Sürdürülebilir Kalkınma Amaçları
Yazarlar
- Utku, Anıl(3)
- Acharya, U. Rajendra(1)
- Ali Akcayol, M.(1)
- Alpsülün, Mustafa(1)
- Amoozegar, Azadeh(1)
- Ay, Berhan Turku(1)
- Ayaz, Zafer(1)
- Baloglu, Ulas Baran(1)
- Balıkçı, Hasan Celal(1)
- Barut, Abdulkadir(1)
- Can, Umit(1)
- Ciftci, Derya(1)
- Coskun, Musab(1)
- Demir, Yakup(1)
- Dogan, Gurkan(1)
- Doğan, Gürkan(1)
- Ergen, Burhan(1)
- Karakose, Mehmet(1)
- Pilatin, Abdulmuttalip(1)
- Ucar, Aysegul(1)
- Yildirim, Ozal(1)
- Yilmazer, Merve(1)
- Yıldırım, Özal(1)
Yayınlar
- MakaleYalnız künye2025Scopus SJRQ2Enhanced gastrointestinal disease classification using a convvit hybrid model on endoscopic images
Utku, Anıl
Endoscopy is a procedure that allows examination of the gastrointestinal system, including the stomach, esophagus, large intestine, and duodenum, with the help of an endoscope. Processing of endoscopic images is important for early detection and treatment of gastrointestinal diseases. In this study, hybrid ConvViT was developed using CNN and ViT to increase the classification accuracy of pathologies in gastrointestinal endoscopic images. CNNs are well-suited for capturing local spatial features through hierarchical convolutions, making them highly effective in detecting fine-grained textures and edge patterns. These capabilities complement the ViT’s global attention mechanism, which excels at modeling long-range dependencies in images. The motivation of this study is to increase the classification accuracy and reliability with the ConvViT model, which was developed by combining the practical features of CNN and ViT models, which are individually successful in different aspects of image processing. The ConvViT model was compared with VGG-16, ResNet-50, Inception-V3 and ViT. Comparable models were tested using a gastrointestinal endoscopic image dataset containing ulcers, polyps, inflammation, bleeding, and regular anatomical features. Experiments showed that ConvViT had better prediction performance than compared models, with 95.87% classification accuracy.
AtıfScopus5WoS2OpenAlex2
İncele - MakaleAçık Erişim2025Scopus SJRQ2Advancing Air Quality Monitoring: Deep Learning-Based CNN-RNN Hybrid Model for PM2.5 Forecasting
Utku, Anıl; Can, Umit; Alpsülün, Mustafa; Balıkçı, Hasan Celal; Amoozegar, Azadeh; Pilatin, Abdulmuttalip; Barut, Abdulkadir
Particulate matter, particularly PM2.5, poses a significant threat to public health due to its ability to disperse widely and its detrimental impact on the respiratory and circulatory systems upon inhalation. Consequently, it is imperative to maintain regular monitoring and assessment of particulate matter levels to anticipate air pollution events and promptly mitigate their adverse effects. However, predicting air quality is inherently complex, given the multitude of variables that influence it. Deep learning models, renowned for their ability to capture nonlinear relationships, offer a promising approach to address this challenge, with hybrid architectures demonstrating enhanced performance. This study aims to develop and evaluate a hybrid model integrating Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) for forecasting PM2.5 levels in India, Milan, and Frankfurt. A comparative analysis with established deep learning and machine learning techniques substantiates the superior predictive capabilities of the proposed CNN-RNN model. The findings underscore its potential as an effective tool for air quality prediction, with implications for informed decision-making and proactive intervention strategies to safeguard public health.
AtıfScopus10WoS6OpenAlex11İncele - BildiriYalnız künye2024Performance Analysis of Attention Mechanisms for Melanoma Cancer Detection
Dogan, Gurkan
The deadliest type of skin cancer worldwide is melanoma. If melanoma skin cancer is detected and treated at an early stage, the mortality rate can be significantly reduced. In particular, convolutional neural networks, one of the deep learning techniques that have achieved remarkable success in recent years in areas such as computer vision, natural language processing, signal processing, etc., can detect this type of cancer automatically, quickly and with high accuracy. Moreover, the accuracy performance of the neural network can be further increased by using attention mechanisms together with convolutional neural networks. However, attention mechanisms have not been used correctly and effectively in the neural network models proposed so far for the detection of melanoma skin cancer. This study examines the effect of attention mechanisms on the accuracy performance of a convolutional neural network and guides researchers to use these mechanisms correctly and effectively. To achieve this, a base model based on a convolutional neural network was built. Then, seven different attention mechanisms were integrated separately into this basic model and the results of the experimental tests of each were presented.
