APR in enhancing software efficiency and reliability, representing significant progress in software development and maintenance practices.Öğe APPLICATION OF ARTIFICIAL INTELLIGENCE TECHNIQUES FOR DEFECT PREVENTION AND QUALITY CONTROL IN ARC WELDING PROCESSES: A COMPREHENSIVE REVIEW(Bilal GÜMÜŞ, 2024) Bilgin, Turgay Tugay; Kunduracı, Musa Selman; Metin, Ahmet; Doğru, Merve; Nayir, ErdalThis study presents a comprehensive review of research applying artificial intelligence (AI) techniques to prevent defects in arc welding processes. Arc welding is essential across various industries, but numerous issues can arise, impacting weld quality and production efficiency. The review systematically analyzes relevant studies published since 2018, focusing on three key aspects: datasets used, methodologies and approaches adopted, and performance metrics reported. The findings reveal significant adoption of both machine learning and deep learning techniques, with the choice depending on factors like input data nature, welding process dynamics, and computational requirements. Deep learning models, particularly convolutional neural networks (CNNs) and long short-term memory (LSTM) networks, have demonstrated superior performance in image-based defect detection and time-series analysis for quality prediction. However, traditional machine learning algorithms have also been utilized, often coupled with dimensionality reduction or feature selection techniques. The review highlights the diverse range of performance metrics employed, such as accuracy, precision, recall, F1-score, mean squared error (MSE), and root mean squared error (RMSE). Metric selection depends on the specific task (classification or regression) and the desired trade-off between different performance aspects. While many studies reported promising results with accuracy rates frequently exceeding 90%, challenges remain in real-world industrial settings due to factors like noise, occlusions, and rapidly changing welding conditions. This review serves as a comprehensive guide for researchers and practitioners in AI-assisted defect prevention and quality control for arc welding processes, highlighting current trends, methodologies, and future research directions.Öğe Automated machine learning for fabric quality prediction: a comparative analysis(PeerJ Inc., 2024) Metin, Ahmet; Bilgin, Turgay TugayThe enhancement of fabric quality prediction in the textile manufacturing sector is achieved by utilizing information derived from sensors within the Internet of Things (IoT) and Enterprise Resource Planning (ERP) systems linked to sensors embedded in textile machinery. The integration of Industry 4.0 concepts is instrumental in harnessing IoT sensor data, which, in turn, leads to improvements in productivity and reduced lead times in textile manufacturing processes. This study addresses the issue of imbalanced data pertaining to fabric quality within the textile manufacturing industry. It encompasses an evaluation of seven open-source automated machine learning (AutoML) technologies, namely FLAML (Fast Lightweight AutoML), AutoViML (Automatically Build Variant Interpretable ML models), EvalML (Evaluation Machine Learning), AutoGluon, H2OAutoML, PyCaret, and TPOT (Tree-based Pipeline Optimization Tool). The most suitable solutions are chosen for certain circumstances by employing an innovative approach that finds a compromise among computational efficiency and forecast accuracy. The results reveal that EvalML emerges as the top-performing AutoML model for a predetermined objective function, particularly excelling in terms of mean absolute error (MAE). On the other hand, even with longer inference periods, AutoGluon performs better than other methods in measures like mean absolute percentage error (MAPE), root mean squared error (RMSE), and r-squared. Additionally, the study explores the feature importance rankings provided by each AutoML model, shedding light on the attributes that significantly influence predictive outcomes. Notably, sin/cos encoding is found to be particularly effective in characterizing categorical variables with a large number of unique values. This study includes useful information about the application of AutoML in the textile industry and provides a roadmap