Department of Information Technology-Scopes
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Publication Open Access Utalk: Sri Lankan Sign Language Converter Mobile App using Image Processing and Machine Learning(2020 2nd International Conference on Advancements in Computing (ICAC), SLIIT, 2020-12-10) Dissanayake, I.S.M.; Wickramanayake, P.J.; Mudunkotuwa, M.A.S; Fernando, P.W.N.Deaf and mute people face various difficulties in daily activities due to the communication barrier caused by the lack of Sign Language knowledge in the society. Many researches have attempted to mitigate this barrier using Computer Vision based techniques to interpret signs and express them in natural language, empowering deaf and mute people to communicate with hearing people easily. However, most of such researches focus only on interpreting static signs and understanding dynamic signs is not well explored. Understanding dynamic visual content (videos) and translating them into natural language is a challenging problem. Further, because of the differences in sign languages, a system developed for one sign language cannot be directly used to understand another sign language, e.g., a system developed for American Sign Language cannot be used to interpret Sri Lankan Sign Language. In this study, we develop a system called Utalk to interpret static as well as dynamic signs expressed in Sri Lankan Sign Language. The proposed system utilizes Computer Vision and Machine Learning techniques to interpret sings performed by deaf and mute people. Utalk is a mobile application, hence it is non-intrusive and cost-effective. We demonstrate the effectiveness of the our system using a newly collected dataset.Publication Embargo Smart Intelligent Advisory Agent for Farming Community(2020 2nd International Conference on Advancements in Computing (ICAC), SLIIT, 2020-12-10) Illandara, T.S.; De Silva, H.L.H.; Madurawala, K.S.H.; Dayasena, B.R.D.; Srimath, U.; Samaratunge Arachchillage, S.; Buddhika, T.The currently available agricultural services have few limitations because of the traditional cultivation methods and the unavailability of experts. This research attempts to solve the major problems faced by farmers using an Intelligent Expert Advisory Agent (EAA) that would act as a human counterpart to provide reliable solutions in real-time to the farmers using Machine Learning (ML), Image Processing (IP), and Internet of Things (IoT) technologies. A web application is developed to provide meaningful information to the user by representing agriculture instructors. Using the web application, the farmer can obtain information about predicted weather up to two months. Once the crop is selected, suitable organic fertilizers are suggested to maximize the productivity of the cultivation. After planting, the farmer can continuously monitor the condition of the plants in real-time using the IoT system. Based on this information, the farmer can check if the conditions are optimum for the growth of the plant by interacting with the knowledge base system. If the plants get infected with diseases, the user can capture an image of the diseased plant using the implemented mobile application and send to the IP system to identify the diseases and suggests remedies to overcome the situation.Publication Embargo 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.Publication Embargo Remote Treatment Management Approach to Rural Healthcare in Sri Lanka(2020 2nd International Conference on Advancements in Computing (ICAC), SLIIT, 2020-12-10) Shashishka, P.W.T.; Madhusankar, M.S.; Perera, M.T.K.; Gnanasena, A.M.J.C.; Kuruppu, T.A.; Kahandawaarachchi, K.A.D.C.P.With the complications of society, people tend to ignore their medical status and focus on other priorities. Majorly due to lack of time to consult medical help, also in a pandemic situation like Corona Virus people have tended to detour their health issues. Patients lack options to contact their medical consultant without being physically present. “Remote Treatment” is designed to address this issue. App offers video conferencing between the patient and the medical consultant. Patients can choose the quality of the stream depending on the internet connection's capability, implemented utilizing WebRTC. Additionally, Remote Treatment provides patient data management and prescription handling along with improved security. Medical consultants has access to patient's previous and current reports also detailed graphs on their progress, implemented by utilizing Fusion charts, prescription handling has digitalized the prescription process hence patients wouldn't have the risk of acquiring inaccurate medicines. prescription handling is based on voice recognition built on using sphinx package In a telemedicine feature information security is utmost importance since these information violations could result in devastating situations. Information is secured with the aid of blockchain technology to provide maximum security. Remote Treatment allows patients to access medical help quickly with optimized condition.Publication Embargo