Yazılım Mühendisliği Bölümü

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Citation Count: Jafari, Aref...et al. (2014). "A combined spatial and frequency based texture model for organsegmentation in computed tomography examinations" Journal Of Medical Imaging And Health Informatics, Vol.4, No.2, pp.230-236.
A combined spatial and frequency based texture model for organsegmentation in computed tomography examinations
(Amer Scientific Publishers, 2014) Jafari, Aref; Hassanpour, Reza; Shahbahrami, Asadollah; Wong, Stephan
The organ segmentation in computed tomography (CT) examination is a tedious and error prone task. The local similarity of the pixels from different organs, and the differences between the pixels of the same organ observed in different examinations are two most challenging problems affecting the segmentation process. In this study, statistical and spectral texture properties are combined with the a-priori knowledge about the human body to develop a model for reliably segmenting organs in CT examinations. The main goal of the developed model is fusing local and global statistics to support spatial-frequency analysis and to maximize the simultaneous localization of energy in both spatial and frequency domains. The feature space dimension is reduced by means of a wrapper technique applied as a pre-processing filter. The proposed classifier utilizes a linear combination (ensemble) of two support vector machines (SVM) where the first SVM classifies the input samples according to their textural information and the second one correct the results of the first classifier by searching the spatial information of those samples in a statistical atlas.
Citation Count: A Comparative Evaluation of Popular Search Engınes On Finding Turkish Documents For A Specific Time Period (2017). Bitirim, Yiltan; Gorur, Abdul Kadir, Tehnicki Vjesnik-Technical Gazette, 24(2), 565-569.
A Comparative Evaluation Of Popular Search Engines On Finding Turkish Documents For A Specific Time Period
(Univ Osijek, Tech Fac, 2017) Bitirim, Yıltan; Görür, Abdül Kadir; 107251
This study evaluates the popular search engines, Google, Yahoo, Bing, and Ask, on finding Turkish documents by comparing their current performances with their performances measured six years ago. Furthermore, the study reveals the current information retrieval effectiveness of the search engines. First of all, the Turkish queries were run on the search engines separately. Each retrieved document was classified and precision ratios were calculated at various cut-off points for each query and engine pair. Afterwards, these ratios were compared with the six years ago ratios for the evaluations. Besides the descriptive statistics, Mann-Whitney U and Kruskal-Wallis H statistical tests were used in order to find out statistically significant differences. All search engines, except Google, have better performance today. Bing has the most increased performance compared to six years ago. Nowadays: Yahoo has the highest mean precision ratios at various cut-off points; all search engines have their highest mean precision ratios at cut-off point 5; dead links were encountered in Google, Bing, and Ask; and repeated documents were encountered in Google and Yahoo.
Citation Count: Dokeroglu, Tansel; Deniz, Ayça; Kiziloz, Hakan E. (2022). "A comprehensive survey on recent metaheuristics for feature selection", Neurocomputing, Vol.494, pp.269-296.
A comprehensive survey on recent metaheuristics for feature selection
(2022) Dokeroglu, Tansel; Deniz, Ayça; Kiziloz, Hakan Ezgi; 234173
Feature selection has become an indispensable machine learning process for data preprocessing due to the ever-increasing sizes in actual data. There have been many solution methods proposed for feature selection since the 1970s. For the last two decades, we have witnessed the superiority of metaheuristic feature selection algorithms, and tens of new ones are being proposed every year. This survey focuses on the most outstanding recent metaheuristic feature selection algorithms of the last two decades in terms of their performance in exploration/exploitation operators, selection methods, transfer functions, fitness value evaluations, and parameter setting techniques. Current challenges of the metaheuristic feature selection algorithms and possible future research topics are examined and brought to the attention of the researchers as well.
Citation Count: Nasser, Ahmed; Sever, Hayri (2020). "A Concept-based Sentiment Analysis Approach for Arabic", The International Arab Journal of Information Technology, Vol. 17, No. 5, pp. 778-788.
