<rdf:RDF xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#" xmlns:dcterms="http://purl.org/dc/terms/">
<rdf:Description rdf:about="https://omeka.ibu.edu.ba/items/show/2248">
    <dcterms:title><![CDATA[Could government legalize illegal settlement by improving their energy efficiency?]]></dcterms:title>
    <dcterms:abstract><![CDATA[In recent months we are faced with serious budget problems in Montenegro, the solution of  which, among other things is seen in reducing the number of employees in state  administration. On the other hand, the costs of living are significantly above the disposable  budget of households. Particular problem is the high cost of electricity, which recently resulted in the street protests of discontented citizens. On one hand we have a government that  alerts the lack of electricity, and on the other hand we have citizens that may hardly cover  these costs. In addition, Montenegro is dealing with a double-challenge of inefficient space  use (country features over 100,000 illegal homes, if distributed evenly implying that every  other family lives in an illegal home) and inefficient energy use (Montenegro needs on  average 8.5 times more energy per unit produced than an average EU country).]]></dcterms:abstract>
    <dcterms:date><![CDATA[2012-05-31]]></dcterms:date>
    <dcterms:extent><![CDATA[1238]]></dcterms:extent>
</rdf:Description><rdf:Description rdf:about="https://omeka.ibu.edu.ba/items/show/2249">
    <dcterms:title><![CDATA[Using Artificial Neural Networks To Forecast Gdp For Turkey]]></dcterms:title>
    <dcterms:abstract><![CDATA[Artificial Neural Networks (ANN) is a system resembling biological neural systems and uses  working principles of human brain as a base. ANN can be applied in various fields for the  purposes of forecasting, classification, optimization, data binding and so on. ANN has been  frequently used in financial applications in recent years. In this study, ANN is used in  forecasting Gross Domestic Product of Turkey. Gross Domestic Product (GDP) refers to the  market value of all final goods and services produced within a country in a given period. GDP  can be thought as the size of an economy and it is the foremost important measure of  macroeconomic performance of a country, a country’s health and standard of living.  Therefore, expectations about future GDP can be the primary determinant of investments,  employment, wages, profits and even stock market activities. With respect to its economic significance mentioned above, the purpose of this study is to forecast Gross Domestic Product  (GDP) for Turkey and to test the ability of ANN Method in forecasting GDP.  Keywords: Importance of Gross Domestic Product, Forecasting, Artificial Neural Networks.]]></dcterms:abstract>
    <dcterms:date><![CDATA[2012-05-31]]></dcterms:date>
    <dcterms:extent><![CDATA[1129]]></dcterms:extent>
</rdf:Description><rdf:Description rdf:about="https://omeka.ibu.edu.ba/items/show/2250">
    <dcterms:title><![CDATA[Clustering Balkan Countries Based on Competitiveness Factors: A Strategic  Perspective]]></dcterms:title>
    <dcterms:abstract><![CDATA[Prior to directing their investments, strategy makers at national and firm level need to know  competitive advantages and disadvantages in a country or region. By bearing this need in  mind, this study aims to examine competitive factors in Balkan countries to develop a road  map for investors. To do this, we used World Economic Forum’s “Global Competitivenes  Index” to analyse the case of Balkan countries as a region to cluster and compare them based  on Global competitiveness factors. Analysis results pointed out that Balkan countries were  clustered in two groups and scored lower or medium level on almost all competitive factors  as the region. Based on these findings, authors suggested various strategic recommendations  at micro and macro level.  Keywords: Cluster, Competitiveness, Strategic Management, Balkan Countries]]></dcterms:abstract>
    <dcterms:date><![CDATA[2012-05-31]]></dcterms:date>
    <dcterms:extent><![CDATA[1113]]></dcterms:extent>
</rdf:Description><rdf:Description rdf:about="https://omeka.ibu.edu.ba/items/show/2251">
    <dcterms:title><![CDATA[Clustering Balkan Countries Based on Competitiveness Factors: A Strategic Perspective]]></dcterms:title>
    <dcterms:abstract><![CDATA[Prior to directing their investments, strategy makers at national and firm level need to know  competitive advantages and disadvantages in a country or region. By bearing this need in mind,  this study aims to examine competitive factors in Balkan countries to develop a road map for  investors. To do this, we used World Economic Forum’s “Global Competitivenes Index” to  analyse the case of Balkan countries as a region to cluster and compare them based on Global  competitiveness factors. Analysis results pointed out that Balkan countries were clustered in two  groups and scored lower or medium level on almost all competitive factors as the region. Based  on these findings, authors suggested various strategic recommendations at micro and macro level.  Keywords: Cluster, Competitiveness, Strategic Management, Balkan Countries]]></dcterms:abstract>
