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 Real Time Accident Detection and Emergency Response Using Drones, Machine Learning and LoRa Communication(Science and Information Organization, 2025) Bandara H.M.S.I.D; Maduhansa H.K.T.P; Jayasinghe S.S; Samararathna A.K.S.R; Fernando, H; Lokuliyana, SRoad accidents and delayed emergency responses remain a major concern in urban environments, contributing to over 1.4 million fatalities globally each year. With rapid urbanization and increasing vehicle density, timely detection and efficient traffic management are critical to reducing the impact of such events. This study proposes a real time Accident Detection and Emergency Response System with integrating Machine Learning IoT enabled drones and LoRa communication. The system combines real time accident detection using CCTV, drone assisted fire detection for post accident scenarios, crime activity monitoring and automated traffic management to reduce congestion and improve public safety. LoRa ensure long range, energy-efficient communication. ML models improve detection accuracy across accidents, fires, crimes and vehicles. Figures and sensor data are analyzed in real time to trigger alerts and assist emergency responders. The system supports scalable integration with existing urban infrastructure, promoting the development of smart city safety frameworks. By minimizing emergency response time, limiting secondary incidents and improving situational awareness, the proposed solution addresses critical gaps in current urban safety systems. It offers a practical, intelligent and adaptive approach to accident mitigation and traffic control in smart cities.Item Open Access Enhancing Cognitive and Metacognitive Domains of Autistic Children Using Machine Learning(Multidisciplinary Digital Publishing Institute (MDPI), 2025-08-21) Tharaki, D; Rupasinghe, Y; Ruhunage, P; Pehesarani, A; Rathnayake, S.CASD poses special difficulty in both cognitive and metacognitive development, necessitating specialized educational strategies. This research proposes LearnMate, a web-based application powered by machine learning techniques that aims to improve the abilities of children with autism. Utilizing classification models learned from medical data, LearnMate forecasts skill acquisition and suggests personalized learning activities according to the strengths and developmental requirements of the child. The system permits instructors to monitor progress through real-time feedback, enabling adaptive learning approaches. Pilot application to more than 100 children showed significant gains in their skills. The results demonstrate the immense potential for change through machine learning in special education to facilitate data-driven, personalized learning opportunities that enhance the capabilities of both autistic students and teachers.Publication Embargo Machine learning study of shoreline change in Western and Southwestern coastlines of Sri Lanka(Emerald Publishing, 2025-12-05) Dananjaya, H.G. D.V; Gomes,P.I.AShoreline change per year, also known as end point rate (EPR), showed a skewed normal distribution but without a clear spatial trend for the period 2013–2023 in the western and southern coastal belts. The performance of four machine learning (ML) algorithms was evaluated by dividing the EPR into three or five classes. The three-class EPR approach gave more predictive power. With hyperparameter tuning, the random forest (RF) algorithm demonstrated 0.69 accuracy in EPR prediction, whereas the artificial neural network, support vector machine, and k-nearest neighbour showed accuracies at 0.63, 0.58, and 0.52, respectively. The RF model in any EPR class showed more than 50% accuracy and was thus used as the ML prediction tool. Global Shapely additive explanations illustrated that the presence of port structures, distance to the river mouth, and geomorphology contributed significantly to the overall predictions. Model validation using a separate coastal stretch resulted in a 0.66 accuracy, demonstrating the model’s generalisation ability.Publication Embargo Cloud-based Salesman-Bot for Ontology-based Negotiation(IEEE, 2023-04-06) Fernando, A; Rahubedda, T; Jayasinghe, B; Mallikahewa, S; Hettiarachchi, O; Rajapaksha, SWe have proposed a cloud-based ChatBot (Salesman-Bot) approach to handling multiple negotiation scenarios in a supermarket environment. The web application is a simple interface that can be implemented on a single standalone device or interacted with through a mobile phone. The Salesman-Bot responds both via text and speech. By introducing a Salesman-Bot, efficient negotiation, with quick preferences and suggestions can be provided. A new architecture proposed to operate the Salesman-Bot together with Google APIs and libraries such as Natural Language AI, Vision AI, Speech to Text API, Text to Speech API and Machine Learning using TensorFlow. The application also uses the Google Cloud Platform with related services such as Google App Engine. The goal is to make ChatBots more efficient