Scopus İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/395
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Article BrAIn: A Comprehensive Artificial Intelligence-Based Morphology Analysis System for Brain Organoids and Neuroscience(Wiley, 2026-03-12) Polatli, Elifsu; Guner, Huseyin; Bastanlar, Yalin; Karakulah, Gokhan; Evranos, Ali Eren; Kahveci, Burak; Guven, SinanHuman-induced pluripotent stem cells (iPSCs) offer transformative potential for biomedical research, with iPSC-derived organoids providing more physiologically relevant models than traditional 2D cell cultures. Among these, brain organoids (BO) are particularly valuable for drug screening, disease modeling, and investigations into molecular pathways. Accurate representation of brain morphology is critical, as more complex organoid structures better mimic the human brain. Deep learning (DL) and machine learning (ML) approaches have become integral to analyzing organoid morphology, yet tools for comprehensive, time-resolved assessments are scarce. Here, we introduce BrAIn, a DL-based application for analyzing the developmental progression of BOs. BrAIn tracks their evolution from embryoid bodies (EBs) and quantifies parameters including area, Feret diameter, perimeter, roundness, and circularity. It also classifies budding and abnormal morphologies of 3D organoids and detects monolayer neural rosette structures, key features of neuronal differentiation. Designed with accessibility in mind, BrAIn provides a no-code interface, enabling researchers of all technical backgrounds to conduct advanced morphological analyses with ease. Our study demonstrates the application of BrAIn to evaluate the effects of different growth conditions-static, orbital shaker, and microfluidic chip-based-on BO development. Orbital shaker cultures resulted in the largest organoids, while chip-based systems achieved more homogeneous growth. Both conditions produced organoids with greater morphological complexity compared to static culture. BrAIn emerges as a robust, user-friendly tool to quantify BO development and explore how versatile growth conditions influence their morphology and maturation.Conference Object Generating Lost Urban Fabric: Exploration of Generative Adversarial Networks as a Design Tool in Post-Disaster Urban Recovery(Education and Research in Computer Aided Architectural Design in Europe, 2025) Takış, F.N.; Akyüz, S.This study investigates the use of GANs, particularly the Pix2PixHD, for reconstructing urban fabric and preserving urban memory in post-disaster contexts, focusing on Hatay, Türkiye, after the 2023 earthquakes. Models were trained on pre-disaster urban maps and tested on incomplete post-earthquake data to regenerate damaged urban areas. Evaluation metrics, including FID scores, SSIM values, and visual inspections, demonstrated the model's ability to produce contextually accurate designs. The trained model effectively maintained road networks, building geometries, and spatial coherence. In addition to spatial consistency, the model produced outputs with sharp edges and high visual clarity. These results highlight the significant potential of GANs as generative design tools, offering valuable support to urban planners and architects in balancing urgent reconstruction needs with the long-term preservation of urban identity and memory in disaster-affected areas. © 2025, Education and research in Computer Aided Architectural Design in Europe. All rights reserved.Article Citation - Scopus: 5University Librarians’ Perceptions Of Artificial Intelligence, Its Application Areas İn Libraries, And The Future(University and Research Librarians Association (UNAK), 2024-12-26) Cuhadar, S.; Mert, S.; Gezer, Ç.; Helvacioğlu, E.; Arus, O.; Aslan, Ö.; Atli, S.; Gurdal, Gultekin; Erken, MehmetToday, libraries are among the institutions affected by changing technology and innovations. The popularization of artificial intelligence (AI) technologies has also begun to transform library services. In this research, a survey was conducted to determine the adjustments that university libraries in Turkey have made and plan to make during the development process of AI technologies and applications, and to identify the services they have developed specific to the relevant period. The survey was carried out with the participation of 111 university library managers from 208 university libraries in Turkey. Through the analysis of the data, the status, knowledge, and awareness levels of university libraries regarding AI technologies and applications were determined, and measures and recommendations were presented to improve deficiencies and weaknesses. This research is the first and most comprehensive study conducted in Turkey by obtaining opinions and suggestions from university library managers on artificial intelligence. The research findings revealed that university libraries use AI applications such as ChatGPT, Gemini, and Grammarly to a certain extent; however, they have needs in developing institutional policies, enhancing personnel competencies, and planning related to AI. © 2024 University and Research Librarians Association (UNAK). All rights reserved.Article Deep-Learning Detection of Open-Apex Teeth on Panoramic Radiographs Using YOLO Models(Springer, 2025-12-23) Edik, Merve; Celebi, Fatma; Cukurluoglu, AykaganObjectivesThe use of deep learning in detecting teeth with open apices can prevent the need for additional radiographs for patients. The presented study aims to detect open-apex teeth using You