XL In e na ional Con e ence
INFOTECH 2025
PROCEEDINGS
Edi ed by
Vladan Pan o ić
Dušan S a če ić
A anđelo ac, 4 – 5, June, 2025
XL In e na ional Con e ence
INFOTECH 2025
A anđelo ac, 4 – 5, June, 2025
O ganize
ASIT - Associa ion o Compu ing, In o ma ics,
Telecommunica ions and New Media o Se bia
Co-o ganize s o he scien i ic pa o he con e ence:
• Facul y o P ojec and Inno a ion Managemen p o . d Pe a Jo ano ić, Belg ade
• Facul y o In o ma ion Technology and Enginee ing, Belg ade
• Facul y o Business Economy and En ep eneu ship, Belg ade
• Labo a o y o mul imedia communica ion, FON, Belg ade
• Uni e si y “Bijeljina”, Bijeljina, Bosnia and He zego ina
• “Dosi ej” Facul y o Economics and In o ma ics, Belg ade
INFOTECH 2025 P oceedings
Edi ed by Vladan Pan o ić & Dušan S a če ić
Publishe : ASIT, Belg ade, Nikola Mi ko ić, P esiden
Co-Publishe : Facul y o P ojec and Inno a ion Managemen p o . d Pe a Jo ano ić, Belg ade
Co e Design: Vladimi Jablano / Digi al p in ing: GRAFOPAN doo / Ci cula ion: 200
ISBN-978-86-900491-3-4
CIP - Каталогизација у публикацији
Народна библиотека Србије, Београд
004(082)
007:004(075.8)(082)
INTERNATIONAL Con e ence INFOTECH (40 ; 2025 ; A anđelo ac)
P oceedings / XL In e na ional Con e ence INFOTECH 2025, A anđelo ac, 4 – 5, June, 2025 ; edi ed
by Vladan Pan o ić, Dušan S a če ić ; [o ganize ] ASIT [i. e.] Associa ion o Compu ing, In o ma ics,
Telecommunica ions and New Media o Se bia ; [co-o ganize s Facul y o P ojec and Inno a ion
Managemen ... [e al.]]. - Belg ade : ASIT : Facul y o P ojec and Inno a ion Managemen , 2025
(Beog ad : G a opan). - 138 s . : ilus . ; 30 cm
Ti až 200. - S . 5: P e ace / Nikola Mi ko ić, Vladan Pan o ić. - Bibliog a ija uz s aki ad.
ISBN 978-86-900491-3-4 (ASIT)
1. Pan o ić, Vladan, 1961- [u ednik] [au o doda nog eks a] 2. S a če ić, Dušan, 1949- [u ednik]
a) Информациона технологија -- Зборници b) Информациони системи -- Зборници
COBISS.SR-ID 169843977
XL In e na ional Con e ence INFOTECH 2025 P oceedings
3
Table o Con en s
PREFACE ………………………………………………………………………………………………………………………………….. 5
PROGRAM COMMITTEE …………………………………………………………………………………………………………… 6
INVITED KEYNOTE LECTURE: T us in he Age o AI: Decoding he Fu u e o Da a-D i en Insigh s
Nikola Voj ek, Vladan Pan o ić ……….…. 9
1. A i icial In elligence ………………………………………………………………………………………………………….. 13
• Applica ion o A i icial In elligence Technologies in Au onomous Vehicles – Ad an ages and
Challenges, Radosla Rako ić…………………………………………………………………………………………. 15
• Code Gene a o s in he Age o La ge Language Models, Mladen Opačić, Nikola Dimi ije ić,
Nemanja Zd a ko ić…………………………….…………………………………………………………………………. 21
• AI’s In luence on O ganiza ional Cul u e in So wa e SMEs in Sou heas Eu ope: Challenges
and Me ics, Aleksanda Milinčić…………………………………………………………………………………… 27
• D i ing Inno a ion h ough In elligen Au oma ion, D agan Me ikoš………………………………. 33
• Tackling Da a Sca ci y in AI: A Compa a i e Analysis o Da a Augmen a ion and Syn he ic
Da a Gene a ion, Nikola Voj ek, Bojan Smudja ……………………………………………………………….. 37
• Using A i icial In elligence in In o ma ion Technology Audi s, D agan Jo ičić, K is ijan Lazić,
Vladan Pan o ić, Ma ina Jo ano ić-Milenko ić, I an Vulić …………………………………………..…. 43
• O ganiza ional Resilience and Compe i i eness wi h ISO/IEC 42001: A F amewo k o AI
Da a Supe ision, Nikola Voj ek, Vladan Pan o ić……………………………………………………………. 49
