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Bireylerin Covid-19’a dair tükenmişliklerinin algıladıkları Covid-19 riski, dünyaya ilişkin varsayımları ve ebeveyn biçimleri ile ilişkisi
(Türk Psikologlar Derneği, 2023-07-26) Erdem, Büşra; Ünver, Buket
Dünyaya İlişkin Varsayımları Ve Ebeveyn Biçimleri İle İlişkisi Tükenmişlik kavramı özellikle endüstri ve sağlık psikolojisi çatısı altında yer almakla birlikte Covid-19 pandemisi ile birlikte tükenmişlik ve salgın hastalıklar arasındaki ilişki klinik literatürde de dikkat çekmeye başlamıştır. Mevcut çalışma kapsamında bireylerin kitlesel bir dış faktör karşısında (Covid-19 pandemisi), süreç içerisinde yaşayacakları tükenmişlikleri ile olaya dönük algıladıkları risk, anlamsal dünyaları ve algılanan ebeveynlik biçimlerinin ilişkisini araştırmanın, olası pandemiler ya da paylaşılan toplumsal olaylar karşısında yaşanabilecek tükenmişlik olgusuna ve komorbidite tanıların ayrımına dair bütüncül bir bakış açısı sunması beklenmektedir. Bu doğrultuda gerçekleştirilen mevcut çalışmanın temel amacı bireylerin Covid-19’a dair tükenmişlikleri ile algıladıkları Covid-19 riski, dünyaya ilişkin varsayımları (DİV) ve algılanan ebeveynlik biçimlerinin ilişkisinin incelenmesidir. Aynı zamanda, sosyodemografik değişkenler ve Covid-19’a dair değişkenlerin Covid-19 tükenmişliği üzerindeki etkilerinin incelenmesi de araştırmanın diğer amacını oluşturmaktadır. Bu doğrultuda gerçekleştirilen çalışmanın örneklem grubunu 18-65 yaş aralığında yer alan 368 katılımcı (yaş ort. 33.85, SS=9.75; %58:4’ü kadın, %41.6’sı erkek) oluşturmaktadır. Çalışmanın veri toplama araçları Sosyodemografik Bilgi Formu, Koronavirüs Tükenmişlik Ölçeği (COVID-19-BS), Algılanan Covid-19 Risk Ölçeği (CPRS), Dünyaya İlişkin Varsayımlar Ölçeği (DİVÖ) ve Young Ebeveynlik Ölçeği (YEBÖ) şeklindedir. Yapılan analizlere göre kadın olanların, çocuk sahibi olmayanların, düşük eğitim düzeyi ve ekonomik durumu orta-alt ve orta-üste göre düşük ya da yüksek olanların ve anne babası ile yaşayanların Covid-19 tükenmişlik düzeylerinin daha yüksek olduğu görülmüştür. Öte yandan Covid-19’a dair değişkenlerden pozitif tanı alanların, fiziksel/sosyal izolasyon yaşayanların, iş yerinde ve pandemi öncesine göre daha yoğun çalışanların, Covid-19 nedeniyle yakın kaybı yaşayanların ve yakınları risk grubunda olanların Covid-19 tükenmişliklerinin daha yüksek olduğu bulgulanmıştır. Ayrıca yapılan korelasyon analizlerine göre Covid-19 tükenmişliği ile yaş arasında negatif yönde; algılanan Covid-19 riski ve bilişsel/duygusal alt boyutları ile pozitif yönde; DİV’in iyilik, adalet, şans ve kendilik değeri alt boyutları ve toplam DİV puanı ile negatif yönde; algılanan anne ebeveynlik biçimi ile pozitif yönde; algılanan baba ebeveynlik biçiminin ise küçümseyici/kusur bulucu, duygusal bakımdan yoksun bırakıcı, sömürücü/istismar edici ve kötümser/endişeli alt boyutları ile pozitif yönde anlamlı ilişkiler bulunmuştur. Son olarak hiyerarşik regresyon analizine dahil edilen tüm değişkenlerin toplam varyansın %40.8’ini anlamlı olarak [F= 8.690, p<.001] açıkladığı görülmüştür. Sonuçlar COVID 19 tükenmişliği üzerinde sosyodemografik ve Covid-19’a bağlı özelliklerin, algılanan ebeveynlik biçimlerinin, dünyaya ilişkin varsayımların, algılanan Covid19 risk algısının ve yordayıcı gücünün önemli olabileceğini düşündürmektedir. Mevcut bulgular ilgili literatür ışığında tartışılmış, çalışmanın sınırlılıklarına ve gelecek çalışmalar için önerilere yer verilmiştir.
