Ballı, Tuğçe

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Profile URL
https://hdl.handle.net/20.500.12469/6111
Name Variants
T. Ballı
Ballı, TUĞÇE
Altuğlu T.
Altuǧlu T.
Ballı, T.
Balli, Tugce
B., Tugce
Tugce, Balli
TUĞÇE BALLI
Tuğçe Ballı
BALLI, Tuğçe
B., Tuğçe
Ballı T.
Ballı, Tuğçe
Ballı,T.
Balli T.
Balli,Tugce
Balli,T.
B.,Tugce
Tuğçe BALLI
BALLI, TUĞÇE
Job Title
Dr. Öğr. Üyesi
Email Address
[email protected]
Main Affiliation
Management Information Systems
Status
Current Staff
Website
ORCID ID
0000-0002-6509-3725
Scopus Author ID
Turkish CoHE Profile ID
Google Scholar ID
WoS Researcher ID

Sustainable Development Goals

NO POVERTY1
NO POVERTY
0
Research Products
ZERO HUNGER2
ZERO HUNGER
0
Research Products
GOOD HEALTH AND WELL-BEING3
GOOD HEALTH AND WELL-BEING
0
Research Products
QUALITY EDUCATION4
QUALITY EDUCATION
0
Research Products
GENDER EQUALITY5
GENDER EQUALITY
0
Research Products
CLEAN WATER AND SANITATION6
CLEAN WATER AND SANITATION
0
Research Products
AFFORDABLE AND CLEAN ENERGY7
AFFORDABLE AND CLEAN ENERGY
0
Research Products
DECENT WORK AND ECONOMIC GROWTH8
DECENT WORK AND ECONOMIC GROWTH
0
Research Products
INDUSTRY, INNOVATION AND INFRASTRUCTURE9
INDUSTRY, INNOVATION AND INFRASTRUCTURE
3
Research Products
REDUCED INEQUALITIES10
REDUCED INEQUALITIES
0
Research Products
SUSTAINABLE CITIES AND COMMUNITIES11
SUSTAINABLE CITIES AND COMMUNITIES
1
Research Products
RESPONSIBLE CONSUMPTION AND PRODUCTION12
RESPONSIBLE CONSUMPTION AND PRODUCTION
0
Research Products
CLIMATE ACTION13
CLIMATE ACTION
0
Research Products
LIFE BELOW WATER14
LIFE BELOW WATER
0
Research Products
LIFE ON LAND15
LIFE ON LAND
0
Research Products
PEACE, JUSTICE AND STRONG INSTITUTIONS16
PEACE, JUSTICE AND STRONG INSTITUTIONS
1
Research Products
PARTNERSHIPS FOR THE GOALS17
PARTNERSHIPS FOR THE GOALS
0
Research Products
This researcher does not have a Scopus ID.
This researcher does not have a WoS ID.
Scholarly Output

