Bilgisayar Mühendisliği Bölümü Koleksiyonu

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https://hdl.handle.net/20.500.13055/43

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Dynamic virtual credit cards in OTA B2B payments: An industrial case study
(Euroasia Journal of Social Sciences & Humanities, 2026) Bağrıyanık, Selami; Mutlu, Behçet
Introduction and Purpose: Online travel agencies (OTAs) rely on secure B2B payment integrations. Conventional models often use a "stored-card exposure" approach, storing customer cards reversibly without usage limits, creating severe security vulnerabilities. This study evaluates an architectural transformation to a centralized Virtual Credit Card (VCC) pipeline, WingieVCC, designed for transaction isolation and automated balance management within a high-volume OTA ecosystem, specifically targeting the hospitality sector. Materials and Methods: An industrial case study methodology evaluated this transformation over a one-year deployment period (Q2 2024 to Q1 2025). The system dynamically generates transaction isolated, disposable VCCs with strict, currency-based limits. It integrates multi-currency balance tracking, automated threshold routing, and an idempotent API framework with retry mechanisms to handle network timeouts. During deployment, the system utilized shadow traffic and staged rollout strategies to ensure seamless integration. The evaluation focused on architectural resilience, security posture, and operational improvements. Results: The architectural shift yielded profound qualitative improvements. By migrating from a limitless, stored-card model to a transaction-isolated, disposable VCC architecture, card data exposure was structurally eliminated. Defining strict currency-based limits for each transaction guaranteed that even in breach scenarios, unauthorized usage is technically impossible; unused or failed cards are automatically invalidated. Operationally, centralized balance management significantly reduced failed transactions caused by insufficient personal card limits, minimizing the need for manual interventions. Performance-wise, the system successfully met a Service Level Agreement (SLA) target of < 600 ms for transaction response times. Replacing traditional, stored-card infrastructures with a dynamic, transaction-isolated VCC pipeline fundamentally mitigates critical security risks in OTA platforms. This architectural transformation demonstrates that integrating strict transaction limits, shadow traffic deployment, and idempotent APIs maximizes transaction reliability and operational efficiency. Ultimately, this isolated architecture offers a highly scalable and secure blueprint for advancing fintech integrations within the global tourism sector.
Machine learning-guided green ultrasound processing enhances polyphenols, antioxidant capacity, and in vitro bioaccessibility of black fig vinegar
(Frontiers Media S. A., 2026) Çakmak Sancar, Burcu; Aktaran Bala, Deniz; Demirel, Selinay; Gürfidan, Remzi; Yıkmış, Seydi; Kilim, Oğuzhan; Açıkgözoğlu, Enes; Şimşek, Mehmet Ali; Tokatlı Demirok, Nazan; Ibrahim, Marwa Ezz El-Din; Tokatlı, Nazlı
Black fig vinegar is a functional fermented product rich in phenolic compounds and antioxidants; however, conventional thermal pasteurization may reduce its bioactive composition and functional quality. Although ultrasound processing has emerged as a promising non-thermal alternative, its optimization, predictive modeling, and influence on the in vitro bioaccessibility of black fig vinegar have not yet been comprehensively investigated. Therefore, this study aimed to optimize ultrasound processing conditions and evaluate their effects on phenolic composition, antioxidant capacity, and in vitro bioaccessibility of traditionally produced black fig vinegar using response surface methodology (RSM) integrated with ensemble machine learning algorithms. Untreated, pasteurized, and ultrasound-treated vinegars were compared, while ultrasound processing variables (processing time and amplitude) were optimized through RSM. The experimental dataset was subsequently expanded using data augmentation, and Extra Trees, CatBoost, Gradient Boosting, and AdaBoost models were developed to predict total phenolic content (TPC) and ferric reducing antioxidant power (FRAP). Among the tested algorithms, the Extra Trees model achieved the best predictive performance, with coefficients of determination (R2) exceeding 0.998 and prediction accuracies above 99%. Under the optimized ultrasound conditions (8 min and 58% amplitude), TPC (79.77 ± 3.69 mg GAE/100 mL), FRAP (8.87 ± 0.24 mmol TE/L), and total flavonoid content were significantly higher than those of untreated and pasteurized samples (p < 0.05). Ultrasound processing also significantly increased caffeic acid, catechin hydrate, chrysin, vanillin, p-coumaric acid, o-coumaric acid, and trans-ferulic acid concentrations. During simulated gastrointestinal digestion, all samples exhibited reductions in bioactive compounds; however, ultrasound-treated vinegar consistently retained higher TPC, total flavonoid content, and FRAP values, with bioaccessibility remaining approximately 30%–32%. Pearson correlation and principal component analyses further identified caffeic acid, trans-ferulic acid, rutin, and o-coumaric acid as the principal contributors to antioxidant capacity. This study integrates ultrasound optimization, RSM, data enhancement, community machine learning, phenolic profiling, and in vitro bioavailability assessment for black fig vinegar, presenting a robust and sustainable strategy for process optimization and the development of high-quality functional vinegar products.
