Sürdürülebilir Kalkınma Amaçları
Yayınlar
- MakaleAçık Erişim2026Sentiment Analysis of Monkeypox Tweets Using a Hybrid CNN-BiGRU Based Deep Learning Model
Can, Umit
Recently, epidemics have disrupted people's lives. Following the COVID-19 pandemic, cases of monkeypox, a rare disease, began to appear worldwide. These incidents raised societal tension and anxiety. Understanding public attitudes, sentiments, and perceptions regarding the epidemic is essential for developing and implementing effective policies and countermeasures. Online social media platforms are essential venues for expressing feelings and views. In particular, Twitter provides valuable information for measuring societal sentiment. Machine learning and deep learning methods have recently been widely used in social network analysis. Deep learning models, which deliver strong performance, particularly on complex and nonlinear problems, are used in natural language processing and text mining. In this study, a Convolutional Neural Network Bidirectional Gated Recurrent Unit (CNN-BiGRU) hybrid deep learning model was developed by combining a CNN and a BiGRU and applied for the monkeypox sentiment analysis. The dataset was labeled as positive, negative, or neutral with Valence Aware Dictionary and sEntiment Reasoner (VADER). In addition, FastText and Term Frequency-Inverse Document Frequency (TF-IDF) word-embedding methods were used for feature extraction, and the performance of machine-learning and deep-learning methods was evaluated separately. As a result of the experimental studies, the FastText-CNN-BiGRU model obtained the highest results with 0.9382 accuracy, 0.9347 macro precision, 0.9367 macro recall, 0.9355 macro F-score, and 0.9875 macro ROC-AUC. The FastText-CNN model was the second most successful, achieving 0.9303 accuracy, 0.9268 macro precision, 0.9280 macro recall, 0.9271 macro F-score, and 0.9862 macro ROC-AUC. The FastText-BiGRU model was the third most successful, achieving 0.9262 accuracy, 0.9224 macro precision, 0.9255 macro recall, 9236 macro F-score, and 0.9838 macro ROC-AUC.
- MakaleAçık Erişim2026Application of machine learning techniques for multi-class classification performance on mental disorders
Can, Umit
The contemporary social framework is predicated on a fiercely competitive system, which amplifies individual pressure and subsequently elevates the incidence of mental diseases, significantly affecting public health. Consequently, governmental and non-governmental groups allocate considerable resources to tackle these concerns. The precise identification of such illnesses via artificial intelligence techniques has become critically significant. These methodologies, widely used across several healthcare sectors, have been increasingly adopted in psychiatry. Evaluating psychiatric data to forecast disease accurately enables doctors to make better decisions and establishes a robust decision-support framework. This work employed prevalent machine learning techniques to identify mental illnesses utilizing a dataset with labels for seven distinct mental states—six disorders and one normative condition. The approaches were initially evaluated on an imbalanced dataset and subsequently on a balanced dataset, facilitating comparison of the outcomes. Among Categorical Boosting, Gradient Boosting, Extreme Gradient Boosting, Support Vector Machine, and Logistic Regression, the Support Vector Machine method demonstrated superior performance on both balanced and imbalanced datasets.
- MakaleAçık Erişim2026Scopus SJRQ1Surface EMG-Based Hand Gesture Recognition Using a Hybrid Multistream Deep Learning Architecture
Çelik, Yusuf; Can, Umit
Surface electromyography (sEMG) enables non-invasive measurement of muscle activity for applications such as human–machine interaction, rehabilitation, and prosthesis control. However, high noise levels, inter-subject variability, and the complex nature of muscle activation hinder robust gesture classification. This study proposes a multistream hybrid deep-learning architecture for the FORS-EMG dataset to address these challenges. The model integrates Temporal Convolutional Networks (TCN), depthwise separable convolutions, bidirectional Long Short-Term Memory (LSTM)–Gated Recurrent Unit (GRU) layers, and a Transformer encoder to capture complementary temporal and spectral patterns, and an ArcFace-based classifier to enhance class separability. We evaluate the approach under three protocols: subject-wise, random split without augmentation, and random split with augmentation. In the augmented random-split setting, the model attains 96.4% accuracy, surpassing previously reported values. In the subject-wise setting, accuracy is 74%, revealing limited cross-user generalization. The results demonstrate the method’s high performance and highlight the impact of data-partition strategies for real-world sEMG-based gesture recognition.
