fuat türk | KIRIKKALE UNIVERSITY-TURKEY (original) (raw)

Papers by fuat türk

Journal of medicine and palliative care, Aug 29, 2023

Aims: Diabetes mellitus is a metabolic disease caused by elevated blood sugar. If this disease is... more Aims: Diabetes mellitus is a metabolic disease caused by elevated blood sugar. If this disease is not diagnosed on time, it has the potential to pose a risk to other organs and tissues. Machine learning algorithms have started to preferred day by day in the detection of this disease, as in many other diseases. This study suggests a diabetes prediction approach incorporating optimized machine learning (ML) algorithms. Methods: The framework presented in this study starts with the application of different data pre-processing processes. Random forest (RF), support vector machine (SVM), K-nearest neighbor (K-NN) and decision tree (DT) algorithms are used for classification. Grid search is utilized for hyperparameter optimization of algorithms. Different performance evaluation measures are used to find the algorithm that best predicts diabetes. PIMA Indian dataset (PID) is chosen for testing the experiments. In addition, it is investigated to what extent the attributes in the data set affect the result using Shapley additive explanations (SHAP) analysis. Results: As a result of the experiments, the RF algorithm achieved the highest success rate with 89.06%, 84.33%, 84.33%, 84.33% and 0.88% accuracy, precision, sensitivity, F1-score and AUC scores. As a result of the SHAP analysis, it is found that the "Insulin", "Age" and "Glucose" attributes contributed the most to the prediction model in identifying patients with diabetes. Conclusion: The hyperparameter optimized RF approach proposed in the framework of the study provided a good result in the prediction and diagnosis of diabetes mellitus when compared with similar studies in the literature. As a result, an expert system can be designed to detect diabetes early in real time using the proposed method.

Hittite journal of science and engineering-b/Hittite journal of science and engineering, Mar 31, 2024

O ver the last ten years, the swift rise of cryptocurrencies has triggered sweeping shifts in the... more O ver the last ten years, the swift rise of cryptocurrencies has triggered sweeping shifts in the worldwide economy, reshaping financial landscapes and remoulding transactional systems [1]. These seismic shifts owe much to rapid advancements in Information Technology (IT), enabling the emergence of blockchain and the birth of Bitcoin in 2009 by the enigmatic entity known as Satoshi Nakamoto [2]. The soaring popularity of digital currencies like Bitcoin and Ethereum is fuelled by an expanding community of users and the allure of substantial financial returns. These currencies use a decentralized architecture anchored by blockchain technology for the secure verification and logging of transactions. However, this decentralization poses complex challenges for regulatory bodies and traditional financial institutions [3]. As fascination with cryptocurrencies grows, so does academic interest in blockchain and its foundational technology. Digital currencies come into the blockchain each time a new block is formed, and they can be traded for various goods and services [4]. Mainstream cryptocurrencies like Bitcoin and Ethereum have attained widespread acknowledgement, notably for their hefty trading volumes and market capitalizations. For

Signal, image and video processing, Mar 4, 2024

Selçuk üniversitesi mühendislik, bilim ve teknoloji dergisi, Feb 28, 2024

The two-stage system has been successfully tested in wheat classification and this is a prelude t... more The two-stage system has been successfully tested in wheat classification and this is a prelude to more staged systems. • Wheats have been successfully classified according to both their vitreous/yellow berry status and their varieties. • Experimental results showed that the accuracy for binary classification was 98.71% and the multilabel classification average accuracy was 89.5%. • This proposed system can be successfully used in wheat trade and breeding studies to help experts. • The proposed U2-Net architecture is an example that can be easily used in all grain groups, especially wheat images.

Bitlis Eren üniversitesi fen bilimleri dergisi, Jun 27, 2023

The use of intelligent devices in almost every sector, and the provision of services by private a... more The use of intelligent devices in almost every sector, and the provision of services by private and public institutions through network servers, cloud technologies, and database systems are now mostly remotely controlled. Due to the increasing demands on network systems, unfortunately, both malicious software and users are showing more interest in these areas. Some organizations are facing almost hundreds or even thousands of network attacks daily. Therefore, it is not enough to solve the attacks with a virus program or a firewall. Detection and accurate analysis of network attacks are crucial for the operation of the entire system. With the use of deep learning and machine learning, attack detection, and classification can be successfully performed. This study conducted a comprehensive attack detection process on the UNSW-NB15 and NSL-KDD datasets using existing machine learning and deep learning algorithms. In the UNSW-NB15 dataset, an accuracy of 98.6% and 98.3% was achieved for two-class and multi-class classification, respectively, and 97.8% and 93.4% accuracy were obtained in the NSL-KDD dataset. The results prove that machine learning algorithms are an effective solution for intrusion detection systems.