• In eg a ion o A i icial In elligence Tools in Pee -Re iew: T anspa ency, E iciency and
Reliabili y o Hyb id Model, D ago ad Milo ano ić, Zo an Jo ičić, Siniša Ris ić……………….. 53
• Le e aging KNIME o AI Model Design in IoT and Edge Compu ing Scena ios, Pe a
P ulo ić, Nemanja Radosa lje ić, Đo đe Babić, Dušan Vujoše ić……………………………………. 57
• In e nal Audi in K–12 Educa ion in he Age o A i icial In elligence: Challenges, Po en ials,
and Recommenda ions, Sonja Djukic Popo ić, S e an Popo ić, D ažen Jo ano ić …………… 63
• Human and A i icial In elligence in Team Wo k, Vesna Buha, Rada Lečić, Mi jana
Dejano ić ……………………………………………………………………………………………………………………….. 69
2. In o ma ion Secu i y ………………………………………………………………………………………………………….. 73
• AI's Role in Cybe secu i y Th ea s and De enses - Examples o Conc e e Solu ions, D agan
Pleskonjić, Luka Tica, Vladimi Jelić, Dušan Todo o ić, An hony English ……….…………………. 75
• Si ius E-Lea ning Pla o m o C ea ing and Deli e ing Acc edi ed Online Semina s - IT
Secu i y in Schools, Vladan S e ano ić ...………………………………………………………..………………. 81
• Risk assessmen o he In o ma ion Sys em by Applying he AHP Me hod, B anko Vuja o ić,
Sanja Klajić, Da ko G ubač, Ma ija Vuja o ć, Nenad S ojano ić ………………………….………….. 85
XL In e na ional Con e ence INFOTECH 2025 P oceedings
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3. In o ma ion Technology and Applica ions ………………………………………………………………………….. 91
• Explo ing he Applica ion o Blockchain and Sma Con ac s in Cons uc ion P og ess
Paymen s, Paolo Eugenio Demagis is, Filippo Ma ia O a iani .…………………………..………… 93
• Tool o Li e a u e Disco e y and Analysis, Nikola Vojičić, Da o Sekulski, Vladimi U oše ić
………………………………………………………………………………………………………………………………………… 99
• S udy on 5G-Ad anced FinIoT Pla om in Eme ging Financial Use Cases, Ca ol Ed ich,
D ago ad Milo ano ić, D ago Indjić …………………………………………………………………………..…. 105
• P ocess o Mig a ing Single Page Applica ions o Me a F amewo ks Like Nux , Pe a K esoja,
Ma ko Ša ac, Mladen Veino ić ……………………………………………………………………………………… 109
• Mode n App oaches o Wa e Sys em Secu i y: In eg a ion Scada Sys em and Blockchain,
I ena Tasić, S đan Tasić …………………………………………………………………………………………………. 113
4. Managemen and In o ma ion Sys ems ……………………………………………………………………………. 119
• Assessmen o he use o in o ma ion and communica ion echnology on he example o an
ag icul u al a m, Mi osla Nedeljko ić, Slađana Vujičić, C ije in Ži ano ić …………………… 121
• Models o Managemen O ganiza ional Changes, Biljana Ilić, Sla ica Anđelić, Sanja
S ojano ić …………………………………………………………………………………………………………………….. 125
• E-HRM Like Po en ial o Business, Julija A akumo ić, Jelena A akumo ić ……………………… 133
WORKSHOP: Implemen a ion o he ISO/IEC 42001 S anda d o AI Managemen Sys em ……. 137
AUTHOR INDEX ……………………………….…………………………………..………………………………………………. 138
XL In e na ional Con e ence INFOTECH 2025 P oceedings
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P R E F A C E
This yea ma ks he 40 h edi ion o INFOTECH, a egula annual in e na ional scien i ic and
p o essional con e ence in he ield o he de elopmen and applica ion o in o ma ion echnologies.
INFOTECH has always ollowed ends in he de elopmen o in o ma ion and communica ion
echnologies, and his yea is no di e en ; he ob ious dominan heme, add essed in mo e han
hal o he pape s, is he applica ion o a i icial in elligence (AI). Due o he impo ance o his
cu en opic, a special AI Panel, AI Wo kshop and AI Keyno e we e o ganized.