Multi-class network intrusion detection: Machine and deep learning benchmark with live stream deployment
(IEEE, 2026) Danışmaz, Betül; Bıyık, Mustafa Emre; Gözüaçık, Necip
The rapid rise of advanced cyber threats is exposing the limitations of traditional, signature-based network intrusion detection systems. While machine learning and deep learning models offer proactive defense mechanisms, current research is often compromised due to temporal data leakage, impractical threat classifications, and significant discrepancies between offline evaluation and online deployment. This study presents a comparative evaluation of traditional machine learning and deep learning architectures integrated into a fully functional real-time inference pipeline. Additionally, we propose an operationally motivated three-class classification encompassing Benign, Volumetric, and Semantic traffic to contextualize incident response. When the trained XGBoost, Random Forest, Decision Tree, LSTM, and BiLSTM models were evaluated and compared under the same constraints, the BiLSTM model achieved the highest Macro F1 score at 97.72%. However, XGBoost closely followed with a Macro F1 of 95.87%, while achieving 15.5 times faster inference. To translate these findings into practice, a Kafka-based live system architecture was designed that integrates real-time stream inference, zero-downtime model switching, and automated threat response capabilities.
Driver fatigue estimation using multimodal deep learning with eeg and eog signals
(IEEE, 2026) Demir, Șevval; Yapar, Rümeysa; Aldaş, Ahsen Nur; Gözüaçık, Necip
Driver fatigue is a critical condition that increases accident risk by causing loss of concentration and perceptual limitations. Therefore, researchers have focused on developing systems aimed at the early detection of driver fatigue. In this study, driver fatigue detection was targeted using Differential Entropy–Linear Dynamic System (DE-LDS) based Electroencephalography (EEG) features and Electrooculography (EOG) features provided by the SEED-VIG dataset. The feature pool employed in the study was enriched through feature engineering methods such as band ratios, temporal delta components, hemispheric asymmetry, and moving standard deviation. Moreover, by eliminating the need for raw signal processing, the focus was directed toward the temporal variation patterns of meaningful features. Experimental results demonstrated that predictions based solely on EEG data remained limited, whereas the inclusion of EOG data significantly improved performance.These findings reveal that EEG and EOG signals provide complementary information in the prediction of driver fatigue. The proposed multimodal approach exhibited greater robustness against inter-subject variability compared to unimodal systems and offered a more generalizable monitoring framework.
