Sürdürülebilir Kalkınma Amaçları
Yayınlar
- MakaleYalnız künye2026Scopus SJRQ1Innovative solutions for aquaculture: detecting fish diseases with hybrid deep learning model and explainable artificial intelligence
Utku, Anıl; Barut, Abdulkadir; Alofaysan, Hind
Early identification of fish diseases is critical to maintain aquaculture and minimize its financial burden. In the diagnosis of diseases, time is critical and scientists’ experience is imperative, which makes the artificial intelligence-based models necessary. It is intended by this study to integrate deep learning with explainable artificial intelligence methods to establish an artificial intelligence-powered framework for early detection of disease in fish. In this study, we developed a hybrid artificial intelligence system designed to detect diseases in freshwater fish by analyzing skin images. The primary objectives of the system are to minimize economic losses, prevent further disease spread, and ultimately increase aquaculture productivity. For this purpose, we used a dataset consisting of images from seven different disease categories, with 250 images in each class. A new hybrid model, Residual Network-50–Vision Transformer, was developed by integrating the strengths of Residual Network-50 and Vision Transformer architectures to improve classification accuracy. The proposed model was subjected to rigorous comparison with established architectures, including Visual Geometry Group 16, Mobile Convolutional Neural Network Version 2, Efficient Neural Network–Baseline Model B0, standalone Vision Transformer, and the original Residual Network-50. The results demonstrated that Residual Network-50–Vision Transformer outperformed the model, achieving an impressive 99.14% accuracy rate. Furthermore, the study employed local interpretable model-independent explanations to explain which image features most significantly influence the model’s classification decisions, thereby enhancing the interpretability and confidence in the model’s output. The application of such advanced image processing models in combination with explainable AI techniques demonstrates significant potential to support sustainable aquaculture by reducing disease outbreaks and associated financial losses.
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İncele - MakaleYalnız künye2026Scopus SJRQ1Analysis of the impact of the social justice index on the COVID-19 pandemic using artificial intelligence in EU and OECD countries
Utku, Anıl; Can, Ümit; BARUT, Abdulkadir; Pilatin, Abdulmuttalip; Sharabati, Abdel-Aziz Ahmad
The COVID-19 disease has evolved into a pandemic that affects the world in multiple dimensions. While COVID-19 spread rapidly to several nations, distinct patterns of spread emerged in each nation. This spreading behavior is related to various characteristics of countries, including social, political, and economic factors. In this study, an analysis of the COVID-19 pandemic in EU and OECD countries was conducted, taking into account the social justice index for the first time. The impact and significance of the social justice index on the COVID-19 analysis results were attempted to be revealed. As a result, it is understood that the countries with high Social Justice Index scores and sub-dimension scores are more successful in combating the COVID-19 pandemic. In addition, a new LSTM-based model was developed to predict the number of cases and deaths in these countries. The designed LSTM approach was compared with some well-known deep learning and machine learning algorithms. As a result of the experimental studies, the LSTM model obtained the best prediction results. Finally, the similarities between these countries in terms of the characteristics of the COVID-19 pandemic, including death and case spread, were analyzed, and exciting results were disclosed.
- MakaleAçık Erişim2026Scopus SJRQ2Federated agricultural yield forecasting under an Edge–Fog–Cloud framework: a multi-country privacy-preserving approach
Utku, Anıl; Barut, Abdulkadir; Alofaysan, Hind; Henni, Mohamed Djafar; Amoozegar, Azadeh
Predicting agricultural yields is crucial for ensuring global food security and adaptation to climate change. However, the centralized collection of agricultural data across countries might pose governance, privacy, and data sovereignty issues. This research presents a privacy-preserving model for predicting agricultural yields using federated learning within an edge-fog-cloud integrated architecture. The experiments have been performed on a European cereal production dataset for the period 1990–2022, using a time-aware forecasting scheme. Centralized, edge-only, federated, and hybrid learning techniques have been systematically compared to examine the influence of distributed optimization on forecasting performance. It can be seen that central classical machine learning algorithms have yielded the best predictive performance. Federated deep learning models, meanwhile, showed relatively poor forecasting accuracy compared to their centralized counterparts under a non-IID distribution of multi-country agricultural data. Edge-only learning, meanwhile, has yielded comparatively low generalization capabilities due to the absence of collaborative learning.
