Bilgisayar Mühendisliği Bölümü Koleksiyonu

Permanent URI for this collectionhttps://hdl.handle.net/20.500.11779/1940

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Churn Prediction for Subscription-Based Applications Using Machine Learning
(Institute of Electrical and Electronics Engineers Inc., 2025) Gozukara H.; Patel J.; Kara E.; Yildiz A.; Mese Y.K.; Obali E.; Cakar T.
In this study, a predictive model was developed using machine learning techniques to forecast customer churn in subscription-based video streaming services. The data such as user login records, content viewing information, subscription details, and content-related features were used to interpret usage patterns and customer churn was defined based on subscription renewal status and renewal timing. Several usage-based features are extracted for users and several algorithms were used for modeling, such as Random Forest, CatBoost, XGBoost, Logistic Regression, K-Nearest Neighbors, and Gradient Boosting. Occurring class imbalance on the target variable is handled via BorderLineSMOTE. The model's performance was evaluated using training-test accuracy plots, classification reports, and hyperparameter tuning. Even though most of the models performed similarly, the CatBoost model emerged as the top performer, achieving a macro F1-score of 0.60. However, while effective in identifying churners, the models struggled to precisely classify non-churning customers, a common challenge in imbalanced datasets even after applying oversampling techniques. The analysis of feature importance yielded a crucial insight, early and consistent user engagement is the strongest predictor of customer retention. These findings provide valuable, actionable insights for streaming platforms, emphasizing that retention strategies should focus on maximizing engagement immediately after a user subscribes. © 2025 IEEE.
Attention-Enhanced Dual-Head LSTM With Rich Feature Engineering for Risk-Adjusted Stock Return Forecasting
(Institute of Electrical and Electronics Engineers Inc., 2025) Patel J.; Gunes P.; Ertugrul S.; Sayar A.; Benli H.; Makaroglu D.; Cakar T.
Stock return forecasting is a challenging task due to the complex, nonlinear, and volatile nature of financial markets. In this paper, we propose a comprehensive deep learning framework that integrates: a two-layer Long Short-Term Memory (LSTM) network augmented with a learnable attention mechanism, a dual-head output for simultaneous regression of next-day returns and classification of price direction, with an extensive suite of technical and macro-financial features. Our feature set comprises lagged log-returns, trend indicators (simple and exponential moving averages), momentum oscillators (RSI, MACD), volatility measures (rolling variance and GARCH conditional volatility), price bands (Bollinger Bands, Donchian channels), volume metrics (On-Balance Volume, Volume Rate of Change), Hidden Markov Model regime states, market index returns, and calendar effects. We train and validate the model using a rolling-window cross-validation scheme with early stopping and hyperparameter tuning to ensure temporal robustness. Empirical results on a large multi-stock dataset demonstrate that our attention-enhanced, dual-task LSTM outperforms single-task LSTMs and traditional machine learning benchmarks, achieving lower forecasting error and more stable generalization. © 2025 IEEE.
Rag Based Interactive Chatbot for Video Streaming Services
(Institute of Electrical and Electronics Engineers Inc., 2025) Gözükara H.; Patel J.; Kara E.; Yildiz A.; Köseoǧlu O.; Makaroǧlu D.; Çakar T.
The proliferation of content within video streaming services presents a significant challenge for users seeking personalized recommendations and specific information. This research addresses this challenge by developing a Retrieval-Augmented Generation (RAG) chatbotn designed to enhance user experience through conversational AI. The primary contribution of this work is a novel Retrieval-Augmented Generation (RAG) architecture featuring a dual-retrieval system that combines semantic search for descriptive requests and structured queries for fact based inquiries. This approach grounds the Large Language Model (LLM) in a factual knowledge base, mitigating the risk of hallucinations. The system is engineered to handle empty data retrieval scenarios by dynamically relaxing search filters, ensuring a robust user experience. The effectiveness of this RAG approach was validated through a comprehensive set of automated evaluations. The system demonstrates high precision in ranked list retrieval with questions like "Recommend me the top 5 action movies with highest IMDb scores", achieving an average NDCG@k of 0.837. While the chatbot shows strong semantic understanding by achieving 91% accuracy with contextual clues such as "Which Batman movies are directed by Christopher Nolan?", its performance with more ambiguous, plot-only queries (59.5% accuracy) indicates clear opportunities for future refinement. These results confirm that the dual-tool architecture successfully combines the flexibility of semantic search with the precision of structured queries, paving the way for more intuitive and efficient content discovery on streaming platforms. © 2025 IEEE.
