In e na ional Jou nal o In en i e Enginee ing and Sciences (IJIES)
ISSN: 2319-9598 (Online), Volume-12 Issue-11, No embe 2025
17
Published By:
Blue Eyes In elligence Enginee ing
and Sciences Publica ion (BEIESP)
© Copy igh : All igh s ese ed.
Re ie al Numbe : 100.1/ijies.K113512111125
DOI: 10.35940/ijies.K1135.12111125
Jou nal Websi e: www.ijies.o g
A i icial In elligence and Machine Lea ning in
Enginee ing Applica ions
Himanshu Gup a, Sanjee Tayal, Pa ag Jain, Abhay Bha ia, Lokesh Kuma
Abs ac : Today, AI and ML a e used in almos all domains, om
enginee ing o medicine. AI is ans o ming he way
esea che s/enginee s used o sol e p oblems. AI is making
p ocesses mo e e icien , lexible and as . AI needs da a o make
decisions. Basically, i cap u es he pa e ns in he da a. Enginee s
oday is using AI ools o add ess p oblems ac oss almos e e y
enginee ing domain, including manu ac u ing, u ban planning,
and anspo a ion. The objec i e o his s udy is o explo e AI
applica ions ac oss a ious enginee ing ields, wi h pa icula
a en ion o elec ic ehicle (EV) cha ging. In his s udy, we used
a da ase om a publicly a ailable eposi o y (Kaggle) in CSV
o ma , con aining da a om 3,395 cha ging sessions by 85 EV
use s a 105 s a ions ac oss 25 wo kplaces. We pe o med an
explo a o y analysis o his da ase and iden i ied se e al
in e es ing ends, including a e age and peak ene gy
consump ion, peak cha ging ime, and he busies cha ging
s a ions. Some indings include ha 5 kWh was consumed in mos
sessions, hough a ew d ew no iceably mo e ene gy. F om he
analysis, i is ound ha on Thu sdays, cha ging ac i i ies a e
mo e han usual, oughly a ound 11 a.m. This may be due o he
egula o ice schedules. I is also obse ed ha ype 3 cha ging
s a ions we e used mos equen ly, and a la ge sha e o ene gy
was consumed om hese s a ions. These insigh s p o ide a
p ac ical unde s anding o how people cha ge hei EVs a he
wo kplace. By unde s anding his challenging pa e n,
o ganisa ions can schedule hei cha ging acili ies mo e
e ec i ely. Fu he o ganiza ions can make s a egy o mo i a e
hei employees o cha ge hei EV ehicles du ing non-peak
hou s.
Keywo ds: A i icial In elligence (AI), Machine Lea ning (ML),
AI Applica ions, Elec ic Vehicle (EV) Cha ging, AI in
Enginee ing, Decision Making.
Nomencla u e:
AI: A i icial In elligence
ML: Machine Lea ning
EV: Elec ic Vehicle
EDA: Explo a o y Da a Analysis
Manusc ip ecei ed on 22 Oc obe 2025 | Fi s Re ised
Manusc ip ecei ed on 28 Oc obe 2025 | Second Re ised
Manusc ip ecei ed on 08 No embe 2025 | Manusc ip
Accep ed on 15 No embe 2025 | Manusc ip published on 30
No embe 2025.
*Co espondence Au ho (s)
D . Himanshu Gup a*, Assis an P o esso , Depa men o Compu e
Science and Enginee ing, Roo kee Ins i u e o Technology, Roo kee, India.
Email ID: hgup [email protected], ORCID ID: 0000-0003-3271-3032
D . Sanjee Tayal, Depa men o Compu e Applica ions, SD College o
Managemen S udies, Muza a naga , India. Email ID:
sanjee [email protected]
D . Pa ag Jain, Depa men o Compu e Science and Enginee ing,
Roo kee Ins i u e o Technology, Roo kee, India. Email ID:
di ec o @ i oo kee.com
D . Abhay Bha ia, Resea che , Depa men o Compu e Science and
Enginee ing, Roo kee Ins i u e o Technology, Roo kee, India. Email ID:
d abhay.cse@ i oo kee.com
D . Lokesh Kuma , Associa e P o esso , Depa men o Compu e
Science and Enginee ing, Roo kee Ins i u e o Technology, Roo kee, India.