AtıfScopus1OpenAlex1İncele - BildiriAçık Erişim2024A New Hybrid CNN Model for Abnormal Behaviour Detection in Smart Campus
Yilmazer, Merve; Karakose, Mehmet; Ay, Berhan Turku
Security and monitoring systems are provided through video cameras in public places such as universities, hospitals, cinemas and theater halls. In smart campuses created by the integration of various sensors and digital technologies, there are many video cameras indoors and outdoors in the campus areas to ensure the safety of students, academics, administrators and visitors. Examining the images recorded with these video cameras in terms of security and detecting abnormal behaviours requires a long time and effort. In this study, a new hybrid convolutional neural network (CNN) model is proposed to detect abnormal behaviors occurring in smart campus areas. In the proposed method, firstly, the features of the UTI public dataset, which includes various student behaviors, are extracted with the hybrid use of InceptionV3 and MobileNetV2 deep transfer learning models. The extracted features of the images were combined and classified by converting them into a single feature vector. 6 different behavior classes, 3 of which are normal and 3 of which are abnormal, have been classified and detected with 92.7 % accuracy. Experimental results show that the proposed method for abnormal behavior detection produces more efficient results than other methods in terms of feature extraction, accuracy and computational costs.
AtıfScopus2OpenAlex1İncele - MakaleAçık Erişim2023Scopus SJRQ4Deep Learning Based Classification for Hoverflies (Diptera: Syrphidae)
Utku, Anıl; Ayaz, Zafer; Ciftci, Derya; Ali Akcayol, M.
Syrphidae is essential in pollinating many flowering plants and cereals and is a family with high species diversity in the order Diptera. These family species are also used in biodiversity and conservation studies. This study proposes an image-based CNN model for easy, fast, and accurate identification of Syrphidae species. Seven hundred twenty-seven hoverfly images were used to train and test the developed deep-learning model. Four hundred seventy-nine of these images were allocated to the training set and two hundred forty-eight to the test dataset. There are a total of 15 species in the dataset. With the CNN-based deep learning model developed in this study, accuracy 0.96, precision 0.97, recall 0.96, and f-measure 0.96 values were obtained for the dataset. The experimental results showed that the proposed CNN-based deep learning model had a high success rate in distinguishing the Syrphidae species.
AtıfScopus3WoS3OpenAlex3TR Dizin1
İncele - 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 - MakaleYalnız künye2020Scopus SJRQ1A deep convolutional neural network model for automated identification of abnormal EEG signals
Yıldırım, Özal; Baloglu, Ulas Baran; Acharya, U. Rajendra
Electroencephalogram (EEG) is widely used to monitor the brain activities. The manual examination of these signals by experts is strenuous and time consuming. Hence, machine learning techniques can be used to improve the accuracy of detection. Nowadays, deep learning methodologies have been used in medical field to diagnose the health conditions precisely and aid the clinicians. In this study, a new deep one-dimensional convolutional neural network (1D CNN) model is proposed for the automatic recognition of normal and abnormal EEG signals. The proposed model is a complete end-to-end structure which classifies the EEG signals without requiring any feature extraction. In this study, we have used the EEG signals from temporal to occipital (T5–O1) single channel obtained from Temple University Hospital EEG Abnormal Corpus (v2.0.0) EEG dataset to develop the 1D CNN model. Our developed model has yielded the classification error rate of 20.66% in classifying the normal and abnormal EEG signals.
AtıfScopus164OpenAlex211İncele - BildiriAçık Erişim2017Face recognition based on convolutional neural network
Coskun, Musab; Ucar, Aysegul; Yildirim, Ozal; Demir, Yakup
Face recognition is of great importance to real world applications such as video surveillance, human machine interaction and security systems. As compared to traditional machine learning approaches, deep learning based methods have shown better performances in terms of accuracy and speed of processing in image recognition. This paper proposes a modified Convolutional Neural Network (CNN) architecture by adding two normalization operations to two of the layers. The normalization operation which is batch normalization provided acceleration of the network. CNN architecture was employed to extract distinctive face features and Softmax classifier was used to classify faces in the fully connected layer of CNN. In the experiment part, Georgia Tech Database showed that the proposed approach has improved the face recognition performance with better recognition results.
AtıfScopus250OpenAlex295İncele