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    A hybrid deep learning approach combining neutrosophic set theory and multi-axis vision transformer for nutrient deficiency classification in plants
    (Pergamon-Elsevier Science Ltd, 2025) Sert, Eser; Kiziloluk, Soner
    Plant nutrient deficiencies in agricultural production, especially nitrogen and potassium deficiencies, threaten global food security. The fact that traditional detection methods are time-consuming, costly, and require expertise increases the need for automatic and reliable classification systems. Current deep learning models lack the ability to assess the reliability of their predictions, which can lead to misclassifications in low-quality or ambiguous images. In order to overcome these limitations, an innovative approach that combines Neutrosophic Set theory with the Multi-Axis Vision Transformer (MaxViT), one of the latest Vision Transformer (ViT) models, namely Neutrosophic-MaxViT, is proposed in this study. The proposed model increases the explicability and reliability of classification results by integrating MaxViT's hybrid architecture, which includes local convolutions and global attention mechanisms, with the truth (T), indeterminacy (I), and falsity (F) components of Neutrosophic Set theory. In addition, a special Neutrosophic Entropy Loss Function has been developed that optimizes the T, I, and F components. The performance of the model was evaluated on the Early Nutritional Stress Detection of Plants (EarlyNSD) dataset, which includes healthy, nitrogen, and potassium-deficient conditions of ashgourd, bittergourd, and snakegourd plants. The experimental results show that the Neutrosophic-MaxViT model outperforms advanced models, including MaxViT, Regular Networks Y (RegNetY), Convolutional Neural Network Next (ConvNeXt), and ViT-Tiny, achieving 95.31 % accuracy and a macro F1-score of 0.9529. This study contributes to artificial intelligence-based agricultural solutions, enhancing productivity and offering a reliable framework for sustainable practices.
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    A new hybrid approach based on AOA, CNN and feature fusion that can automatically diagnose Parkinson's disease from sound signals: PDD-AOA-CNN
    (Springer London Ltd, 2024) Yildirim, Muhammed; Kiziloluk, Soner; Aslan, Serpil; Sert, Eser
    Parkinson's is one of the most rapidly increasing neurological diseases in the world, caused by the deficiency of dopamine-producing cells in the brain. Voice disorders are a significant finding in the early stage of Parkinson's disease (PD). Detection of this finding at an early stage of the disease allows early treatment of the disease. Therefore, in this study, using sound data, a hybrid model for detecting PD has been designed. In the developed method, first of all, the sound data were converted into spectrograms. Then, the feature maps of the obtained spectrogram images were extracted using 3 different CNN architectures. Feature maps with different features obtained by utilizing the accumulation of different architectures were combined. Then, these features were selected using the arithmetic optimization algorithm (AOA), one of the most recent metaheuristic optimization algorithms, and then classified by support vector machine (SVM) and K-nearest neighbors (KNN). One of the important novelties in the study is the reduction of the size of the acquired feature maps with AOA, a new and high-performance metaheuristic approach. The success of the proposed model in diagnosing Parkinson's disease reached up to 98.19%. In addition, feature maps of the sound data in the dataset were acquired by using the MFCC method to compare the performance of the proposed model. Eight different classifiers were used to categorize the acquired feature maps. The highest accuracy value obtained in this method was obtained in the Random Forest classifier with 93.98%.
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    A novel feature fusion and mountain gazelle optimizer based framework for the recognition of jute pests in sustainable agriculture
    (Nature Portfolio, 2025) Kiziloluk, Soner; Karaduman, Mucahit; Aslan, Serpil; Yildirim, Muhammed; Khan, Muhammad Attique; Alhayan, Fatimah; Nam, Yunyoung
    Sustainable agriculture is an approach that involves adopting and developing agricultural practices to increase efficiency and preserve resources, both environmentally and economically. Jute is one of the primary sources of income grown in many countries. At this stage, increasing efficiency in jute production and protecting it from pests is essential. Detecting jute pests at an early stage will not only improve crop yield but also provide more income. In this paper, an artificial intelligence-based model was suggested to detect jute pests at an early stage. In this developed model, two different pre-trained models were used for feature extraction. To improve the performance of the developed model, the features obtained using the DarkNet-53 and DenseNet-201 models were combined. After this stage, the metaheuristic Mountain Gazelle Optimizer (MGO) was used, allowing the developed model to work faster and achieve more successful results. Feature selection was carried out using MGO; thus, more successful results were obtained with fewer, more compelling features. The proposed model was compared with six different models and five different classifiers accepted in the literature. In the developed model, 17 different jute pests were detected with 96.779% accuracy. The accuracy value achieved in the developed model is promising in successfully detecting jute pests.
