Révolange: Your Fashion Guide

International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 09 Issue: 06 | June 2022 www.irjet.net p-ISSN: 2395-0072
© 2022, IRJET | Impact Factor value: 7.529 | ISO 9001:2008 Certified Journal | Page 2180
Révolange: Your Fashion Guide
Mohammed Farooq Abdulla FM1, M. Nishant2, K. Smethaa3, K. Lohit Shakthi4
1Assistant Professor-II, Department of Information Science and Engineering, Kumaraguru College of Technology,
Coimbatore - 641048, Tamil Nadu, India.
2-4UG Scholar, Department of Information Science and Engineering, Kumaraguru College of Technology,
Coimbatore - 641048, Tamil Nadu, India.
---------------------------------------------------------------------***---------------------------------------------------------------------
Abstract - Self-expression, self-empowerment, and self-
assurance are the only things that fashionisabout. Acountry's
culture is reflected in its fashion. Personalization, user
experience, search engine approximation, missing product
information, missingphonyproductreviews, tracking, support,
and no live chat option are all serious faults in online fashion
retail stores. As a result, customers lose faith and interestin E-
commerce and M-commerce, as well as fashion. Another
significant disadvantage is that people spend a significant
amount of time online browsing products, and some of them
wind up with items that are incompatible with their bodies. A
model that suggests the most appropriate attiredepending on
gender, body size, body shape, and skintone is suggested to
gain a better understanding of the circumstance.
Key Words: Cloth Recommendation System
1. INTRODUCTION
The fashion industry is one of the most underrated One of
the most undervalued businesses is design. Design has
forever been a significant piece of how individuals
communicate their thoughts, and it is a very successful
device of impact. The style business plans, fabricates, and
sells dress, footwear, and adornments. Whatisgoingonwith
style? Since design works on individuals' lives as well as
permits them to have an independent mind. The design
business contributes fundamentally by permitting us to
articulate our thoughts and our innovativeness. Style is a
main impetus on the planet's interaction, making things
move dangerously fast. Individuals, then again, excuse style
and have never pondered the story they need to tell with
their garments. The design business makes a larger number
of occupations and more cash than some other industry. A
few parts of the style business require improvement, as the
business is far from perfect.
In numerous ways, the idea of web based business has
changed the style business. One can shop from any area on
the planet and integrate their #1 image into their closet. To
this end E-business gateways that have helped deals of
provincial attire in India, from unique variations of ethnic
wedding dresses to customary outfits, have brought Indian
craftsmanship legacy into the spotlight in the beginning
phases of shopping in the advanced time. The ascent of web
based business is one of the essential explanations behind
the prevalence of customary and territorial apparel. On
account of disconnected shopping, deals partners give close
consideration to their clients and give total item data. In the
event that clients have any inquiries, they can find out if to
get it. Be that as it may, this isn't true with internetshopping.
Clients can see the picture, yet they can peruse the portrayal
and check client surveys. Clients wind upinvestinganexcess
of energy online because of an absence of personalisation,
like shopping, particularly for style, which can transform
into a long distance race of looking over and clicking down
dark holes looking for the perfectoutfit.
There might be a billion issues, however because of
innovation, we can now effectively devise a zillion
arrangements. AI calculations are being utilized in this
undertaking since they permit internet business
organizations to make a more customized and redone
experience. Personalization keeps clients faithful toa brand,
so clients today favor a profoundly customized client
experience. The client additionallydoesn'thaveanydesireto
be dealt with like various different clients. On the off chance
that legitimate consideration isn't given, they will ultimately
change to another brand.Itemproposals,customizedlanding
page ideas, arrangements and gives thoughts, and
customized email suggestions will urge clients to purchase
from a particular brand. AI calculations are utilized in the
hunt cycle, making it simpler for clients to find precise items
that they have composed into the pursuit bar. The results
will likewise be more significant and pertinent.
2. LITERATURE SURVEY
Certifies that the system will needto endorsetheclientto
pick outfits that suit their personality to reduce the outfit
decision and delay [1]. The structure relies upon two
modules: the first is to find the part for the utilization of
outfits like customary, western,daytimeornight, etc,and the
ensuing component is to registerthebodyassessmentlimits.
The proposed system will have a picture getting byusingthe
HAAR part or data contraption which gets body limits from
clients.
Certifies that the structure will help clients with noticing
sensible arrangements of garments considering particularly