The mediating role of difficulties in positive and negative emotion regulation in the relationship between early maladaptive schemas and cyber dating violence
(European Association for Behavioural and Cognitive Therapies (EABCT), 2023-10-07) Ünver, Buket; İnce, Elif Hazal
Introduction: Cyber dating violence includes all kinds of words, attitudes and behaviors that individuals use against their partners in order to harm the partner in the digital environment. In the present study, it was aimed to examine the mediating role of difficulties in positive emotion regulation and negative emotion regulation in the relationship between early maladaptive schemas and cyber dating violence. Method: The sample of the study consists of 298 individuals between the ages of 18-30 who are in a romantic relationship or have had a romantic relationship in the last 1 year. The data of the research was collected through Demographic Information Form, Cyber Dating Abuse Questionnaire, Young Schema Questionnaire-Short Form, Difficulty in Emotion Regulation Scale-Short Form and Multidimensional Measure of Difficulties in the Regulation of Positive Emotions. Results: Pearson Correlation Analysis was used to determine the relationship between early maladaptive schemas, difficulty in positive emotion regulation, negative emotion regulation and applied and exposed cyber dating violence. As a result of the statistical analyzes, significant relationships were found between four schema areas, disconnection&rejection, impaired autonomy and performance, other-directedness, overvigilance&inhibition, and the digital dating violence both applied and exposed. Mediation analysis revealed that difficulty in positive emotion regulation had a partial mediator role in digital dating violence applied and exposed to four schema domains. In addition, a partial mediating role of difficulty in regulating negative emotion was found between the areas of disconnection&rejection and others-directedness schema areas and the digital dating violence exposed. Dissusion: Individuals with an early life in an unhappy family develop schemas that cause them to turn to strategies such as fear, suppression and sabotage instead of feeling guilty for experiencing and enjoying positive emotions. At this point, the sabotage can be seen as the person being exposed to cyberbullying and/or being a cyberbully. The fact that digital dating violence seen in romantic relationships occurs especially through positive emotion regulation strategies reveals a need for how a positive emotion can be regulated especially in the adolescence and emerging adulthood group. Conclusion: The association of early maladaptive schemas and emotion regulation difficulties with digital dating violence suggests that clinicians may be effective in developing interventions for emotion regulation skills. In particular, in terms of regulation of positive emotions, impulse control, goal-oriented behavior, ability to activate emotional strategies, acceptance of emotions and regulation of targetoriented emotions and behaviors will be important therapeutic targets. Finally, awareness of cyber dating violence, cyberbullying and/or being a cyberbully that can be seen in adolescence and emerging adulthood group should be increased and people should be aware of their possible victimization.
