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The development of information technology increases its use in various spheres of human activity, including healthcare. Bundles of data and reports are generated and stored in textual form, such as symptoms, medical history, and doctor’s observations of patients' health. Electronic recording of patient data not only facilitates day-to-day work in hospitals, enables more efficient data management and reduces material costs, but can also be used for further processing and to gain knowledge to improve public health. Publicly available health data would contribute to the development of telemedicine, e-health, epidemic control, and smart healthcare within smart cities. This paper describes the importance of textual data normalization for smart healthcare services. An algorithm for normalizing medical data in Serbian is proposed in order to prepare them for further processing (F1-score=0,816), in this case within the smart health framework. By applying this algorithm, in addition to the normalized medical records, corpora of keywords and stop words, which are specific to the medical domain, are also obtained and can be used to improve the results in the normalization of medical textual data.

Preliminary communicationIn modern computer graphics, the emphasis is on the details of the scene, and taking into account the improvements in hardware performances, it is not allowed to make compromises when it comes to the reality of scenes. Each reflection, shadow, rounded corner and transparency must be brought to perfection and presented in order to make a depicted scene more realistic. In one of the most widely used API for rendering 3D objects, OpenGL, there is nothing similar to a library for adding those phenomena that exist in reality. If the possibility of existence of multiple light sources is considered, rendering all these details becomes a real challenge. The aim of this paper is to provide a method for generating shadows in an efficient way, for the objects commonly used as components of complex 3D objects, in conditions of the presence of moving light sources. Keywords Sjene u realnom vremenu u OpenGL-u uzrokovane prisutnošću više izvora svjetlostiPrethodno priopćenje U suvremenoj računalnoj grafici naglasak je na detaljima prizora, a uzimajući u obzir poboljšanja hardverskih svojstava, nije dopušteno raditi kompromise kada je riječ o stvarnosti scena. Svaki odraz, sjena, zaobljeni kut ili prozirnost mora biti doveden do savršenstva i prikazan u cilju da se scena koja se oslikava učini što realističnijom. U jednom od najšire rabljenih API-ja za renderiranje 3D objekata, OpenGL-u, ne postoji nešto poput knjižnice za dodavanje ovih pojava koje postoje u stvarnosti. Ako se razmatra mogućnost postojanja više izvora svjetlosti, renderiranje svih ovih detalja postaje pravi izazov. Cilj ovog rada je obezbjeđivanje metoda za generiranje sjene na efikasan način, za objekte često rabljene kao komponente složenih 3D objekata, u uvjetima prisutnosti više svjetlosnih izvora s mogućnošću kretanja.

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This paper deals with the context-aware smart healthcare platform, based on IoT and citizen sensing. The proposed platform provides support to smart cities' citizens in the form of air quality visualization in their surroundings and by appropriate notifications in case of dangerous pollutants level is sensed. It also provides medical assistance based on "help needed" function, and where available, on the medical record of a patient that uses the platform services. The platform is interactive, so the information sent by the users and the requests for help will be processed. Platform development is based on a special kind of social machine that is capable to capture the city's sensors data, analyze these data and to interact with appropriate business processes. On return, that interaction results with several goals achieved with the project. Presented dashboard visualization allows decision makers, e.g. medical staff, to take proper actions on time and on-the-fly. On the other side, citizens that suffer from a variety of disease problems are able to report an air pollution incident, and ask for help, if they felt worse. The platform itself has a wider usability value and may be deployed to other smart services in a city, e.g. waste management, smart transportation, energy savings, etc. It is also scalable and open for a variety of sensor devices ranges from smartphones, wearables, and other IoT that resides in a smart city, and for different forms of crowdsensing methods. Finally, concluding remarks emphasize the future research directions.