complex nuances like style, plans, varieties, surfaces, etc in
like manner recollecting client's credits like age,
composition, most loved variety, etc [2]. It endeavorstohelp
the client with wearing garments that are proper for
occasions and helps the client with purchasing those
garments that would suit their style.
The audit on the web shopping and shopper devotion on
Myntra.Myntra, an Indian-style web business focus got
comfortable Bangalore, Karnataka, India.Inthis paper,it has
been explained the way that Internet Marketing has
redesigned associations all around the planet [3]. Electronic
Marketing in its most un-complex terms implies the
promoting and selling of work and items including the web
as the arrangements and allocation medium. The Internet
has diminished the world into an overall town andhasmade
distance inconsequential and time districts negligible in
excess of an irritation. Insists that the idea of garments is
achievedusingtheAdvanced client-basedagreeableisolating
(AUCF) computation [4]. The AUCF computation presents a
client thing associated overview, which can beat the issue of
the tremendous time unpredictability. Taking into account
the impact of different reputation of things, the Advanced
client-based agreeable isolating (AUCF) estimation is
prepared for circulating the antagonistic outcomes of
renowned things, they can construct the idea consideration.
The assessment resultsshow howtheAUCF computation out
and out forms the proposition incorporation and accuracy.
Fig -1: Architecture of Real-Time Clothing
Recommendation
This is about the lead of the purchaser for clothing, the
customer thing buyingconductisimpactedbythehugethree
components like social, mental, and individual components
[5]. In the basic periods of web shopping, purchasers were
not excited about buying garments online as it has various
imperatives. Regardless, nowadays, the business place can
vanquish countless the cutoff points and gather assurance
among the customers to buy on the web. By and by the
example of e-shopping has become fundamental
characteristics with the buyers. The game plan of the Indian
e-business is going on a round outing flip to get back to
where it started to its fundamental stages, in any case, this
time the structure has changed close by the size and
perception of the business place as well. By taking into
account a fundamental course of picking what to wear, this
undertaking gives a phase to discuss the probable impact of
the development in ordinary day to day presence direct [6].
Shrewd storerooms are practicallytypical nowadaysandare
particularly useful to the customers. Locales are given by
clothing retailers and are used to encourage people to make
purchasing decisions. Online media getting sorted out is
moreover considered as they partner their utilizationtotalk
with their sidekicks, accomplices, companions,teachers,and
families in different ways by sharing photos, calls, accounts,
regions, status, and many different pieces of their life. The
essential mark of this adventure is to arrange, make, and
survey a unique thought for managing the buying, storing,
and wearing of garments by using client-centered
procedures and strategies.
The new techniques in therecommenderSystemattempt
to conjecture the inclination of what clients would provide
for a thing [7]. In view of past buys by the client the
prescribed framework attempts to foresee a comparable
item by utilizing a Content-Based approach. Cooperative
Filtering tracks down the client's past buys and predicts
comparable sorts of items. In style, space clients may not
search for comparative items which have been bought
before. Thus, this conductsuggeststhatthething'ssubstance
likeness between the things previously bought by the client
isn't sufficient to make exact expectation.
In this Recommendation System for outfit Selection, the
framework proposes the fitting outfits which will suit
customers' personality [8]. The Recommendation for the
decision of their outfits depends on various genuine limits
that make with the learning of available named and
unlabelled data. There are twomodulesinthiscollaboration;
In the main module is to see the components for use of
outfits like traditional,western,helpful,daytimeornight, etc,
the subsequent module is for figuring the body assessment
limits. The framework will have picture getting by including
HAAR component or data device for getting body limits
suitably. We mean to a gathering and concentrate the best
outfits from the framework by using the HIGEN MINER
computation. By social affair relatively few experiences
concerning the client to endorse legitimate outfits to them.
A superior suggestion computation named Advanced
User-based Collaborative Filtering (AUCF) estimation is
proposed and is executed in thedresssuggestionframework
[9]. An effective suggestion framework is truly fundamental
for clients. Client based Collaborative Filtering (UCF)
estimation is for the most part used to expect the tendencies
of the clients. As web business advancement is
comprehensively creating. We can vanquish the issue of
immense time complexities through, Advanced User-based
Collaborative Filtering (AUCF) estimation presents a client
thing associated once-over. Style firms have permitted their

