Browsing by Author "Perera, D."

Now showing 1 - 5 of 5

Embargo
Computer Vision for Autonomous Driving
(2021 3rd International Conference on Advancements in Computing (ICAC), SLIIT, 2021-12-09) Kanchana, B.; Peiris, R.; Perera, D.; Jayasinghe, D.; Kasthurirathna, D.
Computer vision in self-driving vehicles can lead to research and development of futuristic vehicles that can mitigate the road accidents and assist in a safer driving environment. By using the self-driving technology, the riders can be roamed to their destinations without using human interaction. But in recent times self-driving vehicle technology is still at the early stage. Mostly in the rushed areas like cities it becomes challenging to deploy such autonomous systems because even a small amount of data can cause a critical accident situation. In Order to increase the autonomous driving conditions computer vision and deep learning-based approaches are tended to be used. Finding the obstacles on the road and analyzing the current traffic flow are mainly focused areas using computer vision-based approaches. As well as many researchers using deep learning-based approaches like convolutional neural networks to enhance the autonomous driving conditions. This research paper focused on the evaluation of computer vision used in self-driving vehicles.
Embargo
Computer-Vision Enabled Waste Management System for Green Environment
(2021 3rd International Conference on Advancements in Computing (ICAC), SLIIT, 2021-12-09) Hewagamage, P.; Mihiranga, A.; Perera, D.; Fernando, R.; Thilakarathna, T.; Kasthurirathna, D.
Waste management has become a critical requirement to maintain a green environment in Sri Lanka as well as other countries. Town councils have to regularly collect different types of wastes to clean cities/towns. Hence managing the waste of the cities is a challenging task. However, most of the urban councils currently use a manual approach to managing waste. However, it results in many difficulties for the people and cleaning staff who involve in the process by following strict guidelines. Issues due to waste contamination, no proper information management of waste collection, and no punctuality in removing waste from the garbage bins are some of the significant issues arising from the manual process. Due to the drawbacks of the manual approach, social issues, environmental issues, health issues can occur easily. This paper proposes a better solution to replace this manual system with an automated system to overcome these issues. Hence, the main objective of this research is to introduce an ICT-based innovative design that can be used to develop an effective waste management system in town councils. In the proposed model, we will introduce a Computer Vision-based smart waste bin system with real-time monitoring that incorporates various technologies such as computer vision, sensor-based IoT devices, and geographical information system (GIS) related technologies. Our proposed solution consists of a waste bin system, which is capable of automated waste segregation. Our design facilitates the admin users to expand the waste bin kit by adding more waste categories in a user-friendly manner, making our product adaptive in any environment. At the same time, waste bins can notify the real-time waste status. Our system generates the optimum collection routing path and displays it in a mobile app using those real-time status details. We also demonstrate a lowcost prototype.
Embargo
Individualized Edutainment and Parent Supportive Tool for ADHD Children
(2020 2nd International Conference on Advancements in Computing (ICAC), SLIIT, 2020-12-10) Thennakoon, A.; Perera, D.; Sugathapala, S.; Weerasingha, S.; Samarasinghe, P.; Dahanayake, D.; Piyawardan, V.S.
Attention-Deficit/Hyperactivity Disorder (ADHD) is a comorbid disorder that can impact a child and his/her family. ADHD children have considerable obstacles in managing time, understanding instructions, and paying attention to the activities. To address these perplexities, this research has designed a mobile application to help parents to have better interaction with the children and for the children to enjoy their learning activities. The specialty of this application is the models are trained on individual child skills and needs. Issues with time management are handled by the Scheduler component while the Instruction Predictor module supports the parent in recognizing the child's understandability level. Furthermore, the children are provided with edutainment activities based on their attention and ability levels. Different models have been used in predicting the results through these modules and the prediction result accuracy exceeds 90% in most of the cases. Out of the many models, The Random Forest model resulted in the best overall performance. The application was tried by many parents and health professionals and received satisfactory and commendable reviews.
Open Access
Investigation of the Carbon Offsetting Targets Towards Sustainability: A Focus on 3PL Companies
(ICSDB 2024 and SLIIT Business School, 2024-12-10) Erandika, T.; Pallawala, T.; Wijesinghe, A.; Perera, D.; Karunarathna, N.; Kalansuriya, N.
This study examines the carbon emission reduction goals of third-party logistics (3PL) firms for sustainable activities, assessing their effectiveness and impact on sustainability. It explores the goals and methods used by these companies, their implementation challenges, and the potential effects on sustainability outcomes like reduced emissions, improved operational efficiency, and increased stakeholder participation. This aims to understand how 3PL companies reduce their carbon footprints and identify opportunities. This study examines the leading 3PL providers in the global content market using a comprehensive analysis of literature articles. The effect of carbon footprints on efficiency in third-party logistics companies is examined in the present research. With an emphasis on carbon mitigation, the effects of carbon footprints on organisations, and the tactics 3PL entities employ to lower their carbon footprints, it included 76 research publications during 2019–2024. The study examines supply chain management, sustainability, and emission reduction using the theories of carbon management, stakeholders, and the environment. The outcomes demonstrate how well the retention of carbon techniques works to raise industrial sustainability standards. This study evaluates the interest and difficulties experienced by 3PL companies by comparing the results with those of other countries. Consequently, the goal of the research is to raise the general understanding of environmental issues and accomplish long-term sustainability objectives in the transportation industry, as a finding of this research, Direct, indirect, and fugitive carbon footprints have considerable impacts on the environment and economy. Compared to individuals, businesses are more responsible for global warming, which damages infrastructure, disrupts supply chains, and lowers productivity. 3PL companies can employ carbon mitigation strategies, such as waste reduction, renewable energy investments, and environmentally friendly transportation while interacting with stakeholders and governments to minimise the negative environmental effects. Likewise,20% of the world's greenhouse gas (GHG) emissions come from the transportation sector, which includes 3PL companies.
Embargo
Production of Disease Free Rumohra adiantiformis (Leatherleaf Fern) Using In Vitro Propagation
(Faculty of Humanities and Sciences - SLIIT, 2021-03-26) Perera, D.; Peiris, S.E.
This study investigated the use of rhizome tips as explants in regenerating disease-free plantlets of Rumohra adiantiformis (G. Frost) using the micropropagation technique. Rhizome tips of 0.5 cm, 1 cm, 1.5 cm, and 2 cm in length, and with and without outer skin were used as explants.The highest regeneration rate (30%) was achieved with an explant size of one centimetre when the outer skin was not present in rhizome explants. Rhizome tips produced complete plantlets in Murashige and Skoog (MS) medium without any growth regulators in 80 - 90 days of culture initiation. In vitro multiplication of disease-free plants was achieved by culturing four months old in vitro plantlets in the MS medium supplemented with 2 mg/L Benzyl Amino Purine (BAP) and 0.1mg/L Naphthalene Acetic Acid (NAA). Plantlets produced clusters of shoot primordia which are known as green globular bodies (GGB) in the multiplication medium, 10 weeks after culturing. Plantlets were obtained by culturing 2-3 mm size GGB segments in the basal medium without any plant growth regulators. Disease indexing of the in vitro derived plantlets verified that at in vitro level 60% of the plantlets obtained from the rhizome tips of one centimetre were free from bacterial and fungal diseases.