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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Subject: search.filters.subject.LSTM

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    Comparison of ARIMA and LSTM in Forecasting the Retail Prices of Vegetables in Colombo, Sri Lanka
    (IEEE, 2022-12-09) Fonseka, D.D; Karunasena, A
    Identification of vegetable price trends is important to make better decisions in the production and market. Due to several factors, including seasonality, perishability, an imbalanced supply-demand market, customer choice, and the availability of raw materials, vegetable prices fluctuate quickly and are highly unstable. In this study price prediction was concluded using two models ARIMA and LSTM with retail price data for Cabbage, Carrot, and Green beans in Colombo from 2009 to 2018. According to the decision criteria of RMSE and MAPE, the LSTM model is superior to the ARIMA model in predicting the retail prices of vegetables. There were no studies have focused on predicting prices with novel technology in the Sri Lankan vegetable market. Hence the results of this study can be used to build an advanced forecasting model by the government and decision-makers in agriculture in Sri Lanka.
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    PublicationOpen Access
    Agro-Genius: Crop Prediction Using Machine Learning
    (https://ijisrt.com/agrogenius-crop-prediction-using-machine-learning, 2019-10) Gamage, M. P. A. W; Kasthurirathna, D; Paresith, M. M; Thayakaran, S; Suganya, S; Puvipavan, P
    This paper present a way to aid farmers focusing on profitable vegetable cultivation in Sri Lanka. As agriculture creates an economic future for developing countries, the demand of modern technologies in this sector is higher. Key technologies used for this problem are Deep Learning, Machine Learning and Visualization. As the product, an android mobile application is developed. In this application the users should input their location to start the prediction process. Data preprocessing is started when the location is received to the system. The collected dataset divided into 3 parts. 80 percent for training, 10 percent for testing and 10 percent for validation. After that the model is created using LSTM RNN for vegetable prediction and ARIMA for price prediction. Finally, for given location profitable crop and predicted future price of vegetables are shown in the application. Other than the prediction, optimizing for multiple crop sowing according to the user requirements and visualizing cultivation and production data on map and graphs are also given in the application. This paper elaborates the procedure of model development, model training and model testing.
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    Exploiting Multivariate LSTM Models with Multistep Price Forecasting for Agricultural Produce in Sri Lankan Context
    (2020 2nd International Conference on Advancements in Computing (ICAC), SLIIT, 2020-12-10) Navaratnalingam, S.
    In Sri Lanka agricultural produces possess a large supply which involves various stakeholders and thus, fluctuation of the agricultural produce prices has a direct impact on the purchasing decisions of the consumer. So, the main purpose of this study is to address the problem faced by the consumer due to poor awareness of price fluctuation which consequently astonish the consumers and hinder them from making better purchasing decisions. The research study is being specially developed in a way to adapt the Sri Lankan agricultural consumer market that is mainly based on Pettah and Dambulla trade centers. As the study we exploited different types of LSTM model with multivariate inputs along with the different combination of multistep models. The result of the study reveals that better performance was obtained for the multivariate CNN LSTM model with encoder decoder multistep model which provided an average RMSE of 19.46 Sri Lankan rupees per kilogram with an average RMSPE of 14.9%. Also, study reveals a correlation between price fluctuation and standard days of the week, where a better prediction was obtained for Monday and Tuesday with an average RMSE of 17.2 and 17.7 Sri Lankan rupees per kilogram respectively with an average RMSPE of 12.2%. Based on the input timestep considered for model, though 14 days and 21 days provided a similar result with minor variation result reveals that 14 days provided a lesser standard deviation of 0.17 than 21 days standard deviation which is 0.98.
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    PublicationEmbargo
    PharmaGo-An Online Pharmaceutical Ordering Platform
    (2021 3rd International Conference on Advancements in Computing (ICAC), SLIIT, 2021-12-09) Gamage, R.G.; Bandara, N.S.; Diyamullage, D.D.; Diyamullage, K.U.; Abeywardena, K.Y.; Amarasena, N.
    Pharmacy services are a paramount important pillar of health. People must keep social distance due to the COVID-19 pandemic, hence the availability of online services to give medicine is vital. Due to the quarantine measures implemented in and by various countries to prevent the virus’s breaking out and online pharmacies have become an exceptionally popular way to obtain accurate medication. Currently, in Sri Lanka, there are a few mobile applications separately owned by each of the pharmacies to provide online pharmaceutical services for their customers. But all the medicines the customer needs might not be available in a single pharmacy. PharmaGo provides with its cooperation to the customers to get medicines of his necessity at a single pharmacy, as against avoiding him roaming from pharmacy to pharmacy. Similarly, pharmacy owners can read the prescription by using image processing mechanisms and doubtlessly identify the required medicines. In addition, the system analyzes previous sales records and provides predictions regarding the future demand for drugs to the pharmacy owners. PharmaGo includes a highly trained AI-powered medical chatbot to guide the customers throughout the process. PharmaGo provides a reliable platform for both pharmacy users and pharmacists to fulfill the unique needs of pharmacy services.
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    PublicationOpen Access
    Agro-Genius: Crop Prediction Using Machine Learning
    (2019-10) Gamage, A; Kasthurirathna, D
    This paper present a way to aid farmers focusing on profitable vegetable cultivation in Sri Lanka. As agriculture creates an economic future for developing countries, the demand of modern technologies in this sector is higher. Key technologies used for this problem are Deep Learning, Machine Learning and Visualization. As the product, an android mobile application is developed. In this application the users should input their location to start the prediction process. Data preprocessing is started when the location is received to the system. The collected dataset divided into 3 parts. 80 percent for training, 10 percent for testing and 10 percent for validation. After that the model is created using LSTM RNN for vegetable prediction and ARIMA for price prediction. Finally, for given location profitable crop and predicted future price of vegetables are shown in the application. Other than the prediction, optimizing for multiple crop sowing according to the user requirements and visualizing cultivation and production data on map and graphs are also given in the application. This paper elaborates the procedure of model development, model training and model testing.