Browsing by Author "Canayaz, Murat"

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Analysis of Developmental Dysplasia of the Hip Using Deep Learning Techniques
(Springer Nature, 2025) Çelik, Ramazan; Yokuş, Adem; Gündüz, Ali Mahir; Canayaz, Murat; Toprak, Nurşen; Türkoǧlu, Saim
Purpose: Developmental dysplasia of the hip (DDH) is a relatively common musculoskeletal condition in neonates. Early detection with ultrasound (US) is crucial for effective treatment. This study aimed to evaluate images obtained from hip ultrasonography with deep learning methods. Material and Method: Patients who underwent hip ultrasonography between January 2018 and September 2021 and were found to have normal hips and hip dysplasia were retrospectively screened. A total of 947 patient images, 450 girls and 497 boys, were examined. According to the Graf method, images were classified without any marking. In the first stage, two groups were created: those with Type 1 mature hips and those with dysplastic hips (other types). In the second stage of the study, four groups were created using only the α angle: 451 were classified as Type 1, 326 as Type 2a and 2b, 137 as Type 2c and D, and 33 as Type 3 and Type 4. During the classification, three versions of the EfficientNet model, one of the current deep learning models, were used. Classifiers were included in the study to improve the accuracy values of the models. In our study, two classifiers named support vector machine and K-nearest neighbors were used. Results: In the classification phase with deep learning models, the highest accuracy value of 0.9577 was obtained with the EfficientNetB1 model for 2 classes in the first group, while the highest accuracy value of 0.8571 was obtained with the EfficientNetB0 model for 4 classes in the second group. By including the classifiers in the evaluation, the highest accuracy rate was found to be 0.99 with EfficientNetB1 and 1(100%) with EfficientNetB2 in the first group, while it was 0.97 with EfficientNetB0 in the second group. Conclusion: In the diagnosis of developmental hip dysplasia, high accuracy rates were obtained in deep learning methods using US images. Accuracy rates increased with the addition of classifiers to the models. © 2025 Elsevier B.V., All rights reserved.
Application of Machine Learning Methods To Removal Percentage Prediction for Malachite Green Adsorption on Kaolinite
(desalination Publ, 2022) Canayaz, Murat; Aldemir, Adnan; Kul, Ali Riza
In this study, the removal percentage was estimated using machine learning methods, such as artificial neural network, radial basis function neural network, support vector regressor, and random forest regressors, for data obtained during Malachite green adsorption on kaolinite as an adsorbent in an aqueous solution. Important process parameters, including initial dye concentration, sonication time and temperature, were investigated. Statistical evaluation metrics such as R2, mean squared error, and root mean square error were used to evaluate the performance of the models. Among these models, the artificial neural network was more successful compared to other models with 0.98 R2 values for three temperatures. Radial basis function neural network and random forest regressors were observed to achieve successful results. In this study, the results obtained from the machine learning methods are given comparatively. The initial dye concentrations increased from 10 to 60 mg L-1, the removal percentage of Malachite green on kaolinite increased from 68.71% to 79.61% for 298 K, 72.26% to 82.58% for 308 K and 78.75% to 85.91% for 318 K, respectively. Isotherm, kinetic and thermodynamic calculations for Malachite green removal by kaolinite were completed. The equilibrium of Malachite green adsorption onto kaolinite was best described by the Langmuir isotherm and the kinetics of the process followed the pseudo-second-order model, which had the highest correlation values. Thermodynamic analysis of experimental data suggests that the adsorption process is spontaneous and endothermic in nature.
