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Diabetic Retinopathy Detection Using Deep Learning

Jayasundara, L.J.M.D.N.K.; Jeyamugan, T.; Vijayakanthan, G. (Faculty of Applied Science, University of Vavuniya, 2024-10-30)

The eye is a vital organ in human anatomy, uniquely allowing non-invasive examination of its interior, including vascular and brain tissues, from the outside. While retinal fundus images are traditionally used to diagnose ...

A Deep Learning Approach Utilizing Convolutional Neural Networks for the Detection of Generated Human Face Images

Wanigasekara, W.G.M.G.N.; Jeyamugan, T.; Sobana, S. (Faculty of Applied Science, University of Vavuniya, 2024-10-30)

The proliferation of Artificial intelligence-generated human face images has posed significant challenges to the detection and authentication of digital content, particularly within social networks. These synthetic faces, ...

Securing Large Language Models: Investigating Prompt Injection Attacks and Remediation Tactics

Zahry, M.Z.L. (Faculty of Applied Science, University of Vavuniya, 2024-10-30)

The rapid advancement of Large Language Models (LLMs) has brought about remarkable capabilities in natural language processing, but it has also exposed vulnerabilities such as prompt injection attacks, which pose significant ...

Using Spatial Pyramid Pooling Layer in Convolutional Neural Networks For Image-based Malware Detection

Albuker, H.N.I.; Jeyamugan, T.; Sobana, S. (Faculty of Applied Science, University of Vavuniya, 2024-10-30)

As computer technology rapidly advances, the prevalence of malware has surged, posing significant threats to network security and user data. Malware can infiltrate systems by spreading through the Internet, leading to data ...

Identification of Sri Lankan Commercial Fish Species Using MobileNet SSD and Deep Learning

Mahaliyana, N.S.D.; Vijayakanthan, G. (Faculty of Applied Science, University of Vavuniya, 2024-10-30)

Accurate identification of commercial fish species is essential for sustainable fisheries management, ecological conservation, and market regulation in Sri Lanka, which is home to over 90 commercially significant species. ...

Determination of Weight of Vegetables and Generate Bills for Supermarkets in Sri Lanka using Image Processing and Deep Learning Approaches

Wanasinghe, S.D.K.; Jeyamugan, T.; Sobana, S. (Faculty of Applied Science, University of Vavuniya, 2024-10-30)

Automatic billing systems have emerged as a powerful tool to enhance the efficiency of the food industry through the utilization of information technology and artificial intelligence. Sri Lankan supermarkets often face ...

An Efficient Encryption Algorithm for 3D Medical Images

Senanayake, T.K. (Faculty of Applied Science, University of Vavuniya, 2024-10-30)

With the rapid advancement of digital medical imaging technologies, the need to safeguard sensitive patient data has never been more critical. This thesis presents a robust software solution for the encryption of 3D medical ...

Static Tamil Sign Language Recognition using Mediapipe Hand Landmark Detection and Machine Learning

Perera, G.V.D.P.P.; Jeyamugan, T.; Vijayakanthan, G. (Faculty of Applied Science, University of Vavuniya, 2024-10-30)

Sign language is essential for communication among the deaf and hard-ofhearing communities, addressing the needs of over 430 million people globally, including 34 million children. By 2050, this number is expected to surpass ...

Urban Area Detection in Sri Lanka using Satellite Images and Deep Learning

Lakshika, N.; Bramyah, S. (Faculty of Applied Science, University of Vavuniya, 2024-10-30)

Urbanization is reshaping landscapes globally at a rapid pace, making the automatic detection and segmentation of urban areas critical for effective planning, resource allocation, and sustainable development. This research ...

Omic Data in Identification of Covid-19 Vaccinated In dividuals

Kaluarachchi, N.C.N.; Pratheeba, J.; Yasotha, R. (Faculty of Applied Science, University of Vavuniya, 2024-10-30)

The COVID-19 pandemic has led to significant challenges in public health, highlighting the need for effective monitoring and identification of vaccinated individuals. This research explores the use of machine learning ...

