Research Publications

Permanent URI for this communityhttps://rda.sliit.lk/handle/123456789/4194

This main community comprises five sub-communities, each representing the academic contribution made by SLIIT-affiliated personnel.

Browse

Filters

2016 - 201912020 - 20234

Advanced Search

Filter by

Settings

Subject: search.filters.subject.ANN

Search Results

Now showing 1 - 5 of 5
  • Thumbnail Image
    PublicationEmbargo
    WONGA: The Future of Personal Finance Management – A Machine Learning-Driven Approach for Predictive Analysis and Efficient Expense Tracking
    (IEEE, 2023-07-10) Uyanahewa, M.I.R; Jayawardana, G.V.H.D; Bandara, M.B.D.N; Hapugala, H.A.V.V; Attanayaka, B
    The financial literacy of Sri Lankans is relatively low, leading to difficulties in managing personal finances. This research presents a smart solution to simplify the complexities associated with money management and assist individuals in managing their finances more efficiently to achieve better financial health without requiring a comprehensive knowledge of money management from the user. The proposed system automates personal finance management with minimal user effort, reducing manual data entry by tracking cash flow by utilizing SMS messages and expense bills to extract bank transaction data and cash expenditures. Each extracted expense will automatically be categorized into the correct expense category. The system also generates a custom budget plan for each user based on spending patterns to help them stay on the budget throughout the month and avoid irrational overspending. Furthermore, the system provides a mechanism to predict future expenses associated with upcoming events based on calendar events, allowing users to devise the most efficient budget plan and avoid facing financially unprepared events in the upcoming month. All these smart solutions are bundled up in the "Wonga" mobile application to help users make better financial decisions to achieve personal financial success.
  • Thumbnail Image
    PublicationEmbargo
    A Framework for Tracking And Summarizing Daily Activities with Mobile Phone for Healthy Life of a User
    (Institute of Electrical and Electronics Engineers, 2022-09-16) Wickramasinghe, K.E.T.; Albertjanstin, N; Wijethunga, L.P. P. L; Rajapaksha, U.U.S.K; Panduwawala, P.K.K.G; Harshanath, S.M.B
    People nowadays have incredibly busy lifestyles with little time to do their activities, and smartphones have become a need for many people all over the world as new features and controls have been developed, and smartphone usage has rapidly increased by individuals. People that lead complex lifestyles find it difficult to balance their activities. As a consequence, an automated method of tracking user activity on a smartphone can give a better solution for managing a user's daily life while also saving time. We take a look at some of the previous studies on user behaviour tracking methods approaches. Derived from the previous studies, we noticed some important challenges, including smartphone addiction and unhealthy posture, battery drainage concerns, managing educational activities, and finding necessary information from the huge data. In this paper, we proposed an automated monitoring approach to track user activities on smartphones and give solutions to the aforementioned difficulties. We developed different algorithms such as ANN to understand smartphone usages and battery interactions, CNN for detecting unhealthy neck postures and Word2Vec for generating similar meanings for fast search
  • Thumbnail Image
    PublicationOpen Access
    A comparative study of data mining algorithms in the prediction of auto insurance claims
    (European International Journal of Science and Technology, 2016) Weerasinghe, K. P. M. L. P; Wijegunasekara, M. C
    Insurance claims are a significant and costly problem for insurance companies. The prediction of auto insurance claims has been a challenging research problem for many auto insurance companies. Identifying the risk factors which are affected for the high number of claims and denying them may lead to increased corporate profitability and keep insurance premiums at a below rate. The key objective of conducting this study is to examine the data mining techniques in developing a predictive model in support of auto insurance claim prediction and a comparative study of them. The research was carried out by using Artificial Neural Network (ANN), Decision Tree (DT) and Multinomial Logistic Regression (MLR) to develop the prediction model. The results indicated that the ANN is the best predictor with 61.71% overall classifier accuracy. Decision tree came out to be the second with 57.05% accuracy and the logistic regression model indicated 52.39% accuracy. Parameters of optimal NN model gives 6 input neurons and 7 minimum hidden neurons with 0.15 learning rate. The comparative study of multiple prediction models provided us with an insight into the relative prediction ability of different data mining methods. The comparison of the results of the decision tree and neural network models showed an interesting pattern. Policies that are misclassified by one model are correctly classified by the other. This might be an indication that the combination of the models could result in a better classification performance.
  • Thumbnail Image
    PublicationEmbargo
    Autonomous Cyber AI for Anomaly Detection
    (2021 3rd International Conference on Advancements in Computing (ICAC), SLIIT, 2021-12-09) Madhuvantha, K.A.N.; Hussain, M.H.; De Silva, H.W.D.T.; Liyanage, U.I.D.; Rupasinghe, L.; Liyanapathirana, C.
    Since available signature-based Intrusion Detection systems (IDS) are lacking in performance to identify such cyber threats and defend against novel attacks. It does not have the ability to detect zero-day or advanced malicious activities. To address the issue with signature-based IDS, a possible solution is to adopt anomaly-based detections to identify the latest cyber threats including zero days. We initially focused on network intrusions. This research paper discusses detecting network anomalies using AIbased technologies such as machine learning (ML) and natural language processing (NLP). In the proposed solution, network traffic logs and HTTP traffic data are taken as inputs using a mechanism called beats. Once relevant data has been extracted from the captured traffic, it will be passed to the AI engine to conduct further analysis. Algorithms such as Word2vec, Convolution Neural Network (CNN), Artificial Neural networks (ANN), and autoencoders are used in order to conduct the threat analysis. HTTP DATASET CSIC 2010, that NSL-KDD, CICIDS are the benchmarking datasets used in parallel with the above algorithms in order to receive high accuracy in detection. The outputted data is integrated and visualized using the Kibana dashboard and blockchain model is implemented to maintain and handle all the data.
  • Thumbnail Image
    PublicationOpen Access
    Analyzing relationships between rainfall and paddy harvest using artificial neural network (ANN) approach: case studies from North-western and North-central provinces, Sri Lanka
    (The Faculty of Agricultural Sciences of the Sabaragamuwa University of Sri Lanka, 2022-01-04) Ranasinghe, T; Rathnayake, U. S; Gunawardena, G; Wimalasiri, E. M
    Purpose: Food and agriculture are frequently affected from on-going climate change. A significant percentage of annual harvest is lost due to extreme climatic conditions in different parts of the world. Sri Lanka is considered as a country which is vulnerable to climate change. Therefore, this research presents a detailed analysis to find out the non-linear relationships between the rainfall and paddy harvest in two major provinces of Sri Lanka. Research Method: North-central and North-western provinces as two major agricultural areas were selected for the study. Rainfall trends were identified using non-parametric Mann-Kendall and Sen’s slope estimator tests. The artificial neural network (ANN) approach was used to establish non-linear relationships between rainfall and paddy yield. Findings: There was no significant (p > 0.05) linear correlation between rainfall amount and the rainfed paddy yield in tested locations. However, no clear relationship between the rainfall and rain fed yield were found in the 14 predefined functions (polynomial, logarithmic, exponential and trigonometric) derived using ANN where the calculated coefficients of determination were less than 0.3. Research Limitations: Due to lack of other climate variables such as temperatures, a significant relationship was not observed in this study. Originality/value: We have shown that non-linear artificial neural network approach can be used to study the impact of climate on agricultural production in Sri Lanka.