Email ID: [email protected]
© The Au ho s. Published by Blue Eyes In elligence Enginee ing and
Sciences Publica ion (BEIESP). This is an open-access a icle unde he
CC-BY-NC-ND license h p://c ea i ecommons.o g/licenses/by-nc-nd/4.0/
I. INTRODUCTION
A i icial In elligence (AI) and Machine Lea ning (ML)
[1][2][3] echnologies a e widely used in enginee ing
applica ions; in almos all enginee ing domains, esea che s
use machine lea ning and AI o p edic ion o in elligen
decision-making. AI and ML help esea che s ind hidden
pa e ns in he da a. As compu ing powe g ows and la ge
da ase s a e made publicly a ailable, he use o AI ac oss
c oss-domain applica ions has inc eased. Fu he mode n
echniques, such as a en ion mechanisms and ans o me s,
ha e also boos ed he use o AI o ackling complex
enginee ing p oblems.
A. Applica ions o AI and ML in Enginee ing
This sec ion discusses he a ious use cases o AI & ML in
di e en enginee ing b anches:
Mechanical enginee s make g ea use o AI and ML o
p edic i e main enance and aul de ec ion. Fo example, i
we ake a senso da ase om CNC machines o obo ic a ms
ha has columns such as ib a ion le el o empe a u e.
Classi ica ion models o eg ession models can be applied o
he ob ained da ase o p edic ailu es be o e hey occu . This
can help educe he down ime and main enance cos s [4]. AI-
enabled sys ems can iden i y de ec i e pa s wi h high
accu acy and main ain consis en p oduc ion.
O he enginee ing b anches also ha e applica ion o AI in
hei domain, ci il and s uc u al enginee s using AI o
moni o b idges, oads and buildings. Fo AI o make
p edic ions o pe o m classi ica ion, da a is needed. Fo his,
di e en senso s can be deployed on b idges o eco d
pa ame e s such as s ain, displacemen , and ib a ion. Once
we ha e he da a, we can use ML models such as g adien
boos ing and SVMs o iden i y unusual pa e ns. Ale s can
be igge ed i some pa ame e s exceed limi s, so p oblems
can be ixed be o e hey become se ious. Some ci il
enginee ing p ojec s also use gene ic algo i hms o op imise
b idge designs o inno a i e ci y layou s [5].
AI is also widely applied in he elec ical and powe
sys ems. Sma g ids balance supply and demand, in eg a e
sola /wind ene gy, and educe losses [6]. So basically, AI
models ely on da a. I we ha e da a, we can use AI models
o ind pa e ns and make p edic ions. LSTM is a popula
model ha wo ks well o ime-se ies da a; his company can
o ecas ene gy consump ion. AI helps EV ba e ies, oo. By
op imising cha ging schedules, peak hou s' load can be
minimised, imp o ing o e all e iciency.
AI is used in anspo a ion o con ol public anspo a ion
and a ic. Models a e ed da a
om GPS, came as, and In e ne -
o -Things senso s o o ecas
a ic and ecommend ou es.
A i icial In elligence and Machine Lea ning in Enginee ing Applica ions
18
Published By:
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and Sciences Publica ion (BEIESP)
© Copy igh : All igh s ese ed.
Re ie al Numbe : 100.1/ijies.K113512111125
DOI: 10.35940/ijies.K1135.12111125
Jou nal Websi e: www.ijies.o g
Au onomous ca s p ocess inpu s om lida _da a,
came a_ ames, and ada _signals using deep lea ning,
senso usion, and ein o cemen lea ning. They con e se
wi h o he ca s and make d i ing decisions.
AI o ecas s emissions, wa e le els, and ai quali y o
suppo sus ainabili y and en i onmen al p o ec ion. ML
models iden i y pollu ion and maximise was ewa e ea men
using ai quali y and was ewa e da a ob ained om a ious
senso s. These applica ions inc ease he sus ainabili y and
e iciency o sys ems.
B. Wo kplace EV Cha ging & Da a Analysis
This s udy is di ided in o wo pa s: he i s discusses he
use o AI in a ious enginee ing domains, and he second
ocuses on EV cha ging e ique e. Wo kplace EV cha ge s
can ge messy. Many employees sha e a ew s a ions, and
some igno e ules, which causes long wai s. To analyse his,
we ha e collec ed a public da ase s a ion_da a_da a e se.cs
om a public eposi o y (Kaggle). s a ion_da a has 3,395
sessions om 85 use s a 105 cha ge s in 25 wo kplaces. This
CSV ile includes columns use _id, s a ion_id, s a _ ime,
end_ ime, and kWh_used. We ha e conduc ed explo a o y
da a analysis on his CSV ile and iden i ied mul iple insigh s,
which a e discussed in he esul s and discussion sec ion o
he pape .
