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 Embargo Designing of a Voice-Based Programming IDE for Source Code Generation: A Machine Learning Approach(IEEE, 2022-10-04) Nizzad, A.R. M.; Thelijjagoda, SHumans are precise in recognizing natural languages and responding contextually unlike machines. However, speech recognition or Automatic speech recognition often refers to converting human speech or voice to textual information with the help of artificial intelligence algorithms. With the advancement of Artificial Intelligence technologies and extensive research being conducted in AI, speech recognition has received much attention and has emerged as a subset of Natural Language Processing where the advancement and accuracy in speech recognition will open many ways to provide a high standard of human-computer interaction. In this study, using the pre-trained transformer model with a transfer learning approach, the English to Python dataset was used to train the transformer model to produce syntactically correct source code in python. Additionally, the Word2Vec model was used to generate voice-to-text as input for the model. For the purpose of demonstration, a custom Python IDE is developed to generate source code from voice input. The results and findings suggest that in the transformer model, with the use of transfer learning, any dataset can be trained to produce syntactically correct source code. The model’s training loss and validation loss were below 5 and 2.1, respectively. Future research can focus on generating valid source code from any human spoken language without restricting it to English only.Publication Embargo A Semi Distributed Approach for Feasible Min-Max Fair Agent-assignment Problem with Privacy Guarantees(IEEE, 2018-03) Weeraddana, P. C; Fischione, C; Xu, Y; Alfonsetti, EIn cyberphysical systems, a relevant problem is assigning agents to slots by distributed decisions capable of preserving an agent's privacy. For example, in future intelligent transportation systems, city-level coordinators may optimally assign cars (the agents) to parking slots depending on the cars' distance to final destinations in order to ensure social fairness and without disclosing or even using the car's destination information. Unfortunately, these assignment problems are combinatorial, whereas traditional solvers are exponentially complex, are not scalable, and do not ensure privacy of the agents' intended destinations. Moreover, no emphasis is placed to optimize the agents' social benefit. In this paper, the aggregate social benefit of the agents is considered by an agent-slot assignment optimization problem whose objective function is the fairness among the agents. Due to the problem's complexity, the problem is solved by an approximate approach based on Lagrange duality theory that enables the development of an iterative semidistributed algorithm. It is shown that the proposed algorithm is gracefully scalable compared to centralized methods, and that it preserves privacy in the sense that an eavesdropper will not be able to discover the destination of any agent during the algorithm iterations. Numerical results illustrate the performance and tradeoff of the proposed algorithm compared to the ideal optimal assignment and a greedy method.Publication Embargo Deep Learning Approach for Designing and Development of Risk Level Indicator for Patients with Lung Diseases(IEEE, 2022-02-23) Chathurika, K. B. A. B; Gamage, A"Lung disease" as a medical term, discusses as several disorders that affects both lungs. There are different types of lung disease like Asthma, lungs infections like Influenza, Pneumonia, Tuberculosis, and numerous other types of breathing problems including Lung cancers. These lung diseases can be the main reason for failure in breathing. Due to COVID19 pandemic, Pneumonia and COVID19 were highlighted mostly as fatal diseases if not detected on time. Newly identified COVID19 diseases has caused many deaths and confirmed detections reported worldwide, followed with a greatest risk to community wellbeing, especially for patients with lung diseases. Process of developing a clinically accepted vaccine or specific therapeutic drug for this disease are not finalized, which will contribute to the expansion of actual prevention action plans. Thus, methods to detect lung illness accurately and efficiently is important. Proposed solution will easily and precisely detect the risk level of patients with these two lung diseases Pneumonia and COVID19 using a mobile application with chest radiography (Chest X-rays), which is considered as a cheap, easy to access and speedy manner. Proposed solution will identify, classify and evaluate the risk level of the patient suffering with the use of Image Processing, Machine Learning techniques and Convolutional Neural Networks. So, anybody who use the proposed solution may have the ability to have a precious decision about own medical condition accurately, quickly with low cost. Proposed solution can calculate severity level of a patient with more than 97% accuracy with chest radiography analysis together with patient’s current symptoms and breath holding time evaluation.Publication Embargo