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arXiv 2025年

作者： Demirci, Ege Tüzün, Efe Ün, Ahmet Furkan Sönmez, Taner Giray Varol, Onur Faculty of Engineering and Natural Sciences Sabanci University Istanbul34956 Turkey Mars Athletic Club Istanbul34340 Turkey Center of Excellence in Data Analytics Sabanci University Istanbul34956 Turkey

Exercising regularly is widely recognized as a cornerstone of health, yet the challenge of sustaining consistent exercise habits persists. Understanding the factors that influence the formation of these habits is crucial for developing effective interventions. This study utilizes data from Mars Athletic Club, Türkiye’s largest sports chain, to investigate the dynamics of gym attendance and habit formation. The general problem addressed by this study is identifying the critical periods and factors that contribute to the successful establishment of consistent exercise routines among gym-goers. Here we show that there are specific periods during which gym attendance is most crucial for habit formation. By developing a survival metric based on gym attendance patterns, we pinpoint these critical periods and segment members into distinct clusters based on their visit patterns. Our analysis reveals significant differences in how various subgroups respond to interventions, such as group classes, personal trainer sessions, and visiting different clubs. Using causal inference analysis, we demonstrate that personalized guidance and social dynamics are key drivers of sustained long-term engagement. By systematically examining these variables and considering the specific characteristics of different clusters, our research demonstrates the importance of a tailored, multi-dimensional approach to promoting exercise habits, which integrates social dynamics, personalized guidance, and strategic interventions to sustain long-term engagement. Copyright © 2025, The Authors. All rights reserved.

关键词： Cluster analysis

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Novel Feature Extractor Framework in Conjunction with Supervised Three Class-XGBoost Algorithm for Osteosarcoma Detection from Whole Slide Medical Histopathology Images

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Computers, Materials & Continua 2025年第2期82卷 3337-3353页

作者： Tanzila Saba Muhammad Mujahid Shaha Al-Otaibi Noor Ayesha Amjad Rehman Khan Artificial Intelligence&Data Analytics Lab College of Computer&Information SciencesPrince Sultan UniversityRiyadh11586Saudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh11671Saudi Arabia Center of Excellence in Cyber Security(CYBEX) Prince Sultan UniversityRiyadh11586Saudi Arabia

Osteosarcomas are malignant neoplasms derived from undifferentiated osteogenic mesenchymal cells. It causes severe and permanent damage to human tissue and has a high mortality rate. The condition has the capacity to occur in any bone;however, it often impacts long bones like the arms and legs. Prompt identification and prompt intervention are essential for augmenting patient longevity. However, the intricate composition and erratic placement of osteosarcoma provide difficulties for clinicians in accurately determining the scope of the afflicted area. There is a pressing requirement for developing an algorithm that can automatically detect bone tumors with tremendous accuracy. Therefore, in this study, we proposed a novel feature extractor framework associated with a supervised three-class XGBoost algorithm for the detection of osteosarcoma in whole slide histopathology images. This method allows for quicker and more effective data analysis. The first step involves preprocessing the imbalanced histopathology dataset, followed by augmentation and balancing utilizing two techniques: SMOTE and ADASYN. Next, a unique feature extraction framework is used to extract features, which are then inputted into the supervised three-class XGBoost algorithm for classification into three categories: non-tumor, viable tumor, and non-viable tumor. The experimental findings indicate that the proposed model exhibits superior efficiency, accuracy, and a more lightweight design in comparison to other current models for osteosarcoma detection.

关键词： Medical image processing deep learning healthcare image classification histopathology

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Additive Manufacturing of Ti6Al4V Using Directed Energy Deposition: An Elaborative Approach of Process Modelling via Computational Fluid Dynamics

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Lasers in Manufacturing and Materials Processing 2025年第1期12卷 201-225页