A Concept-based Sentiment Analysis Approach for Arabic
(2020) Nasser, Ahmed; Sever, Hayri; 11916
Concept-Based Sentiment Analysis (CBSA) methods are considered to be more advanced and more accurate when it compared to ordinary Sentiment Analysis methods, because it has the ability of detecting the emotions that conveyed by multi-word expressions concepts in language. This paper presented a CBSA system for Arabic language which utilizes both of machine learning approaches and concept-based sentiment lexicon. For extracting concepts from Arabic, a rule-based concept extraction algorithm called semantic parser is proposed. Different types of feature extraction and representation techniques are experimented among the building prosses of the sentiment analysis model for the presented Arabic CBSA system. A comprehensive and comparative experiments using different types of classification methods and classifier fusion models, together with different combinations of our proposed feature sets, are used to evaluate and test the presented CBSA system. The experiment results showed that the best performance for the sentiment analysis model is achieved by combined Support Vector Machine-Logistic Regression (SVM-LR) model where it obtained a F-score value of 93.23% using the Concept-Based-Features + Lexicon-Based-Features + Word2vec-Features (CBF + LEX+ W2V) features combinations.
Citation Count: Güner, Funda...et al. (2023). "A Constraint Programming Approach To A Real-World Workforce Scheduling Problem For Multi-Manned Assembly Lines With Sequence-Dependent Setup Times", International Journal Of Production Research.
A Constraint Programming Approach To A Real-World Workforce Scheduling Problem For Multi-Manned Assembly Lines With Sequence-Dependent Setup Times
(2023) Güner, Funda; Görür, Abduel K.; Satır, Benhür; Kandiller, Levent; Drake, John H.; 54700
For over five decades, researchers have presented various assembly line problems. Recently, assembly lines with multiple workers at each workstation have become very common in the literature. These lines are often found in the manufacturing of large vehicles, where workers at a workstation may perform their assigned tasks at the same time. Most research on multi-manned assembly lines focuses on balancing tasks and workers among workstations and scheduling tasks for workers. This study, however, concentrates on assigning tasks to workers already assigned to a specific workstation, rather than balancing the entire line. The problem was identified through an industrial case study at a large vehicle manufacturing company. The study presents two methods, one using mixed integer linear programming and the other using constraint programming, to minimise the number of workers required on a multi-manned assembly line with sequence-dependent setup times. The results of the computational experiments indicate that the constraint programming method performs better than the mixed integer linear programming method on several modified benchmark instances from the literature. The constraint programming model is also tested on the real-world scenario of our industrial case study and leads to significant improvements in the productivity of the workstations.
Citation Count: Dökeroğlu, Tansel. (2023). "A new parallel multi-objective Harris hawk algorithm for predicting the mortality of COVID-19 patients", Peerj Computer Science, Vol. 9.
A new parallel multi-objective Harris hawk algorithm for predicting the mortality of COVID-19 patients
(2023) Dökeroğlu, Tansel; 234173
Harris' Hawk Optimization (HHO) is a novel metaheuristic inspired by the collective hunting behaviors of hawks. This technique employs the flight patterns of hawks to produce (near)-optimal solutions, enhanced with feature selection, for challenging classification problems. In this study, we propose a new parallel multi-objective HHO algorithm for predicting the mortality risk of COVID-19 patients based on their symptoms. There are two objectives in this optimization problem: to reduce the number of features while increasing the accuracy of the predictions. We conduct comprehensive experiments on a recent real-world COVID-19 dataset from Kaggle. An augmented version of the COVID-19 dataset is also generated and experimentally shown to improve the quality of the solutions. Significant improvements are observed compared to existing state-of-the-art metaheuristic wrapper algorithms. We report better classification results with feature selection than when using the entire set of features. During experiments, a 98.15% prediction accuracy with a 45% reduction is achieved in the number of features. We successfully obtained new best solutions for this COVID-19 dataset.
Citation Count: Dokeroglu, Tansel; Ozdemir, Yavuz Selim. (2023). "A new robust Harris Hawk optimization algorithm for large quadratic assignment problems", Neural Computing & Applications, Vol. 35, No. 17, pp. 12531-12544.
A new robust Harris Hawk optimization algorithm for large quadratic assignment problems
(2023) Dokeroglu, Tansel; Ozdemir, Yavuz Selim; 234173
Harris Hawk optimization (HHO) is a new robust metaheuristic algorithm proposed for the solution of large intractable combinatorial optimization problems. The hawks are cooperative birds and use many intelligent hunting techniques. This study proposes new HHO algorithms for solving the well-known quadratic assignment problem (QAP). Large instances of the QAP have not been solved exactly yet. We implement HHO algorithms with robust tabu search (HHO-RTS) and introduce new operators that simulate the actions of hawks. We also developed an island parallel version of the HHO-RTS algorithm using the message passing interface. We verify the performance of our proposed algorithms on the QAPLIB benchmark library. One hundred and twenty-five of 135 problems are solved optimally, and the average deviation of all the problems is observed to be 0.020%. The HHO-RTS algorithm is a robust algorithm compared to recent studies in the literature.
