Research Papers - Dept of Information Technology
Permanent URI for this collectionhttps://rda.sliit.lk/handle/123456789/593
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Publication Open Access Diagnosing autism in low‐income countries: Clinical record‐based analysis in Sri Lanka(Wily, 2022-06-16) Samarasinghe, P; Wickramarachchi, C; Peiris, H; Vance, P; Dahanayake, D. M. A.; Kulasekara, V; Nadeeshani, MUse of autism diagnosing standards in low-income countries (LICs) are restricted due to the high price and unavailability of trained health professionals. Furthermore, these standards are heavily skewed towards developed countries and LICs are underrepresented. Due to such constraints, many LICs use their own ways of assessing autism. This is the first retrospective study to analyze such local practices in Sri Lanka. The study was conducted at Ward 19B of Lady Ridgeway Hospital (LRH) using the clinical forms filled for diagnosing ASD. In this study, 356 records were analyzed, from which 79.5% were boys and the median age was 33 months. For each child, the clinical form together with the Childhood Autism Rating Scale (CARS) value were recorded. In this study, a Clinically Derived Autism Score (CDAS) is obtained from the clinical forms. Scatter plot and Pearson product moment correlation coefficient were used to benchmark CDAS with CARS, and it was found CDAS to be positively and moderately correlated with CARS. In identifying the significant variables, a logistic regression model was built based on clinically observed data and it evidenced that “Eye Contact,” “Interaction with Others,” “Pointing,” “Flapping of Hands,” “Request for Needs,” “Rotate Wheels,” and “Line up Things” variables as the most significant variables in diagnosing autism. Based on these significant predictors, the classification tree was built. The pruned tree depicts a set of rules, which could be used in similar clinical environments to screen for autism.Publication Embargo AI-based Behavioural Analyser for Interviews/Viva(IEEE, 2022-01-03) Dissanayake, D. Y; Amalya, V; Dissanayaka, R; Lakshan, L; Samarasinghe, P; Nadeeshani, M; Samarasinghe, PGlobalization and technology have made virtual interviews to be the choice of recruitment. Even though online interviews/viva have eliminated time, budgetary, and geographical barriers, the lack of comprehension regarding the interviewee’s behavioural aspects is yet to overcome. Therefore, a machine-based approach is proposed in this research for detecting and assessing changes in interviewees’ behaviour and personality traits based on nonverbal cues. Additionally, a group analysis of other applicants, as well as a comparison of the interview environment with the non-interview environment is also being obtained. To achieve this, we focus on the candidate’s emotion, eye movement, smile, and head movements. The system was carried out using deep learning and machine learning models which achieved accuracies over 85% for all smile, eye gaze, emotion, and head pose analysis. Furthermore, several machine learning models were developed based on the analysed behavioural outcomes of the interviewee to identify big five personality traits with Random Forest model yielding highest accuracy rate of over 75%. Our findings indicate that nonverbal behavioural cues can be utilized to determine personality traits.Publication Embargo Facial emotion prediction through action units and deep learning(IEEE, 2020-12-10) Nadeeshani, M; Jayaweera, A; Samarasinghe, PWith the recent advancements in deep learning techniques, attention has been given to training and testing facial emotions through highly complex deep learning systems. In this paper we apply machine learning techniques which require less resources to produce comparable results for emotion prediction. As the underlying technique for the emotion prediction in this research is based on clinically recognized Facial Action Coding System (FACS), a further analysis is given on the contribution of each of the Action Units (AUs) for the predicted emotion. This analysis would complement, strengthen and be a main resource for addressing many different health issues related to facial muscle movements.Publication Embargo Pubudu: Deep learning based screening and intervention of dyslexia, dysgraphia and dyscalculia(IEEE, 2019-12-18) Kariyawasam, R; Nadeeshani, M; Hamid, T; Subasinghe, I; Samarasinghe, P; Ratnayake, pDyslexia, Dysgraphia and Dyscalculia are significant learning disabilities that affect around 10% of children in the world. Despite the advancement of technology literacy in the community, limited attention has been given for screening and intervention of these disabilities using mobile applications in Sri Lanka. In this research, one of the first deep learning and machine learning based mobile applications, named “Pubudu” was developed for screening and intervention of dyslexia, dysgraphia and dyscalculia supporting local languages. In “Pubudu” we have followed up clinical screening and diagnostic procedures recommended by health professionals for screening and intervention. The screening of dyslexia, letter dysgraphia and numeric dysgraphia was carried out using deep neural network and the screening for dyscalculia was carried out using machine learning techniques. Intervention techniques are implemented using gamified environments. System testing was carried out using 50 differently abled children and 50 typical children. With the initial dataset 88%, 58%, 99% screening accuracies are achieved in neural networks for letter dysgraphia, dyslexia and numeric dysgraphia screening while dysgraphia, whereas 90% accuracy was achieved for dyscalculia. Handwritten letters and numbers were fed as inputs to CNN model in letter dysgraphia and numeric dysgraphia while embedded audio clips of letter pronunciation were fed in to voice recognition CNN model in dyslexia. “Pubudu” shows significant potential for screening and intervention of dyslexia, dysgraphia and dyscalculia in local languages motivating children and interactively making them able and would be an enabling app for most of the underprivileged children in Sri Lanka.