strategy to give redid experiences to their clients by using
progressed CAD devices like CLO 3D, Marvelous Designer,
Brow wear, Lectra, and a ton something different for
arranging the garment and build 3-Dimensional image for
the changed piece of clothing as well as online organizations
to be united with the web and convenient basedapplications
[10]. The framework structure is moved toward the client's
biometric profile and recorded data of thing solicitations of
the clients. The plan for this proposal framework dependson
different Data Mining techniques like clustering, gathering,
and alliance mining. The substance based (CB) separating
strategy prepares the suggestion by matching the similar
substance of the client's optimal things. The aftereffects of
the tests are beneath in Figures 2 and 3.
Fig -2: Comparison of Precision (UCF, AUCF)
Fig -3: Comparison of Recall (UCF, AUCF)
The precision of the arrangeddatasetusingdifferentdata
mining methods like bundling, gathering connectionmining,
and data mining estimations [11].Taking the most ideal
decision while purchasing and furthermore fabricating the
business, using a proposal framework to help purchasers
with picking their outfit easily. To perform propertydecline,
they have used cfsSubsetEval, consistencySubsetEval and
chisquaredAttributeEval. The computations that areused to
portray the dataset are Random Forest, Naive Bayes, zeroR,
Multilayer Perceptron, RBF Network, and AdaboostM1. The
dataset is changed using SMOTE assessment to gain higher
accuracy's and moreover quality diminishing which is
performed to dissect the precision's they get.
Proposal frameworks are an imaginative game plan that
vanquishes the limitations of the web business
organizations. They use client direct information, and thing
information to perceive the client tendencies, and
proactively propose things that they are probably charmed
to buy [12]. This paper is about a certifiable world
cooperative separating suggestion framework executed
commonly in Korean plan associations which sells style
things both on the web and disengaged.
It expresses that garments are suggested in light of client
shopping history. Clients can purchasegarmentstosupplant
or supplement the garments that theypreviouslypurchased.
Additionally, their organization sells similar items both on
the web and disconnected. The proposal of garments is
accomplished utilizing cooperative separating [13]. Avows
that there are a lot of AI calculations that are utilized for
suggestion. Picking the right calculation for an application is
a troublesome interaction. This paper reasons that the
Bayesian calculation and Decision tree calculation are
broadly utilized in suggestion because of their relative
effortlessness [14]. States that there is a quick development
in design centered informal organizations and web based
shopping. The creator of this paper has attempted two
difficulties, one to suggest individual garments in lightof the
client's advantage and the second to match the suggested
garments for more personalization. This is accomplished
utilizing a profound completely associated brain
organization and profound tangled brain network [15].
Declares that the garments are prescribed to clients in view
of the client's elements as well as founded on the garments'
audit. Their model incorporates two phases, one to remove
the client's elements and the secondtoarrangethegarments
as positive or negative. This is accomplished utilizing a
profound brain organization. More datasets are utilized to
prepare this man-made reasoning model [16].
Broadcasts that design situated internet shopping is a
quickly developing field. Likewise, countless garments are
displayed to clients on internet shopping stages. Toplanand
give garments as per clients' necessities one ought to carry
out AI calculations to suggest garments.Thisisaccomplished
utilizing a characteristic language handling calculation. For
suggestion, the rightdatasetsarerecoveredutilizingblended
type grouping calculations [17]. Declares that sizefitismore
significant contrasted with style fit. More returns of
garments occur in web based shopping because of size and
fit issues. Likewise, the client needs to depend on pictures
and surveys in web based shopping as it were. To carry an
answer for this issue, the creator proposes a size suggestion
framework. This framework prescribes the right size to
clients in view of past request history and the substance in
the garments portrayal. This is accomplished utilizing angle
helping grouping model and gram-based word2vec model
[18].