SURAKSHA e-Caretaker: Elders Falling Detection and Alerting System using Machine Learning(2020 2nd International Conference on Advancements in Computing (ICAC), SLIIT, 2020-12-10) Mendis, L.; Hathurusinghe, S.; Epa, H.; Edirisinghe, T.; Wickramarathne, J.; Rupasinghe, S.People become unable to perform tasks that were done at the younger ages as they were when the ages pass with time. Falls play a major issue in the lives of elderly people as the physical and mental quality of life is dependable on the effects of falls. This research presents an e-Caretaker SURAKSHA which is an elder falling detection and alerting system based on Machine Learning concepts. Researchers that have been done in this area have produced different solutions to detect only the falls but not to automatically detect and notify them to the caretakers. This solution serves as a smart wearable device that is capable of automatically monitoring real-time postures, detecting sudden falls, possible arrhythmia conditions of the heart of the fallen person, and daily route deviations along with the fallen location which is finally notified to the caretakers through a mobile application. According to the performed studies, python model development was used to implement the system through Machine Learning concepts by referring to the Markov model, Prophet model, and Naïve Bayes algorithms. This solution provides the results of this research with an accuracy of around 89.9% leading to a successful product in the domain. Keywords—Publication Embargo Smart Personal Intelligent Assistant for Candidates of IELTS Exams(2020 2nd International Conference on Advancements in Computing (ICAC), SLIIT, 2020-12-10) Senevirathne, S.S.; Fernando, G.U.D.; White, J.B.; Divyanjala, S.T.H.; Samaratunge Arachchillage, U.S.S.; Dias, D.P.N.P.Many IELTS candidates encounter problems at the examinations and majority of them are unable to achieve their goals even though they strive hard to accomplish their targets. Candidates strive to achieve higher band score in exams, but fail to achieve them due to the ignorance of prevailing weaknesses which have to be identified if they were to succeed. At present, IELTS seems to be the most demanding exam among applicants who are planning to embark their higher studies or migration purposes. Currently, there is no proper mechanism to assist candidates and generate an improvement plan by identifying the weaknesses of them. As a solution, Smart Personal Intelligent Assistant for Candidates Exams (SPIACIE) has been proposed to detect IELTS candidates’ weaknesses through an analysis of their answers. The SPIACIE assesses four components (Reading, Writing, Listening, and Speaking) in IELTS exams. This paper is specifically based on the Long Short-Term Memory (LSTM) network model used to analyze the score of grammar and cohesion. To analyze the similarity of the sentences, the cosine proximity technique is proposed to evaluate the paraphrasing of the graph explanations. The final outcome of this application is to generate an improvement plan, developed using Machine Learning (ML) algorithms. The proposed algorithms are; Gaussian naïve base for reading exam, support vector machines for listening exam, decision tree classifier for speaking exam, and k-neighbors classifier for writing exam. An improvement plan on the prediction model is provided to increase the band score of the IELTS exams, based on applicants’ weakness.Publication Embargo Smart Exam Evaluator for Object-Oriented Programming Modules(2020 2nd International Conference on Advancements in Computing (ICAC), SLIIT, 2020-12-10) Wickramasinghe, M.L.; Wijethunga, H.P.; Yapa, S.R.; Vishwajith, D.M.D.; Samaratunge Arachchillage, U.S.S.; Amarasena, N.Worldwide educators considered that, automate the evaluation of programming language-based exams is a more challenging task due to its complexity and the diversity of solutions implemented by students. This research investigates and provides insight into the applicability and development of a java based online exam evaluator as a solution to traditional onerous manual exam assessment methodology. The proposed system allows students to take online exams in Java for an implemented source code in a practical exam, automatically reporting the results to the administrator simultaneously. Accordingly, this research examines existing methods, identifies their limitations, and explores the significance of introducing a smart object-oriented program-based exam evaluator as a solution. This method minimizes all human errors and makes the system more efficient. An automated answer checker checks and marks are given as human-counterpart and generate a report with possible suggestions for improvement of the answer scripts and generate a classification report to predict the student’s final exam marks. This software application uses a Knowledge base, Abstract Syntax tree (AST), ANTLR, Image processing, and Machine Learning (ML) as key technologies. The proposed system gains a higher accuracy of 95% as performed by a separate human-counterpart. These results show a high