SLIIT Conference and Symposium Proceedings

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All SLIIT faculties annually conduct international conferences and symposiums. Publications from these events are included in this collection.

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Has files: YesSubject: search.filters.subject.Prediction System

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    Smart Crop and Fertilizer Prediction System
    (IEEE, 2019-12-05) Wickramasinghe, C. P; Lakshitha, P. L. N; Hemapriya, H. P. H. S; Jayakody, A; Ranasinghe, P. G. N. S
    Agricultural industry plays a major role in the process of economic development as well as the Gross Domestic Product of Sri Lanka. One of the significant issues in the industry is lacking an accurate way to identify the best crop that can be grown with the available soil fertility in a particular land. Since most of the farmers have a lack of knowledge about soil nutrients, they start cultivations by believing myths in society and few of them use scientific approaches. This research mainly focuses on suggesting the best crop according to soil fertility of land and also it recommends a fertilizer plan to optimize the amount of fertilizers applied for suggested crops. The paper presents a tool with embedded sensors that measure soil fertility and developed a cross- platform mobile application to suggest the best crops according to available soil fertility. Further, a fertilizer plan will be suggested to optimize fertilizer usage in order to increase profitability and avoid soil degradation. To evaluate the final product, the same soil sample was tested in the lab and using sensors embedded tool. Results obtained by those tests proven that both generate approximately equal Nitrogen (N), Phosphorus (P) and Potassium (K) values.
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    AI Based Cyber Threats and Vulnerability Detection, Prevention and Prediction System
    (IEEE, 2019-12-05) Amarasinghe, A. M. S. N; Wijesinghe, W. A. C. H; Nirmana, D. L. A; Jayakody, A; Priyankara, A. M. S
    Security of the computer systems is the most important factor for single users and businesses, because an attack on a system can cause data loss and considerable harm to the businesses. Due to the increment of the range of the cyber-attacks, anti-virus scanners cannot fulfil the need for protection. Hence, the increment of the skill level that required for the development of cyber threats and the availability of the attacking tools on the internet, the need for Artificial Intelligence-based systems, is a must to the users. The proposed approach is an automated system that consists of a mechanism to deploy vulnerabilities and a rich database with known vulnerabilities. The Convolutional Neural Networks detects the vulnerabilities and the artificial intelligence-based generative models do the prevention process and improves reliability. The prediction procedure implemented using the algorithm called “Time Series” and the model called “SARIMA”. These implementations give an output with considerable accuracy.