Department of Information Technology-Scopes
Permanent URI for this collectionhttps://rda.sliit.lk/handle/123456789/2227
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Publication Embargo Candidate Selection for the Interview using GitHub Profile and User Analysis for the Position of Software Engineer(2020 2nd International Conference on Advancements in Computing (ICAC), SLIIT, 2020-12-10) Gajanayake, R.G.U.S.; Hiras, M.H.M.; Gunathunga, P.I.N.; Supun, E.G.J.; Karunasenna, A.; Bandara, P.Selecting the most suitable candidates for interviews is an important process for organizations that can affect their overall work performance. Typically, recruiters check Curriculum Vitae (CV), shortlist them and call candidates for interviews which have been the way of recruiting new employees for a long time. To minimize the time spent on the above process, pre-screening mechanisms are nowadays implemented by organizations. However, those mechanisms need sufficient information to evaluate the candidate. For example, in case of a software engineer, the recruiters are interested on the programming ability, academic perfo rmance as well as personality traits of potential candidates. In this research, a pre-screening solution is proposed to screen the applicants for the post of Software Engineer where candidates are screen based on an initial call transcript, GitHub profile, LinkedIn profile , CV, Academic transcript and, Recommendation letters. This approach extracts textual features of different dimensions based on Natural Language Processing to identify the Big Five personality traits, CV and GitHub insights, candidate’s skills, background, and capabilities from Recommendation letters as well as programming skills and knowledge from Academic transcript and Linked Profile. The results obtained from the different areas are presented an d shown that the selected supervised machine learning algorithms and techniques can be used to evaluate the best possible candidates.Publication Embargo Character Modifier Combinations Recognition in Sinhala Handwriting(2020 2nd International Conference on Advancements in Computing (ICAC), SLIIT, 2020-12-10) Silva, C.M.; Jayasundere, N. D.Sinhala script is categorized as a segmental writing system and therefore consonant–vowel sequences are identified as a unit. Depending on the vowel and the consonant combination, the diacritic can attach above, below, following or preceding the consonant. The identification of characters with the modifier symbols is important in recognizing generally written Sinhala text and has not been addressed sufficiently in the existing research. Sinhala handwriting recognition is much difficult compared to the other popular languages due to the complexity of the shapes of the characters and the presence of the modifier symbols. This paper discusses on a projection profile, distance profile, partial distance profile and contour based approach to identify characters along with character modifiers in Sinhala script. The proposed method has given an average recognition rate of 75% for 283 character modifier combinations. The proposed solution can be used to identify Sinhala handwritten text with a proper segmentation mechanism.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.Publication Embargo An Enhanced Virtual Fitting Room using Deep Neural Networks(2020 2nd International Conference on Advancements in Computing (ICAC), SLIIT, 2020-12-10) Ileperuma, I.C.S.; Gunathilake, H.M.Y.V.; Dilshan, K.P.A.P.; Nishali, S.A.D.S.; Gamage, A.I.; Priyadarshana, Y.H.P.P.As the customer's experience in present fit-on rooms is considered as an essential part of the textile industry, these fit-on rooms play a huge role in the textile shops. It is quite an arduous method and generates problems like long queues, having to change clothes individually, privacy problems and wasting time. The proposed convolutional neural network-based Virtual Fit-on Room helps to prevent the above mentioned problems. This product contains a TV screen, two web cameras, and a PC. It captures the customer's body by using two web cameras and displays the customer's dressed body. The combination of CNN in Deep learning and AR processes the body detection and generates the customer's dressed object. The application uses the stereo vision concept to get body measurements. The system detects customer age, gender, face type, and skin tones which are used to recommend cloth styles to customers. Another requirement of this system is customizing styles according to the customer requirements and suggests different styles of clothes. The system achieved 99% accuracy when suggesting different styles using FFNN. Customers can choose clothes for another person who does not physically appear with the customer in the textile shop. The expected output delivers the most realistic dressed object to the customer which allows the efficient customizations for the textile products according to customer requirements. This product can highly influence the textile and fashion industry. Therefore, this product is suitable to compete with other