Journal of Stored Products Research, May 1, 2023

Computer systems science and engineering, 2023

Breast cancer seriously affects many women. If breast cancer is detected at an early stage, it ma... more Breast cancer seriously affects many women. If breast cancer is detected at an early stage, it may be cured. This paper proposes a novel classification model based improved machine learning algorithms for diagnosis of breast cancer at its initial stage. It has been used by combining feature selection and Bayesian optimization approaches to build improved machine learning models. Support Vector Machine, K-Nearest Neighbor, Naive Bayes, Ensemble Learning and Decision Tree approaches were used as machine learning algorithms. All experiments were tested on two different datasets, which are Wisconsin Breast Cancer Dataset (WBCD) and Mammographic Breast Cancer Dataset (MBCD). Experiments were implemented to obtain the best classification process. Relief, Least Absolute Shrinkage and Selection Operator (LASSO) and Sequential Forward Selection were used to determine the most relevant features, respectively. The machine learning models were optimized with the help of Bayesian optimization approach to obtain optimal hyperparameter values. Experimental results showed the unified feature selection-hyperparameter optimization method improved the classification performance in all machine learning algorithms. Among the various experiments, LASSO-BO-SVM showed the highest accuracy, precision, recall and F1-score for two datasets (97.95%, 98.28%, 98.28%, 98.28% for MBCD and 98.95%, 97.17%, 100%, 98.56% for MBCD), yielding outperforming results compared to recent studies.

Neural Network World, 2023

Many women around the world die due to breast cancer. If breast cancer is treated in the early ph... more Many women around the world die due to breast cancer. If breast cancer is treated in the early phase, mortality rates may significantly be reduced. Quite a number of approaches have been proposed to help in the early detection of breast cancer. A novel hybrid feature selection model is suggested in this study. This novel hybrid model aims to build an efficient feature selection method and successfully classify breast lesions. A combination of relief and binary Harris hawk optimization (BHHO) hybrid model is used for feature selection. Then, k-nearest neighbor (k-NN), support vector machine (SVM), logistic regression (LR) and naive Bayes (NB) methods are preferred for the classification task. The suggested hybrid model is tested by three different breast cancer datasets which are Wisconsin diagnostic breast cancer dataset (WDBC), Wisconsin breast cancer dataset (WBCD) and mammographic breast cancer dataset (MBCD). According to the experimental results, the relief and BHHO hybrid model improves the performance of all classification algorithms in all three datasets. For WDBC, relief-BHO-SVM model shows the highest classification rates with an of accuracy of 98.77%, precision of 97.17%, recall of 99.52%, F1-score of 98.33%, specificity of 99.72% and balanced accuracy of 99.62%. For WBCD, relief-BHO-SVM model achieves of accuracy of 99.28%, precision of 98.76%, recall of 99.17%, F1-score of 98.96%, specificity of 99.56% and balanced accuracy of 99.36%. Relief-BHO-SVM model performs the best with an accuracy of 97.44%, precision of 97.41%, recall of 98.26%, F1-score of 97.84%, specificity of 97.47% and balanced accuracy of 97.86% for MBCD. Furthermore, the relief-BHO-SVM model has achieved better results than other known approaches. Compared with recent studies on breast cancer classification, the suggested hybrid method has achieved quite good results.

Bitlis Eren Üniversitesi Fen Bilimleri Dergisi

Recently, the need for Network-based systems and smart devices has been increasing rapidly. The u... more Recently, the need for Network-based systems and smart devices has been increasing rapidly. The use of smart devices in almost every field, the provision of services by private and public institutions over network servers, cloud technologies and database systems are almost completely remotely controlled. Due to these increasing requirements for network systems, malicious software and users, unfortunately, are increasing their interest in these areas. Some organizations are exposed to almost hundreds or even thousands of network attacks daily. Therefore, it is not enough to solve the attacks with a virus program or a firewall. Detection and correct analysis of network attacks is vital for the operation of the entire system. With deep learning and machine learning, attack detection and classification can be done successfully. In this study, a comprehensive attack detection process was performed on UNSW-NB15 and NSL-KDD datasets with existing machine learning algorithms. In the UNSW-NB...

Journal of Stored Products Research

Arabian Journal for Science and Engineering

Lung opacities are extremely important for physicians to monitor and can have irreversible conseq... more Lung opacities are extremely important for physicians to monitor and can have irreversible consequences for patients if misdiagnosed or confused with other findings. Therefore, long-term monitoring of the regions of lung opacity is recommended by physicians. Tracking the regional dimensions of images and classifying differences from other lung cases can provide significant ease to physicians. Deep learning methods can be easily used for the detection, classification, and segmentation of lung opacity. In this study, a three-channel fusion CNN model is applied to effectively detect lung opacity on a balanced dataset compiled from public datasets. The MobileNetV2 architecture is used in the first channel, the InceptionV3 model in the second channel, and the VGG19 architecture in the third channel. The ResNet architecture is used for feature transfer from the previous layer to the current layer. In addition to being easy to implement, the proposed approach can also provide significant cost and time advantages to physicians. Our accuracy values for two, three, four, and five classes on the newly compiled dataset for lung opacity classifications are found to be 92.52%, 92.44%, 87.12%, and 91.71%, respectively.