for employing Industry 4.0 technologies to enhance fabric quality prediction. The research highlights the importance of striking a balance between predictive accuracy and computational efficiency, emphasizes the significance of feature importance for model interpretability, and lays the groundwork for future investigations in this field. © 2024 Metin and BilginÖğe Classification of Turkish Universities by quantity and titles of Academic Staff(Ieee, 2018) Kaşif, Ahmet; Bilgin, Turgay TugayIn this study, universities are clustered based on the quantity and title of the academics employed by Turkish Universities. The data comes from both state and private universities and vocational schools. For clustering, partitional and hierarchical clustering methods were used. The dataset was obtained from the YOK academic search website using "web scraping" techniques implemented by the authors. Agglomerative clustering technique was found to yield better results regarding cluster sizes and intracluster distances. The Silhouette coefficient is used for clustering quality measurement. As a result of the study, Turkish universities were mainly composed of four clusters. These are instructor dominant universities, just instructor based vocational schools, professor and associate professor dominated universities and research assistant dominated universities. The results of this study may be used for academic staff employment planning in Turkish Universities.Öğe D.Ç.P: An Open Source Course Scheduling Program Developed as a Wordpress Plugin(İstanbul Sabahattin Zaim Üniversitesi, 2021) Selvi, Muhammed; Ortaç, Gizem; Bilgin, Turgay TugayPreparing a curriculum schedule in which all the restrictions are met in educational institutions is a very tiring and time consuming process. Although automated syllabus scheduling software saves time for administrators, each educational institution has different and constantly changing requirements. For this reason, in this study, people in charge of preparing the curriculum of a state university can easily access faculty, class and course information through a common platform; It is aimed to prepare the optimum syllabus by minimizing various problems such as conflict. Developed with open source code, this program can be modified and used according to the special needs of educational institutions.Öğe DeepTherapy: A mobile platform for osteoarthritis rehabilitation utilizing chain-of-thought reasoning and deep learning(2025) Bilgin, Turgay Tugay; Avcı, Muhammed Ferit; Günay, Selim Mahmut; Şahin, Büşra; Sayaca, Cetin; Altan, Lale; Ozkal, OzdenObjectives: To develop and evaluate an AI-driven mobile platform that integrates deep learning-based exercise analysis with large language model (LLM) feedback for enhancing osteoarthritis (OA) rehabilitation accessibility and effectiveness. Methods: A deep learning framework was developed using Long Short-Term Memory (LSTM) architecture to classify exercise phases from video data of 10 rehabilitation exercises. The dataset consisted of approximately 800,000 frames collected from 20 healthy volunteers. A feedback system utilizing chain-of-thought reasoning in LLMs (GPT-4o and Claude 3.5 Sonnet) was implemented to generate targeted corrective feedback. Evaluation was conducted with OA patients (n=2) and physiotherapists (n=7) using the Intraclass Correlation Coefficient (ICC) and Likert scales. Results: The developed LSTM models achieved 97.8% accuracy in exercise phase classification. Strong agreement between system-generated scores and expert evaluations was demonstrated (ICC=0.85). Physiotherapists slightly preferred Claude's outputs (52.4% vs 47.6%) but rated GPT-4o higher on clinical relevance (4.57/5 vs 4.13/5), clarity (4.71/5 vs 4.38/5), and helpfulness (4.50/5 vs 4.29/5). Conclusions: DeepTherapy effectively addresses critical limitations in rehabilitation monitoring by providing qualitative movement assessment, identifying incorrect movements, and offering detailed guidance on technique improvement, potentially increasing rehabilitation accessibility while maintaining quality of care.