Kitap / Kitap Bölümü Koleksiyonu | Bilgisayar Mühendisliği Bölümü
Bu koleksiyon için kalıcı URI
Güncel Gönderiler
Yayın Machine learning(Institution of Engineering and Technology, 2020-01-01) Yıldız, Olcay Taner[No abstract available]Yayın Yayın Infrastructure assisted data dissemination for vehicular sensor networks in metropolitan areas(IGI Global, 2012) Tüysüz Erman, Ayşegül; Schwartz, Ramon S.; Dilo, Arta; Schölten, Hans J.; Havinga, PaulVehicular Sensor Networks (VSNs) are an emerging area of research that combines technologies developed in the domains of Intelligent Transport Systems (ITS) and Wireless Sensor Networks. Data dissemination is an important aspect of these networks. It enables vehicles to share relevant sensor data about accidents, traffic load, or pollution. Several protocols are proposed for Vehicle to Vehicle (V2V) communication, but they are prone to intermittent connectivity. In this chapter, the authors propose a roadside infrastructure to ensure stable connectivity by adding vehicle to infrastructure to the V2V communication. They introduce a data dissemination protocol, Hexagonal Cell-Based Data Dissemination, adapting it for VSNs within a metropolitan area. The virtual architecture of the proposed data dissemination protocol exploits the typical radial configuration of main roads in a city, and uses them as the basis for the communication infrastructure where data and queries are stored. The design of the communication infrastructure in accordance with the road infrastructure distributes the network data in locations that are close or easily reachable by most of the vehicles. The protocol performs a geographical routing and is suitable for highly dynamic networks, supporting a high number of mobile sources and destinations of data. It ensures reliable data delivery and fast response. The authors evaluate the performance of the proposed protocol in terms of data delivery ratio and data delivery delay. The simulation results show that HexDD significantly improves the data packet delivery ratio in VANETs.Yayın Medium access control and routing in industrial wireless sensor networks(CRC Press, 2017-01-01) Tüysüz Erman, Ayşegül; Durmaz İncel, ÖzlemWireless Sensor Networks (WSNs) appear as a promising solution for industrial applications, especially for monitoring purposes, due to the advantages that they provide [19]. First of all, they overcome the wiring constraints present in wired industrial monitoring and control systems. Other advantages can be listed as ease of installation and maintenance, reduced cost, and better performance [50]. On the other hand, lack of standardization, strict real-timeliness, and reliability requirements of some industrial applications have limited their use in industrial domains [6]. Today, however, initial examples of industrial wireless sensor networks (IWSNs) do appear especially on process monitoring and control [28, 19], supported with standardization efforts such as IEEE 802.15.4 [2], WirelessHART [40], and ISA100.11a [25].Yayın Bagging soft decision trees(Springer Verlag, 2016) Yıldız, Olcay Taner; İrsoy, Ozan; Alpaydın, Ahmet İbrahim EthemThe decision tree is one of the earliest predictive models in machine learning. In the soft decision tree, based on the hierarchical mixture of experts model, internal binary nodes take soft decisions and choose both children with probabilities given by a sigmoid gating function. Hence for an input, all the paths to all the leaves are traversed and all those leaves contribute to the final decision but with different probabilities, as given by the gating values on the path. Tree induction is incremental and the tree grows when needed by replacing leaves with subtrees and the parameters of the newly-added nodes are learned using gradient-descent. We have previously shown that such soft trees generalize better than hard trees; here, we propose to bag such soft decision trees for higher accuracy. On 27 two-class classification data sets (ten of which are from the medical domain), and 26 regression data sets, we show that the bagged soft trees generalize better than single soft trees and bagged hard trees. This contribution falls in the scope of research track 2 listed in the editorial, namely, machine learning algorithms.