C. Signi icance o he S udy
In his wo k, he i s pa explo es he applica ions o AI
ac oss a ious enginee ing domains, ollowed by an
explo a o y da a analysis o a publicly a ailable da ase o
wo kplace EV cha ging s a ions. This analysis helps iden i y
pa e ns in employees' EV cha ging habi s. This analysis has
shown which s a ions ge c owded, when e ique e is igno ed,
and, using hese insigh s, he wo kplace can imp o e he
scheduling o sha ed esou ces. Wi h p ope scheduling,
p edic ions, and ules, EV cha ging becomes as e , mo e
eliable, and easie o manage. The de ailed analysis o he
s udy is discussed in he esul s sec ion o he pape .
II. LITERATURE REVIEW
The g ow h o A i icial In elligence (AI) and Machine
Lea ning (ML) has changed many enginee ing ields in he
las ew yea s [7]. This powe is demons a ed by one AI
amewo k ha has been success ully used ac oss a eas such
as aul de ec ion, medical sys ems, oil, and space a el [8].
This shows AI can eally help sol e p oblems ac oss di e en
job domains.
In s uc u al enginee ing, esea che s also ind a ious
applica ions o AI. An ex ensi e e iew o abou 4,000 pape s
by [9] concluded ha AI and ML a e widely used in a eas
such as p edic ing ma e ial p ope ies, enginee ing o
ea hquakes, wind, and i e, and assessing he heal h o
s uc u es. In hei ex ensi e e iew, he au ho s no ed ha
machine lea ning and deep lea ning achie e as e , mo e
accu a e esul s han olde me hods. This encou ages hem o
explo e AI u he in his ield. Simila ly, esea che s ha e
ound signi ican applica ions o AI in he oil and gas sec o .
Using AI o analyse da a mo e e ec i ely, iden i y isks, and
plan needed epai s can be done mo e accu a ely and
e icien ly. This leads o be e wo k, mo e us , and sa e
explo a ion [10].
I is wo h no ing ha AI is no jus o one ield. Ano he
s udy by [11] explo ed i s uses in physics, ma e ials
enginee ing, and medicine. They s essed ha AI can help
make decisions as e , p edic hings, and also au oma e asks.
Also, a s udy o o e 1,200 pape s by [12] in soil enginee ing
showed ha A i icial Neu al Ne wo ks a e widely used in he
ield. ANNs a e helping in a eas such as ounda ion design,
unnel cons uc ion, and slope s abili y assessmen . ANNs
handle unce ain y be e , which helps educe cu ing cos s
and make buildings sa e .
Bu e en wi h all i s me i s, he Use o AI and ML in
ma e ials and s uc u es enginee ing has been slow. This
p oblem a ises because ma e ials da a is o en messy and
doesn' align [13]. To mo e quickly adap AI in his domain,
he au ho s sugges ed ha AI be augh in enginee ing
schools. Beyond adi ional enginee ing, AI has quie ly
become pa o ou daily ou ines. We use i when we sea ch
he web, unlock ou phones wi h acial ecogni ion, o le ou
email apps il e ou spam [14].
In ci il enginee ing, esea che s ha e been explo ing how
big da a and deep lea ning can imp o e s uc u al
main enance, managemen , and design in la ge cons uc ion
p ojec s [5]. Howe e , he e a e s ill some eal challenges,
such as a s udy on using AI o disas e planning, inno a i e
design, and s uc u al inspec ion [15], which discussed
echniques such as de ec ing damage om came a images and
using machine lea ning o spo po en ial issues. A p ima y
conce n is ha he e a en’ enough eliable ways o alida e
hese esul s egula ly.