Öğe Determining of the user attitudes on mobile security programs with machine learning methods(Slovene Society Informatika, 2021) Yayla, Rıdvan; Bilgin, Turgay TugaySecurity plays an important role in today's virtual world. Cybersecurity software has been widely used by the development of portable virtual environments. Smartphones occur in an important part of our lives. Daily routines are performed over mobile phones, especially after the COVID-19 pandemic process. Due to its ease of use, compulsory or optional mobile phone use also brought about many security concerns. Mobile security software is used for different purposes such as virus removal and protection of personal information according to user preferences. In the field of natural language processing, user preferences can now be analyzed on the basis of machine learning methods with sentiment analysis. In this paper, the preference reasons for mobile security software have been analysed with machine learning methods based on user comments and sentiment analysis. In the study, all user comments have been classified into 10 main categories and the user preferences of mobile security programs have been analysed.Öğe DOĞAL DİL METİNLERİNDEN PROGRAMLAMA DİLİ KODU OLUŞTURMA ÇALIŞMALARI: BİR DERLEME ÇALIŞMASI(2024) Hatipoğlu, Ayşegül; Bilgin, Turgay TugaySon yıllarda Doğal Dil İşleme (DDİ) alanındaki gelişmelerin hız kazanması, araştırmacıların ve programcıların bu alana olan ilgisini büyük ölçüde artırmıştır. Bilgisayar programlarını doğal dil komutlarıyla yazma konsepti, birçok araştırmacının odak noktası haline gelmiştir. Literatür incelendiğinde, doğal dil ile programlama üzerine yapılan araştırmaların uzun bir geçmişe sahip olduğu açıkça görülmektedir. Bu uzun soluklu araştırmalar, çeşitli çözüm önerilerini beraberinde getirmiş ve kural tabanlı yöntemlerden, olasılık tabanlı yöntemlere, makine öğrenmesi yöntemlerinden derin öğrenme yöntemlerine kadar bir dizi çözüm yaklaşımının ortaya çıkmasına neden olmuştur. Literatürdeki çalışmalar tarihsel olarak kategorize edildiğinde geçmiş tarihli çalışmalarda kural tabanlı ya da istatistik tabanlı modeller üzerine yoğunlaştığı görülürken günümüze yaklaşıldıkça makine öğrenmesi ve derin öğrenme temelli çalışmaların arttığı görülmektedir. Kural tabanlı yöntemler, olasılık tabanlı yöntemler, makine öğrenmesi yöntemleri, derin öğrenme yöntemleri gibi çeşitli yaklaşımların geliştirildiği literatürde, bu çeşitlilik yeni araştırmacıların bu alana giriş yaparken karşılaşabileceği karmaşıklığı artırabilmektedir. Bu çalışma, doğal dil girdileriyle programlama dili kodu oluşturma çalışmalarına yönelik literatürde geliştirilen 32 yöntemin detaylı bir incelenmesini sunmaktadır. Çalışmanın amacı, literatürde tespit edilen çeşitli yöntemlerin zaman içerisindeki değişimlerinin gözden geçirilmesi, çalışmaların geniş bir perspektiften incelenerek genel bir çerçeve içinde toplanması ve bu alanda çalışacak olan araştırmacılara rehberlik edebilmesidir.Öğe ENHANCING MULTI-CLASS TEXT CLASSIFICATION WITH APRIORI-BASED FEATURE SELECTION(Bilal GÜMÜŞ, 2024) Er, Maide Feyza; Bilgin, Turgay TugayIn the field of Natural Language Processing, selecting the right features is crucial for reducing unnecessary model complexity, speeding up training, and improving the ability to generalize. However, the multi-class text classification problem makes it challenging for models to generalize well, which complicates feature selection. This paper investigates how feature selection impacts model performance for multi-class text classification, using a dataset of projects completed by TÜBİTAK TEYDEB between 2009 and 2022. The study employs LSTM, a deep learning method, to classify the projects into nine different industries based on various attributes. The paper proposes a new feature selection approach based on the Apriori algorithm, which reduces the number of attribute combinations considered and makes model training more efficient. Model performance is evaluated using metrics like accuracy, loss, validation scores, and test scores. The key findings are that feature selection significantly affects model performance, and different feature sets have varying impacts on performance.