A o al o 23 accep ed pape s by 58 au ho s a e published in he P oceedings. They a e classi ied
acco ding o hei subjec ma e in o ou sec ions: A i icial In elligence, In o ma ion Secu i y,
In o ma ion Technology and Applica ions, and Managemen and In o ma ion Sys ems. The adi ion
con inues, and his yea INFOTECH also ea u ed au ho s om ab oad (I aly, Canada, Uni ed
Kingdom, Ge many and Bosnia and He zego ina).
ASIT - he Associa ion o Compu ing, In o ma ics, Telecommunica ions and New Media o Se bia,
he o ganize o he con e ence, on he occasion o he impo an jubilee, published, in coope a ion
wi h he Co-Publishe he “Facul y o P ojec and Inno a ion Managemen p o . d Pe a Jo ano ić“,
wo addi ional special edi ions edi ed by p o . d Vladan Pan o ić and p o . eme i us d Dušan
S a če ić:
• INFOTECH 2020 – 2024 Selec ed Pape s
• INFOTECH 1995 – 2000 P oceedings: Digi al e sion he classic p in ed p oceedings
We would like o hank e e yone who ac i ely pa icipa ed in he p epa a ion o he INFOTECH 2025
con e ence, and we expec good coope a ion in he coming yea s as well.
Chai man o he O ganizing Boa d Chai man o he Scien i ic P og am Boa d
Nikola Mi ko ić P o . d Vladan Pan o ić
XL In e na ional Con e ence INFOTECH 2025 P oceedings
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ENHANCING LITERATURE REVIEWS
A TOOL FOR DISCOVERY AND ANALYSIS
Nikola Vojičić, Beli d.o.o. (L d.), nikola. [email p o ec ed]. s
Da o Sekulski, Beli d.o.o. (L d.), [email p o ec ed]. s
Vladimi U oše ić, Beli d.o.o. (L d.), ladimi .u o[email p o ec ed]
Abs ac : This pape in oduces a cus om-buil Li e a u e
Re iew Tool desinged o disco e , ex ac , and ank
ele an con en om a di e se ange o online sou ces
using ad anced web sc aping and con ex ual ull- ex
sea ch echniques. The ool enhances he e iciency and
accu acy o li e a u e e iews by enabling comp ehensi e
analysis o bo h scien i ic publica ions and g ey li e a u e,
including blogs, in o mal epo s, and go e nmen
documen s. The pape p esen s an o e iew o he ool’s
co e unc ionali ies, including sou ce disco e y, a icle
ex ac ion, and con en analysis.
Keywo ds: Web sc aping and con en ex ac ion, ull- ex
sea ch, ele ance sco ing and highligh ing, li e a u e
analysis, g ey li e a u e.
1. INTRODUCTION
The su eying, disco e y, and sys ema iza ion o ele an
g ey li e a u e ma e ials “p oduced on all le els o
go e nmen , academics, business and indus y in p in and
elec onic o ma s, […] which is no con olled by
comme cial publishe s” [1] – such as echnical epo s,
p ojec epo s, wo king pape s, whi e pape s, go e nmen
documen s, and o he o ms – is highly signi ican [2] o
mapping he s a e o he a wi h a mo e balanced iew [3],
pa icula ly in applied esea ch and inno a ion, ye i is
la gely o e looked o unsuppo ed by majo academic
sea ch engines and s uc u ed con en eposi o ies, such as
Scopus, Lens, ScienceDi ec , and Web o Science.
In he absence o e icien ools o s uc u ed egis ies,
li e a u e e iews o his kind o en ely on ei he manual
web sea ching o in e ac ion wi h LLM-based in e aces
(such as OpenAI Cha GPT [4]). Howe e , hese
app oaches emain highly un eliable o asks ha demand
de e minis ic esponses and ully accu a e, comple e
esul s, due o issues such as hallucina ions o
con abula ions [5] and inhe en biases [6].
Manual web sea ches a e e ec i e o ela i ely simple
asks, such as ga he ing in o ma ion on s aigh o wa d,
na owly ocused opics o add essing speci ic p oblems
wi h widely accep ed answe s and minimal con lic ing
iewpoin s. The limi a ions o manual sea ching become
e iden when conduc ing ex ensi e and sys ema ic g ey
li e a u e e iews, especially hose ha equi e di e se
sou ces, mul iple pe spec i es, and co e di e en ime
pe iods. Manual sea ching in such cases is ime-consuming
and labo -in ensi e, o en necessi a ing a well-coo dina ed
eam, ho ough planning, and sys ema iza ion o esul s.
