Sürdürülebilir Kalkınma Amaçları
Yazarlar
- Utku, Anil(2)
- Acharya, U. Rajendra(1)
- Ak, Kübra(1)
- Alparslan, Cem(1)
- Aydın, Baki(1)
- BARUT, Abdulkadir(1)
- Can, U.(1)
- Can, Umit(1)
- Can, Ümit(1)
- Cevher, M. F.(1)
- Cevher, Muhammed Fatih(1)
- Demiralp, Demet(1)
- Dogan, Gurkan(1)
- Er, Akif(1)
- Erdem, Ebubekir(1)
- Félix, Luís M.(1)
- Garipcan, Ali Murat(1)
- Hoseini, Seyyed Morteza(1)
- Ibin, Adil(1)
- Kaner, Seyfullah(1)
- Kangal, Evrim Ersin(1)
- Kurtul, Irmak(1)
- Köse, Özay(1)
- Minaz, Mert(1)
- Pilatin, Abdulmuttalip(1)
- Salti, Mehmet(1)
- Salvi, Massimo(1)
- Sengur, Abdulkadir(1)
- Serdar, Osman(1)
- Sharabati, Abdel-Aziz Ahmad(1)
- Tan, Jen Hong(1)
- Tan, Ru-San(1)
- Utku, A.(1)
- Utku, Anıl(1)
- Uyan, Umut(1)
- Uyanik, Hakan(1)
- Zengin, Bilgin(1)
- Önen, E.(1)
Yayınlar
- MakaleAçık Erişim2026Adoption of artificial intelligence-supported digital banking applications and their impact on customer loyalty
Cevher, M. F.; Önen, E.
The rationale of this paper is that AI-enabled digital banking applications enhance consumer experiences and customer relationships; however, holistic findings on the impact of these applications on customer satisfaction and loyalty, beyond their benefits and adoption, are limited. The study aims to reveal the impact of AI-powered digital banking applications on customer loyalty by examining consumer adoption, experience, and satisfaction levels. The theoretical basis is grounded in an integrated research model that correlates variables from the technology acceptance model and the expectation confirmation model. The data and research methods comprise 510 participants using AI-enabled banking applications in T & uuml;rkiye, employing a quantitative research design and an online survey technique. The data obtained were analysed using structural equation modelling (PLS-SEM). The findings indicate significant effects of perceived ease of use on perceived usefulness, as well as the relationship between perceived usefulness and confirmation on satisfaction. Furthermore, significant effects of satisfaction on continuance intention and continuance intention on customer loyalty were found. However, the direct effect of satisfaction on customer loyalty is not significant. This result suggests that satisfaction plays an indirect role in the formation of customer loyalty through continuance intention. Therefore, satisfaction and continuance intention are critical variables in strengthening customer loyalty in AI-enabled digital banking. Increasing customer satisfaction may not be sufficient for enhancing customer loyalty: banks must devise strategies to support customers' intentions to use applications regularly.
AtıfOpenAlex1
İncele - MakaleYalnız künye2026Scopus SJRQ2From assistance to dependence: The nexus between AI dependence and workplace cognitive engagement
Uyan, Umut; Ibin, Adil
Background The increasing integration of artificial intelligence (AI) tools into workplace practices has reshaped how employees process information and perform cognitive tasks. While these systems are often associated with superior efficiency, their excessive use may entail significant cognitive costs. Objective Grounded in automation complacency perspective and the growing literature on technology addiction, this study investigates how dependence on artificial intelligence is associated with cognitive engagement in the workplace, with particular attention to the mediating role of cognitive fatigue. Methods Adopting quantitative research design, data were collected from 246 academic and administrative staff who actively use AI tools in their daily work routines. Partial Least Squares Structural Equation Modeling (PLS-SEM) was used to test the theoretical model proposed. Results The findings indicate that dependence on AI was positively associated with cognitive fatigue (beta = 0.569; t = 13.468; p = 0.000) and negatively with cognitive engagement (beta = -0.174; t = 2.503; p = 0.012). In addition, the relationship between cognitive fatigue and cognitive engagement was found to be negative and statistically significant (beta = -0.207; t = 2.773; p = 0.006). Mediation analysis further revealed that cognitive fatigue partially mediates the relationship between dependence on AI and cognitive engagement (indirect effect beta = -0.118, t = 2.724, p < .01; VAF = 40.5%). Conclusion These results suggest that higher levels of dependence on AI correspond to higher employee cognitive fatigue, which in turn relates to lower cognitive engagement at work. By empirically demonstrating the cognitive costs of AI reliance, this study contributes to the emerging literature on workplace AI and technology addiction.