Confirms the components which are influencing the
clients to change to M-trade stages from E-business locales.
It furthermore states how Myntra performs through M-
Commerce stages in the style clothing industry [19]. Insists
that web promoting is very famous nowadays. Carrying on
with work on the web enjoys incredible benefits like more
benefit, more deals, and more creation. This paper talks
about how Myntra acts in the design business [20]. Affirms
that it's anything but a simple undertaking to pick what to
wear. Clothing is an essential piece of life. A fundamental tip
is to dress as per one's body shape. Wearing garmentsasper
body shape speeds up great highlights in the body. This
paper gives an unmistakable image of the similarity of body
shapes with the separate sorts of garments. The objective is
to prescribe right garments as indicated byone'sbodyshape
[21]. States that body shape assumes a key part in style
dressing. This paper is an investigation of the connection
between's body shapes and material pieces of clothing and
this is accomplished utilizing a novel and energetic multi-
photo method for managing survey the body conditions of
each and every client and collect the contingent model of
dressing characterizations with a given body shape [22].
Declares that style is a progression of shorttrends.
Design reflects how individuals characterize themselves.
Picking the right outfit in the right tone lights up and
smoothens the coloring, limits the face lines, and gives a
solid gleam to the skin. Certain individuals battle to track
down the right garments for themselves. This paper is a
concentrate on various complexions and the dresses that
match the complexion in like manner [23].
Declares that individuals purchase garments asperwhat
they put stock in their garments to communicate. Clothing is
an approach to putting themselves out there. This paper is a
mental report about garments tone and why individuals
wear them. The brain science behind dress is ordered into
three topical classifications: a) The significance of varieties
in apparel brain research; b) The socio-mental effect of
attire; and c) Gender (in) Equality in regards to attire [24].
Convolutional Neural Networks(CNN)haveshownbetter
precision in distinguishing when contrasted with people in
numerousvisual acknowledgmenterrands.Theconsequence
of this work has outperformed the people execution in
unambiguous and testing errands, the grouping task in the
ImageNet dataset which has 1000 classes . There has been
gigantic improvements in recognizing the presentation,
assembling all the more remarkable models, and planning
viable systems against overfitting are the fundamental
reasons that have made headway in these two specialized
bearings.Neural organizations are turning out to be more
compelling even with greater ensnarement new nonlinear
enactments, and refined layer plansforfittingdataset.Better
speculation is accomplished by useful regularization
strategies, forceful information expansion, and enormous
scope information [25].
Profound Convolutional Neural Networks (DCNN) have
directed to a progression of forward leaps in picture
classification.Theprofundity oftheorganizationisextremely
fundamental and driving consequences of the difficult
ImageNet dataset are exploit "exceptionally profound"
models with a profundity of sixteen to thirty.Other major
visual acknowledgment undertakings have likewise
enormously been helped by extremely profound
models.Bynormalised instatement and moderate
standardization layers which allows to the organizations
with 10 layers to start gathering StochasticGradientDescent
(SGD)with backpropagation. At the point when more
profound organizations can begin gathering, breakdown
issues have been uncovered with the organization
profundity expanding, precision gets soaked and afterward
corrupts fastly. Startlingly, such breakdown isn't brought
about by overfitting, and adding more layers to a right
profound model prompts higher preparation blunder. The
breakdown demonstrates that not all frameworks are
consistently simple to create. Allow us to consider a
shortsighted engineering and more profound identical adds
more layers onto it. There is an answer by development to
the more profound model: the layers which are added are
character planning, and the other recently introducedlayers
are duplicated from the learned shortsighted model.The
presence of this built arrangementdemonstratesthata more
profound model ought to create no higher preparation
blunder than its oversimplified partner [26]. Designs
handling unit (GPU) for AI, present day GPU figuring and
equal registeringtechniques havemonstrouslyexpandedthe
possibility to prepare the Convolutional Neural Networks
(CNN) models, are prime to their ascent in research and in
industry.
We are presently ready to prepare networks with
billions, or trillions , of boundaries on exceptionally huge
datasets like ImageNet, generally effectively. Picture
grouping is one of the phenomenal issues in PC vision:when
a picture is given to it , it perceives just like an individual
from one of different fixed classes. Picture arrangement is a
legitimate issue somewhat in lightofitsendlessapplications.
Independent or self-overseeing driving requirements quick
picture grouping as a uniquely basic crude. In current web-
based entertainment and photograph sharing/capacity
applications like Meta and Google Photos use picture order
to improve and individualize the clients experience on their
items. The picture grouping issue is likewise illustrative of a
few normal difficulties in PC vision, for example,
intraclassvariation, occlusion, deformation, scalevariation,
viewpoint variety, and enlightenment. Techniques that
functionadmirablyforpicturecharacterizationaresupposed
to mean strategies that will help up other key PC vision

