Bilgisayar Mühendisliği Bölümü

Permanent URI for this communityhttps://hdl.handle.net/20.500.12416/13

Browse

Now showing 1 - 19 of 19

Citation - WoS: 31
Citation - Scopus: 40
A 3D virtual environment for training soccer referees
(Elsevier Science Bv, 2019) Güleç, Ulaş; Gulec, Ulas; Yilmaz, Murat; Yılmaz, Murat; Isler, Veysi; O'Connor, Rory V.; Clarke, Paul M.; 47439; Bilgisayar Mühendisliği; Yazılım Mühendisliği
Emerging digital technologies are being used in many ways by and in particular virtual environments provide new opportunities to gain experience on real-world phenomena without having to live the actual real-world experiences. In this study, a quantitative research approach supported by expert validation interviews was conducted to determine the availability of virtual environments in the training of soccer referees. The aim is to design a virtual environment for training purposes, representing a real-life soccer stadium to allow the referees to manage matches in an atmosphere similar to the real stadium atmosphere. At this point, the referees have a chance to reduce the number of errors that they make in real life by experiencing difficult decisions that they encounter during the actual match via using the virtual stadium. In addition, the decisions and reactions of the referees during the virtual match were observed with the number of different fans in the virtual stadium to understand whether the virtual stadium created a real stadium atmosphere for the referees. For this evaluation, Presence Questionnaire (PQ) and Immersive Tendencies Questionnaire (ITQ) were applied to the referees to measure their involvement levels. In addition, a semi-structure interview technique was utilized in order to understand participants' opinions about the system. These interviews show that the referees have a positive attitude towards the system since they can experience the events occurred in the match as a first person instead of watching them from camera as a third person. The findings of current study suggest that virtual environments can be used as a training tool to increase the experience levels of the soccer referees since they have an opportunity to decide about the positions without facing the real-world risks.
Citation - WoS: 180
Citation - Scopus: 264
Adoption of e-government services in Turkey
(Pergamon-elsevier Science Ltd, 2017) Kurfali, Murathan; Tokdemir, Gül; Arifoglu, Ali; Tokdemir, Gul; Pacin, Yudum; 17411; Bilgisayar Mühendisliği
This research aims to investigate underlying factors that play role in citizens' decision to use e-government services in Turkey. UTAUT model which was enriched by introducing Trust of internet and Trust of government factors is used in the study. The model is evaluated through a survey conducted with Turkish citizens who are from different regions of the country. A total of 529 answers collected through purposive sampling and the responses were evaluated with the SEM (Structural Equation Modeling) technique. According to the results, Performance expectancy, Social influence, Facilitating conditions and Trust of Internet were found to have a positive effect on behavioral intention to use e-government services. Additionally, both Trust factors were found to have a positive influence on Performance expectancy of e-government services, a relation which, to our best knowledge, hasn't been tested before in e-government context. Effect of Effort expectancy and Trust of government were found insignificant on behavioral intention. We believe that the findings of this study will guide professionals and policy makers in improving and popularizing e-government services by revealing the citizen's priorities regarding e-government services in Turkey. (C) 2016 Elsevier Ltd. All rights reserved.
