Scopus İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12416/8651
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Article Citation - WoS: 3Citation - Scopus: 5Block Size Analysis for Discrete Wavelet Watermarking and Embedding a Vector Image as a Watermark(Zarka Private Univ, 2019) Sever, Hayri; Sever, Hayri; Senol, Ahmet; Elbasi, Ersin; Bilgisayar MühendisliğiAs 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.Article Citation - WoS: 5Citation - Scopus: 11A Shallow 3d Convolutional Neural Network for Violence Detection in Videos(Cairo Univ, Fac Computers & information, 2024) Kaya, Aydin; Sever, Hayri; Dundar, Naz; Keceli, Ali SeydiWith the recent worldwide statistical rise in the amount of public violence, automated violence detection in surveillance cameras has become a matter of high importance. This work introduces an end-to-end, trainable 3D Convolutional Neural Network (3D CNN) for detecting violence in video footage. The proposed network is inherently capable of processing both spatial and temporal information, thereby obviating the need for additional models that would introduce higher computational requirements and complexity. This work has two main contributions: 1) developing a lightweight 3D CNN suitable for inference on edge devices as mobile systems, and 2) a comprehensive explanation of all components comprising a CNN model, thereby enhances model interpretability. Experiments were conducted to assess the performance of the proposed model using a consolidated dataset combining four benchmark datasets. The results of the experiments support the asserted contributions, which are discussed in detail.Article Citation - WoS: 3Citation - Scopus: 3Binary Background Model With Geometric Mean for Author-Independent Authorship Verification(Sage Publications Ltd, 2023) Sezer, Ebru A.; Sever, Hayri; Canbay, PelinAuthorship verification (AV) is one of the main problems of authorship analysis and digital text forensics. The classical AV problem is to decide whether or not a particular author wrote the document in question. However, if there is one and relatively short document as the author's known document, the verification problem becomes more difficult than the classical AV and needs a generalised solution. Regarding to decide AV of the given two unlabeled documents (2D-AV), we proposed a system that provides an author-independent solution with the help of a Binary Background Model (BBM). The BBM is a supervised model that provides an informative background to distinguish document pairs written by the same or different authors. To evaluate the document pairs in one representation, we also proposed a new, simple and efficient document combination method based on the geometric mean of the stylometric features. We tested the performance of the proposed system for both author-dependent and author-independent AV cases. In addition, we introduced a new, well-defined, manually labelled Turkish blog corpus to be used in subsequent studies about authorship analysis. Using a publicly available English blog corpus for generating the BBM, the proposed system demonstrated an accuracy of over 90% from both trained and unseen authors' test sets. Furthermore, the proposed combination method and the system using the BBM with the English blog corpus were also evaluated with other genres, which were used in the international PAN AV competitions, and achieved promising results.Conference Object Citation - WoS: 8Citation - Scopus: 14Small and Unbalanced Data Set Problem in Classification(Ieee, 2019) Sezer, Ebru Akcapinar; Sever, Hayri; Par, Oznur EsraClassification of data is difficult in case of small and unbalanced data set and this problem directly affects the classification performance. Small and / or the imbalance dataset has become a major problem in data mining. Classification algorithms are developed based on the assumption that the data sets are balanced and large enough. The most of the algorithms ignore or misclassify examples of the minority class, focus on the majority class. Small and unbalanced data set problem is frequently encountered in medical data mining due to some limitations. Within the scope of the study, the public accessible data set, hepatitis, was divided into small and imblanced data subsets, each of the data subsets were oversampled by distance based data generation methods. The oversampled data sets were classified by using four different machine learning algorithms (Artificial Neural Networks, Support Vector Machines, Naive Bayes and Decision Tree) and the classification scores were compared.Conference Object Citation - WoS: 2Citation - Scopus: 15Securing Blockchain Shards by Using Learning Based Reputation and Verifiable Random Functions(Ieee, 2019) Ozsoy, Adnan; Sever, Hayri; Bugday, AhmetIn order to meet the increasing demand of the blockchain, it needs to find a solution to the scalability problem. It has been focused on sharding recently to address the scalability problem. In the sharding method, the blockchain is divided into pieces. Instead of a more extensive network, networks with fewer nodes are created. As a result, it becomes more important that each node in the network is reliable. In this study, studies using sharding method have been investigated, and methods for the assigning nodes to shards are proposed. The use of learning-based adaptive methods for this process will contribute to the safe and reliable use of shards. The probability of the shards to deteriorate and influence the whole blockchain will be reduced.Conference Object Authorship Modelling Approach for Authorship Verification on the Turkish Texts(Ieee, 2018) Akcapinar Sezer, Ebru; Sever, Hayri; Canbay, Pelin; Sezer, Ebru AkcapinarAuthorship attribution which aims to extract information about an author by analyzing the text of the