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
9 results
Filters
Advanced Search
Filter by
Settings
Search Results
Publication Embargo UveaTrack: Uveitis Eye Disease Prediction and Detection with Vision Function Calculation and Risk Analysis Publisher: IEEE Cite This PDF(Institute of Electrical and Electronics Engineers, 2022-10-15) Perera, B. D. K; Wickramarathna, W.A.A.I.; Chandrasiri, S; Wanniarachchi, W.A.P.W; Dilshani, S.H.N; Pemadasa, NUveitis is an inflammatory infection that affects uvea tissue, the middle layer of the eyewall. It can result in swelling or damage to the eye and lead to vision impairments or blindness. Most Uveitis symptoms are associated with many other diseases localized to the eye. Thus, it is hard to determine the responsible symptoms for uveitis. Consequently, early detection of this disease can prevent a perilous situation in the future. The initial motivation behind the design of this mobile application is to help accurately diagnose uveitis with minimal time and effort and thereby minimize the shortage of human specialists in this field. The 'UveaTrack' is a hybrid mobile application that enables the keep tracking of uveitis eye illness and uses machine learning (ML) algorithms, deep learning (DL) architectures, and image processing techniques for developing the system. The 'UveaTrack' application could be able to achieve an average accuracy of more than 85% and had produced overall better results. Furthermore, the 'UveaTrack' application can use as a valuable instructional tool for freshly graduated clinicians, supporting their work with patients and assisting them in making diagnostics conclusions.Publication Embargo CXR Scan:X-Ray Image Scanning Application for Lung Cancer and Tuberculosis(Institute of Electrical and Electronics Engineers, 2022-10-15) Jayasooriya, A.M.U.J.; Wickramasekara, T.M.A.M; Jayasinghe, I.C.; Gunaratne, U.A.; Weerasinghe, L; Dassanayake, G. TThe initial criterion for identifying lung disorders is chest radiographs. The three major lung illnesses that pose the greatest threat to public health are tuberculosis, pneumonia, and lung cancer. Chest X-ray diagnosis of pulmonary illnesses is a challenging undertaking that requires high experience. In rural places, it can be difficult to locate skilled radiologists. Due to the high frequency of TB and lung cancer radiological similarities, many individuals with lung cancer are initially misdiagnosed as having TB and treated incorrectly. According to a recent WHO survey, millions of people die each year as a result of delayed or incorrect diagnoses of lung diseases. This death rate can be reduced, by early detection of certain disorders. This paper proposes a system with 4 main components; Image processing of chest X-rays to identify the disease using Convolutional Neural networks; Predicting the probability of having LC or TB using multivariate data classification techniques; Recommending medicine and related information to support the decision-making process using gaussian naïve bayes, logistic regression model and decision tree classification methods; Visualizing the X-ray image using Augmented Reality.Publication Embargo Multilingual Conversational AI incorporated with Visual Questions Answering and Intelligent Disease Prediction for Healthcare Industry(IEEE, 2022-07-18) Sasmitha, N. U. A.; Wathasha, H. K. G. V.; Guruge, P. P. L.; Silva, W. J. T.; Rupasinghe, L; Gunarathne, G. W. D. A.Artificial intelligence (AI) is becoming more active than ever in everyday life and steadily being incorporated to healthcare. AI, with its seemingly limitless power, affirms a promising future to a revolutionized healthcare system. This paper is proposing a conversational AI solution in two different languages, English and Sinhala, to predict diseases through a conversation, a visual question answering solution to generate answers are based on a given question and a medical image and a disease forecasting module. A robust, accurate prediction is a rather difficult task given the availability of data and absence of preprocessed, clean data. With the aid of outlier rejection, data imputation, vectorization, feature selection and data standardization, the proposed framework gets the advantage of latest machine learning advancements such as AI using DIET classifier and NLU pipelines, for the conversational disease diagnosis which uses support vector machine (SVM) achieved an accuracy of 0.93. Moreover, the visual questions answering module with VGG16 