Faculty of Computing
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Publication Embargo Aspect Based Sentiment Analysis for Evaluating Movies and TV series Publisher: IEEE Cite This PDF(2020 2nd International Conference on Advancements in Computing (ICAC), SLIIT, 2020-12-10) Cooray, T.; Perera, G.; Chandrasena, D.; Alosius, J.; Kugathasan, A.Aspect-based sentiment analysis (ABSA) is used in different fields for analyzing customer reviews to project an overall customer opinion on certain products. With the expansion of the internet, people are provided with an inexpensive and time-saving method to express their opinion to a larger audience, while various industries are handed with the opportunity to gather free information from it to obtain market value. The implementation of machine learning methods for the evaluation of aspects related to movies and television series has not been commenced, and it could be a new development for the industry. This study focuses on conducting an ABSA on a movie or a television series based on genre, story as well as cast and crew aspects. The data collected from social media through web scraping is processed to produce adequate results to get a broad understanding on how the popularity of the movie or the television series related to above mentioned aspects. Then, each aspect is further analyzed to gather precise information belonging to each aspect. The accuracy of the results of the proposed system has been achieved over 79%. The results proved that the solution is highly successful than the former works with high business value.Publication Open Access “iSAY”: Blockchain-based Intelligent Polling System for Legislative Assistance(2021-01) Wattegama, D; Silva, P. S; Elapatha, K; Yapa Abeywardena, K; Kuruwitaarachchi, N; Jayathilake, C. R“iSAY”' is a Blockchain-based polling system created for legislative assistance. Sri Lanka is a democratic country. Country follows a representative democracy and voters in Sri Lanka vote for their preferred government based on their election mandate. However, governments implement legislative decisions that are not stated in the election mandate. People won’t get a chance to state their opinion on this legislative matter and the government also doesn’t know whether people like this or not. To solve this issue, in this paper the authors propose a blockchain-based intelligent polling application for legislative assistance. “iSay” is an application where blockchain technology gets together with machine learning to add value into the public opinion. The government can create a poll about a legislative decision and people can state their opinion which could be further discussed in the legislature. Adding a significant change to the blockchain based e-voting solutions this paper proposes a novel feature where users can add their idea to a relevant poll. Using machine learning algorithms all these user ideas will be classified and analyzed before presenting to the government. Through this research, it is expected to deploy scalable elections among the general public and get their vote and ideas about specific legislations to generate an overview of general public opinion about legislative decisions.Publication Embargo Credit Card Fraud Prevention Using Blockchain(IEEE, 2021-04-02) Balagolla, E. M. S. W; Fernando, W. P. C; Rathnayake, R. M. N. S; Wijesekera, M. J. M. R. P; Senarathne, A. N; Abeywardhana, K. YWith the advancement of online payments in various products and services, the likelihood of credit card fraud has risen compared to the decades-long history of credit cards. When blockchain systems' immutability meets smart contracts, third-party removal and decentralization could be met as a high level of security. Proposed blockchain with fraud detection technology will assist to mitigate fraudulent credit card transactions due to its intermediate parties. Authors propose a solution (B-Box.com) where credit card transactions are modeled on a blockchain so that the credit card processing can be decentralized and verifiable with an accredited set of computing nodes. This solution reduces fraud due to ambiguous contracts with the use of a smart contract between the bank and the customer. Also, this project includes a scaling mechanism to blockchain because the current projects have a lack of scalability. Moreover, the solution introduces a proactive anomaly detection to detect fraudulent credit card transactions, in which the system will resist frauds before the fraudulent transaction enters the blockchain. So the proposed solution will make transparency between the banks and the end-users and at the same time prevents frauds before it happens which helps the banks to save millions in otherwise lost due to fraud.Publication Open Access