Research Papers - Dept of Information Technology

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Author: search.filters.author.Pulasinghe, KStart date: 2010

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Now showing 1 - 10 of 22
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    Step-by-Step Process of Building Voices for Under Resourced Languages using MARY TTS Platform
    (IEEE, 2022-12-09) Senarathna, M; Pulasinghe, K; Reyal, S
    This paper presents a comprehensive guide for creating synthetic voices to support under resourced languages for the MaryTTS platform. Although researchers have extensively contributed in the domain of speech synthesis, the lack of a thorough documentation hinders the voice building process for languages not yet supported by MaryTTS, complicating the implementation process for users with inadequate knowledge in the field of Text-to-Speech (TTS). The step-by-step process discussed in this study is further demonstrated with the creation of a synthetic voice for the Sinhala language, with unit selection as the voice building approach. A Sinhalese voice was generated with an intelligibility score of 91.7% upon evaluation with Diagnostic Rhyme Test (DRT). Comparison with ground truth data proved a close approximation to human speech where the intelligibility score was identified as 97.9%, when tested with the same participants. The Mean Opinion Score (MOS) revealed a naturalness level of 2.993, indicating a moderately high speech quality for the proposed system in comparison with the ideal score of 4.972.
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    SeizeIT: SEIZURE victims are no longer leashed
    (Institute of Advanced Engineering and Science, 2019-09-18) Wimalarathne, M. A. J. I; Ubeysingha, K. U. K; Imbulana, I. A. D. M; Welikala, W. A. D. R; Pulasinghe, K
    Seizure is considered to be one of the severe and most common type of neurological disorders. Despite the availability of numerous anti-seizure drugs, it is often difficult to control the disease completely and effectively. Lack of close supervision and failure in providing urgent medical care during and after seizure episodes, leads to serious injuries or even death. On the other hand, the use of wireless sensor networks in everyday applications have rapidly increased due to decreased technology costs and improved product reliability. Therefore, developing a wearable device to monitor seizure may complete the anamnesis, help medical staff in diagnosing and acute treatment while preventing seizure related accidents. There are number of seizure detection systems available in the market. Still their performance is far from perfect. This paper explores an application of biomedical wireless sensor networks, which attempts to monitor patients in a completely non-invasive and non-intrusive manner. It describes a wearable device together with seizure prediction and alerting system, which is designed to address some issues with seizure detection systems in the market. Its functional block diagram and operating modes are detailed. Possible application areas of the device are also discussed.
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    Machine learning based automated speech dialog analysis of autistic children
    (IEEE, 2019-10-24) Wijesinghe, A; Samarasinghe, P; Seneviratne, S; Yogarajah, P; Pulasinghe, K
    Children with autism spectrum disorder (ASD) have altered behaviors in communication, social interaction, and activity, out of which communication has been the most prominent disorder among many. Despite the recent technological advances, limited attention has been given to screening and diagnosing ASD by identifying the speech deficiencies (SD) of autistic children at early stages. This research focuses on bridging the gap in ASD screening by developing an automated system to distinguish autistic traits through speech analysis. Data was collected from 40 participants for the initial analysis and recordings were obtained from 17 participants. We considered a three-stage processing system; first stage utilizes thresholding for silence detection and Vocal Activity Detection for vocal isolation, second stage adopts machine learning technique neural network with frequency domain representations in developing a reliant utterance classifier for the isolated vocals and stage three also adopts machine learning technique neural network in recognizing autistic traits in speech patterns of the classified utterances. The results are promising in identifying SD of autistic children with the utterance classifier having 78% accuracy and pattern recognition 72% accuracy.
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    Picture Archiving and Communications System (PACS) for Government Hospitals in Sri Lanka
    (eHealth Asia 2015At: Colombo, 2015-10) Amarathunga, S. D. D; Jayasundara, P. P. A. S; Somaweera, E. G. P. P; Weerasena, P. D. C; Pulasinghe, K; Samarathunga, S. A. U. S
    In this modern world, Healthcare medical imaging system plays and important and central role in critical factor for the quality of diagnostic and treatments. Picture archiving and Communication System (PACS) is the backbone of the analysis of medical images as it is well adapt with several standards such as DICOM and HL7.
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    Web Based Voice Controlled Advanced PACS to Diagnose Lungs Cancer and Related Anomalies
    (www.ijisrt.com, 2019-10) Ratnasingam, T; Sayanthan, A; Velummylum, E. S; Archchana, K; Pulasinghe, K
    - PACS (picture archiving and communication system) is a medicinal imaging technology that is utilized basically in medicinal services associations to safely store and dissect carefully transmit electronic pictures. PACS a needed asset in contemporary hospitals, has demonstrated its key position in the department of radiology for archiving and collecting medical images, followed by its inclusion with the department of radiology. In this paper we have included work 3D displaying of DICOM pictures, calculation of segmented cancer part with fine calculations, voice recognition for a program to get and translate correspondence or to comprehend and complete spoken directions, and forecast of malignancy utilizing examinations of cancer symptoms.