- MakaleYalnız künye2025Artificial Intelligence-Driven Risk Assessment in Civil Engineering Projects: A Comparative Analysis of Machine Learning Models
Utku, Anıl
Successful completion of civil engineering projects involves complex interactions among various components such as managing costs, maintaining schedules, ensuring structural stability, and minimizing environmental impacts. Accurate risk prediction at the project level is important to support efficient resource planning and prevent potential delays or disruptions. Since traditional risk assessment methods based on expert judgments involve lengthy and subjective procedures, the development of data-driven and automated estimation systems is at the forefront. In this study, a comparative analysis of various machine learning methods such as RF, SVM, XGBoost, CatBoost, and LGBM for project risk assessments in civil engineering is presented. The dataset used in the study includes information on project type, cost change records and planning, and real-time history, as well as environmental data and safety documentation, and structural health measurements. Precision, accuracy, sensitivity, and F-score metrics were used to evaluate the models along with ROC-AUC measurements. Experimental studies showed that CatBoost and LGBM outperform the compared models with 95.5% accuracy.
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İncele - MakaleAçık Erişim2025Enhancing water quality prediction with artificial intelligence: A hybrid convlstm model for spatio-temporal analysis
Utku, Anıl; Kutlu, Banu; Utku, Esen Damla Balo
Water quality plays a vital role in public health, environmental sustainability and ecosystem balance. However, industrialization, urbanization, and agricultural activities cause water pollution to increase, threatening both human health and aquatic ecosystems. Traditional water quality monitoring methods are usually time-consuming, costly, and require manual intervention. Therefore, developing automatic, data-driven, and high-accuracy prediction models is crucial for sustainable water management. This study created a hybrid ConvLSTM model to increase prediction accuracy. The created model was comparatively analyzed with RF, SVR, XGBoost, MLP, CNN, and LSTM. The dataset used includes historical measurement values of chemical pollutants. Input data includes geographical coordinates of sample points, chemical parameter type, and compliance with regulatory standards. Experimental results show that ConvLSTM provides the lowest prediction errors by learning spatial and temporal dependencies and reaches the highest accuracy rate with 0.994 R2 compared to other models.
- MakaleYalnız künye2025Scopus SJRQ4Deep learning-based hybrid traffic flow prediction model for freight transport
Utku, Anıl; Kaya, Sema Kayapinar; Can, Umit
Urban freight transportation is crucial to sustaining domestic and international trade and fulfilling the regular requirements of community companies and customers. Therefore, this study developed an efficient convolutional neural network (CNN) and long short-term memory (LSTM) based hybrid deep learning model for predicting traffic flow in freight transportation. The number of freight trucks that would travel along 663 Street in Anderlecht, Belgium, and their average speed was predicted. The created model was compared with support vector machine (SVM), linear regression (LR), multilayer perceptron (MLP), random forest (RF), LSTM, and CNN using the success measures. The critical finding of this study is that combining the feature extraction ability of the 1D CNN model with the learning ability of the LSTM model from multivariate traffic data increases the traffic density prediction success compared to other traditional methods. Based on the results of the experiments, the CNN-LSTM model outperformed all the other models employed in this study. With this model, freight density can be successfully predicted, assisting in planning freight transport activities.
- MakaleAçık Erişim2025Scopus SJRQ2Hybrid ConvViT model for classification of dental radiography images; [Dental radyografi görüntülerinin sınıflandırılmasına yönelik hibrit ConvViT modeli]
Utku, Anıl
Dental radiography is used as an important diagnostic tool in the field of dentistry. Dental radiography provides detailed visualization of teeth, jaws and surrounding structures. Dental radiographs are manually interpreted by dentists for diagnosis and treatment purposes. Artificial intelligence methods are successfully applied to automate various image-related processes such as diagnosis and classification. In this study, a hybrid ConvViT model was developed to detect caries, fillings, impacted teeth, implants and normal teeth from dental radiography images. ConvViT combined the advantages of CNN and ViT architectures and provided more successful classification of dental radiography images. ConvViT was compared with popular pre-trained deep learning models such as ResNet-50, VGG-16, EfficientNetB0, and DenseNet201. Experimental results showed that ConvViT outperformed the compared models with 95% accuracy, 95% precision, 94% recall and 94% F-score. ConvViT makes a significant contribution to the development of artificial intelligence-based automatic diagnosis systems in the field of dentistry by effectively modeling both local and long-range features thanks to its hybrid structure.