undertakings also,likeidentification,restriction,anddivision
[27].
2.1 CNN Frameworks
Convolutional Neural Networks (CNNs) are a model or
strategy for Deep Learning (DL), and thusly, Deep Learning
is a part of Machine Learning (ML). In this manner, while
discussing a system for Convolutional Neural Networks, we
need to discuss a Machine Learning Framework overall. A
Machine Learning Framework is a connection point, library,
or device that permits in making Machine Learning models.
There are assortments of Machine Learning frameworks,
which are unique in relation to other people.
Underneath recorded are a few most popular structures
for Machine Learning:
 TensorFlow: Developed by Google is an open
source ML library TensorFlow gives an assortment
of workflows to create and prepare models
usingPython,Java,C++,JavaScript.
 Caffe: Developed by Berkeley AI Research
(BAIR)Convolutional Architecture for Fast Feature
Embedding (Caffe) is a DL system. An open-source,
under a Berkeley Standard Distribution (BSD)
licence.Which is written in C++, with a
Pythoninterface.
 Theano:Theano has been one of the most utilized
CPUand GPU numerical compilers, particularly in
Machine Learning.It is a Python library which will
permit to define, improve, and assess numerical
articulations including complex exhibits efficiently.
 PyTorch: This is utilized by Facebook, IBM, among
others. PyTorch upholds the Lua programming
language for the UI. It is all around upheld on
significant cloud stages, giving frictionless turn of
events and extremely simple scaling. PyTorch
isopen-source.
 MatLab:MATLAB tool kit givesa systemtoplanning
and carrying out Deep Neural Networks(DNN)with
the calculations, pre-prepared models,and
applications. It can exchange models with
TensorFlow and PyTorch, and furthermore import
models fromTensorFlow-Keras.
 MatConvNet: This is a MATLAB tool stash carrying
out Convolutional Neural Networks (CNNs) for PC
vision applications.It can run cutting edge
Convolutional Neural Networks (CNNs) models,
pre-prepared Convolutional Neural Networks for
picture classification, segmentation,face
acknowledgment, and text location [28].
2.2 CNN for Face Recognition
Face acknowledgment is one of the main PC vision errands
from the 1970s. Face acknowledgment frameworks for the
most part have four steps.Given an information picture with
at least one faces, a face locator recognizes and disconnects
each face. Then, at that point, each face is pre-handled and
arranged utilizing either 2D or 3D demonstrating strategies.
Then, a component extractor separates highlights from
arranged countenances to secure a low-layered
representation.Lastly, a classifierwill makeforecastsinview
of the low-layered portrayal. The way to getting great
execution for face acknowledgmentframeworks isobtaining
a viable low-layered portrayal. Face acknowledgment
frameworks utilize hand-created highlights. Lawrence et al.
first proposed involving CNNs for face acknowledgment. By
and by the exhibition of face acknowledgment frameworks,
or at least, Meta's DeepFace and Google's FaceNet, are
absolutely founded on CNNs. Other CNN-based face
acknowledgment frameworks are eased up Convolutional
Neural Networks (CNN) and Visual Geometry Group (VGG)
Face Descriptor.
Alternatively utilizing hand-created highlights, CNNsare
straightforwardly applied to RGB pixel esteems and utilized
as a component to take out and give a low-layered portrayal
order an individual's face. To standardize the information
picture to make the face hearty to various view points,
DeepFace models a face in 3D and adjusts it to show up as a
front facing face.Thenormalised input is taken care of to a
solitary convolution-pooling-convolution filter.3 privately
associated layers and 2 completely associated layers are
utilized to make last predictions.faceacknowledgmentthese
days utilized in versatile processing which is an
exceptionally fascinating subject. While DeepFace and
FaceNet stay private and are of huge size, OpenFace offers a
lightweight,constant,andopen-sourcefaceacknowledgment
framework with serious exactness, which is reasonable for
portable processing [29].
3. PROPOSED WORK
3.1 Clothes Recommendation System
The garments suggestion framework suggests garments
utilizing the subtleties that are gottenfromthephotograph.A
photograph of client is taken in application itself at first,
when the client join or the client can import their image. The
photograph which is importedisutilizedtoacquiresubtleties
like body size, bodyshape,complexion,orientation.Thereare
different subtleties which are given by client incorporates
name, address, telephone number and email id.

