Research Publications

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Start date: 2020Subject: search.filters.subject.NLP

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Now showing 1 - 8 of 8
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    publications.listelement.badgeOpen Access
    Investigating the Locational Accuracy of Crowdsourced Data in the Context of NSW Wildfires Using NLP and Geocoding Techniques
    (Sri Lanka Institute of Information Technology, 2026-05-21) Vishwajith, D; Koswatte, S
    Wildfires are major natural disasters, and timely monitoring is essential for effective response. Recent progress in Crowdsourced Data (CSD) and Remote Sensing (RS) has improved wildfire monitoring, however their combined use for spatial validation is still limited. This study examined the locational accuracy of Twitter-based wildfire reports during the 2019-2020 New South Wales (NSW) - Australia wildfire season by comparing them with satellite-derived hotspots. Wildfirerelated tweets were processed using Natural Language Processing (NLP), Named Entity Recognition (NER), and geocoding through the Nominatim API, while hotspot data was obtained from the VIIRS sensor via Digital Earth Australia. Advanced data cleaning and keyword filtering methods were applied to extract relevant geolocated tweets from a large raw dataset, and geocoded locations were rigorously clipped to the NSW boundary to improve spatial relevance. A 5 km grid overlay and statistical tests, including chi-square, binomial tests and Poisson-Based tests, were used to measure the spatial relationship between the two datasets. The results showed a strong positive association, indicating that Twitter reports often align with satellite-validated hotspots, particularly in densely reported areas. Although CSD can be uneven in coverage and limited by geocoding accuracy, a combined approach using NLP, spatial filtering, and statistical validation improves its reliability. The study highlighted the value of integrating social media data with RS, proposed a reproducible framework for spatial accuracy assessment, and provided practical guidance relevant to real-time wildfire monitoring and disaster management systems.
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    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.
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    Question and Answering System For Investment Promotion Based on NLP
    (IEEE, 2023-04-03) Panditharathna, P M R A; Rajapaksha, U.U.S.
    Smart Question and Answering System for Investment Promotion is one such software/tool which will enable users (Investors, Investment Promotion Officers, Researchers, Professionals, etc.) to systematically ask the questionnaires related to the Investment and giving answers anytime anywhere. Preparing questions will be categorized according to investment sector based on Natural Processing Language. The levels would be, Knowledge, Comprehension, Application, Analysis, Synthesis and evaluation. Accordingly, Smart Question Generator will be modified to achieve the aspects related to each of the levels in investment promotion concept.Most likely these prepared questions would be of high standard predicting the questions as expected. This will also be a greater advantage for the investors to gain the potential out of this enabling them to invest money for projects and various information accordingly. Time consumption and communication gap is one such major issue which has not yet been practically attempted to. In order to overcome this issue, Smart Question and Answering will be a good assistant.
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    Farming Through Technology Driven Solutions For Agriculture Industry Ceylon E-Agro mobile application-find technology based solutions for agricultural problems
    (Institute of Electrical and Electronics Engineers, 2022-09-16) Imalka, L.A.; Gunawardana, K.G.A.; Kodithuwakku, K.M.S.K; Arachchi, H.K.E; Harshanath, S.M.B; Rajapaksha, S
    Many developing countries are based on the agricultural sector. More than 60 percent of the population depends on this sector. This project is focused on maize cultivation. In agriculture, farmers play the most important role. Currently, farmers are facing many problems related to maize cultivation in Sri Lanka. This mobile application will help the maize farmers to overcome these difficulties and provide a good consumer demand for maize cultivation. Through this mobile application, the farmer can find solutions for pest & diseases in maize, fire threat in the farm field. AI based Agri Agent will be provide real-time solutions, bring the farmers and the buyers into the one platform, and provide price prophesying, price index feature. IoT based smart farming features will be provided to remain soil moisture and quality of soil for maize plantation.
