Sürdürülebilir Kalkınma Amaçları
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- MakaleYalnız künye2026Scopus SJRQ1MILA: An Innovative Approach to Identifying AI-Generated Content Using BERT, CNN, and BiLSTM
Utku, Anil
AI ethical principles are essential in balancing the social impacts of rapidly developing technology today. The widespread use of AI technologies has heightened the need for ethical and legal regulations in this area. In this process, fundamental principles such as data privacy, accountability, transparency, and human oversight take center stage to enhance the reliability of artificial intelligence and foster social trust. Generative AI refers to machines producing text, images, audio, and video content. However, generative AI brings ethical discussions since it can produce original content. Detecting AI-generated content is becoming increasingly complex due to the ability of AI models to produce increasingly more fluent and meaningful texts. This study created a hybrid MILA model using BERT, CNN, and BiLSTM models to detect AI-generated content. MILA was compared in detail with SVM, XGBoost, CNN, LSTM, and BiLSTM using sentence-level and article-level datasets. Experiments demonstrated that MILA outperformed the compared models, achieving 94.34% accuracy at the sentence-level and 93.62% accuracy at the article-level.
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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.
AtıfScopus1WoS1OpenAlex3İncele - MakaleAçık Erişim2026Scopus SJRQ1Quantifying agricultural resilience under climate variability: a data-driven climate resilience index for European cereal systems
Utku, Anil; Radulescu, Magdalena; Barut, Abdulkadir; Alofaysan, Hind
Climate variability and extreme weather events pose an increasingly serious threat to agricultural productivity in Europe. Although many studies have focused on the mean yield response to climate change, relatively few have attempted to offer a comprehensive, multi-dimensional view of agricultural resilience, separating long-term productivity trends from short-term climate-induced fluctuations. In this paper, a data-driven Climate Resilience Index is proposed to assess the resilience of European cereal systems under climate stress. Annual yield time series are decomposed into trend and anomaly components to identify interannual deviations driven by climate variability. Machine learning and deep learning algorithms, including RF, CatBoost, CNN, LSTM, TCN, and a TCN-LSTM hybrid model, are used to assess the predictive validity of climate extreme indices and persistence patterns. Among the evaluated models, the TCN-LSTM architecture achieved the highest predictive performance with 0.8347 R2, outperforming standalone temporal and ensemble-based models. The PCA-based exposure framework explained 68.4% of the total climate variability within the first principal component, indicating a highly concentrated climate-stress structure. Feature importance analyses further revealed that lagged yield anomalies and rolling volatility indicators were the dominant predictors of resilience dynamics. The findings show that temporal models perform better than traditional ensemble methods, emphasizing the significance of multi-year recovery mechanisms in anomaly evolution. To provide a consistent measure of exposure, principal component analysis is used to reduce correlated climate indicators into a multi-dimensional climate stress index. The CRI captures these three essential elements: climate exposure, yield variability, and recovery. The results at the country level show substantial variability across Europe, indicating that while exposure intensity is important, recovery dynamics are also critical to understanding resilience. The results also show that there are structurally distinct resilience profiles, and robustness tests indicate that country rankings are not sensitive to different weights and recovery functions. The results clearly show that recovery and adaptive capacity are critical to agricultural sustainability amid growing climate uncertainty. The CRI developed in this paper provides a clear, empirically valid framework for comparing resilience in agricultural systems.