Fig -4: Clothes Recommendation System
Table -1: Algorithm Comparison
Decision Tree
and Random
Forest
(Proposed)
k-Nearest
Neighbor
Algorithm
(Existing)
Linear
Regression
(Existing)
DT:
Tree-like Structure
Quick and easy to
implement
RF:
To increase
accuracy in real-
time
Complications in
classifyingproducts
cause the scatter
plots are not in
groups
Complications
in classifying
products
cause the
scatter plots
are not in
groups
The garments are prescribed to clients in view of body
size, body shape, complexion, and orientation. This proposal
framework is partitioned into two modules.
 Module 1: This provides users a normal shopping
experience in which the garments are not
customized and not redid.
 Module 2: This recommends clothes
recommendations from the subtletiesthataregiven
by the client. The proposal of garments is
accomplished utilizing AI calculations, for example,
the Decision tree truck calculation, Random Forest,
and Logistic relapse.
3. CONCLUSION
Open to shopping is slowly turning into a reality, all you
want is a gadget that should be associated with the web and
a helpful location where you can accept your arranged
things. Giving proposals in view of a client's past request
history, inclinations, and individual data will makeitsimpler
for the client to make a buy and this application makes the
essential ideas by considering boundaries like orientation,
body size, body shape, and complexion utilizing ML models.
While buying items over the web, you have themostchoices.
You are not restricted to what a singularstoresellsorstocks.
Expecting a thing exists, you can in all likelihood getitonline
somewhere anyway making an electronic purchase,
distortion and discount misrepresentation are a consistent
bet. There are likewise critical worries about the security of
purchaser information gave to retailers. To guarantee safe
shopping, counterfeit items and retailers should be
authoritatively verified. Except if you pick in-store pickup,
web based shopping implies that the arranged things are
conveyed straightforwardly to your entryway. You don't
need to stress over heading to your objective, paying forgas,
tracking down stopping, or holding up in line to be served
regardless, when you buy something on the web, you can't
offer it a chance first. You also can't contact or see a thing
exceptionally shut with your own eyes. With respect to
specific kinds of things, this can be an immense weight. Our
application will be modified to show itemsthatarepertinent
to the client's very own data and inclinations. Subsequently,
the client doesn't need to be worried about item fit.
REFERENCES
[1] Atharv Pandit, ManavJain, Kunal Goel, Neha Katre, “A
Review on Clothes Matching and Recommendation
Systems based on User Attributes,” Department of
Information Technology D. J. Sanghvi College of
Engineering Mumbai, India.
[2] Samit Chakraborty, Md. Saiful Hoque, Naimur Rahman
Jeem, Manik Chandra Biswas, Deepayan Bardhan and
Edgar Lobaton, “Fashion Recommendation Systems,
Models and Methods: A Review”, Wilson College of
Textiles, North Carolina State University, Raleigh, NC
27695, USA.
[3] Anupindi Sravani, “M-Commerce Platform and Fashion:
Myntra.com,” Kirloskar Institute of Advanced
Management Studies Harihar-577601.
[4] Dr. T. Shenbhagavadivu, M. Sharanya, S. SaiPriyanka, V.
Ajithkumar, A. Irshad Khan, “A Study on Online
Shopping and Customer Satisfaction on Myntra,” Sri
Krishna Arts and Science College, Coimbatore - 641008.
[5] Illa Pavan Kumar, Swathi Sambangi, “Content Based
Apparel RecommendationSystemforFashionIndustry”,
Kakatiya University and M.Tech. (CSE) from K L
University.
[6] Prof. Mahalaxmi K. R., Nagamanikandan P. A, “Study on
Online Shopping Behavior for Apparel: Literature
Review,” Anna University – BIT Campus, Trichy -
620024.