AtıfScopus1İncele - 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.
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İncele - MakaleAçık Erişim2025Scopus SJRQ1Hybrid GA-ConvLSTM for data-driven prediction of climate variables: a case study of the most biodiverse cities in India
Utku, Anıl; Akyol, Sinem
Climate variables affect ecosystems’ structure, sustainability, and biodiversity. Monitoring and analysis of climate variables are essential for the management of ecosystems and the conservation of biodiversity. Temperature affects the distributions, habitats, and phonological events of living things in ecosystems. Humidity is essential for meeting plants’ water needs and the habitat’s quality and health. Dew point is essential for water quality, balance, plant health, and microclimate. Wind speed is essential for pollinating plants, microhabitats, and the health of living things. For such reasons, accurate prediction of climate variables using artificial intelligence methods can effectively predict the future of ecosystems and biodiversity and develop future strategic plans. This study aimed to predict the climate variables of temperature, humidity, dew point, and wind speed of Bhubaneshwar, Dehradun, Guwahati, Mysore, and Nagpur, among the cities with the highest biodiversity in India. It aimed to determine the long-term impacts of climate change on managing ecosystems and biodiversity protection. The hyperparameters of the ConvLSTM model, developed using CNN and LSTM, were optimized with a genetic algorithm, and the GA-ConvLSTM hybrid model was created. GA-ConvLSTM was tested with XGBoost, SVM, ANFIS, CNN, LSTM, and base ConvLSTM using approximately 15 years of hourly, up-to-date, and real-time climate variable data of cities. This work is one of the pioneering studies using GA to optimize deep learning hybrid models for multi-city climate prediction through extensive long-term high-resolution datasets. The experimental results reveal that GA-ConvLSTM outperforms conventional models, producing an R2 score exceeding 0.9 for each city across all climate variables. GA-ConvLSTM produced temperature predictions with 0.833 RMSE and 0.995 R2 for Bhubaneswar, humidity predictions with 3.461 RMSE and 0.990 R2, dew point predictions with 0.632 RMSE and 0.995 R2, and wind speed predictions with 2.278 RMSE and 0.993 R2. The experimental findings confirm the improved performance over conventional models due to SVM and XGBoost failing to achieve these R2 values and high RMSE scores. The research meets three principal obstacles because of the combination of stochastic climate datasets and complex nonlinear patterns, which also encounter limitations in conventional methods for managing scale-dependency relationships and manual parameter adjustments. This approach delivers three main strengths because it combines CNN and LSTM networks through genetic programming while tracking temporal and spatial relationships and processing noisy data automatically, leading to improved results compared to other methods. Unlike other published models in the field, the hybrid model GA-ConvLSTM combines optimized CNN and LSTM elements with genetic algorithm elements. The proposed model received its first application in this research for multi-city climate prediction through training with 15 years of hourly data. Standard methods encounter challenges when detecting long-term dependencies, but GA-ConvLSTM successfully addresses this issue.
AtıfScopus1OpenAlex1İncele - 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 - MakaleYalnız künye2024A Comparative Study for Privacy-Aware Recommendation Systems
Utku, Anıl; Canbay, Yavuz
Recommendation systems are sophisticated processes for information filtering designed to offer users tailored recommendations based on their preferences and interests. Users need help to choose between options as the amount of information on the web grows. As a result, it is critical to deliver personalized recommendations to consumers to promote user loyalty and satisfaction. Because recommender systems use sensitive user information such as ratings, comments, likes, and dislikes, this information can be leaked if no privacy measures are taken. As a result, we presented a comparison of privacy-aware recommendation systems in this paper. Two experiments are carried out. In the first experiment, we examined collaborative filtering algorithms on perturbed ratings and then compared hybrid, collaborative, and content-based algorithms on perturbed ratings in the second experiment. According to the results, the Singular Value Decomposition++ (SVDpp) algorithm presented the lowest Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) values for epsilon 100, with 0.8889 and 0.6822, respectively. Furthermore, for epsilon 100, the hybrid filtering technique had the lowest RMSE and MAE rates of 0.90664 and 0.69813, respectively.