Application Software Development for Web Based Management of Network Devices
(2020) Gümüş, Osman; Canayaz, Murat
Yapısal olarak büyük bir ağda bulunan cihazların birçoğu gelişen teknolojiyle birlikte programlanabilir özeliğine sahip olmuştur. Cihazların yönetilebilmeleri ağın yapısını değiştirmemiz ve ağın sürekliliği sağlaması açısından büyük bir öneme sahiptir. Bu cihazların yönetimi için üretici firmaların birçoğu kendi komut setlerini geliştirmişlerdir. Her bir marka için ayrı komut setinin olması ağ alt yapısı kurulumu ve bakımını zorlaştırmaktadır. Kurumlar için bilgisayar ağlarının devamlığı çok önemlidir. Kısa süreli kesintilerde bile kurumlar büyük ekonomik kayıpların yanında kurumsal itibarları da zedelenebilmektedir. Bu durum göz önünde bulundurulduğunda, ağ alt yapısında yeni başlayan elemanların eğitim süreçleri boyunca bu elemanların bütün ağa ulaşması ve ağ yönetimde bütün yetkilere sahip olması büyük sakınca oluşturmaktadır Bu çalışma farklı komut setlerine sahip cihazların tek bir uygulamada yönetilmesini sağlayan ve böylelikle kurumun mevcut durumda cihaz yönetiminde maruz kaldığı finansal yükümlüğünü ve nitelikli personel bulma zorluğunu ortadan kaldırmayı amaçlamaktadır. Bu çalışma ağın kullanım kolaylığı ve tasarımsal özeliklerinden dolayı personelin oryantasyon süresini kısaltıp eğitim gerektirmeyen bir durumu da sunmayı hedeflemektedir. Personellerin ağ cihazlarına, yöneticinin belirlediği yetkiler dahilinde erişip komutlar uygulaması sayesinde ağ cihazlarının daha güvenli bir şekilde yönetilebilmesini sağlamak da bu yazılım çalışmasının amaçları arasındadır.
C Plus Effxnet: a Novel Hybrid Approach for Covid-19 Diagnosis on Ct Images Based on Cbam and Efficientnet
(Pergamon-elsevier Science Ltd, 2021) Canayaz, Murat
COVID-19, one of the biggest diseases of our age, continues to spread rapidly around the world. Studies continue rapidly for the diagnosis and treatment of this disease. It is of great importance that individuals who are infected with this virus be isolated from the rest of the society so that the disease does not spread further. In addition to the tests performed in the detection process of the patients, X-ray and computed tomography are also used. In this study, a new hybrid model that can diagnose COVID-19 from computed tomography images created using EfficientNet, one of the current deep learning models, with a model consisting of attention blocks is proposed. In the first step of this new model, channel attention, spatial attention, and residual blocks are used to extract the most important features from the images. The extracted features are combined in accordance with the hyper-column technique. The combined features are given as input to the EfficientNet models in the second step of the model. The deep features obtained from this proposed hybrid model were classified with the Support Vector Machine classifier after feature selection. Principal Components Analysis was used for feature selection. The approach can accurately predict COVID-19 with a 99% accuracy rate. The first four versions of EfficientNet are used in the approach. In addition, Bayesian optimization was used in the hyper parameter estimation of the Support Vector Machine classifier. Comparative performance analysis of the approach with other approaches in the field is given. (C) 2021 Elsevier Ltd. All rights reserved.
Classification of Diabetic Retinopathy With Feature Selection Over Deep Features Using Nature-Inspired Wrapper Methods
(Elsevier, 2022) Canayaz, Murat
Diabetic retinopathy (DR) is the most common cause of blindness in middle-aged people. It shows that an automatic image evaluation system is needed in the diagnosis of this disease due to the low number of scans. It is critical to meet this need that these systems are large-scale, cost-effective, and minimally invasive screening programs. With the use of deep learning techniques, it has become possible to develop these systems faster. In this study, a new approach based on feature selection with wrapper methods used for fundus images is presented that can be used for the classification of diabetic retinopathy. The fundus images used in the approach were improved with image processing techniques, thus eliminating unnecessary dark areas in the image. In this new approach, the most effective features are selected by wrapping methods over 512 deep features obtained from EfficientNet and DenseNet models. Binary Bat Algorithm (BBA), Equilibrium Optimizer (EO), Gravity Search Algorithm (GSA), and Gray Wolf Optimizer (GWO) were chosen as wrappers for the proposed approach. Selected features are classified by support vector machines and random forest machine learning methods. Considering the performance of this new approach, it gives the highest value of 96.32 accuracy and 0.98 kappa. These performance values were obtained with a minimum of 250 selected features. The Asia Pacific Tele-Ophthalmology Society (APTOS) dataset used to obtain these values was taken from a competition organized by Kaggle. The highest kappa value in this competition was reported as 0.93. This parameter clearly demonstrates the success of our approach. (C) 2022 Elsevier B.V. All rights reserved.