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İncele - MakaleAçık Erişim2026Scopus SJRQ1EBA-Net: A hybrid EfficientNetB3-BiLSTM-Attention model for species-level diatom classification
Balo Utku, Esen Damla; Kutlu, Banu; Utku, Anil
Species identification in diatoms is important for assessing water bodies, determining environmental quality, and analyzing biodiversity. Nonetheless, diatom classification has proven difficult due to intra-class variation, interclass similarity, and data imbalance in microscopic images. In this regard, we propose a new deep learning approach for species-level diatom classification. Various deep learning models, including traditional convolutional neural networks or CNNs (ResNet50, DenseNet121), state-of-the-art CNNs (EfficientNetB3, Xception, MobileNetV3), transformer-based architectures (ViT-B16, Swin-Tiny), and our proposed EBA-Net (EfficientNetB3-BiLSTM-Attention Network), were extensively compared under identical experimental settings. The EBA-Net integrates the advantages of convolutional feature extraction, sequence modeling, and an attention mechanism to improve classification accuracy. It first uses the EfficientNetB3 model as the backbone network to obtain higher-level features, followed by a BiLSTM layer to capture dependencies among the extracted features. Then, it applies the attention mechanism to the BiLSTM output to highlight the most informative features. Experimental results showed that the proposed EBA-Net model outperforms other models considered in the study, yielding 95.8% accuracy and 85.1% macro F1-score. In addition, Top-3 (98.9%) and Top-5 (99.6%) accuracies confirmed the efficiency of the proposed model for classifying objects from visually similar classes. An analysis of the model's interpretability was conducted using Gradient-weighted Class Activation Mapping, showing that the proposed model's attention is concentrated on relevant morphological features of diatoms. To the best of our knowledge, the suggested approach achieves state-of-the-art accuracy on the data set of diatoms used.
- MakaleAçık Erişim2026Scopus SJRQ2Data-Driven Prediction of Climate Variables in Agricultural Cities of India With Hybrid GA-TCN-LSTM Model
Utku, Anil
Accurate prediction of climate variables is important in reducing the effects of natural disasters and taking preventive measures for agriculture and food security, energy sector, public health, and water resources management. For agricultural production, it is essential for producers to determine strategies for their agricultural activities and to increase agricultural productivity. Issues such as determining planting and harvest times, determining pesticides to be used against agricultural pests, selecting products to be planted, irrigation, and sustainable agriculture are directly related to climate variables. In this study, a GA-TCN-LSTM hybrid prediction model was created to determine climate variables such as relative humidity, temperature, wind speed, and dew point in Bhopal, Indore, Kanpur, Ludhiana, and Patna, which are among the prominent cities of India in terms of agricultural production. The hyper-parameters of the developed model using the temporal convolutional networks (TCN) and long short-term memory (LSTM) were optimized with the genetic algorithm (GA), and the GA-TCN-LSTM hybrid model was created. GA-TCN-LSTM was extensively compared with the base TCN-LSTM, convolutional neural network (CNN), LSTM, TCN, and CNN-LSTM. The compared models were tested using approximately 15 years of hourly, up-to-date, and real-time data of the cities. Experiments showed that GA-TCN-LSTM outperformed the compared models and had above 0.9 R-Squared (R2) for the majority of cities and climate variables.
AtıfScopus2WoS1OpenAlex2İncele - MakaleAçık Erişim2025Scopus SJRQ2Unraveling user perceptions and biases: A comparative study of ML and DL models for exploring twitter sentiments towards ChatGPT
Baker, Mohammed Rashad; Utku, Anil
ChatGPT is a powerful chatbot that used to generate human-like text. Chat GPT, developed by OpenAI, can answer many questions honestly, like a personal teacher who knows almost everything. It can perform various tasks such as question and answer, solving mathematical equations, writing text, debugging, and translating between languages. Compared to traditional chatbots, ChatGPT has been adopted by many users since its introduction. Some users feel that ChatGPT will override most content creation professions. This study analyzed users’ feelings about ChatGPT by analyzing tweets shared about ChatGPT. For this purpose, a hybrid Deep Learning (DL) model was developed using Convolutional Neural Networks (CNN) and Bi- Long Short-Term Memory (Bi-LSTM) models. The study has been compared with a number of DL and Machine Learning algorithms, LSTM, Bi-LSTM, CNN, Gated Recurrent Unit, Random Forests and Support Vector Machines. The experimental outcomes demonstrated that, the FastText-trained CNN-Bi-LSTM model exceeded the other models in terms of accuracy, reaching 96.59%.
