2020
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Publication Embargo Comparative analysis of the application of Deep Learning techniques for Forex Rate prediction(2019 1st International Conference on Advancements in Computing (ICAC), SLIIT, 2019-12-05) Aryal, S.; Nadarajah, D.; Kasthurirathna, D.; Rupasinghe, L.; Jayawardena, C.Forecasting the financial time series is an extensive field of study. Even though the econometric models, traditional machine learning models, artificial neural networks and deep learning models have been used to predict the financial time series, deep learning models have been recently employed to do predictions of financial time series. In this paper, three different deep learning models called Long Short-Term Memory (LSTM), Convolutional Neural Network (CNN) and Temporal Convolution Network (TCN) have been used to predict the United States Dollar (USD) to Sri Lankan Rupees (LKR) exchange rate and compared the accuracy of the models. The results indicate the superiority of CNN model over other models. We conclude that CNN based models perform best in financial time series prediction.Publication Embargo Aerodynamic modeling of simplified wind turbine rotors targeting small-scale applications in Sri Lanka(Elsevier, 2020-09-11) Sugathapala, T. M; Boteju, S; Withanage, P. B; Wijewardane, SA design and optimization procedure of simplified wind turbine rotors for small-scale applications is presented. The need for this research has arisen from the recent national initiative of the government of Sri Lanka titled ‘Battle for Wind Energy’ in promoting small scale grid connected wind plants for electricity customers under Net Metering scheme. The main objective of this research is to assist local developers to design optimum rotors for given electrical generators (as determined by customer requirements), suitable for wind characteristics at specific locations. Another objective is to enhance local manufacturing capabilities by providing a design option of a simplified rotor blade geometry. A study on the correlation between population density of electricity customers and wind energy potentials was carried out to categorize the demand centres based on wind energy potentials in proposing series of small-scale wind turbine designs. A unique and improved rotor design procedure is presented which attempts to match the point of maximum performance of a rotor (design tip speed ratio) with the design wind speed of a given location by considering generator performance. The new design procedure showed successful convergence on a unique blade diameter for each rotor configuration that allowed the design tip speed ratio to match the design wind speed. The performance evaluation of rotor designs showed that high solidity rotors work better on the low wind potential region while low solidity rotors dominate medium and high wind potential regions. The performance reductions of simplified rotor designs are not significant and therefore would be an effective way to enhance value addition through local manufacture.Publication Open Access Comparative Analysis of Deep Learning Models for Multi-Step Prediction of Financial Time Series(researchgate.net, 2020-10-21) Aryal, S; Nadarajah, D; Rupasinghe, P.L; Jayawardena, C; Kasthurirathna, DFinancial time series prediction has been a key topic of interest among researchers considering the complexity of the domain and also due to its significant impact on a wide range of applications. In contrast to one-step ahead prediction, multi-step forecasting is more desirable in the industry but the task is more challenging. In recent days, advancement in deep learning has shown impressive accomplishments across various tasks including sequence learning and time series forecasting. Although most previous studies are focused on applications of deep learning models for single-step ahead prediction, multi-step financial time series forecasting has not been explored exhaustively. This paper aims at extensively evaluating the performance of various state-of-the-art deep learning models for multiple multi-steps ahead prediction horizons on real-world stock and forex markets dataset. Specifically, we focus on Long-Short Term Memory (LSTM) network and its variations, Encoder-Decoder based sequence to sequence models, Temporal Convolution Network (TCN), hybrid Exponential SmoothingRecurrent Neural Networks (ES-RNN) and Neural Basis Expansion Analysis for interpretable Time Series forecasting (N-BEATS). Experimental results show that the latest deep learning models such as NBEATS, ES-LSTM and TCN produced better results for all stock market related datasets by obtaining around 50% less Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE) scores for each prediction horizon as compared to other models. However, the conventional LSTM-based models still prove to be dominant in the forex domain by comparatively achieving around 2% less error values.Publication Embargo Arogya -An Intelligent Ayurvedic Herb Management Platform(IEEE, 2020-11-04) Pathiranage, N; Nilfa, N; Nithmali, M; Kumari, N; Weerasinghe, L; Weerathunga, IAyurvedic means a science of life and well-being with its unique approaches to social and spiritual life. Especially in Sri Lanka we have our own set of rare Ayurvedic herbs which have been utilized by generations as medicinal treatments for a variety of diseases. Absence of specialists