Faculty of Computing

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Subject: search.filters.subject.Analytics Platform

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    PublicationEmbargo
    Predictive Analytics Platform for Airline Industry
    (IEEE, 2020-12-10) Tissera, P. H. K; Waduge, K. T; Perera, M. A. l; Nawinna, D. P; Kasthurirathna, D
    The research is to develop accurate demand forecasting model to control the availability in Airline industry. The primary outcome of the model is that the Airline organization can maximize the revenue by controlling the availability. The product in airline industry is the seat, which is an expensive, unstock able product. The demand for the seats is almost uncertain, the capacity is constraint and difficult to increase and the variable costs are very high. Hence the priority of the expected demand forecast is very high for airline industry. An accurate mechanism to predict the revenue for future months of ODs (Origin destinations) is done using fare and passenger data. The revenue is derived by the number of passengers and the fares they pay which vary for each flight. Airline travel is very susceptible to the social, political and economic changes. Therefore, passenger buying patterns change quite dynamically. Hence, it is challenging to develop an accurate method to project the revenue for each route. To overcome this, we are going to use semi-supervised learning mechanism. We have the current ticketed revenue plus we have the current booked passengers. We also have the ticketed passenger details of previous flights. Hence most of the information is available, however changing market conditions is an unknown variable which can have a significant impact on passenger travel patterns. Through this research We are going to design and develop the best fit model to forecast flight OD level passenger demand based on the historical data.
  • Thumbnail Image
    PublicationEmbargo
    Predictive Analytics Platform for Airline Industry
    (IEEE, 2020-12-10) Tissera, P. H. K; Waduge, K. T; Perera, M. A. l; Nawinna, D. P; Kasthurirathna, D
    The research is to develop accurate demand forecasting model to control the availability in Airline industry. The primary outcome of the model is that the Airline organization can maximize the revenue by controlling the availability. The product in airline industry is the seat, which is an expensive, unstock able product. The demand for the seats is almost uncertain, the capacity is constraint and difficult to increase and the variable costs are very high. Hence the priority of the expected demand forecast is very high for airline industry. An accurate mechanism to predict the revenue for future months of ODs (Origin destinations) is done using fare and passenger data. The revenue is derived by the number of passengers and the fares they pay which vary for each flight. Airline travel is very susceptible to the social, political and economic changes. Therefore, passenger buying patterns change quite dynamically. Hence, it is challenging to develop an accurate method to project the revenue for each route. To overcome this, we are going to use semi-supervised learning mechanism. We have the current ticketed revenue plus we have the current booked passengers. We also have the ticketed passenger details of previous flights. Hence most of the information is available, however changing market conditions is an unknown variable which can have a significant impact on passenger travel patterns. Through this research We are going to design and develop the best fit model to forecast flight OD level passenger demand based on the historical data.
  • Thumbnail Image
    PublicationEmbargo
    Predictive Analytics Platform for Organic Cultivation Management
    (IEEE, 2019-12-05) Rathnayake, R. M. S. M; Ekanayake, E. W. L. M. B; Kahandawala, K. A. I. P; de Silva, W. G. S. C; Nawinna, D. P; Kasthurirathna, D
    There is an increasing demand for organic farming as an environmentally friendly alternative to industrial agricultural system. It is a method of farming that does not involve pesticides, fertilizers, genetically modified organisms, and growth hormones. Organic farming yields vital benefits such as preservation of soil's organic composition, fertility, structure and biodiversity, reduce erosion and reduce the risks of human, animal, and environmental exposure to toxic materials. This paper presents design and development of a software platform for supporting sustainability of organic agriculture system, which has been implemented as a proof of concept in Sri Lanka. The predictive analytics based service platform that not only supports farming decisions of organic farmers but also offers an electronic market place for organic foods. The proposed system is capable of predicting organic harvests, prices and provide decision support on crop selection for upcoming cultivations. To implement this system, machine learning and optimization techniques have been used. In addition, it uses block chain technology to maintain authentication and identity management of organic farmers so that the consumers can trust they get genuine organic food.