Sürdürülebilir Kalkınma Amaçları

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• MakaleYalnız künye2026Scopus SJRQ1/r/munzur/1519
  Innovative 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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  AtıfScopus3·OpenAlex2

  İncele
• MakaleYalnız künye2026Scopus SJRQ1/r/munzur/1470
  MILA: 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.

  AtıfOpenAlex1

  İncele
• MakaleYalnız künye2026Scopus SJRQ1/r/munzur/1429
  From 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.

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  AtıfScopus1·WoS1·OpenAlex3

  İncele
• MakaleAçık Erişim2026Scopus SJRQ1/r/munzur/1136
  Quantifying 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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  AtıfScopus1·OpenAlex2

  İncele
• MakaleYalnız künye2026Scopus SJRQ1/r/munzur/1031
  Analysis 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.

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• MakaleAçık Erişim2026Scopus SJRQ2/r/munzur/906
  Federated 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.

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• MakaleAçık Erişim2026Scopus SJRQ1/r/munzur/782
  EBA-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.

  İncele
• MakaleAçık Erişim2026Scopus SJRQ2/r/munzur/693
  Data-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.

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  AtıfScopus2·WoS1·OpenAlex2

  İncele
• MakaleYalnız künye2025/r/munzur/6154
  Artificial 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.

  AtıfTR Dizin1

  İncele
• MakaleAçık Erişim2025/r/munzur/6146
  Enhancing 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.

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