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    Automatic detection of harmful cyanobacterial genera using deep CNN models and artemisinin optimization
    (Nature Portfolio, 2025) Topaloglu, Fatih; Kiziloluk, Soner; Sert, Eser; Yildirim, Muhammed
    Concerns over the spread of Cyanobacteria, which can lead to dangerous blooms that harm drinking water quality and, therefore, the health of plants and animals, are being raised by global warming. Traditional methods for assessing the amount of toxic species in water samples are often time-consuming, require intensive manual effort, are prone to subjective errors, and can lead to delays in necessary water management interventions. This emphasizes the pressing need for a quick and precise automated method. Both aquatic and terrestrial environments include cyanobacteria, and under some circumstances, poisonous cyanobacteria can grow in large numbers and form harmful blooms called harmful cyanobacterial blooms (Cyano-HABs). In addition, cyanoHABs cause hypoxia, ecological imbalances, the generation of toxins, and other detrimental phenomena that put people, animals, and plants in danger of illness. Climate change is expected to cause these situations to increase in frequency and globally. This study presents a novel approach for the automatic detection of harmful cyanobacteria genera by utilizing a newly introduced and publicly available dataset, TCB-DS. In the initial stage, discriminative features are extracted using two powerful deep Convolutional Neural Network (CNN) models: ShuffleNet and ResNet-50. Subsequently, feature fusion is applied to the extracted features to enhance the representation. Then, to select the most relevant features, feature selection is performed using the Artemisinin Optimization (AO) algorithm, a robust meta-heuristic algorithm inspired by the mechanisms of malaria treatment and recently proposed in 2024. This step aims to reduce feature redundancy and improve the overall efficiency of the model. In classifying microscopic images of cyanobacteria species with the proposed method, GoogleNet, MobileNetV2, EfficientNetb0, DarkNet53, ShuffleNet, and ResNet101 models were used. Among these, the proposed method obtained the highest accuracy, with a mean accuracy of 97.471% and max accuracy of 97.683%. Since these results are the highest accuracy values obtained in the TCB-DS dataset, our proposed method significantly improves water quality monitoring in our world.
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    Detection of Corn Diseases Using Transformer-Based Deep Learning Models
    (Institute of Electrical and Electronics Engineers Inc., 2025) Kiziloluk, Soner
    As a grain crop, corn is one of the most important products grown worldwide. The timely and accurate identification of diseases on corn leaves is essential for highquality yield and sustainable agricultural productivity. At this point, the application of deep learning models for corn leaf disease detection comes into play. These models provide a quick solution to this problem. Traditional deep learning models, such as Convolutional Neural Networks (CNN), typically operate on local spatial features and within a limited range. Long-range dependencies required for more complex recognition patterns are often overlooked, making these models less effective. Transformer-based models are more adept at capturing global context thanks to the self-attention mechanisms that are part of their architectures. In this study, four different transformer models and two different CNN models were used for corn leaf disease detection. According to the experimental results, MaxViT outperformed other transformer-based models and CNN models, achieving an accuracy of 97.85% and an F1 score of 0.9725. © 2025 IEEE.
