Browsing by Author "Sengul, Gokhan"
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Article Citation Count: 9Benchmarking Classification Models for Cell Viability on Novel Cancer Image Datasets(Bentham Science Publ Ltd, 2019) İşgör, Sultan Belgin; Şengül, Gökhan; Sengul, Gokhan; Isgor, Yasemin Gulgun; Özkan, Akın; Chemical Engineering; Computer Engineering; Department of Electrical & Electronics EngineeringBackground: 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 Count: 13Construct and face validity of the educational computer-based environment (ECE) assessment scenarios for basic endoneurosurgery skills(Springer, 2017) Şengül, Gökhan; Ozcelik, Erol; Özçelik, Erol; Çağıltay, Nergiz; Computer Engineering; Software EngineeringBackground In neurosurgery education, there is a paradigm shift from time-based training to criterion-based model for which competency and assessment becomes very critical. Even virtual reality simulators provide alternatives to improve education and assessment in neurosurgery programs and allow for several objective assessment measures, there are not many tools for assessing the overall performance of trainees. This study aims to develop and validate a tool for assessing the overall performance of participants in a simulation-based endoneurosurgery training environment. Methods A training program was developed in two levels: endoscopy practice and beginning surgical practice based on four scenarios. Then, three experiments were conducted with three corresponding groups of participants (Experiment 1, 45 (32 beginners, 13 experienced), Experiment 2, 53 (40 beginners, 13 experienced), and Experiment 3, 26 (14 novices, 12 intermediate) participants). The results analyzed to understand the common factors among the performance measurements of these experiments. Then, a factor capable of assessing the overall skill levels of surgical residents was extracted. Afterwards, the proposed measure was tested to estimate the experience levels of the participants. Finally, the level of realism of these educational scenarios was assessed. Results The factor formed by time, distance, and accuracy on simulated tasks provided an overall performance indicator. The prediction correctness was very high for the beginners than the one for experienced surgeons in Experiments 1 and 2. When non-dominant hand is used in a surgical procedure-based scenario, skill levels of surgeons can be better predicted. The results indicate that the scenarios in Experiments 1 and 2 can be used as an assessment tool for the beginners, and scenario-2 in Experiment 3 can be used as an assessment tool for intermediate and novice levels. It can be concluded that forming the balance between perceived action capacities and skills is critical for better designing and developing skill assessment surgical simulation tools.Article Citation Count: 28Deep learning based fall detection using smartwatches for healthcare applications(Elsevier Sci Ltd, 2022) Şengül, Gökhan; Karakaya, Murat; Karakaya, Kasım Murat; Mısra, Sanjay; Damasevicius, Robertas; Computer EngineeringWe 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 Count: 6Estimation of Polypropylene Concentration of Modified Bitumen Images by Using k-NN and SVM Classifiers(Asce-amer Soc Civil Engineers, 2015) Şengül, Gökhan; Sengoz, Burak; Özçelik, Erol; Topal, Ali; Ozcelik, Erol; Computer EngineeringThe 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 Count: 15Fusion of smartphone sensor data for classification of daily user activities(Springer, 2021) Şengül, Gökhan; Ozcelik, Erol; Özçelik, Erol; Mısra, Sanjay; Maskeliunas, Rytis; Computer EngineeringNew 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 Count: 15GENDER DETECTION USING 3D ANTHROPOMETRIC MEASUREMENTS BY KINECT(Polska Akad Nauk, Polish Acad Sciences, 2018) Şengül, Gökhan; Sengul, Gokhan; Çamalan, Seda; Maskeliunas, Rytis; Mısra, Sanjay; Computer Engineering; Information Systems EngineeringAutomatic gender detection is a process of determining the gender of a human according to the characteristic properties that represent the masculine and feminine attributes of a subject. Automatic gender detection is used in many areas such as customer behaviour analysis, robust security system construction, resource management, human-computer interaction, video games, mobile applications, neuro-marketing etc., in which