Citation Count: Hassanpour, Reza, "A Two-Stage Matching Method for Multi-Component Shapes", Advances in Electrical and Computer Engineering, 15, No. 1, pp. 143-150, (2013).
A Two-Stage Matching Method for Multi-Component Shapes
(Univ Suceava, 2015) Hassanpour, Reza
In this paper a shape matching algorithm for multiple component objects has been proposed which aims at matching shapes by a two-stage method. The first stage extracts the similarity features of each component using a generic shape representation model. The first stage of our shape matching method normalizes the components for orientation and scaling, and neglects minor deformations. In the second stage, the extracted similarity features of the components are combined with their relative spatial characteristics for shape matching. Some important application areas for the proposed multi-component shape matching are medical image registration, content based medical image retrieval systems, and matching articulated objects which rely on the a-priori information of the model being searched. In these applications, salient features such as vertebrae or rib cage bones can be easily segmented and used. These features however, show differences from person to person on one hand and similarities at different cross-sectional images of the same examination on the other hand. The proposed method has been tested on articulated objects, and reliable registration of 3-dimensional abdominal computed tomography images.
Citation Count: Uzun, Yusuf;...et.al. (2022). "An intelligent system for detecting Mediterranean fruit fly", Journal of Agricultural Engineering, Vol.53, No.3.
An intelligent system for detecting Mediterranean fruit fly
(2022) Uzun, Yusuf; Tolun, Mehmet Resit; Eyyuboglu, Halil Tanyer; Sarı, Filiz
Nowadays, the most critical agriculture-related problem is the harm caused to fruit, vegetable, nut, and flower crops by harmful pests, particularly the Mediterranean fruit fly, Ceratitis capitata, named Medfly. Medfly's existence in agricultural fields must be monitored systematically for effective combat against it. Special traps are utilised in the field to catch Medflies which will reveal their presence and applying pesticides at the right time will help reduce their population. A technologically supported automated remote monitoring system should eliminate frequent site visits as a more economical solution. This paper develops a deep learning system that can detect Medfly images on a picture and count their numbers. A particular trap equipped with an integrated camera that can take photos of the sticky band where Medflies are caught daily is utilised. Obtained pictures are then transmitted by an electronic circuit containing a SIM card to the central server where the object detection algorithm runs. This study employs a faster region-based convolutional neural network (Faster R-CNN) model in identifying trapped Medflies. When Medflies or other insects stick on the trap's sticky band, they spend extraordinary effort trying to release themselves in a panic until they die. Therefore, their shape is badly distorted as their bodies, wings, and legs are buckled. The challenge is that the deep learning system should detect these Medflies of distorted shape with high accuracy. Therefore, it is crucial to utilise pictures containing trapped Medfly images with distorted shapes for training and validation. In this paper, the success rate in identifying Medflies when other insects are also present is approximately 94%, achieved by the deep learning system training process, owing to the considerable amount of purpose-specific photographic data. This rate may be seen as quite favourable when compared to the success rates provided in the literature.
Citation Count: Dokeroglu, Tansel ; Sevinc, E. (2022). "An island parallel Harris hawks optimization algorithm", Neural Computing and Applications, Vol.34, No.21, pp.18341-18368.
An island parallel Harris hawks optimization algorithm
(2022) Dokeroglu, Tansel; Sevinc, Ender; 234173
The Harris hawk optimization (HHO) is an impressive optimization algorithm that makes use of unique mathematical approaches. This study proposes an island parallel HHO (IP-HHO) version of the algorithm for optimizing continuous multi-dimensional problems for the first time in the literature. To evaluate the performance of the IP-HHO, thirteen unimodal and multimodal benchmark problems with different dimensions (30, 100, 500, and 1000) are evaluated. The implementation of this novel algorithm took into account the investigation, exploitation, and avoidance of local optima issues effectively. Parallel computation provides a multi-swarm environment for thousands of hawks simultaneously. On all issue cases, we were able to enhance the performance of the sequential version of the HHO algorithm. As the number of processors increases, the suggested IP-HHO method enhances its performance while retaining scalability and improving its computation speed. The IP-HHO method outperforms the other state-of-the-art metaheuristic algorithms on average as the size of the dimensions grows.
Citation Count: Timoçin, Erdinç...at al. (2021). "Assessment of improvement of the IRI model for foF2 variability over three latitudes in different hemispheres during low and high solar activities", Acta Astronautica, Vol. 180, pp. 305-3016.