- MakaleAçık Erişim2026Predicting middle school students' academic achievement in mathematics with aAnew hybrid approach
Dogan, Gurkan; Demiralp, Demet
The academic achievement of middle school students is one of the critical steps in their educational journey that affects their future academic and career prospects. During this transition period, students are faced with a variety of challenges related to their families, schools, and the individual themselves. Research shows that academic performance during these formative years is critical for future educational success and overall life success. Therefore, addressing students' academic achievement, which depends on various factors during this period, is of particular importance for both institutional and individual planning and orientation in the future. In this study, a new hybrid approach based on artificial neural networks that enables automatic analysis of data on family, school, and individual factors affecting middle school students' academic achievement in mathematics is proposed. A publicly available student performance dataset was used for training and testing the proposed hybrid approach and other models. This dataset consists of data such as mathematics grades, family information, residential information, and health status information for 395 students enrolled in two public schools in the Alentejo region of Portugal. The proposed approach achieved an R2 score of 88.6% in experimental studies with this data set, providing approximately 3% higher accuracy than its closest competitor among other methods in the literature.
AtıfOpenAlex1
İncele - MakaleYalnız künye2026Scopus SJRQ1From trash to technology: Ai-driven waste classification with hybrid CNN-transformer approach for sustainable recycling
Utku, Anil
The increasing amount of waste globally requires more efficient recycling processes and increases the importance of intelligent, automatic classification systems. Since traditional waste separation methods are time-consuming and have high error rates, artificial intelligence-based solutions have great potential in this area. In this study, the effectiveness of deep learning-based waste classification models was investigated, and nine different waste types were classified using DenseNet121, EfficientNetB0, ResNet50, ViT, DenseNet121-ViT, EfficientNetB0-ViT, ResNet50-ViT. Experimental results show that hybrid CNN-ViT models achieved the highest accuracy rates. Mainly, the EfficientNetB0-ViT model exhibited the best performance with a 95% accuracy rate and made a balanced classification for all waste types. The model can optimize recycling processes by ensuring the correct separation of wastes and contribute to environmental sustainability by reducing misclassification. Artificial intelligence-supported waste management systems offer an effective solution in critical areas such as reducing greenhouse gas emissions, saving energy, and supporting environmentally friendly urban planning. The findings of this study reveal that deep learning-based artificial intelligence models are an important tool for sustainable environmental management.
AtıfScopus1WoS1OpenAlex3
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.
- MakaleYalnız künye2026Scopus SJRQ1Evaluation of rainbow trout response to the optimal anesthetic concentration of citronella oil (Cymbopogon nardus) predicted by artificial neural network
Minaz, Mert; Félix, Luís M.; Er, Akif; Köse, Özay; Ak, Kübra; Alparslan, Cem; Serdar, Osman; Aydın, Baki; Kurtul, Irmak; Hoseini, Seyyed Morteza
Anesthesia practices in aquaculture are important for preventing potential stress in fish. For this purpose, interest in natural anesthetic agents as substitute for currently used synthetic anesthetics has increased. In this context, the present study demonstrated the anesthetic efficacy of citronella oil (Cymbopogon nardus) in rainbow trout (Oncorhynchus mykiss) individuals. Based on an artificial neural network (ANN) model derived from preliminary studies, the optimal anesthetic concentration for rainbow trout individuals was estimated as 90 μL L−1. Validation of the concentration predicted by ANN revealed no time-dependent hematological or histological effects on fish to citronella oil. Furthermore, biochemical tests revealed that the partial post-anesthesia increase in ALT and AST values was transient and negligible in light of histological findings. Although oxidative stress was observed during the acute period, the enzymatic activity of SOD and CAT effectively reduced the oxidative damage, demonstrating their role in protecting the fish from oxidative stress. The antioxidant activity observed was temporary, returning to baseline levels without any accompanying tissue damage at the tested concentration. Behavioral monitoring further indicated a swift reduction in motor activity followed by a smooth recovery process, with no evident signs of stress or avoidance behaviour. Overall, these results position citronella oil as a promising natural anesthetic for rainbow trout under the studied parameters. That said, further studies involving various solvents, temperature ranges, and fish species are necessary to comprehensively establish its efficacy and safety.
