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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    FarmCare: Location-based Profitable Crop Recommendation System with Disease Identification
    (IEEE, 2022-12-09) Weerasooriya, W.M.M.S; Wanigaratne, A.D; De Silva, H.G.O; Hansaka, S.A.H; Perera, J; Rukgahakotuwa, L
    Sri Lanka is an agricultural country since ancient times. Today’s agriculture field is in a dangerous situation because farmers are losing their yield. There are many factors to consider when planting crops like rainfall, temperature, soil conditions, future prices, diseases, etc. We decided to help them through the android application we are making. Here we identified four main problems. First, it was wrong crop cultivation. This is the main reason crops and cultivation are destroyed. To give a solution to that problem, we suggest the five most suitable crops to cultivate according to their location. The second problem is a lack of knowledge about future market prices. As a solution to that problem, we predict prices for each cop for the next 12 months. Another problem is an inability to sell their product at a reasonable price. Here, we directly connect buyers and sellers by removing intermediaries. The last problem is the difficulty to identify diseases affected by crops. Using our mobile app farmers can identify which disease affected their crops by uploading an image to the app. To give solutions to the above-mentioned problems Machine Learning algorithms are used like Random Forest, k-means clustering, and Convolution Neural Network algorithms.
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    Optimum Music: Gesture Controlled, Personalized Music Recommendation System
    (IEEE, 2021-12-09) Wijekoon, R; Ekanayaka, D; Wijekoon, M; Perera, D; Samarasinghe, P; Seneweera, O; Peiris, A
    Music plays an important role in everyone’s life since it helps to relax the mind when appropriate music is played. This paper presents a music recommendation system based on the user’s current emotions, activities as well as demographic information such as age, gender, and ethnicity. In addition, the system can be controlled by hand gestures and vocal commands. Unsupervised learning methods in were used to recommend music according to the demographic data and emotions of the user. Finally, the important idea is to recommend music based on all of the user’s data, such as demographics, emotions, and activities. The overall system performance was manually tested and evaluated with a group of individuals, yielding a 70% satisfaction rate for the recommendation; additionally, supporting models such as demographic identification, emotion identification, and hand gesture identification have received a higher proportion of accuracies, contributing to the research’s success. Unlike other systems, ours utilizes all of the user’s information while making music recommendations.
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    Product Recommendation System for Supermarket
    (IEEE, 2020-12-14) Satheesan, P; Haddela, P. S; Alosius, J
    Customers who seek the services at supermarkets are subjected to inconsistencies & ambiguities over choosing their desired products from a wide range of products with the closest quality. Meanwhile, supermarkets find it very difficult to satiate the customers' demand. Therefore, proposing a method to analyze the customers' need plays an important role in attracting new and regular customers. The purpose of this study is to formulate a product recommendation system which analyze customers' needs and thus recommend the best products. This system recommends products to the regular customers and to the new customers as well. New customers mean obviously the customers with no purchasing history at the supermarket in question. The system referred to recommends the products to the new customers using up two methods. One method recommends the most popular products while the other method solely focuses on the product description for recommendation. The system recommends the products to the regular customers using up user-based collaborative filtering, item based collaborative filtering and association rule mining. It recommends products to regular customers based on purchasing history and priority ratings given by other users who bought the products. Initially, the recommendation algorithm finds a set of customers who purchased and rated the products that overlap with the user who purchased and rated the products. The algorithm aggregates products from the customers with similar preference and eliminates the products the user has already purchased or rated. The proposed methodology improves the shopping experience of customers by recommending accurately and efficiently the products that are personalized to the need of the customers.
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    Smart Talents Recruiter-Resume Ranking and Recommendation System
    (IEEE, 2018-12-21) Mohamed, A; Bagawathinathan, W; Iqbal, U; Shamrath, S; Jayakody, A
    During the previous decade, the augmentation of automatic e-recruitment has led to the enlargement of web channels that are devoted to candidate dissemination. In an economic and strategic context where cost-effective is basal, the recommendation of the candidates for the given job requirements has become mandatory. The purpose of this work is to acquaint the actual results that we have achieved on a new recommendation system named Smart Applicant Ranker which is a candidate recommendation tool designed to supervise recruiters while they input their job requirements into the system. This system is designed using Ontology where we can compare the resume models with the given job requirements to match the best comparable candidates. Two ranking algorithms are underlined in this system which will be invoked to assign a ranking point to the recommended candidates against the other candidates on the recommendation pool. The Smart Applicant Ranker system will be kept in a Semantic Web approach that provides IT recruitment firms to seek experts in an efficient way.
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    Smart Intelligent Floriculture Assistant Agent (SIFAA)
    (2021 3rd International Conference on Advancements in Computing (ICAC), SLIIT, 2021-12-09) Samaratunge, U.S.S.; Amarasinghe, D.H.L.; Kirindegamaarachchi, M.C.; Asanka, B.L.
    Technology has become a vital aspect for various functional purposes throughout the world and some industries like floriculture have not adapted technology to solve and facilitate currently facing problems and provide the supply to the demand. Consequently, we have identified and implemented a solution that will address major aspects of such industry barriers. To address these major aspects we proposed a system Smart Intelligent Floriculture Assistant Agent (SIFAA), which uses expert knowledge with solutions and guideline such as identify diseases based on deep learning techniques. It also suggests remedies for diseases based on the expert knowledge, recommend best products for customers by using Reinforcement Learning (RL) technique, motivate cultivators by using demand forecasting, and apply feature engineering by using Linear Regression (LR) and ensemble advance LightGBM Regressors techniques.