applications in the industry.Publication Embargo Evaluating Teaching Content and Assessments Based on Learning Outcomes(2020 2nd International Conference on Advancements in Computing (ICAC), SLIIT, 2020-12-10) Pallegama, P.M.O.N.; Kumari, K.A.M.R.; Dissanayaka, D.M.D.P.M.; Ravihansi, A.V.Y.; Karunasenna, A.; Samarakoon, U.A modularized syllabus content assigned to different units of a subject proves very useful to both teachings as well as the student community. In each module, learning outcomes are defined. In each learning outcome, lesson learning outcomes are defined. When the Teaching Content (Lecture content), Learning activities (Labs sheets and Tutorials), Final Question Papers are being made the subject learning outcome should be considered and it should be made within the subject learning outcomes. Then the teaching and learning process will be done properly. Nowadays Revised Bloom's Taxonomy standard is used to structure the Teaching Content, Learning Activities, and Final Question paper of a course in the best way. Currently, there is no proper solution to corporate above areas according to the Revised Bloom's Taxonomy. This paper discusses an automated system that provides the features to verify the module and lesson learning outcomes and their levels according to Revised Bloom's taxonomy and to verify that the teaching content and learning activities are within the learning outcomes. Beyond that, this system uses various technologies and algorithms to improve the accuracy and efficiency of this research. This automated system is able to achieve to the final outcome with the best accuracy and efficiency than the manual process.Publication Embargo Ontological Knowledge Inferring Approach based on Term-Clustering and Intra-Cluster Permutations(2020 2nd International Conference on Advancements in Computing (ICAC), SLIIT, 2020-12-10)Ontological representation of knowledge has the advantage of being easy to reason with, but ontology construction with knowledge facts, automatically acquiring them from open domain text is often challenging. This research introduces a novel approach to infer new ontological knowledge in a fully automated manner. Such ontological knowledge can be utilized in both constructing new ontologies and extending existing ontologies. Basic level triples that can be extracted from open domain text are used as the data source for this study. A simple mechanism has been introduced to convert the triple into an ontological knowledge fact and such ontological knowledge facts are further processed to infer new ontological knowledge. The main focus of this research is to infer new ontological knowledge using an advanced term-clustering mechanism followed by an intra-cluster permutation generation task. Generated permutations are potential to be selected as good ontological knowledge facts. Inferred ontological knowledge was tested with inter-rater agreement method with high reliability and variability. Results demonstrated that, out of 43,103 triples, this method inferred 127,874 ontological knowledge (approximately 3 times) of which 66% were estimated to be effective. Finally, this research contributes a reliable approach which requires a single pass over the corpus of triples to infer a large number of ontological knowledge facts that can be used to construct/extend ontologies.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.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 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 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 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 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 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 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 A Story of Two Surveys: for the Advancement of Sinhalese Mobile Text Entry Research(2020 2nd International Conference on Advancements in Computing (ICAC), SLIIT, 2020-12-10) Reyal, S.; Piyawardana, V.; Kaveendri, D.This paper presents two surveys: a literature survey on the current progress on Sinhalese mobile text entry research and a user survey on how Sri Lankans experience Sinhalese mobile text entry. The first survey concludes that Sinhalese mobile text entry is limited in scope and size compared to western text entry research. The second survey attempts to bridge this gap by providing deep insight into aspects in Sinhalese mobile text entry such as language switching, using English within Sinhalese e.g. mixed-mode and Singlish, and the popularity of various input modalities, keyboard vendors, and keyboard layouts. This is also the first research publication that unveils the current state-of-the-art in Sinhalese mobile text entry, along with user-preferences such as using autocorrect, glide-typing, and speech. Results from this survey deepens our understanding of the Sinhalese mobile text entry domain resulting in a stronger empirical footing and more innovative Sinhalese mobile text entry solutions.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 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.