The mediator role of schema modes in the relationship between parentification and co-dependency
(European Association for Behavioural and Cognitive Therapies (EABCT), 2023-10-07) Ünver, Buket; Önürme, Beyza; Bayram Kuzgun, Tubanur; Köse Karaca, Bahar; Kahveci, Ceyhun
Introduction: The disruption of the hierarchy between the parent and the child obscures the role of the child in the family. Parentification is characterized by the child taking emotional and/or instrumental responsibilities and caring for parents and siblings. Therefore, lead to significant difficulties in the child's development of a self, and these difficulties may be reflected in the child's romantic relationships in adulthood in the form of difficulties in thinking independently. This situation is conceptualized as codependency and is defined as excessive focus on others, assuming full responsibility, and low selfesteem. It is hoped that discovering the roles of schema modes, which are defined as emotional and behavioral states that emerge suddenly when people are hypersensitive, in these relationship styles will be a significant guide, especially in therapy sessions. Therefore, the main purpose of this study is to determine which schema modes mediate the relationship between parentification and co-dependence. Method: The research was conducted with 355 participants aged 18-69 years. The Sociodemographic Form, Parentification Inventory, Co-Dependency Assessment Scale, and Schema Mode Scale-Short Form were used in the study. Process Macro analysis Model 4 developed by Hayes (2013) was used to test the mediating role of schema modes between parentification and co-dependency. Results: According to the results of the analysis, the level of co-dependency is higher in women. Eight different mediator effect models were tested, including child modes, coping modes, parent modes, and healthy adult mode, between parent-focused parentification and sibling-focused parentification and codependency. The mediating role of the angry child mode, self-aggrandiser mode, and demanding parent mode was found between parent-focused parentification and co-dependency. In addition, the mediating role of the punitive and demanding parent mode was found between sibling-focused parentification and co-dependency. Discussion: It is noteworthy that the same mediating effect between both parent-focused and siblingfocused parentification and co-dependency is the demanding parent mode. The demanding parent mode, which prioritizes the needs of others, predicts co-dependency and shows the mode that should be studied first in treatment. The attention is drawn to the mediating variable between the punitive parenting mode, characterized by self-blaming aspects in individuals who assumed the responsibility of caring for their sibling during childhood, and perfectionism, which is co-dependency. Similarly, it is observed that the self-aggrandiser mode compensates for the emotional deprivation caused by parentification. These modes, which develop in root family interaction, mediate similar imbalances in adult roles. The prominence of the angry child and self-aggrandiser mode suggests that these individuals can be evaluated especially in terms of narcissism in studies and/or therapy sessions that examine the relationship between parentification and co-dependency. Conclusion: The schema modes come from the experiences of their root families and continue actively in the adulthood romantic relationships of individuals who take responsibilities that are not suitable for their developmental level in their childhood. It is thought that this study will enable individuals who experience parentification to define their unhealthy roles and explore their relational problems and will provide a new perspective on the predictor of childhood experiences on adulthood.
Impact of vaccines on the COVID-19 pandemic in Turkey
(2022-06-01) Yelmenoğlu, Elif Deniz; Elmas, Dilara
COVID-19 (coronavirus disease-2019 pandemic continues to threaten public health and this situation is raising great concern all over the world. With the development of different vaccines, it was aimed to end the epidemic and increase community immunity in the past years. The research reduced public anxiety but the extent of the impact of vaccines in the pandemic is should be under investigation. Because the degree of availability of the COVID-19 vaccines was differing both nationally and globally. This makes it important to investigate how effective vaccination is on the epidemic. The main aim of this study is to investigate the possible recovery impact of vaccination on the COVID-19 pandemic in Turkey. In addition, the rates of severe disease during the first 3 doses of vaccination were also examined in this study. The analyses are conducted based on Spearman, Kendall and Pearson's correlation by using the data of the Ministry of Health of the Republic of Turkey. The obtained results showed that there are strong correlations between vaccination and recovery.