Öğe Entegre Bir Yazılım Çözümü ile Endüstriyel Ortamlarda Enerji Verimliliğinin Artırılması ve Gerçek Zamanlı İzleme: NIGHTWATCH(Kirklareli University, 2023) Selçuk, Kenan; Nikbay, Kader; Bilgin, Turgay TugayKüresel olarak, ısınma, aydınlatma, ulaşım ve çeşitli cihazlar için yakıt temini gibi faktörler nedeniyle enerjiye olan talep artmaya devam etmektedir. Enerjinin böylesine büyük önem kazandığı bir bağlamda, enerji verimliliğinin sağlanması, enerji üretimi, iletimi ve tüketimiyle ilgili faaliyetleri kapsayan kapsamlı bir yaklaşım gerektirmiştir. Enerji alanındaki olumlu ya da olumsuz gelişmeler, sürdürülebilir kalkınmanın odak noktalarını oluşturan hem insani hem de çevresel faktörleri önemli ölçüde etkilemektedir. Sunulan çalışma, endüstriyel ortamlarda çalışmakta ve enerji analizörleri ile arayüz oluşturarak enerji tüketimi, anlık akım ve voltaj gibi verileri toplayıp analiz etmektedir. Bu girişim, fabrika tesislerinde enerjiyle ilgili operasyonlara gerçek zamanlı görünürlük sağlamayı amaçlamaktadır. Buna ek olarak, yazılımın yetenekleri geriye dönük veri analizine kadar uzanmakta ve gelecekteki tahminler için bilinçli içgörüler sağlamaktadır. Ayrıca, analizörlerden gelen enerji tüketimi verilerinin Üretim Yürütme Sistemine (MES) entegrasyonu, iş bazında enerji takibini kolaylaştırır. Yazılımın gösterge paneli bileşeni, kullanıcılara izlenen enerji parametreleri için özelleştirilmiş eşik değerleri belirleme yetkisi verir. Bu eşikler aşıldığında veya değerler beklenen seviyelerden saptığında, yazılım e-posta ve diğer iletişim kanalları aracılığıyla uyarıları ve bildirimleri tetikleyerek ilgili bilgilerin zamanında yayılmasını sağlar.Öğe Forecasting electricity demand in Türkiye: A comprehensive review of methods, determinants, and policy implications(Erol Kurt, 2025) Elbaş, Hakan; Bilgin, Turgay TugayThis review examines the methods, determinants, and forecasting horizons used in electricity demand forecasting in Türkiye. The study investigates how Türkiye's electricity demand is influenced by economic, climatic, socio-demographic, and technological factors, and explores the evolving landscape of forecasting techniques, from traditional statistical models to advanced machine learning and hybrid approaches. The research addresses three key questions: The significant determinants of electricity demand in Türkiye, the most effective forecasting methods, and the application of these insights to improve energy planning and policy development. Through a systematic analysis of peer-reviewed literature, official reports, and case studies, the study reveals the complex interplay of factors affecting electricity demand and the increasing sophistication of forecasting methodologies. Economic growth, industrial production, climate change, urbanization, and technological advancements emerge as primary drivers of demand, while artificial neural networks and hybrid models demonstrate superior forecasting capabilities. The study highlights the importance of integrated modeling approaches, sector-specific strategies, and the incorporation of climate projections in long-term planning. It also emphasizes the need for aligning energy policies with broader economic and environmental objectives. This review provides valuable insights for researchers, policymakers, and industry stakeholders, offering a comprehensive framework for understanding and improving electricity demand forecasting. © 2025 Erol Kurt. All rights reserved.Öğe Forecasting urban forest recreation areas in Turkey using machine learning methods(2024) Özbalcı, Mehmet Cüneyt; Dikici, Sena; Bilgin, Turgay TugayRecreation is the process of revitalizing and renewing human existence through optional activities, serving as a broad description. It has prominently arisen as a reaction to personal requirements for stress reduction, especially in developed urban areas. Engaging in this recreational activity provides a way to utilize one's spare time, providing refreshment for both the physical and mental aspects, whether done alone or with others, in countryside or city environments. Urban forests are important leisure places within city environments. An expanded presence of urban forest places can greatly enhance the general well-being of society. The estimation of urban forest areas in the future may receive increased attention, leading to measures to extend current areas or prepare for future activities and services. We utilized official statistics from the years 2013 to 2021, sourced from the Republic of Turkey official