AtıfScopus4WoS4OpenAlex7
İncele - MakaleYalnız künye2025The Position of Metaheuristic Optimization Methods in Psychiatric Practice: A Bibliometric Analysis
Tuğrul, Feride; Can, Umit
Mental diseases affect billions of people worldwide to varying degrees. This poses a risk to public health. Although the levels and effects of diseases vary, in general, mental diseases prevent people from having a healthy relationship with themselves and their environment and prevent them from living their daily lives efficiently. Individuals who move away from the comfort of daily life face many difficulties in their social and professional lives. People with long-term mental disorders may also experience physiological disorders due to stress. When this situation is not treated and prevented, it becomes a public health problem. Therefore, centers such as health institutions, research centers, and universities operating in the field of health carry out many studies to detect and treat mental disorders. These methods include traditional methods, but today, they also include artificial intelligence-supported methods. AI-powered methods are used for early diagnosis and treatment of mental illnesses by analyzing large amounts of mental illness data of various types. Researchers are also using metaheuristic optimization algorithms to improve the performance of these methods. These optimization methods, which seek fast and high-performance solutions to very complex nonlinear problems, are increasing the success of machine learning and deep learning methods. Nonetheless, there is no distinct bibliometric analysis demonstrating the quantity and quality of research in this domain for scholars. This study aims to fill this gap in this field and to be a preliminary guide for researchers.
AtıfTR Dizin1
İncele - 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 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 - 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.
- 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 - MakaleYalnız künye2023Scopus SJRQ2Detection of hateful twitter users with graph convolutional network model
Utku, Anıl; Can, Umit; Aslan, Serpil
Today, hate speech is widespread and persistent in various forms on social networking platforms, targeting different minority groups. These attacks can be carried out using various factors such as racial, religious, gender, and physical disability, etc. Considering the number of people and their interactions, social networks are the most important channels through which these discourses spread. The social network structure is considered a set of nodes and edges and is very suitable for the graph structure. The multidimensional structure of social networks carries social network data from Euclidean space to non-Euclidean space. In non-Euclidean space, the graph structure is used to represent data effectively. In this respect, solving the hate speech problem with graph-based methods in a complex dimensional space can produce more impressive results. In this study, a powerful method based on the Graph Convolutional Network (GCN) model, which is rarely used in this field, was proposed for the detection of hateful Twitter users in social networks. Well-known machine learning methods were used to measure the performance of this method. According to the results obtained, the proposed GCN model gave the most successful result.
AtıfScopus15WoS10OpenAlex16İncele - MakaleAçık Erişim2022Covid-19'un Yayılım Tahminine Yönelik Makine Öğrenmesi ve Derin Öğrenme Tabanlı Karşılaştırmalı Bir Analiz Türkiye İçin Örnek Bir Çalışma
Utku, Anıl; Can, Umit
Salgınlar tarih boyunca çeşitli zaman dilimlerinde ortaya çıkmış ve insan topluluklarına ciddi zararlar vermiştir. Günümüzde ise bu salgınların modern versiyonu Covid-19 milyonlarca insanın hayatını kaybetmesine ve bir o kadarının da sağlık sorunları yaşamasına yol açmıştır. Tüm dünya, altyapı, finans, veri kaynakları, koruyucu donanımlar, hayati risk tedavileri ve diğer birçok kaynak açısından bu ölümcül hastalığın yayılmasına karşı mücadele etmek için inanılmaz çaba sarf etmektedir. Araştırmacılar ülke çapında paylaşılan verileri kullanarak bu salgın durumunu analiz etmek için matematiksel modeller geliştirmektedirler. Ülkeler salgın hızına bağlı olarak bu salgınla mücadele etmeye çalışmaktadırlar. Bu çalışmada Türkiye özelinde vaka ve ölüm sayılarının tahmin edilmesi için LSTM tabanlı bir tahmin modeli oluşturulmuştur. Bu modelin tahmin başarısını ölçmek için RF, SVM, XGBoost, MLP, CNN ve RNN olmak üzere popüler derin öğrenme yöntemleri dâhil altı makine öğrenmesi yöntemi kullanılmıştır. MSE, RMSE, MAE ve R2 metrikleri cinsinden elde edilen sonuçlara göre vaka ve ölüm sayılarının tahmininde en başarılı model LSTM olmuştur.