author is a challenging field that has been studied for years. This study becomes even more difficult when there is limited data on this field. The need for this study carried out under the name of Authorship Verification is increasing day by day with the increase of anonymous authors in the electronic environments. In this study, a model-based solution approach is presented for the authorship verification problem. With the presented approach, it was determined what should be the success interval to be considered in the authorship verification problem.Article Citation - WoS: 7Citation - Scopus: 13A Concept-Based Sentiment Analysis Approach for Arabic(Zarka Private Univ, 2020) Sever, Hayri; Nasser, AhmedConcept-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.Conference Object Citation - Scopus: 2Web Service-Based Turkish Automatic Speech Recognition Platform(Ieee, 2020) Polat, Huseyin; Sever, Hayri; Oyucu, SaadinIn response to the similar challenges in building large-scale distributed applications and platforms on the Web, microservice architecture has emerged and gained a lot of popularity in recent years. Therefore, both for the use of microservices and for the provided of the necessary interface for Automatic Speech Recognition (ASR), a web-based platform has been developed. Within firstly the scope of the study, a Turkish ASR system was developed. A web service structure was created to facilitate access to the ASR system. The access of methods and data in the web service structure was provided through Representational State Transfer (REST) web services and service layer. An interface was developed to enable interaction with the web service. The platform was developed using a combination of different technologies such as ASR, web services, microservices, and interface technologies. The developed platform can be used via a standard web browser or an Application Programming Interface (API). In this study, Docker packages were used to improve system performance instead of using different virtual machines on a single server. In the experiments performed, it was shown that the Turkish ASR system had a word error rate of 24.70%. In web service performance tests, it was shown that the platform responded in an average of 9.6 seconds for a 59-second speech recording. The developed user interface was tested in both mobile and desktop web browsers and was shown to function properly. Applications and other services were given access to the platform without the need to use an interface via API support provided by the platform. As a result, a web service-based Turkish ASR platform working seamlessly on the ever-increasing number of mobile devices, the Internet of Things ecosystem, or other access devices was developed.Conference Object Citation - WoS: 2Citation - Scopus: 2Clinical Decision Support Systems: From the Perspective of Small and Imbalanced Data Set(Ios Press, 2019) Akcapinar Sezer, Ebru; Sever, Hayri; Par, Oznur EsraClinical decision support systems are data analysis software that supports health professionals' decision - making the process to reach their ultimate outcome, taking into account patient information. However, the need for decision support systems cannot be denied because of most activities in the field of health care within the decision-making process. Decision support systems used for diagnosis are designed based on disease due to the complexity of diseases, symptoms, and disease-symptoms relationships. In the design and implementation of clinical decision support systems, mathematical modeling, pattern recognition and statistical analysis techniques of large databases and data mining techniques such as classification are also widely used. Classification of data is difficult in case of the small and / or imbalanced data set and this problem directly affects the classification performance. Small and/or imbalance dataset has become a major problem in data mining because classification algorithms are developed based on the assumption that the data sets are balanced and large enough. Most of the algorithms ignore or misclassify examples of the minority class, focus on the majority class. Most health data are small and imbalanced by nature. Learning from imbalanced and small data sets is an important and unsettled problem. Within the scope of the study, the publicly accessible data set, hepatitis was oversampled by distance-based data generation methods. The oversampled data sets were classified by using four different machine learning algorithms. Considering the classification scores of four different machine learning algorithms (Artificial Neural Networks, Support Vector Machines, Naive Bayes and Decision Tree), optimal synthetic data generation rate is recommended.Conference Object Citation - WoS: 6Citation - Scopus: 7Blocked-Dwt Based Vector Image Watermarking(Ieee, 2015) Dincer, Kivanc; Sever, Hayri; Elbasi, Ersin; Senol, AhmetImage watermarking is in use for proving ownership for a fairly long time. For most of the study on this area, a pseudo random number sequence PRSN or a binary image logo is embedded as watermark. Nowadays the owner's face or sound is also embedded as biometric watermark. Image is transferred to discrete wavelet transform domain, watermark is embedded to DWT values, then DWT values are retransformed to spatial domain to obtain watermarked image. Embedding a vector image logo as watermark was not tried in previous works. In this work, non-blind robust watermarking is applied using a vector image as watermark. Various attacks are applied to watermarked images and for each of these attacks vector image watermark is obtained equal or almost equal to the original. Embedding vector image as watermark will bring a new discipline for image watermarking and a new development will arise in this perspective.