WoS İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/394
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Article High-Accuracy Identification of Durian Leaf Diseases: A Convolutional Neural Network Approach Validated with K-Fold Cross-Validation and Bayesian Optimization(Springer, 2025-11-18) Soylemez, Ismet; Nalici, Mehmet Eren; Unlu, RamazanTo address the economic losses caused by plant diseases in durian farming, this study presents an optimized deep learning model that diagnoses diseases from leaf images with high accuracy. The model's performance is maximized through Bayesian optimization and hyperparameter tuning, while its reliability is maximized through layered five-fold cross-validation. Training the convolutional neural network model on 2595 leaf images displaying six different states (five diseased and one healthy) resulted in an average test accuracy of 91.98%. This high, consistent success rate demonstrates the model's generalizability to different datasets without overfitting. While the 'Healthy' and 'Algal' classes were successfully detected with high F1-scores, there are difficulties distinguishing between the 'Blight' and 'Colletotrichum' classes due to visual similarities. This study establishes a new reference point for durian disease classification and makes a significant contribution to the development of reliable artificial intelligence-based diagnostic tools for precision agriculture.Article Citation - WoS: 15Citation - Scopus: 11T Cells in Tumor Microenvironment(Springer, 2015-10-18) Kiraz, Yagmur; Baran, Yusuf; Nalbant, AytenTumors progress in a specific area, which supports its development, spreading or shrinking in time with the presence of different factors that effect the fate of the cancer cells. This specialized site is called "tumor microenvironment" and has a composition of heterogenous materials. The immune cells are also residents of this stromal, cancerous, and inflammatory environment, and their types, densities, or functional differences are one of the key factors that mediate the fate of a tumor. T cells as a vital part of the immune system also are a component of tumor microenvironment, and their roles have been elucidated in many studies. In this review, we focused on the immune system components by focusing on T cells and detailed T helper cell subsets in tumor microenvironment and how their behaviors affect either the tumor or the patient's outcome.Article Citation - WoS: 7Citation - Scopus: 9Split-Attention Effects in Multimedia Learning Environments: Eye-Tracking and EEG Analysis(Springer, 2022-02-02) Mutlu-Bayraktar, Duygu; Ozel, Pinar; Altindis, Fatih; Yilmaz, BulentThis study aimed to evaluate the split-attention effect in multimedia learning environments via objective measurements as EEG and eye-tracking. Two different multimedia learning environments in a focused (integrated) and split-attention (separated) format were designed. The experimental design method was used. The participants consisted of 44 students divided into two groups for focused attention and split-attention. There were significant differences between the fixation, brain wave, and retention performance of the two groups. Fixations of the split-attention group were higher than the focused attention group. A significant difference was found in the focused attention group in the alpha brain wave in the frontal region for intra-group comparisons and in the split-attention group in the beta brain wave in the frontal area for the inter-group comparison. The retention performance of the focused attention group was higher than the split-attention group. Accordingly, more cognitive activity emerged in environments where the text was not integrated into the picture. Additionally, the narration of text instead of printed text is effective for focusing attention. To prevent the emergence of a split-attention effect, the text should be integrated into the picture in designs. Due to the split-attention effect, the eye-tracking and EEG data were different between the groups.Article Citation - WoS: 3Citation - Scopus: 5Multi-Focus Image Fusion by Using Swarm and Physics Based Metaheuristic Algorithms: A Comparative Study With Archimedes, Atomic Orbital Search, Equilibrium, Particle Swarm, Artificial Bee Colony and Jellyfish Search Optimizers(Springer, 2023-09-07) Cakiroglu, Fatma; Kurban, Rifat; Durmus, Ali; Karakose, ErcanThe lenses focus only on the objects at a specific distance when an image is captured, the objects at other distances look blurred. This is referred to as the limited depth of field problem, and several attempts exist to solve this problem. Multi-focus image fusion is one of the most used methods when solving this problem. A clear image of the whole scene is obtained by fusing at least two different images obtained with different focuses. Block-based methods are one of the most used methods for multi-focus fusion at the pixel-level. The size of the block to be used is an important factor for determining the performance of the fusion. Thus, the block size must be optimized. In this study, the comparison between the swarm-based and physics-based algorithms is made to determine the optimal block size. The comparison has been made among the following optimization methods which are, namely, Archimedes Optimization Algorithm (AOA), Atomic Orbital Search (AOS) and Equilibrium Optimizer (EO) from the physics-based algorithms and Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC) and Jellyfish Search Algorithm (JSA) from swarm-based algorithms. The swarm-based ABC and JSA algorithms have shown a better performance when compared to physics-based methods. Moreover, meta-heuristic algorithms, in general, are more adaptive compared to the traditional fusion methods.Article Citation - WoS: 6Citation - Scopus: 10Determining Critical Success Factors Related to the Effect of Supply Chain Integration and Competition Capabilities on Business Performance(Springer, 2014-08-03) Ozdemir, Ali Ihsan; Simonetti, Biagio; Jannelli, RobertoThis study analyzes those critical success factors related to supply chain integration (SCI) and competition capabilities (CC) and which have more effect on business performance (BP) by using a structural equation model. For this purpose, the relationship between integration, CC and BP has been analyzed. Data was obtained from the survey that applied to Turkish Small and Medium Sized Enterprises (SMEs) and we examined the critical factors by using a Structural Equation Model to analyze which factors have more effect on BP. As a result of the study it was found that there are positive associations between SCI and CC, and both SCI-CC and BP and it was also found that most critical factor that affects BP is reliability and the least important one is lower price.Conference Object Citation - WoS: 30Citation - Scopus: 30Compatibility of Superplasticizers With Limestone-Metakaolin Blended Cementitious System(Springer, 2015) Zaribaf, Behnaz H.; Uzal, Burak; Kurtis, KimberleyThis study investigates the performance of polycarboxylate ether (PCE), polymelamine sulfonate (PMS), sodium lignosulfonate and naphthalene formaldehyde condensate (PNS) superplasticizers (SPs) with ASTM C595 Type IL cement (with up to 15% calcium carbonate) combined with 10 and 30 % metakaolin (MK) substitutions by mass. The required dosage of each SP for 10 % and 30 % MK substitutions were determined based on mini slump test to establish equivalent paste flow. At these dosage rates, the effects of SPs on setting time, hydration kinetics, and strength development were measured. Life cycle assessment (LCA) was carried out on different cement compositions used in this study to evaluate the greenhouse gas emissions and embodied energy of limestone-metakaolin blended cement with SP addition. While MK substitution decreases the workability of samples and shortens the setting time, this study shows that adequate dosages of a compatible type of SP can be used to compensate for these effects. Of the SPs examined, PCE and PMS are found to be more compatible, compared to PNS and sodium lignosulfonate, with limestone-metakaolin blended cements.