    <dcterms:date><![CDATA[2012-05-31]]></dcterms:date>
    <dcterms:extent><![CDATA[1372]]></dcterms:extent>
</rdf:Description><rdf:Description rdf:about="https://omeka.ibu.edu.ba/items/show/2252">
    <dcterms:title><![CDATA[An Empirical On Knowledge Sharing In Learning Organizations In Kutahya, Turkey]]></dcterms:title>
    <dcterms:abstract><![CDATA[Comunities today and in the future have to process, evaluate and internalize the information  more than past. Comunities and enterprises, which don&#039;t understand the environment, and are  unconscious about changes, and which don&#039;t read the world, are obliged to deteriorate, even  to die. Fiber speed and continious changes of present world, makes compulsory to learn  continiously and to educe information. Enterprises have to be open to continiously learning to  carry on their growth and development and they have to gain capability to share  knowledge.This paper undertakes to contribute to this search by addressing some  fundamental questions about the nature, domain, conceptual foundations, and practical  challenges of knowledge management and organizational learning. A positive relationship  has been found between continiously learning which are learner dimensions of organization,  dialog and research, team learning, sharing systems, empowered workers, connection  between the systems, sharing information of supportive leadership and openness of in-house  cognitive canals through the correlation and multiple regression analysis done in the result of  the research.  Keywords: Knowledge Management, Knowledge Share, learning organization.]]></dcterms:abstract>
    <dcterms:date><![CDATA[2012-05-31]]></dcterms:date>
    <dcterms:extent><![CDATA[1196]]></dcterms:extent>
</rdf:Description><rdf:Description rdf:about="https://omeka.ibu.edu.ba/items/show/2253">
    <dcterms:title><![CDATA[An Empirical Research On Relation Between Learning Organization And Visionary  Leadership In Kutahya, Turkey]]></dcterms:title>
    <dcterms:abstract><![CDATA[EINSTEIN, has seen the future dream and information power, then performed his genius  by dreaming. To dream the aimed future and to focus on, to endeavour on targets, to build a  &#039;&#039;vision&#039;&#039; are the powers which a leader has to have. Visionary leadership is persuading the  communities and formuizing the targets. Enterprises today, can not brand , grown and carry  on without having a vision. In the organizations which aim continious development and  continious learning, it will be easier to carry the enterprise to the future and to show visionary  leadership qualifications if they achieve to become open to changes and should be in  interaction with the others and if they should be a living organization. At the end of the study,  by making a multiple regression analysis, a positive relationship has been found between  learning organisation dimensions; (continious learning, dialog and research, learning as a  team, sharing systems, empowered employees,connection between systems and supporting leadership) and visionary leadership dimensions(planning, visionary organizational  leadership, visionary creative leadership)  Keywords: Learning organizations, Vision, Visionary Leadership, Living Organism, Future.]]></dcterms:abstract>
    <dcterms:date><![CDATA[2012-05-31]]></dcterms:date>
    <dcterms:extent><![CDATA[1112]]></dcterms:extent>
</rdf:Description><rdf:Description rdf:about="https://omeka.ibu.edu.ba/items/show/2254">
    <dcterms:title><![CDATA[Menu Planning With Fuzzy 0-1 Integer Programming]]></dcterms:title>
    <dcterms:abstract><![CDATA[For the sustainability of development, effective usage of sources and the determination of  their optimal usage levels are very important. Healthiness, as one of the main components of  sustainable development, is under influences of many factors one of which is nutrition, and  the number of people who benefit from public nutrition services are increasing every day.The growth in the number of people necessitates that an effective menu planning must be  done in order to keep the continuity of sustainable public nutrition systems.  In this study, detailed plans of 20 days’ lunch menu lists are prepared for workers who are at  the age of between 19 to 30 years old. Fuzzy 0-1 integer linear programming technique was  used during the planning process with the consideration of data’s fuzziness. Carlsson-  Korhenon approach, which is offered for the situations when all parameters are fuzzy in the  model configuration, is applied.  Keywords: Menu Planning, Nutrition, Fuzzy, 0-1 Linear Programming.]]></dcterms:abstract>
