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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: 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: 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.Conference Object Citation - WoS: 1Self Archiving in Atilim University(Springer-verlag Berlin, 2012) Erturk, Korhan Levent; Sengul, GokhanSelf archiving is defined as storing the scientific research outputs in researchers' own web pages/sites, organizational web sites or institutional repositories. In this study the self archiving activities of academicians of Atilim University are investigated. For the purpose of the study the web pages of the university, personal web pages of the academicians and open repository of the university are explored. We found the details of 2176 academic activities of the instructors in web pages. More than half of these activities (1147 - 53%) consist of refereed journal papers. Almost a quarter of the instructors saved their research outputs in the university's open repository. Yet, those instructors have not published their works in their personal web pages or institutional web pages. Only 4% of the works are published in personal/organizational web pages. According to the results obtained, the usage of institutional repository is the common self archiving method in the Atilim University. On the other hand, the personal/organizational web pages should be as a point of attraction in self archiving. While discussing the efficient usage of the institutional repository, we suggest that the social networks as a meeting point should include links to personal/institutional web pages containing academicians' papers.Article Citation - WoS: 1Citation - Scopus: 3Three-Dimensional Visualization With Large Data Sets: a Simulation of Spreading Cortical Depression in Human Brain(Hindawi Publishing Corporation, 2012) Erturk, Korhan Levent; Sengul, GokhanWe developed 3D simulation software of human organs/tissues; we developed a database to store the related data, a data management system to manage the created data, and a metadata system for the management of data. This approach provides two benefits: first of all the developed system does not require to keep the patient's/subject's medical images on the system, providing less memory usage. Besides the system also provides 3D simulation and modification options, which will help clinicians to use necessary tools for visualization and modification operations. The developed system is tested in a case study, in which a 3D human brain model is created and simulated from 2D MRI images of a human brain, and we extended the 3D model to include the spreading cortical depression (SCD) wave front, which is an electrical phoneme that is believed to cause the migraine.Conference Object Deep Learning and Current Trends in Machine Learning(Ieee, 2018) Bostan, Atila; Sengul, Gokhan; Tirkes, Guzin; Ekin, Cansu; Karakaya, MuratAcademic interest and commercial attention can be used to identify how much potential a novel technology may have. Since the prospective advantages in it may help solving some problems that are not solved yet or improving the performance of readily available ones. In this study, we have investigated the Web of Science (WOS) indexing service database for the publications on Deep Learning (DL), Machine Learning (ML), Convolutional Neural Networks (CNN), and Image Processing to reveal out the current trend. The figures indicate the strong potential in DL approach especially in image processing domain.Conference Object Citation - WoS: 1An Undergraduate Curriculum for Deep Learning(Ieee, 2018) Tirkes, Guzin; Ekin, Cansu Cigdem; Sengul, Gokhan; Bostan, Atila; Karakaya, MuratDeep Learning (DL) is an interesting and rapidly developing field of research which has been currently utilized as a part of industry and in many disciplines to address a wide range of problems, from image classification, computer vision, video games, bioinformatics, and handwriting recognition to machine translation. The starting point of this study is the recognition of a big gap between the sector need of specialists in DL technology and the lack of sufficient education provided by the universities. Higher education institutions are the best environment to provide this expertise to the students. However, currently most universities do not provide specifically designed DL courses to their students. Thus, the main objective of this study is to design a novel curriculum including two courses to facilitate teaching and learning of DL topic. The proposed curriculum will enable students to solve real-world problems by applying DL approaches and gain necessary background to adapt their knowledge to more advanced, industry-specific fields.Conference Object An Iot Application for Locating Victims Aftermath of an Earthquake(Ieee, 2017) Karakaya, Murat; Sengul, Gokhan; Gokcay, ErhanThis paper presents an Internet of Things (IoT) framework which is specially designed for assisting the research and rescue operations targeted to collapsed buildings aftermath of an earthquake. In general, an IoT network is used to collect and process data from different sources called things. According to the collected data, an IoT system can actuate different mechanisms to react the environment. In the problem at hand, we exploit the IoT capabilities to collect the data about the victims before the building collapses and when it falls down the collected data is processed to generate useful reports which will direct the search and rescue efforts. The proposed framework is tested by a pilot implementation with some simplifications. The initial results and experiences are promising. During the pilot implementation, we observed some issues which are addressed in the proposed IoT framework properly.Article Citation - WoS: 11Citation - Scopus: 12A Comprehensive Assessment Plan for Accreditation in Engineering Education: A Case Study in Turkey(Tempus Publications, 2015) Turhan, Cigdem; Sengul, Gokhan; Koyuncu, Murat; Information Systems Engineering; Software Engineering; Computer EngineeringThis paper describes the procedure followed by Computer Engineering and Software Engineering programs at Atilim University, Ankara, Turkey, which led to the granting of five years of accreditation by MUDEK, the local accreditation body authorized by The European Network for Accreditation of Engineering Education (ENAEE) to award the EUR-ACElabel, and a full member signatory of Washington Accord of International Engineering Alliance (IEA). It explains the organizational structure established for preparation, determination and measurement of the educational objectives, program outcomes, course outcomes, and the continuous improvement cycle carried out during the preparation period. The aim of the paper is to share methods and experiences which may be beneficial for the other programs that are intended for accreditation.
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