33 research outputs found
Novel Hybrid Scaffolds for the Cultivation of Osteoblast Cells
No full textTurkoglu Sasmazel, Hilal/0000-0002-0254-4541In this study, natural biodegradable polysaccharide, chitosan, and synthetic biodegradable polymer, poly(epsilon-caprolactone) (PCL) were used to prepare 3D, hybrid polymeric tissue scaffolds (PCL/chitosan blend and PCL/chitosan/PCL layer by layer scaffolds) by using the electrospinning technique. The hybrid scaffolds were developed through HA addition to accelerate osteoblast cell growth. Characteristic examinations of the scaffolds were performed by micrometer, SEM, contact angle measurement system, ATR-FTIR, tensile machine and swelling experiments. The thickness of all electrospun scaffolds was determined in the range of 0.010 +/- 0.001-0.012 +/- 0.002 mm. In order to optimize electrospinning processes, suitable bead-free and uniform scaffolds were selected by using SEM images. Blending of PCL with chitosan resulted in better hydrophilicity for the PCL/chitosan scaffolds. The characteristic peaks of PCL and chitosan in the blend and layer by layer nanofibers were observed. The PCL/chitosan/PCL layer by layer structure had higher elastic modulus and tensile strength values than both individual PCL and chitosan structures. The layer by layer scaffolds exhibited the PBS absorption values of 184.2; 197.2% which were higher than those of PCL scaffolds but lower than those of PCL/chitosan blend scaffolds. SaOs-2 osteosarcoma cell culture studies showed that the highest ALP activities belonged to novel PCL/chitosan/PCL layer by layer scaffolds meaning better cell differentiation on the surfaces. (C) 2011 Elsevier B.V. All rights reserved.Turkish Academy of Science (TUBA) L'Oreal; L'OrealThe author is greatly thankful to Turkish Academy of Science (TUBA) & L'Oreal for honoring this study with the award "Young Women in Science" in Materials Science in 2009. Her special thanks also go to L'Oreal for the precious financial support. The author also appreciates the invaluable contribution of AWAC (Academic Writing Advisory Center) to this study in linguistic terms
Assessment of urban seismic resilience of a town in Eastern Turkiye: Turkoglu, Kahramanmaras before and after 6 February 2023 M 7.8 Kahramanmaras earthquake
No full textOn 6 February 2023, two earthquakes occurred approximately 9 h apart, with Mw 7.8 and 7.5, and epicenters located in Pazarc & imath;k and Elbistan districts of Kahramanmaras province, respectively. As part of a national project team which was funded by the Disaster and Emergency Management Presidency of Turkiye (AFAD) between June 2021 and June 2023, the authors of this article had proposed a framework to assess the seismic resilience of an urban region. The pilot area of this national project was a small-scale industrial town named Turkoglu located to the south of Kahramanmaras, at the intersection of Amanos and Pazarcik segments of the East Anatolian Fault zone. The proposed framework encompasses the assessment of active faults in the region, construction of regional velocity models, ground motion simulations of potential earthquakes, structural vulnerability, and study of seismic resilience indicators. The Pazarcik earthquake occurred 4 months before the end of the project on the exact fault system, which was modeled in ground motion simulations within the project in 2022. The objective of this article is multifold: first, to present our findings before the earthquake (2021-2022) in the region, including regional velocity models, ground motion simulations, street survey-based building classifications, and vulnerability classes; and second, to compare the after-event modeling of damage distributions in comparison with the observed damages as well as resilience evaluations of the region from multiple perspectives. A third objective is to assess the seismic resilience framework used in the project, as there are multiple seismically active areas in Turkiye and the world where similar large events are anticipated. This study constitutes a significant case study in the Turkoglu region, which involves critical evaluations of seismic resilience from before and after event data
Turkish truffles I: 18 new records for Turkey
No full textWOS: 000352486200014We report the first records of 18 truffle species in Turkey. Three belong to the Ascomycota: Elaphomyces leucocarpus, E. muricatus, and Genea sphaerica; and 15 to the Basidiomycota: Alpova corsicus, Gautieria otthii, G. retirugosa, G. trabutii, Hymenogaster citrinus, H. hessei, H. luteus, H. lycoperdineus, Hysterangium clathroides, H. epiroticum, H. fragile, H. nephriticum, Leucogaster tozzianus, Octaviania asterosperma, and Protoglossum aromaticum. We also report new localities within Turkey for Picoa juniperi, P. lefebvrei, Geopora cooperi, Terfezia arenaria, T. claveryi, Tuber aestivum, and T. nitidum in the Ascomycota; and Leucogaster nudus, Hymenogaster thwaitesii, H. vulgaris, and Melanogaster broomeanus in the Basidiomycota.Scientific and Technological Research Council of Turkey projectTurkiye Bilimsel ve Teknolojik Arastirma Kurumu (TUBITAK) [T-BAG-111T530, BIDEB-2221]The first author received funding from the Scientific and Technological Research Council of Turkey project number T-BAG-111T530 and BIDEB-2221. We appreciate the help from Abdulkadir Simsek, Ahmet Oksuzoglu, Cemhan Bucak, Coskun Bilgi, Duran Celik, Ekrem Toprak, Esra Er, Fatih Kaya, Gulsum Turkoglu, Idris Sener, Kadir Bazlica, Kadir Ceryan, Mehmet Halil Solak, Mehmet Metin, Mehmet Yucel, Murat Kilic, Mustafa Demir, Mustafa Turuncoglu, Niyazi Ulucoban, Okan Kursun, Osman Coban, Serkan Sevinc, Seyit Ahmet Akay, Tolga Keser, Ugur Demirbilek, Veysel Kodalak, and Yavuzalp Turkoglu in the collection of some of the specimens
Plant Recognition System based on Deep Features and Color-LBP method