TURSpider veri kümesinde Temsilcilerin Karışımı Tabanlı Text-to-SQL çalışması
(IEEE, 2025) Kanburoğlu, Ali Buğra; Tek, Faik Boray
Bu çalışma, Türkçe Text-to-SQL için geliştirilen TURSpider veri kümesi üzerindeki deneyleri ele almaktadır. TURSpider, çeşitli zorluk seviyelerine sahip SQL sorgularını içeren geniş kapsamlı bir Türkçe veri kümesidir ve bu alandaki araştırmalar için önemli bir kaynak niteliğindedir. Çalışmada, geri bildirim odaklı temsilcilerin karışımı yaklaşımının (ing. feedback driven Mixture-of-Agents - MoAF) başarımı incelenmiştir. MoAF yapısında, birden fazla büyük dil modeli (BDM) iş birligi içinde çalışarak SQL oluşturma başarımını artırmayı hedeflemektedir. Bu yapıda temsilci (ing. agent) işbirliği, modellerin birbirinden ögrenmesini ve geri bildirim mekanizmaları aracılığıyla hataların düzeltilmesini sağlamaktadır. Deney sonuçlarına göre, MoAF yaklaşımı ile %60.63 yürütme doğruluğuna ulaşılmış ve TURSpider veri kümesi üzerindeki en iyi sonuç elde edilmiştir.
Reproduction of social biases through AI: a study on AI developers' awareness on social biases
(BIDGE Publications, 2023-12-11) Şahin, Aylin; Pandır, Müzeyyen
Artificial Intelligence (AI) stands as an evolving and controversial force, changing the way of work in numerous sectors including education, art, finance, health, transportation, and security, also having influences over daily lives. While discussions often revolve around the technological breakthroughs and economic ramifications of AI, it is imperative to recognize and address the social impacts and consequences it carries, particularly with respect to potential social biases and discriminations that it will (re)produce or contribute to. This study explores AI developers' awareness and perspectives on gender, race and ethnicity-based biases in general, and how AI may contribute to these in particular. Surveys were conducted with 60 professionals working in different areas of AI and related issues. The findings revealed relatively informed understandings of the concepts of gender, race and ethnicity, whereas a lack of awareness among participants about prejudice. The study discusses that while considering what new technologies bring to society, it is crucial to understand how these new technologies may perpetuate existing social problems. To prevent such developments, it is crucial that those who play a role in the development of these technologies have an informed and ethical perspective towards the reproduction of social inequalities, for building more inclusive societies.
Sosyal medyanın mekân tasarımına etkisi
(BZT Akademi Yayınevi, 2021-12) Yalgın, Beste; Özker, Serpil
Endüstri Devrimi ile birlikte gelişen dünya düzeni içerisinde, toplumun kalıplaşmış özellikleri değişmeye başlamıştır. Sanayileşme öncesinde ürün ya da hizmetleri değerli kılan üretim kültürü, yerini tüketim kültürüne bırakmıştır. Tüketim kültürü bireylere sürekli olarak daha fazlasını istemeyi aşılayarak, tüketim eylemini bir statü göstergesi olarak dayatmıştır. Tüketim kültürü gölgesinde değişen dünyaya adapte olmak isteyen bireyler ise etkileşim kurma yeteneklerini ilerleterek, toplumda yer bulmayı hedeflemektedir. Tüketim kültürünün özünü oluşturan olgulardan biri de iletişimdir. Günümüzde, bireylerin rahatlıkla iletişim kurabildiği sosyal medya platformlarının kullanılmaya başlanması üzerine, mekân tasarım algısında da psikolojik, ekonomik, sosyal ya da kültürel değişiklikler başlamıştır. Sosyal medya platformlarında popülerleşen mekânları keşfetme arzusu duyan bireyler, mevcut ziyaretçi kitlesi tarafından paylaşılan fotoğraf, video ve yorumların etkisinde kalarak, o mekânları ziyaret etme eğilimi duymuştur. Çeşitli görsel ve işitsel ögelerin tesiri altındaki bireyler giderek içerik üretimine elverişli olan mekân tasarımlarına yönelmiştir. Mekân işletmeleri de bireylerin istek ve ihtiyaçlarını karşılayacak atmosferler oluşturmaya çalışmıştır. Bu doğrultuda araştırmada; sosyal medya platformlarının mekân tasarımı üzerindeki etkilerinin belirlenmesi amaçlanmıştır. Bu kapsamda sosyal medya, mekân, sosyal medya ve mekân tasarımı ilişkisi irdelenmiştir. Sonuç olarak, sosyal medya platformlarının yeme-içme, turizm ve mağaza mekânlarının tasarımı çerçevesindeki etkisinin var olduğu tespit edilmiştir.
