Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection

Permanent URI for this collectionhttps://hdl.handle.net/11147/7148

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Now showing 1 - 8 of 8
  • Conference Object
    Combining Generative Adversarial Networks and Reinforcement Learning for Floor Plan Layout Generation
    (Education and Research in Computer Aided Architectural Design in Europe, 2025) Güldilek, M.; Ilal, M.E.; Ekici, B.
    Generative Adversarial Networks (GANs) are among artificial intelligence (AI) methods for generating architectural floor plan layouts to approximate spatial distribution with a reasonable degree of accuracy. However, when used exclusively, GAN-based tools may fail to capture architectural patterns and often produce unrealistic layouts. To address this limitation, researchers have proposed integrating Reinforcement Learning (RL) into GANs. While RL has been combined with generative algorithms in other fields, a systematic multi-scenario integration of GANs and RL remains underexplored in architecture. This paper introduces a new solution by combining RL and GANs to generate floor plan layouts tailored to user requirements. The research design involves three different integration strategies: (1a) mere generation, where RL refines GAN outputs by eliminating inconsistencies and errors; (1b) objective optimization, where RL targets measurable attributes such as spatial size and morphological legibility; and (1c) refinement of non-quantifiable attributes, where RL incorporates user feedback to improve flexibility and perceived comfort. Additionally, the study employs House-GAN++ as the GAN model and the PPO algorithm as the RL framework. Three case studies are presented to evaluate performance. Results demonstrate that integrating RL with GANs yields floor plan layouts more responsive to user needs than those produced by GANs alone. Each scenario illustrates how RL optimizes GAN-generated outputs according to functional, measurable, and perceptual goals. The methodology acknowledges user expectations and translates them into realistic, adaptable plans. Key outcomes include more realistic layouts, designs with distinctive characteristics, and user-customized floor plans created through interaction. The proposed framework enables automatic floor plan generation that combines design, optimization, and user input at the conceptual stage. This integration enhances architectural design processes by balancing computational efficiency with user-oriented adaptability, thus broadening the potential of AI-assisted design. © 2025, Education and research in Computer Aided Architectural Design in Europe. All rights reserved.
  • Article
    Automated Detection and Quantification of Honey Adulteration Using Thermal Imaging and Convolutional Neural Networks
    (Pergamon-Elsevier Science Ltd, 2026) Unluturk, Mehmet S.; Berk, Berkay; Unluturk, Sevcan
    Honey is a valuable natural food rich in bioactive substances beneficial to health. Despite strict regulations prohibiting adulteration, honey remains one of the most frequently adulterated foods, often with low-cost commercial syrups. Conventional detection methods require expensive instruments, expert operators, and lengthy analysis times, limiting their practical use. This study introduces a rapid and automated method for detecting and quantifying honey adulteration using thermal image analysis combined with a tailored Convolutional Neural Network (CNN) architecture. Thirty-six pure honey samples (blossom and honeydew) from different regions of T & uuml;rkiye were adulterated with inverted sugar, maltose, and glucose syrups at varying levels (3 %-60 % weight/weight (w/w)). Samples were heated to 60 degrees C and thermal images were captured during cooling using a custom image-capturing unit. The CNN model employed a multi-layer structure, starting with a shallow network for binary classification (pure vs adulterated honey) achieving 100 % accuracy, followed by specialized deeper CNN regressors to quantify adulterant levels with mean squared errors of 0.0003, 0.001, and 0.0002 for glucose, maltose, and inverted sugar, respectively. This layered CNN approach leverages thermal patterns linked to adulteration, enabling sensitive, rapid, and non-destructive quality control. Furthermore, the method is integrated into a user-friendly hardware-software system called Compact Adulteration Testing Cabinet on Honey (CATCH), requiring no specialized expertise, demonstrating strong potential for automated honey authenticity verification in practical settings.
  • Article
    Comprehensive Analysis and Machine Learning-Based Solutions for Drift Behavior in Ambient Atomic Force Microscope Conditions
    (Pergamon-Elsevier Science Ltd, 2025) Deveci, D. Gemici; Barandir, T. Karakoyun; Unverdi, O.; Celebi, C.; Temur, L. O.; Atilla, D. C.
    This study outlines the effectiveness of combining numerical methods, Computer Vision (CV) and Machine Learning (ML) approaches to analyze and predict drift behavior in high-resolution Atomic Force Microscope (AFM) scanning procedures. Using Long Short-Term Memory (LSTM) models for time series analysis and the Light Gradient Boosting Machine (LightGBM) algorithm for predictive modeling, significant progress was achieved in understanding the dynamic and variable nature of drift and mitigating its impact on scanning. The models demonstrated a robust predictive capability, achieving approximately 94% accuracy in drift predictions. The study emphasizes the nonstationary characteristics of drift and demonstrates how the selection of features directly related to the target variable enhances the efficiency of the model and enables adaptive real-time correction. These findings confirm the predictive strength of the models and highlight the potential for integrating ML predictions with real-time feedback mechanisms to improve the resolution and stability of AFM imaging in both scientific and industrial applications.
  • Conference Object
    Current Sensing in Phase-OTDR Systems Using Deep Learning
    (SPIE, 2025) Yeke, M.C.; Sirin, S.; Yüksel, K.; Gumus, A.