AtıfScopus18WoS11OpenAlex13İncele - MakaleAçık Erişim2025Scopus SJRQ2A New Hybrid ConvViT Model for Dangerous Farm Insect Detection
Utku, Anil; Kaya, Mahmut; Canbay, Yavuz
This study proposes a novel hybrid convolution and vision transformer model (ConvViT) designed to detect harmful insect species that adversely affect agricultural production and play a critical role in global food security. By utilizing a dataset comprising images of 15 distinct insect species, the suggested approach combines the strengths of traditional convolutional neural networks (CNNs) with vision transformer (ViT) architectures. This integration aims to capture local-level morphological features effectively while analyzing global spatial relationships more comprehensively. While the CNN structure excels at discerning fine morphological details of insects, the ViT’s self-attention mechanism enables a holistic evaluation of their overall configurations. Several data preprocessing steps were implemented to enhance the model’s performance, including data augmentation techniques and strategies to ensure class balance. In addition, hyperparameter optimization contributed to more stable and robust model training. Experimental results indicate that the ConvViT model outperforms commonly used benchmark architectures such as EfficientNetB0, DenseNet201, ResNet-50, VGG-16, and standalone ViT, achieving a classification accuracy of 93.61%. This hybrid approach improves accuracy and strengthens generalization capabilities, delivering steady performance during training and testing phases, thereby increasing its reliability for field applications. The findings highlight that the ConvViT model achieves high efficiency in pest detection by integrating local and global feature learning. Consequently, this scalable artificial intelligence solution can support sustainable agricultural practices by enabling the early and accurate identification of pests and reducing the need for intensive pesticide use.
AtıfScopus9WoS5OpenAlex11İncele - MakaleAçık Erişim2023Deep Learning Based Video Event Classification
Gencaslan, Serim; Utku, Anil; Akcayol, M. Ali
In recent years, due to the growth of digital libraries and video databases, automatic detection of activities from videos and obtaining patterns from large datasets have come to the fore. Object detection from images is used as a tool for various applications and is the basis of video classification. Objects in videos are more difficult to identify than in single images, as the information in videos has a time-continuity constraint. Following the developments in the field of computer vision, the use of open source software packages for machine learning and deep learning and the developments in hardware technologies have enabled the development of new approaches. In this study, a deep learning-based classification model has been developed for the classification of sports branches in video. In the model developed using CNN, transfer learning has been applied with VGG-19. Experimental studies on 32827 frames using CNN and VGG-19 models showed that VGG-19 has a more successful classification performance than CNN with an accuracy rate of 83%.
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İncele - MakaleAçık Erişim2023Scopus SJRQ1Deep learning based hybrid prediction model for predicting the spread of COVID-19 in the world's most populous countries
Utku, Anil
COVID-19 has a disease and health phenomenon and has sociological and economic adverse effects. Accurate prediction of the spread of the epidemic will help in the planning of health management and the development of economic and sociological action plans. In the literature, there are many studies to analyse and predict the spread of COVID-19 in cities and countries. However, there is no study to predict and analyse the cross-country spread in the world's most populous countries. In this study, it was aimed to predict the spread of the COVID-19 epidemic. The motivation of this study is to reduce the workload of health workers, take preventive measures and optimize health processes by predicting the spread of the COVID-19 epidemic. A hybrid deep learning model was developed to predict and analyse COVID-19 cross-country spread and a case study was carried out for the world's most populous countries. The developed model was tested extensively using RMSE, MAE and R2. The experimental results showed that the developed model was more successful in predicting and analysis of COVID-19 cross-country spread in the world's most populous countries than LR, RF, SVM, MLP, CNN, GRU, LSTM and base CNN-GRU. In the developed model, CNN performs convolution and pooling operations to extract spatial features from the input data. GRU provides learning of long-term and non-linear relationships inferred by CNN. The developed hybrid model was more successful than the other models compared, as it enabled the effective features of the CNN and GRU models to be used together. The prediction and analysis of the cross-country spread of COVID-19 in the world's most populated countries can be presented as a novelty of this study.