[7] Dina Etebari, “Intelligent Wardrobe: Using mobile
devices, Recommender systems and social networks to
advise on Clothing Choice,” University of Birmingham.
[8] Gabriele Prato, “New Methodologies for Fashion
Recommender Systems,” School of Electrical
Engineering and Computer Science.
[9] Shiv H. Sutar, Akshata H. Khade, “Recommendation
System for Outfit Selection (RSOS),” University of Pune
city Pune in Information Technology Nanded,
Maharashtra, India.
[10] Yu Liu, Jingwen Nie, LexiXu,Yue Chen, Bingyu Xu,
“Clothing Recommendation System Based on Advanced
User-Based Collaborative Filtering Algorithm,” Beijing
University of Posts and Telecommunications, Beijing
100876, China.
[11] A Shukla Sharma, Ludovic Koehl,Pascal Bruniaux,Xianyi
Zeng, “Customized Garment Fashion Recommendation
System Using Data Mining Techniques,” GEMTEX
Laboratory, France.
[12] N. Srinivasa Gupta, D. Sai Thanishvi and B. Valarmathi,
“Outfit Selection Recommendation System using
Classification Techniques”, Vellore Institute of
Technology, Vellore (Tamil Nadu), India.
[13] Hyunwoo Hwangbo, Yang Sok Kim, Kyung Jin Cha,
“Recommendation system development for fashion
retail e-commerce,” Graduate School of Information,
YonseiUniversity, 50 Yonsei-ro, Seodaemun-Gu, Seoul
03722, South Korea.
[14] Ivens Portugal, Paulo Alencar, Donald Cowan, “The Use
of Machine Learning Algorithms in Recommender
Systems: A Systematic Review,”DavidR.CheritonSchool
of Computer Science University of Waterloo Waterloo,
ON, Canada.
[15] Tong He, Yang Hu, “FashionNet: Personalized Outfit
Recommendation with Deep Neural Network”,
University of California, Los Angeles, University of
Electronic Science and Technology of China.
[16] Ying Huang A, Tao Huang, “Outfit Recommendation
System Based on Deep Learning,” School of Information
Engineering, Wuhan University of Technology, Wuhan
430070, China.
[17] Maria Th. Kotouza,“Towards FashionRecommendation:
An AI System for Clothing Data Retrieval and Analysis,”
Part of the IFIP Advances in Information and
Communication Technology book series (IFIPAICT,
volume 584).
[18] G. Mohammed Abdulla, Sumit Borar, “Size
Recommendation System for Fashion E-commerce,”
Myntra Designs, India.
[19] Dr. Prathiba Devi R, Sindhu. P, “Fashion
Recommendation Study on the Clothing Style for The
Personal Body Shape,” PSG College of Technology,
Coimbatore, 641004, Tamil Nadu, India.
[20] Hosnieh Satta, GerardPons-Moll,MarioFritz,“Fashionis
Taking Shape: UnderstandingClothingPreferenceBased
on Body Shape from Online Sources,” Max Planck
Institute for Informatics Saarland Informatics Campus,
Saarbrucken, Germany.
[21] Economy Duje Kodzoman, “The Psychology of Clothing:
Meaning of Colors”, University of Zagreb, Faculty of
Textile Technology, Zagreb, Croatia.
[22] Venkatesh J, Vivekanandan. K, Balaji. D, “Fashion is
Taking Shape: UnderstandingClothingPreferenceBased
on Body Shape from Online Sources,” School of
Management Studies, Anna University of Technology
Coimbatore, Jothipuram Post, Coimbatore - 641 047.
Tamil Nadu, India.
[23] Patrizia Gazzola, Enrica Pavione, Roberta Pezzetti,
Daniele Grechi, “Trends in the Fashion Industry. The
Perception of Sustainability and Circular”. Department
of Economics, University of Insubria, 21100 Varese,
Italy.
[24] Fernando H.S.M, Kumara H.H.S.N,MendisH.I.A,Wettawa
W.M.B.S, Samarasinghe H.M.U.S.R, “Relationship among
emotions, mood, personality and clothing: an
exploratory study”.
[25] KaimingHe, XiangyuZhang, ShaoqingRen, JianSun,
“Delving Deepinto Rectifiers: Surpassing Human-Level
Performance on ImageNet Classification,” Microsoft
Research.
[26] Kaiming He, Xiangyu Zhang, ShaoqingRen, Jian Sun,
“Deep Residual Learning for Image Recognition,”
Microsoft Research.
[27] Sachin Padmanabhan, ”Convolutional Neural Networks
for Image Classification and Captioning,” Departmentof
Computer Science, Stanford University.
[28] José Naranjo-Torres, Marco Mora, Ruber Hernández-
García, Ricardo J. Barrientos, Claudio FredesandAndres
Valenzuela, “A Review of Convolutional Neural Network
Applied to Fruit Image Processing,” 1-Laboratory of
Technological Research in Pattern Recognition(LITRP),
Universidad Católica del Maule,3480112Talca,Chile; 16
May2020.

