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Öğe Discovering socially similar users in social media datasets based on their socially important locations(Elsevier Ltd, 2018) Celik M.; Dokuz A.S.Socially similar social media users can be defined as users whose frequently visited locations in their social media histories are similar. Discovering socially similar social media users is important for several applications, such as, community detection, friendship analysis, location recommendation, urban planning, and anomaly user and behavior detection. Discovering socially similar users is challenging due to dataset size and dimensions, spam behaviors of social media users, spatial and temporal aspects of social media datasets, and location sparseness in social media datasets. In the literature, several studies are conducted to discover similar social media users out of social media datasets using spatial and temporal information. However, most of these studies rely on trajectory pattern mining methods or take into account semantic information of social media datasets. Limited number of studies focus on discovering similar users based on their social media location histories. In this study, to discover socially similar users, frequently visited or socially important locations of social media users are taken into account instead of all locations that users visited. A new interest measure, which is based on Levenshtein distance, was proposed to quantify user similarity based on their socially important locations and two algorithms were developed using the proposed method and interest measure. The algorithms were experimentally evaluated on a real-life Twitter dataset. The results show that the proposed algorithms could successfully discover similar social media users based on their socially important locations. © 2018 Elsevier LtdÖğe Discovering socio-spatio-temporal important locations of social media users(Elsevier B.V., 2017) Celik M.; Dokuz A.S.Socio-spatio-temporal important locations (SSTILs) are places which are frequently visited by social media users in their social media history. Discovering SSTILs is important for several application domains, such as, recommender systems, advertisement applications, urban planning, etc. However, discovering SSTILs is challenging due to spatial, temporal, and social dimensions of the datasets, the lack of sufficient interest measures, and the need for developing computationally-efficient algorithms. In the literature, several methods are proposed to discover social important locations. However, these studies, usually, do not take into account temporal and social dimensions of the datasets and preferences of each user in a social group. In this study, we define SSTILs and SSTIL mining problem by taking into account spatial, temporal, and social dimensions of the social media datasets. We propose methods and interest measures to discover SSTILs efficiently based on both user and group preferences. The proposed algorithms were compared with a naïve alternative using real-life Twitter dataset. The results showed that the proposed algorithms outperform the naïve alternative. © 2017 Elsevier B.V.Öğe Examining combustion and emission characteristics of cotton methyl ester to which manganese additive material was added(Korean Society of Mechanical Engineers, 2017) Celik M.Important researches is being conducted in order to decrease the emissions and fuel consumption. Emissions and fuel consumption has been dramatically decreased by engine design studies. On the other hand it is difficult to obtain the desired results by engine desing only. Studies are being made on fuel additives so as to improve the engine performance, combustion and emission characteristics. In this study manganese additive was added into a cotton methyl ester (C0) and accordingly combustion and emission characteristics at full load and different speed intervals were examined. In the experiments conducted for this mixture a diesel engine with single cylinder was used and the maximum power was obtained at 2750 rpm. Maximum cylinder pressure was obtained at 2200 rpm where as maximum moment was obtained in C0Mn12 fuel. At the same time heat release rate was at 0.021 kJ/°CA and maximum rate of pressure rise was found to be 6.73 bar/°CA. Minimum CO and THC emission were obtained at 2200 rpm and maximum decrease in THC emisssion occured with the C0Mn12 fuel when compared to the C0 fuel, maximum decrease in THC was found to be 5.01 %. Maximum increase in emissions when compare to C0 fuel was 11.58 % in C0Mn12 fuel at 3250 rpm while they were 33.14 % at 1750 rpm. At 2750 rpm maximum decrease of emissions in the C0Mn12 fuel was found to be 2.74 %. © 2017, The Korean Society of Mechanical Engineers and Springer-Verlag GmbH Germany, part of Springer Nature.