In i e enginee ing, a ious machine lea ning models, such
as SVMs, decision ees, and KNNs, as well as deep lea ning,
a e being ex ensi ely used o s udy how ma e ials pe o m
and how i es beha e [16]. This wo k shows ha AI can
imp o e i e sa e y checks. Machine lea ning is widely used
in s uc u al enginee ing because i can ack complex
pa e ns. A sepa a e s udy demons a ed how machine
lea ning is used o p edic ma e ial p ope ies, check i e
esis ance, moni o heal h, and conduc s uc u al analysis. I
ocused on using da a se s, Py hon code, and machine
lea ning ools o make i easie o enginee s o adop AI [17].
AI is also being used o enhance Elec ic Vehicle (EV)
cha ging spo s. Resea ch on sha ed EV cha ging s a ions in
he US examined incen i es and o he ac o s o encou age
mo e e icien cha ging habi s [18]. I ound ha changing
p ices, along wi h ag eed social ules, signi ican ly in luence
whe he people ollow hose ules. Addi ionally, o he
esea ch explo ed a ious AI models, including KNN,
Random Fo es , SVM, and LSTM, o inno a i e g id
managemen o EV cha ging [19]. I is obse ed ha he
LSTM model helped s abilise ol age, educe ene gy loss,
and manage cha ging o p e en o e loading he powe g id.
Reliabili y in EV cha ging is likewise essen ial [20].
analysed 12,720 EV s a ions ac oss 651 egions in he US and
used ML o ca ego ise consume opinions. P i a e s a ions
don’ always pe o m be e han public ones, indica ing ha
public EV in as uc u e needs imp o emen [21]. showed
ha eliabili y is a p ima y ba ie o
EV adop ion. This esea ch
sugges s ha ML can be highly
bene icial o analysing la ge-
In e na ional Jou nal o In en i e Enginee ing and Sciences (IJIES)
ISSN: 2319-9598 (Online), Volume-12 Issue-11, No embe 2025
19
Published By:
Blue Eyes In elligence Enginee ing
and Sciences Publica ion (BEIESP)
© Copy igh : All igh s ese ed.
Re ie al Numbe : 100.1/ijies.K113512111125
DOI: 10.35940/ijies.K1135.12111125
Jou nal Websi e: www.ijies.o g
scale EV da a and planning ad anced cha ging ne wo ks.
III. METHODOLOGY
This s udy is di ided in o wo main sec ions. The i s
discusses he use o machine lea ning and a i icial
in elligence in di e en enginee ing ields, while he second
pa concen a es on he explo a o y analysis o he public EV
cha ging da ase . We conduc ed he explo a o y da a analysis
by ollowing he s eps depic ed in Figu e 1. The sec ions ha
ollow p o ide g ea e de ail on each o hese s eps.
[Fig.1: Me hodology]
A. Da a Collec ion
We used he s a ion_da a_da a e se.cs da ase , a ailable
on Kaggle. I includes ho ough logs o EV cha ging sessions.
Among he many columns in he CSV ile a e sessionId, kWh
To al, dolla s, c ea ed, ended, s a Time, endTime, cha ge
Time H s, weekday, pla o m, dis ance, manage Vehicle,
acili y Type, daily usage lags (Mon-Sun), and epo ed Zip.
We used a ious p ep ocessing echniques and explo a o y
analysis on he clean da a o unco e insigh s.
B. Da a P ep ocessing
A good analysis is only possible on good da a. So be o e
pe o ming he explo a o y da a analysis, he da ase wen
h ough he ollowing p e-p ocessing s eps: -
i. Handling Missing Values:
The da ase was checked o missing alues, and he column's
mean was used o impu e hem.
ii. Da a Type Con e sion:
To accu a ely calcula e cha ging du a ions, he s a Time
and end Time ields in he CSV ile we e con e ed o
da e ime objec s. To con i m da a accu acy, he compu ed
ime di e ence was c oss-checked wi h he cha ge Time H s
column. To pe o m g oup-based analyses, he ca ego ical
columns in he CSV ile, such as weekday and acili y Type,
we e also con e ed o ca ego ical da a ypes.
iii. Fea u e Enginee ing:
Some new ea u es (columns) a e de i ed om he exis ing
ields o he CSV ile:
▪ A new column, Day o Week, is de i ed om he
weekday ield o s udy cha ging ends ac oss days.
▪ The dolla s column in he da ase was c oss-checked
wi h kWh To al o ensu e p icing consis ency.
▪ A new ield cha ging Ra e was also added o ep esen
he a e age powe consump ion a e pe cha ging
session.
