SEARCH RESULT

As known, web crawlers are programs that automatically browse on the web. Their purpose is to automatically navigate pages, saving source links that have target links, marking pages according to the words in those links, saving, indexing, collecting data to bring personalized ads, etc. Although the web crawlering algorithm is simple, it has various difficulties with respect to the existing pages on the web and the resulting amount of data. The semantic web works on generating computer readable data and is intended to overcome the quantity of data generated. Ontologies represent a pivoting source for semantic web applications. Ontology based crawlers scan the web by focusing on related web pages along with a specific ontology based on area ontology. The main advantage of the ontology based web crawlers over other crawlers is that no Conformance Feedback or Training Procedure is required to move wisely. In addition, both the number of documents and the more effective and efficient results will be obtained during the scanning process. As a result; The main advantage of an ontology based web crawler over other web crawlers is that it does not require intelligent, efficient operation and relevant feedback. In this study, traditional and ontology based web crawlers approaches and its infrastructure are examined. In addition, differences between ontology based web crawlers and traditional web crawlers have been investigated. A brief of literature summary on the subject has been included.

International Conference on Advanced Technologies, Computer Engineering and Science
ICATCES

Yasemin Gültepe A.B. ÖNCÜL E. ALTINTAŞ F. UĞUR

Data mining and machine learning algorithms are utilized in order to discover meaningful information by thorough analysis of dataset. They are used in multi-disciplinary field. Wart is caused by the human papillomavirus. It inhibits body growth by activating ecdysone steroid production systematically. There are several treatment methods for this illness. These methods focused on offering a solution for people. In this framework, a study on the analysis of the best two wart treatment methods, Cryotherapy and Immunotherapy, is carried out. The first one of these datasets collected by applying the cryotherapy method consists of seven features. The second dataset collected by applying the immunotherapy method consists of eight features. Fuzzy Rule, Naive Bayes and Random Forest based models are designed in order to evaluate the effectiveness of these methods in wart treatment. The performances of these algorithms are judged within the frame of Accuracy and Sensitivity performance measures.

International Conference on Advanced Technologies, Computer Engineering and Science
ICATCES

Kemal Akyol Abdulkadir Karaci Yasemin Gültepe

The main purpose of the semantic web is to develop standards and technologies that will enable well-defined and linked information and services to be easily computer-readable and computer-understandable in the web environment. Linked data is one of the approaches used to acquire meaningful integrity by gathering data-related data collections by creating semantic links between the web pages that make up the content of the semantic web. Linked data is based on RDF (Resource Description Framework) technology. RDF is a data model that provides space-independent formal semantics with respect to chart resources. In a linked data application, the most important decision point is how to access the linked data. Linked data crawler is a program that explores linked data in web by tracking RDF links. In this work, DBLP (Database Systems and Logic Programming) data set is used as a source of Linked Data. DBLP gradually expanded toward all fields of computer science. An example will be presented related to pageRank sorting of RDF resources in the DBLP dataset. As a result; the search area has shrunk and search results have improved.

International Conference on Advanced Technologies, Computer Engineering and Science
ICATCES

Yasemin Gültepe Kemal Akyol Abdulkadir Karaci

Sign language recognition is used to help communicate effectively between normal hearing peoples and hearing-impaired. According to literature review, Turkish sign language recognition studies are very few. For this reason, this study has been performed on Turkish sign language recognition. Depth cameras, such as the Leap Motion controller, allows the researchers to exploit depth knowledge to better understand hand movements. In this study, data of 10 letters in Turkish sign language was taken from Leap Motion. Five of these data are composed of letters (I, C, L, V, O) that It can be expressed with one hand, while the other five are composed of letters (B, D, M, N, K) that It can be expressed with two hands. The dataset was taken by two different people. Each person made five trials for each letter. Ten samples were taken at each trial. In this study, Artificial Neural Network, Deep Learning and Decision Tree based models were designed and the effectiveness of these models in recognizing the Turkish sign language is evaluated. Regression (R), Mean Square Error (MSE) and Estimation Accuracy performance metrics are used to evaluate models' performance. The data set was randomly divided into 30% for training and 70% for testing. According to the experimental results, the most successful models for the data set with 120 features are decision tree and DNN models. For the data set with 390 features, DNN is the most successful model.

International Conference on Advanced Technologies, Computer Engineering and Science
ICATCES

Abdulkadir Karaci Kemal Akyol Yasemin Gültepe

The Semantic Web is a web environment that allows well defined information and services to be easily understood by machines. The main component of the Semantic Web is ontologies, which formally define a set of concepts for a domain and the relationships between concepts. One of the areas where ontologies can be used is the field of healthcare. In particular, the use of ontologies in the field of healthcare is recommended because of the formal representation of a subject area and its support for reusability. Many medical classification systems are used in the field of medical informatics. The deficiency seen in the proposed approaches for health information systems is that there is no meaningful reference and sharing system that enables the collection of classification and coding systems. Considering the general classification and coding systems, it is clear that a system is required for faster, accurate and efficient processing. In accordance with the interoperability needs of health information systems that are constituted by different ontology combinations, this study propose ontology based of Systemized Nomenclature of Medical-Clinical Terms (SNOMED CT) Concept Model. This model remarks reasonable definition of concepts in SNOMED CT. The official semantics of ontology enhance the ability to automate information management of complex terminology, facilitate the maintenance of clinical decision support materials, and significantly improve interoperability.

International Conference on Cyber Security and Computer Science
ICONCS

Yasemin Gültepe

In recent years, the growing and large amounts of data, which have been associated with the widespread use of social media, smart devices and internet, define big data. With big data; The vast majority of things that were formerly never measured, stored, analyzed, or shared, were converted into processed and usable data. The big data typically describes both the type of managed data and the technology used to collect and operate it. Data can be transformed into information that can only have a value, but if without the wisdom, information can be allowed to really useful to people. Nowadays the big data attract attention with such qualities for his volume, speed and variety. With the increased use of big data, a major breakthrough in productivity, profitability and innovation in different sectors is expected. Examples are many successful applications of big data in different areas of the world; Public sector, health, insurance, banking, education, etc. Big data can help improve productivity, profitability, performance and reduce data exhaustion, etc. Education, health, banking, retail sales, government resources, defense industry, production and energy sectors, as well as facilitating human life will increase the efficiency of institutions and will constitute the infrastructure of further progress towards the future. In the study, big data was handled conceptually, relations with many concepts, big data technologies and methods used for big data processing were introduced and different examples were given about usage areas of big data in the world.

International Conference on Cyber Security and Computer Science
ICONCS

Yasemin Gültepe