[29] Esam Kamal Maried, Mansour AbdallaEldhali, Osama
Omar Ziada, “A Literature Survey of Deep learning and
its application in Digital Image Processing,” University
Of Turkish Aeronautical Association Department Of
Electrical And Electronics Engineering.
BIOGRAPHIES
Mr. Mohammed FarooqAbdullaFM
He is a self-confident and enthusiastic
person who helps students to
improve high achievements in
academics. He has moved from
Industry to Academica. He has
completed his M.Tech (Information
Technology) from B.S.Abdur Rahman
University, Chennai.Hehasworkedas
a Team Lead in domain of Mobile
Application Development for more
than 5 years and also has the teaching
experience for more 3 years. He has
also conducted many Workshops,
Value Added Programs, STTP, FDP on
Mobile Apps Development forvarious
Universities andInstitutionslikeVIT–
Vellore, B.S.Abdur Rahman Crescent
Institute of Science and Technology –
Chennai, HKBK College -Bangalore.
Mr. M. Nishant He is currently doing
final year at Kumaraguru College of
Technology,Coimbatore. His majoring
in B.E Information Science and
Engineering.
Ms. K. Smethaa Sheiscurrentlydoing
final year at Kumaraguru College of
Technology, Coimbatore. Her
majoring in B.E Information Science
and Engineering.
Mr. K. Lohit Shakthi He is currently
doing final year at Kumaraguru
College of Technology, Coimbatore.
His majoring in B.E Information
Science and Engineering.

Révolange: Your Fashion Guide

Recommended

Recommended

More Related Content

Similar to Révolange: Your Fashion Guide

Similar to Révolange: Your Fashion Guide (20)

More from IRJET Journal

More from IRJET Journal (20)

Recently uploaded

Recently uploaded (20)

Révolange: Your Fashion Guide