16

Articles

8

Views / Downloads

132/195

Supervised MSc Theses

3

Supervised PhD Theses

0

WoS Citation Count

12

Scopus Citation Count

20

Patents

0

Projects

0

WoS Citations per Publication

0.75

Scopus Citations per Publication

1.25

Open Access Source

8

Supervised Theses

3

JournalCount
International Conference on Digital Presentation and Preservation of Cultural and Scientific Heritage -- Sep 25-28, 2025 -- Burgas, Bulgaria2
Digital Presentation and Preservation of Cultural and Scientific Heritage -- 15th International Conference on Digital Presentation and Preservation of Cultural and Scientific Heritage, DiPP 2025 -- 25 September 2025 through 28 September 2025 -- Burgas -- 2132352
Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi1
IEEE Access1
International Conference on Information Systems Security and Privacy -- 11th International Conference on Information Systems Security and Privacy, ICISSP 2025 -- 20 February 2025 through 22 February 2025 -- Porto -- 3289591
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Now showing 1 - 10 of 16
  • Thumbnail Image
    Article
    Adaptive Segmentation of IIoT Time Series Data via Change Point Detection for Machinery Fault Classification
    (IEEE-Inst Electrical Electronics Engineers Inc, 2026) Balli, Tugce; Kacar, Saygin; Yetkin, E. Fatih; Yetkin, Fatih
    Predictive maintenance (PdM) is a critical concept in Industry 4.0 that aims to improve manufacturing processes by predicting the remaining useful time of machinery. The development of PdM models relies on access to sufficient data, including condition monitoring and maintenance data from industrial applications. One of the critical aspects of modern PdM approaches is the classification of potential fault signals using time series data collected from IoT devices. However, in most cases, the non-stationary nature of these time series data often causes difficulties with the validity of traditional segmentation techniques when applied to such dynamically changing data patterns. In this work, we propose an adaptive segmentation approach through change point detection to address the inherent non-stationarity of time series, thereby improving the classification performance of traditional classifiers for fault detection problems. By using an adaptive segmentation scheme, we aim to extract more relevant features that will lead to improved classification performance. Taking the time-sensitive nature of the problem into account, we employed three well-known change point detection algorithms (Pruned Exact Linear Time -PELT- algorithm, Binary Segmentation, and Bottom-up segmentation). The effectiveness of the proposed methods is demonstrated by experiments using two different datasets widely used in the PdM literature.
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    Conference Object
    Citation - Scopus: 1
    Restorative: Improving Accessibility to Cultural Heritage With AI-Assisted Virtual Reality
    (Inst Mathematics & Informatics, Bulgarian Acad Sciences, 2025) Balli, Tugce; Peker, Hasan; Piskin, Senol; Yetkin, E. Fatih
    Digitalization of the cultural heritage can be considered from multiple perspectives. In this work, we present a case study based on the ancient city of Karkemish to propose a structured pipeline for developing an Artificial Intelligence (AI)-assisted Virtual Reality (VR) system. The framework outlines a roadmap for creating a user-friendly and gamified VR interface, incorporating qualitative and quantitative evaluation methods before deployment. Qualitative assessments focus on User Interface/User Experience (UI/UX) design, while quantitative evaluations utilize electroencephalogram (EEG) data to monitor cognitive and emotional responses, aiming to promote a positive user experience. Moreover, we introduce a privacy-preserving approach to ensure the user's privacy during the system interaction. The study's aim is twofold: a) preservation and dissemination of endangered cultural heritages, and b) improving the quality of life for individuals with limited mobility (handicapped, elderly, heritage site restrictions, poverty) by enabling virtual access to cultural heritages.
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    Article
    Citation - WoS: 2
    Citation - Scopus: 2
    Neural Signatures of Depression: Classifying Drug-Naive Mdd Patients With Time- and Frequency-Domain Eeg Features During Emotional Processing
    (Iop Publishing Ltd, 2025) Sutcubasi, Bernis; Balli, Tugce; Metin, Baris; Tulay, Emine Elif; Elif Tülay, Emine
    Accurate classification of major depressive disorder (MDD) remains a significant challenge, particularly because of the confounding effect of medications. This study bridges this gap by focusing on the classification of drug-na & iuml;ve individuals diagnosed with MDD and healthy controls (HCs) using electroencephalogram (EEG) data recorded during emotional processing tasks. This study involved 14 HCs and 14 drug-na & iuml;ve individuals diagnosed with MDD (aged 18-31, 12+ years of education, 12 F/2 M). The participants were presented with positive, neutral, and negative images collected from the International Affective Picture System. The mean power amplitudes of event-related potentials (ERP), including the P200, P300, early, middle, and late components of the late positive potential (LPP), were computed, along with band power features, and used as features for classifiers. A support vector machine model was employed for classification to evaluate the individual contributions of ERP components and band power features and explore the combined effects of ERP components and band power features