in negotiating in different business scenarios. This paper presents the work carried out with ontology and machine learning in a cloud-based environment to handle multiple negotiation scenarios based on a negotiation hierarchy. It also proposes the opportunities and drawbacks of such a system.Publication Embargo AgroPro: Optimizer for Traditional Agricultural System in Sri Lanka(IEEE, 2022-12-09) De Silva, D.I.; Suriyawansa, G.M. T. K. D. S.; Senevirathna, M.R. U. M. T.; Balasuriya, I.D. I.; Deshapriya, A. G. S. P.; Gadiarachchi, G. A. D. K. M.Today, in many countries around the world, big data analysis and machine learning methods are used for industrial development. However, such techniques are rarely used in Sri Lankan agricultural industry. The success of agriculture depends heavily on the selection of the right crop. Choosing the right crop depends primarily on predicting future yields. Machine learning methods can be used very successfully to make future predictions about crop yields. Crop prediction mainly depends on the soil, geography, and climate of the growing location. Hence historical data with agricultural facts such as temperature, humidity, pH, and rainfall are used to predict yield as parameters in the machine learning function. Sri Lanka uses a traditional approach to distribute fertilizers among farmers. Not having an organized way to distribute fertilizers to the needed areas leads to many abnormalities along the way. As a result, the country is facing economic losses and resource wastage. Having an optimized distribution network is the key to overcoming those abnormalities. This research assesses the efficiency of the fertilizer distribution system and consists of time-series predictions on fertilizer usage to gain future value. The aim is to identify performance gaps in distribution management that lead to delayed fertilizer distribution affecting agricultural productivity.Publication Embargo An Automated Tool for Student Journey Orchestration & Optimization using Machine Learning(IEEE, 2022-12-09) Ramanayaka, D.Y; Liyanagunawardana, A.P; Ekanayake, E.M.T.K.B; Weerarathna, U.U; Jayasingha, T.B; Thilakarthna, TThe customer journey is a full interaction that a customer has with a business. Every touchpoint of the business is an opportunity to provide good experiences that encourage future opportunities to become customers and consumers to be committed loyal customers through the customer journey. This research paper refers to the student’s journey at university as a customer journey & considers the student’s actions to map the next suitable actions. This paper proposed a machine learningbased novel approach to recommending the suitable next best action for the students based on their past performance at university by using customer journey orchestration and optimization. Customer journey orchestration is the process of coordinating customer experiences in real-time to encourage better engagement with the systems and organization. The journey orchestration of university students is currently a manual flow. The main goal of this research is to convert the manual flow of university journey orchestration into an automated flow. The proposed system orchestrates and optimizes the student journeys at each milestone of the university by recommending the suitable path or next best action as the outcome to help students make a successful path throughout their university journey. This research contributes to achieving the educational goals and professional career goals of university students successfully. Furthermore, from the perspective of the university, this proposed system supports everything to facilitate better directions for the students to complete their studies successfully.Publication Open Access Hashtag Generator and Content Authenticator(researchgate.net, 2018-01) Yapa Abeywardena, K; Ginige, A. R; Herath, N; Somarathne, H; Thennakoon, T. M. N. SIn the recent past, Online Marketing applications have been a focus of research. But still there are enormous challenges on the accuracy and authenticity of the content posted through social media. And if the social media business platforms are considered, majority of the users who try to add a market value to their own product face the problem of not getting enough attention from their target audience. The purpose of this research is to develop a safe and efficient trending hashtag generating application solution for social media business users which generates trending and relevant hashtags for user content in order to get a broad reach of target audience, automatically generates a meaningful caption to their relevant posts and guarantees the authenticity of the product at the same time. The user content is analyzed and filters the important keywords, generates a meaningful caption, suggest related trending keywords and generates trending hashtags to get the required reach for online marketers. Additionally, the marketing products’ content authentication is ensured. The application uses Natural Language