Browsing by Author "Krishara, J"

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Automated Sinhala Voice Assistant to Manage Tasks using Natural Language Processing - ශ්‍රී Voice
(IEEE, 2022-12-26) Senarathne, K.H.I.R.; Nirash, J.M.I.; Herath, H.M.C.P.; Bandara, V.D.; Wijendra, D; Krishara, J
Voice assistants are programs on digital devices that listen and respond to verbal commands. In this dynamic world, users can use these voice assistants to manage daily tasks, plan their day, get answers to problems, and for entertainment purpose. Most of existing voice assistant applications functioned using the English language. Since Sinhala is the native language in Sri Lanka, it is not recognized internationally as well as within Sri Lanka for technical applications. As Sri Lankans are more inclined to use the Sinhala language, it is expected to develop this for the benefit of all the Sri Lankans despite their age and to use their native language through a technical application. Furthermore, the lack of English knowledge will lead to the decline of Information technology literacy. This project expects to take the Sinhala language forward to a standard where it is recognized locally and internationally. Thus, building a mobile application that supports Sinhala voice commands will solve the above-mentioned issues. It will facilitate the individuals to do daily activities efficiently and effortlessly within a less time. Machine Learning and Natural Language Processing are the man technologies used in this project. A computer vision-based algorithm from face detection technology is trained. This application is capable of answering questions and following the instructions for daily tasks and also can be used for entertainment. Apart from the mentioned specialty, it also supports and responds to the Sinhala language that will be shown off on a display.
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A Comprehensive Mobile Platform for Fostering Communication, Literacy, Numeracy, and Emotion Understanding in Children with ASD
(IEEE, 2024-07-25) Bandara, T.W.M.I.P.S; Deshan, M.A.D.; Prasanth, P.; Nadeera, M.S.; Krishara, J
This study presents SIPNENA, a novel mobile application designed to aid the learning and communication development of Sinhala-speaking autistic children aged six, particularly in rural areas of Sri Lanka. It offers a unique approach to teaching challenging subjects like English and Mathematics, tailored to the specific needs of children with Autism Spectrum Disorder (ASD). The application integrates interactive methodologies and gamification elements to facilitate better communication, understanding, and engagement. Additionally, it incorporates real-time emotion recognition features to monitor and respond to children's emotional states during learning activities. This research evaluates SIPNENA's effectiveness in improving communication abilities, academic skills, and emotion understanding among autistic children. The findings indicate promising results in catering to the unique educational needs of this target population, particularly in under-resourced rural regions, where specialized interventions are often scarce.
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Face Skin Disease Detection and Community based Doctor Recommendation System
(IEEE, 2022-12-09) Udara, M.A.A.; Wimalki Dilshani, D.G.; Mahalekam, M.S.W.; Wickramaarachchi, V.Y.; Krishara, J; Wijendra, D
In our country, skin diseases are more common than other diseases because of the climate. Skin diseases are occurring almost on all groups of ages among people. It is one of the most common types of diseases where some can be painful, and some can cause fatal to human life. The delay of the disease detection, difficulties of identify the infected area, Ignorance of the spread of the disease and treatments may threat to the patient’s life. Most of the time this process is performed manually which can lead to human errors and takes days for providing the results. This paper reports a smart solution that assists the patients by detecting the disease, identify the current infected area of the disease, recommend best doctors, provide community-based prevention guidelines, and predict the future risk. Also due to this economic crisis, we suggest that it’s much easier if the patient can do these skin check-ups systematically to continuously monitor and detect skin disease to get proper medical attention. As treatment procedures can be different from each doctor and impact will be different, we are working on community-based platform where we can get patients’ reviews about doctors and preventive guidelines. Depending on the performance evaluations, the results obtained from the proposed method for disease identifications are in the range of 90% - 95% of accuracy.
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IoT-Based Solution for Fish Disease Detection and Controlling a Fish Tank Through a Mobile Application
(IEEE, 2024-04-05) Bodaragama, B.D.T; Miyurangana, E.H.A.D.M; Jayakod, Y.T.W.S.L; Vipulasiri, D.M.H.D; Rajapaksha, U. U. S; Krishara, J
This research project seeks to enhance fish tank management and improve the well-being of aquatic life by leveraging modern technological solutions. It focuses on four key areas: monitoring water quality, detecting fish diseases, preventing algae growth, and developing an automatic fish feeder with remote control capabilities. The project’s first goal is to establish a comprehensive water quality monitoring and control system that predicts future water conditions, continuously assesses key parameters, and provides real-time data to users for proactive interventions. Additionally, the research project aims to develop an image-processing-based mobile application for early detection of fish diseases, eliminating the need for manual inspection and improving overall fish health management. The project also involves the creation of a mobile app to predict and prevent algae growth by analyzing factors like lighting, nutrient levels, and water flow, providing personalized recommendations for algae control. Lastly, an automatic fish feeder with remote control capabilities will be designed, allowing fish owners to schedule and adjust feeding times and portion sizes through a mobile app. This innovative approach ensures fish receive consistent and appropriate nutrition even when owners are away from home.