Automatic classification of Sri Lankan lichen families using deep learning technique

Sribavatharani, R.; Jeyamugan, T.; Keerthanaram, T. (Faculty of Applied Science, University of Vavuniya, 2024-10-30)

Lichen species play an important role in ecosystems, acting as bio indicators and contributing to biodiversity. Traditionally, identifying lichen families requires a great deal of expertise and is time-consuming, prone to ...

Identification and Classification of Diseases in Pomegra nates using Deep Learning

Keerththiga, R.; Thirukumaran, S. (Faculty of Applied Science, University of Vavuniya, 2024-10-30)

Pomegranate diseases, such as bacterial blight and fungal infections, are major threats to agricultural productivity. Since these diseases can greatly affect crop yields and quality, early detection can help preventing ...

Machine Learning Algorithms to Improve Early Diagnosis of Thyroid Disease: A Sri Lankan-Based Study

Senani, W.M.U.; Thirukumaran, S. (Faculty of Applied Science, University of Vavuniya, 2024-10-30)

Thyroid diseases are a major health, well-being, and health care problem across the world. This study proposes a new thyroid diagnosis system using efficient feature selection techniques and machine learning methods for ...

An Improved Algorithm for Fitting High-Dimensional Linear Regression Models

Kamsitha, S.; Yogarajah, B. (Faculty of Applied Science, University of Vavuniya, 2024-10-30)

High-dimensional linear regression (HDLR) is a critical tool in fields such as finance, statistics, and machine learning, particularly for feature selection and predictive modeling when there are many predictor variables ...

Image-Based Identification and Health Assessment of Coral Reefs in Trincomalee Using Convolutional Neural Networks

Nirupa, A.; Jeyamugan, T.; Keerthanaram, T. (Faculty of Applied Science, University of Vavuniya, 2024-10-30)

Coral reefs are one of the most biodiverse and play an important role in the ecosystem that support a vast array of marine life and provide numerous ecological and economic benefits. But, due to various anthropogenic factors ...

Fog removal in single images using deep learning

Komathy, G.; Kokul, T. (Faculty of Applied Science, University of Vavuniya, 2024-10-30)

With the rapid development of artificial intelligence, computer vision systems based on image recognition, object detection, target tracking are widely used in aviation, military industry, agriculture and other fields. ...

Segmentation of Diabetics Foot Ulcers Using Deep Learning: A Sri Lankan Study

Herath, W.R.G.A.D.P.; Vinoharan, V.; Bramyah, S. (Faculty of Applied Science, University of Vavuniya, 2024-10-30)

Segmentation of Diabetics Foot Ulcers Using Deep Learning: A Sri Lankan Study Abstract Diabetes is a chronic metabolic disorder that affects millions of people worldwide, including in Sri Lanka, and often leads to severe ...

A Comprehensive Study of Hate Speech Detection in Child Abuse-Related YouTube Comments: Insights from Traditional and Deep Learning Approaches

Sweshthika, S.; Yasotha, R. (Faculty of Applied Science, University of Vavuniya, 2024-10-30)

The widespread use of social media has significantly contributed to the proliferation of user comments by posing serious threats to individuals and communities. Due to the emotionally charged and nuanced nature of the ...

Early Detection and Explainable Analysis of Alzheimer's Disease Using Resting-State fMRI and Convolutional Neural Networks

Pavithira, S.; Tuvensha, J. (Faculty of Applied Science, University of Vavuniya, 2024-10-30)

Alzheimer’s disease (AD) is an incurable neurodegenerative disorder that progressively damages the brain, leading to memory loss and cognitive decline. Early detection of AD is essential, as it can help delay or slow the ...

Detection of Skin Diseases using Deep Learning: A Study of Sri Lankan Patients

Kayalvizhy, T.; Vinoharan, V.; Pirabaharan, S. (Faculty of Applied Science, University of Vavuniya, 2024-10-30)

Skin disease detection is a critical component of dermatological healthcare, traditionally dependent on dermatologists’ expertise for accurate diagnosis. However, manual diagnosis can be time-consuming and prone to errors, ...

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