Research Papers - Dept of Software Engineering

Permanent URI for this collectionhttps://rda.sliit.lk/handle/123456789/1022

Browse

Filters

2020 - 20222

Advanced Search

Filter by

Settings

Author: search.filters.author.Weerasinghe, LSubject: search.filters.subject.Deep Learning

Search Results

Now showing 1 - 2 of 2
  • Thumbnail Image
    PublicationEmbargo
    Cocopal - A Deep Learning Based Intelligent System to Certify and Standardize the Quality of Coconut Based Products
    (IEEE, 2022-12-09) Gunawardana, K.H.R.; Deshan, M.P.N.; Hemachandra, M.G.S.P.; Ganegoda, D; Hettiarachchi, N. M; Weerasinghe, L
    The procedure of certifying and standardizing the quality of the coconut-based products is done manually in Sri Lanka at precent. It is a time consuming and labor-intensive task and is conducted by experts. In most cases, the quality is decided solely by visual inspections by buyers and suppliers, with no scientific basis. The paper reports the capacity of bringing modern technology solutions such as Artificial Intelligence (AI), Machine Learning (ML), Image Processing (IP), and decentralized storage to aid in the certification and standardization of the quality of raw materials.Results showed that the accuracy of the proposed system is in the 86% to 90% range and showed that this technique could beimproved and used as an alternative to manual techniques.
  • Thumbnail Image
    PublicationEmbargo
    Deep Learning Based Dog Behavioural Monitoring System
    (IEEE, 2020-12-03) Boteju, W. J. M; Herath, H. M. K. S; Peiris, M. D. P; Wathsala, A. K. P. E; Samarasinghe, P; Weerasinghe, L
    Dogs are one of the most popular pets in the world. It is usual that pet owners are always concerned about the health and the wellbeing of their pets. The activity levels of the dogs vary from each other based on breed and age. Tracking the behavioral changes using image processing and machine learning concepts and notifying the pet owners via a mobile application is the main objective of this research. Breed recognition has been done applying deep learning concepts to the user-uploaded video or the photograph of the dog. This research mainly focuses on walking, running, resting, and barking activity patterns of the dog. A surveillance camera and sensors were the main equipment for data collection. The audio feature of the surveillance camera is used to identity the barking behavior of the dog. Dogs from different ages belonging to Pomeranian and German Shepherd breeds have been selected for this experiment. Transfer learning with ResNet50, Inception V3, and support vector machines have been used to recognize and classify the activities of the dogs. The research study was able to achieve the accuracy levels as follows: - breed recognition - 89%+, walking pattern recognition - 99.5%, resting pattern recognition - 97% and barking pattern recognition - 60%. With the above accuracy levels, the research was able to identify the unusual behaviour of the dogs.