A metric-driven IT risk scoring framework: incorporating contextual and organizational factors
(Institute of Electrical and Electronics Engineers Inc., 2025-09-24) Ünal, Nezih Mahmut; Çeliktaş, Barış
Risk analysis is a critical process for organizations seeking to manage their cybersecurity posture effectively. However, traditional risk analysis frameworks, such as the Common Vulnerability Scoring System (CVSS), primarily evaluate technical impacts without incorporating organizational context and dynamic risk factors. This paper presents a metric-based risk analysis framework designed to provide a more adaptable and context-aware risk-scoring framework. The proposed model enables risk owners to define customized threat scenarios and dynamically adjust metric weights based on organizational needs. Unlike traditional approaches, our method integrates contextual parameters to improve the accuracy and relevance of risk calculations. Experimental evaluations demonstrate that the proposed framework enhances risk prioritization and provides more actionable insights for decision-makers. This study contributes to the field by addressing the limitations of existing risk analysis models and offering a systematic approach for cybersecurity risk management.
LLM’leri kullanarak otel incelemelerini görüntü manipülasyonu ile görselleştirme
(Institute of Electrical and Electronics Engineers Inc., 2025-08-15) Özdemir, Ata Onur; Giritli, Efe Batur; Can, Yekta Said
Dijital çağda müşteri yorumları, özellikle otelcilik sektöründe, karar verme sürecinde önemli bir rol oynamaktadır. Metin tabanlı yorumlar değerli bilgiler sunsa da, potansiyel müşteriler genellikle öznel ifadeleri doğru şekilde yorumlamakta zorlanmaktadır. Araştırmalar, görsel temsillerin anlaşılırlığı artırdığını ve kullanıcı etkileşimini güçlendirdiğini göstermektedir. Bu çalışma, metin tabanlı görüntü manipülasyonu ile yazılı otel yorumlarını orijinal otel görselleri üzerinde değişiklikler yaparak görsel incelemelere dönüştürmeyi amaçlamaktadır. Stable Diffusion modeli kullanılarak yazılı otel yorumları girdileriyle otel odası görüntüleri manipüle edilmiştir. Manipüle edilen görsellerin değerlendirilmesinde SSIM (Structural Similarity Index Measure) ve PSNR (Peak Signal-to-Noise Ratio) metrikleri uygulanmıştır. Ayrıca, manipüle edilmiş ve orijinal görüntü örnekleri karşılaştırmalı olarak sunulmuştur. Sonuçlar, modelin küçük ölçekli değişikliklerde başarılı olduğunu, ancak büyük değişikliklerde kalite kaybı yaşadığını göstermektedir.
Mahremiyeti koruyan, merkezi, hibrit film öneri sistemi: araçlar arası internet için bir yaklaşım
(Institute of Electrical and Electronics Engineers Inc., 2025-08-15) Şimşek, Musa; Tüysüz Erman, Ayşegül
Bu çalışmada, kullanıcı verilerinin gizliliğini korurken öneri doğrulu günü artırmayı hedefleyen, diferansiyel mahremiyet destekli hibrit bir öneri modeli sunulmuştur. Model mimarisi, Matris Çarpanlaması (MF), Çok Katmanlı Algılayıcı (MLP) ve Uzun Kısa Süreli Bellek (LSTM) ağlarını birleştirmektedir. Laplace mekanizmasına dayalı gürültü enjeksiyonu ile eğitim sürecinde diferansiyel mahremiyet sağlanmış ve ayrıca hiperparametre optimizasyonu uygulanmıştır. Model, kullanıcı film etkileşimlerini içeren MovieLens 100K veri kümesi üzerinde değerlendirilmiştir. Performans değerlendirmesi MSE, MAE ve NDCG metrikleriyle yapılmış; hiperparametre optimizasyonu ile MSE bazında yaklaşık %4 iyileşme sağlandığı, yüksek gizlilik düzeyinde ise doğrulukta yaklaşık %39 oranında bozulma yaşandığı gözlemlenmiştir.