C. Explo a o y Da a Analysis (EDA)
To unde s and cha ging beha iou and ack any
i egula i ies, we ca ied ou a ho ough da a analysis:
i. Desc ip i e S a is ics: a ious desc ip i e
pa ame e s, such as mean, median, min, max, and
s anda d de ia ion, a e compu ed o mul iple
columns o he da ase .
ii. Visualisa ion Techniques: To isualise ends
clea ly, a ious plo s such as his og ams, boxplo s,
coun plo s, sca e plo s, and hea maps a e used.
D. Peak Usage Analysis
To iden i y busies pe iods:
i. Peak Cha ging Day: The da ase is analysed o ind
ou he busies day o he week.
ii. Peak Cha ging Hou : Explo a o y analysis is also
pe o med o ind he peak cha ging hou .
iii. Pla o m and Loca ion Analysis: Analysis is also
ca ied ou o check he mos p e e ed pla o m and
loca ion by he employees o EV cha ging.
E. Cha ging Facili y Analysis
We also analyzed he acili y le el o gain mo e de ailed
insigh s. Fo example:
i. Session Coun by Facili y Type: We analysed how
many cha ging sessions occu ed o each acili y ype
o iden i y which cha ge ypes we e used he mos .
ii. Ene gy Consump ion by Facili y Type: We calcula ed
he o al ene gy consumed (in kWh) o each acili y
ype and used ba cha s o compa e and isualise how
much ene gy each ca ego y u ilised.
iii. Daily Usage Pa e ns: To unde s and how cha ging
ac i i y a ied h oughou he week, we examined he
columns ep esen ing Monday o Sunday and s udied
he daily ends and occupancy le els.
IV. RESULTS AND ANALYSIS
This sec ion discusses he esul s o he explo a o y analysis
ca ied ou on he EV cha ging da ase . Resul s a e p esen ed
in h ee main ca ego ies, namely desc ip i e s a is ics, peak
usage ends, and acili y-speci ic obse a ions. The
ollowing sec ions discuss each ca ego y in de ail: -
A. Desc ip i e S a is ics
The s a is ical analysis o he da ase p o ides key insigh s
in o cha ging session cha ac e is ics:
i. As pe he analysis, he maximum eco ded ene gy
consump ion is 23.68 kWh, while he a e age
consump ion pe session is 5.81 kWh.
[Fig.2: Dis ibu ion o Cha ging Du a ion]
ii. The A e age cha ging
ime is 2.84 hou s.
Howe e , he mos
A i icial In elligence and Machine Lea ning in Enginee ing Applica ions
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Published By:
Blue Eyes In elligence Enginee ing
and Sciences Publica ion (BEIESP)
© Copy igh : All igh s ese ed.
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DOI: 10.35940/ijies.K1135.12111125
Jou nal Websi e: www.ijies.o g
ex ended eco ded session las ed 55.23 hou s,
sugges ing a possible ou lie .
[Fig.3: Boxplo o Ene gy Consump ion]
I we obse e closely, he cha ging du a ions plo is igh -
skewed. This sugges s ha some sessions a e signi ican ly
longe han o he s, while mos a e ela i ely sho e . The
analysis shows ha an a e age o 5 kWh o ene gy is
consumed pe session, bu some sessions consume mo e han
20 kWh, which may be due o he la ge ba e y. Some
ou lie s sugges ha a small pe cen age o use s ha e used a
disp opo iona ely high amoun o ene gy.
B. Peak Usage T ends
To be e unde s and how cha ging demand changes o e
ime, we also analysed how sessions we e dis ibu ed ac oss
di e en weekdays and hou s o he day. The key indings
om his analysis a e as ollows:
[Fig.4: EV Cha ging Sessions by Weekday]
i. Busies Day: Thu sday saw he highes numbe o
cha ging sessions, wi h 735 in o al. This shows ha
mos use s p e e cha ging hei ehicles a ound
midweek (see Figu e 4).
ii. Quie es Day: Sunday, wi h jus 24 sessions, had he
lowes ac i i y. This d op clea ly poin s o less
cha ging on weekends.
iii. Peak Ene gy Use: Ene gy demand also peaked on
Thu sday, wi h o al consump ion eaching 4,235.13
kWh. This again highligh s ha midweek is he busies
cha ging pe iod.