Combining Similar Trajectories and XGBoost via Residual Learning for Traffic Flow Forecasting
(Institute of Electrical and Electronics Engineers Inc., 2025) Işlak U.; Yilmaz E.; Arslan I.; Çakar T.
In this study, we propose novel hybrid forecasting models that integrate the method of similar trajectories with machine learning techniques, particularly the XGBoost algorithm, for traffic flow prediction. Traditional statistical models, such as ARIMA, often struggle to accurately capture the complex, non-linear patterns characteristic of traffic flow data. To address these limitations, we develop an additive hybrid forecasting framework that combines the strengths of linear models (similar trajectories method) and non-linear models (XGBoost). Our proposed methods are evaluated on two different stations from the California PEMS dataset. Experimental results demonstrate that the proposed hybrid models consistently outperform individual benchmark models, including ARIMA, standalone similar trajectories, and XGBoost. The superiority of the hybrid approach, particularly the XGBST model, is further validated through the Diebold-Mariano statistical test, confirming significant predictive improvements at various significance levels. Additionally, using weighted Euclidean distance within the similar trajectories method further enhanced forecasting accuracy. The interpretability and flexibility of our hybrid framework make it especially suitable for practical implementation in traffic management systems. These findings underline the effectiveness of hybrid modeling strategies in traffic flow forecasting and suggest future research directions, such as comprehensive hyperparameter optimization and broader validation across diverse datasets. © 2025 IEEE.
A Multimodal AI and ML Framework for Fashion Image Segmentation, Recommendation, and Similarity Recognition
(Institute of Electrical and Electronics Engineers Inc., 2025) Soyhan M.E.; Ay T.B.; Memis E.C.; Fatih Capal M.; Cakar T.; Gunay S.; Coskun H.
This study presents a scalable multimodal Artificial Intelligence (AI) and Machine Learning (ML) framework designed to enhance decision making in the fashion industry. The proposed system integrates garment segmentation, multimodal feature extraction, and similarity recommendation into a unified pipeline. Using Segformer for segmentation, along with the convolutional neural network (CNN)-based feature extraction models ResNet152V2 and Xception, and the transformer-based vision-language model LLaVA, the framework generates visual and semantic embeddings of garments. These representations are processed through similarity detection using OpenAI embedding models and stored in the Pinecone vector database for efficient retrieval. Real-time similarity scoring is enabled through FastAPI endpoints, offering interactive search capabilities. Preliminary results demonstrate the system's strong ability to identify conceptually and visually similar items across a large catalog, providing actionable insights for designers. This framework lays the groundwork for intelligent, interpretable, and production-ready AI systems in the fashion industry. © 2025 IEEE.
Predicting Customer Churn in Retail Using Machine Learning on Transaction Data
(Institute of Electrical and Electronics Engineers Inc., 2025) Bozan M.T.; Gozukara H.; Patel J.; Kizilay A.; Sahin Z.; Tosun B.; Cakar T.
Customer churn prediction is critical for businesses to retain customers and reduce revenue loss. This paper presents a retail customer churn prediction study. We preprocess transactional data from a retail dataset comprising approximately 19.7 million transactions involving over 1 million customers. Temporal behavioral features, such as purchase frequency, monetary value, product variety, and promotional engagement metrics, are engineered using a four-month observation window. A Random Forest classifier is trained, utilizing balanced class weighting to address churn class imbalance. The churn label is defined as customers not purchasing in the subsequent six-month period. Our Random Forest model achieves approximately 84% accuracy, 86% precision, 85% recall, and an F1- score of 85%. Additionally, an XGBoost model achieves similar accuracy (≈ 84%) but higher recall (93%) and F1-score (89%), indicating improved churn prediction. The confusion matrix illustrates clear model performance. This study demonstrates that carefully engineered RFM-based features and ensemble learning approaches significantly enhance churn prediction in retail contexts. © 2025 IEEE.
Graph Theory-Based Fraud Detection in Banking Check Transactions
(Institute of Electrical and Electronics Engineers Inc., 2025) Behsi Z.; Memis E.C.; Ertugrul S.; Sayar A.; Gunes P.; Seydioglu S.; Cakar T.