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    Early detection of colorectal cancer using a hybrid model with enhanced image quality and optimized classification
    (Springer, 2025) Bozdag, Ahmet; Karaduman, Mucahit; Kiziloluk, Soner; Karaduman, Gulsah; Yildirim, Muhammed; Yildirim, Ozal; Acharya, U. Rajendra
    Colorectal cancer starts in the large intestine and rectum. It develops when small, usually harmless growths called polyps become cancerous over time. Early diagnosis increases the chances of successfully treating colorectal cancer. A new hybrid model was developed to detect colorectal tissue types. In the first step of the model, the quality of the images was increased using Denoising Convolutional Neural Network (DNCNN) networks. The feature maps of the images were then obtained using DarkNet53 and shrunk using the Gorilla Troops Optimization Algorithm (GTO) to speed up the proposed model's performance and boost the performance. Finally, a support vector machine (SVM) classifier was used to classify the feature maps. The proposed model obtained an accuracy of 95.5% in classifying eight tissue types in colorectal cancer histopathology specimens (Adipose, Complex, Debris, Empty, Lympho, Mucosa, Stroma, and Tumor). To make the developed model more generalizable, robust, and accurate, it needs to be tested with a huge dataset collected from various centers and races.
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    EO-CNN: Equilibrium Optimization-Based hyperparameter tuning for enhanced pneumonia and COVID-19 detection using AlexNet and DarkNet19
    (Elsevier, 2024) Kiziloluk, Soner; Sert, Eser; Hammad, Mohamed; Tadeusiewicz, Ryszard; Plawiak, Pawel
    Convolutional neural networks (CNN) have been increasingly popular in image categorization in recent years. Hyperparameter optimization is a critical stage in enhancing the effectiveness of CNNs and achieving better results. Properly tuning hyperparameters allows the model to exhibit improved performance and facilitates faster learning. Misconfigured hyperparameters can prolong the training time or lead to the model not learning at all. Manually tuning hyperparameters is a time-consuming and challenging process. Automatically adjusting hyperparameters helps save time and resources. This study aims to propose an approach that shows higher classification performance than unoptimized convolutional neural network models, even at low epoch values, by automatically optimizing the hyperparameters of AlexNet and DarkNet19 with equilibrium optimization, the newest metaheuristic algorithm. In this respect, the proposed approach optimizes the number and size of filters in the first five convolutional layers in AlexNet and DarkNet19 using an equilibrium optimization algorithm. To evaluate the efficacy of the suggested method, experimental analyses were conducted on the pneumonia and COVID-19 datasets. An important advantage of this approach is its ability to accurately classify medical images. The testing process suggests that utilizing the proposed approach to optimize hyperparameters for AlexNet and DarkNet19 led to a 7% and 4.07% improvement, respectively, in image classification accuracy compared to nonoptimized versions of the same networks. Furthermore, the approach displayed superior classification performance even in a few epochs compared to AlexNet, ShuffleNet, DarkNet19, GoogleNet, MobileNet-V2, VGG-16, VGG-19, ResNet18, and Inceptionv3. As a result, automatic tuning of the hyperparameters of AlexNet and DarkNet-19 with EO enabled the performance of these two models to increase significantly.
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    MidFusionEfficientV2: Improving Ophthalmic Diagnosis with Mid-Level RGB-LBP Fusion and SE Attention
    (Mdpi, 2026) Keles, Julide Kurt; Kiziloluk, Soner; Sert, Eser; Talo, Furkan; Yildirim, Muhammed
    Background/Objectives: Early diagnosis of eye diseases is critically important for enhancing individuals' quality of life and reducing the risk of vision loss. In this study, a deep learning-based hybrid model called MidFusionEfficientV2 has been proposed to classify eye diseases, including uveitis, conjunctivitis, cataract, eyelid drooping, and normal conditions. Methods: The model presents a dual-branch architecture that combines an RGB image branch with an EfficientNetV2-S architecture and a specialized texture branch based on Local Binary Pattern (LBP) transformation at an intermediate level. Thanks to the Squeeze-and-Excitation (SE) blocks integrated into the LBP branch, channel-based attention mechanisms have been activated, enhancing the prominence of textural features. The features obtained from the RGB and LBP branches were combined at an intermediate level and transferred to the classification stage. Results: Experimental studies on the five-class eye disease dataset from the Mendeley Data platform have shown that the proposed model outperformed six strong models (ResNetV2, ConvNeXt, DenseNet-121, EfficientNet-B1, MobileNetV3 Large, and EfficientNetV2-S) with an accuracy of 98%. Especially in the difficult-to-diagnose uveitis class, recall and F1 scores of 97% and 94%, respectively, were achieved. Conclusions: The results show that a moderate combination of color and texture features significantly improves classification performance, and that MidFusionEfficientV2 offers a reliable and effective solution for the automatic diagnosis of eye diseases.