Speech Master: Natural Language Processing and Deep Learning Approach for Automated Speech Evaluation(IEEE, 2021-12-06) Kooragama, K.G.C.M; Jayashanka, L. R. W. D; Munasinghe, J. A; Jayawardana, K. W; Tissera, M; Jayasingha, T. BEvery English speaker wishes to expertise his/her public speaking skills sharply. However, it is extremely difficult and requires a significant amount of practice and experience on an individual basis. This paper introduces a novel online tool “Speech Master” to practice and improve public English speech delivering skills in a professional manner. Using natural language processing, machine learning, and deep learning approaches, the proposed system analyzes the user's speech in terms of content, grammatical accuracy, grammatical richness, facial expressions, and flow. The accuracy was checked by comparing actual results taken from experts with the predicted results obtained from the tool. “Speech Master” achieves an average accuracy of more than 80% and produces a better overall result. This novel tool benefits English speakers all over the world by meeting the demand for a simple and easy-to-use solution for improving or practicing English speech delivery skills; enhancing oratory skills, boosting confidence, and delivering well-articulated speeches.Publication Embargo Shilpa: A Novel Neural Based Approach for Measuring Human Stress Level(IEEE, 2020-11-04) Perera, B. T. N; Jayarathne, B. G. D. N; Dharmakeerthi, T. G. G. M; Thanthilage, K. T. D. D. K21st century is far more advanced than the 20th century because of its new innovations along with the relevant technological mappings. Technology makes our day to day work easy. However, this has been led our simple life to be very complex. We have become really busy, money minded and most importantly we don't have time to spend with our families or thinking about ourselves. As Millennials form our childhood what we have experienced is the stress to be the best. The competition which has been generated around and among us cannot be handled; so, people have been depressed and this would let them even committing suicide. Therefore, a Learning Assistant for advanced level students, which has been named as “Shilpa”, would be a practical remedy and a companion to overcome such difficulties. Shilpa has been stepped forward to monitor students' stress levels and to help them understand their weak areas considering the curriculum of a particular subject. Once identifying a particularly weak area, Shilpa navigates the user to the summarized version of a weakly identified content of a lesson in which the user doesn't have to go through the entire course curriculum to improve his/her weak areas. The implemented system has been tested considering all the novel components, and an overall value of 0.81 has been experimented as per the precision. It can be concluded that this novel approach has achieved an overall 81% accuracy over the existing state-of-the-art baselines.Publication Embargo CEYLAGRO: Information Technological Approach for an Optimized and Centralized Agriculiture Platform(IEEE, 2020-12-10) Kaushalya, T. V. H; Wijewardana, B. Y. S; Karunasena, A; Kavishika, M. G. G; Gamage, S. T. A; Weerasinghe, LSri Lankan Agriculture sector can be considered as a crucial component as it contributes 18% of country GDP. As native farmers still cling to inapplicable traditional theorems and practices to track customer's vegetable consumption trends, they failed to assure a “good price” for their harvest. Also, the plants are prone to many diseases and pests' attacks which causes loss of the harvest. Unreliable problem identification, poor knowledge on application of fertilizers and pesticides have caused the farmers to lose their profits. As a solution to mitigate these problems, this study has built a computerized system with a vegetable price prediction system and a plant disease, pest identification system. Taking Potato as an example, the parameters of the time series model were analyzed through experiment and has built the price predictor using ARIMA model. Also, with advanced Image processing and CNN techniques Plant disease, pest identifier has built. Desirable results of the entire system have been achieved with more than 94%-97% rate of accuracy. The ultimate goal of this study is to achieve the optimal growth of the sector by navigating the users for a quality and effective decision making by reliable market trends and problem identification.Publication Open Access Human-Robot Interaction Research to Improve Quality of Life in Elder Care—An Approach and Issues(Workshops at the Twenty-Fifth AAAI Conference on Artificial Intelligence, 2011-08-24) Broadbent, E; Jayawardena, C; Kerse, N; Stafford, R. Q; MacDonald, B. AThis paper describes a program of research that aims to develop and test healthcare robots for elder care. We describe the aims of the project, the robots developed, and studies we have performed in HRI in elder care. We highlight research design issues that have become apparent in the retirement home setting when testing robots. These issues are relevant to robotics researchers wishing to evaluate the effects of robotic care on older people’s quality of life.