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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Publication Open Access A cost effective machine learning based network intrusion detection system using Raspberry Pi for real time analysis(PLOS ONE, 2025-12-29) Wijethilaka R.W.K.S; Yapa, K; Siriwardena, DIn an increasingly interconnected world, the security of sensitive data and critical operations is paramount. This study presents the development of a Network Intrusion Detection System (NIDS) that analyzes both inbound and outbound network traffic to detect and classify various cyber attacks. The research begins with an extensive review of existing intrusion detection techniques, highlighting the limitations of traditional methods when addressing the unique security challenges posed by distributed networks. To overcome these limitations, advanced machine learning algorithms, including Random Forest, Long Short Term Memory (LSTM) networks, Artificial Neural Networks (ANN), XGBoost, and Naive Bayes, are employed to create a robust and adaptive intrusion detection system. The practical implementation utilizes a Raspberry Pi as the central processing unit for real time traffic analysis, supported by hardware components such as Ethernet cables, LEDs, and buzzers for continuous monitoring and immediate threat response. A comprehensive alert system is developed, sending email notifications to administrators and activating physical indicators to signify detected threats. Our proposed NIDS achieves 96.5 detection accuracy on the NF-UQ-NIDS dataset, with a significantly reduced false positive rate after applying SMOTE. The system processes real time network traffic with an average response time of 50 milliseconds, outperforming traditional IDS solutions in accuracy and efficiency. Evaluation using the NF-UQ-NIDS dataset demonstrates a significant improvement in detection accuracy and response time, establishing the system as an effective tool for safeguarding networks against emerging cyber threats.Publication Embargo CricSquad: A System to Recommend Ideal Players to a Particular Match and Predict the Outcome of the Match(IEEE, 2023-06-12) Lekamge, E. L.; Wickramasinghe, K. R.; Gamage, S. E.; Thennakoon, T. M. K. L.; Haddela, P.S; Senaratne, SSelection of the cricket squad plays a very important role in the outcome of the match. This work is about selecting ideal players for a cricket match and predicting the outcome of the match according to the selected cricket team. A cricket squad consist of around 15 to 16 players, with different expertise in batting, bowling, fielding. To select players for the squad, points were calculated using a statistical approach considering player’s overall career data. And then for the further use of selecting players for the squad next match performance of each and every player were predicted using Machine Learning techniques. Association rule mining was used to find frequent winning player combinations with day/night, home/away, batting first/second, against different opponent combinations. Finally calculate points for each player in both teams, then predict the outcome of the match with classification algorithms by considering the calculated total points of each team and other factors such as toss outcome, batting inning, day night conditions and venue. As for the results, XG boost regressor has produced the highest R2 score of 0.92 for batsman runs prediction model while random forest regressor has produced the highest R2 score of 0.66 for bowler wickets prediction model. The Gradient Boost Classifier predicted the Outcome of a match with the highest accuracy of 0.92 while the K Nearest Neighbor achieved the lowest accuracy of 0.82 score.Publication Embargo A Machine Learning Approach to Predict the Personalized Next Payment Date of An Online Payment Platform(IEEE, 2022-12-09) Karunathunge, L. C. R.; Dewapura, B. N.; Perera, V. A. S.; Kavirathne, G. P. R. A.; Karunasena, A.; Pemadasa, M. G. N.Use of digital payments has risen exponentially in the recent past especially due to the COVID-19 pandemic. This is because online payment methods offer many benefits in performing their day-to-day transactions and paying utility bills such as electricity bills, water bills, telephone bills and etc. Knowing when a consumer will perform a specific online transaction, or bill payment is beneficial to an online payment platform to plan marketing campaigns since targeted marketing has become very prevalent nowadays. However, predicting this is not an easy task since thousands of transactions are happening in each and every minute of an online payment platform. This paper presents the results of a study that investigated predicting the customer personalized, utility bill payment type wise next payment date of a financial company in Sri Lanka by using machine learning techniques. This is accomplished by analyzing not only online transaction history but also customer characteristics and a holiday calendar which is specific to Sri Lanka. At the end of the study, it was identified that XGBoost Regressor is the most suitable machine learning algorithm, etc deal with this scenario which provided 91.02% accuracy. These predictions will be used for sending personalized reminders and discount offers to customers without sending general common notifications