No full text27th Signal Processing and Communications Applications Conference (SIU) -- APR 24-26, 2019 -- Sivas Cumhuriyet Univ, Sivas, TURKEYIn recent years, deep learning, which is widely used in machine learning and computer vision, offers many new solutions, especially for agricultural problems. In this study, an approach based on the combination of Convolutional Neural Networks (CNN) and Color-Local Binary Pattern (C-LBP) method is recommended for the determination of plant species. Deep features have been obtained from the fc6 layer of the AlexNet model, a pre-trained ESA architecture. Then, LBP method is applied to each channel of color images (R, G, B). Finally, the deep features and LBP features from each color channel were combined and classified by Support Vector Machine (SVM). To test the accuracy of the proposed approach, ICL and Folio leaf data sets commonly used in the literature have been used. According to this results, accuracy rates of 98.50% and 99.48% were calculated for ICL and Folio data sets, respectively. The experimental results indicate that the proposed model achieves better accuracy compared to previous studies.IEEE Turkey Sect,Turkcell,Turkhavacilik Uzaysanayii,Turitak Bilgem,Gebze Teknik Univ,SAP, Detaysoft,NETAS,Havelsa
Plant disease and pest detection using deep learning-based features
No full textThe timely and accurate diagnosis of plant diseases plays an important role in preventing the loss of productivity and loss or reduced quantity of agricultural products. In order to solve such problems, methods based on machine learning can be used. In recent years, deep learning, which is especially widely used in image processing, offers many new applications related to precision agriculture. In this study, we evaluated the performance results using different approaches of nine powerful architectures of deep neural networks for plant disease detection. Transfer learning and deep feature extraction methods are used, which adapt these deep learning models to the problem at hand. The utilized pretrained deep models are considered in the presented work for feature extraction and for further fine-tuning. The obtained features using deep feature extraction are then classified by support vector machine (SVM), extreme learning machine (ELM), and K-nearest neighbor (KNN) methods. The experiments are carried out using data consisting of real disease and pest images from Turkey. The accuracy, sensitivity, specificity, and F1-score are all calculated for performance evaluation. The evaluation results show that deep feature extraction and SVM/ELM classification produced better results than transfer learning. In addition, the fc6 layers of the AlexNet, VGG16, and VGG19 models produced better accuracy scores when compared to the other layers
Leaf-based plant species recognition based on improved local binary pattern and extreme learning machine
No full textOver the past 15 years, many feature extraction methods have been used and developed for the recognition of plant species. These methods have mostly been performed using separation operations from the background based on a pre-processing stage. However, the Local Binary Patterns (LBP) method, which provides high performance in object recognition, is used to obtain textural features from images without need for a pre-processing stage. In this paper, we propose different approaches based on LBP for the recognition of plant leaves using extracted texture features from plant leaves. While the original LBP converts color images to gray tones, the proposed methods are applied by using the R and G color channel of images. In addition, we evaluate the robustness of the proposed methods against noise such as salt & pepper and Gaussian. Later, the obtained features from the proposed methods were classified and tested using the Extreme Learning Machine (ELM) method. The experimental works were performed using various plant leaf datasets such as Flavia, Swedish, ICL, and Foliage. According to the obtained performance results, the calculated accuracy values for Flavia, Swedish, ICL and Foliage datasets were 98.94%, 99.46%, 83.71%, and 92.92%, respectively. The results demonstrate that the proposed method was more successful when compared to the original LBP, improved LBP methods, and other image descriptors for both noisy and noiseless images. (C) 2019 Elsevier B.V. All rights reserve
Apricot Disease Identification based on Attributes Obtained from Deep Learning Algorithms
No full textSurface Defect Detection Using Deep U-Net Network Architectures
No full text29th IEEE Conference on Signal Processing and Communications Applications (SIU) -- JUN 09-11, 2021 -- ELECTR NETWORKSurface defects detection in products used in industry such as steel, fabric and marble is very important in terms of increasing product quality and preventing financial losses. However, automatic surface defects detection is a very difficult problem due to the complexity and diversity of surface defects. In this study, U-net based VGG16-Unet and Resnet34-Unet network models are proposed for Surface defects detection. The proposed model used spatial features in the first layers together with deep semantic features. In the proposed network models, the trained weights of the VGG16 and Resnet34 network architectures were used for the input parameters of the Unet architecture. In experimental studies, the highest F1-score value for MT and AITEX data sets was obtained as 91.07% and 94.67%, respectively, with the proposed Resnet34-Unet model. According to the results, it was observed that the defective areas showing similarity with the background were successfully separated by using the proposed model.IEEE,IEEE Turkey Sec
PlantDiseaseNet: convolutional neural network ensemble for plant disease and pest detection
No full textPlant diseases and pests cause significant losses in agriculture, with economic, ecological and social implications. Therefore, early detection of plant diseases and pests via automated methods are very important. Recent machine learning-based studies have become popular in the solution of agricultural problems such as plant diseases. In this work, we present two classification models based on deep feature extraction from pre-trained convolutional neural networks. In the proposed models, we fine-tune and combine six state-of-the-art convolutional neural networks and evaluate them on the given problem both individually and as an ensemble. Finally, the performances of different combinations based on the proposed models are calculated using a support vector machine (SVM) classifier. In order to verify the validity of the proposed model, we collected Turkey-PlantDataset, consisting of unconstrained photographs of 15 kinds of disease and pest images observed in Turkey. According to the obtained performance results, the accuracy scores are calculated as 97.56% using the majority voting ensemble model and 96.83% using the early fusion ensemble model. The results demonstrate that the proposed models reach or exceed state-of-the-art results for this problem