E en when ele an documen s a e ound, he ou comes o
manual sea ches o en ail o jus i y he ime and e o
in es ed. This is especially ue when esul s, despi e being
well-documen ed and s uc u ed, a e no s o ed in
da abases ha suppo complex e alua ions, agg ega ions,
and analyses. While ad-hoc sc ip s may assis wi h some
p elimina y asks, hey only pa ially add ess he challenge
o assembling a ele an co pus. Signi ican addi ional
e o is s ill equi ed o ho oughly ead h ough hese
documen s and selec he mos ele an ones.
Among all hese ac i i ies, only wo uly equi e human
e o : de ining sea ch que ies and eading he disco e ed
a icles. E e y hing else can be au oma ed, including
anked sea ches ac oss he in e ne o websi es and wi hin
websi es o a icles, sc aping a icle con en , s o ing da a
in s uc u ed, que yable da abase, and isualizing, il e ing,
agg ega ing sco es, and o de ing he da a. E en he manual
asks can be op imized: sea ch que ies can be p io i ized
based on he sco es o e ie ed esul s, and eading can be
minimized by using con ex ual ull- ex sea ch o highligh
only he mos ele an pa ag aphs and keywo ds wi hin he
ex ac ed a icle con en s.
This a icle desc ibes a ool o au oma ed li e a u e
disco e y and e iew designed o add ess key challenges
in he ield. De eloped by a dedica ed p ojec eam, i is
ac i ely used in he ongoing FishEUT us p ojec o
explo e, disco e , and sys ema ize g ey li e a u e on
se e al o i s co e opics and esea ch ques ions, such as
ba ie s o and d i e s o sea ood pu chasing and
consump ion beha iou . The ool’s de elopmen was
mo i a ed by he clea absence o any sui able exis ing
solu ions a he ime (mid-2023).
XL In e na ional Con e ence INFOTECH 2025 P oceedings
100
2. MAIN FEATURES AND WORKFLOW
The Li e a u e Re iew Tool is designed o o e come he
challenges o manual in e ne a icle sea ches by
au oma ing he disco e y o ele an a icles and sec ions,
while also suppo ing use collabo a ion du ing con en
analysis. The wo k low consis s o ou key s ages:
1. Websi e sea ch which in ol es sea ching websi es as
sou ces o speci ic a icles. This s ep is op ional, as
use s can manually p o ide a se o websi es i hey
al eady know whe e o sea ch.
2. A icle sea ch on speci ic websi es in ol es sea ching
o a icles wi hin he subse o websi es iden i ied in
s ep 1, websi es manually en e ed by he use , o bo h.
3. Pa ag aph sea ch wi hin a icles akes place a e s ep
2, once a icles ha e been collec ed and he sea ch is
comple e. The ool downloads con en om he op
4,000 anked a icles, ex ac s and indexes hei
con en , and enables ull- ex sea ch on ha da a.
4. Tagging websi es and a icles wi h s a uses such as
ele an , po en ially ele an , o no ele an o helps
wi h collabo a ion among mul iple use s. This
agging can be done a any s age in he wo k low.
Figu e 1. Li e a u e Re iew Tool wo k low
Figu e 1 illus a es he possible s eps in his wo k low,
de ailing he inpu s and ou pu s o each s age. Disco e ing
websi es in ol es websi e sea ching and analysis. The
a icle sea ch is pe o med on he iden i ied ele an
websi es o collec ma e ial o con en ex ac ion.
Pa ag aph sea ch loca es ele an sec ions wi hin
documen s, educing he need o eading he ull ex .
2.1 Disco e ing Si es
Sea ching he en i e in e ne o a icles on a speci ic opic
is challenging. The as amoun o in o ma ion o en
causes sea ch engines o a o highe - anked si es
op imized o sea ch, which may no always be he mos
ele an . This can na ow he di e si y o esul s. To
add ess his issue, i is necessa y o es ic he sea ch o a
p ede ined se o ele an websi es.
In some cases, use s may al eady know he speci ic
websi es o a ge o a icle sea ches. In o he cases,
addi ional websi es need o be iden i ied o expand he
sou ce pool. This ool conduc s websi e sea ches by
scanning he in e ne and sc aping con ex pa hs om he
i s i e pages o sea ch esul s. Use s p o ide sea ch
que ies, and each sea ch esul is s o ed as a iple
consis ing o si e, que y, and page.