AtıfOpenAlex2TR Dizin1İncele - MakaleAçık Erişim2024Neural Network Based a Comparative Analysis for Customer Churn Prediction
Akcayol, M. Ali; Utku, Anıl
Customer churn refers to a customer's disconnection from a business. The expense associated with customer churn encompasses both the forfeited revenue and the marketing expenditures required to acquire new customers. Mitigating customer churn stands as the foremost objective for every business. Customer churn prediction will contribute to developing strategies enabling businesses to retain these customers by identifying customers with a high risk of loss. In the digital world, the importance of developing customer churn prediction models is increasing daily. In this study, MLP based artificial neural network model was developed for customer churn prediction using customer data from an anonymous telecommunications company. The developed model was compared with kNN, LR, NB, RF, and SVM. The prediction results of the applied models were discussed, and the experimental results showed that all the models compared had over 70% accuracy. Experimental results showed that the developed MLP-based artificial neural network model has the most successful classification performance compared to other models with approximately 95% accuracy.
AtıfOpenAlex3TR Dizin2İncele - MakaleYalnız künye2024Deprem seviye sınıflandırması için hibrit bir convlstm modeli: karşılaştırmalı bir analiz
Utku, Anıl
Deprem, yer kabuğunda depolanan enerjinin açığa çıkması sonucu yer yüzeyinin aniden sarsılmasıdır. Depremler genellikle yer altı kayalarının aniden kırılması ve bir fay boyunca hızlı etmesi nedeniyle meydana gelir. Binaların ve altyapının düzgün inşa edilmediği ve nüfusun hazırlıklı olmadığı bir ortamda, orta şiddette bile olsa bir deprem yıkıcı olabilir. Yapay zekâ yöntemleri, deprem tahmini gibi doğal afetlerin öngörülmesinde önemli bir rol oynamaktadır. Bu amaçla geliştirilen hibrit ConvLSTM modeli ile yer kabuğundaki karmaşık enerji dinamikleri ve hareketleri, büyük miktardaki jeolojik verilerden analiz edilerek deprem olasılıklarının tahmin edilmesi amaçlandı. ConvLSTM, LR, RF, SVM, XGBoost, MLP, CNN ve LSTM gibi popüler yöntemlerle USGS tarafından sunulan gerçek zamanlı deprem verileri kullanılarak karşılaştırıldı. Deneysel sonuçlar, ConvLSTM’in 0,9951 doğruluk ve 0,9993 AUC ile karşılaştırılan modellerden daha başarılı olduğunu göstermiştir
- MakaleAçık Erişim2024Sentetik Atık Takviyeli Poliolefinlerin Mekanik Özelliklerinin GA-LSTM Hibrit Modeli ile Tahmini
Utku, Anıl; Kismet, Yilmaz; Can, Umit
Bu çalışmada, parçacık takviyeli termoplastiklerin enjeksiyon kalıplamasında kullanılan üretim parametrelerinin ürün kalitesi ve üretilen parçanın mekanik özellikleri üzerindeki etkileri optimize edilmiş bir Genetik Algoritma-Uzun Kısa Süreli Bellek (GA-LSTM) hibrit derin öğrenme yöntemi kullanılarak modellenmiştir. Burada termoplastik olarak poliolefinler grubunun en önemli üyesi olan AYPE, YYPE ve PP kullanılırken takviye elemanı olarak ise toz halde sentetik boya atıkları kullanılmıştır. Farklı parametreler kullanılarak enjeksiyon kalıplama yoluyla 819 numune üretilmiş ve her numune üzerinde mekanik çekme, üç nokta eğme ve izod darbe testleri gerçekleştirilmiştir. GA-LSTM modeli, kullanılan parametreler ve deneysel süreç boyunca elde edilen sonuçlarla eğitilmiş ve tahmin edilen değerlerin gerçek değerlere karşılık geldiği belirlenmiştir. Hibrit GA-LSTM modelinin başarısını ölçmek için iyi bilinen yöntemler kullanılmıştır. Elde edilen sonuçlara göre tasarlanan GA-LSTM modeli en iyi sonuçları üretmiştir.
