Sürdürülebilir Kalkınma Amaçları
Yayınlar
- MakaleAçık Erişim2025Scopus SJRQ2Modeling and analysis of WEDM with a new deep neural network
Mahmat, Aybars; Dogan, Gurkan; Ergen, Burhan; Tosun, Nihat
In some cases, the models targeted in machining processes are difficult to apply to industrial production. Artificial intelligence techniques applied to industrial production methods in recent times eliminate these problems in machining. The use of deep learning methods in solving these problems has recently increased. In this study, a new deep neural network (DNN) based on artificial neural networks is proposed to model the machining of AISI4140 steel by wire electrical discharge machining (WEDM). The proposed DNN model predicts surface roughness (SR) and wire wear ratio (WWR), which are the most important inferences in cutting with WEDM. Obtaining inferences such as SR and WWR in a real experimental environment means a lot of time and cost. With the proposed model, there is a significant reduction in time and cost factors. The dataset used to train and test the proposed model was obtained from the real experimental environment. For training and testing the model, the data set is divided into two parts: 20% test data and 80% training data. In the experimental tests carried out with the proposed model, error rates such as Mean Absolute Error (MAE), R2, and Mean Squared Error (MSE) accuracy rate between SR and WWR values estimated according to the SR and WWR values obtained from the real environment were examined. MAE, MSE, and R2 for SR reached 0.0495, 0.0005, and 98.48%, respectively, while MAE, MSE, and R2 for WWR reached 0.0156, 0.0036, and 98.39%, respectively. These results of the proposed model provided a lower error rate and higher accuracy than the state-of-the-art models in the literature.
AtıfScopus5WoS5OpenAlex7
İncele - MakaleAçık Erişim2025Scopus SJRQ2HASK-Net: new hybrid attention selective kernel network for automatic colon cancer detection from colonoscopy images
Imak, Andaç; Doğan, Gürkan; Sengur, Abdulkadir; Ergen, Burhan
Colorectal cancer is a frequently analyzed cancer type seen today. It is the third leading of cancer in terms of incidence. It is among the second leading cases resulting in death. However, when detecting pre-cancerous polyps using traditional methods, the rate of being overlooked is high. Therefore, the rate of missed polyps can be greatly reduced with deep learning-based computer-aided diagnosis (CADx) methods. In this study, a novel convolutional neural network based on CADx, called HASK-Net, is proposed for automatic polyp detection. While HASK-Net uses ResNet50 as a feature extractor in the encoder network, the novely designed hybrid attention selective kernel convolution uses it in the decoder network to increase the representation power in feature maps and learn more complex features. In experimental results on the publicly available Hiper-Kvasir and EndoTech 2020 benchmark datasets, HASK-Net showed extremely promising performance with a dice similarity score of 97.02% and 84.83% and an mIoU score of 95.38% and 84.32%, respectively.
- MakaleAçık Erişim2025Scopus SJRQ1A new lightweight convolutional neural network model for detecting drivable road regions
Doğan, Gürkan; Uyanık, Hakan; Ergen, Burhan
Nowadays, due to the rapid increase in the number of autonomous vehicles on the market, the safe navigation of these vehicles in drivable road areas has become extremely important. One of the most crucial factors in ensuring safe navigation is addressing the detection of drivable road areas as a task of semantic segmentation. Considering that autonomous vehicles are modular, the algorithm to perform this task must have the optimum trade-off in terms of lightweight, computational complexity, and segmentation accuracy. In this study, RoNet, a new model based on convolutional neural networks that provides an optimum trade-off for the detection of drivable road regions, was designed and proposed. The standard convolution types for the encoder and decoder bottleneck module of the RoNet model, as well as the spatial edge attention mechanism, have been optimized by developing asymmetric convolution types using asymmetric atrous convolution, asymmetric convolution types using Prewitt and Sobel kernels. Spatial edge attention mechanism is designed to reduce the loss of detailed information in small-resolution feature maps. In experimental tests performed with CamVid and FUVid datasets, RoNet achieved a better trade-off in terms of segmentation accuracy, number of parameters, and computational complexity compared to other state-of-the-art methods.