in this area makes proper identification as well as classification of valuable herbal plants a tedious task, which is essential for better treatment. Hence, a fully automated system for herb detection and classification, information visualization regarding them is highly desirable. There are existing applications which can identify plants with low prediction accuracies, as well as to give information regarding them. However, these applications are based on foreign plant data sets that do not include valuable herbs and shrubs with medicinal qualities. Hence this research proposes an application unique to medicinal plants, which can perform all these functionalities in both online and offline approach. Here, a new Ayurvedic plant dataset prepared from scratch, and preliminary results for classification of 5 types of herbs, compared with several deep Convolutional Neural Network (CNN) models based on transfer learning are presented. Experimental results indicate Marker-based Watershed algorithm as the best object detection algorithm in a complex background, VGG-16 as the best deep CNN classification model which reached a promising testing accuracy of 99.53%, and Seq2Seq LSTM model as the best deep learning model with optimum accuracy in abstractive information summarization.Publication Embargo An Integrated Framework for Predicting Health Based on Sensor Data Using Machine Learning(2020 2nd International Conference on Advancements in Computing (ICAC), SLIIT, 2020-12-10) Jayaweera, K.N.; Kallora, K.M.C.; Subasinghe, N.A.C.K.; Rupasinghe, L.; Liyanapathirana, C.According to recent studies, the majority of the world's population shows a lack of concern in their health. As a consequence, the non-communicable disease rate has increased dramatically. Amongst these diseases, heart diseases have caused the most catastrophic situations. Apart from the busy lifestyle, studies also show that stress is another factor that causes these diseases. Therefore, the focus of our research is to provide a user-friendly health monitoring system that causes minimum disturbance to its users. However, many studies have focused on predicting health; very few have focused on its usability. The objective of our research is to predict the possibility of cardiac arrests and the presence of stress in real-time using a wearable device prototype. The system uses biometric signals obtained from the photoplethysmogram sensor embedded in the wearable device to perform real-time predictions. We trained three models using random forest, k-nearest neighbor, and logistic regression classification algorithms to predict sudden cardiac arrests with accuracies 99.93%, 99.10%, and 94.47%, respectively. Further, we trained three additional models to predict stress using the same algorithms with accuracies 99.87%, 96.83%, and 65.00%, respectively. Thus, the results of this study show that an integrated framework, capable of predicting different health-related conditions, through sensor data collected from wearable sensors, is feasible.Publication Open Access Utalk: Sri Lankan Sign Language Converter Mobile App using Image Processing and Machine Learning(2020 2nd International Conference on Advancements in Computing (ICAC), SLIIT, 2020-12-10) Dissanayake, I.S.M.; Wickramanayake, P.J.; Mudunkotuwa, M.A.S; Fernando, P.W.N.Deaf and mute people face various difficulties in daily activities due to the communication barrier caused by the lack of Sign Language knowledge in the society. Many researches have attempted to mitigate this barrier using Computer Vision based techniques to interpret signs and express them in natural language, empowering deaf and mute people to communicate with hearing people easily. However, most of such researches focus only on interpreting static signs and understanding dynamic signs is not well explored. Understanding dynamic visual content (videos) and translating them into natural language is a challenging problem. Further, because of the differences in sign languages, a system developed for one sign language cannot be directly used to understand another sign language, e.g., a system developed for American Sign Language cannot be used to interpret Sri Lankan Sign Language. In this study, we develop a system called Utalk to interpret static as well as dynamic signs expressed in Sri Lankan Sign Language. The proposed system utilizes Computer Vision and Machine Learning techniques to interpret sings performed by deaf and mute people. Utalk is a mobile application, hence it is non-intrusive and cost-effective. We demonstrate the effectiveness of the our system using a newly collected dataset.Publication Embargo Smart Intelligent Advisory Agent for Farming Community(2020 2nd International Conference on Advancements in Computing (ICAC), SLIIT, 2020-12-10) Illandara, T.S.; De Silva, H.L.H.; Madurawala, K.S.H.; Dayasena, B.R.D.; Srimath, U.; Samaratunge Arachchillage, S.; Buddhika, T.The currently available agricultural services have few limitations because of the traditional cultivation methods and the unavailability of experts. This research attempts to solve the major problems faced by farmers using an Intelligent Expert Advisory Agent (EAA) that would act as a human counterpart to provide reliable solutions in real-time to the farmers using Machine Learning (ML), Image Processing (IP), and Internet of Things (IoT) technologies. A web application is developed