Thermosonication improves the bioactive properties and in vitro bioaccessibility of broccoli juice through integrated RSM and metaheuristic optimization
(Frontiers Media S. A., 2026) Arpa Zemzemoğlu, Tuba Eda; Tokatlı Demirok, Nazan; Türkol, Melikenur; Gürfidan, Remzi; Kilim, Oğuzhan; Tokatlı, Nazlı; Yıkmış, Seydi; Mohamed Ahmed, Isam A.; Aljobair, Moneera; Yinanç, Abdullah
Broccoli juice is highly nutritious, containing phenolic compounds, chlorophyll derivatives, antioxidants, and other biologically active phytochemicals; however, conventional thermal processing may reduce their stability and bioaccessibility. The aim of this study was to analyse the impact of thermosonication on the following properties of broccoli juice: bioactivity, antioxidant capacity, phenolic profile and in vitro bioaccessibility. Thermosonication conditions were optimized using Response Surface Methodology (RSM) integrated with the Grey Wolf Optimizer (GWO) and Particle Swarm Optimization (PSO) algorithms. Broccoli juice samples were subjected to conventional pasteurization and thermosonication under different processing conditions, and total chlorophyll, ascorbic acid (AA), ferric reducing antioxidant power (FRAP), total phenolic content (TPC), and individual phenolic compounds were evaluated. Digestive stability and post-digestion bioaccessibility were also assessed using a standardized in vitro gastrointestinal digestion model. The developed quadratic models exhibited high statistical significance for TPC and FRAP responses (p < 0.001), with coefficients of determination (R²) of 0.9949 and 0.9853, respectively. The optimum thermosonication conditions were identified as 40 °C, 6.75 min, and 64.04% amplitude, under which experimentally validated FRAP and TPC values reached 12.86 mmol TE/L and 149.77 mg GAE/100 mL, respectively. HPLC-DAD analysis showed that gallic acid and naringin were the predominant phenolic compounds, and that thermosonication promoted greater phenolic retention than conventional pasteurization. In vitro digestion results demonstrated that thermosonicated broccoli juice maintained significantly higher levels of TPC, FRAP, chlorophyll, and AA throughout gastrointestinal digestion, with higher TPC recovery (30.45%) than pasteurized samples (26.22%), indicating improved post-digestion bioaccessibility of phenolic compounds. Similar improvements were observed for antioxidant capacity and chlorophyll retention. Furthermore, PSO and GWO optimization results showed strong agreement with RSM predictions, confirming the robustness of the proposed optimization strategy. Overall, thermosonication was shown to effectively and sustainably enhance the functional quality, antioxidant properties, phenolic stability, and digestive bioaccessibility of broccoli juice.
Mathematical and numerical modeling of drilling system dynamics using CF fractional differentiation
(AIMS Press, 2026) Erdoğan, Mahir Ceylan
In this work, I propose a Caputo–Fabrizio fractional mathematical model of a drilling system (CFFMMDS) to investigate the coupled dynamics of an induction motor-driven drilling assembly within a unified fractional-order framework. The governing equations were formulated using a nonsingular Caputo–Fabrizio fractional derivative with an exponential memory kernel to characterize the temporal dependencies among the system state variables. The resulting fractional-order system was solved using the Caputo–Fabrizio q-Elzaki homotopy analysis transform method (CFq-EHATM) to derive semi-analytical solution series and to conduct a numerical investigation of the system response under varying fractional orders. The results indicated that a decrease in the fractional order 𝜇 results in attenuated growth and decay rates. This trend is consistent with the intrinsic memory structure of the Caputo–Fabrizio operator, which distributes the influence of past states over time. The proposed framework enables the analysis of the temporal evolution of coupled state variables in dynamic systems exhibiting history-dependent behavior.
An NLP-driven framework for automated radiology–pathology concordance assessment in breast biopsy
(MDPI Publishing, 2026) Esmerer, Emel; Nazlı, Mehmet Ali; Uzun-Per, Meryem; Gümüş Değidiben, Melike; Söyleyici, Merve; Tahir, Eren; Bal, Mert
Background/Objectives: To develop and assess the feasibility of a natural language processing (NLP) framework for automated assessment of radiology-pathology concordance in breast biopsy using machine learning-based analysis of unstructured reports. Methods: This retrospective study included 766 paired radiology and pathology reports from ultrasound- or mammography-guided breast biopsies (August 2020-May 2024). Reports underwent translation, normalization, tokenization, lemmatization, and synonym expansion, followed by structured encoding of BI-RADS and pathology categories. Three models were trained: a Decision Tree, a LightGBM classifier, and a fine-tuned BioBERT model. Concordance labels were defined by multidisciplinary consensus. Performance metrics included accuracy, sensitivity, specificity, F1-score, area under the curve (AUC), and Cohen's kappa. SHapley Additive exPlanations (SHAP) analysis was used to identify influential features. Results: Among 766 cases, 707 (92.3%) were concordant and 59 (7.7%) were initially discordant. After excluding B3 lesions (n = 46), 13 true discordant cases remained (1.7%). Including B3 lesions increased clinically non-concordant or indeterminate cases from 1.7% to 7.7%, indicating that the apparent performance of the models is likely sensitive to case definition and dataset composition. BI-RADS 4a was the most common category (31.3%), and benign pathology (B2) accounted for 64.4% of biopsies. Within this dataset, LightGBM yielded the highest apparent AUC (0.999) (however, given the extremely small number of true discordant cases, this estimate is likely unstable and should be interpreted with caution), while BioBERT showed the strongest agreement with expert consensus (κ = 0.89). SHAP analysis identified clinically meaningful terms such as calcification, hypoechoic, ductal, and carcinoma as key contributors to model predictions. Given the very limited number of true discordant cases, these performance estimates are likely unstable and should be regarded as preliminary, requiring validation in larger, multi-center cohorts. Conclusions: This study presents a proof-of-concept NLP-based framework for radiology-pathology concordance assessment. The models showed promising performance in identifying potentially discordant cases; however, given the limited number of true discordant samples, these findings should be considered preliminary and require further validation in larger, multi-center datasets before clinical implementation.