Comparative Analysis of TGAN and Other GAN Models for Synthetic Earthquake Data: A Case Study With Data From Türkiye
(Springer, 2025) Urcan, Hayrullah; Cengil, Emine; Canayaz, Murat
Early detection of earthquakes is very important to prevent possible loss of life and injuries. T & uuml;rkiye faces frequent earthquake disasters due to its geographical location. In order to predict the possible earthquake risk with artificial intelligence methods, data is required. This paper explores the potential of synthetic data generation, focusing on the data constraint in simulating earthquake data and further analyzing disaster scenarios. TGAN, CTGAN and CopulaGAN models are compared using real earthquake dataset obtained from Istanbul Metropolitan Municipality open data portal. The results show that the TGAN model achieves the highest performance in both statistical and structural metrics. TGAN produced results close to the real data in terms of mean (19.53 vs. 19.84) and cumulative total (27,269.58), and obtained the highest value (0.9022) in correlation analysis. Kolmogorov-Smirnov (KS) test and chi-squared (CS) test results showed that all models modeled discrete attributes better, while the logistic regression classifier TGAN performed moderately well in distinguishing real data from synthetic data. These findings reveal that the TGAN model is an effective tool in the synthetic generation of earthquake data and offers new perspectives in disaster management processes. As one of the first comprehensive comparisons of the potential of GAN models for synthetic generation of earthquake data, this study makes an innovative contribution to the literature in terms of both model selection guidelines and synthetic data applications.
A Comparative Study of Segmentation Algorithms for Intracerebral Hemorrhage Detection
(2024) Canayaz, Murat; Milanlıoğlu, Aysel; Sehrıbanoglu, Sanem; Yalın, Abdulsabır; Yokuş, Adem
Segmentation in the medical field has special importance. One of the purposes of segmentation is to visualize the area affected by the disease after disease detection in any organ. In recent years, efficient studies have been carried out for this purpose with deep learning models. In this study, three segmentation algorithms were compared for the detection of hemorrhage in brain parenchyma. These algorithms are the most familiar: U-net, LinkNet, and FPN algorithms. For the background of these algorithms, five backbones consisting of deep learning models were used. These backbones are Resnet34, ResNet50, ResNet169, EfficientNetB0, and EfficientNet B1. An original dataset was created for the study. The dataset in the study was verified by experts. In the study, the Dice coefficient and Jaccard index, which are the most common metrics in the medical field, were chosen as evaluation metrics. Considering the performance results of the algorithms, the FPN architecture with a 0.9495 Dice coefficient value for the training data and LinkNet with a 0.9244 Dice coefficient for the test data gave the best results. In addition, EfficientNetB1 provided the best results among the backbones used. When the results obtained were examined, better segmentation performance was obtained than in existing studies.
Comparison of Development Strawberry Fruit With Deep Learning Methods
(2023) Dalgın, Levent; Canayaz, Murat
Tarımda yapay zeka uygulamalarının kullanımı her geçen gün artmakta, buna paralel olarak bilgisayarlı görü dünyasında geliştirilen modeller ile ürün tespit ve takibi daha doğru sonuçlar vermektedir. Ülkemizde Ege, Akdeniz ve Marmara bölgelerinde yoğun yetiştirilen, ayrıca seralarda da çok geniş yetişme alanı bulunan çilek meyvesi dar bir hasat zamanına sahiptir. Hasat (derim) için çileğin hasat zamanının geciktirilmesi çileğin yumuşamasına, erken hasadı ise verimin düşmesine neden olmaktadır. Çilek gelişim evrelerinin yapay zeka destekli görüntü işleme teknikleriyle takip edilmesi, olgunlaşan çileklerin tespit edilerek bunların zamanında hasat edilmesini kolaylaştıracaktır. Çilek meyvesinden sabit zaman aralıklarında alınan görüntülerin Yolo, FasterCNN ve SSD görüntü işleme algoritmalarıyla olgunlaşma tespiti yapılarak hangi algoritmanın daha hızlı ve doğru sonuçlar verdiği bilimsel veriler ışığında grafiksel olarak karşılaştırılmıştır. Cep telefonundan farklı zaman ve benzer açılardan alınan görüntüler kullanılarak nesne algılama çalışmaları için veri seti oluşturulmuştur. Evrişimsel sinir modelleri dünyası sürekli artan ihtiyaçlara binaen daha hızlı ve sağlıklı sonuçlar üreten sistemler geliştirmektedir. Evrişimsel sinir ağları modellerinde başarının artması tarımda yapay zeka uygulamalarını destekleyecek ve insansız tarım, hasat robotları gibi teknolojilerin daha doğru çalışmasını sağlayacaktır. Hazırlanan bu tez evrişimsel sinir ağları modellerinin başarı analizlerini matematiksel olarak hesaplayıp çıkan sonuçları grafik ve tablolarla anlaşılır bir şekilde karşılaştırmıştır. Sıkça kullanılan sinir ağı modellerinin deneysel analizlerle elde edilen sonuçlarının karşılaştırmalı grafikleri bu alanda yapılan çalışmalara katkı sağlayacaktır. Anahtar kelimeler: Bitki gelişimi, FasterCNN, Görüntü işleme, OpenCv, Python, SSD, Yolo, Yapay zeka