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A Mobile-Based Screening and Refinement System to Identify the Risk of Dyscalculia and Dysgraphia Learning Disabilities in Primary School Students
(IEEE, 2021-08-11) Hewapathirana, C; Abeysinghe, K; Maheshani, P; Liyanage, P; Krishara, J; Thelijjagoda, S
Learning Disability is a condition that has a direct effect on the brain and there is no cure or any identified medical treatments. Most of these cases remain undiagnosed due to the lack of awareness from their parents and teachers in underdeveloped countries like Sri Lanka. Mobile application-based solution ‘Nana Shilpa’ was developed for the screening and intervention processes for the specific Learning Disabilities which are Verbal and Lexical Dyscalculia, Operational and Practognostic Dyscalculia, Letter Level Dysgraphia and Numeric Dysgraphia. Deep Learning with Machine Learning techniques is used in the screening process to provide a better solution. To detect the written letters/numbers, trained Convolutional Neural Networks (CNN) achieved the accuracy of 92%, 99%, 99% for Verbal and Lexical Dyscalculia, Letter Level Dysgraphia and Number Dysgraphia respectively. The Machine Learning algorithms used for screening processes are Support Vector Machine (SVM) and Random Forest (RF). In the machine learning models, it is achieved the accuracy of 98%, 97% for Operational and Practognostic Dyscalculia and Number Dysgraphia respectively. In Sri Lanka, this has been recognized as an acceptable solution for screening and intervention via a mobile-based application for above mentioned four variants of learning disability conditions which are developed based on the gaming environment.
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Revolutionizing Tamil Language Analysis: A Natural Language Processing Model Development Approach
(IEEE, 2024-07-25) Ravichandira, G; Sivabaskaran, V; Uthayakumar, T; Vyravanathan, S; Krishara, J; Rajendran, K
This study proposes a web-based platform utilizing Natural Language Processing (NLP) techniques to identify and rectify spelling and grammar errors in Tamil, a language with intricate nuances. Users can input Tamil text, which undergoes automated scrutiny for linguistic inaccuracies. Additionally, the research delves into contextual text summarization and real-time transcription of spoken Tamil. The overarching aim is to devise a holistic solution amalgamating various components to facilitate the detection and rectification of Tamil spelling and grammatical errors. The envisioned subgoals encompass a spell-checking tool capable of identifying misspelled words and suggesting appropriate replacements based on context, a grammar correction feature adept at identifying and rectifying grammatical inaccuracies while accommodating the unique grammatical structures of Tamil, a summarization component adept at condensing paragraphs while retaining core concepts, and a transcription feature enabling the real-time conversion of spoken Tamil into accurate text. By addressing the complexities of the Tamil language, this research endeavor seeks to contribute to the expansion of language processing tools. The ultimate objective is to empower users with the means to detect and rectify errors while enhancing their proficiency in spoken Tamil. This synthesis of components represents a significant stride towards the development of a comprehensive web-based platform for identifying and rectifying Tamil spelling and grammar errors.
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Sell-Net: Mobile Based Solution to Strengthen the Seller-Buyer Relationship of Small-Scale Businesses in Sri Lanka
(IEEE, 2022-01-21) Wickramarathne, N; Siriwardana, U; Karunarathna, A; Jeremiah, P; Thelijjagoda, S; Krishara, J
As a developing country, Sri Lanka is getting a greater number of people who are using an online application and online purchase for various kinds of items nowadays. The number of users is highly increasing day by day. These online applications and their usages have a major role in the economy of Sri Lanka. The usage of online purchasing is widely observed in Sri Lanka as well it has been observed that most of the delivery persons and small-scale business people are facing many challenges while using these applications in practical ways. This is an application that connects small-scale business sellers directly to the buyer on an online platform. Goods Delivery vehicles are widely spread in Sri Lanka with traveling their paths. Most of the time they traveled with some extra free spaces. Authors are trying to manage these vehicles to give up an extra service and get extra income with proper management. It tries to improve the service to get more people an advantage and build up their businesses. This app aims at three main user areas: seller, delivery persons, and buyer. The buyer can place the order for these goods through the online platform and then the system calculates and find the nearest driver to the seller and choose the driver. From using the app driver sees the best path to the seller. Also, there's an algorithm to calculate ordered goods type, quantity, size and select which vehicle is suited for delivering these goods. The delivery person delivers those goods to the buyer by their path. The System tracks down until the buyer received their goods. and also detecting the fake review also if buyer using this application never get unsatisfied for the practical usage. The outcome of this project will help to develop a reliable seller-buyer relationship for small-scale business parties and improve their business activities.
Embargo
A Smart Waste Disposal System: To Encourage Proper Waste Disposal
(IEEE, 2022-12-09) Alwis, D; Munasinghe, P; Rajapaksha, S; Ranawaka, B; Krishara, J; Tissera, W
Waste disposal is one of the most important industries in the world. If not maintained properly it would lead to the destruction of the environment. Improper waste disposal is becoming a critical issue in Sri Lanka and the lack of waste segregation, inadequate waste collection methods, the lack of support for waste management from the public are among the root causes of the problem. As a solution we propose an IoT-based solid waste management system that allows garbage bin monitoring, routing of garbage collector trucks, a prediction model and a point rewarding system. As the end result of this research the following prototypes was built; a prototype model of a smart bin with the capabilities of opening and closing by itself and detecting the waste level of the bin, a prototype mobile application for garbage collectors which delivers analysed data on truck position and ensures timeliness, a prototype mobile application for the public which receives the weight and type of solid waste discarded as an input and calculate reward points to encourage the public in proper waste disposal, a prototype web application which delivers statistical data for detailed reports and a prediction model which predicts the amount of waste to be collected in the coming month using machine learning. This is a low-cost IoT-based solution that uses existing resources to handle the massive amounts of garbage collected each day.