Browsing by Subject "Deep learning"

Browsing by Subject "Deep learning"

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  • Attendance Marking System using QR codes with Facial Features 
    Adikaram, K.K.J.K.B.; Mohomed Wazeem, S.K.; Ranaweera, S.D.; Sakuntharaj, R. (Korea Database Strategy Society (KDSS), 2026)
    This research presents the design and implementation of a secure, AI-powered attendance system utilizing dual authentication through facial recognition and QR code verification. The study addresses the prevalent issues ...
  • Audio Deepfake Detection Methods: A Review 
    Janani, W.V.M.; Nissanka, N.B.A.S.C.; Thisaravi, K.G.A. (Faculty of Technological Studies, University of Vavuniya, 2025)
    The emergence of audio deepfake technologies has raised new concerns in the area of digital security, privacy, and trust in media. Audio deepfakes are AI-generated synthetic audio clips that are capable of accurately ...
  • Automated Cashew Nut Grading using Deep Learning Techniques 
    Jalaxshi, R.; Merenchige, A.C.S.; Sutharsan, M. (Faculty of Applied Science University of Vavuniya Sri Lanka, 2025)
    Grading cashew nuts is an integral process and one of the factors that greatly impacts the quality, price and value of cashew products, especially for export. Traditionally, in Sri Lanka, grading entails the manual ...
  • Automated Detection of Multiple Plant Diseases in Paddy and Jackfruit using Machine Learning 
    Liyanwala, D.K.G.; Jayasundara, T.N.; Amarasinghe, P.Y.; Sakuntharaj, R. (Korea Database Strategy Society (KDSS), 2026)
    Early identification of plant diseases plays an important role in reducing crop damage and improving agricultural productivity. This study presents a deep learning–based system for the automatic detection of multiple ...
  • 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 ...
  • Colour-Based Feature Analysis for Aloe Vera Leaf Disease Classification using Deep Learning 
    Tharsika, P.; Thirukumaran, S. (Faculty of Applied Science University of Vavuniya Sri Lanka, 2025)
    Accurate classification of Aloe vera leaf diseases is challenging due to overlapping colour patterns between healthy and infected regions under varying lighting conditions. Traditional methods often fail to capture these ...
  • Comparing Similarity Between the Title and the Content of News Articles Using Deep Learning 
    Jayasundara, M.H.E.U.; Yasotha, R. (Faculty of Applied Science, 2020-12-02)
    The volume of the online news has rapidly increased for recent years. People who consume the news online have increased. New technologies have changed the way of consuming online news. It has become a huge adoption of ...
  • A Comprehensive Introduction to Convolutional Neural Networks: A Case Study for Leaf Image Classification 
    Satheeskumar, T.; Selvarajan, P. (South Eastern University of Sri Lanka, 2023)
    Most of the today’s new and innovative artificial intelligence applications are based on the artificial neural types of networks to capture, interpret and analyze various kind of data. A Convolutional Neural Network is a ...
  • 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, ...
  • Deep Learning for Intelligent Waste Sorting in Sri Lanka: A Conceptual Review 
    Herath, H.M.P.H.S.; Kuruneru, R.P.D.; Logeesan, R.; Venuja, N. (Faculty of Technological Studies, University of Vavuniya, 2025)
    Urban waste management in Sri Lanka is increasingly challenged by rising waste volumes, reliance on manual sorting, and inefficient collection systems. This review paper aims to examine how deep learning, particularly ...
  • Deep Learning-Based Coral Reef Segmentation: A Satellite Imagery Study of the Trincomalee District 
    Ariyaraththinam, N.; Kumaralingam, L.; Sinthathurai, B.; Thanabalasingam, K.; Thirunavukkarasu, J.; Ratnarajah, N. (2025 Moratuwa Engineering Research Conference (MERCon), 2025)
    Coral reefs, vital yet endangered ecosystems, face rising threats from climate change and humans. Accurate assessment of coral reef health is essential for early detection of ecosystem decline and effective conservation ...
  • Deep Learning-Based Detection of Diseases in Mangifera indica (Mango) 
    Bandara, B.M.A.U.; Chathuranga, T.D.K.; Madhuwantha, D.A.J.A.; Saranga, T.A.D.S.; Mishawrnilanthi, S.; Hasitha Viduranga, G.R.; Dhanaranga, K.W.I.B.; Vinoharan, V.; Suthaharan, S. (Faculty of Technological Studies, University of Vavuniya, 2025)
    Mango (Mangifera indica) is one of the most commercially viable fruit crops in Sri Lanka. However, its cultivation faces increasing challenges due to various pests and diseases, which significantly reduce both yield quantity ...
  • 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, ...
  • Disease Classsification in Sugarcane using Deep Learning Technologies 
    Nivithasini, P.; Ann Sinthusha, A.V. (Faculty of Applied Science University of Vavuniya Sri Lanka, 2025)
    This document presents a study on improving sugarcane disease detection in Sri Lanka, where productivity is significantly hindered by diseases such as White Leaf Disease, Red Rot, Rust, and Yellow Leaf Disease. Although ...
  • E-Document Classification Using Deep Learning 
    Wijesuriya, M.W.A.S.P.; Sakuntharaj, R. (Faculty of Technological Studies, University of Vavuniya, 2025)
    The proliferation of digital content in the form of electronic documents necessitates efficient classification methods to manage and analyze this growing body of information. This research explores the application of ...
  • Electrocardiogram Signal Classification for Heart Disease Diagnosis with Deep Learning A Sri Lankan Study 
    Ushanthika, B.; Thirukumaran, S. (Faculty of Applied Science University of Vavuniya Sri Lanka, 2025)
    Heart disease remains a significant health concern worldwide, including in Sri Lanka, where timely and accurate diagnosis is crucial for improving patient outcomes. The 12-lead electrocardiogram (ECG) is a widely used, ...
  • Ensemble Deep Learning Framework for Multiclass Classification of Femur and Pelvic Fractures 
    Minuja, K.; Luxshi, K.; Abishethvarman, V.; Prasanth, S.; Kumara, B.T.G.S. (Faculty of Applied Science University of Vavuniya Sri Lanka, 2025)
    The fractures of the femur and pelvis are life-threatening orthopedic conditions that are common among older individuals, causing severe complications and reducing mobility. This paper introduces a deep learning method ...
  • Extraction of Sentiments in Tamil Sentences Using Deep Learning 
    Loganathan, H.; Sakuntharaj, R. (2022 International Research Conference on Smart Computing and Systems Engineering (SCSE), 2022)
    Sentiment analysis is the process of extracting information from the given text in which the text consists of various sensations such as happiness, perturbation, pride, worry, and so on about various functions, human beings, ...
  • 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. ...
  • Identification of Cotton Species and Luxury Product Recommendation using Deep Learning 
    Ariyarathna, P.S.; Edwin Linosh, N. (Faculty of Applied Science University of Vavuniya Sri Lanka, 2025)
    Cotton species determination has major implications for agriculture, textile manufacturing, and luxury product design. A brief yet deployable deep learning pipeline is proposed for accurately classifying four cotton ...