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.Conference Object Feature Enrichment Via Similar Trajectories for Xgboost Based Time Series Forecasting(Ieee, 2024-05-15) Yilmaz, Elif; Islak, Umit; Çakar, Tuna; Arslan, Ilker; Matematik, ; Bilimi, Bilgisayar; Mühendisliği, MakineIn this study, new time series forecasting models are developed based on XGBoost, and the similar trajectories method (ST), which can be interpreted as a regression based on nearest neighbors. Both the similar trajectories method and XGBoost model are known to have successful applications in traffic flow prediction. In our case, the focus is on similar trajectories used in the former method, and features based on these trajectories are used in the training of XGBoost. The success of the proposed models is confirmed through metrics such as the mean absolute error. Also, statistical tests are performed among the compared benchmark models. The study is concluded with discussions and questions about how these models can be further developed.Conference Object Reliability Study of Psychometric Tests in a Credit Scoring Model(Ieee, 2024-05-15) Nicat, Sahin; Filiz, Gozde; Ozvural, Ozden Gebizlioglu; Çakar, TunaThis study investigates the effectiveness and reliability of using psychometric tests in the credit decision-making processes within the finance sector. Psychometric tests, by measuring individuals' cognitive and psychological traits, hold the potential to broaden access to credit and identify high credit risk. However, after the literature review, it was seen that there was a need for more studies on the reliability and validity of these tests in finance. This study is designed to measure the test-retest reliability of a machine learning model and its inputs that utilize psychometric test results. Within the scope of the research, 115 participants were re-subjected to the same psychometric tests after an average of 6 months. Findings showed that psychometric tests and the machine learning model were generally consistent over time. This work has the potential to fill the gaps in the literature regarding the use of psychometric tests in the finance sector and lays a foundation for future research.Conference Object Citation - WoS: 1Citation - Scopus: 2Determination of Alzheimer's Disease Levels by Ordinal Logistic Regression and Artificial Learning Algorithms(Ieee, 2024-05-15) Bulut, Nurgül; Çakar, Tuna; Arslan, Ilker; Akinci, Zeynep Karaoglu; Oner, Kevser SetenayThis study compares artificial learning algorithms and logistic regression models in determining different levels of Alzheimer's disease (AD). The research uses demographic, genetic, and neurocognitive inventory results obtained from the National Alzheimer's Coordination Center (NACC) database, along with brain volume/thickness measurements derived from MRI scanners. Deep Neural Networks, Ordinal Logistic Regression, Random Forest, Gaussian Naive Bayes, XGBoost, and LightGBM models were employed to determine the 4 different ordinal levels of AD. Although there were similarities between the accuracy rate, F1 score, AUC value, and sensitivity, specificity, and precision performance measures of each class, the highest classification rate was achieved by the Random Forest model where the oversampling was not applied. (F1 score: 0.86; accuracy: 0.86 and AUC: 0.95). The outputs of the model with the best performance were explained with the SHAP (SHapley Additive exPlanations) method. These findings indicate that non-invasive markers and artificial learning models can be used effectively in early diagnosis and decision support systems to predict different levels of Alzheimer's disease.Conference Object Citation - Scopus: 1Distinguishing Cognitive Processes: a Machine Learning Approach To Decode Fnirs Data for Third-Party Punishment and Credit Decision-Making(Ieee, 2024-05-15) Filiz, Gozde; Son, Semen; Sayar, Alperen; Ertugrul, Seyit; Sahin, Turkay; Akyurek, Guclu; Çakar, TunaFunctional near-infrared spectroscopy (fNIRS) has seen increasingly widespread use in examining brain activity and cognitive processes. However, the existing literature provides insufficient information on distinguishing between different decision-making mechanisms. This study explores the application of fNIRS in differentiating between two distinct decision-making processes: third-party punishment decisions and credit decisions. The research includes analyzing fNIRS data collected during these processes and classifying the associated neural patterns using machine learning. The findings reveal that fNIRS, in conjunction with ML, holds substantial potential to enhance the depth of understanding of decision-making processes in neuroscience research.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.Conference Object Fault-Tolerant Strassen-Like Matrix Multiplication(IEEE, 2020) Arslan, Şuayb Şefik; Güney, Osman B.; Oblokulov, MuhtashamIn this study, we propose a simple method for fault-tolerant Strassen-like matrix multiplications. The proposed method is based on using two distinct Strassen-like algorithms instead of replicating a given one. We have realized that using two different algorithms, new check relations arise resulting in more local computations. These local computations are found using computer aided search. To improve performance, special parity (extra) sub-matrix multiplications (PSMMs) are generated (two of them) at the expense of increasing communication/computation cost of the system. Our preliminary results demonstrate that the proposed method outperforms a Strassen-like algorithm with two copies and secures a very close performance to three copy version using only 2 PSMMs, reducing the total number of compute nodes by around 24% i.e., from 21 to 16.