Assessment of improvement of the IRI model for foF2 variability over three latitudes in different hemispheres during low and high solar activities
(2021) Timoçin, Erdinç; Temuçin, Hüseyin; Inyurt, Samed; Shah, Munawar; Jamjareegulgarn, Punyawi; 182651
This paper discusses the diurnal and seasonal variations of the F2 layer critical frequency (foF2) and the improvement of performance of the IRI-2016 model in predicting foF2 over three latitudes in different hemispheres during low and high solar activities. We extracted the foF2 data from six ionosonde stations which are Manila (14.7 degrees N, 121.1 degrees E), Yamagawa (31.2 degrees N, 130.6 degrees E), Yakutsk (62.0 degrees N,129.6 degrees E), Townsville (19.6 degrees S, 146.8 degrees E), Hobart (42.9 degrees S, 147.3 degrees E) and Terre Adelie (66.6 degrees S, 140.0 degrees E). The data of both low solar activity (LSA) period and high solar activity (HSA) periods were divided into three seasons as Northern Summer (May, June, July and August), Equinoxes (March, April, September and October) and Northern Winter (November, December, January and February). The present study showed that the IRI-2016 performance is strongly dependent on the solar activity, latitude, season, local time and hemisphere. For both hemispheres, the foF2 values at low latitude station are larger than those at middle latitude station, whereas the foF2 values at middle latitude station are larger than those at high latitude station. The agreement between IRI2016-modelled foF2 and foF2 measurements on all stations selected in the northern hemisphere is best for North Summer and worst for North Winter. For northern hemisphere, the values of relative deviations during both solar activities are largest in high latitudes and smallest in middle latitudes. As for southern hemisphere, the values of relative deviations during LSA are largest in middle latitudes and smallest in high latitudes, whereas the values of relative deviations during HSA are largest in low latitudes and smallest in high latitudes. It is thought that the relative deviations in the observed foF2 values are caused by solar activity that strongly alter chemical and electromagnetic processes in the ionosphere. These results are important for future improvements depending on solar activity and seasons in the IRI model for foF2 values over three latitudes in different hemispheres.
Citation Count: Akyol, H.; Kızılduman, H.S.; Dökeroğlu, T. (2022). "Big Data Reduction and Visualization Using the K-Means Algorithm", Ankara Science University, Researcher, Vol.2, No.1., pp.40-45.
Big Data Reduction and Visualization Using the K-Means Algorithm
(2022) Akyol, Hakan; Kızılduman, Hale Sema; Dökeroğlu, Tansel; 234173
A huge amount of data is being produced every day in our era. In addition to high-performance processing approaches, efficiently visualizing this quantity of data (up to Terabytes) remains a major difficulty. In this study, we use the well-known clustering method K-means as a data reduction strategy that keeps the visual quality of the provided huge data as high as possible. The centroids of the dataset are used to display the distribution properties of data in a straightforward manner. Our data comes from a recent Kaggle big data set (Click Through Rate), and it is displayed using Box plots on reduced datasets, compared to the original plots. It is discovered that K-means is an effective strategy for reducing the amount of huge data in order to view the original data without sacrificing its distribution information quality
Citation Count: Medeni, İ.T.; Aktaş, Z.A.; Tolun, M.R. "Bilgi Biliminin Mühendislik Gereksinimi ve Bilgi Mühendisliği", Elektrik Elektronik Bilgisayar Biomedikal Mühendisleri Eğitimi 4. Ulusal Sempozyumu, 2009.
Bilgi Biliminin Mühendislik Gereksinimi ve Bilgi Mühendisliği
(2009) Medeni, İhsan Tolga; Aktaş, A. Ziya; Tolun, Mehmet R.; 1863
Yirminci yüzyılın ikinci yarısında bilgisayar, bilgi ve iletişim teknolojilerindeki gelişmeler bilgiye dayalı yeni bilim ve mühendislik disiplinleri oluşturma ihtiyacını doğurmuştur. Bu ihtiyaç nedeniyle doğan yeni bilim ve mühendislik disiplinlerinin gelişiminin aslında (veri, enformasyon ve bilgi ) üçlüsüne yönelik oluşumlar olduğu gözlemlenmektedir.Bu makalede bilgi sözcüğü bu üçlü için genel bir ad olarak kullanılacaktır. Bilgi disiplini bir taraftan, bu üçlü arasındaki bağların örgütler ve bireyler açısından ortaya koyulmasını amaçlar; açık ve örtük bilginin birbirine dönüşümünü sağlamaya çalışırken, diğer taraftan da ortaya çıkan yeni dallar ve var olan dalların bilgi temelli ilişkisini kurmaya yönelik çalışmalar yapmaktadır. Bu üçlünün ve bilgi disiplininin bilim / mühendislik, işletme / yönetim disiplinleriyle olan ilişkisi ve oluşturulacak bir bilgi mühendisliği lisans programının bu kavramlarla olabilecek ilgisi bu bildirinin konusudur.