MNet-Sim: A Multi-layered Semantic Similarity Network to Evaluate Sentence Similarity(2021-11) Kasthurirathna, D; Jeyaraj, M. NSimilarity is a comparative - subjective measure that varies with the domain within which it is considered. In several NLP applications such as document classification, pattern recognition, chatbot questionanswering, sentiment analysis, etc., identifying an accurate similarity score for sentence pairs has become a crucial area of research. In the existing models that assess similarity, the limitation of effectively computing this similarity based on contextual comparisons, the localization due to the centering theory, and the lack of non-semantic textual comparisons have proven to be drawbacks. Hence, this paper presents a multi-layered semantic similarity network model built upon multiple similarity measures that render an overall sentence similarity score based on the principles of Network Science, neighboring weighted relational edges, and a proposed extended node similarity computation formula. The proposed multi-layered network model was evaluated and tested against established state-of-the-art models and is shown to have demonstrated better performance scores in assessing sentence similarityPublication Embargo A steganography-based fingerprint authentication mechanism to counter fake physical biometrics and trojan horse attacks(IEEE, 2021-12-06) Karunathilake, H; Shahan, A. R. M; Shamry, M. N. M; De Silva, M. W. D. S; Senarathne, A. N; Yapa, KIn the modern world, unique biometrics of every individual play a vital role in authentication processes. However, as convenient as it seems, biometrics come with their own set of drawbacks. For instance, if a passphrase is compromised (which is highly likely), changing it to a new passphrase would solve the issue. However, when someone's biometrics are compromised, there is no turning back. Simultaneously, biometric systems are often compromised due to the use of fake physical biometrics and trojan horse attacks that are capable of modifying the authentication process to fulfill a malicious user's intents. This research focuses on proposing a novel and secure authentication process that uses steganography. This “all-in-one” solution also focuses on mitigating the aforementioned drawbacks with the use of four modules, namely, the feature extraction module, the payload generation and authentication module, the fake physical biometrics countering module and the trojan horse countering module. This solution is implemented such that the idea behind it can be easily adopted to enhance the existing biometric authentication systems as well as improve the overall condition and user experience of the multi-factor authentication processes that are widely in use today.Publication Embargo Assisting Wheelchair: Assist W(IEEE, 2021-12-07) Ranaweera, D; Athalage, C; Sri Virajamana, M; Kaveesha, C; De Silva, D. I; De Silva, HTraditional wheelchairs used by disabled people are required to be controlled manually. Hence, continuous monitoring and assistance of a caretaker is a mandatory requirement. This paper introduces an autonomous assisting wheelchair - Assist W, which would facilitate disabled people to do their day-to-day activities independently in a very safe manner, thereby managing their mental and physical health. Assist W can scan the location and design a 2D map of the house using SLAM algorithm and LIDAR sensor. After generating the map, Assist W is able to move automatically according to the commands (Voice and touch) given by the user, with the help of the map data. There is an AR (Augmented Reality) chat-bot that acts as a good companion to manage the mental health of the disabled person. Assist W is also able to manage the security and physical health of the disabled person by providing a fall detection system and automatic lifting system, and sending emergency alerts to the caretakers. This system was tested using simulation.Publication Embargo SentinelPlus: A Cost-Effective Cyber Security Solution for Healthcare Organizations(2021 3rd International Conference on Advancements in Computing (ICAC), SLIIT, 2021-12-09) Janith, K.; Iddagoda, R.; Gunawardena, C.; Sankalpa, K.; Abeywardena, K.Y.; Yapa, K.Electronic Protected Health Information (ePHI) has proven to be quite lucrative by cybercriminals due to their long shelf life and multiple possible avenues of monetization. These highly sensitive data has become an easy target for cyber attackers due to the poor cyber resiliency strategies exercised by Healthcare Organizations. The reasoning behind the poor cyber security management in the healthcare sector sums to the collective impact of budgetary restriction, lack of cyber security competency and talent in the domain, prioritizing convenience