Browsing by Author "Gamage, M. P"

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Open Access
A step towards a Natural Language Programming Tool (NLPT)
(Researchget.net, 2014-06-13) Perera, K. J. A; Kuruppu, K. A. I; Gamage, M. P; Jayakody, J. A. P. B; Gunasekara, K. S. G. S; Kodagoda, N
This research project is taking a step towards developing a tool that can facilitate Natural Language Programming (NLP). As a result Natural Language Programming Tool (NLPT) is designed that generates source codes in response to Natural Language (NL) instructions. The major issue that needs to be addressed is identifying the meaning of the user’s NL instruction. The research is aimed at solving the fundamental problems of symbolic NL understanding which is an evolving research area. As NL consists of a broad vocabulary Basic Mathematical Calculations and Basic Input/Output Operations are selected as the project domain. NLPT can be used as a programming tool to generate the relevant source code, plays as an interactive programming learning tool and serves as a brainstorming tool.
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User-friendly Enhanced Machine Learning-based Railway Management System for Sri Lanka
(IEEE, 2021-12-06) Mihiranga, G.L.V; Weerasooriya, W. K. M; Palliyaguruge, T. L. P; Gunasekera, P. N. G; Gamage, M. P; Kumari, S. P. K
The railway service is a convenient and low-cost transport method in Sri Lanka, widely employed by both local and foreign passengers. Major railway lines in Sri Lanka cover unique and very different areas in the country. For example, the Northern province's weather and geography conditions significantly differ from Southern or Central provinces. Majority of the tourists lack understanding in identifying appropriate or attractive places that best suits them, close by to the Railway Stations. Therefore, a passenger needs to spend more time identifying their railway tour destinations. When passengers are booking tickets, even though they are able to reserve seats beforehand, they are unable to reserve a specific seat. Also, there is no process to identify the most suitable seat for them amidst many other travelers, especially if they are travelling alone. Considering the aforementioned, authors propose a more innovative and user-friendly system for the Railway Department of Sri Lanka. Depending on various passenger attributes the system is capable of suggesting a travel plan with railway lines which cover most suitable destination suggestions; identifying the best seats with a relaxing atmosphere; providing an interactive chatbot to satisfy user queries on specific location information; and facility for 24×7 user interaction. A travel plan can save passengers time and allows them to identify the desired railway line and relevant attractions without much hassle. And they are saved of an unpleasant experience through the suggestion of the best seating location. Machine Learning and Deep Learning technologies are used in developing the proposed system.
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Virtual Makeover and Makeup Recommendation Based on Personal Trait Analysis
(IEEE, 2021-12-09) Perera, P. R. H; Soysa, E. S. S; De Silva, H. R. S; Tavarayan, A. R. P; Gamage, M. P; Weerasinghe, K. M. L. P
The utilization of facial makeup is an important attribute in modern society as a means of self-expression, and as a method to feel more confident during social interactions. A makeover has become a necessity when attending divergent functions, and makeup used on diverse occasions varies in style. Choosing the perfect makeup that best suits a person is challenging unless they have years of expertise with cosmetics. This paper proposes a “Virtual Makeover and Makeup Recommendation System” to eliminate the need to be concerned about the appearance after applying makeup. The proposed system enables real-time makeup simulation in an Augmented Reality (AR) environment and recommends makeup styles considering skin tone, colour of clothing and hair, type of clothing and occasion to be attended with makeup. The personal traits of a user would be automatically detected and processed to generate recommendations for makeup products, namely lipstick, foundation and eyeshadow. Complications of wasting makeup products and time, and cleaning makeup can be mitigated by using a real-time makeup simulation system. Recommendations generated by the application assist the users to decide on makeup styles and provides a better user experience. The proposed system is developed with the aid of Deep Learning (DL) algorithms.
Open Access
A Web Application to Support Customer Churn Management for Retail Grocery Stores
(United International Journal for Research & Technology |, 2022) Mallawarachchi, S. N; Rodrigo, M. N. D; Gunaratne, M. A. S. N; Gamage, M. P; Qamra, N. N
— In the business world ‘Customer Churn’ is a principal issue. The retail grocery business holds a peak point in churning customers due to various reasons. Churn means gradually breaking every connection with the business by the customers. According to the experts, retaining the existing customers cost less, than attracting new customers. Therefore, a web-based prediction model; “CRetention” with some additional features is proposed as a solution. The main features in the proposed system are to analyze data and predict customers who are about to churn, manage the storage of inventory items, provide marketing strategies by market basket analysis, and offer personalized marketing recommendations to retain customers. Machine Learning and Deep Learning technologies are used to implement the solution. The main advantage and novelty of the product are that a definition for churn adjusted to a retail business is created and churners and results are obtained are based on a real scenario. It is clear that the retail grocery store owners highly recommend and appreciate the proposed system from a survey conducted.