manual gender detection may be not feasible. In this study, we have developed a fully automatic system that uses the 3D anthropometric measurements of human subjects for gender detection. A Kinect 3D camera was used to recognize the human posture, and body metrics are used as features for classification. To classify the gender, KNN, SVM classifiers and Neural Network were used with the parameters. A unique dataset gathered from 29 female and 31 male (a total of 60 people) participants was used in the experiment and the Leave One Out method was used as the cross-validation approach. The maximum accuracy achieved is 96.77% for SVM with an MLP kernel function.Article Citation Count: 12Gesture-based interaction for learning: time to make the dream a reality(Wiley, 2012) Şengül, Gökhan; Sengul, Gokhan; Özçelik, Erol; Computer Engineering[No Abstract Available]Conference Object Citation Count: 1Haptic User Interface Integration for 3D Game Engines(Springer-verlag Berlin, 2014) Şengül, Gökhan; Cagiltay, Nergiz Ercil; Özçelik, Erol; Çağıltay, Nergiz; Erol, Batuhan; Computer Engineering; Software EngineeringTouch 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 Count: 0A Hybrid Approach for Semantic Image Annotation(Ieee-inst Electrical Electronics Engineers inc, 2021) Sezen, Arda; Turhan, Çiğdem; Sengul, Gokhan; Şengül, Gökhan; Computer Engineering; Software EngineeringIn 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 Count: 5An improved random bit-stuffing technique with a modified RSA algorithm for resisting attacks in information security (RBMRSA)(Cairo Univ, Fac Computers & information, 2022) Şengül, Gökhan; Misra, Sanjay; Mısra, Sanjay; Abayomi-Alli, Olusola; Sengul, Gokhan; Computer EngineeringThe recent innovations in network application and the internet have made data and network security the major role in data communication system development. Cryptography is one of the outstanding and powerful tools for ensuring data and network security. In cryptography, randomization of encrypted data increases the security level as well as the Computational Complexity of cryptographic algorithms involved. This research study provides encryption algorithms that bring confidentiality and integrity based on two algorithms. The encryption algorithms include a well-known RSA algorithm (1024 key length) with an enhanced bit insertion algorithm to enhance the security of RSA against different attacks. The security classical RSA has depreciated irrespective of the size of the key length due to the development in computing technology and hacking system. Due to these lapses, we have tried to improve on the contribution of the paper by enhancing the security of RSA against different attacks and also increasing diffusion degree without increasing the key length. The security analysis of the study was compared with classical RSA of 1024 key length using mathematical evaluation proofs, the experimental results generated were compared with classical RSA of 1024 key length using avalanche effect in (%) and computational complexity as performance evaluation metrics. The results show that RBMRSA is better than classical RSA in terms of security but at the cost of execution time. (C) 2022 THE AUTHORS. Published by Elsevier B.V. on behalf of Faculty of Computers and Information, Cairo University.Article Citation Count: 1The Integrated Usage of LBP and HOG Transformations and Machine Learning Algorithms for Age Range Prediction from Facial Images(Univ Osijek, Tech Fac, 2018) Şengül, Gökhan; Sengul, Gokhan; Computer EngineeringAge 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%.Article Citation Count: 5A Study on the Performance of Magnetic Material Identification System by SIFT-BRISK and Neural Network Methods(Ieee-inst Electrical Electronics Engineers inc, 2015) Şengül, Gökhan; Nazlibilek, Sedat; Nazlıbilek, Sedat; Citak, Hakan; Kalender, Osman; Karacor, Deniz; Sengul, Gokhan; Computer Engineering; Department of Mechatronics EngineeringIndustry 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.Article Citation Count: 1Three-Dimensional Visualization with Large Data Sets: A Simulation of Spreading Cortical Depression in Human Brain(Hindawi Publishing Corporation, 2012) Şengül, Gökhan; Sengul, Gokhan; Ertürk, Korhan Levent; Computer Engineering; Information Systems EngineeringWe 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.