Öğe FAST SS-ILM: A COMPUTATIONALLY EFFICIENT ALGORITHM to DISCOVER SOCIALLY IMPORTANT LOCATIONS(Copernicus GmbH, 2017) Dokuz A.S.; Celik M.Socially important locations are places which are frequently visited by social media users in their social media lifetime. Discovering socially important locations provide several valuable information about user behaviours on social media networking sites. However, discovering socially important locations are challenging due to data volume and dimensions, spatial and temporal calculations, location sparseness in social media datasets, and inefficiency of current algorithms. In the literature, several studies are conducted to discover important locations, however, the proposed approaches do not work in computationally efficient manner. In this study, we propose Fast SS-ILM algorithm by modifying the algorithm of SS-ILM to mine socially important locations efficiently. Experimental results show that proposed Fast SS-ILM algorithm decreases execution time of socially important locations discovery process up to 20 %. © Authors 2017.Öğe Investigation of combustion and emission characteristics of n-hexane and n-hexadecane additives in diesel fuel(Korean Society of Mechanical Engineers, 2019) Bayindirli C.; Celik M.One of the most important basic requirements of diesel-powered vehicles that they have lower pollutant emissions and fuel consumption. In diesel engines, combustion and engine performance are influenced by the physical and chemical properties of the used fuel. Engine design studies are not enough to increase engine performance and reduce exhaust emissions alone. By adding fuel additives in diesel fuel, the physical and chemical properties of the fuel can be improved. Fuel additives affect engine performance, combustion and emissions positively by exerting catalyst effect during combustion. In this study, n-hexane and n-hexadecane were added in diesel fuel (D0) by volume of 4, 12 % and 20 %. With respect to D0 fuel, in DHD20 and DHX20 fuels engine torque increased by 1.60 % and 1.32 %, respectively, while the brake specific fuel consumption decreased by 3.12 % and 1.98 %, respectively. Maximum cylinder pressures and heat release rate values of the ingredient added fuels increased. It was seen that NO x emissions increased while HC, CO and soot emissions decreased with increasing contribution ratio. © 2019, KSME & Springer.Öğe Mining periodic spatio-temporal co-occurrence patterns: A summary of results(2012) Celik M.; Azginoglu N.; Terzi R.Periodic spatio-temporal co-occurrence patterns (PECOPs) represent subsets of object-types that are often periodically located together in space and time. Discovering PECOPs is an important problem with many applications such as discovering interactions between animals and identifying tactics in games. However, mining PECOPs is computationally very expensive because the interest measures are computationally complex, databases are larger due to the archival history, and the set of candidate patterns is exponential in the number of object-types. In this paper, we define the problem of mining PECOPs, and propose a novel PECOP mining algorithm. The experimental results show that the proposed algorithm is computationally more efficient than the naïve alternatives. © 2012 IEEE.Öğe The investigation of the effects of washing process on biodiesel production to fuel properties and engine performance(2012) Celik M.; Bayindirli C.; Demiralp M.The major part of all energy consumed worldwide comes from fossil sources (petroleum, coal and natural gas). The search for alternative fuels, which promise a harmonious correlation with sustainable development, energy conservation, efficiency and environmental preservation, has become highly pronounced in the present context. One of the more promising approaches is the conversion of vegetable oils (VOs) and other feed stocks, which primarily contain triglycerides (TGs) and free fatty acids (FFAs), into biodiesel. Injection, atomization and combustion characteristics of vegetable oils are very different from those of diesel fuel. Experiments were made on single-cylinder, four-stroke, water-cooled, direct injection diesel engine. Crude biodiesel was produced utilizing refined cottonseed oil through the transesterification method. Crude biodiesel was subjected to wash with pure water (distilled water DW) and deionized water (DEW). At the end of this process biodiesel is obtained. The effect of the specifications of diesel fuel with these produced-biodiesels on engine performance has been observed. Result of the experiments prove that COME (DEW)fuel obtained by being washed with deionized water gives better performances than COME (DW) fuel obtained by being washed with distilled water on engine torque, effective power and specific fuel consumption respectively at the values as following %0.8, %0.75 and %0.81. © Sila Science.
