Research Publications Authored by SLIIT Staff

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This collection includes all SLIIT staff publications presented at external conferences and published in external journals. The materials are organized by faculty to facilitate easy retrieval.

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Subject: search.filters.subject.Convolutional Neural Network

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    Advancing Canine Health and Care: A Multifaceted Approach using Machine Learning
    (IEEE, 2023-06-26) Wimukthi, Y; Kottegoda, H; Andaraweera, D; Palihena, P; Fernando, H; Kasthurirathnae, D
    This research paper proposes a comprehensive approach to enhance the well-being of dogs through a range of innovative technologies. Firstly, we develop an automated system for dog breed and age identification using a Convolutional Neural Network (CNN) and a transfer learning model. This system aims to provide an efficient and reliable solution for dog owners and new adopters who are interested in discovering more about their canine companions. Secondly, we propose the development of a system that uses Reinforcement Learning to generate personalized meal plans based on a variety of factors such as the dog's breed, age, weight, health status, and emotional state. The system aims to provide dog owners with a reliable and effective tool for generating personalized meal plans that will enhance their pets' overall health and well-being. Thirdly, we present a dog disease recognition application that utilizes an artificial neural network (ANN) for identifying dog diseases based on their symptoms. Lastly, we introduce a real-time remote dog monitoring system using loT devices with edge computing to detect aggressive and anxious sounds. Our system provides an accurate classification of dog sounds related to aggression and anxiety, which can help dog owners detect and respond to potential issues early on. This research aims to provide dog owners and veterinarians with a range of technologies that can help them better understand and care for their furry friends.
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    Comprehensive Analysis of Convolutional Neural Network Models for Non-Instructive Load Monitoring
    (IEEE, 2020-10-20) Herath, G. M; Thilakanayake, T. D; Liyanage, M. H; Angammana, C. J
    Non-Instructive Load Monitoring (NILM) schemes have become more popular in recent years with the availability of smart meters. It provides energy use data to utilities and per-appliance energy consumption details to end users. This study carries out a comprehensive analysis of existing Convolutional Neural Network (CNN) architectures that have been used for NILM. Nevertheless, it provides an unbiased comparison of the existing architectures thereby helping to select the best performing model for NILM applications. The commonly used CNN disaggregation models were categorized into distinctive groups based on their architectures which considered structure of the Neural Network (NN) and outputs. It considers regression-based sequence to sequence and sequence to point mapping, classification-based sequence to point hard association and soft association-based mapping. The CNN models are improved and modified to bring them onto a common platform for comparison. Thereafter, a rigorous comparison was performed using indices which included accuracy, precision, F-measure and recall. The results reveal interesting relationships between architectures, appliances and measures.
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    Vehicle Insurance Policy Document Summarizer, AI Insurance Agent and On-The-Spot Claimer
    (IEEE, 2021-04-02) Samarasinghe, H. T. D; Herath, N. A. D. M; Dabare, H. S. S; Gamaarachchi, Y. R; Pulasinghe, K; Yapa, P
    This paper proposes an automated vehicle insurance policy summarizing application. “Explain to Me” is one such software/tool which enable you to summarize the content of documents regarding vehicle insurance policies by using the NLP, machine learning and deep learning applications. The program targets mainly insurance users and suppliers of insurance services. Due to the increase of vehicle accidents, the vehicle insurance industry has gained more popularity currently. Therefore, different insurance companies have introduced a variety of insurance policies to customers. Vehicle insurance policy documents consist lot of insurance terms that should be read with more attention. As the main objective, this system filters unnecessary data in the particular document, and finalize a summary as the output. As another major component, the application “On the spot claimer” which is never before in Sri Lankan vehicle insurance industry, is another major part of this project that works as suggesting the most relevant insurance claiming that can be claimed by the user after detection of the type of damage through mobile phone camera. Another part of this research project, the function known as the Recommender, which works along with the summarization tool, is a recommendation system with a view of recommending more favorable rules for the assertion of alternatives that exist in the corresponding, equivalent documents of other companies. Finally, in order to interact with custody concerns about how to insure an automobile, CNN, which are based on the extraction of images, are used for the implementation of the ETM system in NLP.