Citation - WoS: 32
Citation - Scopus: 43
Automated Classification of Rheumatoid Arthritis, Osteoarthritis, and Normal Hand Radiographs with Deep Learning Methods
(Springer, 2022) Ureten, Kemal; Maraş, Hadi Hakan; Maras, Hadi Hakan; 34410; Bilgisayar Mühendisliği
Rheumatoid arthritis and hand osteoarthritis are two different arthritis that causes pain, function limitation, and permanent joint damage in the hands. Plain hand radiographs are the most commonly used imaging methods for the diagnosis, differential diagnosis, and monitoring of rheumatoid arthritis and osteoarthritis. In this retrospective study, the You Only Look Once (YOLO) algorithm was used to obtain hand images from original radiographs without data loss, and classification was made by applying transfer learning with a pre-trained VGG-16 network. The data augmentation method was applied during training. The results of the study were evaluated with performance metrics such as accuracy, sensitivity, specificity, and precision calculated from the confusion matrix, and AUC (area under the ROC curve) calculated from ROC (receiver operating characteristic) curve. In the classification of rheumatoid arthritis and normal hand radiographs, 90.7%, 92.6%, 88.7%, 89.3%, and 0.97 accuracy, sensitivity, specificity, precision, and AUC results, respectively, and in the classification of osteoarthritis and normal hand radiographs, 90.8%, 91.4%, 90.2%, 91.4%, and 0.96 accuracy, sensitivity, specificity, precision, and AUC results were obtained, respectively. In the classification of rheumatoid arthritis, osteoarthritis, and normal hand radiographs, an 80.6% accuracy result was obtained. In this study, to develop an end-to-end computerized method, the YOLOv4 algorithm was used for object detection, and a pre-trained VGG-16 network was used for the classification of hand radiographs. This computer-aided diagnosis method can assist clinicians in interpreting hand radiographs, especially in rheumatoid arthritis and osteoarthritis.
Citation - WoS: 268
Citation - Scopus: 344
Context-Aware Computing, Learning, and Big Data in Internet of Things: A Survey
(Ieee-inst Electrical Electronics Engineers inc, 2018) Sezer, Omer Berat; Doğdu, Erdoğan; Dogdu, Erdogan; Ozbayoglu, Ahmet Murat; Bilgisayar Mühendisliği
Internet of Things (IoT) has been growing rapidly due to recent advancements in communications and sensor technologies. Meanwhile, with this revolutionary transformation, researchers, implementers, deployers, and users are faced with many challenges. IoT is a complicated, crowded, and complex field; there are various types of devices, protocols, communication channels, architectures, middleware, and more. Standardization efforts are plenty, and this chaos will continue for quite some time. What is clear, on the other hand, is that IoT deployments are increasing with accelerating speed, and this trend will not stop in the near future. As the field grows in numbers and heterogeneity, "intelligence" becomes a focal point in IoT. Since data now becomes "big data," understanding, learning, and reasoning with big data is paramount for the future success of IoT. One of the major problems in the path to intelligent IoT is understanding "context," or making sense of the environment, situation, or status using data from sensors, and then acting accordingly in autonomous ways. This is called "context-aware computing," and it now requires both sensing and, increasingly, learning, as IoT systems get more data and better learning from this big data. In this survey, we review the field, first, from a historical perspective, covering ubiquitous and pervasive computing, ambient intelligence, and wireless sensor networks, and then, move to context-aware computing studies. Finally, we review learning and big data studies related to IoT. We also identify the open issues and provide an insight for future study areas for IoT researchers.
Citation - WoS: 32
Citation - Scopus: 42
Development of a recurrent neural networks-based calving prediction model using activity and behavioral data
(Elsevier Sci Ltd, 2020) Keceli, Ali Seydi; Catal, Cagatay; Kaya, Aydin; Tekinerdogan, Bedir; 3530
Accurate prediction of calving time in dairy cattle is crucial for dairy herd management to reduce risks like dystocia and pain. Prediction of calving using traditional, manual observation such as observing breeding records and visual cues, however, is a complicated and error-prone task whereby even experts can fail to provide a proper prediction. Moreover, manual prediction does not scale for larger farms and becomes very soon time-consuming, inefficient, and costly. In this context, automated solutions are considered to be promising to provide both better and more efficient predictions, thereby supporting the health of the dairy cows and reducing the unnecessary overhead for farmers. Although the first automated solutions appear to have mainly focused on statistical solutions, currently, machine learning approaches are now increasingly being considered as a feasible and promising approach for accurate prediction of calving. In this context, the objective of this study is to develop machine learning-based prediction models that provide higher performance compared to the existing tools, methods, and techniques. This study shows that the calving of the cattle can be predicted by applying several behaviors of cattle, behavioral monitoring sensors, and machine learning models. Bi-directional Long Short-Term Memory (Bi-LSTM) method has been applied for the prediction of the calving day, and the RusBoosted Tree classifier has been used to predict the remaining 8 h before calving. The experimental results demonstrated that Bi-LSTM provides better performance compared to the LSTM algorithm in terms of classification accuracy, while the RusBoosted Tree algorithm predicts the remaining 8 h accurately before calving. Furthermore, Recurrent Neural Networks provide high performance for the prediction of calving day.
