Istanbul Technical University

About: Istanbul Technical University is a education organization based out in Istanbul, Turkey. It is known for research contribution in the topics: Fuzzy logic & Large Hadron Collider. The organization has 12889 authors who have published 25081 publications receiving 518242 citations. The organization is also known as: İstanbul Teknik Üniversitesi & Technical University of Istanbul.

Heterograft Copolymers via Double Click Reactions Using One-Pot Technique

Abstract: The double click reactions (Cu catalyzed Huisgen and Diels–Alder reactions) were used as a new strategy for the preparation of well-defined heterograft copolymers in one-pot technique. The synthetic strategy to the various stages of this work is outlined: (i) preparing random copolymers of styrene (St) and p-chloromethylstyrene (CMS) (which is a functionalizable monomer) via nitroxide mediated radical polymerization (NMP); (ii) attachment of anthracene functionality to the preformed copolymer by the o-etherification procedure and then conversion of the remaining CH2Cl into azide functionality; (iii) by using double click reactions in one-pot technique, maleimide end-functionalized poly(methyl methacrylate) (PMMA-MI) via atom transfer radical polymerization (ATRP) of MMA and alkyne end-functionalized poly (ethylene glycol) (PEG-alkyne) were introduced onto the copolymer bearing pendant anthryl and azide moieties. © 2008 Wiley Periodicals, Inc. J Polym Sci Part A: Polym Chem 46: 6969–6977, 2008

Towards Disaster Resilient Smart Cities: Can Internet of Things and Big Data Analytics Be the Game Changers?

Abstract: Disasters (natural or man-made) can be lethal to human life, the environment, and infrastructure. The recent advancements in the Internet of Things (IoT) and the evolution in big data analytics (BDA) technologies have provided an open opportunity to develop highly needed disaster resilient smart city environments. In this paper, we propose and discuss the novel reference architecture and philosophy of a disaster resilient smart city (DRSC) through the integration of the IoT and BDA technologies. The proposed architecture offers a generic solution for disaster management activities in smart city incentives. A combination of the Hadoop Ecosystem and Spark are reviewed to develop an efficient DRSC environment that supports both real-time and offline analysis. The implementation model of the environment consists of data harvesting, data aggregation, data pre-processing, and big data analytics and service platform. A variety of datasets (i.e., smart buildings, city pollution, traffic simulator, and twitter) are utilized for the validation and evaluation of the system to detect and generate alerts for a fire in a building, pollution level in the city, emergency evacuation path, and the collection of information about natural disasters (i.e., earthquakes and tsunamis). The evaluation of the system efficiency is measured in terms of processing time and throughput that demonstrates the performance superiority of the proposed architecture. Moreover, the key challenges faced are identified and briefly discussed.

Applying Bayesian Belief Network approach to customer churn analysis

Abstract: Research highlights? Bayesian Belief Network is utilized to explain the causal relations between the factors that affect customer churn in telecommunication industry. ? The important factors are found out. ? We examine the results with different scenarios. ? Telecommunication provider should offer campaigns to appropriate customer groups to retain them. ? The expected savings are calculated after each promotion that will be gained if the churners are not lost. In telecommunication industry, for many organizations, it is really important to take place in the market. As competition increases between companies, customer churn becomes a great issue to deal with by the telecommunication providers. For an effective churn management, companies try to retain their existing customers, instead of acquiring new ones. Previous researches focus on predicting the customers with a propensity to churn in telecommunication industry. In this study, a model is constructed by Bayesian Belief Network to identify the behaviors of customers with a propensity to churn. The data used are collected from one of the telecommunication providers in Turkey. First, as only discrete variables are used in Bayesian Belief Networks, CHAID (Chi-squared Automatic Interaction Detector) algorithm is applied to discretize continuous variables. Then, a causal map as a base of Bayesian Belief Network is brought out via the results of correlation analysis, multicollinearity test and experts' opinions. According to the results of Bayesian Belief Network, average minutes of calls, average billing amount, the frequency of calls to people from different providers and tariff type are the most important variables that explain customer churn. At the end of the study, three different scenarios that examine the characteristics of the churners are analyzed and promotions are suggested to reduce the churn rate.