Prof. Dr. Danijel Djosic
'%3e%3cpath%20d='M22.1642%2019.0924C21.7317%2018.068%2021.1041%2017.1375%2020.3163%2016.3527C19.5309%2015.5656%2018.6006%2014.9381%2017.5766%2014.5048C17.5674%2014.5002%2017.5582%2014.4979%2017.5491%2014.4933C18.9774%2013.4616%2019.9059%2011.7811%2019.9059%209.88504C19.9059%206.74408%2017.3611%204.19922%2014.2201%204.19922C11.0792%204.19922%208.53429%206.74408%208.53429%209.88504C8.53429%2011.7811%209.46282%2013.4616%2010.8912%2014.4956C10.882%2014.5002%2010.8728%2014.5025%2010.8636%2014.5071C9.83652%2014.9404%208.91487%2015.5617%208.1239%2016.355C7.33685%2017.1404%206.70933%2018.0707%206.276%2019.0947C5.85031%2020.0972%205.62072%2021.172%205.59967%2022.2609C5.59905%2022.2853%205.60335%2022.3097%205.61229%2022.3325C5.62123%2022.3553%205.63465%2022.376%205.65174%2022.3935C5.66883%2022.4111%205.68926%2022.425%205.71182%2022.4345C5.73437%2022.444%205.7586%2022.4489%205.78308%2022.4489H7.15868C7.25956%2022.4489%207.3398%2022.3686%207.3421%2022.2701C7.38795%2020.5001%208.09868%2018.8425%209.35506%2017.5861C10.655%2016.2862%2012.3814%2015.5709%2014.2201%2015.5709C16.0588%2015.5709%2017.7852%2016.2862%2019.0852%2017.5861C20.3415%2018.8425%2021.0523%2020.5001%2021.0981%2022.2701C21.1004%2022.3709%2021.1807%2022.4489%2021.2815%2022.4489H22.6571C22.6816%2022.4489%2022.7058%2022.444%2022.7284%2022.4345C22.751%2022.425%2022.7714%2022.4111%2022.7885%2022.3935C22.8056%2022.376%2022.819%2022.3553%2022.8279%2022.3325C22.8369%2022.3097%2022.8412%2022.2853%2022.8406%2022.2609C22.8176%2021.165%2022.5907%2020.0989%2022.1642%2019.0924ZM14.2201%2013.8284C13.1678%2013.8284%2012.1773%2013.418%2011.4322%2012.6729C10.6871%2011.9278%2010.2767%2010.9374%2010.2767%209.88504C10.2767%208.83271%2010.6871%207.84227%2011.4322%207.09715C12.1773%206.35204%2013.1678%205.94165%2014.2201%205.94165C15.2724%205.94165%2016.2629%206.35204%2017.008%207.09715C17.7531%207.84227%2018.1635%208.83271%2018.1635%209.88504C18.1635%2010.9374%2017.7531%2011.9278%2017.008%2012.6729C16.2629%2013.418%2015.2724%2013.8284%2014.2201%2013.8284Z'%20fill='%238590A2'/%3e%3c/g%3e%3c/svg%3e)
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Academic Career Length
13Years (2014-Present)
(Calculated once Scilit publication data is synced: current year − year of first publication + 1)
Representative Publications
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Journal Article
Milan Dejanovic
Stefan PanicNKNataša Kontrec
Nataša KontrecDanijel Djosic
Saša T. MilojevićPerformance-Based Logistics (PBL) frameworks prioritize system availability by optimizing maintenance strategies, with repair rate estimation playing a critical role in predictive maintenance planning. This study proposes a machine learning-based approach for repair rate prediction, leveraging fully connected neural networks (FCNNs) and Long Short-Term Memory (LSTM) networks trained on repair rate samples generated from a stochastic model. The FCNN estimates maximum repair rates, while the LSTM predicts minimum repair rates, capturing both steady-state and sequential dependencies in repair rate variations. By eliminating the need for complex mathematical formulations, the proposed methodology provides a scalable and computationally efficient alternative to traditional stochastic models. Extensive performance evaluations demonstrate that the neural networks achieve higher accuracy and lower computational costs compared to stochastic approaches, making them well-suited for real-time predictive maintenance applications. This research enhances decision-making in maintenance planning, optimizes resource allocation, and improves overall system reliability within PBL frameworks.