Citation Count: Sever, Hayri; Senol, Ahmet; Elbasi, Ersin, "Block Size Analysis for Discrete Wavelet Watermarking and Embedding a Vector Image as a Watermark", International Arab Journal of Information Technology, Vol. 16, No. 6, pp. 1036-1043, (2019).
Block Size Analysis for Discrete Wavelet Watermarking and Embedding a Vector Image as a Watermark
(Zarka Private Univ, 2019) Sever, Hayri; Şenol, Ahmet; Elbaşı, Ersin; 11916
As telecommunication and computer technologies proliferate, most data are stored and transferred in digital format. Content owners, therefore, are searching for new technologies to protect copyrighted products in digital form. Image watermarking emerged as a technique for protecting image copyrights. Early studies on image watermarking used the pixel domain whereas modern watermarking methods convert a pixel based image to another domain and embed a watermark in the transform domain. This study aims to use, Block Discrete Wavelet Transform (BDWT) as the transform domain for embedding and extracting watermarks. This study consists of 2 parts. The first part investigates the effect of dividing an image into non overlapping blocks and transforming each image block to a DWT domain, independently. Then, effect of block size on watermark success and, how it is related to block size, are analyzed. The second part investigates embedding a vector image logo as a watermark. Vector images consist of geometric objects such as lines, circles and splines. Unlike pixel-based images, vector images do not lose quality due to scaling. Vector watermarks deteriorate very easily if the watermarked image is processed, such as compression or filtering. Special care must be taken when the embedded watermark is a vector image, such as adjusting the watermark strength or distributing the watermark data into the image. The relative importance of watermark data must be taken into account. To the best of our knowledge this study is the first to use a vector image as a watermark embedded in a host image.
Citation Count: Altunel, Yusuf; Tolun, Mehmet R.; Sobh, T. (2008). "Component-Based Project Estimation Issues for Recursive Development", Advances in Computer and Informatiom Sciences and Engineering, pp. 577-+.
Component-Based Project Estimation Issues for Recursive Development
(2008) Altunel, Yusuf; Tolun, Mehmet R.; Sobh, T.; 1863
In this paper we investigated the component-based specific issues that might affect project cost estimation. Component-based software development changes the style of software production. With component-based approach the software is developed as the composition of reusable software components. Each component production process must be treated as a stand-alone software project, which needs individual task of management. A typical pure component-based development can be considered as decomposition/integration activities successively applied at different levels and therefore results in recursive style of development. We analyzed and presented our results of studies on the component-based software development estimation issues from recursive point of view.
Citation Count: Bugday, Ahmet...et al. (2019). "Creating consensus group using online learning based reputation in blockchain networks", Pervasive and Mobile Computing, Vol. 59.
Creating consensus group using online learning based reputation in blockchain networks
(Elsevier, 2019) Buğday, Ahmet; Özsoy, Adnan; Öztaner, Serdar Murat; Sever, Hayri; 11916
One of the biggest challenges to blockchain technology is the scalability problem. The choice of consensus algorithm is critical to the practical solution of the scalability problem. To increase scalability, Byzantine Fault Tolerance (BFT) based methods have been most widely applied. This study proposes a new model instead of Proof of Work (PoW) for forming the consensus group that allows the use of BFT based methods in the public blockchain network. The proposed model uses the adaptive hedge method, which is a decision-theoretic online learning algorithm (Qi et al., 2016). The reputation value is calculated for the nodes that want to participate in the consensus committee, and nodes with high reputation values are selected for the consensus committee to reduce the chances of the nodes in the consensus committee being harmful. Since the study focuses on the formation of the consensus group, a simulated blockchain network is used to test the proposed model more effectively. Test results indicate that the proposed model, which is a new approach in the literature making use of machine learning for the construction of consensus committee, successfully selects the node with the higher reputation for the consensus group. (C) 2019 Elsevier B.V. All rights reserved.