Boundary element method for EEG single-dipole localization: a study in patients with OCD
(Institute of Electrical and Electronics Engineers Inc., 2025-08-15) Abdullahi, Fatima I.; Demirer, Rüştü Murat
This study investigates EEG dipole localization in patients diagnosed with obsessive-compulsive disorder (OCD) using the Boundary Element Method (BEM) implemented via Brainstorm and OpenMEEG. EEG signals from 33 OCD patients were analyzed using a realistic, multi-layer head model consisting of scalp, skull, and brain tissues with respective conductivity values. Dipoles were accurately localized for each discrete time instant within the gamma frequency range (20-50 Hz) using a single dipole assumption per time point. EEG potentials measured from 19 standard electrodes were numerically computed by solving the forward EEG problem with the boundary element approach provided by OpenMEEG. Spectral clustering analysis identified distinct neural patterns corresponding to clinically recognized OCD subtypes, facilitating better diagnostic interpretations. Our results address previous methodological limitations by combining realistic head geometry modeling and precise temporal and spatial dipole estimation, offering promising directions for enhanced EEG-based diagnostic tools in psychiatry.
Comparing pre-trained and fine-tuned transformer-based models for sentiment analysis in Turkish comments in student surveys
(Institute of Electrical and Electronics Engineers Inc., 2025-08-15) Pourjalil, Kajal; Ekin, Emine; Recal, Füsun
Student surveys are essential for evaluating teaching quality and course content, but analyzing open-ended responses is challenging due to their unstructured and multilingual nature. This study applies sentiment analysis to Turkish educational survey responses using three transformer-based models: SAVASY, DBMDZ BERT Base Turkish Cased, and XLM-RoBERTa Base. A labeled dataset of real-world student comments was used, with sentiment labels assigned using the Gemini AI tool to facilitate model fine-tuning. Evaluation metrics included accuracy, F1-score, precision, recall, and confidence scores. Results show that fine-tuning improves sentiment classification, effectively identifying positive, negative, and neutral sentiments. This highlights the value of transformer models in analyzing Turkish student feedback.
Çok ölçekli görsel benzerlik analizi ile oltalama saldırısı tespiti
(Institute of Electrical and Electronics Engineers Inc., 2025-08-15) Kılıç, Bartu; Çeliktaş, Barış
Oltalama saldırıları teknolojinin gelişmesiyle günümüzün en yaygın siber güvenlik tehditlerinden biri haline gelmiştir. Bu çalışma, web sitelerinin ekran görüntülerini gelişmiş bir görsel benzerlik analizi yöntemiyle inceleyerek oltalama saldırılarını yüksek doğrulukla tespit eden bir yaklaşım sunmaktadır. Oltalama tespiti için önerilen yöntemde, algısal özütleme tabanlı çoklu çözünürlük analizi, akıllı ilgi bölgesi (ROI) tespiti ve çoklu metrik füzyonu gibi teknikler birleştirilerek yüksek doğrulukta tespit yapılabilmektedir. Veri seti, popüler bankacılık, e-posta ve sosyal medya platformlarının gerçek ve oltalama sayfalarından oluşan 23 gerçek ve 3 oltalama sayfası ekran görüntülerinden derlenmiştir. Yapılan testler, yöntemin %85 doğruluk oranı ile tekil metrik tabanlı yaklaşımlardan daha iyi performans gösterdiğini ortaya koymuştur. Dil bağımsız çalışan bu yöntem, URL ve HTML manipülasyonlarına karşı dayanıklıdır ve gerçek zamanlı oltalama tespiti için güçlü bir çözüm sunmaktadır.