Traditional banking fraud detection systems rely on rule-based approaches that analyze individual transactions in isolation, failing to capture complex relationship patterns indicative of coordinated fraud schemes such as check-kiting and artificial credit score manipulation. We p resent our study, a novel similarity-based graph theory approach that constructs weighted networks between check issuers using Jaccard Similarity Index and employs advanced graph analysis to identify suspicious entity clusters without requiring complete transaction relationship data. Our approach combines Jaccard Similarity Index for behavioral pattern analysis (addressing payee information unavailability) with comprehensive graph analysis including centrality measures, community detection, and anomaly identification. Through comprehensive evaluation on real banking data containing 458,399 transactions from 121,647 unique issuers - the largest confirmed dataset in fraud detection literature - we demonstrate the effectiveness of our methodology. Following parameter optimization using grid search methodology (similarity threshold: 0.55, risk percentile: 0.75), our study achieves competitive detection rates in optimal configurations with an average F1-score of 0.447 (±0.164) and peak performance reaching an F1-score of 0.557, while providing superior network topology analysis with 0.923 clustering coefficient. The system operates under significant data privacy constraints, lacking personal identification information (names, account numbers, IDs) and complete payee data. Despite these limitations, our study outperforms traditional approaches by leveraging similarity-based indirect relationships, and we project that performance could reach 85-95% levels with complete data access. © 2025 IEEE.
Developing Autonomous Steering Algorithm To Improve Cornering Slip Performance of a Four-Wheel Car Using Neural Network Tools
(Institute of Electrical and Electronics Engineers Inc., 2025) Alatciyan D.R.; Emeryan B.J.; Barbaros B.; Cakar T.; Kilic N.
This study investigates a neural network-based predictive steering control using simulation data generated from ADAMS Car. A Long Short-Term Memory (LSTM) architecture is employed to estimate steering angle and longitudinal velocity from sequential input features, with the goal of analyzing the model's behavior in cornering scenarios. The experimental setup includes multiple simulation runs under varying configurations, particularly exploring the effect of different sliding window sizes on prediction performance. Results show that the proposed model can effectively capture temporal patterns in the input data and produce consistent estimations across test conditions. While the study is limited to a simulation environment, it provides initial insights into how AI-based models may support steering control tasks and lays the groundwork for future extensions involving additional vehicle dynamics inputs. © 2025 IEEE.
Does Prompt Engineering Help Turkish Named Entity Recognition?
(Institute of Electrical and Electronics Engineers Inc., 2024) Pektezol, A.S.; Ulugergerli, A.B.; Öztoklu, V.; Demir, Şeniz
The extraction of entity mentions in a text (named entity recognition) has been traditionally formulated as a sequence labeling problem. In recent years, this approach has evolved from recognizing entities to answering formulated questions related to entity types. The questions, constructed as prompts, are used to elicit desired entity mentions and their types from large language models. In this work, we investigated prompt engineering in Turkish named entity recognition and studied two prompting strategies to guide pretrained language models toward correctly identifying mentions. In particular, we examined the impact of zero-shot and few-shot prompting on the recognition of Turkish named entities by conducting experiments on two large language models. Our evaluations using different prompt templates revealed promising results and demonstrated that carefully constructed prompts can achieve high accuracy on entity recognition, even in languages with complex morphology. © 2024 IEEE.
Citation - Scopus: 1
Artificial Intelligence Driven Multivariate Time Series Analysis of Network Traffic Prediction
(Institute of Electrical and Electronics Engineers Inc., 2024) Filiz, G.; Yıldız, A.; Kara, E.; Altıntaş, S.; Çakar, T.
The primary objective of this research is to employ artificial intelligence, machine learning, and neural networks in order to construct a network traffic prediction model. The analysis of network traffic data obtained from a digital media and entertainment provider operating in Turkey is conducted through the application of multivariate time-series analysis techniques in order to get insights into the temporal patterns and trends. In model development, Vector Autoregression (VAR), Vector Error Correction Model (VECM), Long-Short Term Memory (LSTM), and Gated Recurrent Unit (GRU) algorithms have been utilized. LSTM and GRU models have performed better with low Mean Absolute Percentage Error (MAPE) and high R-squared Score (R2). LSTM model has reached 0.98 R2 and 8.95% MAPE. These results indicate that the models can be utilized in network management optimization as resource allocation, congestion detection, anomaly detection, and quality of service. © 2024 IEEE.