    <dcterms:date><![CDATA[2012-05-31]]></dcterms:date>
    <dcterms:extent><![CDATA[1095]]></dcterms:extent>
</rdf:Description><rdf:Description rdf:about="https://omeka.ibu.edu.ba/items/show/2255">
    <dcterms:title><![CDATA[Report on : Students expenditure and the economic recession]]></dcterms:title>
    <dcterms:abstract><![CDATA[All subjects were selected from International Islamic University Malaysia (IIUM),  data was collected using questionnaire which is attached to the research paper. There are two  types of data which is local student’s data and foreign student’s data. The findings from  research are representing that foreign students as well as local students are affected by current  economic recession.]]></dcterms:abstract>
    <dcterms:date><![CDATA[2012-05-31]]></dcterms:date>
    <dcterms:extent><![CDATA[1338]]></dcterms:extent>
</rdf:Description><rdf:Description rdf:about="https://omeka.ibu.edu.ba/items/show/2256">
    <dcterms:title><![CDATA[Classification of EEG signals for epileptic seizure prediction using ANN]]></dcterms:title>
    <dcterms:abstract><![CDATA[In this paper, we developed a model for classification of EEG signals. The aim of the study is  to determine whether this model can be used for epileptic seizure prediction if “pre-ictal”  stages were successfully detected. We analyzed long-term Freiburg EEG data. Each of 21  patients contains datasets called “ictal” (seizure) and “inter-ictal” (seizure-free). We extracted  4096-samples (or 16 seconds) long segments from both datasets of each patient. These  segments were decomposed into time-frequency representations using Discrete Wavelet  Transform (DWT). The statistical features from the DWT sub-bands of EEG segments were  calculated and fed as inputs to Multilayer Perceptron (MLP) and Radial Basis Function  (RBF) network classifiers using 10-fold cross validation. We also applied multiscale PCA  (MSPCA) de-noising method to determine if it can further enhance the classifiers’  performance. MLP-based approach outperformed RBF classifier with or without MSPCA,  which significantly improved the classification accuracy of both classifiers. The proposed  MLP-approach with MSPCAachieved a classification accuracy of 95.09%. We showed that a  high classification accuracy of EEG signals can be accomplished in cases when additional  “pre-ictal” class is introduced. Therefore, the proposed approach may become an efficient  tool to predict epileptic seizures from EEG recordings.  Keywords: Electroencephalogram (EEG); Epileptic seizure; Discrete Wavelet Transform  (DWT); Multilayer Perceptron (MLP); Radial Basis Function (RBF) network; Multiscale  PCA (MSPCA); Machine learning.]]></dcterms:abstract>
    <dcterms:date><![CDATA[2012-05-31]]></dcterms:date>
    <dcterms:extent><![CDATA[1208]]></dcterms:extent>
</rdf:Description><rdf:Description rdf:about="https://omeka.ibu.edu.ba/items/show/2257">
    <dcterms:title><![CDATA[Economic Costs And Benefits Of The Eu Enlargement: The Impact On The Eu And  Seec’s]]></dcterms:title>
    <dcterms:abstract><![CDATA[The South-eastern enlargement of the European Union will be the sixth enlargement since  establishing the European Community in 1957. The research uses the Gravity model, and  measures the factors that have an influence on trade. The Gravity model involves coefficients that explain the pattern of trade with GDP, geographical distance, population, and several  dummy variables. Trade that is explained by Gravity model includes two regions, EU-15  (inclusive Bulgaria and Romania) and SEEC’s. The reason why Bulgaria and Romania are  included, even if they are part of the SEEC’s, is to acquire as accurate pattern of trade as  possible. Comparing the data from 2010, the gravity model describes trade flows between 23  countries. Thus, the purpose of this study is to analyze trade flows between two regions.  Taking into consideration the costs of enlargement, this research examines the effects of the  trade, its significance on the development of SEEC’s after enlargement, well-being of  countries that are not part of the EU, as well as it offers a solution for the South-east European  countries. Therefore, the solution that this research proposes is a model based on creation of  the Balkan Union.  Keywords: EU-Enlargement, Gravity model, South-eastern Europe, European union, Trade  flows.]]></dcterms:abstract>
    <dcterms:date><![CDATA[2012-05-31]]></dcterms:date>
    <dcterms:extent><![CDATA[1325]]></dcterms:extent>
</rdf:Description></rdf:RDF>