Browsing by Author "Rajapakse, S"

Now showing 1 - 3 of 3

item: Conference-Full-text
Alexza: a mobile application for dyslexics utilizing artificial intelligence and machine learning concepts
(Information Technology Research Unit, Faculty of Information Technology, University of Moratuwa, Sri Lanka, 2018) Rajapakse, S; Polwattage, D; Guruge, U; Jayathilaka, I; Edirisinghe, T; Thelijjagoda, S; Wijesiriwardana, CP
Dyslexia can be explained as a neurological learning disability which causes difficulties in reading, word decoding, comprehension, short-term memory, writing, spelling, and speaking. People who are diagnosed with dyslexia tend to show signs of low self-esteem and anxiety since they can't interact with the society in a way that their peers do. Many applications available in this domain help them by correcting their issues by playing games and reading some hard-coded texts or pdf books. This correcting process takes time and dyslexics become helpless when coping with their day-to-day activities. This paper describes results of an evaluation of a prototype mobile application which helps the dyslexic users to deal with their reading difficulties in real life successfully, while they are receiving proper treatments. This prototype can identify the texts around them and read it loudly so that user can understand and will be allowed to customize the chunking, scrolling and highlighting of words according to their disability levels. By integrating dictionary support with the phonic and morphological structure of the word, the user will be able to comprehend difficult and complex words easily. In addition, the study also explores the use of a machine learning approach to improve the effectiveness of the learning dyslexic complex words.
item: Article-Abstract
Morphological, antimicrobial, durability, and physical properties of untreated and treated textiles using silver-nanoparticles
(2014-06-27) Perera, S; Bhushan, B; Bandara, R; Rajapakse, G; Rajapakse, S; Bandara, C
Silver nanoparticles are often applied to textiles for their strong antimicrobial activity and potential uses in various applications. The treatment of fabrics with silver nanoparticles has often involved complex or expensive processes, required surface post treatment, lacked durability and altered desirable properties related to the comfort of the fabric. In this paper, a systematic study has been performed to identify a simple yet durable and economical approach to apply silver nanoparticles on cotton fabrics with minimum alterations to the fabrics' physical properties. An ex situ chemical and in situ photo reduction approaches of silver-nanoparticle treatment on cotton fabrics were investigated, comparing the morphology, antimicrobial, durability of the treatment after wash and physical properties. Results indicate that the in situ approach was favorable toward aforementioned requirements and could retain its properties close to the original fabric. Methodology presented here to study effects of ex situ and in situ treatments of silver nanoparticles on textiles could be of interest to other applications.
item: Conference-Abstract
Patient alert and decision support system
Gunawardane, TSFW; Koggalage, R; Rodrigo, BKRP; Rajapakse, S
Safety of critically ill patients in intensive care units is an important aspect of medical care. There are many factors contributing to shortcomings and errors in patient care in the intensive care setting, such as long working hours, high levels of stress, lack of enough people, may cause human errors and affecting the effectiveness of the decisions of the physician. Several attempts have been made to increase the effectiveness of such decisions by issuing early alerts on adverse patient conditions. However, such alerts are based on single parameter variations but not on the relationship between multiple parameter variations. Thus, inability to provide an effective communication model causes a considerable bottleneck in intensive care unit (ICU) operations. The proposed model is an integrated solution which identifies the adverse patient conditions on multiple parameter variations and then provides predictive treatment suggestions on those identified conditions. It follows an interactive communication cycle in order to properly notify the responsible physicians. Results show that the system is capable of early identification of adverse conditions and providing suitable treatment suggestions compared to physicians themselves make decisions on same patient conditions.