Bilgisayar Mühendisliği Bölümü Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.11779/1940
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Conference Object Influence of Local Soil Conditions on Damages in Kahramanmaras during the 2023 Turkey Earthquake(Springer Science and Business Media Deutschland GmbH, 2024-11-16) Milev, Nikolay; Kiyota, Takashi; Tobita, Tetsuo; Briones, Juan; Briones, Othon; Cinicioglu, Ozer; Torisu, SedaThe 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.Conference Object Citation - WoS: 3Citation - Scopus: 3Detecting Autism From Head Movements Using Kinesics(Assoc Computing Machinery, 2024-11-04) Gokmen, Muhittin; Sariyanidi, Evangelos; Yankowitz, Lisa; Zampella, Casey J.; Schultz, Robert T.; Tunc, BirkanHead 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.Article Citation - WoS: 16Citation - Scopus: 20Physicians’ Ethical Concerns About Artificial Intelligence in Medicine: a Qualitative Study: “the Final Decision Should Rest With a Human”(Frontiers Media SA, 2024-11-27) 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.Conference Object Citation - WoS: 2Citation - Scopus: 1Fuzzy Elephant Herding Optimization and DBSCAN for Emergency Transportation: A Case Study for the 2023Turkiye Earthquake(Springer international Publishing Ag, 2024) Drias, Yassine; Drias, HabibaIn 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.Article Mention Detection in Turkish Coreference Resolution(Tubitak Scientific & Technological Research Council Turkey, 2024-09-23) Demir, Seniz; Akdag, Hanifi IbrahimA 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.Article Citation - WoS: 15Citation - Scopus: 15Enhanced Primordial Gravitational Waves From a Stiff Postinflationary Era Due To an Oscillating Inflaton(Amer Physical Soc, 2024-09-25) Chen, Chao; Dimopoulos, Konstantinos; Eroncel, Cem; Ghoshal, AnishWe 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.Conference Object Citation - Scopus: 2The Application of Two Bayesian Personalized Ranking Approaches Based on Item Recommendation From Implicit Feedback(Ieee, 2024-05-15) Tagtekin, Burak; Sahin, Zeynep; Çakar, Tuna; Drias, YassineThe present study has aimed to provide a different ranking approach that will be used actively in a sector-specific application regarding the optimization of item ranking presented to the users. The current online approach in several different applications still holds a manual ranking algorithm whose parameters are determined by the data specialists with adequate domain-knowledge. The obtained findings from the present study indicate that the optimized Bayesian Personalized Ranking models will be used for providing a suitable, data-driven input for the ranking system that would serve to be personalized. The outcomes of the present study also demonstrate that the model using LearnBPR optimized with a stochastic gradient descent algorithm outperform the other similar methods. The sample model outputs were also investigated by a user sample to ensure that the algorithm was working correctly. The next potential step is to provide a normalization process to include the extracted information to the current ranking system and observe the performance of this new algorithm with the A/B tests conducted.Article Citation - WoS: 6Citation - Scopus: 8Designing restorative landscapes for students: A Kansei engineering approach enhanced by VR and EEG technologies(Elsevier, 2024-09-01) Karaca, Elif; Çakar, Tuna; Karaca, Mehmet; Gul, Hasan Huseyin Mirac; Hüseyin Miraç Gül, HasanThis study explores the alignment of specific landscape features within school environments with the core elements of Attention Restoration Theory (ART) that includes Coherence, Fascination, Compatibility, and Being Away. Utilizing Kansei Engineering, this research integrates emotional analysis into landscape design by employing Virtual Reality (VR) and Electroencephalogram (EEG) technologies to record students' responses to different landscape simulations. Analytical techniques, including the Taguchi Method and Analysis of Variance (ANOVA), were applied to evaluate the data. The findings have revealed that students associate a sense of enclosure with a coherent landscape and openness with a fascinating landscape, the lawn's significance was also highlighted for coherent landscape. However, limited insights were gained regarding Compatibility and Being Away. The study advocates for diverse cognitive zones within school landscapes to promote mental restoration, emphasizing the need for varied design elements that cater to the elevated experience of students.Article Citation - WoS: 1Citation - Scopus: 1Understanding the Psychological and Financial Correlates for Consumer Credit Use;(Sosyoekonomi Society, 2024-01-31) Ertuğrul, Seyit; Sayar, Alperen; Şahin, Türkay; Çakar,Tuna; Ertuğru, SeyitThis study investigated the behavioural and cognitive predictors of consumer credit usage to develop a behavioural credit risk assessment procedure for a factoring company. Participants completed surveys measuring personality traits, self-esteem, material and monetary values, compulsive and impulsive buying tendencies, self-control, and impulsiveness. Financial surveys also assessed financial literacy and knowledge of financial concepts. The results indicated that extraversion, conscientiousness, emotional stability, and experiential self-control were significant predictors of consumer credit usage. These findings suggest that a finance company can use these personality traits and financial characteristics to develop a more accurate and effective credit risk assessment procedure, such as psychometric tests. © 2024, Sosyoekonomi Society. All rights reserved.Article Citation - WoS: 2Citation - Scopus: 4Unlocking the Neural Mechanisms of Consumer Loan Evaluations: an Fnirs and Mlbased Consumer Neuroscience Study(Frontiers Media SA, 2024-02-05) Girişken, Yener; Son, Semen; Demircioğlu, Esin Tuna; Filiz, Gözde; Çakar, Tuna; Ertuğrul, Seyit; Sayar, Alperen; Tuna, Esin; Son-Turan, SemenThis study conducted a comprehensive exploration of the neurocognitive processes underlying consumer credit decision-making using cutting-edge techniques from neuroscience and artificial intelligence (AI). Employing functional Near-Infrared Spectroscopy (fNIRS), the research examines the hemodynamic responses of participants while evaluating diverse credit offers. The study integrates fNIRS data with advanced AI algorithms, specifically Extreme Gradient Boosting, CatBoost, and Light Gradient Boosted Machine, to predict participants' credit decisions based on prefrontal cortex (PFC) activation patterns. Findings reveal distinctive PFC regions correlating with credit behaviors, including the dorsolateral prefrontal cortex (dlPFC) associated with strategic decision-making, the orbitofrontal cortex (OFC) linked to emotional valuations, and the ventromedial prefrontal cortex (vmPFC) reflecting brand integration and reward processing. Notably, the right dorsomedial prefrontal cortex (dmPFC) and the right vmPFC contribute to positive credit preferences. This interdisciplinary approach bridges neuroscience and finance, offering unprecedented insights into the neural mechanisms guiding financial choices. The study's predictive model holds promise for refining financial services and illuminating human financial behavior within the burgeoning field of neurofinance. The work exemplifies the potential of interdisciplinary research to enhance our understanding of human financial decision-making.