SLTC e-Repository
The SLTC e-Repository is a digital platform designed to store, manage, and provide access to academic and research outputs of the Sri Lanka Technological Campus. It serves as a central hub for theses, dissertations, research papers, and other scholarly content produced by students and faculty.
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Recent Submissions
Edge-Magic Total Labelling of Cyclic and Bicyclic Bridge Graphs
(Sri Lanka Technology Campus, 2024-11-06) Sakalasooriya,Kaushalya.; Perera, A.A.I; Ranasinghe,P.G.R.S.
Edge-magic total labelling is an interesting area in
graph theory with significant implications. In this study, we
explore the edge-magic total labelling of cyclic graphs with n
vertices and bicyclic bridge graphs with 2n vertices,
demonstrating that these graphs can be labelled with a magic
sum k=2n. An edge- magic total labelling on a graph G is a
one-to-one map ๐ from ๐ฝ(๐ฎ) โช๐ฌ(๐ฎ) onto the integers
1,2,โฆ,๐ + ๐, where ๐ = |๐ฝ(๐ฎ)| and ๐ = |๐ฌ(๐ฎ)|. This
mapping has the property that for any edge ๐๐, ๐(๐) +
๐(๐๐) + ๐(๐) = ๐, a constant called the magic sum of ๐ฎ.
Graphs that satisfy this condition are termed edge-magic.
For cyclic graphs with ๐ vertices, we start by labelling the
vertices from ๐ to ๐ in a clockwise direction. Edges are then
labelled by starting from the (๐ โ ๐)th edge, labelling from ๐
to ๐๐โ๐ in steps of ๐ in an anti-clockwise direction, and the
๏ฟฝ
๏ฟฝth edge is labelled ๐ โ ๐. Considering any edge ๐๐ with
adjacent vertices labelled ๐ + ๐ and ๐, the edge receives the
label ๐๐ โ ๐๐โ๐. The magic sum ๐ is calculated as ๐ +
(๐+๐)+๐(๐โ๐)โ๐=๐๐, proving that cyclic graphs
with n vertices are edge-magic with the magic sum ๐๐. For
bicyclic bridge graphs, two cyclic graphs each with ๐ vertices
are connected by a bridge. Each cycle is labelled similarly to
the cyclic graph. The bridge connects the vertex labelled ๐ of
each cycle and is labelled ๐๐ โ ๐. For the bridge edge, the
magic sum remains ๐๐. Thus, the bicyclic bridge graphs are
also edge-magic with the magic sum ๐๐. This study confirms
that both cyclic graphs with ๐ vertices and bicyclic bridge
graphs with ๐๐ vertices can achieve edge-magic total
labelling with a consistent magic sum of ๐๐, contributing
Sentiment Analysis on Consumer Reviews of Amazon Products
(International Journal of Computer Theory and Engineering, 2021-05-02) Aamir Rashid; Ching-yu Huang
Sentiment Analysis for Amazon Reviews to Identify Customer Interests
(Sri Lanka Technology Campus, 2024) Abeysirigunawardana, K. Aloka; Rajapaksha, Dr. Rasika
In the twenty-first century, consumers now
frequently buy online and use reviews to judge the quality
of the products they purchase. These reviews are also
examined by businesses to enhance their offerings. In
order to glean business insights from massive datasets,
this study uses sentiment analysis on Amazon product
reviews. To determine the specific subjects upon which
the entire collection of reviews is predicated, an LDA
model is created for the Amazon review dataset. Word
frequencies for each topic are visualised by us. After
determining which machine learning model is most
appropriate for sentiment analysis, the analysis is carried
out on a topic-by-topic basis. Using logistic regression,
the themes derived from the LDA model are categorised
as either positive or negative product review subjects.
Analysis is done on both the positive and negative
subjects.
DreamyMirror A Machine Learning-Based Personalized Outfit Recommendation System Integrating Skin Tone Classification and Size
(Sri Lanka Technology Campus, 2024-11-06) Serasinghe,S. J. A. S. K.; Jayarathne,P. V. C. G.
Due to the increasing popularity of e-commerce, buying
has undergone a tremendous shift and belongs to a
different world. โ But there is still some difficulties with
personalization when it comes to the proposed size and
the choice of the color taking into consideration the skin
color of the person. In this paper, the authors introduce
DreamyMirror, a novel system for recommending outfits
based on the individual userโs skin tone and size, along
with allowing for virtual fitting. Based on a skin tone
classifier CNN model built from CelebA database and a
pre-trained Pose_iter_440000 of OpenPose. size
prediction caffemodel, DreamyMirror provides unique
suggestions of outfits according to the appearance of the
person. The system also contains the Virtual Try on
system wherein users can anticipate what the selected
apparels look like at a glance. The proposed system can
automatically recognize skin tone with an overall
classification accuracy of 92% and the size prediction
accuracy is found to be 95%. Moreover, the user
engagement metrics also show an 85% level of
satisfaction with the features โ this is about the virtual
try-on. This work shows how the application of AI in
fashion retail can help better engage the customer
through a truly individualized shopping experience and,
at the same time, minimize the return rate because of the
wrong size or color choice.
Intelligent Road Safety Navigation in Sri Lanka: A Review of Machine Learning Techniques and Proposal of a Model for Predicting Accident Hotspots and Severity
(Sri Lanka Technology Campus, 2024-11-06) Jayamanna,JMCT; Kalansooriya,Pradeep
Both public safety and the stability of the economy
are seriously threatened by traffic accidents. Like many other
regions, Sri Lanka is faced with the challenge of road accidents,
which hinder the path to safer roads. One of the reasons that
accidents occur is that people are unaware of common accident
locations. The government has already enforced other tactics,
like traffic signals and fines, to reduce these incidents, but they
have been ineffective. In order to decrease road accidents, people
must change their driving patterns. While looking for a solution
to that problem, existing studies around the world have proposed
predictive machine learning models for accident-prone locations
known as hotspots and severity levels. But there were not any
existing studies that proposed a solution that was suitable for Sri
Lanka. This research seeks to address these challenges by
conducting an in-depth review of existing machine learning
techniques and proposing the most suitable model approaches
for prediction of accident hotspots and severity in Sri Lanka
based on the availability of the accident data. One of the main
objectives is to identify the correct machine learning techniques.
According to studies, 81% used the 'Random Forest' algorithm,
which is a supervised machine learning algorithm for the
prediction. And Random Forest performed better in
approximately 69% of the studies. And this research is not just
proposing suitable model approaches for predictions. It provides
the foundation to revolutionize road safety through the
development of an intelligent road safety mobile navigation
application