within themselves. The alpha band power achieved the highest individual classification accuracy among the band power features for negative stimuli (92.86%). The late LPP component was the most discriminative ERP component for positive stimuli, yielding an accuracy rate of 89.29%. Combined analysis of the band power features exhibited high accuracy for both positive and negative stimuli (92.86% each). When the ERP components were combined, the classifier achieved the highest accuracy of 89.29% for both negative and neutral stimuli. Our findings suggest that alpha band power and LPP responses to negative and positive stimuli, respectively, can be used to detect MDD. The comparable performance of individual features to that of the combined feature sets indicates their strength as indicators of emotional processing in MDD. These findings provide valuable insights into the development of more reliable diagnostic tools and treatment monitoring strategies that focus on emotional processing in MDD.
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    Article
    Motor İmgeleme EEG Sinyallerinde Sınıflandırma Performansını Arttırmaya Yönelik Adaptif Segmentasyon Yaklaşımı
    (2025) Balli, Tugce
    Elektroansefalogram (EEG) tabanlı beyin-bilgisayar arayüzlerinin (BBA) performansı, sistem tasarımında kullanılan sinyal işleme yöntemlerine doğrudan bağlıdır. EEG sinyal işleme süreci; önişleme, öznitelik çıkarma, seçme ve sınıflandırma adımlarını içerir. EEG verilerinden öznitelik çıkarımı genellikle sabit uzunlukta pencerelere ayrıldıktan sonra yapılmaktadır. Bu çalışmada özniteliklerin veri temsiliyetini artırmak için değişim noktası tespitine (change point detection, CPD) dayalı adaptif bir segmentasyon yaklaşımı önerilmiştir. Bu amaçla budanmış kesin doğrusal zaman (pruned exact linear time, PELT) algoritması kullanılmıştır. Bu yöntem, değişim noktalarını uygun bir maliyet fonksiyonu ile duyarlılık parametresinin belirlenmesi yoluyla tespit etmektedir. Önerilen yöntemin, sabit segmentasyona kıyasla etkinliği, BCI Competition IV 2a veri seti kullanılarak değerlendirilmiştir. Bu veri seti, 9 katılımcının sol el, sağ el, ayak ve dil imgeleme görevlerini gerçekleştirirken kaydedilmiş EEG verilerini içermektedir. Sonuçlar, CPD tabanlı yöntemin hem ikili hem de dört sınıflı sınıflandırmada test verisi üzerindeki sınıflandırma başarımını artırdığını göstermiştir. İkili sınıflandırma senaryosunda, önerilen yöntemin performans artışı %5,81 ile %8,72 arasında değişmiştir. En yüksek sınıflandırma performansı, sol el ve dil görevleri arasında gözlemlenmiş; katılımcı bazında performans artışları %4,16 ve %12,73 aralığında değişmiştir. Dört sınıflı sınıflandırma görevinde ise ortalama %7,5 oranında bir başarı artışı sağlanmış olup, katılımcı bazlı performans artışları %3,93 ile %11,11 aralığında değişmiştir.
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    Master Thesis
    Sax-lstm'ye Dayalı Tahmini Bakım için Zaman Serisinin Sembolik Tahmini
    (2024) Güler, Aykut; Yetkin, Emrullah Fatih; Ballı, Tuğçe
    Bu çalışma, Sembolik Toplam Yaklaşım (SAX) ve Parçalı Toplam Yaklaşım (PAA) gibi gelişmiş yaklaşımları makine öğrenme algoritmalarıyla birleştirerek endüstriyel ortamlarda tahmine dayalı bakım tahminine yönelik yeni bir yaklaşımı araştırıyor. Çalışma, üretim süreçlerinin dijitalleşmesinin hem fırsatlar hem de karmaşıklıklar getirdiği Endüstri 4.0 bağlamında bakım tahmini konularını ele almayı amaçlıyor. Çalışma, sentetik verileri kullanarak ve çeşitli veri kümesi boyutları, PAA segment uzunlukları ve SAX alfabe boyutlarıyla denemeler yaparak bakım gereksinimlerini doğru şekilde tahmin edebilen sağlam bir algoritma oluşturmayı amaçlıyor. Süreç, SAX ve PAA teknikleri kullanılarak elde edilen etiketli veriler üzerinde makine öğrenimi modellerinin, özellikle de Uzun Kısa Süreli Bellek (LSTM) ağlarının eğitilmesini gerektirir. Algoritmanın performansı, işletme verimliliğini artırmak ve arıza süresini azaltmak için zamanında bakımın kritik olduğu çelik üretim fırınlarından elde edilen gerçek dünya endüstri verileri kullanılarak değerlendirilir. Çalışmanın bulguları, modern veri işleme ve makine öğrenimi yaklaşımlarının endüstriyel varlık yönetimini ve karar verme süreçlerini nasıl iyileştiribileceğine dair öngörüler sağlayarak tahmine dayalı bakım yöntemlerinin artmasına yardımcı oluyor.
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    Conference Object
    Evaluating Cognitive and Emotional Engagement in AI-Assisted Virtual Reality Through EEG
    (Inst Mathematics & Informatics, Bulgarian Acad Sciences, 2025) Balli, Tugce; Yetkin, E. Fatih
    This study proposes an EEG-based evaluation pipeline for an AI-assisted VR platform designed to deliver immersive cultural heritage experiences for elderly people. EEG data is used to evaluate emotional and cognitive responses while performing real-world versus virtual tasks, offering a reusable evaluation framework for future immersive heritage applications.
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    Conference Object
    Citation - Scopus: 1
    Privacy Preservation for Machine Learning in Iiot Data Via Manifold Learning and Elementary Row Operations
    (Science and Technology Publications, Lda, 2025) Yetkin, E.F.; Ballı, T.