AtıfScopus3WoS3OpenAlex6İncele - MakaleAçık Erişim2024Scopus SJRQ1A new hybrid mobile CNN approach for crosswalk recognition in autonomous vehicles
Doğan, Gürkan; Ergen, Burhan
While automobile transportation is increasing worldwide, it also negatively affects the safety of road users. Along with the neglect of traffic rules, pedestrians account for 22% of all highway traffic deaths. Millions of pedestrians suffer non-fatal injuries from these accidents. Most of these injuries and deaths occur at crosswalks, where the highway and pedestrians intersect. In this study, deep learning-based a new hybrid mobile CNN approaches are proposed to reduce injuries and deaths by automatically recognizing of crosswalks in autonomous vehicles. The first of these proposed approaches is the HMCNet approach, which is a hybrid model in which the MobileNetv3 and MNasNet CNN models are used together. This model achieves approximately 2% more accuracy than the peak performance of the lean used MobileNetv3 and MNasNet models. Another proposed approach is the FHMCNet approach, which increases the success of the HMCNet approach. In the FHMCNet approach, LSVC feature selection method and SVM classification method are used in addition to HMCNet. This approach increased the classification success of HMCNet by more than approximately 2%. Finally, the proposed FHMCNet offered approximately 3% more classification accuracy than state-of-the-art methods in the literature.
AtıfScopus10WoS7OpenAlex8İncele - MakaleAçık Erişim2024Scopus SJRQ1A new hybrid approach for grapevine leaves recognition based on ESRGAN data augmentation and GASVM feature selection
Doğan, Gürkan; Imak, Andaç; Ergen, Burhan; Sengur, Abdulkadir
Grapevine leaf is a commodity that is collected only once a year and has a high return on investment due to its export. However, only certain types of grapevine leaves are consumed. Therefore, it is extremely important to distinguish the types of grapevine leaves. In particular, performing this process automatically on industrial machines will reduce human errors, workload, and thus cost. In this study, a new hybrid approach based on a convolutional neural network is proposed that can automatically distinguish the types of grapevine leaves. In the proposed approach, firstly, the overfitting of network models is prevented by applying data augmentation techniques. Second, new synthetic images were created with the ESRGAN technique to obtain detailed texture information. Third, the top blocks of the MobileNetV2 and VGG19 CNN models were replaced with the newly designed top block, effectively extracting features with the data. Fourthly, the GASVM algorithm was adapted and used to create a subset of the features to eliminate the ineffective and unimportant ones from the obtained features. Finally, SVM classification was performed with the feature subset consisting of 314 features, and approximately 2% higher accuracy and MCC score were obtained compared to the approaches in the literature.
AtıfScopus8WoS6OpenAlex9İncele - MakaleAçık Erişim2024Scopus SJRQ1A new CNN-based semantic object segmentation for autonomous vehicles in urban traffic scenes
Doğan, Gürkan; Ergen, Burhan
Semantic segmentation is the most important stage of making sense of the visual traffic scene for autonomous driving. In recent years, convolutional neural networks (CNN)-based methods for semantic segmentation of urban traffic scenes are among the trending studies. However, the methods developed in the studies carried out so far are insufficient in terms of accuracy performance criteria. In this study, a new CNN-based semantic segmentation method with higher accuracy performance is proposed. A new module, the Attentional Atrous Feature Pooling (AAFP) Module, has been developed for the proposed method. This module is located between the encoder and decoder in the general network structure and aims to obtain multi-scale information and add attentional features to large and small objects. As a result of experimental tests with the CamVid data set, an accuracy value of approximately 2% higher was achieved with a mIoU value of 70.59% compared to other state-of-art methods. Therefore, the proposed method can semantically segment objects in the urban traffic scene better than other methods.
