Bilgisayar Mühendisliği Bölümü Koleksiyonu

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

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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.
Ultrasound technology in environmental sustainability: Vinegar production from black carrot pulp
(Codon Publications, 2025) Türkol, Melikenur; Yıkmış, Seydi; Tokatlı, Nazlı; Sağcan, Nihan; Khalid, Waseem; Yinanç, Abdullah; Aksu, Harun; Althawab, Suleiman A.; Alsulami, Tawfiq
In this study, vinegar obtained from untreated traditional black carrot pulp was compared with vinegar obtained from black carrot pulp subjected to thermal pasteurization and ultrasound treatment. Ultrasound treatment sig nificantly improved the preservation and bioavailability of bioactive compounds, with higher total carotenoid content (TCC), total anthocyanin (TAC), and antioxidant (FRAP) values. It efficiently released bioactives from cell walls, enhancing bioavailability. RSM optimization revealed optimal conditions at 8 minutes processing time and 59.7% amplitude. However, ultrasound-treated vinegar (UT-BCV) was preferred in sensory analysis. Utilizing black carrot pulp supports sustainability, circular economy, and bioavailability goals.
Thermosonication-enhanced bioaccessibility and functional quality of dill juice: An in vitro digestion approach
(Frontiers Media S. A., 2025) Yıkmış, Seydi; Ateş, Abdullah; Demirel, Selinay; Levent, Okan; Tokatlı, Nazlı; Tokatlı Demirok, Nazan; Aljobair, Moneera; Karrar, Emad; A. Athawab, Suleiman; Mohamed Ahmed, Isam A.
This study, the effects of thermosonication, a non-thermal treatment, on the functional components of dill (Anethum graveolens) juice were investigated and its effect on post-digestion bioaccessibility was evaluated. Control (CDJ), thermally pasteurized (P-DJ), and thermosonicated (TS-DJ) samples were compared in terms of total phenolic content (TPC), β-carotene, total chlorophyll, and ferric reducing antioxidant power (FRAP). The stabilities and recovery rates of volatile aroma compounds and bioactive components throughout the digestion process (oral, gastric, and intestinal stages) were analyzed by an in vitro digestion model. Thermosonication provided higher preservation of bioactive components both initially and during 21 days of storage, and significantly higher post-digestion bioaccessibility values were observed. Process optimization was performed using Response Surface Methodology (RSM) and Equilibrium Optimization algorithms, with models validated with high predictive accuracy. Pearson correlation analysis revealed strong positive correlations between total phenolic content (TPC), β-carotene, and specific volatile compounds such as limonene and carvone, indicating that higher levels of these bioactives were associated with enhanced characteristic aroma profiles of dill juice. These results suggest that thermosonication may be a promising alternative to traditional thermal treatments for improving the functional quality and post-digestion bioaccessibility of dill water. In this context, while further research is needed to assess consumer acceptance and industrial scalability, this study provides valuable insights into the development of improved processing methods for plant-based beverages.
Thermosonication-assisted fortification of kiwi juice with bee bread: Enhancing nutritional and functional properties through ANFIS-RSM optimization
(Frontiers Media S. A., 2025) Yıkmış, Seydi; Duman Altan, Aylin; Türkol, Melikenur; Tokatlı, Nazlı; Yıldırım Maviş, Çiğdem; Tokatlı Demirok, Nazan; Aadil, Rana Muhammad; Karrar, Emad; Aljobair, Moneera O.; Mohamed Ahmed, I. A.
This study investigated the effects of thermosonication on the preservation and enhancement of bioactive components in kiwi juice fortified with bee bread. Response Surface Methodology (RSM) and Adaptive Neuro-Fuzzy Inference System (ANFIS) were employed to optimize processing parameters by evaluating FRAP, total phenolics, total chlorophyll, and ascorbic acid levels. Thermosonication significantly enhanced the levels of phenolic compounds (127.97 GAE mg/100 mL) and ascorbic acid (14.89 mg/100 mL), while a reduction in chlorophyll content was observed. The ANFIS model provided more accurate predictions compared to RSM, particularly under optimal processing conditions. Additionally, the thermosonication-treated kiwi juice with bee bread (TS-KJB) exhibited the highest antioxidant capacity, total flavonoid, and dietary fiber content. The findings demonstrate that thermosonication is an effective and sustainable technique for improving the functional and nutritional properties of bee bread-fortified kiwi juice. This approach offers a promising alternative for the production of additive- and preservative-free functional fruit juices.
