Sportsko sponzorstvo jedno je od najbrže rastućih oblika marketinške komunikacije te je kao takvo izrazito privlačno brojnim tvrtkama koje su u potrazi za efikasnijim načinima privlačenja kupaca. U ...radu je prikazan teorijski pregled literature, od sportskog marketinga i sponzorstva u širem smislu do sportskog sponzorstva, njegovih glavnih oblika te pozitivnih i negativnih aspekata vezanih uz njega. Dan je osvrt i na stanje sportskog sponzorstva na području Republike Hrvatske te na njegovo kretanje nakon ekonomske krize. Naglasak u radu stavljen je na istraživanje sponzorstva u sportu na primjeru hrvatskih košarkaških klubova u okviru kojeg se ispituje koliko klubovi ulažu u svoj marketing i kakva im je strategija u privlačenju sponzora. Istraživanje je provedeno na hrvatskim muškim prvoligaškim košarkaškim klubovima te je pokazalo kako oni nemaju odgovarajuću strategiju u pronalaženju sponzorstva, odnosno ne pristupaju navedenom problemu na odgovarajući način.
Sportsko sponzorstvo jedno je od najbrže rastućih oblika marketinške komunikacije te je kao takvo izrazito privlačno brojnim tvrtkama koje su u potrazi za efikasnijim načinima privlačenja kupaca. U ...radu je prikazan teorijski pregled literature, od sportskog marketinga i sponzorstva u širem smislu do sportskog sponzorstva, njegovih glavnih oblika te pozitivnih i negativnih aspekata vezanih uz njega. Dan je osvrt i na stanje sportskog sponzorstva na području Republike Hrvatske te na njegovo kretanje nakon ekonomske krize.
Naglasak u radu stavljen je na istraživanje sponzorstva u sportu na primjeru hrvatskih košarkaških klubova u okviru kojeg se ispituje koliko klubovi ulažu u svoj marketing i kakva im je strategija u privlačenju sponzora. Istraživanje je provedeno na hrvatskim muškim prvoligaškim košarkaškim klubovima te je pokazalo kako oni nemaju odgovarajuću strategiju u pronalaženju sponzorstva, odnosno ne pristupaju navedenom problemu na odgovarajući način.
With the exponential growth of the amount of data being generated, stored and processed on a daily basis in the machine learning, data analytics and decision-making systems, the data preprocessing ...established itself as the key factor for building reliable high-performance machine learning models. One of the roles in preprocessing is variable reduction using feature selection methods; however, the processing time needed for these methods is a major drawback. This study aims at mitigating this problem by migrating the algorithm to a MapReduce implementation suitable for parallelization on a high number of commodity hardware units. The genetic algorithm-based methods were put in the focus of this study. Hadoop, an open-source MapReduce library, was used as a framework for implementing parallel genetic algorithms within our research. The representative machine learning methods, SVM (support vector machine), ANN (artificial neural network), RT (random tree), logistic regression and Naive Bayes, were embedded into implementation for feature selection. The feature selection methods were applied to four NSL-KDD data sets, and the number of features is reduced from cca 40 to cca 10 data sets with the accuracy of 90.45%. These results have both significant practical and theoretical impact. On the one hand, the genetic algorithm has been parallelized in the MapReduce manner, which has been considered unachievable in a strict sense. Furthermore, the genetic algorithm allows randomness-enhanced feature selection and its parallelization reduces overall data preprocessing and allows larger population count which in turn leads to better feature selection. On the practical side, it has been shown that this implementation outperforms the existing feature selection methods.
A car price prediction has been a high-interest research area, as it requires noticeable effort and knowledge of the field expert. Considerable number of distinct attributes are examined for the ...reliable and accurate prediction. To build a model for predicting the price of used cars in Bosnia and Herzegovina, we applied three machine learning techniques (Artificial Neural Network, Support Vector Machine and Random Forest). However, the mentioned techniques were applied to work as an ensemble. The data used for the prediction was collected from the web portal autopijaca.ba using web scraper that was written in PHP programming language. Respective performances of different algorithms were then compared to find one that best suits the available data set. The final prediction model was integrated into Java application. Furthermore, the model was evaluated using test data and the accuracy of 87.38% was obtained.
The main aim of the study is to develop a real-time epilepsy prediction approach by using the ensemble machine learning techniques that might predict offline seizure paradigms. The proposed seizure ...prediction algorithm is patient-specific since generalization showed no satisfactory results in our previous studies. The algorithm is tested on CHB-MIT database comprised of EEG data from pediatric epileptic patients. Based on relations to number of seizures and number of files, gender and age, three patients have been chosen for this study. The special majority voting algorithm is proposed and used for raising an alarm of upcoming seizure. EEG signals are denoised using MSPCA (Multiscale PCA), the features were extracted by WPD (wavelet packet decomposition), and EEG signals were classified using Rotation Forest. The significance of the study lies in the fact that the proposed seizure prediction algorithm could be used in novel diagnostic and therapeutic applications for pediatric patients.
Cancer classification is one of the main steps during patient healing process. This fact enforces modern clinical researchers to use advanced bioinformatics methods for cancer classification. Cancer ...classification is usually performed using gene expression data gained in microarray experiment and advanced machine learning methods. Microarray experiment generates huge amount of data, and its processing via machine learning methods represents a big challenge. In this study, two-step classification paradigm which merges genetic algorithm feature selection and machine learning classifiers is utilized. Genetic algorithm is built in MapReduce programming spirit which makes this algorithm highly scalable for Hadoop cluster. In order to improve the performance of the proposed algorithm, it is extended into a parallel algorithm which process on microarray data in distributed manner using the Hadoop MapReduce framework. In this paper, the algorithm was tested on eleven GEMS data sets (9 tumors, 11 tumors, 14 tumors, brain tumor 1, lung cancer, brain tumor 2, leukemia 1, DLBCL, leukemia 2, SRBCT, and prostate tumor) and its accuracy reached 100% for less than 25 selected features. The proposed cloud computing-based MapReduce parallel genetic algorithm performed well on gene expression data. In addition, the scalability of the suggested algorithm is unlimited because of underlying Hadoop MapReduce platform. The presented results indicate that the proposed method can be effectively implemented for real-world microarray data in the cloud environment. In addition, the Hadoop MapReduce framework demonstrates substantial decrease in the computation time.
This study presents a short-term prediction approach for honey production using ensemble regression technique. The data were recorded as a part of Habeetat project in Sarajevo, Bosnia and Herzegovina ...for 2016 season. This season has been entitled as one of the worst seasons for beekeepers in our country, which makes the problem of honey production prediction even more challenging. Random Tree regression algorithm was used for such purpose showing that the mean absolute error in predicting total honey production was less than 1.16 kg in all three hives monitored between November 2016 and April 2017. These findings are very significant for beekeepers since they can be notified in advance to visit individual hives and collect the honey. Besides, they can monitor trends in honey production throughout the season and perhaps change the position of hives in the current season and for the next upcoming season.
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