AtıfScopus19WoS10İncele - MakaleAçık Erişim2024Scopus SJRQ2A novel convolutional neural network model with hybrid attentional atrous convolution module for detecting the areas affected by the flood
Şener, Abdullah; Doğan, Gürkan; Ergen, Burhan
Natural disasters are sudden and unexpected events that occur as a result of natural processes and often have a significant impact on people, animals, and plants, often resulting in material and emotional losses. Two of the most devastating disasters are floods and tsunamis. After these disasters, search and rescue operations to identify flooded areas are very important to save the lives of affected people and to ensure the health and safety of rescue workers. In this study, a novel semantic segmentation model called Flood Area Segmentation Network (FASegNet) was proposed to guide search and rescue teams in disaster areas to speed up search and rescue operations after natural disasters such as floods, high tides, and tsunamis. The obtained results were compared with common image segmentation models used in various fields. It was found that the developed model achieved higher accuracy rates with fewer parameters. When tested with the "Flood Area" and "Water Body" datasets without pretraining and data augmentation, FASegNet achieved mIoU accuracy of 84.3% and 84.5% respectively.
AtıfScopus23WoS21OpenAlex23İncele - MakaleAçık Erişim2023A New Method Based on Extracting, Combining and Selecting Deep Features from Natural and Synthetic Data for Classification of Grapevine Leaf Species
Sengur, Abdulkadir; Ergen, Burhan; Doğan, Gürkan; Imak, Andac
In recent years, the production and consumption of grapevine leaves containing rich minerals, dietary fiber and vitamins has been intensively realized in Turkey. However, the demand for stuffed food made from grapevine leaves in the convenience food sector increases the export opportunities to different countries In commercial agricultural activities such as this, it is important to determine quality standards for sustainable marketing. Researchers have made positive progress with deep learning in smart farming applications. In this study, we propose a new method for recognizing the type of grapevine leaves to be used for consumption. In the proposed method, a dataset containing 500 images of 5 different grapevine leaf species, namely Ak, Ala Idris, Büzgülü, Dimnit and Nazli, was used. From these images, 3500 images were obtained with data augmentation techniques. In addition, a dataset consisting of more detailed textures was obtained by applying the ESRGAN model to the obtained images. VGG 19 deep learning model was used to extract features from these images. The features obtained from the two separate data sets were combined. In this way, a hybrid feature extraction model was created. The best 175 feature subsets were selected using the PCA algorithm. Finally, 96.14% accuracy was calculated using a Support Vector Machine (SVM) for classification of the obtained features.
AtıfOpenAlex10TR Dizin8
İncele - MakaleAçık Erişim2023A new approach based on convolutional neural network and feature selection for recognizing vehicle types
Doğan, Gürkan; Ergen, Burhan
The number of vehicles used in traffic life has reached enormous dimensions today. The increase in the number of vehicles day by day causes some traffic problems along with it; such as traffic congestion, accidents, pollution, and safety. To overcome all these problems, convolutional neural networks (CNN) methods are one of the trend methods used in recent years due to their success. In this study, a new approach is proposed to use this power of CNN in low-power devices. First of all, MobileNetv1, MobileNetv2, and NASNetMobile models were optimized to increase accuracy performance. Then, an approach is proposed in which these optimized mobile CNN approaches are used only as feature extractors, and methods such as combining, selecting, and classifying the obtained features are used together. As a result of the classification made with this approach, the classification accuracy has increased by approximately 5%.
AtıfScopus10OpenAlex12İncele - DüzeltmeAçık Erişim2023Correction to: A new approach based on convolutional neural network and feature selection for recognizing vehicle types(Iran Journal of Computer Science, 10.1007/s42044-022-00125-6)
Doğan, Gürkan; Ergen, Burhan
The sentence beginning 'Bu nedenle bu …' in this article should have read 'Therefore, in this study, a combination of OMobileNetv1, OMobileNetv2, ONASNetMobile CNN methods for feature extraction, LSVC method for feature selection and SVM method for classification is proposed.' The original article has been corrected.