A context-aware, AI-driven load balancing framework for incident escalation in SOCs
(Institute of Electrical and Electronics Engineers Inc., 2025-08-12) Abuaziz, Ahmed; Çeliktaş, Barış
SOCs face growing challenges in incident management due to increasing alert volumes and the complexity of cyberattacks. Traditional rule-based escalation models often fail to account for the workload of the analyst, the severity of the incident, and the organizational context. This paper proposes a context-aware, AI-driven load balancing framework for intelligent analyst assignment and incident escalation. Our framework leverages large language models (LLMs) with retrievalaugmented generation (RAG) to evaluate incident relevance and historical assignments. A reinforcement learning (RL)-based scheduler continuously optimizes incident-to-analyst assignments based on operational outcomes, enabling the system to adapt to evolving threat landscapes and organizational structures. Planned simulations in realistic SOC environments will compare the model with traditional rule-based models using metrics such as Mean Time to Resolution (MTTR), workload distribution, and escalation accuracy. This work highlights the potential of AIdriven approaches to improve SOC performance and enhance incident response effectiveness.
Retinal disease diagnosis in OCT scans using a foundational model
(Springer Science and Business Media Deutschland GmbH, 2025) Nazlı, Muhammet Serdar; Turkan, Yasemin; Tek, Faik Boray; Toslak, Devrim; Bulut, Mehmet; Arpacı, Fatih; Öcal, Mevlüt Celal
This study examines the feasibility and performance of using single OCT slices from the OCTA-500 dataset to classify DR (Diabetic Retinopathy) and AMD (Age-Related Macular Degeneration) with a pre-trained transformer-based model (RETFound). The experiments revealed the effective adaptation capability of the pretrained model to the retinal disease classification problem. We further explored the impact of using different slices from the OCT volume, assessing the sensitivity of the results to the choice of a single slice (e.g., “middle slice”) and whether analyzing both horizontal and vertical cross-sectional slices could improve outcomes. However, deep neural networks are complex systems that do not indicate directly whether they have learned and generalized the disease appearance as human experts do. The original dataset lacked disease localization annotations. Therefore, we collected new disease classification and localization annotations from independent experts for a subset of OCTA-500 images. We compared RETFound’s explainability-based localization outputs with these newly collected annotations and found that the region attributions aligned well with the expert annotations. Additionally, we assessed the agreement and variability between experts and RETFound in classifying disease conditions. The Kappa values, ranging from 0.35 to 0.69, indicated moderate agreement among experts and between the experts and the model. The transformer-based RETFound model using single or multiple OCT slices, is an efficient approach to diagnosing AMD and DR.
Assessing ChatGPT's accuracy in dyslexia inquiry
(Institute of Electrical and Electronics Engineers Inc., 2024) Eroğlu, Günet; Harb, Mhd Raja Abou
Dyslexia poses challenges in accessing reliable information, crucial for affected individuals and their families. Leveraging chatbot technology offers promise in this regard. This study evaluates the OpenAI Assistant's precision in addressing dyslexia-related inquiries. Three hundred questions commonly posed by parents were categorized and presented to the Assistant. Expert evaluation of responses, graded on accuracy and completeness, yielded consistently high scores (median=5). Descriptive questions scored higher (average=4.9568) than yes/no questions (average=4.8957), indicating potential response challenges. Statistical analysis highlighted the significance of question specificity in response quality. Despite occasional difficulties, the Assistant demonstrated adaptability and reliability in providing accurate dyslexia-related information.