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    Multi-feature fusion and dandelion optimizer based model for automatically diagnosing the gastrointestinal diseases
    (Peerj Inc, 2024) Kiziloluk, Soner; Yildirim, Muhammed; Bingol, Harun; Alatas, Bilal
    It is a known fact that gastrointestinal diseases are extremely common among the public. The most common of these diseases are gastritis, reflux, and dyspepsia. Since the symptoms of these diseases are similar, diagnosis can often be confused. Therefore, it is of great importance to make these diagnoses faster and more accurate by using computer-aided systems. Therefore, in this article, a new artificial intelligence-based hybrid method was developed to classify images with high accuracy of anatomical landmarks that cause gastrointestinal diseases, pathological findings and polyps removed during endoscopy, which usually cause cancer. In the proposed method, firstly trained InceptionV3 and MobileNetV2 architectures are used and feature extraction is performed with these two architectures. Then, the features obtained from InceptionV3 and MobileNetV2 architectures are merged. Thanks to this merging process, different features belonging to the same images were brought together. However, these features contain irrelevant and redundant features that may have a negative impact on classification performance. Therefore, Dandelion Optimizer (DO), one of the most recent metaheuristic optimization algorithms, was used as a feature selector to select the appropriate features to improve the classification performance and support vector machine (SVM) was used as a classifier. In the experimental study, the proposed method was also compared with different convolutional neural network (CNN) models and it was found that the proposed method achieved better results. The accuracy value obtained in the proposed model is 93.88%.
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    TSA-CNN-AOA: Twitter sentiment analysis using CNN optimized via arithmetic optimization algorithm
    (Springer London Ltd, 2023) Aslan, Serpil; Kiziloluk, Soner; Sert, Eser
    COVID-19, a novel virus from the coronavirus family, broke out in Wuhan city of China and spread all over the world, killing more than 5.5 million people. The speed of spreading is still critical as an infectious disease, and it causes more and more deaths each passing day. COVID-19 pandemic has resulted in many different psychological effects on people's mental states, such as anxiety, fear, and similar complex feelings. Millions of people worldwide have shared their opinions on COVID-19 on several social media websites, particularly on Twitter. Therefore, it is likely to minimize the negative psychological impact of the disease on society by obtaining individuals' views on COVID-19 from social media platforms, making deductions from their statements, and identifying negative statements about the disease. In this respect, Twitter sentiment analysis (TSA), a recently popular research topic, is used to perform data analysis on social media platforms such as Twitter and reach certain conclusions. The present study, too, proposes TSA using convolutional neural network optimized via arithmetic optimization algorithm (TSA-CNN-AOA) approach. Firstly, using a designed API, 173,638 tweets about COVID-19 were extracted from Twitter between July 25, 2020, and August 30, 2020 to create a database. Later, significant information was extracted from this database using FastText Skip-gram. The proposed approach benefits from a designed convolutional neural network (CNN) model as a feature extractor. Thanks to arithmetic optimization algorithm (AOA), a feature selection process was also applied to the features obtained from CNN. Later, K-nearest neighbors (KNN), support vector machine, and decision tree were used to classify tweets as positive, negative, and neutral. In order to measure the TSA performance of the proposed method, it was compared with different approaches. The results demonstrated that TSA-CNN-AOA (KNN) achieved the highest tweet classification performance with an accuracy rate of 95.098. It is evident from the experimental studies that the proposed approach displayed a much higher TSA performance compared to other similar approaches in the existing literature.