作者： Ur Rehman, Asif Azher, Kashif Rehman, Aqsa Shah, Mussadiq Mahmood, Muhammad Arif Tüfekci, Celal Sami Salamci, Metin Uymaz Department of Mechanical Engineering Gazi University Ankara Turkey Bartlett School of Sustainable Construction University College London LondonWC1E 6BT United Kingdom Department of Mechanical Engineering King Fahd University of Petroleum & Minerals Dhahran31261 Saudi Arabia Data Science and Analytics Institute The University of Oklahoma NormanOK73019 United States Institute of Chemical Technologies and Analytics TU Wien Getreidemarkt 9 Vienna1060 Austria Intelligent Systems Center Missouri University of Science and Technology RollaMO65409 United States Advanced Manufacturing Technologies Center of Excellence-URTEMM Ankara Turkey Additive Manufacturing Technologies Application and Research Center-EKTAM Ankara Turkey

Additive manufacturing (AM) has revolutionized many industries. Directed energy deposition (DED) offers remarkable utility in size and is an effective method in terms of energy efficiency. The interaction of powder with a laser before coming in contact with the substrate helps to attain fair melting and deposition. However, there is a problem associated with non-uniformity and accumulation of metallic melt on the edges of the manufactured parts which needs to be understood. Herein, the DED process of Ti6Al4V alloy is presented. The phenomenon of recoil pressure and Marangoni actions along the single tracks of the melt pool is defined via the computational fluid dynamics (CFD) analysis. The discrete element modeling (DEM) improves the simulation model output as variable particle diameters are considered, thus mimicking the actual powder size distribution. The simulation results disclosed intricate facts about the flow dynamics of molten material under varying conditions. The stream traces and thermal gradients are evaluated to recognize the effect of laser movement on material cooling and surface tension. The material accumulation occurred at curves and borders due to cooling-induced surface tension alterations, affecting the attribute of the deposited layers. The bi-directional flow patterns are prompted by Marangoni convection and recoil pressure, causing distinct features, including lateral menisci and depression modes. The cooling rates impacted the liquid portion and dilution levels, critical for attaining optimal bonding between deposited layers and substrate. Experimental endorsement using Synchrotron X-ray imaging favored the simulation results, confirming the simulated Marangoni convection effects. This comparison highlights the efficiency of CFD models in predicting molten material behavior, offering a worthy tool for adjusting DED printing parameters. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature

关键词： Titanium alloys

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MVLA-Net:A Multi-View Lesion Attention Network for Advanced Diagnosis and Grading of Diabetic Retinopathy

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Computers, Materials & Continua 2025年第4期83卷 1173-1193页

作者： Tariq Mahmood Tanzila Saba Faten S.Alamri Alishba Tahir Noor Ayesha Artificial Intelligence and Data Analytics(AIDA)Lab College of Computer&Information SciencesPrince Sultan UniversityRiyadh11586Saudi Arabia Faculty of Information Sciences University of EducationVehari CampusVehari61100Pakistan Department of Mathematical Sciences College of SciencePrincess Nourah bint Abdulrahman UniversityRiyadh84428Saudi Arabia Shifa College of Medicine Shifa Tameer-E-Millat UniversityIslamabad44000Pakistan Center of Excellence in Cyber Security(CYBEX) Prince Sultan UniversityRiyadh11586Saudi Arabia

Innovation in learning algorithms has made retinal vessel segmentation and automatic grading tech-niques crucial for clinical diagnosis and prevention of diabetic *** traditional methods struggle with accuracy and reliability due to multi-scale variations in retinal blood vessels and the complex pathological relationship in fundus images associated with diabetic *** the single-modal diabetic retinopathy grading network addresses class imbalance challenges and lesion representation in fundus image data,dual-modal diabetic retinopathy grading methods offer superior ***,the scarcity of dual-modal data and the lack of effective feature fusion methods limit their potential due to multi-scale *** paper addresses these issues by focusing on multi-scale retinal vessel segmentation,dual feature fusion,data augmentation,and attention-based *** proposed model aims to improve comprehensive segmentation for retinal images with varying vessel *** employs a dual-branch parallel architecture that integrates a transformer encoder with a convolutional neural network encoder to extract local and global information for synergistic saliency *** that,the model uses residual structures and attention modules to extract critical lesions,enhancing the accuracy and reliability of diabetic retinopathy *** evaluate the efficacy of the proposed approach,this study compared it with other pre-trained publicly open models,ResNet152V2,ConvNext,Efficient Net,DenseNet,and Swin Transform,with the same developmental *** models achieved approximately 85%accuracy with the same image preparation ***,the proposed approach outperforms and optimizes existing models by achieving an accuracy of 99.17%,99.04%,and 99.24%,on Kaggle APTOS19,IDRiD,and EyePACS datasets,*** results support the model’s utility in helping ophthalmologists diagnose diabetic retinopathy more rapidly and accurat