- MakaleAçık Erişim2024Hybrid ConvLSTM Model for Wind Speed Prediction: A Case Study for India's Highest Wind Speed Cities
Utku, Anıl; Akyol, Sinem
Wind speed prediction is very important for logistics, energy production and renewable energy sources, aviation and maritime, agriculture, disaster management, environmental monitoring, construction, life planning and economic activities. In this study, it was aimed to predict the wind speeds of Jaisalmer, Kochi, Mangalore, Puri and Rameswaram, which are among the cities with the highest wind speeds in India. ConvLSTM hybrid model was developed by taking advantage of the effective features of CNN and LSTM models for wind speed prediction. With ConvLSTM, it is aimed to better determine the dynamics of wind speed by processing spatial and temporal data simultaneously. ConvLSTM has been extensively tested using an approximately 15-year hourly dataset consisting of RF, SVM, ANFIS, CNN and LSTM and wind speed measurements at 10 meters and 100 meters’ altitudes. Experimental results show that ConvLSTM has R2 values above 0.9 for almost all of the city and wind speed parameters, and outperforms the compared models with the lowest error values in terms of RMSE and MAE error metrics.
AtıfOpenAlex1TR Dizin1İncele - MakaleYalnız künye2024Hybrid CNN-LSTM Model for Accurate Long-Term and Short-Term Temperature Prediction: A Case Study for Bingöl and Tunceli
Utku, Anıl
Extreme and sudden weather events experienced with global warming and climate change reveal the importance of accurate air temperature prediction. For this reason, it can be used to optimize decision-making processes for a wide range of applications from health and agricultural planning to energy consumption strategies. Artificial intelligence methods are successfully applied in many application areas due to their flexibility and efficiency. Traditional weather forecasting models are inefficient in detecting sudden fluctuations and complex, irregular patterns in data. Artificial in-telligence methods overcome these limitations thanks to their ability to process big data and capture long-term temporal dependencies. In this study, the aim is to predict future temperature changes more accurately by capturing patterns in past data with the developed CNN-LSTM hybrid model. The developed hybrid model is compared in detail with RF, SVM, CNN, and LSTM. The compared models were tested using daily average temperature data between 1961-2024 and hourly temperature data between 2020-2024. Experiments have shown that CNN-LSTM outperforms the compared models with R2 value above 0.97 in all scenarios.
AtıfOpenAlex2TR Dizin1İncele - MakaleYalnız künye2024Güvenlik Duvarlarındaki Ağ Trafiği Log Kayıtlarının Analizinde Açıklanabilir Yapay Zekâ ve Derin Sinir Ağlarının Kullanımı: Karşılaştırmalı Bir Analiz
Utku, Anıl
İnternet kullanımında yaşanan hızlı büyüme, siber tehditlerin çeşitlenmesine ve karmaşıklığının artmasına neden olmuştur. Bu durum, özellikle ağ güvenliği konusunda daha gelişmiş ve dinamik çözümler geliştirilmesini zorunlu kılmaktadır. Geleneksel güvenlik yöntemleri, özellikle güvenlik duvarları, belirli kurallar çerçevesinde çalıştığından, yeni nesil siber tehditlere karşı yetersiz kalabilmektedir. Güvenlik duvarları, ağ trafiğini izleyerek potansiyel tehditleri tespit eden ve engelleyen önemli araçlar olarak uzun yıllardır kullanılmaktadır. Ancak, statik kurallara dayanan geleneksel güvenlik duvarları, karmaşık ve dinamik tehditleri algılamada yetersiz kalmakta ve yüksek oranda yanlış pozitif/negatif sonuçlar üretmektedir. Bu durum, daha esnek ve kendini sürekli olarak geliştirebilen yapay zekâ tabanlı güvenlik çözümlerine olan ihtiyacı artırmıştır. Bu çalışmada, açıklanabilir yapay zekâ ve DNN tabanlı bir derin öğrenme modeli kullanarak güvenlik duvarlarındaki ağ trafiği loglarının analiz edilmesi hedeflenmiştir. Geliştirilen DNN tabanlı model, RF, kNN, SVM, LR ve XGBoost gibi popüler makine öğrenmesi algoritmalarıyla karşılaştırılarak performansı değerlendirilmiştir. Deneysel sonuçlar, geliştirilen DNN tabanlı modelin %99,87 doğruluk oranıyla karşılaştırılan modellerden ve literatürdeki çalışmalardan daha başarılı olduğunu göstermiştir.