over security, and various work culture malpractices. Further-more, a substantial number of data breaches in the healthcare sector are known to be caused by human errors, security misconfigurations, and information mismanagement. Secondly, the increasing prevalence of ransomware and botnet attacks has hampered the efficiency and availability of healthcare services. As a result, in order to provide a holistic security mechanism, this paper presents "SentinelPlus," a machine learning-based security management suite.Publication Embargo Use of Natural Language Processing and Deep Learning towards Guiding Healthy Cholesterol Free Life(2021 3rd International Conference on Advancements in Computing (ICAC), SLIIT, 2021-12-09) Sasanka, D.; Malshani, H. K. N.; Wickramaratne, U.I.; Kavindi, Y.; Tissera, M.; Attanayaka, B.High blood cholesterol is a key risk factor for cardiovascular diseases such as coronary heart disease and stroke. This has become a severe health problem, because it causes a considerable amount of deaths annually. The major risk factors that affect a person’s cholesterol level include unawareness of cholesterol risk, unhealthy dietary habits, lack of proper exercises, and high stress conditions. In this research, novel approaches are introduced to provide an automated and personalized guidance to maintain healthy cholesterol level and raise the awareness of each risk factors mentioned above. This research associates with four novel approaches. Natural Language Processing (NLP) based Cholesterol risk analyzer, Fuzzy based Food management with Meal predictor, Machine Learning based Physical exercise planner and Stress controller. Altogether with results, this research will provide a complete and facts-proven solution to reduce and guide people towards a cholesterol-free healthy lifestyle.Publication Embargo Effectiveness of Stock Index Forecasting using ARIMA model: Evidence from New Zealand(2021 3rd International Conference on Advancements in Computing (ICAC), SLIIT, 2021-12-09) Dassanayake, W.; Ardekani, I.; Gamage, N.; Jayawardena, C.; Sharifzadeh, H.Time series of stock market indices are dynamic, interdependent, and considered sensitive to many factors. Thus, the prediction of such indexes is always challenging. A comprehensive review carried out by the authors finds that no attempts have yet been carried out to test ARIMA models’ predictive efficacy applied to the New Zealand financial markets. Thus, technical analysis based ARIMA prediction models are developed and empirically tested on the New Zealand stock market (NZX50) index. Daily NZX50 index data are used, and the forecasting precision of the models is assessed based on Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and Root Mean Square Error (RMSE]. Our study finds that ARIMA (1, 1, 0) plus intercept is the best order forecasting model out of the models we examined. Once a substantiate algorithm training is implemented, formulated ARIMA models could be successfully employed to forecast the time series of other stock market indexes or the same index for varied periods. Future researchers could compare the forecasting efficiencies of ARIMA with a deep-learning model such as long short-term memory (LSTM). The presence of limited published research of ARIMA applied to the financial markets of New Zealand validates the need and the contribution of this paper.Publication Embargo Consumer and Farmer Centric Subscription Based Organic Vegetable/Fruit Delivery System(2021 3rd International Conference on Advancements in Computing (ICAC), SLIIT, 2021-12-09) Karunarathna, T.M.N.P.; Kumari, M.M.A.; Kulasinghe, W.D.K.A.; Liyanage, T.L.P.; Kodagoda, N.; Suriyanwsa, K.; Kumari, D.For centuries, the economy of Sri Lanka has been backed by the agricultural sector. Even though it has contributed to the development of the nation on a large scale, the economies and living standards of the farmers have not been improved much. Most of the farmers remain poor mainly due to not knowing the market demand and the low buying prices of the middleman. Meanwhile, the consumers in Sri Lanka are buying non-organic vegetables and fruits which are highly contaminated with dangerous chemicals which in turn has contributed to the rapidly increasing non-communicable disease rates in both consumers and farmers. The proposed system is an attempt to solve these problems faced by both farmers and consumers by reducing the farmers-consumer knowledge gap and providing consumers a trustworthy and personalized subscription for organic vegetables/fruits with a donation platform.Publication Embargo Digital Preservation and Noise Reduction using Machine Learning(2021 3rd International Conference on Advancements in Computing (ICAC), SLIIT, 