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Article Citation - WoS: 6Classification of Parasite Egg Cells Using Gray Level Cooccurence Matrix and Knn(Scientific Publishers india, 2016) Sengul, GokhanParasite eggs are around 20 to 80 mu m dimensions, and they can be seen under microscopes only and their detection requires visual analyses of microscopic images, which requires human expertise and long analysis time. Besides visual analysis is very error prone to human procedures. In order to automatize this process, a number of studies are proposed in the literature. But there is still a gap between the preferred performance and the reported ones and it is necessary to increase the performance of the automatic parasite egg classification approaches. In this study a learning based statistical pattern recognition approach for parasite egg classification is proposed that will both decrease the time required for the manual classification by an expert and increase the performance of the previously suggested automated parasite egg classification approaches. The proposed method uses Gray-Level Co-occurrence Matrix as the feature extractor, which is a texture based statistical method that can differentiate the parasite egg cells based on their textures, and the k-Nearest Neighbourhood (kNN) classifier for the classification. The proposed method is tested on 14 parasite egg types commonly seen in humans. The results show that proposed method can classify the parasite egg cells with a performance rate of 99%.Article Citation - WoS: 9Citation - Scopus: 9Benchmarking Classification Models for Cell Viability on Novel Cancer Image Datasets(Bentham Science Publ Ltd, 2019) Ozkan, Akin; Isgor, Sultan Belgin; Sengul, Gokhan; Isgor, Yasemin GulgunBackground: Dye-exclusion based cell viability analysis has been broadly used in cell biology including anticancer drug discovery studies. Viability analysis refers to the whole decision making process for the distinction of dead cells from live ones. Basically, cell culture samples are dyed with a special stain called trypan blue, so that the dead cells are selectively colored to darkish. This distinction provides critical information that may be used to expose influences of the studied drug on considering cell culture including cancer. Examiner's experience and tiredness substantially affect the consistency throughout the manual observation of cell viability. The unsteady results of cell viability may end up with biased experimental results accordingly. Therefore, a machine learning based automated decision-making procedure is inevitably needed to improve consistency of the cell viability analysis. Objective: In this study, we investigate various combinations of classifiers and feature extractors (i.e. classification models) to maximize the performance of computer vision-based viability analysis. Method: The classification models are tested on novel hemocytometer image datasets which contain two types of cancer cell images, namely, caucasian promyelocytic leukemia (HL60), and chronic myelogenous leukemia (K562). Results: From the experimental results, k-Nearest Neighbor (KNN) and Random Forest (RF) by combining Local Phase Quantization (LPQ) achieve the lowest misclassification rates that are 0.031 and 0.082, respectively. Conclusion: The experimental results show that KNN and RF with LPQ can be powerful alternatives to the conventional manual cell viability analysis. Also, the collected datasets are released from the "biochem.atilim.edu.tr/datasets/ " web address publically to academic studies.Article Citation - WoS: 4Citation - Scopus: 7The Integrated Usage of Lbp and Hog Transformations and Machine Learning Algorithms for Age Range Prediction From Facial Images(Univ Osijek, Tech Fac, 2018) Khalifa, Tariq; Sengul, GokhanAge prediction is an active study field that can be used in many computer vision problems due to its importance and effectiveness. In this paper, we present extensive experiments and provide an efficient and accurate approach for age range prediction of people from facial images. First, we apply image resizing to unify all images' size, and Histogram Equalization technique to reduce the illumination effects on all facial images taken from FG-NET and UTD aging databases. Second, Histogram of Oriented Gradient (HOG) and Local Binary Pattern (LBP) are used to extract the features of these images, and then we combined both HOG and LBP features in order to attain better prediction. Finally, Support Vector Machine (SVM) and k-Nearest Neighbour (k-NN) are used for the classification processes. In addition, k-fold, Leave-One-Out (LOO) and Confusion Matrix (CM) are used to evaluate the performance of proposed methods. The extensive and intensified experiments show that combining HOG and LBP features improved the age range predicting performance up to 99.87%.Conference Object Citation - WoS: 1Haptic User Interface Integration for 3d Game Engines(Springer-verlag Berlin, 2014) Sengul, Gokhan; Cagiltay, Nergiz Ercil; Ozcelik, Erol; Tuner, Emre; Erol, BatuhanTouch and feel senses of human beings provide important information about the environment. When those senses are integrated with the eyesight, we may get all the necessary information about the environment. In terms of human-computer-interaction, the eyesight information is provided by visual displays. On the other hand, touch and feel senses are provided by means of special devices called "haptic" devices. Haptic devices are used in many fields such as computer-aided design, distance-surgery operations, medical simulation environments, training simulators for both military and medical applications, etc. Besides the touch and sense feelings haptic devices also provide force-feedbacks, which allows designing a realistic environment in virtual reality applications. Haptic devices can be categorized into three classes: tactile devices, kinesthetic devices and hybrid devices. Tactile devices simulate skin to create contact sensations. Kinesthetic devices apply forces to guide or inhibit