Browsing by Author "Akyol, K"
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Publication A comprehensive comparison study of traditional classifiers and deep neural networks for forest fire detection(2023-01-01) Akyol K.; Akyol, KForest fires cause great harm to people, environment, and nature. Fire detection using forest landscape images can play a critical role in the design of expert systems required to solve the forest fire problem. The main aim of this study is to evaluate the classification accuracy of different classifier models for efficiently detecting forest fires and to present an effective and successful model. At this point, classification performances of traditional and deep neural networks (DNN) based classifiers were compared on landscape images dataset taken from the Mendeley repository within the frame of well-known metrics such as accuracy, sensitivity, specificity, precision and false negative rate. The DNN-3 classifier performed very well on the ResNet50 deep features extracted from images with 97.11% accuracy, 96.84% sensitivity, 3.16% false negative rate, 97.37% specificity, and 97.35% precision. This model (ResNet50+DNN-3) offered the most area under the curve with 0.971. In this context, it is thought that the proposed model could play an active role in the design of expert systems that will support the forest protection and monitoring units by easily integrating with real-time internet of things and embedded system applications.Web of Science Web of Science A Decision Support System for Early-Stage Diabetic Retinopathy Lesions(2017.01.01) Akyol, K; Bayir, S; Sen, BPublication Assessing the importance of autistic attributes for autism screening(2020-10-01) Akyol K.; Akyol, KAutistic Spectrum Disorder (ASD) is a cognitive disease which leads to the loss of linguistic, communicative, cognitive, and social skills and abilities. Patients with ASD have diverse troubles such as sleeping problems. The role of genetic and environmental factors is of great importance in its pathophysiology. Early diagnosis provides an improved overall mental health of the patients. This study aimed to identify the important attributes for the best detection of this disorder in children, adolescents and adults. To achieve this aim, Recursive Feature Elimination and Stability Selection methods that consider important attributes for target class were proposed. The attributes collected from autism screening methods and other attributes such as age and gender were examined for the disease. The results verified the combining of feature selection method and machine learning algorithm within the frame of accuracy, sensitivity and specificity evaluation metrics.Web of Science Assessing the importance of autistic attributes for autism screening(2020.01.01) Akyol, KWeb of Science Automated detection of Covid-19 disease using deep fused features from chest radiography images(2021.01.01) Ucar, E; Atila, U; Ucar, M; Akyol, KWeb of Science Publication Automatic classification of brain magnetic resonance images with hypercolumn deep features and machine learning.(2022-09-01T00:00:00Z) Akyol, Kemal; Akyol, KBrain tumours are life-threatening and their early detection is very important in a patient's life. At the present time, magnetic resonance imaging is one of the methods used for detecting brain tumours. Expert decision support systems serve specialist physicians to make more accurate diagnoses by minimizing the errors arising from their subjective opinions in real clinical settings. The model proposed in this study detects important keypoints and then extracts hypercolumn deep features of these keypoints from some convolutional layers of VGG16. Finally, Random Forest and Logistic Regression classifiers are fed with a set of these features. Random Forest classifier offered the best performance with 94.51% accuracy, 91.61% sensitivity, 8.39% false-negative rate, 97.42% specificity, and 97.29% precision using fivefold cross-validation in this study. Consequently, it is thought that the proposed model could contribute to field experts by integrating it into computer-aided brain magnetic resonance imaging diagnosis systems.Web of Science Automatic Detection of Covid-19 with Bidirectional LSTM Network Using Deep Features Extracted from Chest X-ray Images(2022.01.01) Akyol, K; Sen, BWeb of Science Care4HIP: An Embedded System Design for Discerning Hear-Impaired People in Traffic(2022.01.01) Akyol, K; Karaci, A; Titikci, MEWeb of Science Chaotic Multi-swarm Particle Swarm Optimization for Welded Beam Design Engineering Problem(2022.01.01) Feneaker, SOF; Akyol, KWeb of Science Classification of Different Tympanic Membrane Conditions Using Fused Deep Hypercolumn Features and Bidirectional LSTM(2022.01.01) Ucar, M; Akyol, K; Atila, U; Ucar, EPublication Comparing of deep neural networks and extreme learning machines based on growing and pruning approach(2020-02-01) Akyol K.; Akyol, KRecently, the studies based on Deep Neural Networks and Extreme Learning Machines have become prominent. The models of parameters designed in these studies have been chosen randomly and the models have been designed in this direction. The main focus of this study is to determine the ideal parameters i.e. optimum hidden layer number, optimum hidden neuron number and activation function for Deep Neural Networks and Extreme Learning Machines architectures based on growing and pruning approach and to compare the performances of the models designed. The performances of the models are evaluated on two datasets; Parkinson and Self-Care Activities Dataset. Multi experiments have verified that the Deep Neural Networks architectures present a good prediction performance and this architecture outperforms the Extreme Learning Machines.Web of Science Web of Science Decolorization of the Reactive Blue 19 from Aqueous Solutions with the Fenton Oxidation Process and Modeling with Deep Neural Networks(2020.01.01) Degermeoci, N; Akyol, KWeb of Science Publication Growing and pruning based deep neural networks modeling for effective Parkinson⇔s disease diagnosis(2020-01-01) Akyol K.; Akyol, KParkinson's disease is a serious disease that causes death. Recently, a new dataset has been introduced on this disease. The aim of this study is to improve the predictive performance of the model designed for Parkinson's disease diagnosis. By and large, original DNN models were designed by using specific or random number of neurons and layers. This study analyzed the effects of parameters, i.e., neuron number and activation function on the model performance based on growing and pruning approach. In other words, this study addressed the optimum hidden layer and neuron numbers and ideal activation and optimization functions in order to find out the best Deep Neural Networks model. In this context of this study, several models were designed and evaluated. The overall results revealed that the Deep Neural Networks were significantly successful with 99.34% accuracy value on test data. Also, it presents the highest prediction performance reported so far. Therefore, this study presents a model promising with respect to more accurate Parkinson's disease diagnosis.Web of Science Publication Handling hypercolumn deep features in machine learning for rice leaf disease classification(2022-01-01) Akyol K.; Akyol, KRice leaf disease, which is a plant disease, causes a decrease in rice production and more importantly, environmental pollution. 10–15% of the losses in rice production are due to rice plant diseases. Automatic recognition of rice leaf disease by computer-assisted expert systems is a promising solution to overcome this problem and to bear the shortage of field experts in this field. Many studies have been conducted using features extracted from deep learning architectures, so far. This study includes keypoint detection on the image, hypercolumn deep feature extraction from CNN layers, and classification stages. The hypercolumn is a vector that contains the activations of all CNN layers for a pixel. Keypoints are prominent points in the images that define what stands out in the image. The first step of the model proposed in this study includes the detection of keypoints on the image and then the extraction of hypercolumn features based on the interest points. In the second step, machine learning experiments are carried out by running classifier algorithms on the features extracted. The evaluation results show that the proposed approach in this paper can detect rice leaf diseases. Furthermore, the Random Forest classifier presented a very successful performance on hypercolumn deep features with 93.06% accuracy, 89.58% sensitivity, 94.79% specificity, and 89.58% precision. As a result, the proposed approach can be integrated into computer-aided rice leaf disease diagnosis systems and so support field experts.Web of Science HANDLING THE EFFECT OF ATTRIBUTE SELECTION ON SUPPORT VECTOR MACHINES FOR DETECTING CHRONIC KIDNEY DISEASE(2022.01.01) Akyol, K; Sen, B