A Comprehensive Exploration of Deep Learning Approaches for Pulmonary Nodule Classification and Segmentation in Chest Ct Images
(Springer London Ltd, 2024) Canayaz, Murat; Sehribanoglu, Sanem; Ozgokce, Mesut; Akinci, M. Bilal
Accurately determining whether nodules on CT images of the lung are benign or malignant plays an important role in the early diagnosis and treatment of tumors. In this study, the classification and segmentation of benign and malignant nodules on CT images of the lung were performed using deep learning models. A new approach, C+EffxNet, is used for classification. With this approach, the features are extracted from CT images and then classified with different classifiers. In other phases of the study, a segmentation between benign and malignant was performed and, for the first time, a comparison of nodes was made during segmentation. The deep learning models InceptionV3, DenseNet121, and SeResNet101 were used as backbone models for feature extraction in the segmentation phase. In the classification phase, an accuracy of 0.9798, a precision of 0.9802, a recognition of 0.9798, an F1 score of 0.9798, and a kappa value of 0.9690 were achieved. During segmentation, the highest values of 0.8026 Jacard index and 0.8877 Dice coefficient were achieved.
Covid-19 Diagnosis on Ct Images With Bayes Optimization-Based Deep Neural Networks and Machine Learning Algorithms
(Springer London Ltd, 2022) Canayaz, Murat; Sehribanoglu, Sanem; Ozdag, Recep; Demir, Murat
Early diagnosis of COVID-19, the new coronavirus disease, is considered important for the treatment and control of this disease. The diagnosis of COVID-19 is based on two basic approaches of laboratory and chest radiography, and there has been a significant increase in studies performed in recent months by using chest computed tomography (CT) scans and artificial intelligence techniques. Classification of patient CT scans results in a serious loss of radiology professionals' valuable time. Considering the rapid increase in COVID-19 infections, in order to automate the analysis of CT scans and minimize this loss of time, in this paper a new method is proposed using BO (BO)-based MobilNetv2, ResNet-50 models, SVM and kNN machine learning algorithms. In this method, an accuracy of 99.37% was achieved with an average precision of 99.38%, 99.36% recall and 99.37% F-score on datasets containing COVID and non-COVID classes. When we examine the performance results of the proposed method, it is predicted that it can be used as a decision support mechanism with high classification success for the diagnosis of COVID-19 with CT scans.
Cricket Behaviour-Based Evolutionary Computation Technique in Solving Engineering Optimization Problems
(Springer, 2016) Canayaz, Murat; Karci, Ali
Meta-heuristicalgorithms are widely used in various areas such as engineering, statistics, industrial, image processing, artificial intelligence etc. In this study, the Cricket algorithm which is a novel nature-inspired meta-heuristic algorithm approach which can be used for the solution of some global engineering optimization problems was introduced. This novel approach is a meta-heuristic method that arose from the inspiration of the behaviour of crickets in the nature. It has a structure for the use in the solution of various problems. In the development stage of the algorithm, the good aspects of the Bat, Particle Swarm Optimization and Firefly were experimented for being applied to this algorithm. In addition to this, because of the fact that these insects intercommunicate through sound, the physical principles of sound propagation in the nature were practiced in the algorithm. Thanks to this, the compliance of the algorithm to real life tried to be provided. This new developed approach was applied on the familiar global engineering problems and the obtained results were compared with the results of the algorithm applied to these problems.