关键词： Diabetic retinopathy grading retinal vessel segmentation dual-modal deep learning attention mecha-nism health risks

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Checks and Balances and Institutional Gridlock: Implications for Authoritarianism

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Governance 2025年第2期38卷

作者： Adiguzel, Fatih Serkant Faculty of Arts and Social Sciences Sabancı University İstanbul Turkey Center of Excellence in Data Analytics Sabancı University İstanbul Turkey

Recently, the world saw a wave of elected leaders attack democracy. Why do people support leaders who remove checks and balances? I argue that aspiring autocrats gain more popular support when they present these institutions as obstacles to getting things done. In doing so, they exploit a critical tension between the possibility of gridlock and the abuse of power, which is inherent in democratic institutions. Using cross-national data and leveraging an original survey experiment from Turkey, I show that effective checks and balances decrease democracy satisfaction and that aspiring autocrats gain more popular support when they present these institutions as obstacles. More interestingly, respondents perceive the aspiring autocrats' gridlock justification to dismantle checks and balances as a pro-democratic attempt to remove the obstacles to a policy-responsive regime. These results show that aspiring autocrats exploit the tension in democracies, making it harder for citizens to perceive the threat they face. © 2025 Wiley Periodicals LLC.

关键词： abuse of power democratic backsliding gridlock

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Fast Variational Boosting for Latent Variable Models

arXiv

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arXiv 2025年

作者： Gunawan, David Nott, David Kohn, Robert School of Mathematics and Applied Statistics University of Wollongong Australia School of Economics University of New South Wales Australia Australian Center of Excellence for Mathematical and Statistical Frontiers Australia Department of Statistics and Data Science National University of Singapore Singapore Institute of Operations Research and Analytics National University of Singapore Singapore

We consider the problem of estimating complex statistical latent variable models using variational Bayes methods. These methods are used when exact posterior inference is either infeasible or computationally expensive, and they approximate the posterior density with a family of tractable distributions. The parameters of the approximating distribution are estimated using optimisation methods. This article develops a flexible Gaussian mixture variational approximation, where we impose sparsity in the precision matrix of each Gaussian component to reflect the appropriate conditional independence structure in the model. By introducing sparsity in the precision matrix and parameterising it using the Cholesky factor, each Gaussian mixture component becomes parsimonious (with a reduced number of non-zero parameters), while still capturing the dependence in the posterior distribution. Fast estimation methods based on global and local variational boosting moves combined with natural gradients and variance reduction methods are developed. The local boosting moves adjust an existing mixture component, and optimisation is only carried out on a subset of the variational parameters of a new component. The subset is chosen to target improvement of the current approximation in aspects where it is poor. The local boosting moves are fast because only a small number of variational parameters need to be optimised. The efficacy of the approach is illustrated by using simulated and real datasets to estimate generalised linear mixed models and state space models. Copyright © 2025, The Authors. All rights reserved.

关键词： Stochastic systems

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VRLLab at HSD-2Lang 2024: Turkish Hate Speech Detection Online with TurkishBERTweet 7

VRLLab at HSD-2Lang 2024: Turkish Hate Speech Detection Onli...

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7th Workshop on Challenges and Applications of Automated Extraction of Socio-Political Events from Text, CASE 2024

作者： Najafi, Ali Varol, Onur Faculty of Engineering and Natural Sciences Sabanci University Turkey Center of Excellence in Data Analytics Sabanci University Turkey

ISBN: (纸本)9798891760707

Social media platforms like Twitter - recently rebranded as X - produce nearly half a billion tweets daily and host a significant number of users that can be affected by content that is not properly moderated. In this work, we present an approach that ranked third at the HSD-2Lang 2024 competition's subtask-A, along with additional methodology developed for this task and evaluation of different approaches. We utilize three different models, and the best-performing approach uses the publicly available TurkishBERTweet model with low-rank adaptation (LoRA) for fine-tuning. We also experiment with another publicly available model and a novel methodology to ensemble different hand-crafted features and outcomes of different models. Finally, we report the experimental results, competition scores, and discussion to improve this effort further. © 2024 Association for Computational Linguistics.