Advanced low-thermal fortification strategy for dill juice: Enhanced bioaccessibility and functional properties through MLP-RSM optimization
(Frontiers Media S. A., 2025) Yıkmış, Seydi; Duman Altan, Aylin; Demirel, Selinay; Türkol, Melikenur; Tokatlı, Nazlı; Tokatlı Demirok, Nazan; Aljobair, Moneera O.; Karrar, Emad; Mohamed Ahmed, Isam A.
In this study, a combination of ultrasound and microwave technologies (USMW) was applied to increase the functional properties of Anethum graveolens L. (dill) juice and the obtained samples were comprehensively evaluated in terms of biofunctionality. Total phenolic content (TPC), β-carotene, total chlorophyll, antioxidant capacity (FRAP) and antidiabetic enzyme inhibition (α-glucosidase, α-amylase) were determined. The optimum process parameters were successfully estimated by Response Surface Methodology (RSM) and Multilayer Perceptron (MLP) models. USMW process increased the extraction of phenolic compounds and carotenoids, providing significant increases in TPC (126.08 mg GAE/100 mL), β-carotene (42.82 mg/100 mL) and chlorophyll (4.42 g/100 mL) levels (*p < 0.05). In the simulated post-digestion bioavailability assessments, the ultrasound and microwave (DJ-USMW) group showed the highest recovery rates. In addition, potential antidiabetic effects were confirmed by the inhibition of α-glucosidase (61.65%) and α-amylase (53.11%). PCA and clustering analyses showed that USMW application significantly separated the samples. The obtained results demonstrate that USMW technology is a sustainable and effective method, especially for the development of functional beverages, as an alternative to traditional heat treatments.
Sustainable valorization of yellow cherry juice using natural propolis and non-thermal techniques
(Frontiers Media S. A., 2025) Yıkmış, Seydi; Türkol, Melikenur; Tokatlı Demirok, Nazan; Tokatlı, Nazlı; Rüzgar, Ezgi; Mohamed Ahmed, Isam A.; Aljobair, Moneera O.
This study investigated the effects of propolis enrichment and thermosonication conditions on bioactive components, amino acid profile, antioxidant capacity and sensory properties of yellow cherry juice. Temperature (40–50°C), time (4–10 min), amplitude (40–80%) and propolis concentration (40–80 mg/100 mL) were optimized as independent variables using response surface methodology (RSM). Principal component analysis (PCA) analysis revealed that thermosonicated samples (TS-YCJ) were positively correlated with functional components such as chlorogenic acid, caffeic acid, epicatechin and total soluble solids (TSS). The malic acid content reached its highest level at 1,174.38 mg/L in thermosonicated optimized propolis yellow cherry juice (TS-YCJ), whereas this value remained at 1,078.34 mg/L in the pasteurized samples. Thermosonication application significantly increased the antioxidant capacity measured by total phenolic content (TPC), total flavonoid content (TFC) and DPPH radical inhibition. While TPC content reached 268.72 mg GAE/L in thermosonicated optimized propolis yellow cherry juice samples, it remained at 256.27 mg GAE/L in control samples. Among the phenolic compounds, chlorogenic acid (35.42 mg/L) and caffeic acid (12.67 mg/L) increased significantly after thermosonication. In terms of amino acid profile, components such as proline (42.21 mg/L), glycine (38.45 mg/L) and phenylalanine (24.32 mg/L) were found at higher levels in control samples. In sensory analysis, thermosonication samples received high scores in terms of taste, odor and overall acceptability. High R2 values (98.94–99.80%) reveal the strong explanatory power and reliability of the model. These findings indicate that thermosonication and propolis offer an effective combination to improve the functional properties, sensory quality and phenolic compound profile of yellow cherry juice.