body movement, and hybrid devices attempt to combine tactile and kinesthetic feedback. Among these kinesthetic devices exerts controlled forces on the human body, and it is the most suitable type for the applications such as surgical simulations. The education environments that require skill-based improvements, the touch and feel senses are very important. In some cases providing such educational environment is very expensive, risky and may also consist of some ethical issues. For example, surgical education is one of these fields. The traditional education is provided in operating room on real patients. This type of education is very expensive, requires long time periods, and does not allow any error-and-try type of experiences. It is stressfully for both the educators and the learners. Additionally there are several ethical considerations. Simulation environments supported by such haptic user interfaces provide an alternative and safer educational alternative. There are several studies showing some evidences of educational benefits of this type of education (Tsuda et al 2009; Sutherland et al 2006). Similarly, this technology can also be successfully integrated to the physical rehabilitation process of some diseases requiring motor skill improvements (Kampiopiotis & Theodorakou, 2003). Hence, today simulation environments are providing several opportunities for creating low cost and more effective training and educational environment. Today, combining three dimensional (3D) simulation environments with these haptic interfaces is an important feature for advancing current human-computer interaction. On the other hand haptic devices do not provide a full simulation environment for the interaction and it is necessary to enhance the environment by software environments. Game engines provide high flexibility to create 3-D simulation environments. Unity3D is one of the tools that provides a game engine and physics engine for creating better 3D simulation environments. In the literature there are many studies combining these two technologies to create several educational and training environments. However, in the literature, there are not many researches showing how these two technologies can be integrated to create simulation environment by providing haptic interfaces as well. There are several issues that need to be handled for creating such integration. First of all the haptic devices control libraries need to be integrated to the game engine. Second, the game engine simulation representations and real-time interaction features need to be coordinately represented by the haptic device degree of freedom and force-feedback speed and features. In this study, the integration architecture of Unity 3D game engine and the PHANToM Haptic device for creating a surgical education simulation environment is provided. The methods used for building this integration and handling the synchronization problems are also described. The algorithms developed for creating a better synchronization and user feedback such as providing a smooth feeling and force feedback for the haptic interaction are also provided. We believe that, this study will be helpful for the people who are creating simulation environment by using Unity3D technology and PHANToM haptic interfaces.Article Citation - WoS: 2Citation - Scopus: 3A Hybrid Approach for Semantic Image Annotation(Ieee-inst Electrical Electronics Engineers inc, 2021) Sezen, Arda; Turhan, Cigdem; Sengul, GokhanIn this study, a framework that generates natural language descriptions of images within a controlled environment is proposed. Previous work on neural networks mostly focused on choosing the right labels and/or increasing the number of related labels to depict an image. However, creating a textual description of an image is a completely different phenomenon, structurally, syntactically, and semantically. The proposed semantic image annotation framework presents a novel combination of deep learning models and aligned annotation results derived from the instances of the ontology classes to generate sentential descriptions of images. Our hybrid approach benefits from the unique combination of deep learning and semantic web technologies. We detect objects from unlabeled sports images using a deep learning model based on a residual network and a feature pyramid network, with the focal loss technique to obtain predictions with high probability. The proposed framework not only produces probabilistically labeled images, but also the contextual results obtained from a knowledge base exploiting the relationship between the objects. The framework's object detection and prediction performances are tested with two datasets where the first one includes individual instances of images containing everyday scenes of common objects and the second custom dataset contains sports images collected from the web. Moreover, a sample image set is created to obtain annotation result data by applying all framework layers. Experimental results show that the framework is effective in this controlled environment and can be used with other applications via web services within the supported sports domain.Article Citation - WoS: 8Estimation of Polypropylene Concentration of Modified Bitumen Images by Using K-Nn and Svm Classifiers(Asce-amer Soc Civil Engineers, 2015) Tapkin, Serkan; Sengoz, Burak; Sengul, Gokhan; Topal, Ali; Ozcelik, ErolThe goal of this study is to design an expert system that automatically classifies the microscopic images of polypropylene fiber (PPF) modified bitumen including seven different contents of fibers. Optical microscopy was used to capture the images from thin films of polypropylene fiber modified bitumen samples at a magnification scale of 100 x. A total of 313 images were pre-processed, and features were extracted and selected by the exhaustive search method. The k-nearest neighbor (k-NN) and multiclass support vector machine (SVM) classifiers were applied to quantify the representation capacity. The k-NN and multiclass SVM classifiers reached an accuracy