Fo analysis and isualiza ion, he la ows a e g ouped by
si e, wi h sco es agg ega ed by summing indi idual
con ibu ions and que ies agg ega ed in o se s. Each
appea ance o a si e on a page con ibu es
10
𝑝𝑎𝑔𝑒 𝑛𝑢𝑚𝑏𝑒𝑟
(1)
o he o al sco e o ha si e.
Fo example, i a si e appea s ou imes on he i s page,
i s sco e will be 40, and i i appea s once on he second
page, i will add 5 o he sco e.
Figu e 2. Le column o he si e disco e e page wi h
websi es and sco es
Ho e ing o e he sco e numbe , as shown in Figu e 2,
opens a d opdown lis displaying all he que ies o which
he si e appea s. Si es can be ma ked wi h he ollowing
s a uses: “ ejec ed” ( ed), “unce ain” (yellow), and
XL In e na ional Con e ence INFOTECH 2025 P oceedings
101
“accep ed” (g een). Use s can also add cus om s a uses –
o example, Figu e 2 shows an addi ional s a us,
“academic” (pink), used o websi es con aining academic
li e a u e. Once a si e is ma ked as “accep ed,” i is
au oma ically queued o a icle sea ch (see he nex
chap e ), while o he s a uses emain desc ip i e and
sea chable only.
Figu e 3. Righ column o he si e disco e e page wi h
que ies and sco es
Since his p ocess in ol es sea ching ac oss he en i e
in e ne , que ies should be longe and mo e speci ic o
na ow he esul s. The que y sco e helps guide use s in
e ining hei que ies. Fo each que y, he sco e is
calcula ed as he sum o
1
𝑠𝑖𝑡𝑒 𝑟𝑎𝑛𝑘
(2)
o each si e ound by ha que y, whe e he si e wi h he
highes sco e is assigned ank 1 and subsequen si es
ecei e inc emen ally highe anks. This sum is hen
mul iplied by 100.
Que ies can be dele ed, which also emo es all associa ed
esul s. This ac ion canno be undone, al hough use s can
manually e-en e he same que y i needed. Checking he
boxes o speci ic que ies il e s he websi es lis ed in he
le column, showing only hose wi h sco es ha include
he selec ed que ies (see Figu e 3). Addi ionally, use s can
pe o m a uzzy sea ch by si e o combine hese il e s.
2.2 Disco e ing A icles
A e op ionally sea ching o and analyzing websi es, and
ma king some as accep ed, he nex s ep is o sea ch o
a icles. I websi es we e no p e iously iden i ied h ough
a sea ch, hey mus be manually en e ed, since bo h
websi es and que ies a e equi ed inpu s o he a icle
sea ch. This sea ch hen looks o a icles ma ching he
speci ied que ies on he designa ed websi es.
Fo he ini ial sea ch, bo h a si e and a que y a e equi ed.
Subsequen sea ches allow en e ing ei he o bo h. When a
new si e is added, all p e ious que ies will be sea ched on
ha si e. When a new que y is added, i will be sea ched
ac oss all p e iously speci ied si es. I bo h a new si e and
a new que y a e p o ided, he new que y will be sea ched
ac oss all exis ing si es, and all p e ious que ies will be
sea ched on he new si e. Sea ches o exis ing si e-que y
pai s a e no epea ed. To e esh esul s, use s mus
emo e and e-en e exis ing en ies. This app oach
ensu es consis en sco ing and ele an esul s.
The a icle sc ape ex ac s a icle URIs and i les om he
op h ee pages o sea ch esul s. Each si e-que y pai
igge s h ee sc apes, one o each page. Limi ing
ex ac ion o he i s h ee pages has p o en su icien , as
he sea ch is ocused on speci ic websi es a he han he
en i e in e ne , esul ing in less bias compa ed o he si e
sea ch phase.
The ou come o each sea ch con ains: si e, que y, page,
sc aped a icle URI and i le. These la ows a e g ouped
by a icle, sco es a e agg ega ed by summing indi idual
con ibu ions, while que ies and si es a e combined in o
se s. Each appea ance o a URI on a page con ibu es
10
𝑝𝑎𝑔𝑒 𝑛𝑢𝑚𝑏𝑒𝑟
(3)
o he a icle's o al sco e. Fo example, an a icle ha
appea s wice on he i s page will ha e a sco e o 20, and
a sco e o 5 i i appea s once on he second page.
Figu e 4. Le column o he a icle disco e e page wi h
a icles and sco es
Ho e ing o e he sco e numbe opens a d opdown lis ing
all que ies o which he a icle was ound. Like websi es,
a icles can be ma ked wi h s a uses such as “ ejec ed”