level of accuracy and automate marking is the major emphasis to save human evaluation effort and maximize productivity.Publication Embargo Secured, Intelligent Blood and Organ Donation Management System - “LifeShare”(2020 2nd International Conference on Advancements in Computing (ICAC), SLIIT, 2020-12-10) Wijayathilaka, P.L.; Pahala Gamage, P.H.; De Silva, K.H.B.; Athukorala, A.P.P.S.; Kahandawaarachchi, K.A.D.C.P.; Pulasinghe, K.N.The scarcity and exigency for blood and organs has created many discrepancies in current approaches. These have created the criteria for malpractices such as organ trafficking and black market selling. This research presents a solution with a secured-smart blood and organ donation web developed system, allowing both patients and healthcare providers to access information about the blood and organ processing records. The database would be managed using the Blockchain technology which could be only accessed by authorized users. Finally, tracking all registered donors, the proposed system generates a smart identity developed by Ethereum Smart Contract (ESC). System predicts blood demand for the future ten years using Linear Regression Model with 0.998 of high R-squared accuracy value. This reduces shortages and wastage of blood. Also, using global positioning system and K-Nearest Neighbors Machine Learning algorithm, the system finds the best matches among donors and seekers according to the nearest location. Further, the system will automatically send questionnaires for registered users to identify and evaluate their awareness and issues about organ donation. Overall, this study aims for a secured and transparent web application. Thus, it facilitates an innovative and a productive blood donation and organ transplantation process in Sri Lankan healthcare sector.Publication Embargo Science Zone : An Augmented Reality based Mobile Application for Science(2020 2nd International Conference on Advancements in Computing (ICAC), SLIIT, 2020-12-10) De Silva, W.; Naranpanawa, P.; Hettihewa, U.; Liyanage, P.; Samarakoon, U.; Amarasena, N.In recent years, technology has rapidly developed, and it has provided many technological advancements for the field of education with an attempt to improve and overcome its limitations. Augmented Reality is among these latest technologies which support to improve learning environment around the world. It can bring education to a new level which can help students in many significant ways. In Sri Lanka, augmented reality is rarely been used for the purpose of educational enhancements. Therefore, it was decided to develop an augmented reality embedded mobile application for the G.C.E Ordinary Level Students in order to make it easy for them to learn Science with more enthusiasm and interest. This research has been used marker-based approach to transmit images or objects in the text book into the real-world scenes in order to create a more productive learning environment for the students. The first version of the application covers four main areas in the Science curriculum, such as; Preparation of Acids, Human Anatomy, Organization of Plants and Biosphere Cycles. Feedback for the application was taken from randomly selected ten science teachers and twenty grade eleven students and accordingly the application was further developed. Their feedback proves that the application would satisfy the common requirements of students, and it would be an immense support in scoring good results for science.Publication Embargo PatientCare: Patient Assistive Tool with Automatic Hand-written Prescription Reader(2020 2nd International Conference on Advancements in Computing (ICAC), SLIIT, 2020-12-10) Kulathunga, D.; Muthukumarana, C.; Pasan, U.; Hemachandra, C.; Tissera, M.; De Silva, H.Most people in the world prefer to be conscious of the medications prescribed by physicians. Especially, the importance of handwritten prescriptions is prodigious in Sri Lanka because they are widely used in the healthcare sector. However, due to the illegible handwriting and the medical abbreviations of the physicians, patients are unable to find the prescribed medication information. This research is an attempt to assist the patients in identifying the prescribed medicine information and minimizes misreading errors of medical prescriptions. When a patient uploads the image of a prescription, the system converts it into unstructured text data by using OCR and segmentation, then NER is used to categorize medical information from given text. According to the other research, some solutions exist in other domains for the above mechanisms. But they gave less accuracy when tried to apply for this research due to the domain specialty. Therefore, as a solution to overcome the above discrepancy this approach allows users to scan handwritten medical prescriptions and blood reports and obtain analyzed reports in medical history. Results have shown that this approach will give 64%-70% accuracy level in doctor's handwriting recognition and 95%- 98% accuracy in medical information categorization of the prescription format.