2021-12-09) Aravinda, K.P.; Sandeepa, K.G.H.; Sedara, V. V.; Chamodya, A.K.Y.L.; Dharmasena, T.; Abeygunawardhana, P.K.W.This paper proposes a digital preservation solution for Sinhala audios to conserve those as documents with noise reduction. The solution has implemented multiple noise reduction techniques as a pre-processing step to remove unwanted internal and external noises. A two-step, two-way noise reduction process is applied to produce clean audios based on Deep Convolutional Neural Network (DCNN) and adaptive filter-based techniques. This approach implements two separate noise reduction models for internal and external noises. After that, the speech recognition decoder recognizes the speech and converts it to a Unicode document by acoustic, language, and pronunciation models using extracted audio features from the denoised audio. Further, noise reduction models are decoupled from the preservation solution and exposed as a sub solution for multilingualism noise reduction, supporting English and Sinhala audios.Publication Embargo Personalized Assistive Learning System for Primary Education(2021 3rd International Conference on Advancements in Computing (ICAC), SLIIT, 2021-12-09) Yapa, Y.M.T.S.; Fernando, W.S.I.; Sampath, W.H.M.K.; Kodithuwakku, K.D.D.I.; Samaratunge, U.S.S.; Lunugalage, D.Due to the COVID 19 pandemic, almost all educational institutions, including schools, remain closed. This caused a dramatic change in the educational systems. The sudden shift away from the classroom made the profound transformation of the teacher-centered education system prevail so far; consequently, the education of primary level students has been collapsed. Therefore, the primary students from grades 1 to 3 cannot acquire the primary education given by the school. This research proposes a personalized assistive learning system for primary education from grade 1 to 3 students, aiming to improve their learning skills. The proposed system aims to increase the automation and self-learning of students. A novel method is proposed to acquire personalized course materials for students of grades 1 to 3 according to their knowledge level. The system is founded on a solid theoretical foundation and enables children to grow cognitive and psycho-therapeutic skills such as drawing, writing, recognizing numbers, enabling self-learning, and focusing on measuring the progress of the students and reporting it to parents. CNN is the primary classifier used in image recognition and classification tasks in computer vision. The components' median accuracy is 94.74%.Publication Embargo Early Warning for Pre and Post Flood Risk Management by Using IoT and Machine Learning(2021 3rd International Conference on Advancements in Computing (ICAC), SLIIT, 2021-12-09) Ilukkumbure, S.P.M.K.W.; Samarasiri, V.Y.; Mohamed, M.F.; Selvaratnam, V.; Rajapaksha, U.U.S.Flooding has been a very treacherous situation in Sri Lanka. Therefore, developing a structure to forecast risky weather conditions will be a great aid for citizens who are affected from flood d isasters. I n t his s tudy, t he a uthors explore the use of Machine Learning (ML), Deep Learning (DL), Internet of Things (IoT), and crowdsourcing to provide insights into the development of the pre and post flood r isk management system as a solution to manage and mitigate potential flood risks. Machine learning and deep learning algorithms are used to predict upcoming flooding s ituations and r ainfall occurrences by using predicted weather information and historical data set of flood a nd r ainfall. Crowdsourcing i s u sed a s a n ovel method for identifying flood t hreatening a reas. Weather i nformation is gathered from citizens and it will help to build a procedure to notify the public and authorities of imminent flood risks. The IoT device tracks the real-time meteorological conditions and monitors continuously. The overall outcome showcases that machine learning models, deep learning algorithms, IoT and crowdsourcing information are equally contributing to predict and forecast risky weather conditions. The integration of the above components with machine learning techniques, together with the availability of historical data set, can forecast flood occurrences and disastrous weather conditions with above 0.70 accuracy in specific areas of Sri Lanka.Publication Embargo E-Learn Detector: Smart Behaviour MonitoringSystem to Analyze Student Behaviours DuringOnline Educational Activities(2021 3rd International Conference on Advancements in Computing (ICAC), SLIIT, 2021-12-09) Bamunuge, H.K.T.; Perera, H.M.; Kumarage, S.; Savindri, P.A.P.; Kasthurirathna, D.; Kugathasan, A.With the rise of online education more attention is being paid to the deficiencies in online learning platforms. Online Learning environments aim to deliver