AtıfScopus23WoS20OpenAlex31İncele - MakaleAçık Erişim2023Scopus SJRQ1DEEP LEARNING BASED AN EFFICIENT HYBRID PREDICTION MODEL FOR COVID-19 CROSS-COUNTRY SPREAD AMONG E7 AND G7 COUNTRIES
Utku, Anil
The COVID-19 pandemic has caused the death of many people around the world and has also caused economic problems for all countries in the world. In the literature, there are many studies to analyze and predict the spread of COVID-19 in cities and countries. However, there is no study to predict and analyze the cross-country spread in the world. In this study, a deep learning based hybrid model was developed to predict and analysis of COVID-19 cross-country spread and a case study was carried out for Emerging Seven (E7) and Group of Seven (G7) countries. It is aimed to reduce the workload of healthcare professionals and to make health plans by predicting the daily number of COVID-19 cases and deaths. Developed model was tested extensively using Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE) and R Squared (R2). The experimental results showed that the developed model was more successful to predict and analysis of COVID-19 cross-country spread in E7 and G7 countries than Linear Regression (LR), Random Forest (RF), Support Vector Machine (SVM), Multilayer Perceptron (MLP), Convolutional Neural Network (CNN), Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM). The developed model has R2 value close to 0.9 in predicting the number of daily cases and deaths in the majority of E7 and G7 countries.
AtıfScopus9OpenAlex11İncele - 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 - BildiriYalnız künye2022Privacy Concerns and Measures in Metaverse: A Review
Canbay, Yavuz; Utku, Anil; Canbay, Pelin; Ozbudak, F.; Sagiroglu, S.; Selcuk, A.A.
Metaverse is the new buzzword in today's digital world. It is a new internet application and a new form of the social world that integrates new technologies such as; Artificial Intelligence (AI), Virtual Reality (VR), Augmented Reality (AR), Extended Reality (XR), Blockchain (BC),etc. The metaverse can be seen as a virtual world that mimics the real world. Most things that can be done in the real world, such as shopping, making new friends, attending concerts, playing games, and so on, can be done differently in Metaverse. While a person who represents himself with his presence in the real world realizes this representation in the digital world with his personal data. Hence, the Metaverse exploits personal data to create and maintain this virtual world. At this point, privacy concerns of users in Metaverse occur, and Metaverse Service Providers (MSP) should take these concerns into account. This paper focuses on privacy concerns in Metaverse, presents some measures in order to minimize these concerns, and provides a comprehensive list of personal data collected and processed in Metaverse.
AtıfScopus44OpenAlex53İncele - MakaleAçık Erişim2022Scopus SJRQ2Using network traffic analysis deep learning based Android malware detection; [Aǧ trafiǧi analizi ile derin öǧrenme tabanli Android kötücül yazilim tespiti]
Utku, Anil
Mobile devices, which are becoming more and more widespread today, have turned into hand-held computers thanks to the multimedia communication and applications. Today, the multimedia applications have been supported by traditional mobile phones. With their increasing functionality, mobile devices are used for many purposes such as enriched internet experience, financial transactions, access to social media platforms, sharing, music and video. Performing transactions where sensitive personal data transfers such as banking and shopping are carried out on mobile devices make mobile devices the target of attackers. In this study, a deep learning based malware detection system has been developed based on the interactions of mobile applications on the network. The developed LSTM-based deep learning model has been analysed comparatively with NB, RF, SVM, MLP, CNN, RNN and GRU using accuracy, precision, recall and F-1 metrics. The experimental results showed that the developed LSTM-based deep learning model is more successful in malware detection than others with 95% accuracy.
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