AI-focused peer assessment in internship programs: Effects on virtual learning competencies and attitudes towards AI
(International Society for Technology, Education, and Science, 2025) Kayhan, Osman; Tokatlı, Nazlı; Altun, Halis; Korkmaz, Özgen
This research aims to investigate the impact of peer-evaluated internship activities centered on artificial intelligence on engineering students, specifically focusing on their virtual learning competencies, attitudes towards artificial intelligence, and ascertain student perspectives. For this purpose, mixed-methods research has been used. In this study, which used a sequential explanatory design, a preliminary experimental design was employed in the quantitative part and basic qualitative research methods were used in the qualitative part. The study group consists of 34 engineering students. Data were collected using the Project-Based Virtual Learning Competencies Scale, the General Attitude Towards Artificial Intelligence Scale, and semi-structured interview forms. The research demonstrated that internship activities centered on peer evaluation related to artificial intelligence in virtual learning environments considerably improved the virtual learning competencies of engineering students. However, these activities did not significantly influence their attitudes towards artificial intelligence, whether positive or negative. The tasks during the internship program are thought to have improved students' collaboration, problem-solving, communication, creative thinking, technical knowledge, project writing skills, and research skills. Furthermore, it has been determined that students have acquired experience and enhanced their skills in process management, leadership, and tolerance.
Enhanced nearest centroid model tree classifier
(Springer Nature Link, 2025) Özçelik, Mehmet Hamdi; Duman, Ekrem; Bağrıyanık, Selami; Bulkan, Serol
In this study, frst, we improved an existing variant of the Nearest Centroid algorithm. In this new version, the predic tive power of features and within-class variances are used as weights in distance calculation. This version is called the Enhanced Nearest Centroid (ENC). Second, we proposed a new model tree algorithm for binary classifcation. It is named as the Enhanced Nearest Centroid Model Tree (ENCMT). The model tree is built using ENC at each leaf node of the decision tree. To evaluate the performance of the new model tree, we used an independent test platform and ran the algorithm on 30 binary datasets available therein. Results showed that ENCMT improves the performance of the decision tree algorithm. We also compared ENCMT with the Logistic Model Tree (LMT) algorithm and showed that it outperforms LMT as well. We also designed a bagging algorithm where ENCMT is used to build a random forest. Our comparison results show that its performance is signifcantly better than the Random Forest (RF) algorithm.
On new exact traveling wave solutions of the hamiltonian amplitude equation
(Bibliotheca, 2025) Özkan, Ayten; Özdemir, Nagehan
Nonlinear differential equations have an important place in mathematical physics. In this paper, the G′/G2 expansion method is used to obtain new exact traveling wave solutions of the Hamiltonian amplitude equation that arise in the analysis of various problems in fluid mechanics and theoretical physics. All calculations in this study are made using the software program and the solutions obtained are substituted in the equations. The solutions obtained have important areas of use in the fields of mathematical physics and engineering.
Advancing sustainable food preservation: Ultrasound and thermosonication as novel approaches to enhance nutritional and bioactive properties of broccoli juice
(Elsevier, 2025) Yıkmış, Seydi; Türkol, Melikenur; Dülger Altıner, Dilek; Duman Altan, Aylin; Sağlam, Kübra; Abdi, Gholamreza; Tokatlı, Nazlı; Çelik, Güler; Aadil, Rana Muhammad
To meet the challenges of sustainability and nutritional quality, innovative food processing technologies are essential. This study investigates the application of ultrasound and thermosonication- emerging non-thermal preservation techniques- to improve the functional properties of broccoli juice. Using Response Surface Meth odology (RSM) and Adaptive Neuro-Fuzzy Inference System (ANFIS), the processes were optimised to maximize chlorophyll and ascorbic acid content. Optimal ultrasound parameters (4 min, 91.1 % amplitude) achieved 12.29 mg/100 mL chlorophyll and 79.38 mg/100 g ascorbic acid. Thermosonication (6.9 min, 66 % amplitude, 40 ◦C) gave comparable results. Both treatments significantly improved phenolic composition and mineral content, demonstrating superior preservation of bioactive compounds and reduced nutrient degradation compared to traditional methods. The results highlight the potential of ultrasound and thermosonication for sustainable food systems by improving nutritional quality and shelf life, thereby contributing to reduced food waste and environmentally friendly processing. This research provides valuable insights into the integration of non-thermal technologies in the production of functional beverages, supporting the development of circular food systems and sustainable innovation.