Comparative benchmarking of 2d lidar slam algorithms with ros 2 on raspberry pi 5
(IEEE, 2026) Şahin, Ulaş; Can, Göktürk; Altıok, Ezgi; Çavdar, İbrahim; Gözüaçık, Necip
Mobile robotics increasingly relies on SLAM for robust autonomous navigation. While many algorithms exist, systematic comparisons within the ROS 2 framework under real-world conditions remain limited. This study addresses this gap by benchmarking three widely used 2D LiDAR-based methods—GMapping, Hector SLAM, and Cartographer—on a wheeled mobile robot. Using both simulation and on-device experiments, we evaluate mapping accuracy, localization quality, and computational efficiency. Results show that Cartographer achieves the highest accuracy in structured environments, Hector SLAM demonstrates robustness without odometry, and GMapping performs reliably only in small-scale settings. These findings highlight trade-offs relevant to embedded deployment. The main contributions are: (i) a reproducible evaluation pipeline on ROS 2, (ii) quantitative analysis of accuracy versus resource usage on Raspberry Pi 5, and (iii) practical guidelines for algorithm selection in autonomous systems. This work advances the understanding of ROS 2-based SLAM and supports informed deployment in robotics applications.
Meta-learning analysis of deep neural network architectures on diverse numeric datasets via geometric complexity descriptors
(Wiley, 2026) Bulut, Faruk; Dönmez, İknur
Meta-learning techniques aim to predict the most suitable learning algorithm for a given dataset based on its intrinsic structural characteristics. These techniques provide a robust framework for understanding algorithmic behavior across diverse data dis tributions and attributes. Although these state-of-the-art models (CNNs and transformers) are widely applied in various machine learning tasks, their use on numerical datasets remains underexplored due to the complexity of their internal structures. This study aims not only to predict the performance of two black-box deep learning models on static datasets but also to conduct a behavioral analysis in order to identify which meta-features most strongly infuence their outcomes. It seems unclear which specifc attributes of a dataset positively or negatively afect the performance of these deep learning models. To bridge this gap, we constructed a meta dataset consisting of 296 datasets, each characterized by 20 meta-features describing the dataset’s statistical, geometric, and structural properties. The analysis identifes which intrinsic dataset properties infuence model accuracy, without relying on raw data or hyperparameter tuning. Results show that both models perform best on datasets with high feature discriminability, as captured by meta-features such as maximum feature efciency, collective feature efciency, and directional separability. In contrast, performance declines with increasing class boundary complexity and nonlinearity, refected in features like class separability measures and the linear classifer nonlinearity metric. While CNNs are more sensitive to local geometric complexity, transformers respond more strongly to global statistical measures such as mutual information and entropy, highlighting their distinct inductive biases. The proposed meta-model accurately predicts the performance of both architectures on unseen datasets (0.96 correlation coefcient, 0.019 MAE, and 0.025 RMSE for CNNs; 0.92 correlation coefcient, 0.027 MAE, and 0.036 RMSE for transformers), enabling performance estimation without costly training. These fndings emphasize the importance of aligning model architecture with dataset geometry and structure. Additionally, the framework supports more interpretable, efcient, and sustainable deep learning model selection in structured data settings.