Influence of Local Soil Conditions on Damages in Kahramanmaras during the 2023 Turkey Earthquake
(Springer Science and Business Media Deutschland GmbH, 2025) Milev, Nikolay; Kiyota, Takashi; Tobita, Tetsuo; Briones, Juan; Briones, Othon; Cinicioglu, Ozer; Torisu, Seda
The 2023 Turkey-Syria earthquake affected an area of 99000 km2 on Turkish side where two million people were left without home. The PGA values which have been recorded by various stations show values as high as 1.2g as well as relatively spectacular maximum vertical component (PGV). The focus of the paper is to focus on a noticeable phenomenon in the city of Kahramanmaras where, on one hand, almost all buildings in the historical centre have either collapsed or been severely damaged by the two earthquakes (Pazarcik at 4:17 AM and Elbistan at 1:24 PM, respectively) of February 6th 2023, whereas, on the other hand, structures in the surrounding areas have significantly less damage. Moreover, it is evident from seismic stations’ recordings that impact (in terms of PGA, acceleration and velocity time histories) of first major shock (M7.7 Pazarcik) is higher than the one of the second major shock (M7.6 Elbistan) at similar magnitude and comparable distance to the epicenter. For the sake of investigating further the influence of local soil conditions as possible reason for the observed events shear wave velocity and soil deposit fundamental frequency have been measured in two spots – first, where multiple collapsed structures were detected and second, a neighbouring area with mostly standing buildings. Results indicate that the on-site measurement of only S-waves might lead to wrong assumptions in terms of microseismical zonation and further considerations shall be accounted. Furthermore, some comments and preliminary assumptions regarding seismic motion amplification effects have been presented in the study. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.
Citation - WoS: 3
Citation - Scopus: 3
Detecting Autism From Head Movements Using Kinesics
(Assoc Computing Machinery, 2024) Gokmen, Muhittin; Sariyanidi, Evangelos; Yankowitz, Lisa; Zampella, Casey J.; Schultz, Robert T.; Tunc, Birkan
Head movements play a crucial role in social interactions. The quantification of communicative movements such as nodding, shaking, orienting, and backchanneling is significant in behavioral and mental health research. However, automated localization of such head movements within videos remains challenging in computer vision due to their arbitrary start and end times, durations, and frequencies. In this work, we introduce a novel and efficient coding system for head movements, grounded in Birdwhistell's kinesics theory, to automatically identify basic head motion units such as nodding and shaking. Our approach first defines the smallest unit of head movement, termed kine, based on the anatomical constraints of the neck and head. We then quantify the location, magnitude, and duration of kines within each angular component of head movement. Through defining possible combinations of identified kines, we define a higher-level construct, kineme, which corresponds to basic head motion units such as nodding and shaking. We validate the proposed framework by predicting autism spectrum disorder (ASD) diagnosis from video recordings of interacting partners. We show that the multi-scale property of the proposed framework provides a significant advantage, as collapsing behavior across temporal scales reduces performance consistently. Finally, we incorporate another fundamental behavioral modality, namely speech, and show that distinguishing between speaking- and listening-time head movements significantly improves ASD classification performance.
Citation - WoS: 11
Citation - Scopus: 11
Physicians’ Ethical Concerns About Artificial Intelligence in Medicine: a Qualitative Study: “the Final Decision Should Rest With a Human”
(Frontiers Media SA, 2024) Kahraman, F.; Aktas, A.; Bayrakceken, S.; Çakar, T.; Tarcan, H.S.; Bayram, B.; Ulman, Y.I.
