KDU Institutional Repository (IR@KDU)
Institutional Repository of General Sir John Kotelawala Defence University is a collection of the University’s intellectual or research output. This service allows members of the University to share scholarly and research output with the wider community. KDU Library is responsible in establishing, collaborating, managing, maintaining and disseminating the content of IR@KDU.
Communities in IR@KDU
Select a community to browse its collections.
ACADEMIC JOURNALS [262]
ARCHIVAL COLLECTION [193]
SYMPOSIUM ABSTRACTS [239]
THESES AND DISSERTATIONS [1107]
Recently Added
-
Enhancing Human-Computer Interaction on Educational Websites: Color Preferences for 7-8 Years Old
(2024-07)For any application development first impression will always be a matter to attract users. User Interfaces (UI) will be the first handshake of an application with the user. This concern brings more impact when creating ... -
Monocular 3D Reconstruction in Poorly Visible Environments
(2024-07)3D reconstruction of real physical environments can be a challenging task, often requiring depth cameras such as LIDAR or RGB-D to capture the necessary depth information. However, this method is resource-intensive ... -
Automatic Bug Priority Prediction using LSTM and ANN Approaches during Software Development
(2024-07)The process of manually assign a priority value to a bug report takes time. There is a high chance that a developer may allocate the wrong value, and this can affect several important software development processes. To ... -
Artificial Neural Network Based Grey Exponential Smoothing Approach for Forecasting Electricity Demand in Sri Lanka
(2024-07)The electricity supply of the country has greatly impacted the economy and the nation’s standard of living; an accurate forecast of electricity demand is essential for any country to enhance industrialization, farming, ... -
Empowering the Captioning of Fashion Attributes from Asian Fashion Images
(2024-07)Fashion image captioning, an evolving field in AI and computer vision, generates descriptive captions for fashion images. This paper addresses the prevalent bias in existing studies, which focus predominantly on Western ...