A comprehensive review on the use of artificial intelligence, internet of things, sensors, and green energy in non-invasive agricultural techniques
(FRUCT, 2025) Serdaroğlu, Kemal Çağrı; Tokatlı, Nazlı
Feeding a burgeoning global population amid cli mate change and dwindling resources presents a profound chal lenge for agriculture. This paper examines ”smart agriculture” (Agriculture 4.0) as a pivotal solution, integrating technologies like IoT, AI, and robotics to cultivate data-driven, efficient, and sustainable farming. We emphasize the growing effectiveness of multi-modal data fusion—combining diverse sensor inputs—for improved pest detection, water management, and yield predic tion. A critical shift towards decentralized edge intelligence is also explored, facilitating real-time, on-farm decisions and overcoming connectivity hurdles. While acknowledging that successful implementations are highly context-specific and that synthetic data can address scarcity, we also confront persistent obstacles: high adoption costs, the digital divide, unreliable rural connectivity, and cybersecurity risks. Ultimately, realizing smart agriculture’s full potential—a more resilient and productive global food system—requires sustained investment in affordable sensors, robust and explainable AI, and autonomous robotics to translate data insights into actionable field-level strategies.
ThermoMicrowave-sonication improves the stability and digestive bioaccessibility of phenolic compounds in parsley juice
(Elsevier, 2026) Yıkmış, Seydi; Tokatlı Demirok, Nazan; Duman Altan, Aylin; Paçal, İshak; Türkol, Melikenur; Tokatlı, Nazlı; Paçal, Nurettin; Abdi, Gholamreza; Aadil, Rana Muhammad
These are indications of the effects of ThermoMicrowave Sonication (TMS) on the bioactive compounds of parsley (Petroselinum crispum) juice and their bioaccessibility during in vitro digestion. Total phenolic content (TPC), iron-reducing antioxidant power (FRAP), chlorophyll, and ascorbic acid levels were measured in TMS treated and pasteurized samples. TMS minimized the loss of heat-sensitive proteins and significantly increased the phenolic content and antioxidant structure (p < 0.05). By following simulated oral, gastric, and intestinal digestion, TPC, chlorophyll, and FRAP levels were better in TMS samples than in controls or pasteurized samples. The highest recoverable levels were observed in the intestinal phase, highlighting the role of TMS in supporting functional quality after digestion. Prediction models using linear regression and LASSO showed strong accuracy (R2 > 0.99) for antioxidant capacity. Overall, TMS offers a promising, environmentally friendly, and industrially applicable tool for preserving and ensuring bioaccessibility of bioactive images in parsley juice and valuable information for functional electrical development. Chemical compounds: Gallic acid (PubChem CID:370); flovone (PubChem CID: 10680); vanillic acid (PubChem CID: 8468); rutin (PubChem CID: 5280805); naringin (PubChem CID: 442428); p- coumaric acid (PubChem CID: 637542); o- coumaric acid (PubChem CID: 637540); quercetin (PubChem CD: 5280459); alizarin (PubChem CD: 6293).
SFNN: A secure and diverse recommender system through graph neural network and regularized variational autoencoder
(Elsevier, 2025) Bahi, Abderaouf; Gasmi, Ibtissem; Bentrad, Sassi; Azizi, Mohamed Walid; Khantouchi, Ramzi; Uzun-Per, Meryem
Recommender systems are frequently improved to filter information and provide users with the most relevant items. However, they face limitations in balancing appropriate and diverse recommendations while ensuring the security and integrity of user data. A new recommender system based on secure fusion neural network is pre sented in this paper. It guarantees data integrity and confidentiality while balancing accuracy and diversity. It integrates a graph neural network that models user-item interactions to improve accuracy, with a regularized variational autoencoder whose evidence lower bound loss function is enhanced by a diversity-promoting regu larization term that favors latent-space dispersion, thereby improving recommendation diversity. To optimize the combination of the two neural networks scores, an adaptive fusion mechanism is introduced to generate final predictions that consider diverse user preferences while maintaining relevance. Furthermore, our approach uses blockchain technology to encrypt and secure data storage, ensuring the integrity and confidentiality of users’ data. The experiments conducted on three datasets show that the proposed model can achieve an accuracy of 78.13 % with an intra-list diversity of 46.82 % for Retail Rocket dataset, an accuracy of 82.44 % with an intra-list diversity of 37.78 % for clothing dataset, and an accuracy of 86.16 % with an intra-list diversity of 47.65 % for MovieLens-1 M dataset.