Computer Science and Information Systems

Although age determination by radiographs of the hand and wrist before the age of 18 is an area w... more Although age determination by radiographs of the hand and wrist before the age of 18 is an area where there is a lot of radiological knowledge and many studies are carried out, studies on age determination for adults are limited. Studies on adult age determination through sternum multidetector computed tomography (MDCT) images using artificial intelligence algorithms are much fewer. The reason for the very few studies on adult age determination is that most of the changes observed in the human skeleton with age are outside the limits of what can be perceived by the human eye. In this context, with the dual-channel Convolutional Neural Network (CNN) we developed, we were able to predict the age groups defined as 20-35, 35-50, 51-65, and over 65 with 73% accuracy over sternum MDCT images. Our study shows that fusion modeling with dual-channel convolutional neural networks and using more than one image from the same patient is more successful. Fusion models will make adult age determin...

Journal of Medical Imaging and Health Informatics, 2021

Hundreds of thousands of people worldwide are diagnosed with kidney cancer each year, and this di... more Hundreds of thousands of people worldwide are diagnosed with kidney cancer each year, and this disease is more common in developed societies. Approximately 30% of patients with kidney cancer are recognized at the metastatic stage. Segmentation is an important process in the computer-aided treatment planning of kidney diseases. For this reason, more importance should be given to studies focused on segmentation as accurate segmentation is of high importance in the medical sense. This paper focuses on an improved version of the existing U-Net3D models. The aim is to assist physicians struggling with kidney segmentation. The improved U-Net3D model showed better performance than U-Net, U-Net+ResNet, and U-Net++ models, with 97.89% accurate segmentation.

Tez (Yüksek Lisans) -- Kırıkkale Üniversitesi88094

Sakarya University Journal of Computer and Information Sciences

COVID-19 is a deadly virus that first appeared in late 2019 and spread rapidly around the world. ... more COVID-19 is a deadly virus that first appeared in late 2019 and spread rapidly around the world. Understanding and classifying computed tomography images (CT) is extremely important for the diagnosis of COVID-19. Many case classification studies face many problems, especially unbalanced and insufficient data. For this reason, deep learning methods have a great importance for the diagnosis of COVID-19. Therefore, we had the opportunity to study the architectures of NasNet-Mobile, DenseNet and Nasnet-Mobile+DenseNet with the dataset we have merged. The dataset we have merged for COVID-19 is divided into 3 separate classes: Normal, COVID-19, and Pneumonia. We obtained the accuracy 87.16%, 93.38% and 93.72% for the NasNet-Mobile, DenseNet and NasNet-Mobile+DenseNet architectures for the classification, respectively. The results once again demonstrate the importance of Deep Learning methods for the diagnosis of COVID-19.

Uluslararası Muhendislik Arastirma ve Gelistirme Dergisi, 2021

This article is an open access article distributed under the terms and conditions of the Creative... more This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY

Osmaniye Korkut Ata Üniversitesi Fen Bilimleri Enstitüsü Dergisi, 2021

Worldwide, hundreds of thousands of people are diagnosed with kidney cancer and this disease is m... more Worldwide, hundreds of thousands of people are diagnosed with kidney cancer and this disease is more common in developed and industrialized countries. Previously, kidney cancer was known as an elderly disease and was seen in people over a certain age; nowadays it is also seen in younger individuals and it is easier to diagnose thanks to new radiological diagnostic methods. A kidney tumor is a type of cancer that is extremely aggressive and needs surgical treatment rapidly. Today, approximately 30% of patients diagnosed with kidney cancer are unfortunately noticed at the stage of metastatic disease (spread to distant organs). The biggest factor that pushes us to this study is that kidney tumors progress unlike other cancer types with little or no symptoms. Therefore, conducting such studies is extremely important for early diagnosis. In this study, we compare the Unet3D models in order to help people who are dealing with difficulties in the diagnosis of kidney cancer. Unet, Unet+ResN...

Intelligent Automation & Soft Computing, 2014

In this study, from 150 individuals over the age of 30 taken no drugs, sex, age, height, weight, ... more In this study, from 150 individuals over the age of 30 taken no drugs, sex, age, height, weight, HDL, LDL, Triglyceride, smoking and uric acid were measured. 65 of them are normal but 85 consist of the patients. This data was transferred to the computer by processing methods of quantitative analysis. Data obtained of each patient was applied Artificial Neural Network (ANN) models. The results obtained will be classified as either normal or the patient. Using Multi Layer Perceptron (MLP) neural network, 80.4% of patient individuals and 81.8% of normal individuals were classified correctly. Using Jordan Elman neural network, 85.3% of the patient individuals and 87.8% of normal individuals were classified correctly.