Bilgisayar Mühendisliği Bölümü Koleksiyonu

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

Browse

Now showing 1 - 20 of 142

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: 2
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: 5
Citation - Scopus: 5
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 - 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.
Citation - WoS: 8
Citation - Scopus: 7
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.
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.
Predicting Credit Repayment Capacity With Machine Learning Models
(Ieee, 2024) Filiz, Gozde; Bodur, Tolga; Yaslidag, Nihal; Sayar, Alperen; Çakar, Tuna
This study examines the transformation in the financial services sector, particularly in banking, driven by the rapid development of technology and the widespread use of big data, and its impact on credit prediction processes. The developed credit prediction model aims to more accurately predict customers' credit repayment capacities. In pursuit of this goal, demographic and financial data along with credit histories of customers have been utilized to employ data preprocessing techniques and test various classification algorithms. Findings indicate that models developed with XGBoost and CATBoost algorithms exhibit the highest performance, while the effective use of feature engineering techniques is revealed to enhance the model's accuracy and reliability. The research highlights the potential for financial institutions to gain a competitive advantage in risk management and customer relationship management by leveraging machine learning models.
Physical Activity Monitoring With Smartwatch Technology in Adolescents and Obtaining Big Data: Preliminary Findings
(Ieee, 2024) Filiz, Gozde; Arman, Nilay; Ayaz, Nuray Aktay; Yekdaneh, Asena; Albayrak, Asya; Bozkan, Tunahan; Çakar, Tuna
This study assesses the potential of smartwatch technology in monitoring adolescents' physical activity and health parameters. It focuses on the role of physical activity in preventing chronic diseases and improving quality of life. The primary aim of the project is to perform statistical analysis of the large data sets collected from both healthy adolescents and those with chronic rheumatic diseases, and to develop a machine learning-based classification model to distinguish between these two groups. This analysis highlights the issue of physical inactivity observed during the Covid-19 pandemic, while showcasing the capacity of technology to offer solutions. The study aims to evaluate the collected data in a way that forms the basis for personalized activity plans for adolescents, demonstrating how wearable technology and big data can be effectively used in health services and to promote physical activity.
Reliability Study of Psychometric Tests in a Credit Scoring Model
(Ieee, 2024) Nicat, Sahin; Filiz, Gozde; Ozvural, Ozden Gebizlioglu; Çakar, Tuna
This 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.
Determination of Alzheimer's Disease Levels by Ordinal Logistic Regression and Artificial Learning Algorithms
(Ieee, 2024) Bulut, Nurgül; Çakar, Tuna; Arslan, Ilker; Akinci, Zeynep Karaoglu; Oner, Kevser Setenay
This 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.
Affection for Nouvel Architecture: on Contemporary (islamic) Architecture and Affect
(Intellect Ltd., 2022) Yücel, Şebnem
[No abstract available]
Feature Enrichment Via Similar Trajectories for Xgboost Based Time Series Forecasting
(Ieee, 2024) Yilmaz, Elif; Islak, Umit; Çakar, Tuna; Arslan, Ilker
In 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.
The Application of Two Bayesian Personalized Ranking Approaches Based on Item Recommendation From Implicit Feedback
(Ieee, 2024) Tagtekin, Burak; Sahin, Zeynep; Çakar, Tuna; Drias, Yassine
The 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.
Citation - Scopus: 1
Distinguishing Cognitive Processes: a Machine Learning Approach To Decode Fnirs Data for Third-Party Punishment and Credit Decision-Making
(Ieee, 2024) Filiz, Gozde; Son, Semen; Sayar, Alperen; Ertugrul, Seyit; Sahin, Turkay; Akyurek, Guclu; Çakar, Tuna
Functional 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.

Browse

Recent Submissions