A comparative study of deep learning models for automated liver and tumor segmentation in 2d contrast-enhanced MRI images
(IEEE, 2025) Tokatlı, Nazlı; Bilmez, Yakuphan; Bayram, Mücahit; Bayır, Beyzanur; Özalkan, Helin; Tekin, Zeynep; Örmeci, Necati; Altun, Halis
This paper presents a comprehensive investigation into deep learning techniques for the automated segmentation of the liver and tumors from 2D abdominal contrast-enhanced Magnetic Resonance Imaging (MRI) slices. Addressing a significant challenge in medical image analysis, our study leverages the public ATLAS dataset [1], using a selection of 60 3D abdominal MRI scans, from which we extracted approximately 3,750 2D slices for model training and evaluation. The core objective was the precise identification and delineation of both the liver organ and any intrahepatic lesions. A comparative analysis was conducted on three U-Net-based architectures: the standard Attention U-Net model incorporating EfficientNet-b3 and CBAM but without Focal Loss, the Attention U-Net model with integrated Focal Loss, and the ResNet34-Based U-Net model. To optimize performance, we explored the efficacy of different loss functions, namely DiceLoss and a hybrid DiceLoss with Focalcoss. Our findings are promising: Among the evaluated models, the ResNet34-Based U-Net demonstrated the highest performance with a Dice score of 91.36% and an IoU score of 89.52%. It was followed by the Attention U-Net with Focal Loss, which achieved 86.41% Dice and 81.61% IoU scores, and the standard Attention U-Net, which obtained 85.93% Dice and 81.19% IoU scores. These results underscore the significant potential of our 2D-based methodology to enhance the precision and efficiency of liver and tumor detection from abdominal scans, offering a valuable tool to support clinicians in early diagnosis and to alleviate their workload.
An AI-powered mobile application for aroid identification and interactive learning: Enhancing pharmacognosy education through deep learning and NLP
(IEEE, 2025) Tokatlı, Nazlı; Bilmez, Yakuphan; Kılıç, Yusuf; Alpınar, Abdülkerim
Aroid plants (Araceae family), recognized for their distinct inflorescence, possess significant botanical, pharmaceutical, and practical importance due to their content of both beneficial compounds and toxins such as calcium oxalate crystals. Accurate identification of these species is particularly crucial in pharmacy education; however, morphological similarities among Aroid species often lead to confusion among students. This paper presents a deep learning-based mobile application designed to support both plant identification and interactive learning. The solution leverages EfficientNet and Convolutional Neural Network (CNN) architectures, achieving up to 96 % accuracy in classifying Aroid species. The visual classification model, trained on a comprehensive dataset, is deployed via a RESTful API and integrated within a Flutter-based mobile application. In addition, the app incorporates a Natural Language Processing (NLP)-powered chatbot to address user inquiries regarding plant characteristics and care. While technical evaluations demonstrate robust model performance, a comprehensive user evaluation aimed at assessing the system's educational value, usability, and chatbot interaction is planned as future work. This study underscores the potential of AI-driven mobile solutions in advancing pharmacognosy education, with future developments aimed at expanding the app's botanical scope and enhancing user engagement based on forthcoming survey results.
Comparative evaluation of deep learning models for the classification of impacted maxillary canines on panoramic radiographs
(MDPI Publishing, 2026) Tokatlı, Nazlı; Erdem, Buket; Özcan, Mustafa; Turan Maviş, Begüm; Şar, Çağla; Özdemir, Fulya
Background/Objectives: The early and accurate identification of impacted teeth in the maxilla is critical for effective dental treatment planning. Traditional diagnostic methods relying on manual interpretation of radiographic images are often time-consuming and subject to variability. Methods: This study presents a deep learning-based approach for automated classification of impacted maxillary canines using panoramic radiographs. A comparative evaluation of four pre-trained convolutional neural network (CNN) architec tures—ResNet50, Xception, InceptionV3, and VGG16—was conducted through transfer learning techniques. In this retrospective single-center study, the dataset comprised 694 an notated panoramic radiographs sourced from the archives of a university dental hospital, with a mildly imbalanced representation of impacted and non-impacted cases. Models were assessed using accuracy, precision, recall, specificity, and F1-score. Results: Among the tested architectures, VGG16 demonstrated superior performance, achieving an accuracy of 99.28% and an F1-score of 99.43%. Additionally, a prototype diagnostic interface was developed to demonstrate the potential for clinical application. Conclusions: The findings underscore the potential of deep learning models, particularly VGG16, in enhancing diag nostic workflows; however, further validation on diverse, multi-center datasets is required to confirm clinical generalizability.