Only Look Once (YOLO)-based deep learning models and compare these models.MethodsA total of 966 panoramic radiographs were included in the study. Open-apex teeth in panoramic radiographs were labeled. During the labeling process, they were divided into 6 classes in the maxilla and mandible, namely incisors, premolars, and molars. AI models YOLOv3, YOLOv4, and YOLOv5 were used. To evaluate the performance of the three detection models, both overall and separately for each class in the test dataset, precision, recall, average precision (mAP), and F1 score were calculated.ResultsYOLOv4 achieved the highest overall performance with a mean average precision (mAP) of 87.84% at IoU (Intersection over Union) 0.5 (mAP@0.5), followed by YOLOv5 with 85.6%, and YOLOv3 with 84.46%. Regarding recall, YOLOv4 also led with 90%, while both YOLOv3 and YOLOv5 reached 89%. Moreover, the F1 score was the highest for YOLOv4 (0.87), followed by YOLOv3 (0.86) and YOLOv5 (0.85).ConclusionsIn this study, YOLOv3, YOLOv4, and YOLOv5 were evaluated for the detection of open-apex teeth, and their mAP, recall, and F1 scores exceeded 84%. Deep learning-based systems can provide faster and more accurate results in the detection of open-apex teeth. This may help reduce the need for additional radiographs from patients and aid dentists by saving time.Article Citation - WoS: 1Citation - Scopus: 1Prediction of the Diffusible Hydrogen Concentration After Electrochemical Charging Utilizing Artificial Intelligence(IOP Publishing Ltd, 2025-09-01) Sivesoglu, Abdurrahman; Li, Yang; Bal, BurakThe concentration of diffusible hydrogen in a material is of high importance as it helps to predict the hydrogen embrittlement effect in the material, and the amount of mechanical properties' degradation after reaching a critical concentration. Despite that, a simple experimental setup is not available to measure hydrogen concentration at service. In this paper, a multi-layer perceptron (MLP) model is developed using weight initialization, which can estimate the diffusible hydrogen concentration of Face-Centred-Cubic (FCC) metals after electrochemical charging. The input properties of the model include the electrochemical charging parameters of current density, temperature, and charging time as well as the grain size of the specimen. The MLP model with and without the weight initialization was validated and tested with unseen test dataset. The model in both cases showed an excellent predictive performance with a higher accuracy and faster convergence when using weight initialization. A linear correlation of 89% between the experimental and predicted hydrogen concentration was observed. This demonstrates that for the family of FCC metals under electrochemical charging, the estimation of diffusible hydrogen concentration is a feasible path for material safety design analysis.Article Citation - WoS: 9Citation - Scopus: 13The Impact and Future of Artificial Intelligence in Medical Genetics and Molecular Medicine: An Ongoing Revolution(Springer Heidelberg, 2024-08) Ozcelik, Firat; Dundar, Mehmet Sait; Yildirim, A. Baki; Henehan, Gary; Vicente, Oscar; Sanchez-Alcazar, Jose A.; Dundar, MunisArtificial intelligence (AI) platforms have emerged as pivotal tools in genetics and molecular medicine, as in many other fields. The growth in patient data, identification of new diseases and phenotypes, discovery of new intracellular pathways, availability of greater sets of omics data, and the need to continuously analyse them have led to the development of new AI platforms. AI continues to weave its way into the fabric of genetics with the potential to unlock new discoveries and enhance patient care. This technology is setting the stage for breakthroughs across various domains, including dysmorphology, rare hereditary diseases, cancers, clinical microbiomics, the investigation of zoonotic diseases, omics studies in all medical disciplines. AI's role in facilitating a deeper understanding of these areas heralds a new era of personalised medicine, where treatments and diagnoses are tailored to the individual's molecular features, offering a more precise approach to combating genetic or acquired disorders. The significance of these AI platforms is growing as they assist healthcare professionals in the diagnostic and treatment processes, marking a pivotal shift towards more informed, efficient, and effective medical practice. In this review, we will explore the range of AI tools available and show how they have become vital in various sectors of genomic research supporting clinical decisions.Article Citation - WoS: 8Citation - Scopus: 9Priceless: Privacy Enhanced AI-Driven Scalable Framework for IoT Applications in Serverless Edge Computing Environments(John Wiley & Sons Ltd, 2024-02-14) Golec, Muhammed; Golec, Mustafa; Xu, Minxian; Wu, Huaming; Gill, Sukhpal Singh; Uhlig, SteveServerless edge computing has emerged as a new paradigm that integrates the serverless and edge computing. By bringing processing power closer to the edge of the network, it provides advantages such as low latency by quickly processing data for time-sensitive Internet of Things (IoT) applications. Additionally, serverless edge computing also brings inherent problems of edge and serverless computing such as cold start, security and privacy that are still waiting to be solved. In this paper, we propose a new Blockchain-based AI-driven scalable framework called PRICELESS, to offer security and privacy in serverless edge computing environments while performing