to provide meaningful information to the user by representing agriculture instructors. Using the web application, the farmer can obtain information about predicted weather up to two months. Once the crop is selected, suitable organic fertilizers are suggested to maximize the productivity of the cultivation. After planting, the farmer can continuously monitor the condition of the plants in real-time using the IoT system. Based on this information, the farmer can check if the conditions are optimum for the growth of the plant by interacting with the knowledge base system. If the plants get infected with diseases, the user can capture an image of the diseased plant using the implemented mobile application and send to the IP system to identify the diseases and suggests remedies to overcome the situation.Publication Embargo PatientCare: Patient Assistive Tool with Automatic Hand-written Prescription Reader(2020 2nd International Conference on Advancements in Computing (ICAC), SLIIT, 2020-12-10) Kulathunga, D.; Muthukumarana, C.; Pasan, U.; Hemachandra, C.; Tissera, M.; De Silva, H.Most people in the world prefer to be conscious of the medications prescribed by physicians. Especially, the importance of handwritten prescriptions is prodigious in Sri Lanka because they are widely used in the healthcare sector. However, due to the illegible handwriting and the medical abbreviations of the physicians, patients are unable to find the prescribed medication information. This research is an attempt to assist the patients in identifying the prescribed medicine information and minimizes misreading errors of medical prescriptions. When a patient uploads the image of a prescription, the system converts it into unstructured text data by using OCR and segmentation, then NER is used to categorize medical information from given text. According to the other research, some solutions exist in other domains for the above mechanisms. But they gave less accuracy when tried to apply for this research due to the domain specialty. Therefore, as a solution to overcome the above discrepancy this approach allows users to scan handwritten medical prescriptions and blood reports and obtain analyzed reports in medical history. Results have shown that this approach will give 64%-70% accuracy level in doctor's handwriting recognition and 95%- 98% accuracy in medical information categorization of the prescription format.Publication Embargo Assist: Rendering, Pipeline Management, and Pipeline Tracking Software Publisher: IEEE Cite This PDF(2020 2nd International Conference on Advancements in Computing (ICAC), SLIIT, 2020-12-10) Salgado, M.V.I; Hettiarachchi, H.A.D.D.; Munasinghe, T.U.; Fernando, K.A.U.; Gamage, I.; Thilakarathna, T.; Cooray, N.C.Video production is one of the most dominant industries in the 21st century, and research into the automation of tasks associated with it has drastically increased. The production of videos take place in three stages: pre-production, production, and post-production. These three stages consist of script writing, scheduling, logistics, and other administration work. There are commercial products to automate these individual tasks. Incorporating all these software into video production can be expensive and difficult to manage. This study proposes the “Assist” software to handle all processes in video production. It has resulted in a product that covers the three main stages featuring scripts, storyboards, inventory management, production progress tracking and management, and rendering. The mentioned features were designed and developed using decision tree algorithm, PyQt5, general decimation algorithm, mesh simplification algorithm, and multi-variable regression.Publication Embargo A Story of Two Surveys: for the Advancement of Sinhalese Mobile Text Entry Research(2020 2nd International Conference on Advancements in Computing (ICAC), SLIIT, 2020-12-10) Reyal, S.; Piyawardana, V.; Kaveendri, D.This paper presents two surveys: a literature survey on the current progress on Sinhalese mobile text entry research and a user survey on how Sri Lankans experience Sinhalese mobile text entry. The first survey concludes that Sinhalese mobile text entry is limited in scope and size compared to western text entry research. The second survey attempts to bridge this gap by providing deep insight into aspects in Sinhalese mobile text entry such as language switching, using English within Sinhalese e.g. mixed-mode and Singlish, and the popularity of various input modalities, keyboard vendors, and keyboard layouts. This is also the first research publication that unveils the current state-of-the-art in Sinhalese mobile text entry, along with user-preferences such as using autocorrect, glide-typing, and speech. Results from this survey deepens our understanding of the Sinhalese mobile text entry domain resulting in a stronger empirical footing and more innovative Sinhalese mobile text entry solutions.Publication Embargo Smart Backpack for Travelers(2020 2nd International Conference on Advancements in Computing (ICAC), SLIIT, 2020-12-10) Gunarathne, P.D.R.P.; Amarasuriya, R.M.C.I.; Wickramasinghe, W.A.D.D.; Witharana, A.H.T.N.; Abeygunawardhana, P.K.W.Smart backpack is an application-specific design which guarantees a safe journey for travelers. The smart backpack has a different combination of services connected to a single system. It has a