    Modern large-scale production sites are highly data-driven and need large computational power due to the amount of the data collected. Hence, relying only on in-house computing systems for computational workflows is not always feasible. Instead, cloud environments are often preferred due to their ability to provide scalable and on-demand access to extensive computational resources. While cloud-based workflows offer numerous advantages, concerns regarding data privacy remain a significant obstacle to their widespread adoption, particularly in scenarios involving sensitive data and operations. This study aims to develop a computationally efficient privacy protection (PP) approach based on manifold learning and the elementary row operations inspired from the lower-upper (LU) decomposition. This approach seeks to enhance the security of data collected from industrial environments, along with the associated machine learning models, thereby protecting sensitive information against potential threats posed by both external and internal adversaries within the collaborative computing environment. © 2025 by SCITEPRESS – Science and Technology Publications, Lda.
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    Article
    Citation - WoS: 1
    Citation - Scopus: 1
    Verbal Harassment Detection in Online Games Using Machine Learning Methods
    (Elsevier Sci Ltd, 2025) Hibatullah, Helmi; Balli, Tugce; Yetkin, E. Fatih
    Video games have been an inseparable aspect for many throughout their upbringing. The widespread adoption of the internet in the early 2000s has brought video games from the traditional offline media to the online environment. Consequently, people from different parts of the world can play together and communicate in-game with each other. Nowadays, most massively multiplayer online games (MMOs) incorporate voice communication features. Playing video games online with a certain degree of anonymity, along with the ability to verbally communicate with each other, has proven to be a dangerous combination that can breed toxic and abusive behaviors if left unmoderated. This paper proposes a new approach to integrating Whisper, a pre-trained automatic speech recognition (ASR) model, with the well-researched topic of text-based abusive behavior detection. Our proposed verbal harassment detection pipelines yielded an average F-score of 0.899 for all variants tested.
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    Master Thesis
    Yassı Çelik Sanayisinde, Çalışan Ekipmanlarda Titreşım Tabanlı Arıza Potansiyelinin Değerlendirilmesi için Öz Nitelik Çıkarım Yöntemlerinin İncelenmesi
    (2024) Kaçar, Saygın; Ballı, Tuğçe; Yetkin, Emrullah Fatih
    Tahmine dayalı bakım (PdM), sanayide bakım verimliliğini ve üretim süreçlerini iyileştirmek için kullanılan önemli bir veri bilimi uygulamasıdır. Sensör tabanlı izleme ve bakım raporları gibi güvenilir verilere sahip olmak, PdM modellerinin başarısı için kritiktir. Ancak, bakım verilerinin kullanımıyla ilgili zorluklar nedeniyle, bu modellerin uygulanmasında bakım ekiplerinin ve uzmanların desteğine ihtiyaç duyulur. En büyük sorun, zaman kısıtlamaları nedeniyle bakım ekiplerinin kapsamlı ve etiketli veri sağlamasının zor olmasıdır, bu da verilerin eksik veya sınırlı kalmasına yol açar. Çok sayıda durum izleme veri seti bulunsa da küçük çaplı bakım işlemleri için etiketlenmiş veri setleri nadirdir. Bu boşluğu doldurmak için, bu tez çalışmasında insan müdahalesine gerek kalmadan etiket üretmeyi hedefleyen bir yaklaşım önerilmektedir. Bu tezde, kritik varlıklardan toplanan titreşim verilerinden bilgi çıkarmak için gerçek zamanlı değişim noktası tespiti (CPD) algoritmalarının kullanılması önerilmektedir. Değişim noktalarını otomatik olarak tespit ederek ham veriyi anlamlı özelliklere dönüştürmek, makine öğrenmesi modellerini iyileştirir ve PdM modellerinin doğruluğunu artırır.CPD yönteminin uygulanabilirliğini göstermek için bir üretim şirketinden alınan titreşim verileri kullanılmıştır. Çalışmanın bulgularını desteklemek için etiketli bir veri seti de kullanılmıştır. Sonuçlar, CPD yaklaşımlarının tahmine dayalı bakım operasyonlarını iyileştirme potansiyelini göstermektedir. Bu kapsamlı yaklaşım, bakım uygulamalarının güvenilirliğini ve endüstriyel sistemlerin uzun vadeli güvenilirliğini artırmada uygulama alanları sunmaktadır
  • Thumbnail Image
    Article
    Citation - WoS: 2
    Citation - Scopus: 3
    Decoding Functional Brain Data for Emotion Recognition: A Machine Learning Approach
    (Assoc Computing Machinery, 2024) Tulay, Emine Elif; Balli, Tugce
    The identification of emotions is an open research area and has a potential leading role in the improvement of socio-emotional skills such as empathy, sensitivity, and emotion recognition in humans. The current study aimed at using Event Related Potential (ERP) components (N100, N200, P200, P300, early Late Positive Potential (LPP), middle LPP, and late LPP) of EEG data for the classification of emotional states (positive, negative, neutral). EEG datawere collected from 62 healthy individuals over 18 electrodes. An emotional paradigm with pictures from the International Affective Picture System (IAPS) was used to record the EEG data. A linear Support Vector Machine (C = 0.1) was used to classify emotions, and a forward feature selection approach was used to eliminate irrelevant features. The early LPP component, which was the most discriminative among all ERP components, had the highest classification accuracy (70.16%) for identifying negative and neutral stimuli. The classification of negative versus neutral stimuli had the best accuracy (79.84%) when all ERP components were used as a combined feature set, followed by positive versus negative stimuli (75.00%) and positive versus neutral stimuli (68.55%). Overall, the combined ERP component feature sets outperformed single ERP component feature sets for all stimulus pairings in terms of accuracy. These findings are promising for further research and development of EEG-based emotion recognition systems.