cold start prediction. In PRICELESS framework, we used deep reinforcement learning for the cold start latency prediction. For experiments, a cold start dataset is created using a heart disease risk-based IoT application and deployed using Google Cloud Functions. Experimental results show the additional delay that the blockchain module brings to cold start latency and its impact on cold start prediction performance. Additionally, the performance of PRICELESS is compared with the current state-of-the-art method based on energy cost, computation time and cold start prediction. Specifically, it has been observed that PRICELESS causes 19 ms of external latency, 358.2 watts for training, and 3.6 watts for prediction operations, resulting in additional energy consumption at the expense of security and privacy.Article Citation - WoS: 3Citation - Scopus: 4Prediction of Biomechanical Properties of Ex Vivo Human Femoral Cortical Bone Using Raman Spectroscopy and Machine Learning Algorithms(Elsevier, 2025-09) Unal, Mustafa; Unlu, Ramazan; Uppuganti, Sasidhar; Nyman, Jeffry S.This study applied Raman spectroscopy (RS) to ex vivo human cadaveric femoral mid-diaphysis cortical bone specimens (n = 118 donors; age range 21-101 years) to predict fracture toughness properties via machine learning (ML) models. Spectral features, together with demographic variables (age, sex) and structural parameters (cortical porosity, volumetric bone mineral density), were fed into support vector regression (SVR), extreme tree regression (ETR), extreme gradient boosting (XGB), and ensemble models to predict fracture-toughness metrics such as crack-initiation toughness (Kinit) and energy-to-fracture (J-integral). Feature selection was based on Raman-derived mineral and organic matrix parameters, such as nu 1Phosphate (PO4)/CH2-wag, nu 1PO4/ Amide I, and others, to capture the complex composition of bone. Our results indicate that ensemble models consistently outperformed individual models, with the best performance for crack initiation toughness (Kinit) prediction being achieved using the ensemble approach. This yielded a coefficient of determination (R2) of 0.623, root-mean squared error (RMSE) of 1.320, mean absolute error (MAE) of 1.015, and mean percentage absolute error (MAPE) of 0.134. For prediction of the overall energy to propagate a crack (J-integral), the XGB model achieved an R2 of 0.737, RMSE of 2.634, MAE of 2.283, and MAPE of 0.240. This study highlights the importance of incorporating mineral quality properties (MP) and organic matrix properties (OMP) for enhanced prediction accuracy. This work represents the first-ever study combining Raman spectroscopy with other clinical and structural features to predict fracture toughness of human cortical bone, demonstrating the potential of artificial intelligence (AI) and ML in advancing bone research. Future studies could focus on larger datasets and more advanced modeling techniques to further improve predictive capabilities.Article Citation - WoS: 1Citation - Scopus: 2Is Artificial Intelligence a Trustworthy Route Navigation System for Smart Urban Planning(Univ Alexandru Ioan Cuza, Centrul Studii Europene, 2024) Kourtit, Karima; Nijkamp, Peter; Osth, John; Turk, UmutIn the age of smart or intelligent cities, the use of Artificial Intelligence (AI) presents a spectrum of new opportunities and challenges for both the research and policy community. The present study explores the intricate interplay between AI-generated content and actual choice spectra in urban planning. It focuses on the concept of 'city intelligence' and related AI concepts, underscoring the pivotal role of AI in addressing and understanding the quality of life in contemporary urban environments. As AI continues its transformative impact on communication and information systems in the realm of urban planning, this study brings to the forefront key insights into the challenges of validating AI-based information. Given the inherently subjective nature of AIgenerated content, and its influential role in shaping user-perceived value, AI will most likely be a game changer catalyzing enhancements in the urban quality of life and inducing favorable urban developments. Additionally, the study also addresses the significance of the so-called 'Garbage-in Garbage-out' (GiGo) principle and 'Bullshitin Bullshit out' (BiBo) principle in validating AI-generated content, and seeks to enhance our understanding of the spatial information landscape in urban planning by introducing the notion of an urban X'XQ' performance production function.Article Citation - WoS: 1Citation - Scopus: 1Intelligent Traffic Light Systems Using Edge Flow Predictions(Elsevier, 2024-01) Thahir, Adam Rizvi; Coskun, Mustafa; Kilic, Sultan Kubra; Gungor, Vehbi CagriIn this paper, we propose a novel graph-based semi-supervised learning approach for traffic light management in multiple intersections. Specifically, the basic premise behind our paper is that if we know some of the occupied roads and predict which roads will be congested, we can dynamically change traffic lights at the intersections that are connected to the roads anticipated to be congested. Comparative performance evaluations show that the proposed approach can produce comparable average vehicle waiting time and reduce the training/learning time of learning adequate traffic light configurations for all intersections within a few seconds, while a deep learning-based approach can be trained in a few days for learning similar light configurations.