when they are planning to do an online payment. Such reminders and offers will be notified on the mobile devices of the customers and, ultimately both customers and the business owners will be benefited by this.Publication Embargo Banana Disease Identification Using Machine Learning Based Technologies and Weather-Based Dispersion Analysis(IEEE, 2022-12-09) Kothalawala, M.U.; Gaveshith, M.G. K; Tharaka, A.H.D.H.; Punchihewa, I.A; Sriyaratna, DBanana is the fourth most important food crop in the world as well as the most important and popular fruit crop in Sri Lanka. Banana leaf diseases are becoming one of the most important factors affecting agricultural products. As a result of these diseases, the quantity and quality of agricultural produce have drastically decreased. Hence, early detection and classification of banana leaf diseases are becoming more important than ever. But the ancient method of disease identification, visual observation is no longer helpful in this matter as it requires significant knowledge and experience related to banana diseases and symptoms which present farmers severely lacks. Therefore, using ICT-based approaches such as autoML, deep learning, natural language processing and APIs are very important towards the efficiency of the disease identification process and the accuracy of the diagnosis as well as keeping farmers synced with the information related to their plantation such as recent threats and nearby threats.Publication Embargo SMART DIARY: Autonomous System for Daily Diary Creation and Prioritization of Daily Activities for Improved Well-Being Using Neural Networks and Machine Learning(IEEE, 2022-12-09) Abraar, S.F.M.; Thuduhenage, D.T.; Balasubramaniyam, V.P.; Mohanraj, S.R.; Wimalaratne, G; Rajapaksha, SIn the present world, the IT (Information Technology) industry is so advanced that it has opened many opportunities to communities with numerous roles. Even though the industry is growing day by day and providing more opportunities, it has had serious effects on human well-being. If a person fails to control the demands of work or study, such as tasks with higher complexity, an unmanageable workload, pressure, enduring conflicts within the team, and other physical and emotional demands, it could lead that person to exhaustion, anxiety, and stress. Such factors can affect the health of a person in an extremely negative way. The proposed topic “Smart Diary: Auto generation of diary and Prioritization of Daily Activities for Improved Well-Being” is a solution for people with uncontrolled job demands and busy work schedules. This helps to keep track of day-to-day life activities and review them to make better plans for the future. It also helps the user prioritize their daily tasks and provides suggestions for people who are stressed and showcasing negative emotions based on text analysis.Publication Embargo SmartPredi – Development of Agricultural Crop Wastage Reduction System using Machine Learning(IEEE, 2022-12-09) Weerasinghe, W.M.K.I.B.; Somawansha, K.W.A.M.; Chandrasiri, K.A. Jayanga; Thalagahagedara, T.M.S.Y.B.; Chathurika, K.B.A Bhagyanie; Swarnakantha, N.H.P Ravi SupunyaThe culture and economy of Sri Lanka heavily depend on agriculture. The All-Island Farmers Federation (AIFF) claims that post-harvest produce loss is a problem that has plagued farmers in all regions of Sri Lanka and occurs both on farms and in commercial locations. The lack of a suitable system to handle produce, such as fruits and vegetables, has been identified as the key problem. The process of sowing seeds to generating the harvest and transporting it to the consumers is an overly complex process. If this process is not correctly identified the demand and supply may not be at equilibrium. Farmers tend to take decisions based on their experiences or from the knowledge gathered from past generations. Over the year environmental factors as well as economic factors have changed, therefore there is a high chance that the decisions taken by farmers might lead to wastage of crops. This research hopes to produce a mobile application for the farmers by considering some factors that affect the wastage in crops and try to provide timely relevant information to minimize the crop wastage by deploying machine learning, one of the advanced technologies in crop prediction.Publication Embargo Mobile Application for Mental Health Using Machine Learning(IEEE, 2022-12-09) Mendis, E.S; Kasthuriarachchi, L.W; Samarasinha, H.P.K.L; Kasthuriarachchi, S; Rajapaksa, SIn present era, mental health has become one of the most neglected, yet critically important, factors of our overall well-being. A large number of people are affected by various types of mental illnesses and mental health disorders. Stress, anxiety, and depression are the most common disorders among children and adolescents in Sri Lanka, and their prevalence has increased over the years, likely to require immediate medical attention. In today’s world, mobile phones and applications play an important role in everyone’s life. With the rapid growth of mental illness, mental health-focused apps and websites have gradually increased globally in recent years. This study aims to develop a mobile application that will primarily serve Sri Lankans with mental health problems, helping them identify their levels of