efficacious instructions, but rarely take providing a conventional classroom experience to the students into consideration. Efficient detection of students’ learning situations can provide information to teachers to help them identify students having trouble in real-time. This idea has been exploited several times for Intelligent Tutoring Systems, but not yet in other types of learning environments that are less structured. “E-Learn Detector is a web application solution to these existing issues in online learning which consists of unique features such as verifying the user during logging procedure and throughout an examination, detecting suspicious behaviors and presence of multiple users during online examinations and detecting low engagement levels of students during online lectures. “E-Learn Detector” is developed with the aim to provide guidance to students to improve their academic performance and behavior during classroom activities and to induce the best out of the educational activities.Publication Embargo SalFix: Solutions for Small Businesses Using Artificial Intelligence and Machine Learning(2021 3rd International Conference on Advancements in Computing (ICAC), SLIIT, 2021-12-09) Perera, T.; Kuganandamurthy, L.; Ameen, T.; Dassanayake, T.; Ganegoda, D.Every large organization was a small business before. There are many businesses starting every day. Most of them are small businesses. Managing a business is always a challenge. The owners face lots of challenges when they engage with a business. Small business owners do not have enough knowledge about advertising or promoting a product. New owners do not know the trendiest product at present, and they need to know to sell which product to be profitable. L ack o f communication with the customers will impact the customer base. These are the main problems that owners face. By introducing SalFix, these challenges can be conquered. SalFix is a web application that is suitable for current owners and new owners. SalFix uses Artificial I ntelligence t o g enerate a utomated a d i mages, predict what will happen to the business next year, predict which product is the trendiest. To improve customer communication, SalFix is embedded with a chatbot plugin that can be integrated into the small business’s website. SalFix can perform a SWOT analysis as well. Owners can use SalFix to fix t heir s ales and boost their income. SalFix is a yearly subscription service and will provide more accurate results.Publication Embargo Machine Learning-based Prediction Model for Academic Performance(2021 3rd International Conference on Advancements in Computing (ICAC), SLIIT, 2021-12-09) Tharsha, S.; Dilogera, J.; Mohanashiyaam, B.; Kirushan, S.; Chathurika, K.B.A.B.; Swarnakantha, N.H.P.R.S.This paper represents the work of a new integrated and collaborative Smart application for managing students online through data mining techniques. Nowadays especially in this pandemic situation, there is a necessity for academic management to incorporate and change all study methods online. By considering all these conditions this research is focused to discuss the solution to manage and engage students smartly and easily. Thou technology advancements have a serious impact on the day-to-day life people face troubles when using complex applications, this implemented Smart application is simple to use and a great tool for Student Management systems. The survey feedback from students, academic staff, and the public illustrate that this project helps to improve the effectiveness and efficiency of learning capability among the targeted group. The main objective of this project is to build up a smart model using Machine Learning, Deep Learning, and Artificial Intelligence to overcome generic learning problems. Therefore, this paper aims to present the concept behind the development and implementation of the Smart Study Application for Student Management System.Publication Embargo AI Based Monitoring System for Social Engineering(2021 3rd International Conference on Advancements in Computing (ICAC), SLIIT, 2021-12-09) Yapa, K.; Udara, S.W.I.; Wijayawardane, U.P.B.; Kularatne, K.N.P.; Navaratne, N.M.P.P.; Dharmaphriya, W.G.V.USocial media is one of the most predominantly used online platforms by individuals across the world. However, very few of these social media users are educated about the adverse effects of obliviously using social media. Therefore, this research project, is to develop an advisory system for the benefit of the general public who are victimized by the adverse impacts of their ignorant and oblivious behavior on social media. The system was implemented using a decision tree model with the use of customized datasets; and for the proceeding operational implementations, Python