Citation Count: Canbay, Pelin; Sezer, Ebru; Sever, Hayri (2020). "Deep Combination of Stylometry Features in Forensic Authorship Analysis", International Journal of Information Security Science, Vol. 9, no. 3, pp. 154-163.
Deep Combination of Stylometry Features in Forensic Authorship Analysis
(2020) Canbay, Pelin; Sezer, Ebru; Sever, Hayri; 11916
Authorship Analysis (AA) in forensic is a process aim to extract information about an author from his/her writings. Forensic AA is needed for detection characteristics of anonymous authors to make better the security of digital media users who are exposed to disturbing entries such as threats or harassment emails. To analyze whether two anonymous short texts were written by the same author, we propose a combination of stylometry features from different categories in different progress. In the majority of the previous AA studies, the stylometric features from different categories are concatenated in a preprocess. In these studies, during the learning process, no category-specific operations are performed; all categories used are evaluated equally. On the other hand, the proposed approach has a separate learning process for each feature category due to their qualitative and quantitative characteristics and combines these processes at the decision phase by using a Combination of Deep Neural Networks (C-DNN). To evaluate the Authorship Verification (AV) performance of the proposed approach, we designed and implemented a problem-specific Deep Neural Network (DNN) for each stylometry category we used. Experiments were conducted on two English public datasets. The results show that the proposed approach significantly improves the generalization ability and robustness of the solutions, and also have better accuracy than the single DNNs.
Citation Count: Canbay, Pelin; Sezer, Ebru Akçapınar; Sever, Hayri (2020). "Detection of Stylometric Writeprint from the Turkish Texts", 28th Signal Processing and Communications Applications Conference (SIU).
Detection of Stylometric Writeprint from the Turkish Texts
(2020) Canbay, Pelin; Sezer, Ebru Akçapınar; Sever, Hayri; 11916
Authorship attribution studies aim to extract information about the author by analyzing the data in the text form. With the increase of anonymous authors in digital environments, the need for these works is increasing day by day. Although there exists lots of studies focuse on stylometric writeprint detection in different languages using different attributes, there is no standard feature set and detection algorithm to be evaluated in these studies. Giving priority to Turkish texts, in this study, which features are more distinctive for determining stylistic writeprint of text, and which methods will contribute to increase the success to be achieved are shown with experimental studies.
Citation Count: Çağıltay, Nergiz Ercil; Toker, Sacip; Çağıltay, Kürşat (2024). "Exploring MOOC Learners’ Behavioural Patterns Considering Age, Gender and Number of Course Enrolments: Insights for Improving Educational Opportunities", Open Praxis, Vol. 16, No. 1, pp. 70-81.
Exploring MOOC Learners’ Behavioural Patterns Considering Age, Gender and Number of Course Enrolments: Insights for Improving Educational Opportunities
(2024) Çağıltay, Nergiz Ercil; Toker, Sacip; Çağıltay, Kürşat; 113411
Massive Open Online Courses (MOOCs) now offer a variety of options for everyone to obtain a high-quality education. The purpose of this study is to better understand the behaviours of MOOC learners and provide some insights for taking actions that benefit larger learner groups. Accordingly, 2,288,559 learners’ behaviours on 174 MITx courses were analysed. The results show that MOOCs are more attractive to the elderly, male, and highly educated groups of learners. Learners’ performance improves as they register for more courses and improve their skills and experiences on MOOCs. The findings suggest that, in the long run, learners’ adaptation to MOOCs will significantly improve the potential benefits of the MOOCs. Hence, MOOCs should continue by better understanding their learners and providing alternative instructional designs by considering different learner groups. MOOC providers’ decision-makers may take these findings into account when making operational decisions.
Citation Count: Choupani, Roya; Tolun, Mehmet R. (2005). "Hand gesture recognition in variable length sequences", WSEAS Transactions on Information Science and Applications, Vol. 2, No. 9, pp. 1294-1301.
Hand gesture recognition in variable length sequences
(2005) Choupani, Roya; Tolun, Mehmet R.; 1863
Using hand gestures in human computer interaction has been a major challenge during the recent years. Many of the hand gesture recognition systems however, have been based on the recognition of hand postures and estimating the related gesture which is restricted to a few numbers of possible movements. However when dealing with applications such as understanding sign languages which include a large number of classes, an automatic learning method based on matching a sequence of postures with the characterizing feature sequence of each class is necessary. An important characteristic of this method is that each sample sequence of a class may have a variable length and different position of the key features. In this paper a syntactic method has been proposed for classifying the input sequences. An algorithm foe extracting the grammar of the method during training stage is also given.

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