Deep Learning Aplications for Network Intrusion Detection
(2019) Kapar, Mesut; Canayaz, Murat
Ağ saldırı tespit sistemleri günümüz bilişim sistemlerinde kritik bir yer teşkil ederken, önemli bir araştırma alanı olarak yükselmeye ve yapay sinir ağlarının kullanımı bu alanda giderek daha popüler hale gelmeye başlamıştır. Buna rağmen, bu alanda yapay sinir ağı mimarileri ve bu mimarilerin parametreleri hakkında karşılaştırma çalışmalarında eksiklikler vardır. Bu çalışmada, ağ saldırı tespit sistemleri alanında kullanılan yapay sinir ağları mimarileri ile ileride yapılacak olan mühendislik ve akademik çalışmalar için bir temel oluşturulması amaçlanmıştır. Bu doğrultuda kıyaslama veri seti olarak kabul edilen KDD-99 veri seti kullanılmıştır. İleri beslemeli yapay sinir ağı mimarisi ve evrişimsel sinir ağı mimarisi bu veri seti üzerinde kullanılmış ve % 90 üzeri başarı elde edilerek sonuçlar karşılaştırılmıştır.
Detection and Censorship of Abusive Sounds in Turkish Videos Using Deep Learning and Audio Processing Methods
(2022) Tayiz, Muhammed Mustafa; Canayaz, Murat
Sosyal mecralardaki içeriklerin sayısı ve çeşitliliği her geçen gün artmaktadır. Bunun yanında internete erişebilen kullanıcılar bütün içeriklere doğrudan ulaşabilmektedir. Bu ortamlara yüklenen içeriklerin belirli filtrelerden geçmemesi ve toplumun ahlaki yapısını bozabilecek söylemlerin sansürlenmemesi sorun teşkil etmektedir. Özellikle çocuklar duydukları kelime ve kelime gruplarını doğru ya da yanlış olarak ayırmadan günlük hayatlarında kullanmaktadırlar. İçeriklerin artışındaki hız, kontrol ve sansür gibi işlemlerin uygulanma noktasında aynı oranda bir zorluk ortaya çıkarmaktadır. İçeriklerin sayısı bu kadar hızlı artarken manuel yöntemler kullanılarak, yüksek doğruluk ile sansür uygulamak mümkün olmamaktadır. Bu soruna bir çözüm üretebilmek için en az bahsedilen artış hızı ile aynı hızda ve mümkün oldukça az insan müdahalesi ile çalışan otonom sistemlere ihtiyaç vardır. Bu kapsamda yapay zeka ve ses işleme yöntemleri kullanılarak, videolardaki ya da ses dosyalarındaki küfürlü seslerin bilgisayarlar tarafından otomatik olarak tespit edilmesi ve sansürlenmesi sureti ile bir çalışma yapılmıştır. Tez sürecinde, çeşitli yöntemler ile toplanan küfür seslerinden oluşan bir ses veri seti oluşturulmuştur. Tahmin ve sınıflandırma aşamasında, Evrişimsel Sinir Ağı (CNN) ve Tekrarlayan Sinir Ağı (RNN) modelleri, oluşturulan veri seti kullanılarak eğitilmiş ve karşılaştırılmıştır. Tez kapsamında, belirlenen 3 tane Türkçe küfür için sinir ağları eğitilmiştir. Son durumda Türkçe küfür içeren videoyu girdi olarak alıp, küfürlü seslerin sansürlendiği, video çıktısı oluşturma kabiliyetine sahip bir uygulama geliştirilmiştir.
Investigation of Cricket Behaviours as Evolutionary Computation for System Design Optimization Problems
(Elsevier Sci Ltd, 2015) Canayaz, Murat; Karci, Ali
In this study, the behaviours of an insect species called cricket were investigated and tried to develop a new meta-heuristic algorithm approach that may be used in solving optimization problems by modelling these behaviours. These insect species make a sound by flapping their wings and attract the other crickets around them. While creating this algorithm, the physics laws related to propagation of sound as well as the crickets ability to predict the temperature with the number of flaps were also considered. The approach performance was tried to be shown by applying the developed approach at the end of the study to both numeric problems and cantilever stepped and welded beam that are of system design optimization problems. (C) 2015 Elsevier Ltd. All rights reserved.
Marble Classification Using Deep Neural Networks
(2020) Uludağ, Fatih; Canayaz, Murat
Deep learning, which has been described as the processing and interpretation of data, is now widely used. In this study, deep neural networks are used for the classification of marbles which can be used in the industry. For this purpose most used marbles images were obtained from companies in Turkey and 28-class dataset was created. Then VGG16, ResNet and LeNet models were trained on this dataset. Data augmentation was performed to have class balance. To evaluate the models performance accuracy metric is used. In the VGG16 model, fine tunning was applied and %97 accuracy was achieved. In experimental studies, models were trained with different parameter settings. The performances of the models are given comparatively. The fact that both new dataset and deep neural networks are used for the first time in marble classification are among the positive aspects of this study. It is planned to integrate the models produced in the future studies into mobile based expert systems.