- MakaleAçık Erişim2024Hindistan'daki Turistik Şehirlerin İklim Değişkenlerinin Tahminine Yönelik Hibrit ConvGRU Modeli
Utku, Anıl
Hava durumu tahmini tarım, sağlık, ulaşım, turizm, hava kalitesi ve endüstriyel üretim gibi alanlarda etkinliğin ve verimliliğin sağlanabilmesi açısından oldukça önemlidir. Geleneksel hava durumu tahmin modelleri uzun vadeli tahminlerde yetersiz kalmaktadır. Yapay zekâ yöntemleri, sıcaklık, nem, rüzgâr hızı ve hava basıncını gibi hava durumu verileri arasındaki karmaşık ilişkileri öğrenerek geleceğe dönük başarılı tahminler üretebilmektedir. Bu çalışmada, Hindistan'ın önemli turizm şehirlerinden olan Agra, Jaipur, Jodhpur, New Delhi ve Rishikesh'in sıcaklık, nem ve çiğ noktası gibi uzun vadeli iklim değişkenlerinin tahmin edilmesi amaçlanmıştır. Bu amaçla, geliştirilen ConvGRU hibrit modeli, RF, SVM, CNN, LSTM ve GRU modelleriyle kapsamlı bir şekilde karşılaştırılmıştır. 2010-2024 yılları arasına ait gerçek zamanlı ve güncel bir veriseti kullanılmıştır. Deneysel sonuçlar, ConvGRU’nun tüm şehirler ve iklim değişkenleri için 0,9’un üzerinde R2 değeriyle karşılaştırılan modellerden daha başarılı olduğunu göstermiştir.
AtıfOpenAlex3TR Dizin3İncele - DüzeltmeAçık Erişim2024Scopus SJRQ2Correction to: Detection of hateful twitter users with graph convolutional network model (Earth Science Informatics, (2023), 16, 1, (329-343), 10.1007/s12145-023-00940-w)
Utku, Anıl; Can, Umit; Aslan, Serpil
In this article the affiliation details for Serpil Aslan were incorrectly given as ‘Software Engineering Department, Turgut Üzal University, 44210 Malatya, Turkey’ but should have been ‘Department of Software Engineering, Faculty of Engineering and Natural Sciences, Malatya Turgut Ozal University, Malatya, Turkey.’ The original article has been corrected.
AtıfScopus1OpenAlex2İncele - MakaleAçık Erişim2024Scopus SJRQ1Spread patterns of COVID-19 in European countries: hybrid deep learning model for prediction and transmission analysis
Utku, Anıl; Akcayol, M. Ali
The COVID-19 pandemic has profoundly impacted healthcare systems and economies worldwide, leading to the implementation of travel restrictions and social measures. Efforts such as vaccination campaigns, testing, and surveillance have played a crucial role in containing the spread of the virus and safeguarding public health. There needs to be more research exploring the transmission dynamics of COVID-19, particularly within European nations. Therefore, the primary objective of this research was to examine the spread patterns of COVID-19 across various European countries. Doing so makes it possible to implement preventive measures, allocate resources, and optimize treatment strategies based on projected case and mortality rates. For this purpose, a hybrid prediction model combining CNN and LSTM models was developed. The performance of this hybrid model was compared against several other models, including CNN, k-NN, LR, LSTM, MLP, RF, SVM, and XGBoost. The empirical findings revealed that the CNN-LSTM hybrid model exhibited superior performance compared to alternative models in effectively predicting the transmission of COVID-19 within European nations. Furthermore, examining the peak of case and death dates provided insights into the dynamics of COVID-19 transmission among European countries. Chord diagrams were drawn to analyze the inter-country transmission patterns of COVID-19 over 5-day and 14-day intervals.
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