website. Ministry of Agriculture and Forestry's General Directorate of Forestry. We used statistics that contained information about urban forests, classified as Type D recreational areas, to create a dataset. We performed provincial-level area projections for the year 2021. Using the KNIME platform, we used three different analysis techniques: linear regression analysis, gradient-boosted regression trees and artificial neural networks. It is seen that the results of linear regression and artificial neural networks are close to each other and give good results. The peak performance was attained using artificial neural networks, resulting in an R2 score of 0.99. This study differs from other similar projects by concentrating on calculating urban forest recreational spaces per province throughout Turkey, using data provided by government agencies. The accomplishments highlight the ability to make reliable predictions about future forest resources by using analogous forecasts in the upcoming years.Öğe Görüntülerden veya Çizimlerden Otomatik Kod Oluşturma Teknikleri: Bir Derleme Çalışması(2023) Kunduracı, Musa Selman; Bilgin, Turgay TugayBir yazılımın geliştirilmesi sürecinde, tasarım ve öncül üretim en önemli ve zaman alıcı aşamalardır. Kullanıcılar yazılımların görsel arayüzlerine ve tasarımlarına oldukça önem vermektedir. İyi bir görsel arayüz tasarımına sahip bir yazılım daha iyi işleve sahip olup fakat arayüzü kullanışsız olan benzerinden daha fazla tercih edilmektedir. Görsel arayüz tasarımı sürecinde geliştiriciler öncelikle kâğıt üzerinde tasarım gerçekleştirip ardından görsel arayüz tasarım programları ile dijital tasarıma dönüştürürler. Sonraki aşamada, tasarımın çeşitli biçimlendirme dilleriyle (xml, html, css vb.) veya doğrudan programlama dilleriyle kodlanması gerekmektedir. Otomatik kot üretme yaklaşımlarında amaç minimum yazılım geliştirici maliyeti ile kısa zamanda verimli ve hızlı uygulamalar geliştirmektir. Bu çalışmada, çeşitli yöntemleri kullanarak otomatik kot üretimi gerçekleştiren çalışmaları içeren geniş bir yayın taraması oluşturulmuştur. İncelenen makalelerde çoğunlukla derin öğrenme, görüntü işleme, yapay sinir ağları veya makine öğrenmesi yöntemleri kullanılmıştır. Bu derleme çalışması ile bu alanda çalışma yapacak araştırmacılara rehber olunması amaçlanmıştır.Öğe Improving initial flattening of convex-shaped free-form mesh surface patches using a dynamic virtual boundary(CRL Publishing admin@crlpublishing.co.uk, 2019) Yavuz, Erdem; Yazıcı, Rıfat; Kasapbaşı, Mustafa Cem; Bilgin, Turgay TugayThis study proposes an efficient algorithm for improving flattening result of triangular mesh surface patches having a convex shape. The proposed approach, based on barycentric mapping technique, incorporates a dynamic virtual boundary, which considerably improves initial mapping result. The dynamic virtual boundary approach is utilized to reduce the distortions for the triangles near the boundary caused by the nature of convex combination technique. Mapping results of the proposed algorithm and the base technique are compared by area and shape accuracy metrics measured for several sample surfaces. The results prove the success of the proposed approach with respect to the base method. ©2019CRL Publishing Ltd.Öğe Improving Initial Flattening of Convex-Shaped Free-Form Mesh Surface Patches Using a Dynamic Virtual Boundary(C R L Publishing Ltd, 2019) Yavuz, Erdem; Yazici, Rifat; Kasapbasi, Mustafa Cem; Bilgin, Turgay TugayThis study proposes an efficient algorithm for improving flattening result of triangular mesh surface patches having a convex shape. The proposed approach, based on barycentric mapping technique, incorporates a dynamic virtual boundary, which considerably improves initial mapping result. The dynamic virtual boundary approach is utilized to reduce the distortions for the triangles near the boundary caused by the nature of convex combination technique. Mapping results of the proposed algorithm and the base technique are compared by area and shape accuracy metrics measured for several sample surfaces. The results prove the success of the proposed approach with respect to the base method.