AtıfScopus4İncele - Derleme MakaleAçık Erişim2025Scopus SJRQ1Automated Detection of Neurological and Mental Health Disorders Using EEG Signals and Artificial Intelligence: A Systematic Review
Uyanik, Hakan; Sengur, Abdulkadir; Salvi, Massimo; Tan, Ru-San; Tan, Jen Hong; Acharya, U. Rajendra
Mental and neurological disorders significantly impact global health. This systematic review examines the use of artificial intelligence (AI) techniques to automatically detect these conditions using electroencephalography (EEG) signals. Guided by Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA), we reviewed 74 carefully selected studies published between 2013 and August 2024 that used machine learning (ML), deep learning (DL), or both of these two methods to detect neurological and mental health disorders automatically using EEG signals. The most common and most prevalent neurological and mental health disorder types were sourced from major databases, including Scopus, Web of Science, Science Direct, PubMed, and IEEE Xplore. Epilepsy, depression, and Alzheimer's disease are the most studied conditions that meet our evaluation criteria, 32, 12, and 10 studies were identified on these topics, respectively. Conversely, the number of studies meeting our criteria regarding stress, schizophrenia, Parkinson's disease, and autism spectrum disorders was relatively more average: 6, 4, 3, and 3, respectively. The diseases that least met our evaluation conditions were one study each of seizure, stroke, anxiety diseases, and one study examining Alzheimer's disease and epilepsy together. Support Vector Machines (SVM) were most widely used in ML methods, while Convolutional Neural Networks (CNNs) dominated DL approaches. DL methods generally outperformed traditional ML, as they yielded higher performance using huge EEG data. We observed that the complex decision process during feature extraction from EEG signals in ML-based models significantly impacted results, while DL-based models handled this more efficiently. AI-based EEG analysis shows promise for automated detection of neurological and mental health conditions. Future research should focus on multi-disease studies, standardizing datasets, improving model interpretability, and developing clinical decision support systems to assist in the diagnosis and treatment of these disorders.
AtıfScopus58OpenAlex46İncele - MakaleAçık Erişim2025Scopus SJRQ1A novel deep learning-based statistical randomness evaluation test methodology for cryptographic applications
Kaner, Seyfullah; Garipcan, Ali Murat; Erdem, Ebubekir
The security of cryptographic systems is directly linked to the statistical randomness properties of the random numbers used. Traditional statistical randomness tests can be limited in evaluating the properties of these numbers and can require long processing times. While widely used test suites such as the National Institute of Standards and Technology (NIST) Special Publication (SP) 800–22 play a crucial role in assessing data randomness, they are slow on large data sets, can only evaluate certain statistical properties, and fail to detect complex data patterns and dependency structures. In this paper, we propose a new deep learning (DL) based method to overcome these limitations. In the study, bit sequences produced by an FPGA-based true random number generator (TRNG) and different pseudo-random number generators (PRNGs) were converted into image format, and classification experiments were carried out on the AlexNet, ResNet50, and EfficientNetB0 architectures. The results showed that AlexNet, with 87% accuracy and 99% recall, was by far the most successful method compared to the other models. Furthermore, the ablation analysis revealed that the components of data augmentation, early stopping, and cross-validation play a critical role in the model’s stability and generalizability. Providing a reliable randomness evaluation with high accuracy, this new method, as an alternative to traditional statistical test suites, demonstrates that AI-based solutions can enhance the effectiveness and accuracy of randomness assessment in cryptographic applications. The success of DL methods in complex data structures offers significant potential for improving the security of modern cryptographic systems.
