Bilgisayar Mühendisliği Bölümü / Department of Computer Engineering

Permanent URI for this collectionhttps://hdl.handle.net/11413/6817

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2D UAV path planning with radar threatening areas using simulated annealing algorithm for event detection
(2018) Basbous, Bilal;
Path Planning for Unmanned Aerial Vehicles (UAVs) can be used for many purposes. However, the problem becomes more and more complex when dealing with a large number of points to visit for detecting and catching different type of events and simple threat avoidance such as Radar Areas. In the literature different type of algorithms (especially evolutionary algorithms) are preferred. In this project, Simulated Annealing (SA) Algorithm is used for solving the path planning problem. Firstly, problem is converted to a part of Travelling Salesman Problem (TSP), and then the solutions are optimized with the 2-Opt approach and other simple algorithms. The code is implemented in MATLAB by using its visualization. Circular avoidance approach is developed and applied with the Simulated Annealing in order to escape from circular radar threats. Tests have been made to observe the results of SA algorithm and radar threats avoidance approaches, where the results show that after a period of time, SA algorithm gives acceptable solutions with the capacities of escaping from radar area threats. Where SA algorithm gives better solutions in less period of time when there are no radar threats. Experimental results depicted that the proposed model can result in an acceptable solution for UAVs in sufficient execution time. This model can be used as an alternative solution to the similar evolutionary algorithms.
A tree learning approach to web document sectional hierarchy extraction
(2010) Pembe, F.Canan; Göngör, Tunga
There is an increasing availability of documents in electronic form due to the widespread use of the Internet. Hypertext Markup Language (HTML) which is mostly concerned with the presentation of documents is still the most commonly used format on the Web, despite the appearance of semantically richer markup languages such as XML. Effective processing of Web documents has several uses such as the display of content on small-screen devices and summarization. In this paper, we investigate the problem of identifying the sectional hierarchy of a given HTML document together with the headings in the document. We propose and evaluate a learning approach suitable to tree representation based on Support Vector Machines.
Automatic HTML code generation from mock-up images using machine learning techniques
(2019) Asiroğlu, Batuhan; Yıldız, Eyyüp; Nalçakan, Yağız; Sezen, Alper; Dağtekin, Mustafa; Ensari, Tolga; METE, BÜŞRA RÜMEYSA
The design cycle for a web site starts with creating mock-ups for individual web pages either by hand or using graphic design and specialized mock-up creation tools. The mock-up is then converted into structured HTML or similar markup code by software engineers. This process is usually repeated many more times until the desired template is created. In this study, our aim is to automate the code generation process from hand-drawn mock-ups. Hand drawn mock-ups are processed using computer vision techniques and subsequently some deep learning methods are used to implement the proposed system. Our system achieves 96% method accuracy and 73% validation accuracy.
Content-based publish/subscribe communication model between Iot devices in smart city environment
(2019-04) Öztürk, Fulya; Özdemir, Ayşe Meliha
In recent years the population of the cities has been increasing the getting over half of the whole world population. These peoples are facing with some security and infrastructural needs. Some of these needs can be met with the use of some smart technologies such as Internet of Things Devices (IoT) with different types of sensors. Coordination and communication of these devices are very critical for enabling the digital solutions in a secure and comfortable city life. However, due to the restricted ability of these devices, setting up a flexible communication platform is a very challenging issue. In this paper for setting up the physical security of smart homes/buildings, a content-based publish-subscribe model is proposed by the use of wireless sensor network nodes in the critical environment. Due to the energy constraint of the devices, the broadcasting feature of the wireless communication is not used, instead of this a peer to a communication according to the content of the message is preferred. Experimental results showed that the proposed communication model can be applicable for smart city environments.
Deep learning based forecasting in stock market with big data analytics
(2019) Şişmanoğlu, Gözde; Önde, Mehmet Ali; Koçer, Furkan; ŞAHİNGÖZ, ÖZGÜR KORAY
In recent years, due to the technological improvements in computers' hardware and enhancements in the machine learning techniques, there are two increasing approaches for problem-solving as the use of "Big Data" and "Parallel Processing". Especially with the emergence of Deep Learning algorithms which can be executed parallelly on multi-core computing devices such as GPUs and CPUs, lots of real-world problems are resolved with these approaches. One of the most critical application areas in the Financial Market especially sits on Stock Markets. In this area, the aim is trying to predict the future value of a specific stock by looking at its previous financial data on the exchange process in the market. In this paper, we proposed a system that uses a Deep Learning based approach for training and constructing a knowledge base on a specific stock such as "IBM". We get time series values of the stock from the New York Stock Exchange which starts from 1968 up to 2018. Experimental results showed that this approach produces very good forecasting for specific stocks.
The Effect of Heuristic Methods Toward Performance of Health Data Analysis
(Springer Science and Business Media Deutschland GmbH, 2022) ÖZOĞUR, HATİCE NİZAM; Orman, Zeynep
Analysis and prediction of health data make essential contributions to the detection, control, and prevention of diseases in the early stages without special examinations. In the analysis of health data, the balance of the datasets, the accuracy and completeness of the data, and the selection of features to represent the disease are very important as they affect the performance of machine learning methods. They have also become popular in various health data analysis studies such as classification of diseases, selection of features to represent the disease, imputation of missing value in dataset since heuristic methods give successful result in the optimization of many problems. In this chapter, various studies that combine heuristic methods and machine learning algorithms for health data analysis between 2010 and 2021 have been examined. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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