unique design that helps to fulfill its services. The system provides the health status of travelers and environmental status by measuring the quality level of the nearby atmosphere. As a security feature, it contains a human detective sensor-based security system. As well as the research consists of an undying power resource which charges by solar cells, the power source can be used to power up the system and to recharge traveler's devices through a USB power outlet. The Backpack has a user-friendly mobile application. This system also provides a health monitoring feature, which monitors the heart rate and body temperature of the traveler. The traveler can share his/her health status with the system and compute the real-time health condition from the outputs of the health sensors integrated into the backpack. The bag model design and building play a major role and has removable unique mini compartments for all hardware components. It should carry maximum weight with minimum pressure for the back of the traveler with minimum cost.Publication Embargo Aspect Based Sentiment Analysis for Evaluating Movies and TV series Publisher: IEEE Cite This PDF(2020 2nd International Conference on Advancements in Computing (ICAC), SLIIT, 2020-12-10) Cooray, T.; Perera, G.; Chandrasena, D.; Alosius, J.; Kugathasan, A.Aspect-based sentiment analysis (ABSA) is used in different fields for analyzing customer reviews to project an overall customer opinion on certain products. With the expansion of the internet, people are provided with an inexpensive and time-saving method to express their opinion to a larger audience, while various industries are handed with the opportunity to gather free information from it to obtain market value. The implementation of machine learning methods for the evaluation of aspects related to movies and television series has not been commenced, and it could be a new development for the industry. This study focuses on conducting an ABSA on a movie or a television series based on genre, story as well as cast and crew aspects. The data collected from social media through web scraping is processed to produce adequate results to get a broad understanding on how the popularity of the movie or the television series related to above mentioned aspects. Then, each aspect is further analyzed to gather precise information belonging to each aspect. The accuracy of the results of the proposed system has been achieved over 79%. The results proved that the solution is highly successful than the former works with high business value.Publication Embargo Secured, Intelligent Blood and Organ Donation Management System - “LifeShare”(2020 2nd International Conference on Advancements in Computing (ICAC), SLIIT, 2020-12-10) Wijayathilaka, P.L.; Pahala Gamage, P.H.; De Silva, K.H.B.; Athukorala, A.P.P.S.; Kahandawaarachchi, K.A.D.C.P.; Pulasinghe, K.N.The scarcity and exigency for blood and organs has created many discrepancies in current approaches. These have created the criteria for malpractices such as organ trafficking and black market selling. This research presents a solution with a secured-smart blood and organ donation web developed system, allowing both patients and healthcare providers to access information about the blood and organ processing records. The database would be managed using the Blockchain technology which could be only accessed by authorized users. Finally, tracking all registered donors, the proposed system generates a smart identity developed by Ethereum Smart Contract (ESC). System predicts blood demand for the future ten years using Linear Regression Model with 0.998 of high R-squared accuracy value. This reduces shortages and wastage of blood. Also, using global positioning system and K-Nearest Neighbors Machine Learning algorithm, the system finds the best matches among donors and seekers according to the nearest location. Further, the system will automatically send questionnaires for registered users to identify and evaluate their awareness and issues about organ donation. Overall, this study aims for a secured and transparent web application. Thus, it facilitates an innovative and a productive blood donation and organ transplantation process in Sri Lankan healthcare sector.Publication Embargo Deepfake Audio Detection: A Deep Learning Based Solution for Group Conversations(2020 2nd International Conference on Advancements in Computing (ICAC), SLIIT, 2020-12-10) Wijethunga, R.L.M.A.P.C.; Matheesha, D.M.K.; Al Noman, A.; De Silva, K.H.V.T.A.; Tissera, M.; Rupasinghe, L.The recent advancements in deep learning and other related technologies have led to improvements in various areas such as computer vision, bio-informatics, and speech recognition etc. This research mainly focuses on a problem with synthetic speech and speaker diarization. The developments in audio have resulted in deep learning models capable of replicating naturalsounding voice also known as text-to-speech (TTS) systems. This technology could be manipulated for malicious purposes such as deepfakes, impersonation, or spoofing attacks. We propose a system that has the capability of distinguishing between real and synthetic speech in group conversations.We built Deep Neural Network models and integrated them into a single solution using different datasets, including but not limited to Urban- Sound8K (5.6GB), Conversational (12.2GB), AMI-Corpus (5GB), and