stress, anxiety, and depression (ADS) and receiving advice on how to deal with them. This app’s main objective is to support those who are dealing with mental illnesses and raise awareness of them locally using machine learning and image processing techniques. It does this by serving as a constant reminder of how crucial mental health is and how much of an impact it has on daily life. The GSE Scale, DASS 21 scale has been used to find the users’ mental health illness and the severity of each mental health illness such and Anxiety depression and stress. These methods are put to our mobile application using machine learning techniques such as Decision tree and Random Forest classifiers and uses image processing technologies, CNN machine learning algorithm to offer a variety of activities for reliving stress, depression, and anxiety,Publication Embargo Solid-Waste Management System for Urban Sri Lanka Using IOT and Machine Learning(IEEE, 2022-12-09) Baddegama, T; Ariyasena, H; Wijethunga, S; Bowaththa, M; Nawinna, D; Attanayake, BSolid waste management has become a serious concern in urban areas of Sri Lanka. This paper arises from a study that aims to identify an Information and Communication Technology-based solution for managing solid waste effectively. This solution mainly includes features such as locating common waste hotspots and displaying them on a map, developing a dynamic schedule for collecting garbage, developing an Internet of Things-based smart component to identify the overflowing garbage bins by and automatically notify the municipal council, and a service rating mechanism for garbage collectors. To bring these solutions together, on a single platform, a web application has been designed and developed with all the necessary features. The project’s end goal is to manage disposal methodically before the problem becomes worse and to appraise trash collectors for their service. The findings of this study contribute to the practice and literature on Information and Communication Technology for Development.Publication Embargo A Machine Learning Approach to Predict Default Lease Cases in Sri Lankan Financial Institutions(IEEE, 2022-12-26) Perera, V. A. S.; Kavirathne, G. P. R. A.; Karunathunge, L. C. R.; Dewapura, B. N.; Karunasena, A.; Pemadasa, M. G. N. M.The economic growth of a country can be aided by a strong financial services industry. Therefore, financial companies play a vital role in today’s society. However, by providing credit facilities, they expose themselves to a significant amount of risks, since most of them lack a proper strategy to identify whether the customer is reliable and capable of paying back on time. Hence, it is widely acknowledged that having a proper strategy in place to manage and lessen the credit risks that these companies face is more beneficial, rather than relying on traditional manual techniques. This study is intended to propose a machine learning-based solution to predict possible financial lease defaults beforehand. The dataset used in this work was obtained from a leading finance company in Sri Lanka, where the data were related to leasing contracts and their equipment. According to the final results of this study, a deep learning model implemented using an Artificial Neural Network, which was compared against several other machine learning models, is the best to predict default lease cases in Sri Lankan financial institutions. The finalized model provides 93.93% of classification accuracy, 85.49% of F-measure, 87.69% of AUROC score, and 80.41% of Kappa score.Publication Embargo Machine Learning Based Solution for Improving the Efficiency of Sugar Production in Sri Lanka(IEEE, 2022-12-26) Kulasekara, S; Kumarasiri, K; Sirimanna, T; Dissanayake, D; Karunasena, A; Pemadasa, NAlthough sugar is a popularly used commodity in Sri Lanka, sugar manufactured within the country fulfill only a very small portion of the demanded amount. Sugar production is an intricate process which requires a considerable amount of expertise especially in the areas of cultivation, production and revenue prediction which may not exist in novice farmers. This research proposes a methodology which provides novice sugarcane farmers with expert knowledge on four main areas related to farming including weather forecast, sugarcane maturity estimation, production forecast and prediction of return sugarcane amounts from lands. ARIMA model is used for weather forecast whereas machine learning methods and multiple regression models were used for sugarcane maturity estimation and production of forecasts and returns respectively. The final ARIMA time series model was validated with p-value greater than 0.05 for Ljung-Box test with three different lag values. The Support Vector Machines model was identified as the best model with an accuracy of 81.19% for the sugarcane maturity estimation. The SVM model was trained using the HSV and texture features extracted from sugarcane stalk images using image processing techniques. The prediction of sugar production received a testing R-squared score of 87.75% and mean squared error of 0. Prediction of yield received a mean squared error of approximately 0 and R squared score of 98% on test data. The methodology used in this research could be used by novice farmers to increase their cultivation as well as sugar production.