Background/aim: Artificial Intelligence (AI) is the capability of computational systems to perform tasks that require human-like cognitive functions, such as reasoning, learning, and decision-making. Unlike human intelligence, AI does not involve sentience or consciousness but focuses on data processing, pattern recognition, and prediction through algorithms and learned experiences. In healthcare including neuroscience, AI is valuable for improving prevention, diagnosis, prognosis, and surveillance. Methods: This qualitative study aimed to investigate the acceptability of AI in Medicine (AIIM) and to elucidate any technical and scientific, as well as social and ethical issues involved. Twenty-five doctors from various specialties were carefully interviewed regarding their views, experience, knowledge, and attitude toward AI in healthcare. Results: Content analysis confirmed the key ethical principles involved: confidentiality, beneficence, and non-maleficence. Honesty was the least invoked principle. A thematic analysis established four salient topic areas, i.e., advantages, risks, restrictions, and precautions. Alongside the advantages, there were many limitations and risks. The study revealed a perceived need for precautions to be embedded in healthcare policies to counter the risks discussed. These precautions need to be multi-dimensional. Conclusion: The authors conclude that AI should be rationally guided, function transparently, and produce impartial results. It should assist human healthcare professionals collaboratively. This kind of AI will permit fairer, more innovative healthcare which benefits patients and society whilst preserving human dignity. It can foster accuracy and precision in medical practice and reduce the workload by assisting physicians during clinical tasks. AIIM that functions transparently and respects the public interest can be an inspiring scientific innovation for humanity. Copyright © 2024 Kahraman, Aktas, Bayrakceken, Çakar, Tarcan, Bayram, Durak and Ulman.
İmece-depo: İşbirlikçi Hücresel Ağlarda Veri Önbellekleme için Cihazdan Cihaza Iletişim ile Dağıtık Depolama, Optimale Yakın Kodlama ve Protokol Tasarımı.
(2023) Haytaoğlu, Elif; Pourmandı, Massoud; Kaya, Erdi; Arslan, Şefik Şuayb
Hücresel ağlarda popüler dosyaların cihazlarda önbelleklenmesi ile, cihazlar arası etkileşim baz istasyonu (Bİ) üzerine düşen iletişim yükünü oldukça azaltmaktadır. Dağıtık veri önbellekleme işlemi popüler bir dosyanın parçalarının kodlanmamış orijinal haliyle ya da herhangi bir silinti kodu kullanılarak kodlanmış halinin mobil cihazlar içerisinde dağıtık bir şekilde depolanması yardımıyla gerçekleştirilir. Dosyanın herhangi bir parçası, komşu mobil cihazlardan ya da mümkün değilse, doğrudan Bİ?lerden, yüksek bir iletişim maliyeti pahasına indirilebilir. Bir hücresel ağda, rastgele zamanlarda bazı düğümlerin hücreye katıldığı ve bazılarının ayrıldığı göz önüne alındığında, performans için Bİ ile iletişimin minimum düzeyde olmasını sağlayacak akıllı veri onarım yöntemlerine ihtiyaç duyulacaktır. Tek bir veya birden fazla Bİ?nin sisteme katılımı, önceki onarım paradigmalarına, özellikle de işbirlikçi düğüm onarım süreçlerine farklı bir boyut eklemektedir. Bunun nedeni, çalışma protokolü kurallarının yanı sıra iletişim kısıtlamalarının da değişmesidir. Literatür, bu durum için temel bant genişliği/depolama ödünleşim uzayını inceleyen bir çalışma içermemektedir. Yeni hücre mimarileri buna göre, yeni silinti kod yapılarını, verimli protokol tasarımlarını, veri erişim gecikmesi, gerçekçi kuyruk modelleri ve gerçekçi benzetim platformları dahil ancak bunlarla sınırlı olmamak üzere farklı tasarım değerlendirmelerini gerektirmektedir. Bu projede ilk olarak, daha önceki hiçbir çalışmada düşünülmemiş işbirliği yapan Bİ?lerin cihaz ayrılışlarında yaşanan kayıp verinin onarımı için bant genişliği ve depolama kapasitesinin iyileştirilmiş teorik sınırlarının veri akış diyagramları kullanılarak elde edilmesi amaçlanmıştır. Bununla beraber, bant genişliği ve depolama alanını en iyi kullanan kod yapılarından esinlenerek, veri önbellekleme işlemini optimale yakın bir maliyetle gerçekleştirecek tamamen özgün çizge tabanlı kod yapıları ve bu yeni kodlar için daha önce düğüm onarım problemine uygulanmamış genetik algoritma, optimize edilmiş artık veri dağıtımı gibi yeni yaklaşımlar kullanılarak önceden düşünülmemiş düğüm onarım algoritmaları önerilecektir. Ayrıca, düğümlerin hücreye katılma ve ayrılma süreçleri için, bant genişliği ve veri depolaması gereksinimlerini en aza indirmeye yardımcı olacak enerji tüketimi odaklı son derece özgün protokoller önerilecektir. Bu protokoller, düğümlerin bir hücreden