- MakaleAçık Erişim2022Gömülü Sistemler İçin Performansı Arttırılmış HBONet CNN Yaklaşımı
Ergen, Burhan; Doğan, Gürkan
Son yıllarda, evrişimli sinir ağlarının (CNN) kullanım alanları dikkate değer bir şekilde artmıştır. İş istasyonlarından gömülü cihazlara varıncaya kadar birçok platformda yaygın olarak kullanılmaktadır. Bununla birlikte, her CNN modeli farklı miktarda hafıza, işlemci, depolama birimi kullanmaktadır ve nesne tanımada farklı doğruluk oranlarına sahiptir. Gömülü sistemlerde kullanılacak CNN’lerin daha az maliyetli olması, daha az kaynak tüketmesi ve daha fazla doğruluk oranını başarması gibi bazı zorlukları vardır. Bu zorlukların en iyi üstesinden gelen CNN modellerinden biri de HBONet modelidir. Ancak, bu model gömülü sistemlerde yeterince iyi performans sağlamamaktadır. Bu çalışmada, gömülü sistemler için kullanılan HBONet modelinin kaynak tüketimi ve doğruluk gibi performans metriklerinin daha da iyileştirilmesi amaçlanmıştır. Bu amaçla, HBONet modelini temel alan bir model olan A-HBONet modeli önerilmiştir. CIFAR-10 veri seti kullanılarak gerçekleştirilen deneyler sonucunda, önerilen modelin doğruluğu HBONet modeline göre %3 arttırılırken hafıza ve depolama birimi kullanımı da yaklaşık olarak %80 oranında azaltılmıştır. Bu sonuçlar, önerilen modelin gömülü cihazlarda daha etkin ve verimli çalıştığı göstermektedir.
- MakaleAçık Erişim2022Karayollarındaki Asfalt Çatlaklarının Tespiti İçin Yeni Bir Konvolüsyonel Sinir Ağı Tabanlı Yöntem
Ergen, Burhan; Doğan, Gürkan
Karayolu yüzeyindeki asfalt çatlakları en yaygın kaplama hasarlarından biridir. Bu çatlaklar zamanında tespit edilip bakıma alınmazsa büyüyerek trafik güvenliğini ve yoğunluğunu arttıracak boyutlara ulaşmaktadır. Bu nedenle, asfalt çatlaklarının tespitinin hızlı bir şekilde gerçekleştirilmesi çok önemlidir. Geleneksel manuel çatlak tespiti, son derece zaman alıcı, çok maliyetli ve çok çaba gerektirir. Bu nedenle, araştırmacılar odaklarını otomatik asfalt çatlaklarının tespitine yoğunlaştırdılar. Ancak, son yıllarda otomatik çatlak tespiti kapsamlı olarak araştırılmasına rağmen çatlakların farklı yoğunluğa sahip olması ve kaplama ortamının karmaşıklığı nedeniyle hala zor bir görevdir. Bu çalışmada, bu zorluğun üstesinden gelmek için konvolüsyon sinir ağı temelli bir yöntem önerildi. Önerilen yöntem, sınıflandırma ve segmentasyondaki başarısı ve hafifliği bilinen MobileNetv2 'nin temelde kullandığı konvolüsyon ve ters artıklı blok yapılarını baz alarak geliştirildi. Yapılan deneysel testler sonucunda, önerilen yöntemin performansı literatürdeki diğer yöntemlere göre daha yüksek olduğu görülmektedir. Bu da, otomatik asfalt çatlak tespitinin daha başarılı olması anlamına gelmektedir.
AtıfOpenAlex8TR Dizin7İncele - MakaleAçık Erişim2022Scopus SJRQ1A new mobile convolutional neural network-based approach for pixel-wise road surface crack detection
Doğan, Gürkan; Ergen, Burhan
In recent years, the worldwide road network has grown rapidly and reached an enormous size. Parallel to this size of the road network, the imperfections on the road surfaces are just as large. This has led to a significant increase in road maintenance costs for traffic management departments. Researchers have proposed methods for automatic flaw detection using deep neural networks to reduce road maintenance costs. However, these methods are insufficient in terms of performance and lightness. In this study, a convolutional neural network-based approach is proposed, which provides more successful road surface crack detection and requires less lightweight compared to other studies. Since the performance of the proposed approach is higher and more lightweight, it can be used more effectively and efficiently in mobile systems.