25 March 2021
Conference Paper
ZPZoran Peric
Zoran Peric
MSMilan Savic
Milan Savic
MDMilan Dincic
Milan Dincic
NVNikola Vucic
Nikola Vucic
Danijel DjosicSMSrdjan Milosavljevic
Srdjan Milosavljevic
Floating Point 32-bits (FP32) representation format is proposed by IEEE Standard 754, being widely used in neural networks (NN), signal processing and numerical computation. Also, Fixed Point 32-bits format is widely used for data representation. This paper describes those standard 32-bits formats (Fixed Point 32 and FP32) as quantization schemes, defining quantizers based on them and providing in this way references for comparison of other quantization schemes used in neural networks. Quantization of data with the Laplacian distribution is considered, in a wide range of variance. Theoretical results are proven by an experiment, applying those quantization schemes on weights of a neural network.
7 April 2015 in International Journal of Numerical Modelling: Electronic Networks, Devices and Fields
Journal Article
DMDejan N Milic
Dejan N MilicDanijel DjosicCSCaslav Stefanovic
Caslav StefanovicSPStefan Panic
Stefan Panic
MSMihajlo Stefanovic
Mihajlo Stefanovic
1 April 2021 in Physical Communication
Journal Article
ČSČaslav Stefanović
Časlav Stefanović
SPStefan Panić
Stefan Panić
Danijel DjosicDMDejan N Milic
Dejan N MilicMSMihajlo Stefanović
Mihajlo Stefanović
19 August 2015 in IETE Journal of Research
Journal Article
Danijel DjosicDSDusan M. Stefanovic
Dusan M. StefanovicCSCaslav Stefanovic
Caslav StefanovicIn this article, macro-diversity structure with macro-diversity selection combining (SC) receiver and two micro-diversity SC receivers operating over correlated shadowed multipath fading channel are analysed. Received signal is subjected simultaneously to α–µ short-term fading and gamma long-term fading (shadowing) resulting in system performance degradation. Macro-diversity SC receiver aims to reduce gamma shadowing effect while micro-diversity SC receivers aim to mitigate α–μ small-scale multipath fading effects. Useful infinite series expressions for the second-order statistical measure such as level crossing rate (LCR) of macro-diversity SC receiver output signal is efficiently derived. Numerical results are presented to examine the influence of various gamma shadowing and α–µ multipath fading parameters on LCR of the proposed system.
7 April 2015 in International Journal of Electronics Letters
Journal Article
CSCaslav Stefanovic
Caslav StefanovicStefan PanicNSNegovan Stamenković
Negovan Stamenković
PSPetar Spalević
Petar Spalević
Danijel Djosic
Zoran PerićAbout Me
Danijel Đošić was born in Priština, Serbia in 1983, where he completed his primary and secondary education. He graduated at the Faculty of Science and Mathematics in the area of Informatics, University of Priština, Serbia in 2008, PhD he finished his studies from Faculty of Electronic Engineering, University of Nis, Serbia in 2019 respectively. He has been employed at the Faculty of Science and Mathematics of the University of Priština from 2008 – to present. Currently he works as assistant professor and holds the position of he was the head of the Department of Informatics, Faculty of Science and Mathematics, University of Priština. He is a member of review team at the National Accreditation Body of the Republic of Serbia. He is the author of one textbooks in the field of information and communication technologies in the Serbian language, as well as over 40 papers in the field of information and communication technologies, published in international journals and conferences. He was participant of one scientific projects funded by Serbian Ministry of Science. His major research interests include wireless mobile and computer networks software applications, mathematical modeling of wireless transmission, mobile and multichannel communications include statistical characterization. Member of Organizing Committee of the Conference: Mathematical and Information Technology 2011 and 2013.
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