diğerine geçiş yapabileceği ve hücre içi kaynakların etkin kullanılmasına yardımcı olmak için Bİ?lerin işbirliği yapmasını sağlayan geçiş senaryoları ile güçlendirilecektir. Bu durum, iki onarım işlemi arasındaki sürenin ayarlanması, veri erişim maliyetlerinin azaltılması, hücreye katılan düğüm içeriğinin kullanımı, artık veri kullanımı v.s. gibi yenilikleri içerecektir. Son olarak, önerilen kod yapıları ve protokol mimarisinin performansını analitik olarak türetmek için bilinen çeşitli ve daha gerçekçi kuyruklama modelleri değerlendirilecektir. Analitik sonuçlarımızı doğrulamak için daha sonra hücresel ağ tabanlı büyük ölçekli benzetimler yapılıp sayısal yöntemler ile toplam iletişim ve dosya onarım işlemlerinin maliyet hesaplamaları ve karşılaştırmaları yapılacaktır. MEF Üniversitesi öğretim üyesi Dr. Şuayb Arslan?ın yürütücüsü olduğu ve 36 ay sürecek projede, Pamukkale üniversitesi Bilgisayar Mühendisliği Bölümü öğretim üyesi Dr. Elif Haytaoğlu araştırmacı olarak görev alacaktır. Projede, iki doktora, iki yüksek lisans ve son iki senemizde iki lisans öğrencisi bursiyer olarak görev alacaktır.
Citation - WoS: 13
Citation - Scopus: 13
Enhanced Primordial Gravitational Waves From a Stiff Postinflationary Era Due To an Oscillating Inflaton
(Amer Physical Soc, 2024) Chen, Chao; Dimopoulos, Konstantinos; Eroncel, Cem; Ghoshal, Anish
We investigate two classes of inflationary models, which lead to a stiff period after inflation that boosts the signal of primordial gravitational waves (GWs). In both families of models studied, we consider an oscillating scalar condensate, which when far away from the minimum is overdamped by a warped kinetic term, a la alpha-attractors. This leads to successful inflation. The oscillating condensate is in danger of becoming fragmented by resonant effects when nonlinearities take over. Consequently, the stiff phase cannot be prolonged enough to enhance primordial GWs at frequencies observable in the near future for low orders of the envisaged scalar potential. However, this is not the case for a higher-order scalar potential. Indeed, we show that this case results in a boosted GW spectrum that overlaps with future observations without generating too much GW radiation to destabilize big bang nucleosynthesis. For example, taking alpha=O(1), we find that the GW signal can be safely enhanced up to Omega(GW) (f)similar to 10(-11) at frequency f similar to 10(2) Hz, which will be observable by the Einstein Telescope. Our mechanism ends up with a characteristic GW spectrum, which if observed, can lead to the determination of the inflation energy scale, the reheating temperature, and the shape (steepness) of the scalar potential around the minimum.
Citation - Scopus: 4
Investigation of the Motion of a Spherical Object Located at Soft Elastic and Viscoelastic Material Interface for Identification of Material Properties
(Academic Enhancement Department, King Mongkut's University of Technology North Bangkok, 2024) Körük, Hasan; Pouliopoulos, A.N.
Measuring the properties of soft viscoelastic materials is challenging. Here, the motion of a spherical object located at the soft elastic and viscoelastic material interface for the identification of material properties is thoroughly investigated. Formulations for different loading cases were derived. First, the theoretical models for a spherical object located at an elastic medium interface were derived, ignoring the medium viscosity. After summarizing the model for the force reducing to zero following the initial loading, we developed mathematical models for the force reducing to a lower non-zero value or increasing to a higher non-zero value, following the initial loading. Second, a similar derivation process was followed to evaluate the response of a spherical object located at a viscoelastic medium interface. Third, by performing systematic analyses, the theoretical models obtained via different approaches were compared and evaluated. Fourth, the measured and predicted responses of a spherical object located at a gelatin phantom interface were compared and the viscoelastic material properties were identified. It was seen that the frequency of oscillations of a spherical object located at the sample interface during loading was 10–15% different from that during unloading in the experimental studies here. The results showed that different loading cases have immense practical value and the formulations for different loading cases can provide an accurate determination of material properties in a multitude of biomedical and industrial applications. © 2023 King Mongkut’s University of Technology North Bangkok. All Rights Reserved.