Öğe Insights Into Air Pollution Dynamics and Quality: A Comprehensive Analysis Of Scholarly Research In Türkiye(2025) Bilgin, Saliha Çelikcan; Bilgin, Turgay TugayAir pollution affects human health, the environment, and the economy worldwide. This comprehensive analysis elucidates the intricate characteristics of air pollution and its quality by amalgamating the latest study outcomes derived from papers published on the DergiPark platform in Türkiye throughout the period spanning from 2022 to 2023. The papers in our study are classified according to their research themes. The main findings suggest that weather, urbanization, industry, and wildfires affect air pollution and quality. Additionally, the COVID-19 pandemic has affected air quality dynamics, requiring further study. Scientists have used various methods to forecast, evaluate, and simulate, but challenges remain that require new approaches. Investigating the causal pathways linking air pollution to climate change, urban development, and transportation will help us better understand the problem. Empirical study into the effects of air pollution and quality on human health is essential for making informed policy decisions, especially for vulnerable groups. Evaluating the efficacy of current regulations and establishing new approaches can help guide effective air quality. This paper is a comprehensive synthesis of scholarly studies pertaining to air quality and pollution, providing a comprehensive overview of the extensive ramifications associated with this subject matter.Öğe Integrating A Dialogue Tree Based Turkish Chatbot into an Open Source Python Coding Editor(Institute of Electrical and Electronics Engineers Inc., 2022) Bilgin, Turgay Tugay; Yavuz, ErdemIn this study, a dialogue tree-based Turkish chatbot has been developed and integreted into the Pynar Python code editor in order to help the users with the questions they may ask while learning the python language. The Pynar Python Code editor is developed as a TÜBİTAK supported open source project. The architecture we propose has been developed in such a way that it can respond to users' questions and give emotional feedback through dialogs and emojis. It also runs completely offline, without being connected to any internet site or web service. The chatbot is coded using Python language and a Turkish NLP library and Qt GUI library. Considering the age and education levels of the target user group of the Pynar editor, our chatbot will provide great convenience to those who are trying to learn Python programming on their own, with the possibility of asking questions and getting answers in their native language. © 2022 IEEE.Öğe KALP HASTALIĞI TAHMİNİNDE MAKİNE ÖĞRENİMİ ALGORİTMALARININ PERFORMANS KARŞILAŞTIRMASI(İstanbul Ticaret Üniversitesi, 2024) Abdulhussein, Ayat Bahaa; Bilgin, Turgay TugayMakine öğrenimi, araştırma dünyasını değiştiren, yapay zekanın en bilinen uygulamalarından biridir. Bu araştırmanın hedefi, etkili makine öğrenimi yaklaşımlarını kullanarak Kalp Hastalığı Tahmini için tahminler üretmek ve kişinin kalp hastalığına sahip olup olmadığını tahmin etmektir. Temel amaç, çeşitli makine öğrenimi algoritmalarının kalp hastalığının varlığını veya yokluğunu tahmin etmedeki öngörü doğruluğunu değerlendirmektir. KNIME veri analizi programı genel doğruluk, bu stratejilerin etkinliğini değerlendirmek için temel gösterge olarak seçilmiştir. Göğüs ağrısı, kolesterol seviyeleri, bir kişinin yaşı ve diğer faktörler gibi detaylar kullanılarak ve K En Yakın Komşu (KNN), Naif Bayes ve Lojistik Regresyon gibi farklı makine öğrenimi teknolojileri kullanılarak, 319796 hasta kaydı ve 18 niteliğe sahip bir veri seti kullanılmıştır. Makine öğrenimi teknikleri olarak Naive Bayes, K En Yakın Komşu (KNN) ve Lojistik Regresyon kullanılmış ve tahmin doğrulukları karşılaştırılmıştır. Uygulama sonuçları, lojistik regresyon yaklaşımının kalp hastalığı için tahmin doğruluğu açısından K En Yakın Komşu yönteminden ve Naive Bayes yönteminden daha iyi olduğunu göstermektedir. K-NN'nin tahmin doğruluğu %90.77, Naive Bayes'in %86.633 ve lojistik regresyonun %91.60'dır. Sonuç olarak, makine öğrenimi algoritmalarının kalp hastalığını büyük oranda doğru bir şekilde tanımlayabileceği görülmüştür. Sonuçlar, bu yöntemlerin bir hastada kalp krizi olasılığını belirlemede doktorlara ve kalp cerrahlarına yardımcı olabileceğini göstermektedir.