Improving nutritional quality, aroma profile and bioactive retention of rocket juice via thermosonication: A support vector regression-based optimization
(Frontiers Media S. A., 2026) Levent, Okan; Şimşek, Mehmet Ali; Yıkmış, Seydi; Demirel, Selinay; Türkol, Melikenur; Tokatlı Demirok, Nazan; Er, Hatice; Aljobair, Moneera O.; Karrar, Emad; Tokatlı, Nazlı; Mohamed Ahmed, I. A.
This study investigates the application of thermosonication (TS) to improve the functional properties of roka (Eruca vesicaria subsp. sativia) water. Processing parameters, including time (8–16 min), amplitude (60–100%), and temperature (40–60 °C), were optimised using a comparative approach combining the response surface method (RSM) and support vector regression (SVR). The total phenolic content (TPC) increased to 86.04 mg GAE/100 mL with TS, representing an 8.1% rise compared to the control group and an 18.3% increase over pasteurization. Likewise, the total chlorophyll level reached 16.98 mmol TE/L from 9.67 g/100 mL, and β-carotene rose to 24.90 mg/100 mL (p < 0.05). Pasteurization caused losses of 15–30% in these components. In the phenolic profile, significant increases were observed in chlorogenic acid (42.05 μg/mL), caffeic acid (15.66 μg/mL), and quercetin (4.28 μg/mL). A total of 31 compounds were identified in aroma analysis; with TS treatment, levels of 3-Hexen-1-ol (15.70 μg/kg) and 1-hexanol (2.01 μg/kg) were preserved or increased. In in vitro digestion tests, the TS group demonstrated the highest bioavailability, even during the intestinal phase. For example, RSM demonstrated high compliance coefficients (R2 = 0.99), while SVR showed strong predictive performance (CV R2 = 0.84), particularly for FRAP. Overall, the results suggest that thermosonication is an innovative method for protecting and enhancing bioactive compounds in rocket juice.
Ultrasound-assisted sustainable processing of garden cress juice: Enhancing bioactive compounds and bioaccessibility through xgboost optimization
(American Chemical Society, 2025) Levent, Okan; Şimşek, Mehmet Ali; Yıkmış, Seydi; Demirel, Selinay; Tokatlı Demirok, Nazan; Türkol, Melikenur; Aljobair, Moneera; Tokatlı, Nazlı; Mohamed Ahmed, Isam A.
This study aimed to improve the functional and nutritional properties of garden cress (Lepidium sativum) juice using ultrasound and optimize process parameters by modeling them with advanced machine learning algorithms. Using a Box−Behnken experimental design, the effects of sonication time (8−16 min) and amplitude (60−100%) on total chlorophyll, total phenolic content (TPC), and ferric reducing antioxidant power (FRAP) were investigated. Nonparametric, high-accuracy estimations were made using the XGBoost algorithm. Optimum conditions were determined to be 12 min and 80% amplitude. Under these conditions, TPC (78.44 mg GAE/mL), FRAP (59.80 mg TE/mL), and chlorophyll (7.15 g/100 mL) values were significantly higher than those in control and pasteurized samples (p < 0.05). HPLC-DAD analysis showed that ultrasound treatment positively impacted the phenolic profile by increasing the release of quercetin, quercetin derivatives, caffeic acid, and chrysin. GC-MS data revealed that volatile aroma compounds (especially 1-hexanol, benzaldehyde, and cinnamaldehyde) were preserved mainly by ultrasound. In vitro digestion simulation showed that total postdigestion recovery rates in ultrasound-treated samples were 34.96% for TPC, 32.50% for chlorophyll, and 28.81% for FRAP, demonstrating a significant increase in bioaccessibility. PCA and hierarchical clustering analyses confirmed a significant biochemical separation of ultrasound-treated samples. The findings indicate that ultrasound technology is a superior method for preserving bioactive compounds, maintaining the aroma profile, and enhancing bioaccessibility compared to heat treatment. This enables data-driven process design. The developed model showed a strong predictive performance under optimal conditions. However, the study is limited by the relatively small data set used for model training.
