S
Srđan Jović
Researcher at Universiteti i Prishtinës
Publications - 39
Citations - 421
Srđan Jović is an academic researcher from Universiteti i Prishtinës. The author has contributed to research in topics: Gross domestic product & Soft computing. The author has an hindex of 10, co-authored 37 publications receiving 286 citations.
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An analysis of energy efficiency in Wireless Sensor Networks (WSNs) applied in smart agriculture
TL;DR: The main focus of the paper is on the power consumption of the various WSN components, on the both levels, physical and functional, and the magnitude of the energy savings that can be accomplished by deploying the duty cycle mechanisms within the WSNs.
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Application of artificial neural network with extreme learning machine for economic growth estimation
TL;DR: It was shown that ANN with ELM learning methodology can be applied effectively in applications of GDP forecasting and should be considered as conventional learning methodology.
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Prediction of laser cutting heat affected zone by extreme learning machine
TL;DR: The purpose of this research was to develop and apply the Extreme Learning Machine (ELM) to predict the HAZ according to three inputs and it was demonstrated that ELM can be utilized effectively in applications of HAZ forecasting.
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Vibration analyzing in horizontal pumping aggregate by soft computing
Miloš Milovančević,Vlastimir Nikolić,Dalibor Petković,Ljubomir Vračar,Emil Veg,Natalija Tomic,Srđan Jović +6 more
TL;DR: Soft computing approach was used since the soft computing approach does not require internal knowledge of the vibration model and the approach should rank the influence of the measuring positions vibration on the pumping aggregate.
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Appraisal of natural resources rents and economic development
TL;DR: In this paper, five natural resources rents were investigated in order to determine which of the natural resource rent has the highest influence on the economic development and economic development was analyzed based on gross domestic product (GDP) Soft computing method was used to perform the sensitivity analysis of the GDP according to the 5 natural resource rents Coal rents, forest rents, mineral rents, natural gas rents and oil rents were used as the inputs.