preprocessing, GoogleNews vectors, LSTM networks, scores an accuracy of 0.9721. In addition, time series analysis models such as ARIMA and adaptive models using PROPHET library for forecasting diseases, classification using random forest scoring an accuracy of 0.81, logistic regression scoring an accuracy of 0.84 for predicting diseases. The objective of this research is to compare and select the best fitting models to be used for a centralized framework for healthcare industry.Publication Embargo Towards Smart Farming: Accurate Prediction of Paddy Harvest and Rice Demand(IEEE, 2019-01-31) Hashini Saranga, A. M; Weerakkody, W. A. N. D; Palliyaguru, S. T; Muthusinghe, R; Rankothge, WRice is the predominant staple food in Asian countries. It has a major impact on the social and economic development of these countries. Therefore, it is very important to keep the sustainability between paddy cultivation and consumer demand. Paddy crop yield and demand for rice of a country depend on numerous factors such as rainfall, humidity, citizen's life styles etc. Hence, the prediction of future harvest and demand is a complex process. There is a requirement for a platform that predicts on future harvest and demands based on all affecting factors. We have proposed a platform that targets the smart farming concepts for paddy, with following modules: (1) a prediction module to predict paddy harvest and (2) a prediction module to predict rice demand. We have developed the prediction modules using two machine learning algorithms: (1) Recurrent Neural Network (RNN) and (2) Long Short-Term Memory (LSTM). The performances of algorithms were evaluated using real data sets for the Sri Lankan context. Our results show that the prediction modules are giving accurate results in a short time.Publication Open Access Comparison of different Artificial Neural Network (ANN) training algorithms to predict atmospheric temperature in Tabuk, Saudi Arabia(researchgate.net, 2020-06) Perera, A; Azamathulla, H; Rathnayake, UUse of Artificial neural network (ANN) models to predict weather parameters has become important over the years. ANN models give more accurate results in weather and climate forecasting among many other methods. However, different models require different data and these data have to be handled accordingly, but carefully. In addition, most of these data are from non-linear processes and therefore, the prediction models are usually complex. Nevertheless, neural networks perform well for non-linear data and produce well acceptable results. Therefore, this study was carried out to compare different ANN models to predict the minimum atmospheric temperature and maximum atmospheric temperature in Tabuk, Saudi Arabia. ANN models were trained using eight different training algorithms. BFGS Quasi Newton (BFG), Conjugate gradient with Powell-Beale restarts (CGB), Levenberg-Marquadt (LM), Scaled Conjugate Gradient (SCG), Fletcher-Reeves update Conjugate Gradient algorithm (CGF), One Step Secant (OSS), Polak-Ribiere update Conjugate Gradient (CGP) and Resilient Back-Propagation (RP) training algorithms were fed to the climatic data in Tabuk, Saudi Arabia. The performance of the different training algorithms to train ANN models were evaluated using Mean Squared Error (MSE) and correlation coefficient (R). The evaluation shows that training algorithms BFG, LM and SCG have outperformed others while OSS training algorithm has the lowest performance in comparison to other algorithms used.Publication Embargo Supply and Demand Planning for Water: A Sustainable Water Management System(2020 2nd International Conference on Advancements in Computing (ICAC), SLIIT, 2020-12-10) Athapaththu, A.M.H.N.; Illeperumarachchi, D.U.S.; Herath, H.M.K.U.; Jayasinghe, H.K.; Rankothge, W.H.; Gamage, N.Sustainable water management requires maintaining the balance between the demand and supply, specifically addressing water demand in urban, agricultural, and natural systems. Having an insight on water supply forecasting and water consumption forecasting, will be useful to generate an optimal water distribution plan. A platform that targets the sustainable water management concepts for domestic usage and paddy cultivation is proposed in this paper, with the following components: (1) forecasting water levels of reservoirs, (2) forecasting water consumption patterns, and (3) optimizing the water distribution. We have used Recurrent Neural Network (RNN) and, Long Short-Term Memory (LSTM) for