AtıfScopus9WoS9OpenAlex11İncele - MakaleYalnız künye2025Scopus SJRQ4Examination of Consumer Perceptions Regarding Productive Artificial Intelligence Applications: A Qualitative Study on the Case of ChatGPT
Cevher, Muhammed Fatih
This research intends to examine the perceptions of consumers who use artificial intelligence applications towards these applications and to examine the perceptions, preferences, and usage experiences of users towards artificial intelligence applications from the perspective of marketing and consumer behavior. In line with this purpose, the qualitative analysis method was picked as the analytical approach in the investigation. The sample consists of a total of 25 participants with different demographic characteristics who are experienced in artificial intelligence applications. The information gathered from the participants through interviews was analyzed through the MAXQDA statistical analysis program, and descriptive content analysis was performed. In the study’s findings, 8 different codes were identified, and the relationships between the codes were analyzed. “Speed,” ‘Benefit’ (perceived performance), and ‘Convenience’ (perceived ease of use) are the themes that stand out positively in consumers’ perceptions of productive artificial intelligence applications. On the other hand, “Distrust” (perceived risk) is a negative theme. The study’s findings reveal consumer perceptions and expectations by examining the rapidly developing artificial intelligence technology from the consumers’ perspective. This information obtained for consumers using artificial intelligence offers important solution suggestions for artificial intelligence developers and businesses. Furthermore, the findings obtained in this study provide a different perspective for future studies on this subject.
- MakaleYalnız künye2024Scopus SJRQ2Deep learning-assisted Hubble parameter analysis
Salti, Mehmet; Kangal, Evrim Ersin; Zengin, Bilgin
We turn our attention on evaluating the most recent Hubble parameter data measured via the differential evolution of cosmic-chronometers from a deep learning perspective. To achieve this goal, we start our investigation by introducing the selected theoretical setup and compiling the most recent statistical data obtained in cosmology experiments. Then we implement a tuned version of the long-short term memory (LSTM) architecture and run it to predict possible values of the Cosmic Hubble parameter for different red-shift states. Since we observe a good correlation between the observed and predicted datasets of the Hubble parameter, we conclude that the machine learning approaches can play important roles in the future cosmology investigations.
AtıfScopus2WoS2OpenAlex2İncele - MakaleYalnız künye2023Scopus SJRQ1An efficient hybrid weather prediction model based on deep learning
Utku, A.; Can, U.
Weather events directly affect human activities. In particular, extreme weather events with global warming, forest fires, and high air temperatures that cause drought make human life difficult. Effective and accurate weather prediction models are needed to take precautions against such climatic events. Therefore, it is essential to develop models that make precise weather predictions. Technological developments contributed significantly to developing successful deep learning-based weather prediction models. With a high success rate, this study proposed a hybrid weather prediction model based on Convolutional Neural Networks and Recurrent Neural Networks models. The proposed hybrid model was applied to the Jena dataset, which contains 14-parameter, large-scale meteorological data that were never utilized for weather prediction. The experimental results were compared with popular deep learning, machine learning, and statistical methods such as Auto-Regressive Integrated Moving Average, Convolution Neural Networks, Long-Short Term Memory, Multilayer Perceptron, Random Forest, Recurrent Neural Networks, and Support Vector Machine. As a result of these comparisons, the proposed hybrid model obtained the best prediction result for all metrics. For example, according to the weather prediction results for Jena, Germany, the proposed hybrid model got the results of Mean Squared Error: 0.035, Root-Mean-Squared Error: 0.189, Mean Absolute Error: 0.126, and R-Squared: 0.987.
AtıfScopus15WoS11OpenAlex11İncele - 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.
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