Mention Detection in Turkish Coreference Resolution
(Tubitak Scientific & Technological Research Council Turkey, 2024) Demir, Seniz; Akdag, Hanifi Ibrahim
A crucial step in understanding natural language is detecting mentions that refer to real-world entities in a text and correctly identifying their boundaries. Mention detection is commonly considered a preprocessing step in coreference resolution which is shown to be helpful in several language processing applications such as machine translation and text summarization. Despite recent efforts on Turkish coreference resolution, no standalone neural solution to mention detection has been proposed yet. In this article, we present two models designed for detecting Turkish mentions by using feed-forward neural networks. Both models extract all spans up to a fixed length from input text as candidates and classify them as mentions or not mentions. The models differ in terms of how candidate text spans are represented. The first model represents a span by focusing on its first and last words, whereas the representation also covers the preceding and proceeding words of a span in the second model. Mention span representations are formed by using contextual embeddings, part-of-speech embeddings, and named-entity embeddings of words in interest where contextual embeddings are obtained from pretrained Turkish language models. In our evaluation studies, we not only assess the impact of mention representation strategies on system performance but also demonstrate the usability of different pretrained language models in resolution task. We argue that our work provides useful insights to the existing literature and the first step in understanding the effectiveness of neural architectures in Turkish mention detection.
Citation - WoS: 1
Citation - Scopus: 1
Fuzzy Elephant Herding Optimization and DBSCAN for Emergency Transportation: A Case Study for the 2023Turkiye Earthquake
(Springer international Publishing Ag, 2024) Drias, Yassine; Drias, Habiba
In recent times, our planet has experienced numerous natural disasters across all continents. The damage caused by these disasters has been so extensive that Emergency Medical Services (EMS) proved incapable of handling the situation. In this article, we present a novel approach for urgent disaster transport with the aim of minimizing loss of life. In this context, we are investigating the Density-Based Spatial Clustering of Applications with Noise algorithm (DBSCAN) to cluster the large geographic zone affected by the 2023 earthquake in Turkiye. The clustering is done based on hospitals' capacity on one hand and damages on the other hand. The ambulance dispatching task is then tackled using a new fuzzy version of Elephant Herding Optimization called FEHO. This approach addresses the challenge of dispatching ambulances to cover emergency locations effectively and optimally in the clustered regions. Experiments conducted on real data demonstrate the effectiveness of our approach in managing emergency transportation and highlight its potential to minimize the number of casualties.
Evaluating Electrophysiological Responses Due To Identity Judgments
(Ieee, 2024) Çakar, Tuna; Hohenberger, Annette
This study was conducted to explore how the brain processes decisions about identity, employing event-related potentials (ERPs) as a measure. The aim was to ascertain if the EEG/ERP technique could be used to monitor the cognitive processing of identity judgments as they happen. The investigation focused on comparing two groups of statements: those that used the concept of 'same' and those that used 'different'. The researchers hypothesized that there would be notable differences in the ERPs, particularly around the 400-millisecond mark, correlating with the reaction time disparities observed behaviorally. The ERP data revealed that the 'different' statements generated a unique N400 response when contrasted with the 'same' statements, implying that the participants' cognitive responses to these two types of judgments were not the same.
Neural Decoding of Brand Perception and Preferences: Understanding Consumer Behavior Through Fnirs and Machine Learning
(Ieee, 2024) Çakar, Tuna; Girisken, Yener; Tuna, Esin; Filiz, Gozde; Drias, Yassine
This research examines the link between consumer brand perceptions and neural activity by employing Functional Near-Infrared Spectroscopy (fNIRS) and machine learning techniques. The study analyzes the neural projections of participants' reactions to brand-associated adjectives, processing data collected from 168 individuals through machine learning algorithms. The findings underscore the significance of the lateral regions of the prefrontal cortex in the decision- making process related to brand perceptions. The aim is to understand how brands are perceived when associated with various adjectives and to develop this understanding through neural patterns using machine learning models. This study demonstrates the potential of integrating neural data with machine learning methods in the field of applied neuroscience.

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