[Fig.5: Cha ging Sessions by Time o Day]
[Fig.6: To al Ene gy Consump ion by Hou o he Day]
i . Peak Cha ging Hou : The da a shows ha cha ging
demand eaches i s highes poin a a ound 11 AM, when
o al ene gy consump ion is a i s maximum. This means
mos use s p e e o cha ge hei ehicles in he la e
mo ning. Figu es 5 and 6 clea ly show his end.
C. Cha ging Facili y Analysis
Cha ging beha iou also a ies ac oss di e en s a ion
ypes:
[Fig.7: Cha ging Sessions by Facili y Type]
i. Mos U ilised Facili y Type: F om he g aph, i is clea
ha ype 3 cha ging s a ion is he mos p e e ed
acili y, wi h 1,832 sessions.
ii. Facili y wi h he Highes To al Ene gy Consump ion:
F om ig.7, i is clea ha ype 3 is he main cha ging
in as uc u e, as e idenced by i s 10,703.24-kWh
o al consump ion.
[Fig.8: To al Ene gy Consump ion by Facili y Type]
iii. Facili y wi h he Lowes To al Ene gy Consump ion:
F om he analysis, i is also e iden ha he Type 4
cha ging s a ion has consumed only 779.44 kWh,
which indica es ha hese people ha e less eliance on
his acili y ype.
In e na ional Jou nal o In en i e Enginee ing and Sciences (IJIES)
ISSN: 2319-9598 (Online), Volume-12 Issue-11, No embe 2025
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Published By:
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and Sciences Publica ion (BEIESP)
© Copy igh : All igh s ese ed.
Re ie al Numbe : 100.1/ijies.K113512111125
DOI: 10.35940/ijies.K1135.12111125
Jou nal Websi e: www.ijies.o g
D. Key Insigh s
The key akeaways om his analysis a e:
i. Weekly T ends: Cha ging s a ions a e busie on
weekdays, wi h Thu sday seeing he highes numbe
o sessions and Sunday being he quie es .
ii. Peak Hou s: Mos cha ging happens in he la e
mo ning, sugges ing ha EV owne s p e e o plug in
hei ehicles a ound mid-day a he han ea ly in he
mo ning o la e a nigh .
iii. Facili y Usage: Type 3 cha ging s a ions u ned ou o
be he mos popula . They eco ded he mos sessions
and he highes ene gy use.
V. CONCLUSION
This s udy explo ed how a i icial in elligence (AI) is being
applied in di e en a eas o enginee ing, wi h a special ocus
on an elec ic ehicle (EV) cha ging
da ase om Kaggle. Th ough explo a o y da a analysis, we
unco e ed insigh s such as which days see he mos cha ging
ac i i y, which s a ions a e he busies , and wha imes mos
people p e e o cha ge hei ca s.
These obse a ions can help EV cha ging s a ions,
especially hose a wo kplaces, plan hei ope a ions mo e
e ec i ely. Fo example, wo ks a ions could in oduce small
discoun s o ewa ds o use s who cha ge du ing less busy
hou s. This simple s ep could help educe ush-hou load and
make ene gy use mo e balanced. The s udy shows how
bene icial AI and da a analysis can be in sol ing eal-wo ld
p oblems. As mo e people swi ch o EVs, his pa e n will
de ini ely help wo kplaces u ilise hei cha ging acili ies
mo e e ec i ely.
DECLARATION STATEMENT
A e agg ega ing inpu om all au ho s, I mus e i y he
accu acy o he ollowing in o ma ion as he a icle's au ho .
▪ Con lic s o In e es / Compe ing In e es s: Based on
my unde s anding, his a icle has no con lic s o
in e es .
▪ Funding Suppo : This a icle has no been unded by
any o ganiza ions o agencies. This independence
ensu es ha he esea ch is conduc ed wi h objec i i y
and wi hou any ex e nal in luence.
▪ E hical App o al and Consen o Pa icipa e: The
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A i icial In elligence and Machine Lea ning in Enginee ing Applica ions
22
Published By:
Blue Eyes In elligence Enginee ing
and Sciences Publica ion (BEIESP)
© Copy igh : All igh s ese ed.