Citation - WoS: 4
Citation - Scopus: 5
Feynman, biominerals and graphene - basic aspects of nanoscience
(Elsevier, 2010) Quandt, Alexander; Özdoğan, Cem; Ozdogan, Cem; Ortak Dersler Bölümü
This article is about writing small. Inspired by R.P. Feynman's legendary talk There's plenty of room at the bottom, we recapitulate his famous Gedanken experiment of condensing a lot of useful information on the head of a pin [see Feymnan R, J. MEMS 1 (1992) 60]. These considerations will familiarize LIS with the length scales for a future downsizing of technological components, and they allow for some speculations about ultimate physical or chemical limits of the corresponding nanodevices. Furthermore we will analyze the nano-technological capabilities of Mother Nature in the case of magnetotactic bacteria, and briefly sketch the cornerstones of the rapidly growing field of biomineralization, which might open up a new science of complex functional nanomaterials in the near future. Finally we describe a general scheme to shrink integrated microelectronic circuits towards the very size limits of nanotechnology. (C) 2009 Elsevier B.V. All rights reserved.
Citation - WoS: 106
Citation - Scopus: 130
Hybrid Expert Systems: A Survey of Current Approaches and Applications
(Pergamon-elsevier Science Ltd, 2012) Sahin, S.; Hassanpour, Reza; Tolun, M. R.; Hassanpour, R.; 1863; Yazılım Mühendisliği
This paper is a statistical analysis of hybrid expert system approaches and their applications but more specifically connectionist and neuro-fuzzy system oriented articles are considered. The current survey of hybrid expert systems is based on the classification of articles from 1988 to 2010. Present analysis includes 91 articles from related academic journals, conference proceedings and literature reviews. Our results show an increase in the number of recent publications which is an indication of gaining popularity on the part of hybrid expert systems. This increase in the articles is mainly in neuro-fuzzy and rough neural expert systems' areas. We also observe that many new industrial applications are developed using hybrid expert systems recently. (C) 2011 Elsevier Ltd. All rights reserved.
Citation - WoS: 2
Citation - Scopus: 3
Mining Medlıne for the Treatment of Osteoporosis
(Springer, 2012) Yildirim, Pinar; Hassanpour, Reza; Ceken, Cinar; Hassanpour, Reza; Esmelioglu, Sadik; Tolun, Mehmet Resit; 101956; Yazılım Mühendisliği
In this paper, we consider the importance of osteoporosis disease in terms of medical research and pharmaceutical industry and we introduce a knowledge discovery approach regarding the treatment of osteoporosis from a historical perspective. Osteoporosis is a systemic skeletal disease in which osteoporotic fractures are associated with substantial morbidity and mortality and impaired quality of life. Osteoporosis has also higher costs, for example, longer hospital stays than many other diseases such as diabetes and heart attack and it is an attractive market for pharmaceutical companies. We use a freely available biomedical search engine leveraging text-mining technology to extract the drug names used in the treatment of osteoporosis from MEDLINE articles. We conclude that alendronate (Fosamax) and raloxifene (Evista) have the highest number of articles in MEDLINE and seem the dominating drugs for the treatment of osteoporosis in the last decade.
