Mühendislik Fakültesi / Faculty of Engineering
Permanent URI for this collectionhttps://hdl.handle.net/11727/1401
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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 mirLSTM: A Deep Sequential Approach to MicroRNA Target Binding Site Prediction(2019) Paker, Ahmet; Ogul, Hasan; HJH-2307-2023MicroRNAs (miRNAs) are small and non-coding RNAs of similar to 21-23 base length, which play critical role in gene expression. They bind the target mRNAs in the post-transcriptional level and cause translational inhibition or mRNA cleavage. Quick and effective detection of the binding sites of miRNAs is a major problem in bioinformatics. In this study, a deep learning approach based on Long Short Term Memory (LSTM) is developed with the help of an existing duplex sequence model. Compared with four conventional machine learning methods, the proposed LSTM model performs better in terms of the accuracy (ACC), sensitivity, specificity, AUC (Area under the curve) and F1 score. A web-tool is also developed to identify and display the microRNA target sites effectively and quickly.Item Computer-Aided Breast Cancer Diagnosis from Thermal Images Using Transfer Learning(2020) Cabioglu, Cagri; Ogul, HasanBreast cancer is one of the prevalent types of cancer. Early diagnosis and treatment of breast cancer have vital importance for patients. Various imaging techniques are used in the detection of cancer. Thermal images are obtained by using the temperature difference of regions without giving radiation by the thermal camera. In this study, we present methods for computer aided diagnosis of breast cancer using thermal images. To this end, various Convolutional Neural Networks (CNNs) have been designed by using transfer learning methodology. The performance of the designed nets was evaluated on a benchmarking dataset considering accuracy, precision, recall, F1 measure, and Matthews Correlation coefficient. The results show that an architecture holding pre-trained convolutional layers and training newly added fully connected layers achieves a better performance compared with others. We have obtained an accuracy of 94.3%, a precision of 94.7% and a recall of 93.3% using transfer learning methodology with CNN.Item Feature-level Fusion of Convolutional Neural Networks for Visual Object Classification(2016) Ergun, Hilal; Sert, Mustafa; https://orcid.org/0000-0002-7056-4245; AAB-8673-2019Deep learning architectures have shown great success in various computer vision applications. In this study, we investigate some of the very popular convolutional neural network (CNN) architectures, namely GoogleNet, AlexNet, VGG19 and ResNet. Furthermore, we show possible early feature fusion strategies for visual object classification tasks. Concatanation of features, average pooling and maximum pooling are among the investigated fusion strategies. We obtain state-of-the-art results on well-known image classification datasets of Caltech-101, Caltech-256 and Pascal VOC 2007.Item Early and Late Level Fusion of Deep Convolutional Neural Networks for Visual Concept Recognition(2016) Ergun, Hilal; Akyuz, Yusuf Caglar; Sert, Mustafa; Liu, Jianquan; 0000-0002-7056-4245; 0000-0002-7056-4245; B-1296-2011; D-3080-2015; AAB-8673-2019Visual concept recognition is an active research field in the last decade. Related to this attention, deep learning architectures are showing great promise in various computer vision domains including image classification, object detection, event detection and action recognition in videos. In this study, we investigate various aspects of convolutional neural networks for visual concept recognition. We analyze recent studies and different network architectures both in terms of running time and accuracy. In our proposed visual concept recognition system, we first discuss various important properties of popular convolutional network architecture under consideration. Then we describe our method for feature extraction at different levels of abstraction. We present extensive empirical information along with best practices for big data practitioners. Using these best practices we propose efficient fusion mechanisms both for single and multiple network models. We present state-of-the-art results on benchmark datasets while keeping computational costs at low level. Our results show that these state-of-the-art results can be reached without using extensive data augmentation techniques.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 A real-time approach to recognition of Turkish sign language by using convolutional neural networks(2021) Guney, Selda; 0000-0002-0573-1326Sign language is a form of visual communication used by people with hearing problems to express themselves. The main purpose of this study is to make life easier for these people. In this study, a data set was created using 3200 RGB images for 32 classes (32 static words) taken from three different people. Data augmentation methods were applied to the data sets, and the number of images increased from 3200 to 19,200, 600 per class. A 10-layer convolutional neural network model was created for the classification of the signs, and VGG166, Inception, and ResNet deep network architectures, which are deep learning methods, were applied by using the transfer learning method. Also, the signs are classified using the support vector machines and k-nearest neighbor methods, which are the traditional machine learning methods, by using features obtained from the last layer of the convolutional neural network. The most successful method was determined by comparing the obtained results according to time and performance ratios. In addition to these analyses, an interface was developed. By using the interface, the static words belonging to Turkish sign language (TSL) are translated into real-time written language. With the real-time system designed, its success in recognizing the static words of TSL signs and printing its prediction on the computer screen was evaluated.Item A new framework using deep auto-encoder and energy spectral density for medical waveform data classification and processing(2019) Karim, Ahmad M.; Guzel, Mehmet S.; Tolun, Mehmet R.; Kaya, Hilal; Celebi, Fatih V.This paper proposes a new framework for medical data processing which is essentially designed based on deep autoencoder and energy spectral density (ESD) concepts. The main novelty of this framework is to incorporate ESD function as feature extractor into a unique deep sparse auto-encoders (DSAEs) architecture. This allows the proposed architecture to extract more qualified features in a shorter computational time compared with the conventional frameworks. In order to validate the performance of the proposed framework, it has been tested with a number of comprehensive medical waveform datasets with varying dimensionality, namely, Epilepsy Serious Detection, SPECTF Classification and Diagnosis of Cardiac Arrhythmias. Overall, the ESD function speeds up the deep auto-encoder processing time and increases the overall accuracy of the results which are compared to several studies in the literature and a promising agreement is achieved. (C) 2018 Nalecz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences. Published by Elsevier B.V. All rights reserved.