rate of 87% and 86%, respectively. The results suggest that the proposed expert system can successfully estimate the concentration of PPF in bitumen images with good generalization characteristics. (C) 2014 American Society of Civil Engineers.Article Citation - WoS: 47Citation - Scopus: 66Deep Learning Based Fall Detection Using Smartwatches for Healthcare Applications(Elsevier Sci Ltd, 2022) Sengul, Gokhan; Karakaya, Murat; Misra, Sanjay; Abayomi-Alli, Olusola O.; Damasevicius, RobertasWe implement a smart watch-based system to predict fall detection. We differentiate fall detection from four common daily activities: sitting, squatting, running, and walking. Moreover, we separate falling into falling from a chair and falling from a standing position. We develop a mobile application that collects the acceleration and gyroscope sensor data and transfers them to the cloud. In the cloud, we implement a deep learning algorithm to classify the activity according to the given classes. To increase the number of data samples available for training, we use the Bica cubic Hermite interpolation, which allows us to improve the accuracy of the neural network. The 38 statistical data features were calculated using the rolling update approach and used as input to the classifier. For activity classification, we have adopted the bi-directional long short-term memory (BiLSTM) neural network. The results demonstrate that our system can detect falling with an accuracy of 99.59% (using leave-one-activityout cross-validation) and 97.35% (using leave-one-subject-out cross-validation) considering all activities. When considering only binary classification (falling vs. all other activities), perfect accuracy is achieved.Article Citation - WoS: 18Citation - Scopus: 24Fusion of Smartphone Sensor Data for Classification of Daily User Activities(Springer, 2021) Sengul, Gokhan; Ozcelik, Erol; Misra, Sanjay; Damasevicius, Robertas; Maskeliunas, RytisNew mobile applications need to estimate user activities by using sensor data provided by smart wearable devices and deliver context-aware solutions to users living in smart environments. We propose a novel hybrid data fusion method to estimate three types of daily user activities (being in a meeting, walking, and driving with a motorized vehicle) using the accelerometer and gyroscope data acquired from a smart watch using a mobile phone. The approach is based on the matrix time series method for feature fusion, and the modified Better-than-the-Best Fusion (BB-Fus) method with a stochastic gradient descent algorithm for construction of optimal decision trees for classification. For the estimation of user activities, we adopted a statistical pattern recognition approach and used the k-Nearest Neighbor (kNN) and Support Vector Machine (SVM) classifiers. We acquired and used our own dataset of 354 min of data from 20 subjects for this study. We report a classification performance of 98.32 % for SVM and 97.42 % for kNN.Article Citation - WoS: 8Citation - Scopus: 13White Blood Cells Classifications by SURF Image Matching, PCA and Dendrogram(Allied Acad, 2015) Nazlibilek, Sedat; Karacor, Deniz; Erturk, Korhan Levent; Sengul, Gokhan; Ercan, Tuncay; Aliew, Fuad; Department of Mechatronics Engineering; Information Systems Engineering; Computer EngineeringDetermination and classification of white blood cells are very important for diagnosing many diseases. The number of white blood cells and morphological changes or blasts of them provide valuable information for the positive results of the diseases such as Acute Lymphocytic Leucomia (ALL). Recognition and classification of white cells as basophils, lymphocytes, neutrophils, monocytes and eosinophils also give additional information for the diagnosis of many diseases. We are developing an automatic process for counting, size determination and classification of white blood cells. In this paper, we give the results of the classification process for which we experienced a study with hundreds of images of white blood cells. This process will help to diagnose especially ALL disease in a fast and automatic way. Three methods are used for classification of five types of white blood cells. The first one is a new algorithm utilizing image matching for classification that is called the Speed-Up Robust Feature detector (SURF). The second one is the PCA that gives the advantage of dimension reduction. The third is the classification tree called dendrogram following the PCA. Satisfactory results are obtained by two techniques.Article Citation - WoS: 5Citation - Scopus: 5A Study on the Performance of Magnetic Material Identification System by Sift-Brisk and Neural Network Methods(Ieee-inst Electrical Electronics Engineers inc, 2015) Ege, Yavuz; Nazlibilek, Sedat; Kakilli, Adnan; Citak, Hakan; Kalender, Osman; Karacor, Deniz; Sengul, GokhanIndustry requires low-cost, low-power consumption, and autonomous remote sensing systems for detecting and identifying magnetic materials. Magnetic anomaly detection is one of the methods that meet these requirements. This paper aims to detect and identify magnetic materials by the use of magnetic anomalies of the Earth's magnetic field created by some buried materials. A new measurement system that can determine the images of the upper surfaces of buried magnetic materials is developed. The system consists of a platform whose position is automatically controlled in x-axis and y-axis and a KMZ51 anisotropic magneto-resistive sensor assembly with 24 sensors mounted on the platform. A new identification system based on scale-invariant feature transform (SIFT)-binary robust invariant scalable keypoints (BRISKs) as keypoint and descriptor, respectively, is developed for identification by matching the similar images of magnetic anomalies. The results are compared by the conventional principal component analysis and neural net algorithms. On the six selected samples and the combinations of these samples, 100% correct classification rates were obtained.
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