Browsing by Author "Akyol K."
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Scopus A comprehensive comparison study of traditional classifiers and deep neural networks for forest fire detection(2023-01-01) Akyol K.Forest 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.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.Scopus A Study on Liver disease diagnosis based on assessing the importance of attributes(2017-11-01) Akyol K.; Gültepe Y.Liver is a needful body organ that forms an important barrier between the gastrointestinal blood, which contains large amounts of toxins, and antigens. Liver diseases contain hepatitis B and hepatitis C virus infections, alcoholic liver disease, nonalcoholic fatty liver disease and associated cirrhosis, liver failure and hepatocellular carcinoma are primary causes of death. The main purpose of this study is to investigate which attributes are important for effective diagnosis of liver disorders by performing the machine learning approach based on the combination of Stability Selection and Random Forest methods. In order to generate more accuracy, dataset was balanced by utilizing the Random Under-Sampling method. Important ones in all attributes were detected by utilizing the Stability Selection method which was performed on sub-datasets, which were obtained with 5 fold cross-validation technique. By sending these datasets to the Random Forest algorithm, the performance of the proposed approach was evaluated within the frame of accuracy and sensitive metrics. The experimental results clearly show that the Random Under-Sampling method can potentially improve the performance of the combination of Stability Selection and Random Forest methods in machine learning. And, the combination of these methods provides new perspectives for the diagnosis of this disease and other medical diseases.Publication 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.Scopus Assessing the importance of autistic attributes for autism screening(2020-10-01) Akyol K.Autistic 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.Scopus Automated detection of Covid-19 disease using deep fused features from chest radiography images(2021-08-01) Uçar E.; Atila Ü.; Uçar M.; Akyol K.The health systems of many countries are desperate in the face of Covid-19, which has become a pandemic worldwide and caused the death of hundreds of thousands of people. In order to keep Covid-19, which has a very high propagation rate, under control, it is necessary to develop faster, low-cost and highly accurate methods, rather than a costly Polymerase Chain Reaction test that can yield results in a few hours. In this study, a deep learning-based approach that can detect Covid-19 quickly and with high accuracy on X-ray images, which are common in every hospital and can be obtained at low cost, was proposed. Deep features were extracted from X-Ray images in RGB, CIE Lab and RGB CIE color spaces using DenseNet121 and EfficientNet B0 pre-trained deep learning architectures and then obtained features were fed into a two-stage classifier approach. Each of the classifiers in the proposed approach performed binary classification. In the first stage, healthy and infected samples were separated, and in the second stage, infected samples were detected as Covid-19 or pneumonia. In the experiments, Bi-LSTM network and well-known ensemble approaches such as Gradient Boosting, Random Forest and Extreme Gradient Boosting were used as the classifier model and it was seen that the Bi-LSTM network had a superior performance than other classifiers with 92.489% accuracy.Scopus Automatic classification of brain magnetic resonance images with hypercolumn deep features and machine learning(2022-09-01) Akyol K.Brain 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.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.Scopus Automatic Detection of Covid-19 with Bidirectional LSTM Network Using Deep Features Extracted from Chest X-ray Images(2022-03-01) Akyol K.; Şen B.Coronavirus disease, which comes up in China at the end of 2019 and showed different symptoms in people infected, affected millions of people. Computer-aided expert systems are needed due to the inadequacy of the reverse transcription-polymerase chain reaction kit, which is widely used in the diagnosis of this disease. Undoubtedly, expert systems that provide effective solutions to many problems will be very useful in the detection of Covid-19 disease, especially when unskilled personnel and financial deficiencies in underdeveloped countries are taken into consideration. In the literature, there are numerous machine learning approaches built with different classifiers in the detection of this disease. This paper proposes an approach based on deep learning which detects Covid-19 and no-finding cases using chest X-ray images. Here, the classification performance of the Bi-LSTM network on the deep features was compared with the Deep Neural Network within the frame of the fivefold cross-validation technique. Accuracy, sensitivity, specificity and precision metrics were used to evaluate the classification performance of the trained models. Bi-LSTM network presented better performance compare to DNN with 97.6% value of high accuracy despite the few numbers of Covid-19 images in the dataset. In addition, it is understood that concatenated deep features more meaningful than deep features obtained with pre-trained networks by one by, as well. Consequently, it is thought that the proposed study based on the Bi-LSTM network and concatenated deep features will be noteworthy in the design of highly sensitive automated Covid-19 