Citation - WoS: 13
Citation - Scopus: 19
New knowledge in strategic management through visually mining semantic networks
(Springer, 2017) Ertek, Gurdal; Tokdemir, Gül; Tokdemir, Gul; Sevinc, Mete; Tunc, Murat Mustafa; 17411; Bilgisayar Mühendisliği
Today's highly competitive business world requires that managers be able to make fast and accurate strategic decisions, as well as learn to adapt to new strategic challenges. This necessity calls for a deep experience and a dynamic understanding of strategic management. The trait of dynamic understanding is mainly the skill of generating additional knowledge and innovative solutions under the new environmental conditions. Building on the concepts of information processing, this paper aims to support managers in constructing new strategic management knowledge, through representing and mining existing knowledge through graph visualization. To this end, a three-stage framework is proposed and described. The framework can enable managers to develop a deeper understanding of the strategic management domain, and expand on existing knowledge through visual analysis. The model further supports a case study that involves unstructured knowledge of profit patterns and the related strategies to succeed using these patterns. The applicability of the framework is shown in the case study, where the unstructured knowledge in a strategic management book is first represented as a semantic network, and then visually mined for revealing new knowledge.
Citation - WoS: 2
Citation - Scopus: 3
A New Relational Learning System Using Novel Rule Selection Strategies
(Elsevier, 2006) Uludag, Mahmut; Tolun, Mehmet R.
This paper describes a new rule induction system, rila, which can extract frequent patterns from multiple connected relations. The system supports two different rule selection strategies, namely the select early and select late strategies. Pruning heuristics are used to control the number of hypotheses generated during the learning process. Experimental results are provided on the mutagenesis and the segmentation data sets. The present rule induction algorithm is also compared to the similar relational learning algorithms. Results show that the algorithm is comparable to similar algorithms. (c) 2006 Elsevier B.V. All rights reserved.
Citation - WoS: 7
Citation - Scopus: 10
Prediction Of Similarities Among Rheumatic Diseases
(Springer, 2012) Yildirim, Pinar; Hassanpour, Reza; Ceken, Cinar; Hassanpour, Reza; Tolun, Mehmet Resit; 101956; Yazılım Mühendisliği
We introduce a method for extracting hidden patterns seen in rheumatic diseases by using articles from the widely used biomedical database MEDLINE. Rheumatic diseases affect hundreds of millions of people worldwide and lead to substantial loss of functioning and mobility. Diagnosing rheumatic diseases can be difficult because some symptoms are common to many of them. We use Facta system as a biomedical text mining tool for finding symptoms and then create a dataset with the frequencies of symptoms for each disease and apply hierarchical clustering analysis to find similarities between diseases. Clustering analysis yields four distinct types or groups of rheumatic diseases. Although our results cannot remove all the uncertainty for the diagnosis of rheumatic diseases, we believe they can contribute to the diagnosis of rheumatic diseases to a certain extent. We hope that some similarities exposed can provide additional information at the stage of decision-making.
Citation - WoS: 5
Citation - Scopus: 6
QoS-constrained core selection for group communication
(Elsevier, 2007) Karaman, Ayse; Hassanein, Hossam
The core-based approach in multipoint communication enhances the solution space in terms of QoS-efficiency of solutions in inter-and intra-domain routing. In an earlier work [A. Karaman, H.S. Hassanein, Extended QoS-framework for Delay-constrained Group Communication, International Journal of Communication Systems, in press.], we showed that the constrained cost minimization solutions in core-based approach proposed to date are restrictive in their search to a subrange of solutions, and we proposed SPAN, a generic framework to process in our identified extended solution space. In this paper, we study the core selection component of SPAN and propose two novel algorithms, SPAN/COST and SPAN/ADJUST, which define the core-selection component of SPAN. SPAN/COST mainly optimizes the cost distances to be traveled between the source-core and core-receiver pairs on the multicast trees, while SPAN/ADJUST selects the cores based on the numbers of nodes they dominate and adjusting the set based on cost. Our algorithms consistently outperform their counterparts proposed to date and can be considered pioneering in their optimization range of multiple metrics and processing in the extended solution space. (c) 2007 Elsevier B.V. All rights reserved.