AtıfScopus60WoS55OpenAlex63İncele - MakaleAçık Erişim2021İMobileNet CNN Yaklaşımları ve Özellik Seçme Yöntemleri Kullanarak Araç Türlerini Sınıflandırma
Ergen, Burhan; Doğan, Gürkan
Günümüzde, trafik hayatında seyreden araç yoğunluğu ciddi boyutlara ulaşmıştır. Bu nedenle, mevcut ulaşım ağlarının kullanım kapasitesi maksimum seviyelere çıkmakta ve trafik sıkışıklığına yol açmaktadır. Akıllı Ulaşım Sistemlerinin bir çözümü olan Görsel Trafik Gözetleme Sistemleri trafik sıkışıklığını azaltmak için kullanılan alternatif yöntemlerden biridir. Görsel Trafik Gözetleme Sisteminin temel görevlerinden biri; video veya görüntülerden algılanan araç türlerini doğru bir şekilde sınıflandırmaktır. Bu çalışma, Görsel Trafik Gözetleme Sisteminin araç türlerini sınıflandırma doğruluğunu arttıracak yeni yöntemler sunmayı amaçlamaktadır. Çoğu görüntü sınıflandırma doğruluğunu arttıran çalışmalarda geleneksel yöntemler kullanılırken bu çalışmada günümüzde trend olan mobil evrişimli sinir ağları (MCNN) iki farklı yaklaşımla ele alınmaktadır. İlk olarak, MobileNetv1 ve MobileNetv2 modelleri optimize edilerek İMobileNetv1 ve İMobileNetv2 yaklaşımları önerildi. İkinci olarak, bu önerilen MCNN yaklaşımları sadece özellik çıkarıcı olarak kullanıldığı ve elde edilen özelliklerin birleştirilmesi, seçilmesi ve sınıflandırılması gibi yöntemlerin birlikte kullanıldığı bir yaklaşım önerildi. Önerilen yaklaşımlarla yapılan sınıflandırma sonucunda, %85,05 oranında çok yüksek bir sınıflandırma başarısı elde edilmiştir.
AtıfOpenAlex3TR Dizin2İncele - MakaleAçık Erişim2018Scopus SJRQ3Recognition of road type and quality for advanced driver assistance systems with deep learning
Tumen, Vedat; Yildirim, Ozal; Ergen, Burhan
To develop effective advanced driving assistance systems, it is important to accurately recognize current driving environments and make critical decisions about driving processes. Preventing accidents through the interaction between the driving assistance systems and the environment and ensuring optimum driving dynamics are the main topics in this field. Vehicles need to recognize the road type and quality at a high accuracy to ensure the most suitable driving for the road type. It is also important to use both uncomplicated and cost-effective systems when performing this detection. In this study, a deep learning-based approach that can be used in vehicle driver assistance systems is proposed to automatically recognize road type and quality. Using this approach, it is possible to determine the road type and the quality of the road using only driving images as the input data. A new convolutional neural network model is designed for classification of the driving images. Driving images obtained from Google Street View are used to evaluate the recognition system for an actual driving environment. The proposed approach shows that the road types were determined with accuracy of 91.41 %, and the pothole road–smooth road distinction was successful at 91.07 %. It can be said that the proposed method is an effective structure that can be used for advanced driving support systems, V2I communications systems, and similar intelligent transportation systems.
AtıfScopus22WoS16OpenAlex25İncele - BildiriAçık Erişim2017Facial emotion recognition on a dataset using Convolutional Neural Network
Tümen, Vedat; Söylemez, Ömer Faruk; Ergen, Burhan
Nowadays, deep learning is a technique that takes place in many computer vision related applications and studies. While it is put in the practice mostly on content based image retrieval, there is still room for improvement by employing it in diverse computer vision applications. In this study, we aimed to build a Convolutional Neural Network (CNN) based Facial Expression Recognition System (FER), in order to automatically classify expressions presented in Facial Expression Recognition (FER2013) database. Our presented CNN achieved % 57.1 success rate on FER2013 database.
AtıfScopus68OpenAlex83İncele
