37 research outputs found
Experimental study on cylindrical grinding with helical grooves wheels
No full text#nofulltext# --- Kına, Muammer (Arel Author)
MUAMMER ENISE AZMI: AS A ROMANTIC NOVELIST IN THE SECOND CONSTİTUTİONAL ERA
No full textMeşrutiyet Dönemi, Osmanlı basını açısından oldukça hareketli ve velut bir dönemi içerir. Basın hayatındaki hareketliliğe bağlı olarak okuryazar kadınlar da makalelerini, mektuplarını ve çeşitli türlerde kaleme aldıkları edebî metinlerini dergilere gönderirler. Ancak dönem içerisinde edebî eserler kaleme alan bazı kadın yazarların zaman içerisinde yazma faaliyetlerini bıraktıkları da görülmektedir. Meşrutiyet yıllarında popüler aşk romanı türünde bir eser yazan, edebiyat dergilerinde hikâye ve deneme türünde eserler yayımlayan Muammer Enise Azmi de Cumhuriyet’ten sonra yazma faaliyetlerinden uzaklaşarak eğitim ve politika ile ilgilenmiştir. Bu makalede, hayatı ve eserleri hakkında daha önce herhangi bir çalışma yapılmamış olan Muammer Enise Azmi’nin biyografisi hakkında bilgi verilerek yazarın Tokat milletvekili Muammer Develi olduğu ortaya konulacaktır. Ayrıca yazarın Meşrutiyet Dönemi dergilerinde yayımladığı eserler de incelenerek popüler aşk romanı türüne giren Aşk ve Günah romanı tematik ve kurgusal açıdan incelenecektir. Aşk ve Günah’ın popüler roman özelliklerini taşıyan yönleri belirlenerek karşılaştırmalı bir biçimde yorumlanacaktır.The Second Constitutional Era involves a very dynamic and
productive era for press of the Ottoman Empire. Literate
women sent their articles, letters and literary texts in several
genres to journals of that period, depending on this dynamism
in press activities. However in this era, it is seen that some
women writers abandoned their writing activities over time.
Muammer Enise Azmi wrote a popular romance novel in the
Second Constitutional Era. He also published some works in
the genre of stories and essays in literary journals. After
proclamation of the republic, he became interested in
education and politics by leaving his writing activities.
There are no studies about Muammer Enise Azmi’s life and
works. This article will provide information about his
biography and it will be claimed that this author is Muammer
Develi, a member of parliament from Tokat. In addition, his
works in journals of this era will also be discussed. As a
popular romance novel, Aşk ve Günah will be analyzed
thematically and fictionally. In this way, the characteristic of
popular novel of Aşk ve Günah will be determined and
interpreted comparatively
MUAMMER ENISE AZMI: AS A ROMANTIC NOVELIST IN THE SECOND CONSTİTUTİONAL ERA
No full textMeşrutiyet Dönemi, Osmanlı basını açısından oldukça hareketli ve velut bir dönemi içerir. Basın hayatındaki hareketliliğe bağlı olarak okuryazar kadınlar da makalelerini, mektuplarını ve çeşitli türlerde kaleme aldıkları edebî metinlerini dergilere gönderirler. Ancak dönem içerisinde edebî eserler kaleme alan bazı kadın yazarların zaman içerisinde yazma faaliyetlerini bıraktıkları da görülmektedir. Meşrutiyet yıllarında popüler aşk romanı türünde bir eser yazan, edebiyat dergilerinde hikâye ve deneme türünde eserler yayımlayan Muammer Enise Azmi de Cumhuriyet’ten sonra yazma faaliyetlerinden uzaklaşarak eğitim ve politika ile ilgilenmiştir. Bu makalede, hayatı ve eserleri hakkında daha önce herhangi bir çalışma yapılmamış olan Muammer Enise Azmi’nin biyografisi hakkında bilgi verilerek yazarın Tokat milletvekili Muammer Develi olduğu ortaya konulacaktır. Ayrıca yazarın Meşrutiyet Dönemi dergilerinde yayımladığı eserler de incelenerek popüler aşk romanı türüne giren Aşk ve Günah romanı tematik ve kurgusal açıdan incelenecektir. Aşk ve Günah’ın popüler roman özelliklerini taşıyan yönleri belirlenerek karşılaştırmalı bir biçimde yorumlanacaktır.The Second Constitutional Era involves a very dynamic and
productive era for press of the Ottoman Empire. Literate
women sent their articles, letters and literary texts in several
genres to journals of that period, depending on this dynamism
in press activities. However in this era, it is seen that some
women writers abandoned their writing activities over time.
Muammer Enise Azmi wrote a popular romance novel in the
Second Constitutional Era. He also published some works in
the genre of stories and essays in literary journals. After
proclamation of the republic, he became interested in
education and politics by leaving his writing activities.
There are no studies about Muammer Enise Azmi’s life and
works. This article will provide information about his
biography and it will be claimed that this author is Muammer
Develi, a member of parliament from Tokat. In addition, his
works in journals of this era will also be discussed. As a
popular romance novel, Aşk ve Günah will be analyzed
thematically and fictionally. In this way, the characteristic of
popular novel of Aşk ve Günah will be determined and
interpreted comparatively
Potential geographic distribution and habitat utilization of Lacerta viridis (Squamata: Lacertidae) in northern Türkiye
No full textThe genus Lacerta, particularly the green lizard (Lacerta viridis), is a significant lizard group in the Western Palearctic. This study aims to predict its geographic range in Northern Türkiye and identify key habitat types based on the CORINE Land Cover classification. Occurrence records, gathered from published sources and on-site investigations, were analyzed using a 30 arc-second resolution layer and bioclimatic parameters from WorldClim v.2.1. The distribution was modeled in kuenm, and habitat use was assessed with generalized linear models. Results show that the green lizard’s occurrence is strongly influenced by precipitation dynamics and it prefers areas with little to no shade. These findings highlight the importance of precipitation in determining the species’ distribution and suggest further research on local dynamics for effective conservation. © 2024 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group
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
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
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