Faculty of Computing

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Now showing 1 - 6 of 6
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    An Automated System for Employee Recruitment Management
    (IEEE, 2022-12-09) Silva, G.L.L.I.; Jayasinghe, T.L; Rangalla, R.H.M; Gunarathna, W.K.L; Tissera, W
    Recruitment of employees is an important process in the human resource management of a company. Currently, most of the recruitment process is done manually in many companies. This manual process may be time-consuming and possibly may be erroneous in employing inappropriate individuals. This may result in the loss of time, money, and efficiency of a company. As a solution to the above problem, we are considering developing an automated process for recruitment. The scope of the system is to cover not only the recruitment process but also to provide job seekers a platform to identify their current skills, help them identify the current skill trends that are required by companies, and provide the ability to automatically generate their resumes through the system. On the other hand, employers will save a lot of time and money since the system will automate the processes such as skill matching of the employee and the company, shortlisting of resumes, and scheduling interviews. The platform involves features such as online mock interview hosting, automated scheduling, and a pre-interview quiz with a monitoring background. To achieve the above components, machine learning algorithms are used along with other technologies such as web scraping.
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    Machine Learning to Aid in the Process of Disease Detection and Management in Soilless Farming
    (IEEE, 2022-07-18) Fernando, S. D; Gamage, A; De Silva, D. H
    This research aims at enhancing the methods and techniques that are being used in disease detection when it comes to soilless farming. Soilless farming is quite famous among the Sri Lankan farmers farming in urban areas. A mobile application is launched by us and this application is capable of identifying diseases in plants, therefore, farmers do not have to rely on their years of experience to identify the diseases. A novice farmer may struggle to say what is wrong with their plants, while another farmer with many years of experience may say what the disease is with no hesitation. Both those types of farmers benefit from our mobile application equally. The said mobile application consists of four components and each of them focuses on a different service. One of those components is to detect and manage diseases in plant leaves and that component is what this research paper showcases. This particular component allows the user to capture live-images of plant leaves. Then the application processes the captured image to identify if the plant is suffering from a disease. After that, it generates a report with a set of treatments. It further analyses and alerts the user if this disease detected is going to affect the harvest.
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    Smart Platform for Film Shooting Management
    (IEEE, 2019-12-06) Senarath, S. M. M. M; Perera, M. T. K; Viduranga, D. G. R; Wijayananda, H. M. C. S; Rankothge, W
    Producing a movie involves difficult and time-consuming phases, specially, pre-production and production. It's a challenging task to find out suitable locations for each scene and building a schedule without any clashes. We have proposed and implemented a platform for film shooting management with following modules: (1) identify required background for each scene, (2) classify available film shooting locations, (3) compare the required background and available film shooting locations and (4) schedule the shooting of each scene. We have used natural language processing, image processing, string matching algorithms and optimization techniques to implement the above-mentioned modules. Our results show that, using our proposed modules, the film shooting management related services can be automated efficiently and effectively.
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    Price Optimisation and Management
    (IEEE, 2021-12-09) Shafkhan, M. T. M; Jayasundara, P. R. S. S; Kariyapperuma, K. A. D. R. L; Lakruwan, H. P. S; Rupasinghe, L
    One of the most crucial decisions a company makes is its pricing strategy. When it comes to pricing, a company must consider the present, as well as the future and the past pricing. It enables a company to make sound judgments. In the process of marketing products, price is the only factor that creates income; everything else is a cost. Guessing at product pricing is a little like throwing darts blindfolded; some will hit something, but it probably will not be the dartboard. Large-scale enterprises throughout the world still depend on Excel sheets with numerous manpower or expensive pricing solutions. Expensive pricing systems are difficult to implement for Medium and Large Sized Enterprises in countries like Sri Lanka. Our goal in this research is to propose an affordable, efficient, easy-to-use and secure solution which can be implemented in Medium and Large Sized Enterprises in Sri Lanka. Manufacturing cost, shipping cost, competitor analysis, customer behaviour are taken as the root factors when deciding the price. The proposed solution includes Machine Learning components which is fed with historical data of these four factors to predict the manufacturing cost, shipping cost, competitor price and customer behavioural factors on a given date and as well as an optimisation component which enables the opportunities to minimise the cost and maximise the profit. The four Machine Learning components are implemented using LSTM, ARIMA, Facebook Prophet and a clustering model. The optimisation model is implemented using linear programming optimise these four components. A user-friendly web application is implemented using MEAN stack with micro service architecture to access this.
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    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.
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    Sinhala Conversational Interface for Appointment Management and Medical Advice
    (IEEE, 2020-12-10) Rajapakshe, D. D. S; Kudawithana, K. N. B; Uswatte, U. L. N. P; Nishshanka, N. A. B. D; Piyawardana, A. V. S; Pulasinghe, K
    This paper proposes an intelligent conversational user interface to assist Sinhala speaking users to make appointments with doctors and to obtain medical advices. This Sinhala Conversational Interface for Appointment Management and Medical Advice (SCI-AMMA) consists of Speech Recognition unit, Query Processing unit, Dialog Management unit, Voice Synthesizer unit, and User Information Management unit to handle user requests and maintain a meaningful dialogue. The SCI-AMMA gets the users' speech utterances and recognize the language content of it for further processing. Language content is further processed using query processing unit to identify users' intent. To fulfil the users' intent, a reply is generated from Dialogue Management Unit. This reply/answer will be delivered to the user by means of a voice synthesizer. The proposed system is successfully implemented using state of the art technology stack including Flutter, Python, Protégé and Firebase. Performance of the system is demonstrated using several sample scenarios/dialogues.