A Meta-Heuristic Algorithm-Based Feature Selection Approach To Improve Prediction Success for Salmonella Occurrence in Agricultural Waters
(Ankara Univ, Fac Agriculture, 2024) Demir, Murat; Canayaz, Murat; Topalcengiz, Zeynal
The presence of Salmonella in agricultural waters may be a source of produce contamination. Recently, the performances of various algorithms have been tested for the prediction of indicator bacteria population and pathogen occurrence in agricultural water sources. The purpose of this study was to evaluate the performance of meta -heuristic optimization algorithms for feature selection to increase the Salmonella occurrence prediction success of commonly used algorithms in agricultural waters. Previously collected datasets from six agricultural ponds in Central Florida included the population of indicator microorganisms, physicochemical water attributes, and weather station measurements. Salmonella presence was also reported with PCR-confirmed method in data set. Features were selected by using binary meta -heuristic optimization methods including differential evolution optimization (DEO), grey wolf optimization (GWO), Harris hawks optimization (HHO) and particle swarm optimization (PSO). Each meta -heuristic method was run 100 times for the extraction of features before classification analysis. Selected features after optimization were used in the K -nearest neighbor algorithm (kNN), support vector machine (SVM) and decision tree (DT) classification methods. Microbiological indicators were ranked as the first or second features by all optimization algorithms. Generic Escherichia coli was selected as the first feature 81 and 91 times out of 100 using GWO and DEO, respectively. The meta -heuristic optimization algorithms for the feature selection process followed by machine learning classification methods yielded a prediction accuracy between 93.57 and 95.55%. Meta -heuristic optimization algorithms had a positive effect on improving Salmonella prediction success in agricultural waters despite spatio-temporal variations. This study indicates that the development of computer -based tools with improved meta -heuristic optimization algorithms can help growers to assess risk of Salmonella occurrence in specific agricultural water sources with the increased prediction success.
Mh-Covidnet: Diagnosis of Covid-19 Using Deep Neural Networks and Meta-Heuristic Feature Selection on X-Ray Images
(Elsevier Sci Ltd, 2021) Canayaz, Murat
COVID-19 is a disease that causes symptoms in the lungs and causes deaths around the world. Studies are ongoing for the diagnosis and treatment of this disease, which is defined as a pandemic. Early diagnosis of this disease is important for human life. This process is progressing rapidly with diagnostic studies based on deep learning. Therefore, to contribute to this field, a deep learning-based approach that can be used for early diagnosis of the disease is proposed in our study. In this approach, a data set consisting of 3 classes of COVID19, normal and pneumonia lung X-ray images was created, with each class containing 364 images. Pre-processing was performed using the image contrast enhancement algorithm on the prepared data set and a new data set was obtained. Feature extraction was completed from this data set with deep learning models such as AlexNet, VGG19, GoogleNet, and ResNet. For the selection of the best potential features, two metaheuristic algorithms of binary particle swarm optimization and binary gray wolf optimization were used. After combining the features obtained in the feature selection of the enhancement data set, they were classified using SVM. The overall accuracy of the proposed approach was obtained as 99.38%. The results obtained by verification with two different metaheuristic algorithms proved that the approach we propose can help experts during COVID-19 diagnostic studies.
Neutrosophic Set Based Image Segmentation Approach Using Cricket Algorithm
(Ieee, 2016) Canayaz, Murat; Hanbay, Kazim
Image segmentation is important part of image processing applications. A given image is separated the different regions with homogeneous characteristics at image segmentation process. This paper will introduce an image segmentation approach that can be used in image processing applications. Recently Neutrosophic Set (NS) that use to evulate indeterminacy information, and metaheuristic algorithms are frequently used in image segmentation process. Our study contain both these methods. At first, an image is transformed to NS domain that has T, I, F subset, and then, features of image are extracted. Then, according to Shannon entopy model, threshold values that correspond to the values maximizing the function in the entropy, is found on the image. Finally, image is thresholded with this value. This search of maximum entropy values is made using the Cricket Algorithm, a new metaheuristic algorithm inspired behaviour of cricket, minimizing the complexity of operation and search time. To summarize, this study aims not only to represent image segmentation technique but also introduce the Cricket Algorithm. At the end of study, the performance of this approach on test images will be shown.
