Research Papers - Dept of Computer Systems Engineering

Permanent URI for this collection https://rda.sliit.lk/handle/123456789/1253

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Has files: YesSubject: search.filters.subject.Business Intelligence

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    Data-driven Business Intelligence Platform for Smart Retail Stores
    (IEEE, 2021-12-09) Eheliyagoda, D. R. M. R. R. D. R. S; Liyanage, T. K. G; Jayasooriya, D. C; Nilmini, D. P. Y. C. A; Nawinna, D. P; Attanayaka, B
    The following research paper presents the design and development of a data-driven decision support platform for the effective management of contemporary retail stores in Sri Lanka. This research has four core components, as a solution to the identified shortcomings. These components are Customer Relationship Management (CRM), Supplier Relationship Management (SRM), Price and Demand estimation, and Branch and Employee Performance Monitoring and Rating. The developed system has features such as product replenishment levels, decrease capital movement, reduced material wastage, better item assortment, provide supplier service efficiency, improve employee and branch-level efficiency, and elevated client delivery.This decision support system used Machine Learning (ML) technologies such as LSTM (Long short-term memory) and ARIMA (Autoregressive integrated moving average) models, Regression, Classification, and Associate Rule Mining Algorithms as key technologies. Data were obtained from websites such as Kaggle and other free platforms for the analysis of datasets. The resulting platform was able to perform with an accuracy of over 90% for all four core components with the tested data sets. The system presented would be particularly beneficial for the top management in retail stores to make effective and efficient decisions based on predictions and analyzes provided by the system.
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    A New Approach for Consumer Protection with Business Intelligence and Data Visualization
    (IEEE, 2021-12-09) Kariyawasam, K. S. T. U. S; Liyanaarachchi, L. A. A. S; Chathurabhani, H. M. N. N; Jayakody, A; Attanayaka, B
    According to the current market usage in Sri Lanka, there is no proper system to manage the buying and selling process of consumer goods and services. This paper presents a possibility of developing a systematic and essential food items management system using a mobile application with public and private interventions benefiting both the trade and the consumer is being explored. The authors discussed a methodology for managing essential food items through business intelligence and data visualization. It connects the trade and consumer sectors and the public and responsible private sectors related to this sector through a mobile application and presents data related to this sector through business intelligence forecasting and visualization methods. This research will also help reduce consumer problems by building transparency in the essential foodstuff sector. It will also systematically update the future of the essential food and beverage industry. The findings contribute to the body of knowledge on the New Approach for Consumer Protection with Business Intelligence and Data Visualization.
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    Auto Training an AI for Detecting Plant Disease Using Twitter Data Annexed With a Plant Anthology
    (2021 3rd International Conference on Advancements in Computing (ICAC), SLIIT, 2021-12-09) Vasanthan, N.; Shimran, Mohamed; Ahkam, A.; Ishak, I.; Silva, C.; Kuruppu, T.A.
    Agricultural productivity plays a vital role in contributing to a nation’s economy. Farmers nowadays are concerned due to disease persistence in crops and plants, and it also affects the economy indirectly, so it is important to come up with a solution to detect plant diseases and educate the farmers about the solutions to retaliate against the diseases. Proper care is mandatory to safeguard the quality of plants. The existing traditional methods consume a massive amount of time and resources hence, it’s costly. Due to the importance of continuous monitoring, it seems impractical for a farmer to implement the traditional methods on large scale. The Traditional systems which are used lack the ability to identify diseases out of their predefined scope. As a solution, we came up with an autolearning system that identifies new plant diseases and provides remedies. This paper showcases the image processing techniques to detect plant diseases, Auto ML techniques to create new models for plants and corresponding diseases, Diseases are identified using image processing, Remedies are extracted for the given plant diseases using unstructured data from web data crawling. The business intelligence model uses NLP to provide ideas about the trending plants and plantrelated diseases are also discussed in this paper.