FakeOrReal (4GB). Our proposed approach consists of four main components. The speech-denoising component cleans and preprocesses the audio using Multilayer-Perceptron and Convolutional Neural Network architectures, with 93% and 94% accuracies accordingly. The speaker diarization was implemented using two different approaches, Natural Language Processing for text conversion with 93% accuracy and Recurrent Neural Network model for speaker labeling with 80% accuracy and 0.52 Diarization-Error-Rate. The final component distinguishes between real and fake audio using a CNN architecture with 94% accuracy. With these findings, this research will contribute immensely to the domain of speech analysis.Publication Embargo Smart Exam Evaluator for Object-Oriented Programming Modules(2020 2nd International Conference on Advancements in Computing (ICAC), SLIIT, 2020-12-10) Wickramasinghe, M.L.; Wijethunga, H.P.; Yapa, S.R.; Vishwajith, D.M.D.; Samaratunge Arachchillage, U.S.S.; Amarasena, N.Worldwide educators considered that, automate the evaluation of programming language-based exams is a more challenging task due to its complexity and the diversity of solutions implemented by students. This research investigates and provides insight into the applicability and development of a java based online exam evaluator as a solution to traditional onerous manual exam assessment methodology. The proposed system allows students to take online exams in Java for an implemented source code in a practical exam, automatically reporting the results to the administrator simultaneously. Accordingly, this research examines existing methods, identifies their limitations, and explores the significance of introducing a smart object-oriented program-based exam evaluator as a solution. This method minimizes all human errors and makes the system more efficient. An automated answer checker checks and marks are given as human-counterpart and generate a report with possible suggestions for improvement of the answer scripts and generate a classification report to predict the student’s final exam marks. This software application uses a Knowledge base, Abstract Syntax tree (AST), ANTLR, Image processing, and Machine Learning (ML) as key technologies. The proposed system gains a higher accuracy of 95% as performed by a separate human-counterpart. These results show a high level of accuracy and automate marking is the major emphasis to save human evaluation effort and maximize productivity.Publication Embargo Candidate Selection for the Interview using GitHub Profile and User Analysis for the Position of Software Engineer(2020 2nd International Conference on Advancements in Computing (ICAC), SLIIT, 2020-12-10) Gajanayake, R.G.U.S.; Hiras, M.H.M.; Gunathunga, P.I.N.; Supun, E.G.J.; Karunasenna, A.; Bandara, P.Selecting the most suitable candidates for interviews is an important process for organizations that can affect their overall work performance. Typically, recruiters check Curriculum Vitae (CV), shortlist them and call candidates for interviews which have been the way of recruiting new employees for a long time. To minimize the time spent on the above process, pre-screening mechanisms are nowadays implemented by organizations. However, those mechanisms need sufficient information to evaluate the candidate. For example, in case of a software engineer, the recruiters are interested on the programming ability, academic perfo rmance as well as personality traits of potential candidates. In this research, a pre-screening solution is proposed to screen the applicants for the post of Software Engineer where candidates are screen based on an initial call transcript, GitHub profile, LinkedIn profile , CV, Academic transcript and, Recommendation letters. This approach extracts textual features of different dimensions based on Natural Language Processing to identify the Big Five personality traits, CV and GitHub insights, candidate’s skills, background, and capabilities from Recommendation letters as well as programming skills and knowledge from Academic transcript and Linked Profile. The results obtained from the different areas are presented an d shown that the selected supervised machine learning algorithms and techniques can be used to evaluate the best possible candidates.Publication Embargo Science Zone : An Augmented Reality based Mobile Application for Science(2020 2nd International Conference on Advancements in Computing (ICAC), SLIIT, 2020-12-10) De Silva, W.; Naranpanawa, P.; Hettihewa, U.; Liyanage, P.; Samarakoon, U.; Amarasena, N.In recent years, technology has rapidly developed, and it has provided many technological advancements for the field of education with an attempt to improve and overcome its limitations. Augmented Reality is among these latest technologies which support to improve learning environment around the world. It can bring education to a new level which can help students in many significant ways. In Sri Lanka, augmented reality is rarely been used for the purpose of educational enhancements. Therefore, it was decided to develop an augmented reality embedded mobile application for the G.C.E Ordinary Level Students in order to make it easy for them to learn Science with more enthusiasm and interest. This research has been used marker-based approach to transmit images or objects in the text book into the real-world scenes in order to create a more