programming language, Pandas, Natural Language Processing and TensorFlow were used. This advisory system can monitor user behaviors and generate customized awareness reports for the users based on category and level of their behaviors on social media. Furthermore, the system is also capable of generating graph reports of the use behavior fluctuations for the reference of the user. With the help of these customized awareness reports and the graph reports, the users can identify their potential vulnerabilities and improve their social media habits.Publication Embargo Indoor Autonomous Multi-Robot Communication System(2021 3rd International Conference on Advancements in Computing (ICAC), SLIIT, 2021-12-09) Gunawardhana, K.D.W.; Kularathana, D.G.D.P.; Welagedara, W.H.; Palihakkara, H.E.; Abeygunawardhana, P.K.W.; Wellalage, S.Robotics, and automation systems are a hot issue right now. Controlling multiple robots at the same time has become very popular. On paper, we propose that a wireless robot-to-robot communication infrastructure be implemented to accomplish some specific tasks. The major goal of this proposed project is to showcase communication infrastructure, a dual manipulator system and a mobile charging dock robot have been designed to achieve this. Special topics and services were employed for communication infrastructure. It is more precise than current communication systems. Existing manipulation situations are limited to a single manipulator task; however, in this case, a dual manipulator task has been designed to work corporately. The charging docking stations are the only places where mobile robots may recharge. We presented a Mobile Charging Dock for recharging mobile robots in this project. This proposed project is introducing a secure communication strategy which uses a ROS topic filtering mechanism.Publication Embargo Event-Driven Malicious URL Extractor(2021 3rd International Conference on Advancements in Computing (ICAC), SLIIT, 2021-12-09) Jonathan, S.W.S.; Arunaasalam, R.H.; Senarathne, A. N.; Wishvajith, V.; Ramanayaka, A.M.; Yapa, K.Cyber-attacks are attacks that are commonly carried out in order to obtain sensitive information or disrupt internet-based services. Recent occurrences, both internationally and locally, have shown an influx of these attacks expanding rapidly through the use of malicious URLs (Uniform Resource Locators). Traditional measures, including such blacklisting malicious URLs, make it extremely difficult to respond to such attacks in a timely and efficient manner. Most existing solutions remain restricted in terms of scalability and proactive user safeguarding in situations when freshly formed URLs are correlated with a recent event, such as Covid-19 related frauds. The proposed solution is presented with the primary aim of addressing traditional system limitations and offering an interface for users to protect themselves by detecting phishing/malicious URLs in real time. In this research, we will examine extracting user-input eventrelated keywords and leveraging NLP (Natural Language Processing) algorithms to match them with the accompanying URL (Uniform Resource Locator) token data to determine whether the URLs are malicious or benign.Publication Embargo Enhance the Safety Measurements in Railways with the Aid of IoT and Image Processing(2021 3rd International Conference on Advancements in Computing (ICAC), SLIIT, 2021-12-09) Vedasingha, K. S.; Perera, K. K. M. T.; Hathurusinghe, K. I.; Akalanka, H. W. I.; Amarasena, N.; Dissanayake, N.R.Railways provide the most convenient and economically beneficial mode of transportation, and it has been the most popular transportation method. According to the past analyzed data, it reveals a considerable number of accidents which occurred at railways, caused damages to not only precious lives but also to the economy. The goal of this research is to minimize the railway accidents by developing “Railway Process Automation System” while ensuring human safety with use of Internet of Things (IoT) and image processing techniques. The system can detect the current location of the train and close the railway gate automatically. As usual, if the system fails to close the rail gate due to any failure, the proposed system can identify the current location and close the rail gate through decision making system by using past data. The proposed system introduces further two features which named as Railway track crack detection and motion detection which play a significant role in reducing the risk of railway accidents. Moreover, the system is capable of detecting rule violations at a level crossing by using sensors. The proposed system is implemented through a prototype and tested with real-world scenarios to gain the above 90% of accuracy.