Mühendislik Fakültesi / Faculty of Engineering
Permanent URI for this collectionhttps://hdl.handle.net/11727/1401
Browse
15 results
Search Results
Item Study of Fish Species Discrimination Via Electronic Nose(2015) Guney, Selda; Atasoy, Ayten; 0000-0003-1188-2902; 0000-0002-0573-1326; HJH-3630-2023; AAC-7404-2020Fish freshness is a critical issue in determining fish quality. Since fish freshness changes according to the fish species, fish species has to be identified before examining the freshness. So far, fish species have been distinguished through different methods such as image processing. In this paper, an electronic nose has been used to distinguish between different species of fish. Thus, both freshness and species of fish will be determined just using a single, low cost device. The aim of this study is to distinguish between three different species of fish - horse mackerel, anchovy and whiting - by using an electronic nose composed of 8 different metal oxide gas sensors. In order to distinguish between the species of fish, a whole new method, which is not applied to this kind of data previously, is used and proposed for use in the pattern recognition unit of the electronic nose. It is examined in three parts such as signal pre-processing, feature extraction and classification. In the pre-processing stage, to reduce the negative effect of sensor drift, a new method is applied to the raw signal in addition to the well-known baseline manipulation method. In the feature extraction part, the sub-sampling method which is not frequently used is applied to the pre-processed signal. The extracted features are used in the classification part. The structure of the proposed classification algorithm is based on binary decision tree structure. The binary decision tree structure is composed of nodes. In every node of the decision tree structure, the feature spaces or classification algorithm can be changed according to the problem. Classification results demonstrate the effectiveness of the presented models. The overall accuracy of the identification of fish species achieved with the proposed methods is 96.18%. The performance of the proposed method is also compared to conventional methods such as Naive Bayes, k-Nearest Neighbor and Linear Discriminant Analysis. The successes of these classifiers are 84.73, 80 and 82.4, respectively. (C) 2015 Elsevier B.V. All rights reserved.Item Discrimination of Different Fish Species by E-nose(2015) Guney, Selda; Atasoy, Ayten; 0000-0003-1188-2902; 0000-0002-0573-1326; HJH-3630-2023; AAC-7404-2020The aim of this study is to distinguish three different types of fish which are anchovy, horse mackerel and whiting by an electronic nose. Generally, the electronic noses are composed of three units. These are sensor unit which has 8 metal oxide sensors in this study, an electronic unit and a pattern recognition unit. In the pattern recognition unit, signal preprocessing, feature extraction and classification stages are performed. For distinguishing different fish species, different feature extraction methods and classification methods are compared with each other. Then the best combination of feature extraction and classification method is selected and applied to the fish database.Item Fish Freshness Testing with Artificial Neural Networks(2015) Atasoy, Ayten; Ozsandikcioglu, Umit; Guney, Selda; 0000-0002-0573-1326; AAC-7404-2020In this work, with the use of an electronic nose which has 8 metal oxide gas sensors and was set up at Karadeniz Technical University, a fish freshness system was designed. There are 7 classes (1, 3, 5, 7, 9, 11, 13 day for fish storage) for classification and to perform classification process, Artificial Neural Networks was used in this work. To increase the classification success, Artificial Neural Network architecture, activation functions and input data obtained from different feature extraction method was changed, the storage condition is very important factor for fish freshness and fishes used in this study were stored at fish market conditions. In this study to determine the classification success, 5-Fold Cross Validation method was used and the maximum success rate was obtained as 98.94 %.Item Classification of Human Movements by Using Kinect Sensor(2023) Acis, Busra; Guney, Selda; https://orcid.org/0000-0001-6683-0005; https://orcid.org/0000-0002-0573-1326; HDM-2942-2022In recent years, studies have been carried out to classify human movements in many areas such as health and safety. To classify human movements, image processing methods have also started to be used in recent years. With the help of learning-based algorithms, human posture can be defined in the images obtained by various imaging methods. The predecessor methods of these classification algorithms are machine learning and deep learning. In addition, in recent years, the use of sensors that can detect human joints in perceiving human posture has also increased. The Kinect sensor, developed by Microsoft, is one of the most frequently used sensors because it is not wearable and can detect joints with infrared rays and transfer this information directly to the computer via USB connection. This study used a dataset called CAD60 that included real-time human posture information and images obtained using a Microsoft Kinect sensor, which is available in the literature. This dataset contains data that includes different movements/postures of different people. Within the scope of this study, the performances of these algorithms were obtained by using classification algorithms with the MATLAB program and these performances were compared. The classification algorithms have been used to try to improve the results by using different architectures. When raw data is used, classification accuracy is obtained as 72.60% with one of the machine learning methods, the Cosine K-Nearest Neighbor method. With the feature selection method, this success value has been increased to 74.18%. In addition, when classified by the Support Vector Machines method after the feature extraction process using the Long Short Term Memory method from the deep network architectures, which is the method proposed in this study, the accuracy rate was increased to 98.95%. The best method of classifying human posture was investigated by using different methods and a method was proposed by comparing it with the literature.Item Applications of Deep Learning Techniques to Wood Anomaly Detection(2022) Celik, Yaren; Guney, Selda; Dengiz, Berna; Xu, J; Altiparmak, F.; Hassan, MHA; Marquez, FPGWood products and structures have an important place in today's industry. They are widely used in many fields. However, there are various difficulties in production systems where wood raw material is under many processes. Some difficulty and complexity of production processes result in high variability of raw materials such as a wide range of visible structural defects that must be checked by specialists on line or of line. These issues are not only difficult and biased in manual processes, but also less effective and