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    Remote Doctor: Tele Medicine Unit
    (https://www.ijert.org/, 2017-02) Lakshani, D. G. K; De Silva, W. K. S; Bandara, U. R. R. I. S; Samarasinghe, R. W. H. V; Kahandawaarachchi, K. A. D. C. P; Pulasinghe, K
    Remote Doctor is a solution for transfiguration of telecommunication for healthcare industry to address the prominent aspects identified among the rural hospitals in Sri Lanka such as inadequate specialist consulting, poor medical record storage, poor capacity planning, time consuming prescription writing and report viewing. The proposed telemedicine system inscribes with video conferencing and report sharing among the physician and the patient, automated prescription, taking snapshots and video clips of necessary details such as wounds, ulcers and incision, annotate them and storing them by compressing without damaging the quality of the images and videos. The system has been developed using PHP, JavaScript, Html5, quickBlox API, Web Speech API, WebRTC technology and Sphinx toolkit. The Remote Doctor will be utilizeed in government hospitals to communicate with rural communities.
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    Knowledge Management for Effective Clinical Diagnosis in Developing Countries
    (Journal of Information Technology Review, 2013-05-02) Amararachchi, J. L; Pulasinghe, K; Perera, H. S. C
    In the last two decades, the Information and Communication Technologies (ICTs) revolution has redefined the structure of the 21st century healthcare organization. The fundamental challenge faced by the 21st century clinical practitioner in a developing country is to acquire proficiency in understanding and interpreting clinical information so as to update knowledge that leverage the quality of decisions made at the clinics. An additional challenge must be considered by the clinical practitioners to make potentially life-saving decisions whilst attempting to deal with large amounts of clinical data. Since the Clinical Knowledge Management Systems (CKMS) consist of most related Data, Information and Knowledge, it could be utilized to achieve the above challenges. Shortage of medical experts in Health Institutions located in rural and remote areas in developing countries being a huge problem which effects badly to the quality of healthcare. By providing facilities for medical practitioners to access KMS, this problem can be alleviated substantially. A Knowledge Management (KM) solution would allow healthcare institutions to give clinical data context, so as to allow knowledge derivation for more effective clinical diagnosis. It would also provide a mechanism for effective transfer of the acquired knowledge in order to aid healthcare workers as and when required. This study has identified the factors that affect to the knowledge management initiatives. There is a strong association between accessing and using Information/knowledge in clinical activities and quality of healthcare. Moreover, attitudes of Medical Practitioners (MP), Infrastructure facilities, patient Information systems, patient treatment, staff benefits etc., have shown positive effect to the success of Knowledge Management in Health Institutions. The research has used a case study methodology for accomplishing the research objectives. Rural and remote areas in Sri Lanka have been considered for the case study since it is one of the developing countries situated in the Asian region. Based on the outcome of the study, we introduce a KM framework for Healthcare Institutions which would assist HIs to discover and create new knowledge. The framework has been validated using a sample of 15 hospitals situated in the Kandy district in Sri Lanka.
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    Sinhala Conversational Interface for Appointment Management and Medical Advice
    (IEEE, 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
    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.
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    Vehicle Insurance Policy Document Summarizer, AI Insurance Agent and On-The-Spot Claimer
    (IEEE, 2021-04-02) Samarasinghe, H. T. D; Herath, N. A. D. M; Dabare, H. S. S; Gamaarachchi, Y. R; Pulasinghe, K; Yapa, P
    This paper proposes an automated vehicle insurance policy summarizing application. “Explain to Me” is one such software/tool which enable you to summarize the content of documents regarding vehicle insurance policies by using the NLP, machine learning and deep learning applications. The program targets mainly insurance users and suppliers of insurance services. Due to the increase of vehicle accidents, the vehicle insurance industry has gained more popularity currently. Therefore, different insurance companies have introduced a variety of insurance policies to customers. Vehicle insurance policy documents consist lot of insurance terms that should be read with more attention. As the main objective, this system filters unnecessary data in the particular document, and finalize a summary as the output. As another major component, the application “On the spot claimer” which is never before in Sri Lankan vehicle insurance industry, is another major part of this project that works as suggesting the most relevant insurance claiming that can be claimed by the user after detection of the type of damage through mobile phone camera. Another part of this research project, the function known as the Recommender, which works along with the summarization tool, is a recommendation system with a view of recommending more favorable rules for the assertion of alternatives that exist in the corresponding, equivalent documents of other companies. Finally, in order to interact with custody concerns about how to insure an automobile, CNN, which are based on the extraction of images, are used for the implementation of the ETM system in NLP.
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    Automated Sinhala Speech Emotions Analysis Tool for Autism Children
    (IEEE, 2021-08-11) Welarathna, K. T; Kulasekara, V; Pulasinghe, K; Piyawardana, V
    — Autism Spectrum Disorder (ASD) is a neurological disorder that impairs children's development and symptoms that can be noticed in early childhood. One of the main diagnosis characteristics of ASD is the child having unusual emotions and expressions during social interactions. The main problem is how to distinguish these symptoms. Only 14 out of 100 Autistic kids, before they reach the age of 24 months, get medical treatments since the unavailability of resources to identify them early. If they can be recognized early, a therapeutic engagement can be done to help them overcome those issues in social interactions, when they reach school-going age. The focus of this research is to develop a tool to screen atypical children from typical children. This research attempts to recognize the correct emotion of a child, while the child is talking. The input audio stream of children was normalized into a specific range, sub-framed into 2s length for language-independent, noise reduction, and age independence features, and extracting the most effective 40 audio features. The Convolutional Neural Network (CNN) based model classifies eight different emotions of sad, disgust, surprise, neutral, happy, calm, fear, and angry with an accuracy matrix of F1 score of 0.90, even in the uncontrol environment. If the classifying emotions have small frequency variances, the trained model has the ability to handle them.