AtıfOpenAlex1TR Dizin1İncele - MakaleAçık Erişim2022Deep Learning Based Air Quality Prediction: A Case Study for London
Utku, Anıl; Can, Umit
Although states take various measures to prevent air pollution, air pollutants continue to exist as an important problem in the world. One air pollutant that seriously affects human health is called PM2.5 (particles smaller than 2.5 micrometers in diameter). These particles pose a serious threat to human health. For example, it can penetrate deep into the lung, irritate and erode the alveolar wall and consequently impair lung function. From this, the event PM2.5 prediction is very important. In this study, PM2.5 prediction was made using 12 models, namely, Decision Tree (DT), Extra Tree (ET), k-Nearest Neighbourhood (k-NN), Linear Regression (LR), Random Forest (RF), Support Vector Machine (SVM), Extreme Gradient Boosting (XGBoost), Multi-Layer Perceptron (MLP), Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), Gated Recurrent Unit (GRU), and Long Short-Term Memory (LSTM) models. The LSTM model developed according to the results obtained achieved the best result in terms of MSE, RMSE, MAE, and R2 metrics.
AtıfOpenAlex7İncele - MakaleYalnız künye2022Machine Learning-Based Effective Malicious Web Page Detection
Utku, Anıl; Can, Umit
The use of the Internet is becoming more and more widespread day by day, putting millions of users at risk of cyberattacks. Especially during the Covid-19 epidemic, internet usage has increased significantly and various cyber-attacks have been made through malicious websites. With these attacks, much information such as people’s private information, bank information, and social information can be captured. Many methods have been developed to prevent cyber-attacks. In particular, methods that use machine learning methods other than traditional methods give more successful results. In this study, it has been tried to automatically detect malicious websites by using the URL properties of malicious websites. For this purpose, popular machine learning methods such as DT, kNN, LightGBM, LR, MLP, RF, SVM, and XGBoost were used. According to the experimental results, the RF algorithm achieved 96% accuracy.
- BildiriYalnız künye2022Machine Learning-Based A Comparative Analysis for Air Quality Prediction
Utku, Anil; Can, Umit
Air pollution affects human life negatively, especially in terms of health, and causes the death of millions of people every year. Today, air pollution in many regions is still above the limits indicated by the World Health Organization. In this study, the prediction of the rate of PM2.5, which is an important air pollutant, in the Beijing region of China is emphasized. For this purpose, weather prediction models were created using Random Forest Algorithm, Support Vector Regression, XGBoost and K-Nearest Neighbor Algorithm, which are popular machine learning algorithms, and the results were compared using various metrics. The best prediction result in all the metrics used was obtained with the Support Vector Regression method.
AtıfWoS2OpenAlex8İncele - MakaleAçık Erişim2021Comparison of Current Metaheuristic Optimization Algorithms by Using Benchmark Functions
Kızıloluk, Soner; Can, Umit
There are dozens of metaheuristic optimization methods inspired by the swarm behaviors of creatures in nature, plants, human-specific phenomena, events in scientific fields such as physics, mathematics, biology and chemistry. These methods are successful in certain problems but not in all problems. Therefore, new metaheuristic methods are suggested by researchers frequently. In this study, for the first time, six up-to-date metaheuristic optimization algorithms, namely Jellyfish Search Optimizer, Carnivorous Plant Algorithm, Giza Pyramids Construction Algorithm, Gradient Based Optimizer, Student Based Psychology Optimization and Tunicate Swarm Optimization, were compared by using 10 mathematical benchmark functions with 10, 30 and 50 dimensions. According to the results obtained, Student Based Psychology Optimization gave the best results in 7 out of 10 benchmark functions. It was observed that Gradient Based Optimizer gave the best results in 4 benchmark functions. Carnivorous Plant Algorithm and Tunicate Swarm Optimization showed the worst performance. In order to compare in terms of time, algorithms were run at 1000 iterations in 50- dimensional benchmark functions. When the average run times obtained are examined, it is seen that Jellyfish Search Optimizer and Tunicate Swarm Optimization are the fastest running algorithms. Carnivorous Plant Algorithm and Student Based Psychology Optimization were the slowest running algorithms.
AtıfOpenAlex3TR Dizin3İncele - MakaleAçık Erişim2021Scopus SJRQ1A novel approach for efficient stance detection in online social networks with metaheuristic optimization
Can, Umit; Alatas, Bilal
In the 19th and 20th centuries, social networks have been an important topic in a wide range of fields from sociology to education. However, with the advances in computer technology in the 21st century, significant changes have been observed in social networks, and conventional networks have evolved into online social networks. The size of these networks, along with the large amount of data they generate, has introduced new social networking problems and solutions. Social network analysis methods are used to understand social network data. Today, several methods are implemented to solve various social network analysis problems, albeit with limited success in certain problems. Thus, the researchers develop new methods or recommend solutions to improve the performance of the existing methods. In the present paper, a novel optimization method that aimed to classify social network analysis problems was proposed. The problem of stance detection, an online social network analysis problem, was first tackled as an optimization problem. Furthermore, a new hybrid metaheuristic optimization algorithm was proposed for the first time in the current study, and the algorithm was compared with various methods. The analysis of the findings obtained with accuracy, precision, recall, and F-measure classification metrics demonstrated that our method performed better than other methods.