misleading. To overcome the drawbacks of the manual quality control processes, machine vision-based inspection systems are in great of interest recently for quality control applications. In this study, the wood anomaly has been detected by using deep learning. As it will be a distinction-based method on image processing, the Convolution Neural Network (CNN), which is one of the most suitable methods, has been used for anomaly detection. In addition, it will be tried to obtain the most suitable one among different CNN architectures such as ShuffleNet, AlexNet, GoogleNet for the problem. MobileNet, SqueezeNet, GoogleNet, ShuffleNet among considered methods show promising results in classifying normal and abnormal wood products.Item Human Activity Recognition by Using Different Deep Learning Approaches for Wearable Sensors(2021) Erdas, Cagatay Berke; Guney, Selda; 0000-0003-3467-9923With the spread of wearable sensors, the solutions to the task of activity recognition by using the data obtained from the sensors have become widespread. Recognition of activities owing to wearable sensors such as accelerometers, gyroscopes, and magnetometers, etc. has been studied in recent years. Although there are several applications in the literature, differently in this study, deep learning algorithms such as Convolutional Neural Networks, Convolutional LSTM, and 3D Convolutional Neural Networks fed by Convolutional LSTM have been used in human activity recognition task by feeding with data obtained from accelerometer sensor. For this purpose, a frame was formed with raw samples of the same activity which were collected consecutively from the accelerometer sensor. Thus, it is aimed to capture the pattern inherent in the activity and due to preserving the continuous structure of the movement.Item Classification of Canine Maturity and Bone Fracture Time Based on X-Ray Images of Long Bones(2021) Ergun, Gulnur Begum; Guney, Selda; 0000-0002-0573-1326; 0000-0001-8469-5484Veterinarians use X-rays for almost all examinations of clinical fractures to determine the appropriate treatment. Before treatment, vets need to know the date of the injury, type of the broken bone, and age of the dog. The maturity of the dog and the time of the fracture affects the approach to the fracture site, the surgical procedure and needed materials. This comprehensive study has three main goals: determining the maturity of the dogs (Task 1), dating fractures (Task 2), and finally, detecting fractures of the long bones in dogs (Task 3). The most popular deep neural networks are used: AlexNet, ResNet-50 and GoogLeNet. One of the most popular machine learning algorithms, support vector machines (SVM), is used for comparison. The performance of all sub-studies is evaluated using accuracy and F1 score. Each task has been successful with different network architecture. ResNet-50, AlexNet and GoogLeNet are the most successful algorithms for the three tasks, with F1 scores of 0.75, 0.80 and 0.88, respectively. Data augmentation is performed to make models more robust, and the F1 scores of the three tasks were 0.80, 0.81, and 0.89 using ResNet-50, which is the most successful model. This preliminary work can be developed into support tools for practicing veterinarians that will make a difference in the treatment of dogs with fractured bones. Considering the lack of work in this interdisciplinary field, this paper may lead to future studies.Item Estimation of Concentration Values of Different Gases Based on Long Short-Term Memory by Using Electronic Nose(2021) Bakiler, Hande; Guney, Selda; 0000-0002-0573-1326An electronic nose (e-nose) is commonly used in different areas. In the e-nose studies, one of the most important subjects is the estimation of the different concentration values of different gases. An accurate estimation of gas concentrations plays a very important role in sensitive issues such as disease detection. This study has been carried out to increase the classification and regression successes of concentration values of four different gases detected by 4 metal oxide gas sensors. The different methods are used to compare the success of the classification of the concentration levels and the success of the estimation of concentration values of these all gases. In order to realize these classification and regression processes, first a preprocessing and a feature extraction steps were applied to the raw data. The focus of this study is to increase the success achieved in classification and regression by performing the feature extraction using the proposed method. In the proposed method, "Fully Connected Layer" of Long Short-Term Memory networks was used as a feature extraction. Then, these extracted features were used. The results of the proposed method are compared the other traditional methods. It was observed that there was an improvement in both the classification and regression results with the proposed method. The highest accuracy rate in the classification were obtained in the Support Vector Machine method with 90.8% and in the regression problem, the best mean square errors were obtained with Gaussian Process Regression by using the proposed method.Item Obesity Level Estimation based on Machine Learning Methods and Artificial Neural Networks(2021) Celik, Yaren; Guney, Selda; Dengiz, BernaObesity is a growing societal and public health problem starting from 1980 that needs more attention. For this reason, new studies are emerging day by day, including those looking for obesity in children, especially the impact factors, and how to predict the emergence of the situation under these factors. In this study, different classification methods were applied for the estimation of obesity levels. Based on the evaluation criteria, the results were compared for different machine learning methods. When the Cubic SVM method was applied by selecting the appropriate features specific to the problem, 97.8% accuracy was obtained.Item Automated Tuberculosis Detection Using Pre-Trained CNN and SVM(2021) Oltu, Burcu; Guney, Selda; Dengiz, Berna; Agildere, MuhtesemTuberculosis (TB) is a dreadfully contagious and life-threatening disease if left untreated. Therefore, early and accurate diagnosis is critical for treatment. Today, invasive, expensive, or time-consuming tests are performed for diagnosis. Unfortunately, accurate TB diagnosis is still a major challenge. In the proposed study, a decision support system that can automatically separate normal and TB chest X-ray (CXR) images is presented for objective and accurate diagnosis. In the presented methodology, first various data augmentation methods were applied to the data set, then pre-trained networks (VGG16, MobileNet), were employed as feature extractors from augmented CXR's. Afterward, the extracted features for all images were fed into a support vector machine classifier. In training process, 5-fold cross-validation was applied. As a result of this classification, it was concluded that TB can be diagnosed with an accuracy of 96,6% and an area under the ROC curve (AUC) of 0,99.