Transforming tourism experience: AI-based smart travel platform
(Association for Computing Machinery, 2023) Yöndem, Meltem Turhan; Özçelik, Şuayb Talha; Caetano, Inés; Figueiredo, José; Alves, Patrícia; Marreiros, Goreti; Bahtiyar, Hüseyin; Yüksel, Eda; Perales, Fernando
In this paper, we propose the development of a novel personalized tourism platform incorporating artificial intelligence (AI) and augmented reality (AR) technologies to enhance the smart tourism experience. The platform utilizes various data sources, including travel history, user activity, and personality assessments, combined with machine learning algorithms to generate tailored travel recommendations for individual users. We implemented fundamental requirements for the platform: secure user identification using blockchain technology and provision of personalized services based on user interests and preferences. By addressing these requirements, the platform aims to increase tourist satisfaction and improve the efficiency of the tourism industry. In collaboration with various universities and companies, this multinational project aims to create a versatile platform that can seamlessly integrate new smart tourism units, providing an engaging, educational, and enjoyable experience for users.
Efficient estimation of Sigmoid and Tanh activation functions for homomorphically encrypted data using Artificial Neural Networks
(Institute of Electrical and Electronics Engineers Inc., 2024) Harb, Mhd Raja Abou; Çeliktaş, Barış
This paper presents a novel approach to estimating Sigmoid and Tanh activation functions using Artificial Neural Networks (ANN) optimized for homomorphic encryption. The proposed method is compared against second-degree polynomial and Piecewise Linear approximations, demonstrating a minor loss in accuracy while maintaining computational efficiency. Our results suggest that the ANN-based estimator is a viable alternative for secure machine learning models requiring privacypreserving computation.
Multi-task learning on mental disorder detection, sentiment analysis, and emotion detection using social media posts
(Institute of Electrical and Electronics Engineers Inc., 2024) Armah, Courage; Dehkharghani, Rahim
Mental disorders such as suicidal behavior, bipolar disorder, depressive disorders, and anxiety have been diagnosed among the youth recently. Social media platforms such as Reddit have become popular for anonymous posts. People are far more likely to share on these social media platforms what they really feel like in their real lives when they are anonymous. It is thus helpful to extract people's sentiments and feelings from these platforms in training models for mental disorder detection. This study uses multi-task learning techniques to examine the estimation of behaviors and mental states for early mental disease diagnosis. We propose a multi-task system trained on three related tasks: mental disorder detection as the primary task, emotion analysis, and sentiment analysis as auxiliary tasks. We took the SWMH dataset, which included four main different mental disorders already labeled (bipolar, depression, anxiety, and suicide) and offmychest. We then added labels for emotion and sentiment to the dataset. The observed results are comparable to previous studies in the field and demonstrate that deep learning multi-task frameworks can improve the accuracy of related text classification tasks when compared to training them separately as single-task systems.
Sentiment analysis for hotel reviews in Turkish by using LLMs
(Institute of Electrical and Electronics Engineers Inc., 2024) Özdemir, Ata Onur; Giritli, Efe Batur; Can, Yekta Said
The field of sentiment analysis plays a pivotal role in consumer decision-making and service quality improvement within the hospitality industry. This study explores the application of Large Language Models (LLMs) for sentiment analysis of Turkish hotel reviews, contributing to the understanding of customer feedback and satisfaction. We created a dataset of 5,000 reviews by translating an English corpus into Turkish, which was then utilized to evaluate the performance of a state-of-the-art Turkish language model, TURNA. The study demonstrates that LLMs, particularly TURNA, outperform traditional machine learning algorithms and other advanced models in sentiment classification tasks, achieving an accuracy of 99.4%. This research underscores the potential of LLMs to enhance the accuracy of sentiment analysis, offering valuable insights for the tourism and hospitality sectors. The findings contribute to the ongoing evolution of sentiment analysis methodologies and suggest that LLMs can significantly improve t he understanding a nd processing of customer feedback in Turkish hotel reviews.

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