forecasting modules and, Genetic Programming (GP) for optimizing water distribution. Our results show that, using our proposed modules, sustainable water management related services can be automated efficiently and effectively.Publication Embargo Predictive Analytics Platform for Airline Industry(2020 2nd International Conference on Advancements in Computing (ICAC), SLIIT, 2020-12-10) Tissera, P. H. K.; llwana, A.N.M.R.S.P.; Waduge, K.T.; Perera, M.A.l.; Nawinna, D.P.; Kasthurirathna, D.The research is to develop accurate demand forecasting model to control the availability in Airline industry. The primary outcome of the model is that the Airline organization can maximize the revenue by controlling the availability. The product in airline industry is the seat, which is an expensive, unstock able product. The demand for the seats is almost uncertain, the capacity is constraint and difficult to increase and the variable costs are very high. Hence the priority of the expected demand forecast is very high for airline industry. An accurate mechanism to predict the revenue for future months of ODs (Origin destinations) is done using fare and passenger data. The revenue is derived by the number of passengers and the fares they pay which vary for each flight. Airline travel is very susceptible to the social, political and economic changes. Therefore, passenger buying patterns change quite dynamically. Hence, it is challenging to develop an accurate method to project the revenue for each route. To overcome this, we are going to use semi-supervised learning mechanism. We have the current ticketed revenue plus we have the current booked passengers. We also have the ticketed passenger details of previous flights. Hence most of the information is available, however changing market conditions is an unknown variable which can have a significant impact on passenger travel patterns. Through this research We are going to design and develop the best fit model to forecast flight OD level passenger demand based on the historical data.Publication Embargo Hand Rehabilitation Using Robot-Assisted Physiotherapy(2020 2nd International Conference on Advancements in Computing (ICAC), SLIIT, 2020-12-10) Madhushan, I.H.D.; Charnara, E.B.K.; De Zoysa, A.T.J.; Upeka, G.S.; Abhayasinghe, N.; Abeygunawardhana, P.Robotics technology in the modern world is currently being implemented in medical fields to improve the quality of care and patient outcomes. In the proposed system, the robotics technology is used for physiotherapy. In the existing physiotherapy robot devices, there is no feature that provides exercise for every joint of the fingers and the wrist. Therefore, in this system, we used forward kinematics technologies to address each joint of the fingers and wrist thatcan access by the physiotherapist. We have designed the robot hand using the solid work and implemented 3D model then assembled system was tested again using different scenarios. Most existing robotic systems provide finger and wrist exercises separately, but our system can provide all exercises simultaneously. In here, we can predict the next exercises that are given for the patient and the progress of the rehabilitation of the patient. For the prediction, we developed the models using the FB prophet algorithm. When using this device, the patient's hand exercises are monitored in real-time and the physiotherapist can see the angles of the hand movement while controlling the robot device. To control this robot device, we used a mobile application.Publication Embargo DenGue CarB: Mosquito Identification and Classification using Machine Learning(2020 2nd International Conference on Advancements in Computing (ICAC), SLIIT, 2020-12-10) Mohommed, M.; Rajakaruna, P.; Kehelpannala, N.; Perera, A.; Abeysiri, L.This research paper discusses a web-based application that assists Public Health Officers in the dengue identification process. The mosquito classification is done using image processing and machine learning techniques. The training models are developed using Convolutional Neural Networks Algorithm, Support Vector Machine Algorithm, and K-Nearest Neighbors Algorithm to validate the results to determine the most accurate and suitable algorithm. this paper discusses the previous related research work on its significance and drawbacks while highlighting design, methods, and implementation in the solution. We conclude that the CNN algorithm provides the highest accuracy among the machine learning techniques used.