    Fiber optic current sensors are marked by a number of advantages such as light-weight, small-size and inherently insulated nature when compared to conventional current transformers which get bulkier and costlier as the desired values of current to be measured increase. Phase-OTDR is a widely known technology especially in acoustic and thermal sensing, but it suffers from noise that limits its usage for current sensing especially for low currents. In order to interpret the noisy data retrieved from Phase-OTDR current sensor simulator, deep learning techniques can have promising performance. In this paper, 3 different types of deep learning models were proposed and applied on the data generated by Phase-OTDR current sensor simulator tool to improve the ability to distinguish low and similar current levels. The current measurements were analyzed as a classification problem where different current ranges with different current increments are selected as different classes. The proposed method provided 100% accuracy at a difference of 20 A between the current levels. In addition, other scenarios where the current levels were increased by 15 A and 10 A were also studied. In this case, the accuracies 97% and 89% were obtained, respectively. © 2025 Elsevier B.V., All rights reserved.
  • Article
    Towards Facile Deep Learning Architectures for Chemical Processes: Simultaneous Pseudo-Global Training and Economic Synthesis
    (Institution of Chemical Engineers, 2025) Sildir, Hasan; Yalcin, Damla; Tuncer, Basak; Deliismail, Ozgun; Leblebici, Mumin Enis
    Chemical process data is usually not directly valorized in pure machine learning predictive models due to limited data availability. This limitation often caused from high sensor costs, data variety, and veracity issues. In response, this study proposes a novel formulation based on mixed-integer linear programming (MILP), called Approximated Deep Learning (ADL), to overcome these limitations and enable accurate modeling under data scarcity. The ADL simultaneously performs input selection, outlier filtering, and training of deep learning architectures within a single-level optimization problem. The method approximates the nonlinear and nonconvex components of traditional deep learning models in the mixed-integer domain through sophisticated reformulations, achieving a pseudo-global solution. A key feature of ADL is the integration of sensor pricing as a regularization mechanism, which promotes cost-efficient soft sensor design without compromising predictive performance. The proposed framework is validated on a publicly available bubble column dataset and benchmarked against four conventional deep learning methods. Results show that ADL achieves superior test accuracy with more than 50% reduction in input space, drastically reducing sensor cost. Furthermore, the optimized architecture is a high-quality initial guess for transfer learning on larger datasets. Overall, the method offers a practical and economically viable solution for data-driven chemical process modeling.
  • Article
    Citation - WoS: 5
    Citation - Scopus: 5
    Automated Deep Learning Model Development Based on Weight Sensitivity and Model Selection Statistics
    (Pergamon-elsevier Science Ltd, 2025) Yalcin, Damla; Deliismail, Ozgun; Tuncer, Basak; Boy, Onur Can; Bayar, Ibrahim; Kayar, Gizem; Sildir, Hasan
    Current sustainable production and consumption processes call for technological integration with the realm of computational modeling especially in the form of sophisticated data-driven architectures. Advanced mathematical formulations are essential for deep learning approach to account for revealing patterns under nonlinear and complex interactions to enable better prediction capabilities for subsequent optimization and control tasks. Bayesian Information Criterion and Akaike Information Criterion are introduced as additional constraints to a mixed-integer training problem which employs a parameter sensitivity related objective function, unlike traditional methods which minimize the training error under fixed architecture. The resulting comprehensive optimization formulation is flexible as a simultaneous approach is introduced through algorithmic differentiation to benefit from advanced solvers to handle computational challenges and theoretical issues. Proposed formulation delivers 40% reduction, in architecture with high accuracy. The performance of the approach is compared to fully connected traditional methods on two different case studies from large scale chemical plants.
  • Conference Object
    Citation - WoS: 1
    Konteyner Görüntülerini Kullanarak Hasar Tespiti ve Sınıflandırması
    (IEEE, 2020) Imamoglu, Zeynep Ekici; Tuglular, Tugkan; Bastanlar, Yalin
    In the logistics sector, digital transformation is of great importance in terms of competition. In the present case, container warehouse entry / exit operations are carried out manually by the logistics personnel including container damage detection. During container warehouse entry / exit process, the process of detecting damaged containers is carried out by the personnel and several minutes are required to upload to the IT system. The aim of our work is to automate the detection of damaged containers. This way, the mistakes made by the personnel will be eliminated and the process will be accelerated. In this work, we propose to use a convolutional neural network (CNN) that takes the container images and classify them as damaged or undamaged. We modeled the problem as a binary classification and employed different CNN models. The result we obtained shows that there is no single best method for the classification. It is shown how the dataset was created and how the parameters used in the layered structures affect the models employed in this study.
  • Article
    Citation - Scopus: 20
    Estrus Detection and Dairy Cow Identification With Cascade Deep Learning for Augmented Reality-Ready Livestock Farming
    (Multidisciplinary Digital Publishing Institute (MDPI), 2023) Arıkan, İ.; Ayav, T.; Seçkin, A.Ç.; Soygazi, F.
    Accurate prediction of the estrus period is crucial for optimizing insemination efficiency and reducing costs in animal husbandry, a vital sector for global food production. Precise estrus period determination is essential to avoid economic losses, such as milk production reductions, delayed calf births, and disqualification from government support. The proposed method integrates estrus period detection with cow identification using augmented reality (AR). It initiates deep learning-based mounting detection, followed by identifying the mounting region of interest (ROI) using YOLOv5. The ROI is then cropped with padding, and cow ID detection is executed using YOLOv5 on the cropped ROI. The system subsequently records the identified cow IDs. The proposed system accurately detects mounting behavior with 99% accuracy, identifies the ROI where mounting occurs with 98% accuracy, and detects the mounting couple with 94% accuracy. The high success of all operations with the proposed system demonstrates its potential contribution to AR and artificial intelligence applications in livestock farming. © 2023 by the authors.