A New Metaheuristic Approach Based on Orbit in the Multi-Objective Optimization of Wireless Sensor Networks
(Springer, 2021) Ozdag, Recep; Canayaz, Murat
Wireless sensor networks (WSNs) is a research area which has been used in various applications and has continuously developed up to now. WSNs are used in many applications, especially in military and civilian applications, with the aim of monitoring the environment and tracking objects. For this purpose, increasing the coverage rate of WSNs is one of the important criteria that determine the effective monitoring of the network. Since the sensors that make up the WSNs have a limited capacity in terms of energy, process and memory, various algorithmic solutions have been developed to optimize this criterion. The effective dynamic deployment of sensor nodes, which is the primary goal of these solutions, has a critical role in determining the performance of the network. A new orbit-based dynamic deployment approach based on metaheuristic Whale Optimization Algorithm has been proposed in this study. The goal is to optimize the convergence speed of the nodes, the coverage rate of the network, the total displacement (movement) distances of sensors and the degree ofk-coverage of each target (Grid) point in the area by effectively performing the dynamic deployments of sensors after their random distribution. This approach is compared with MADA-WOA and MADA-EM in the literature. Simulation results indicated that the approach developed in rapidly converging sensors to each other, increasing the network's coverage rate, and in minimizing the total movement distances of the sensors in the area and the degrees ofk-coverage of Grid points covered by the sensors could be proposed.
Pulmoner Nodüllerin Benign ve Malign Olarak Ayrımında Derin Öğrenme
(Baycinar Medical Publ-baycinar Tibbi Yayincilik, 2024) Özgökçe, Mesut; Dündar, İlyas; Akıncı, Muhammed Bilal; Canayaz, Murat; Durmaz, Fatma; Özkaçmaz, Sercan; Göya, Cemil
Amaç: Akciğer kanserinde mortalitenin yüksek olması nedeniyle benign ve malign olguları yüksek doğrulukla ayırt edebilen, tanısal görüntüleme ile erken tanıya yardımcı olabilecek evrişimsel sinir ağı modellerinin bulunması amaçlandı. Ça lışma planı:Kliniğimizde tomografisi çekilen ve akciğerinde nodül saptanan hastalar Ocak 2015 ve Aralık 2020 tarihleri arasında geriye dönük olarak tarandı. Hastalar iki gruba ayrıldı: benign (n=68; 38 erkek, 30 kadın; ort. yaş: 59±12.2 yıl; dağılım, 27-81 yıl) ve malign (n=29; 19 erkek, 10 kadın; ort. yaş: 65±10.4 yıl; dağılım, 43-88 yıl). Ayrıca kesitlerinde herhangi bir patoloji bulunmayan sağlıklı hastalardan oluşan bir kontrol grubu (n=67; 38 erkek, 29 kadın; ort. yaş: 56.9±14.1 yıl; dağılım, 26-81 yıl) oluşturuldu. Derin sinir ağları, oluşturduğumuz üç sınıflı veri setinin %80̓i ile eğitildi ve verilerin %20̓si ile test edildi. Derin sinir ağlarının eğitiminin ardından bu ağlardan özellik çıkarımı yapıldı. Veri setinden çıkarılan özellikler makine öğrenmesi algoritmaları ile sınıflandırıldı. Performans sonuçları karışıklık matrisi analizi kullanılarak elde edildi. Bulgular: Derin sinir ağlarının eğitimi sonrasında kullanılan modeller arasında en yüksek doğruluk oranına %80 ile AlexNET modelinde ulaşıldı. Özellik çıkarımı ve sınıflandırıcı kullanımı sonrasında elde edilen ikinci aşama sonuçlarda ise en yüksek doğruluk oranına %93.5 ile VGG19 modelinde destek vektör makinesi sınıflandırıcısı ile ulaşıldı. Ayrıca destek vektör makinesi sınıflandırıcısının kullanılmasıyla tüm modellerde doğruluk oranlarında artışlar tespit edildi. So nuç: Benign ve malign akciğer nodüllerinin derin öğrenme modelleri ve özellik çıkarımı kullanılarak ayrıştırılması, radyoloji pratiğinde erken tanı açısından önemli avantajlar sağlayacaktır. Çalışmamızda elde edilen sonuçlar da bu görüşü destekler niteliktedir.