AtıfScopus12WoS9OpenAlex11İncele - Derleme MakaleAçık Erişim2019Scopus SJRQ2A new direction in social network analysis: Online social network analysis problems and applications
Can, Umit; Alatas, B.
The use of online social networks has made significant progress in recent years as the use of the Internet has become widespread worldwide as the technological infrastructure and the use of technological products evolve. It has become more suitable to reach online social networking sites such as Facebook, Twitter, Instagram and LinkedIn via the internet and web 3.0 technologies. Thus, people have shared their views on many different topics and their emotions with other users more widely on these platforms. This means that a huge amount of data is created on platforms where millions of people connect with each other through social networks. Nevertheless, the development of computational paradigms at high speed and complexity with technological possibilities allows analysis of valuable data by means of social network analysis methods. Our goal for this paper is to present a review of novel and popular online social network analysis problems with related applications and a reference work for researchers interested in analyzing online social network data and social network problems. Unlike other individual studies we have gathered 21 online social network problems and defined them with related studies. Thus, this study is original by presenting an important source of research by explaining the problems of online social network and the studies performed in this area.
AtıfScopus172WoS113İncele - MakaleAçık Erişim2017Scopus SJRQ3Automatic Mining of Quantitative Association Rules with Gravitational Search Algorithm
Can, Umit; Alatas, Bilal
The classical optimization algorithms are not efficient in solving complex search and optimization problems. Thus, some heuristic optimization algorithms have been proposed. In this paper, exploration of association rules within numerical databases with Gravitational Search Algorithm (GSA) has been firstly performed. GSA has been designed as search method for quantitative association rules from the databases which can be regarded as search space. Furthermore, determining the minimum values of confidence and support for every database which is a hard job has been eliminated by GSA. Apart from this, the fitness function used for GSA is very flexible. According to the interested problem, some parameters can be removed from or added to the fitness function. The range values of the attributes have been automatically adjusted during the time of mining of the rules. That is why there is not any requirements for the pre-processing of the data. Attributes interaction problem has also been eliminated with the designed GSA. GSA has been tested with four real databases and promising results have been obtained. GSA seems an effective search method for complex numerical sequential patterns mining, numerical classification rules mining, and clustering rules mining tasks of data mining.
AtıfScopus27WoS18OpenAlex28İncele - MakaleAçık Erişim2017Scopus SJRQ2Big social network data and sustainable economic development
Can, Umit; Alatas, Bilal
New information technologies have led to the rapid and effective growth of social networks. The amount of data produced by social networks has increased the value of the big data concept, which is one of the popular current phenomena. The immediate or unpredictable effects of a wide array of economic activities on large masses and the reactions to them can be measured by using social media platforms and big data methods. Thus, it would be extremely beneficial to analyze the harmful environmental and social impacts that are caused by unsustainable business applications. As social networks and big data are popular realms currently, their efficient use would be an important factor in sustainable economic development. Accurate analysis of people's consumption habits and economic tendencies would provide significant advantages to companies. Moreover, unknown consumption factors that affect the economic preferences of individuals can be discovered and economic efficiency can be increased. This study shows that the numerous solution opportunities that are provided by social networks and big data have become significant tools in dynamic policy creation by companies and states, in solving problems related to women's rights, the environment, and health.
AtıfScopus42WoS34OpenAlex50İncele - MakaleAçık Erişim2017Performance comparisons of current metaheuristic algorithms on unconstrained optimization problems
Can, Umit; Alatas, Bilal
Nature-inspired metaheuristic algorithms have been recognized as powerful global optimization techniques in the last few decades.Many different metaheuristic optimization algorithms have been presented and successfully applied to different types of problems. In this paper; seven of newest metaheuristic algorithms namely, Ant Lion Optimization, Dragonfly Algorithm, Grey Wolf Optimization, Moth-Flame Optimization, Multi-Verse Optimizer, Sine Cosine Algorithm, and Whale Optimization Algorithm have been tested on unconstrained benchmark optimization problems and their performances have been reported. Some of these algorithms are based on swarm while some are based on biology and mathematics. Performance analysis of these novel search and optimization algorithms satisfying equal conditions on benchmark functions for the first time has given important information about their behaviors on unimodal and multi-modal optimization problems. These algorithms have been recently proposed and many new versions of them may be proposed in future for efficient results in many different types of search and optimization problems.
AtıfScopus12İncele