关键词： Social networking (online)

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Who Follows Turkish Presidential Candidates in 2023 Elections?

Who Follows Turkish Presidential Candidates in 2023 Election...

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IEEE Signal Processing and Communications Applications (SIU)

作者： Onur Varol Center of Excellence in Data Analytics Sabanci University

Social media serves as an important platform for political discussions, especially during election periods. While politicians use the platform to reach out to voters and share their campaigns, it also creates a debate environment for different voter groups. It is possible to see supportive and critical speeches simultaneously. Particularly as the election date approaches, undecided voters follow politicians on online platforms to learn about their campaigns. Changes in politicians' followers can be observed in detail by analyzing women and young voters who may be decisive in the results. In this study, we analyzed candidates for the 2023 Presidential elections of Türkiye and their followers on the Twitter platform. We examined how the demographic of these followers change over time. Prior to analyzing demographics such as gender and age group, we identified bot accounts and isolated them from the dataset. Significant events are reflected in the number of followers and the relationship between the observed anomalies and changes in the demographics have also been studied in this work.

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Unsupervised detection of coordinated fake-follower campaigns on social media

arXiv

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arXiv 2023年

作者： Zouzou, Yasser Varol, Onur Faculty of Engineering and Natural Sciences Sabanci University Turkey Center of Excellence in Data Analytics Sabanci University Turkey

Automated social media accounts, known as bots, are increasingly recognized as key tools for manipulative online activities. These activities can stem from coordination among several accounts and these automated campaigns can manipulate social network structure by following other accounts, amplifying their content, and posting messages to spam online discourse. In this study, we present a novel unsupervised detection method designed to target a specific category of malicious accounts designed to manipulate user metrics such as online popularity. Our framework identifies anomalous following patterns among all the followers of a social media account. Through the analysis of a large number of accounts on the Twitter platform (rebranded as Twitter after the acquisition of Elon Musk), we demonstrate that irregular following patterns are prevalent and are indicative of automated fake accounts. Notably, we find that these detected groups of anomalous followers exhibit consistent behavior across multiple accounts. This observation, combined with the computational efficiency of our proposed approach, makes it a valuable tool for investigating large-scale coordinated manipulation campaigns on social media platforms. © 2023, CC BY-NC-ND.

关键词： Computational efficiency

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TurkishBERTweet: Fast and Reliable Large Language Model for Social Media Analysis

arXiv

引用

arXiv 2023年

作者： Najafi, Ali Varol, Onur Faculty of Engineering and Natural Sciences Sabanci University Turkey Center of Excellence in Data Analytics Sabanci University Turkey

Turkish is one of the most popular languages in the world. Wide us of this language on social media platforms such as Twitter, Instagram, or Tiktok and strategic position of the country in the world politics makes it appealing for the social network researchers and industry. To address this need, we introduce TurkishBERTweet, the first large scale pre-trained language model for Turkish social media built using almost 900 million tweets. The model shares the same architecture as base BERT model with smaller input length, making TurkishBERTweet lighter than BERTurk and can have significantly lower inference time. We trained our model using the same approach for RoBERTa model and evaluated on two text classification tasks: Sentiment Classification and Hate Speech Detection. We demonstrate that TurkishBERTweet outperforms the other available alternatives on generalizability and its lower inference time gives significant advantage to process large-scale datasets. We also compared our models with the commercial OpenAI solutions in terms of cost and performance to demonstrate TurkishBERTweet is scalable and cost-effective solution. As part of our research, we released TurkishBERTweet and fine-tuned LoRA adapters for the mentioned tasks under the MIT License to facilitate future research and applications on Turkish social media. Our TurkishBERTweet model is available at: https://***/ViralLab/TurkishBERTweet. Copyright © 2023, The Authors. All rights reserved.

关键词： Cost effectiveness

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