School of Business

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Open Access
D-REHABIA: A Drug Addiction Recovery Through Mobile Based Application
(SLIIT, 2016-04-06) Somasiri, L. U; Galabada, S. S. G; Wijethunga, H. M; Dayananda, H. M; Nugaliyadde, A; Thelijjagoda, S; Rajasuriya, M
Drug addiction has become a major issue in the world. There are certain governmental and nongovernmental organizations which provide various programs to prevent, recover and rehabilitate drug addicts. The patients who are in the recovery process have a higher tendency of relapsing after being released to the society. The objective of this research is to produce a mobile based Drug Recovery Application and prevent patients from relapsing during the recovery process and to involve both family and rehabilitation center to the recovery of the patient. In order to accomplish this objective, the application contains an artificial intelligent assistant which will guide/help the patient regarding issues occurred during the recovery process, a location tracking mechanism to identify the movements of the patient and possible high risk places where drugs can circulate, a voice analysis mechanism to analyze the voice of the patient and identify emotional states which might cause the patient to relapse and treatments to reduce the stress, anxiety and depression level of the patient. The field of drug rehabilitation has been barely addressed via a proper technological solution, hence the system implemented as the result of this research can be effectively used for the recovery of the patient.
Open Access
Simplifying Law Statements Using Natural Language Processing
(SLIIT, 2016-11-16) Dharmasiri, N; Gunathilake, B; Pathirana, u; Senevirathne, S; Nugaliyadde, A; Thelijjagoda, S
Understanding the law statements for general public is evidently complex. The research derives a computational solution on reducing the complexity of the law statements. Given a law statement, the research will use both wordnet and “LawNet” to create a simpler meaning. The research will focus on information extraction, information retrieval, question analysis and answer generation techniques to derive better meaning of law statements. The law statement will be treated as a question and the “LawNet” and wordnet will be used in as information extraction points. The law statement will be analyzed as a question; more information will be retrieved through the wordnet and “LawNet”. This process mostly acts similar to a search engine’s process. The results provide on average 80% accuracy for a 1500 dataset.
Embargo
Conditional Random Fields based named entity recognition for sinhala
(IEEE, 2015-12-18) Senevirathne, K. U; Attanayake, N. S; Dhananjanie, A. W. M. H; Weragoda, W. A. S. U; Nugaliyadde, A; Thelijjagoda, S
Named Entity Recognition (NER) plays an important role in Natural Language Processing (NLP). Named Entities (NEs) are special atomic elements in natural languages belonging to predefined categories such as persons, organizations, locations, expressions of times, quantities, monetary values and percentages etc. These are referring to specific things and not listed in grammar or lexicons. NER is the task of identifying such NEs. This is a task entwined with number of challenges. Entities may be difficult to find at first, and once found, difficult to classify. For instance, locations and person names can be the same, and follow similar formatting. This becomes tough when it comes to South and South East Asian languages. That is mainly due to the nature of these languages. Even though Latin languages have accurate NER solutions those cannot be directly applied for Indic languages, because the features found in those languages are different from English. Therefore the research was based on producing a mathematical model which acts as the integral part of the Sinhala NER system. The researchers used Sinhala News corpus as the data set to train the Conditional Random Fields (CRFs) algorithm. 90% of the corpus was used in training the model, 10% is used in testing the resulted model. The research makes use of orthographic word-level features along with contextual information, which are helpful in predicting three different NE classes namely Persons, Locations and Organizations. The findings of the research were applied in developing the NE Annotator which identified NE classes from unstructured Sinhala text. The prominent contribution of this research for NER could benefit Sinhala NLP application developers and NLP related researchers in near future.
Embargo
Linguistic features based personality recognition using social media data
(IEEE, 2017-01-27) Sewwandi, D; Perera, K; Sandaruwan, S; Lakchani, O; Nugaliyadde, A; Thelijjagoda, S
Social media has become a prominent platform for opinions and thoughts. This stated that the characteristics of a person can be assessed through social media status updates. The purpose of this research article is to provide a web application in order to detect one's personality using linguistic feature analysis. The personality of a person has classified according to Eysenck's Three Factor personality model. The proposed technique is based on ontology based text classification, linguistic feature-vector matrix using LIWC (Linguistic Inquiry and Word Count) features including semantic analysis using supervised machine learning algorithms and questionnaire based personality detection. This is vital for HR management system when recruiting and promoting employees, R&D Psychologists can use the dynamic ontology for storage purposes and all the other API users including universities and sports clubs. According to the test results the proposed system is in an accuracy level of 91%, when tested with a real world personality detection questionnaire based application, and results demonstrate that the proposed technique can detect the personality of a person with considerable accuracy and a speed.

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