Braindetective: An advanced deep learning application for early detection, segmentation and classification of brain tumours using MRI images
(Springer Nature Link, 2025) Tokatlı, Nazlı; Bayram, Mücahit; Ogur, Hatice; Kılıç, Yusuf; Han, Vesile; Batur, Kutay Can; Altun, Halis
This study aims to create deep learning models for the early identification and classification of brain tumours. Models like U-Net, DAU-Net, DAU-Net 3D, and SGANet have been used to evaluate brain MRI images accurately. Magnetic resonance imaging (MRI) is the most commonly used method in brain tumour diag nosis, but it is a complicated procedure due to the brain’s complex structure. This study looked into the ability of deep learning architectures to increase the accuracy of brain tumour diagnosis. We used the BraTS 2020 dataset to segment and classify brain tumours. The U-Net model designed for the project achieved an accuracy rate of 97% with a loss of 47%, DAU-Net reached 90% accuracy with a loss of 33%, DAU-Net 3D achieved 99% accuracy with a loss of 35%, and SGANet achieved 99% accuracy with a loss of 20%, all demonstrating effective outcomes. These find ings aim to improve patient care quality by speeding up medical diagnosis processes using computer-aided technology. Doctors can detect 3D tumours from MRI pictures using software developed as part of the research. The work packages correctly han dled project management throughout the study’s data collection, model creation, and evaluation stages. Regarding brain tumour segmentation, 3D U-Net architecture with multi-head attention mechanisms provides doctors with the best tools for planning surgery and giving each patient the best treatment options. The user-friendly Turkish interface enables simple MRI picture uploads and quick, understandable findings.
Visual quality assessment of E-commerce product images using convolutional neural networks
(Springer Nature Link, 2025) Tbaileh, Imad; Bağrıyanık, Selami
High-quality product images are vital in shaping consumer trust and driving engagement on e-commerce platforms. This study proposes a deep learning-based approach for evaluating the visual quality of product images, with the aim of improving the overall customer experience and presentation standards in online marketplaces. A custom-labeled dataset was developed, containing thousands of product images categorized into five quality levels. A convolutional neural net work (CNN) was trained to classify these images based on their visual quality. In addition, two well-known architectures, MobileNetV2 and EfficientNetB0, were trained under identical conditions to serve as benchmarks for performance com parison. The proposed CNN model achieved an accuracy of 94.93%, outperforming both MobileNetV2 (76.60%) and EfficientNetB0 (92.77%). It also delivered the highest performance in terms of precision, recall, and F1-score, confirming its effectiveness in this domain. The results highlight the CNN model’s suitability for real-time quality assessment of e-commerce images. Its strong performance and efficiency make it a promising candidate for integration into commercial platforms. Future work will investigate the use of transformer-based models and more diverse training data to further improve accuracy and generalizability.
AI-guided optimization of traditional bulgur pilafs: Enhancing sensory and bioactive properties through rsm-pso modeling
(Frontiers Media S. A., 2025) Yıkmış, Seydi; Türk Aslan, Sinem; Türkol, Melikenur; Şimşek, Mehmet Ali; Aljobair, Moneera; Karrar, Emad; Tokatlı, Nazlı; Mohamed Ahmed, Isam A.
This study aimed to enhance the sensory and bioactive properties of pilafs prepared from three geographically indicated bulgur varieties—Siyez, Firik, and Karakilçik—through an AI-guided optimization approach combining Response Surface Methodology (RSM) and Particle Swarm Optimization (PSO). Different bulgur (130–150 g) and water (350–450 mL) ratios were tested to determine optimal formulations. Sensory evaluation revealed that Firik bulgur pilaf achieved the highest overall acceptability (8.49), while Karakilçik bulgur pilaf scored highest in color (7.68) and aroma (8.58), and Siyez bulgur pilaf received the highest taste score (7.50). In terms of bioactive properties, Karakilçik bulgur pilaf showed the highest antioxidant capacity (75.57% DPPH radical scavenging activity), whereas Firik bulgur pilaf had the highest total phenolic (842.39 mg GAE/kg) and flavonoid contents (6.38 mg CE/g). Color analysis indicated that Siyez bulgur pilaf had the lightest color (L=52.18), while Firik pilaf exhibited the most intense red hue (a=8.12) and Karakilçik pilaf the darkest appearance (L=35.42). PSO-based validation confirmed the accuracy of RSM models by reaching global optima within 40 iterations and minimal deviation from experimental values. This is the first study to apply an integrated RSM–PSO modeling approach to traditional bulgur pilafs, enabling the prediction and optimization of their sensory and bioactive characteristics. The results provide a novel framework for enhancing the nutritional value and consumer appeal of heritage cereal-based foods and support the development of standardized, functional bulgur products for the food industry.

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