Scopus Index Publications
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This collection consists of all Scopus-indexed publications produced by SLIIT researchers. Scopus is recognized worldwide as a leading and reputable academic indexing database.
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Publication Embargo Intelligent System for Skin Disease Detection of Dogs with Ontology Based Clinical Information Extraction(Institute of Electrical and Electronics Engineers Inc., 2022-10-29) Rathnayaka, R. M. N. A; Anuththara, K. G. S. N; Wickramasinghe, R.J.P; Gimhana, P. S; Weerasinghe, L; Wimalaratne, GThe largest organ in dogs, the epidermis, is crucial in supplying immunological responses. Skin will preserve all the nutrients and safeguard the cells while warding off harmful or pathogenic substances. Most dog owners today are not aware that their pet dog has a skin condition. Although they were aware of these ailments, they had no notion of how to cure them. In such a situation, the dog may experience pain and an aggravation of the condition. Owners should therefore take their dogs to the vet, even if the skin condition is minor. It can, however, be a costly procedure. There aren't many forums where dog owners may get advice from professionals and ask inquiries regarding their pets. The solution suggests a fully functional mobile application which is a combination of disease identification feature, disease severity level detection feature, domain specific knowledge base with semantic web development and a domain specific AI based chat-bot to the dog owners to overcome this problem using Convolutional Neural Network (CNN) and natural language processing (NLP).System will extract the necessary features from the images of the lesion to classify the skin condition and Severity level of the disease. The results obtained show disease type classification is within the accuracy range of 77.78% to 100% which tested again 4 CNN base models. As for the severity level identification accuracy situated around 99.62%.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 Intelligent Troubleshooter to Solve Windows Operating System Specific Issues(2020 2nd International Conference on Advancements in Computing (ICAC), SLIIT, 2020-12-10) Rajapakshe, D.I.K.; Shamil, M.P.P.; Paththinisekara, P.M.C.P.; Liyanage, S.K.; Samaratunge Arachchillage, U.S.S.; Kuruppu, A.While working on computers, people frequently confront with various kinds of problems, those beyond their extensive expertise. Microsoft Windows is the widely used Operating System running on numerous personal computers and the reason which gives more irritating problems that require to be addressed. Currently, troubleshooting is considered as a costly and time-consuming approach. The SAITA is an Artificial Intelligent Troubleshooting Agent that utilizes natural language generation, machine learning, and dependency resolving and ontology-based methodologies for solving most common Windows-specific issues within a short period of time than the traditional approach. The assistant learns from the accessible data and accomplishes the task for users as performed by human experts. The main objective of this exploration venture is to distinguish the constraints of existing troubleshooting software and create an AI troubleshooting assistant to provide solutions to fix the identified user issues. The use of this assistant would be economical as an IT help desk alternative in the industry. SAITA is developed to serve as a representative troubleshooter for fundamental user issues, service issues, application issues, and perform environment setup by analyzing software. This system will be able to solve the common Windows user’s issues as same as a human with less time.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 Smart Monitoring and Disease Detection for Robotic Harvesting of Tomatoes(2021 3rd International Conference on Advancements in Computing (ICAC), SLIIT, 2021-12-09) Pasindu, I.; Viraj, S.; Dilshan, R.; Kalhara, A.; Senaweera, O.; de Silva, R.; Jayawardena, C.Tomato is a one of the most popular produced and extensively consumed vegetables in the world. Typical agricultural systems make extensive use of human labor which is more costly and less effective. This research explores the minimization of human labor through automation. The diseases infected by tomato plants are hard to detect. Identifying these diseases in advance would save the cultivation of the disease from spreading, thereby saving the crop.It is also a difficult task to recognize the ripe harvest and experienced labor is required. The efficiency of the harvesting method will be increased by automating the identification process of ripened fruits. Manually picking tomatoes can cause some harm to the fruits during plucking due to inconsistencies in human labor. Such damage will be reduced through a better implemented robotic scheme. This paper presents the development of autonomous system for tomato harvesting and disease detection.