monitoring systems.Scopus Classification of Different Tympanic Membrane Conditions Using Fused Deep Hypercolumn Features and Bidirectional LSTM(2022-06-01) Uçar M.; Akyol K.; Atila; Uçar E.Objectives: Middle ear inflammatory diseases are global health problem that can have serious consequences such as hearing loss and speech disorders. The high cost of medical devices such as oto-endoscope and oto-microscope used by the specialists for the diagnosis of the disease prevents its widespread use. In addition, the decisions of otolaryngologists may differ due to the subjective visual examinations. For this reason, computer-aided middle ear disease diagnosis systems are needed to eliminate subjective diagnosis and high cost problems. To this aim, a hybrid deep learning approach was proposed for automatic recognition of different tympanic membrane conditions such as earwax plug, myringosclerosis, chronic otitis media and normal from the otoscopy images. Materials and methods: In this study we used public Ear Imagery dataset containing 880 otoscopy images. The proposed approach detects keypoints from the otoscopy images and following the obtained keypoint positions, extracts hypercolumn deep features from 5 different layers of the VGG 16 model. Classification of tympanic membrane conditions were realized by feeding the deep hypercolumn features to Bi-LSTM network in the form of non-time related data. Results: The performance of the proposed model was evaluated in three different color spaces as Red-Green-Blue (RGB), Hue-Saturation-Value (HSV) and Haematoxylin-Eosin-Diaminobenzidine (HED). The proposed model achieved acceptable results in all color spaces, moreover it showed a very successful performance in classifying tympanic membrane conditions especially in RGB space. Experimental studies showed that the proposed model achieved Acc of 99.06%, Sen of 98.13% and Spe of 99.38%. Conclusion: As a result, a robust model with high sensitivity was obtained for classification of tympanic membrane conditions and it was shown that Bi-LSTM network, which is generally used with time-related data, could also be used successfully with non-time related data for diagnosis of tympanic membrane conditions.Scopus Comparing of deep neural networks and extreme learning machines based on growing and pruning approach(2020-02-01) Akyol K.Recently, 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.Publication 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.Scopus Decolorization of the Reactive Blue 19 from Aqueous Solutions with the Fenton Oxidation Process and Modeling with Deep Neural Networks(2020-02-01) Değermenci N.; Akyol K.The decolorization of Reactive Blue 19 (RB19) from aqueous solutions using the Fenton oxidation process was researched. The effects of different operating parameters, e.g., H2O2, Fe(II), initial dye concentration, pH, and solution temperature, on the decolorization of RB19 were investigated. Increasing, the H2O2 concentration and temperature increased the rate of the decolorization; however, increasing initial RB19 concentration reduced the decolorization. Additionally, modeling of the decolorization obtained by the Fenton oxidation process was researched based on deep neural networks (DNN) architecture providing the best performance in terms of optimum hidden layers and neuron numbers in addition to ideal activation and optimization function pairs. The performances of the models were analyzed on the training, validation, and test data. According to the experimental results, the seven hidden layers DNN model with “relu” activation function and “RMSProp” optimization function provided the best performance with root mean square error (RMSE) of 3.39 and correlation coefficient (R2) of 0.99.Scopus Growing and pruning based deep neural networks modeling for effective Parkinson’s disease diagnosis(2020-01-01) Akyol K.Parkinson’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.Publication Growing and pruning based deep neural networks modeling for effective Parkinson’s disease diagnosis(2020-01-01) Akyol K.Parkinson’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.Scopus Growing and pruning based deep neural networks modeling for effective Parkinson⇔s disease diagnosis(2020-01-01) Akyol K.Parkinson'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.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.Scopus Handling hypercolumn deep features in machine learning for rice leaf disease classification(2022-01-01) Akyol K.Rice 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.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.Scopus HANDLING the EFFECT of ATTRIBUTE SELECTION on SUPPORT VECTOR MACHINES for DETECTING CHRONIC KIDNEY DISEASE(2022-12-01) Akyol K.; Şen B.Chronic kidney disease is a gradual loss of kidney function. Determining the important attributes that describe this disease plays a key role in screening and examining the disease by field specialists. The main aim of this study is to comprehensively compare the attribute selection algorithms for predicting this disease. With this aim, several models were built and compared using well-known performance metrics such as accuracy, sensitivity, and specificity in the experiments. Two different attribute selection methods; the stability selection and the minimum redundancy maximum relevance were compared comprehensively on the unbalanced and balanced datasets. In this framework, the stability selection method gave the important attributes. The support vector machines with radial bases function kernel successfully performed the classification using these attributes for this problem.