Processing, Machine Learning, API technologies, Java and Python technologies. A unique database is assigned to users which contains rankings for each user. The target audience who engages in buying products get to know about the status of the sellers with respect to authenticity of the content. It is believed that the application provides a promising solution to existing audience reach problems of online marketers and buyers. The significance of this system is to help marketers and buyers to engage in online buying and selling with much effective, reliable and safer ways. This mitigate the vulnerability of bad social media marketing influences and helps to establish a safe and reliable online marketing practice to make both sellers and buyers happy. This paper provides a brief description on how to perform an organized online marketing discipline via the Trending Hashtag Generator & Image Authenticator application.Publication Embargo E-tongue -A smart tool to predict safe consumption of groundwater(IEEE, 2020) Alahakoon, A. M. P. B; Nibraz, M. M; Gunarathna, P. M. S. S. B; Thenuja, S; Kahandawaarchchi, K. A. D. C. P; Gamage, N. D. U— In Sri Lanka, reportedly 59% of the population depends on water from natural sources. The government has taken necessary action to provide a quality water, there has always been a need to educate people about the importance of maintaining water quality, the importance of using betterquality water, and necessary precautions to be taken to avoid the Chronic Kidney Disease (CKD). Prior studies of the problems that has to induce to implement an E-Tongue: a smart device to predict safe consumption of groundwater, which is identify the quality of a groundwater in real-time by designing an Internet of Things (IoT) device to read the value of water quality parameters and GPS to fetch location which will be then transferred to cloud environment for an easy access by the machine learning model to process and identify the Water Quality Index (WQI). It will then predict the water quality parameter levels that could be changed in the future and check the possibility of CKD. All the outputs will be finally displayed via the mobile application with 73% accuracy.Publication Embargo Automatic Sinhala News Classification Approach for News Platforms(Institute of Electrical and Electronics Engineers Inc., 2020-12-18) Kirindage, G; Godewithana, NBecause of generating various news articles in large scale, online sources moved into an automatic categorization mechanism. This research has been conducted using LDA topic modeling approach and using other classification algorithms to establish a news categorization solution. Sinhala news websites have only few news categories and do not have any relationships or hierarchies between the categories. Therefore, some users require to search manually and find the necessary articles which are in those categories. Purpose of this study is to build a news categorization model with categorization hierarchies for Sinhala news articles. The goals of the models are to identify the most suitable news category for a related news article and develop hierarchies using generated news categories and assign the news articles according to the hierarchical structure. The final experiments and evaluations show that the solution performs well to solve the automatic categorization problem in Sinhala news platforms.Publication Open Access Intrusion detection system with correlation engine and vulnerability assessment(SCIENCE & INFORMATION SAI ORGANIZATION LTD, 2018-09-01) Waidyarathna, D. W. Y. O; Nayantha, W. V. A. C; Wijesinghe, W. M. T. C; Abeywardena, K. Y—The proposed Intrusion Detection System (IDS) which is implemented with modern technologies to address certain prevailing problems in existing intrusion detection systems’ is capable of giving an advanced output to the security analyst. Even though the network of an organization has been secured internally as well as externally the intruders find ways to penetrate the network. With the system that is proposed activities of those intruders can be identified with a higher probability even if managed to bypass security controls of the network. The goal of this project is to give a reliable output to the system users where all the alerts are more accurate and correlated using HIDS alerts and NIDS alerts which is similar to the modern SIEM concept. The system will perform as a centralized IDS by getting inputs from both HIDS and NIDS which gives data regarding the activities of hosts and network traffic. With those implementations, the system is capable of monitoring host activities, monitoring network traffic with existing tools and give a correlated output which is more accurate, advanced and reliable prioritizing the possible attacks by using machine learning techniques and rule-based correlation techniques. With all these capabilities final product is a fully automated Intrusion Detection System which gives correlated alerts as outputs with a less rate of false positives compared to the existing systems.