Faculty of Computing

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Author: search.filters.author.Perera, MHas files: Yes

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    IDairy: Intelligence and Secure E-Commerce Platform for Dairy Production and Distribution Using Block Chain and Machine Learning
    (IEEE, 2022-07-18) Liyanage, I; Madhuwantha, N; Perera, M; Ruhunage, S; Mahaadikara, M. D. J. T. H; Rupasinghe, L
    The dairy industry plays an essential role in the Sri Lanka economy. The purpose of this study is to reduce the cost of import dairy products and increase the profit of the dairy industry. IDairy: Intelligence and secure e-commerce platform for dairy production and distribution using blockchain and machine learning has been suggested as a mobile application. As a first step, this research suggested four factors. Develop a business intelligence dashboard using predictive analysis and provide business solutions to dairy companies described the revenue for the coming month using machine learning and the earning data charts for years to come to display in the dashboard. Design IOT device to maintain the temperature of fresh milk cargo while transporting to productions and design smart contract to maintain the optimum temperature for the fresh milk harvest. Develop a system to identify the cows’ diseases using image processing the primary objective was identified cows’ Foot and Mouth diseases and provide notifications to milk farms about existing illnesses. Cows’ disease directly affects dairy productions. Develop a mobile application for farmers to store animal data, do profit calculation, including giving business solutions through the application with location tracking service. With this IDairy application, both farmers and production companies will be able to get an idea about their future profit and will be suggesting the business solutions.
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    Power Profiling: Assessment of Household Energy Footprints
    (IEEE, 2021-03-06) Wijesinghe, V; Perera, M; Peiris, C; Vidyaratne, P; Nawinna, D. P; Wijekoon, J
    Reduced energy footprint is considered an indicator of efficiency around the world. Having insights into electricity consumption behavior of individuals or families across the day is very useful in efficient management of electricity. In this paper, we present s study that focused on identifying patterns in the monthly electricity consumption profiles of a single household with the K-means clustering algorithm. The data required for this study was collected through a survey in the Sri Lankan context. The survey mainly captured the factors affecting electricity consumption. After proving the demand of electricity is dependable on the data that has been collected, they will be keyed into data models/ profiles that will be built using clustering algorithms. A load profile will be designed using K-means to identify usage patterns of a household on a monthly basis. The parameters that affect the electricity consumption were tested and trained using the SVM algorithm. The outcomes of this study include; identifying the factors contributing to the electricity consumption, identifying electricity consumption patterns, identifying the energy footprint of individuals or families and predicting the future electricity requirements. The results of this study provide many advantages for both consumers and suppliers in efficient management of electricity. It also provides significant impacts in both micro and macro levels through enabling efficient decision-making regarding management of electricity.
  • Thumbnail Image
    PublicationEmbargo
    Power Profiling: Assessment of Household Energy Footprints
    (IEEE, 2021-03-06) Wijesinghe, V; Perera, M; Peiris, C; Vidyaratne, P; Nawinna, D; Wijekoon, J
    Reduced energy footprint is considered an indicator of efficiency around the world. Having insights into electricity consumption behavior of individuals or families across the day is very useful in efficient management of electricity. In this paper, we present s study that focused on identifying patterns in the monthly electricity consumption profiles of a single household with the K-means clustering algorithm. The data required for this study was collected through a survey in the Sri Lankan context. The survey mainly captured the factors affecting electricity consumption. After proving the demand of electricity is dependable on the data that has been collected, they will be keyed into data models/ profiles that will be built using clustering algorithms. A load profile will be designed using K-means to identify usage patterns of a household on a monthly basis. The parameters that affect the electricity consumption were tested and trained using the SVM algorithm. The outcomes of this study include; identifying the factors contributing to the electricity consumption, identifying electricity consumption patterns, identifying the energy footprint of individuals or families and predicting the future electricity requirements. The results of this study provide many advantages for both consumers and suppliers in efficient management of electricity. It also provides significant impacts in both micro and macro levels through enabling efficient decision-making regarding management of electricity.