Re ie al Numbe : 100.1/ijies.K113512111125
DOI: 10.35940/ijies.K1135.12111125
Jou nal Websi e: www.ijies.o g
AUTHOR’S PROFILE
D . Himanshu Gup a is an Assis an P o esso in he
Depa men o Compu e Science & Enginee ing a Roo kee
Ins i u e o Technology, Roo kee. He holds a PhD in
Compu e Science, along wi h a B. Tech and wo M. Tech
deg ees in ela ed disciplines. Wi h o e 17 yea s o
academic and esea ch expe ience, his a eas o in e es
include Ja a p og amming, machine lea ning, and c yp og aphy. He has
au ho ed mo e han 30 esea ch pape s in epu ed SCI- and Scopus-indexed
jou nals and has published se e al Indian pa en s. He has also comple ed
mul iple NPTEL ce i ica ions, e lec ing his dedica ion o con inuous
lea ning and academic excellence.
D . Sanjee Tayal, is a dis inguished academician and
Head o he Depa men o Compu e Science a S.D.
College o Managemen S udies, Muza a naga . Wi h
o e 20 yea s o ich eaching and adminis a i e
expe ience, he has played a pi o al ole in shaping he
depa men 's academic amewo k and nu u ing young
minds in compu e science. D Tayal holds a PhD in Compu e Science and
has con ibu ed o esea ch in A i icial In elligence, Da a Mining, and
So wa e Enginee ing. His commi men o academic excellence, esea ch
ad ancemen , and holis ic s uden de elopmen e lec s his passion o
educa ion and con inuous lea ning. Unde his leade ship, he depa men has
wi nessed signi ican g ow h in academic inno a ion, indus y collabo a ion,
and s uden achie emen s.
D . Pa ag Jain, is a dis inguished academician and he
cu en Di ec o o Roo kee Ins i u e o Technology (RIT),
Roo kee — a NAAC A++-acc edi ed ins i u ion and he
only one in U a akhand o achie e his dis inc ion. An
accomplished leade in Compu e Science, he holds an M.
Tech and a PhD in he ield. He has se ed as a Senio
Pos doc o al Resea che a he p es igious Asian Ins i u e o Technology
(AIT) in Bangkok. His isiona y leade ship has ele a ed RIT o na ional
p ominence, placing i among he op 4% ins i u ions in India. D Jain’s
con ibu ions ha e b ough bo h academic excellence and global ecogni ion
o he s a e o U a akhand, making him a espec ed igu e in he Indian
highe educa ion landscape.
D . Abhay Bha ia, is an accomplished academician and
esea che , se ing as an Associa e P o esso in he
Depa men o Compu e Science and Enginee ing a
Roo kee Ins i u e o Technology, U a akhand. Wi h o e
13 yea s o eaching and esea ch expe ience, he holds a
B. Tech and an M. Tech in Compu e Science, and a PhD
in Wi eless Senso Ne wo ks. An ac i e IEEE membe , he has published o e
34 pape s, au ho ed 11 book chap e s, and iled se en pa en s. His au ho ed
books include Fundamen als o IoT and P ac ical App oach o Machine
Lea ning wi h Tenso Flow. His esea ch in e es s include A i icial
In elligence, Machine Lea ning, and Wi eless Senso Ne wo ks.
D . Lokesh Kuma , is a dedica ed academician and
esea che cu en ly se ing as an Associa e P o esso in
he Depa men o Compu e Science and Enginee ing a
Roo kee Ins i u e o Technology (RIT), Roo kee,
U a akhand. Wi h ex ensi e expe ience in eaching and
esea ch, he has made no able con ibu ions o A i icial
In elligence, Machine Lea ning, and Da a Science. D Kuma holds a PhD in
Compu e Science and has published se e al esea ch pape s in epu ed
na ional and in e na ional jou nals. Passiona e abou inno a ion and quali y
educa ion, he ac i ely men o s’ s uden s in esea ch and eme ging
echnologies, os e ing academic excellence and p o essional g ow h.
Disclaime /Publishe ’s No e: The s a emen s, opinions and
da a con ained in all publica ions a e solely hose o he
indi idual au ho (s) and con ibu o (s) and no o he Blue
Eyes In elligence Enginee ing and Sciences Publica ion
(BEIESP)/ jou nal and/o he edi o (s). The Blue Eyes
In elligence Enginee ing and Sciences Publica ion (BEIESP)
and/o he edi o (s) disclaim esponsibili y o any inju y o
people o p ope y esul ing om any ideas, me hods,
ins uc ions, o p oduc s e e ed o in he con en .