productive learning environment for the students. The first version of the application covers four main areas in the Science curriculum, such as; Preparation of Acids, Human Anatomy, Organization of Plants and Biosphere Cycles. Feedback for the application was taken from randomly selected ten science teachers and twenty grade eleven students and accordingly the application was further developed. Their feedback proves that the application would satisfy the common requirements of students, and it would be an immense support in scoring good results for science.Publication Embargo Remote Treatment Management Approach to Rural Healthcare in Sri Lanka(2020 2nd International Conference on Advancements in Computing (ICAC), SLIIT, 2020-12-10) Shashishka, P.W.T.; Madhusankar, M.S.; Perera, M.T.K.; Gnanasena, A.M.J.C.; Kuruppu, T.A.; Kahandawaarachchi, K.A.D.C.P.With the complications of society, people tend to ignore their medical status and focus on other priorities. Majorly due to lack of time to consult medical help, also in a pandemic situation like Corona Virus people have tended to detour their health issues. Patients lack options to contact their medical consultant without being physically present. “Remote Treatment” is designed to address this issue. App offers video conferencing between the patient and the medical consultant. Patients can choose the quality of the stream depending on the internet connection's capability, implemented utilizing WebRTC. Additionally, Remote Treatment provides patient data management and prescription handling along with improved security. Medical consultants has access to patient's previous and current reports also detailed graphs on their progress, implemented by utilizing Fusion charts, prescription handling has digitalized the prescription process hence patients wouldn't have the risk of acquiring inaccurate medicines. prescription handling is based on voice recognition built on using sphinx package In a telemedicine feature information security is utmost importance since these information violations could result in devastating situations. Information is secured with the aid of blockchain technology to provide maximum security. Remote Treatment allows patients to access medical help quickly with optimized condition.Publication Embargo Innovative, Integrated and Interactive (3I) LMS for Learners and Trainers(2020 2nd International Conference on Advancements in Computing (ICAC), SLIIT, 2020-12-10) Fernando, K.J.L.; Jayalath, W.J.D.L.D.D.; Ranasinghe, A.D.R.N.; Bandara, P.K.B.P.S.; De Silva, H.3I-LMS is meant to conquer the insurmountable restraints to class/lecture room education. What are insurmountable restraints to physical classroom education and how does 3I-LMS conquer them. Firstly, the lockdown and physical distancing warranted by the spread of Covid-19. In order to overcome the restraints of the pandemic 3I-LMS would be the obvious answer. 3I-LMS, the medium for delivery and reception, is online for both learners and trainers. Secondly, ineffectiveness resulting in frail in pass rates, fast diminishing memory, faulty study techniques are overcome. 3I-LMS makes studying satisfied, gamified, engaged and enjoying. The outcome would be lasting memory, effective study techniques, higher recall rates leading to improved pass rates. Thirdly, inefficiencies such as slow access to relevant lessons, notes, slow answer evaluation, feedback, clarification to routine questions are also resolved by 3I-LMS. 3I-LMS provides for keyword search, relevant subject wise notes, instant answers for routine questions thus contributing to improved efficiencies. Additionally, 3I-LMS has unique and innovative features to assist assignment completion, emanate milestone alerts, monitoring emotions and time utilization. 3I-LMS provides for utmost security. Thus, the solution deploys face recognition and keystroke dynamics to combat impersonation, copying and unauthorized referencing.Publication Embargo Domain Specific Conversational Intelligence: Voice Based E-Channeling System(2020 2nd International Conference on Advancements in Computing (ICAC), SLIIT, 2020-12-10) Weerathunga, W.A.H.; Lokugamage, G.N.; Hariharan, V.; Yahampath, A.D.N.H.; Kasthurirathna, D.In this research the application of Automatic Speech Recognition, Natural Language Understanding, Neural Networks and Text To Speech Conversion is investigated to create a domain specific end to end voice based E-Channeling system. The novel idea in this research can be extended to any other domain(e.g.: Taxi Application) and build a conversational intelligence system. This system enables the user to avoid the shortcomings in the traditional doctor appointment channeling procedures. The system also have the ability to predict the doctor specialization according to the symptoms of the patient and can give emergency health tips by using the powerful Neural Network module. Domain-specific speech recognition model is created according to Sri Lankan accents and handles the context-specific to this domain(94% accuracy). Extracting the entities, handling e-channeling functions and selecting the most suitable API is done by the RASA backend. Neural Network will give the first aid and doctor specialization recommendations according to user input with a validation accuracy of 90%. Speech synthesis model will output the response in user preferred language(Sinhala, English or Tamil).