Citation - WoS: 21
Citation - Scopus: 27
Reporting and analyzing alternative clustering solutions by employing multi-objective genetic algorithm and conducting experiments on cancer data
(Elsevier, 2014) Peng, Peter; Addam, Omer; Elzohbi, Mohamad; Ozyer, Sibel T.; Elhajj, Ahmad; Gao, Shang; Alhajj, Reda
Clustering is an essential research problem which has received considerable attention in the research community for decades. It is a challenge because there is no unique solution that fits all problems and satisfies all applications. We target to get the most appropriate clustering solution for a given application domain. In other words, clustering algorithms in general need prior specification of the number of clusters, and this is hard even for domain experts to estimate especially in a dynamic environment where the data changes and/or become available incrementally. In this paper, we described and analyze the effectiveness of a robust clustering algorithm which integrates multi-objective genetic algorithm into a framework capable of producing alternative clustering solutions; it is called Multi-objective K-Means Genetic Algorithm (MOKGA). We investigate its application for clustering a variety of datasets, including microarray gene expression data. The reported results are promising. Though we concentrate on gene expression and mostly cancer data, the proposed approach is general enough and works equally to cluster other datasets as demonstrated by the two datasets Iris and Ruspini. After running MOKGA, a pareto-optimal front is obtained, and gives the optimal number of clusters as a solution set. The achieved clustering results are then analyzed and validated under several cluster validity techniques proposed in the literature. As a result, the optimal clusters are ranked for each validity index. We apply majority voting to decide on the most appropriate set of validity indexes applicable to every tested dataset. The proposed clustering approach is tested by conducting experiments using seven well cited benchmark data sets. The obtained results are compared with those reported in the literature to demonstrate the applicability and effectiveness of the proposed approach. (C) 2013 Elsevier B.V. All rights reserved.
Citation - WoS: 16
Citation - Scopus: 25
Scalable and accurate graph clustering and community structure detection
(Ieee Computer Soc, 2013) Djidjev, Hristo N.; Onuş, Melih; Onus, Melih; 103658; Bilgisayar Mühendisliği
One of the most useful measures of cluster quality is the modularity of the partition, which measures the difference between the number of the edges joining vertices from the same cluster and the expected number of such edges in a random graph. In this paper, we show that the problem of finding a partition maximizing the modularity of a given graph G can be reduced to a minimum weighted cut (MWC) problem on a complete graph with the same vertices as G. We then show that the resulting minimum cut problem can be efficiently solved by adapting existing graph partitioning techniques. Our algorithm finds clusterings of a comparable quality and is much faster than the existing clustering algorithms.
Citation - WoS: 0
Citation - Scopus: 2
Software Process Improvement and Capability Determination Conference 2016
(Elsevier Science Bv, 2017) Clarke, Paul; Yılmaz, Murat; Yilmaz, Murat; Yazılım Mühendisliği
Citation - WoS: 12
Citation - Scopus: 14
Sparsity-driven change detection in multitemporal sar images
(Ieee-inst Electrical Electronics Engineers inc, 2016) Nar, Fatih; Nar, Fatih; Ozgur, Atilla; Saran, Ayşe Nurdan; Saran, Ayse Nurdan; 252953; 20868; Bilgisayar Mühendisliği; Matematik
In this letter, a method for detecting changes in multitemporal synthetic aperture radar (SAR) images by minimizing a novel cost function is proposed. This cost function is constructed with log-ratio-based data fidelity terms and an l(1)-norm-based total variation (TV) regularization term. Log-ratio terms model the changes between the two SAR images where the TV regularization term imposes smoothness on these changes in a sparse manner such that fine details are extracted while effects like speckle noise are reduced. The proposed method, sparsity-driven change detection (SDCD), employs accurate approximation techniques for the minimization of the cost function since data fidelity terms are not convex and the employed l(1)-norm TV regularization term is not differentiable. The performance of the SDCD is shown on real-world SAR images obtained from various SAR sensors.