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    Sinhala Conversational Interface for Appointment Management and Medical Advice
    (2020 2nd International Conference on Advancements in Computing (ICAC), SLIIT, 2020-12-10) Rajapakshe, D. D. S.; Kudawithana, K. N. B.; Uswatte, U. L. N. P.; Nishshanka, N. A. B. D.; Piyawardana, A. V. S.; Pulasinghe, K. N.
    This paper proposes an intelligent conversational user interface to assist Sinhala speaking users to make appointments with doctors and to obtain medical advices. This Sinhala Conversational Interface for Appointment Management and Medical Advice (SCI-AMMA) consists of Speech Recognition unit, Query Processing unit, Dialog Management unit, Voice Synthesizer unit, and User Information Management unit to handle user requests and maintain a meaningful dialogue. The SCI-AMMA gets the users' speech utterances and recognize the language content of it for further processing. Language content is further processed using query processing unit to identify users' intent. To fulfil the users' intent, a reply is generated from Dialogue Management Unit. This reply/answer will be delivered to the user by means of a voice synthesizer. The proposed system is successfully implemented using state of the art technology stack including Flutter, Python, Protégé and Firebase. Performance of the system is demonstrated using several sample scenarios/dialogues.
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    A Deep Learning Approach to Outbreak related Tweet Detection
    (2020 2nd International Conference on Advancements in Computing (ICAC), SLIIT, 2020-12-10) Jayawardhana, B. A. S. S. B.; Rajapakse, R. A. C. P.
    Due to the popularity of social media around the world, people use to report and discuss real-world events, personal health complications, and disaster situations through these platforms. These social media data streams can be used to track and detect different types of outbreaks. A mechanism is needed to identify outbreak-related tweets to predict the outbreak in advance. In this paper, we propose a deep learning model that can detect tweets related to different outbreaks Epidemics, Public Disorders, and Disasters. GloVe (Global Vectors for Word Representation) embeddings are used as the feature extraction technique as it can capture the semantic meanings of the tweets. Long Short-term Memory (LSTM) which is a specialized Recurrent Neural Network architecture is used as the classification algorithm. In the process, first, outbreak-related tweets were manually collected and curated. Pretrained GloVe word embeddings of 100 dimensions were then used to represent the words of the tweets. As the next step, a Deep Learning Model was trained by using LSTM technique on the curated dataset. Finally, the performance of the model was evaluated using a different dataset. With the results, it can be concluded that the proposed deep learning model is an accurate approach for outbreak-related tweet detection.
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    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.
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    Accommodation Finder: An Augmented Reality Based Mobile Application Integrated with Smart Contracts
    (2021 3rd International Conference on Advancements in Computing (ICAC), SLIIT, 2021-12-09) Parameswaran, G.; Perera, M.J.F.R.; Aluthgedara, C.R.B.; Amanda, E.D.N.; Ishara, W.G.A.; Ganegoda, D.
    Accommodation is one of the basic needs for travelers, tourists, students, and employees. Accommodations range from low-budget lodges to world-class luxury hotels, but finding the preferable accommodation is undoubtedly a tedious task. And due to the COVID-19 pandemic, it has become problematic state to visit each accommodation property to check whether it's suitable for the accommodation seeker, considering the location, environment, and to check if the property matches the user’s preferences. There have been incidents reported where thousands of people have been victimized because of contract breaches in the accommodation and real estate sectors, recurring from contract alterations. Considering these problems, we have proposed a system to provide solutions using Natural Language Processing (NLP), Automatic Speech Recognition (ASR), Augmented Reality (AR), Block-chain, and K-Nearest Neighbor (KNN). This system provides an efficient approach to viewing the exterior and interior of an accommodation using 360-degree views, providing recommendations to the user based on user preferences using KNN and cosine similarity, providing security in a digital agreement using blockchain technology, and a map navigation system using ASR. With the aid of the previously mentioned techniques, a mobile application prototype is created with the possibility of future testing and implementation.