Ultrasound-assisted enhancement of bioactive compounds in hawthorn vinegar: A functional approach to anticancer and antidiabetic effects
(Elsevier, 2025) Öğüt, Selim; Türkol, Melikenur; Yıkmış, Seydi; Bozgeyik, Esra; Abdi, Gholamreza; Koçyiğit, Emine; Aadil, Rana Muhammad; Seyidoğlu, Nilay; Karakçı, Deniz; Tokatlı, Nazlı
In this study, the effects of ultrasound treatment on bioactive components and functional properties of hawthorn vinegar (Crataegus tanacetifolia) were investigated. Parameters such as total phenolic compound (TPC), total flavonoid content (TFC), ascorbic acid (AA), DPPH radical scavenging activity and CUPRAC reducing capacity were optimised by surface response method (RSM) and 14 min duration and 61.40 % amplitude were determined as the most suitable treatment conditions. The results showed that ultrasound treatment improved the antioxidant properties of hawthorn vinegar by increasing TPC, TFC, DPPH and CUPRAC values. In addition, it was observed that hawthorn vinegar samples exhibited anticancer effects in cell culture experiments. In experiments on A549 (lung), MCF-7 (breast) and HT-29 (colon) cancer cell lines, ultrasound-treated vinegar increased apoptotic effects, suppressed cell migration and reduced necrosis rates in some cell lines. In particular, ultrasound treatment of vinegar resulted in a reduction in the expression of anti-apoptotic genes (BCL-2 and XIAP) and an enhancement in the expression of pro-apoptotic genes (BAX). These findings suggest that ultrasound technology preserves and enhances the bioactive components of hawthorn vinegar, improves its anticancer properties and increases its potential for use as a functional food product.
Optimization of bioactive compounds and sensory quality in thermosonicated black carrot juice: A study using response surface methodology, gradient boosting, and fuzzy logic
(Elsevier, 2025) Yıkmış, Seydi; Türkol, Melikenur; Paçal, İshak; Duman Altan, Aylin; Tokatlı, Nazlı; Abdi, Gholamreza; Tokatlı Demirok, Nazan; Aadil, Rana Muhammad
This study investigates the optimization of bioactive components in thermosonicated black carrot juice using response surface methodology (RSM) and gradient boosting (GB) modeling techniques. Thermosonication, a combination of ultrasound and heat, was applied to enhance the nutritional quality of black carrot juice, which is rich in anthocyanins, phenolic compounds, and antioxidants. The study examined the effects of temperature, processing time, and ultrasonic amplitude on total carotenoid content (TCC), total anthocyanin content (TAC), ferric reducing antioxidant power (FRAP), and total phenolic content. RSM demonstrated higher prediction accuracy compared to GB, identifying optimal processing conditions at 48.68 °C, 11.15 minutes, and 82.62% amplitude. Thermosonication significantly increased total phenolic content to 414.28 mg GAE/L, surpassing traditional pasteurization. Sensory analysis, conducted via fuzzy logic, indicated improved sensory properties, including aroma, taste, and color, in thermosonicated samples. This study undercomes thermosonication as a promising method for improving both bioactive compounds and sensory quality in black carrot juice. Chemical compounds Chlorogenıc acid (PubChem CD:1794427); caffeic acid (PubChem CD: 689043); vanillin (PubChem CD: 1183); rutin (PubChem CD: 5280805); naringin (PubChem CD: 442428); rosmarinic acid (PubChem CD: 5281792); t-ferulic acid (PubChem CD: 445858); o- coumaric acid (PubChem CD: 637540); (PubChem CD: quercetin 5280459); 4-hyroxybenzoic acid (PubChem CD: 135).

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