Citation - WoS: 4
Citation - Scopus: 5
Systematic Investigation of The Effects of Unidirectional Links on The Lifetime of Wireless Sensor Networks
(Elsevier, 2013) Ozyer, Sibel T.; Koyuncu, Murat; Tavli, Bulent; Dursun, Kayhan; Koyuncu, Murat; Uluslararası Ticaret ve Finansman
Link unidirectionality is a commonly encountered phenomenon in wireless sensor networks (WSNs), which is a natural result of various properties of wireless transceivers as well as the environment. Transmission power heterogeneity and random irregularities are important factors that create unidirectional links. Majority of the internode data transfer mechanisms are designed to work on bidirectional links (i.e., due to the lack of a direct reverse path, handshaking cannot be performed between a transmitter and receiver) which render the use of unidirectional links infeasible. Yet, there are some data transfer mechanisms designed specifically to operate on unidirectional links which employ distributed handshaking mechanisms (i.e., instead of using a direct reverse path, a multi-hop reverse path is used for the handshake). In this study, we investigate the impact of both transmission power heterogeneity and random irregularities on the lifetime of WSNs through a novel linear programming (LP) framework both for networks that utilize only bidirectional links and for those that can use bidirectional links as well as unidirectional links. (C) 2013 Elsevier B.V. All rights reserved.
Citation - WoS: 24
Citation - Scopus: 32
Teaching ISO/IEC 12207 software lifecycle processes: A serious game approach
(Elsevier Science Bv, 2017) Aydan, Ufuk; Yılmaz, Murat; Yilmaz, Murat; Clarke, Paul M.; O'Connor, Rory V.; 55248; Yazılım Mühendisliği
Serious games involve applying game design techniques to tasks of a serious nature. In particular, serious games can be used as informative tools and can be embedded in formal education. Although there are some studies related to the application of serious games for the software development process, there is no serious game that teaches the fundamentals of the ISO/IEC 12207:1995 Systems and software engineering Software life cycle processes, which is an international standard for software lifecycle processes that aims to be 'the' standard that defines all the tasks required for developing and maintaining software. "Floors" is a serious game that proposes an interactive learning experience to introduce ISO/IEC 12207:1995 by creating different floors of a virtual environment where various processes of the standard are discussed and implemented. Inherently, it follows an iterative process based on interactive technical dialogues in a 3D computer simulated office. The tool is designed to assess the novice engineering practitioners knowledge and provide preliminary training for ISO/IEC 12207:1995 processes. By playing such a game, participants are able to learn about the details of this standard. The present study provides a framework for the exploration of research data obtained from computer engineering students. Results suggest that there is a significant difference between the knowledge gained among the students who have played Floors and those who have only participated in paper-based learning sessions. Our findings indicate that participants who played Floors tend to have greater knowledge of the ISO/IEC 12207:1995 standard, and as a result, we recommend the use of serious games that seem to be superior to traditional paper based approach.
Citation - WoS: 16
Citation - Scopus: 17
The impact of incapacitation of multiple critical sensor nodes on wireless sensor network lifetime
(Ieee-inst Electrical Electronics Engineers inc, 2017) Yildiz, Huseyin Ugur; Doğdu, Erdoğan; Tavli, Bulent; Kahjogh, Behnam Ojaghi; Dogdu, Erdogan; Bilgisayar Mühendisliği
Wireless sensor networks (WSNs) are envisioned to be utilized in many application areas, such as critical infrastructure monitoring, and therefore, WSN nodes are potential targets for adversaries. Network lifetime is one of the most important performance indicators in WSNs. The possibility of reducing the network lifetime significantly by eliminating a certain subset of nodes through various attacks will create the opportunity for the adversaries to hamper the performance of WSNs with a low risk of detection. However, the extent of reduction in network lifetime due to elimination of a group of critical sensor nodes has never been investigated in the literature. Therefore, in this letter, we create two novel algorithms based on a linear programming framework to model and analyze the impact of critical node elimination attacks on WSNs and explore the parameter space through numerical evaluations of the algorithms. Our results show that critical node elimination attacks can significantly shorten the network lifetime.

Browse

Browsing Bilgisayar Mühendisliği Bölümü by WoS Q "Q1"