Research Publications Authored by SLIIT Staff

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This collection includes all SLIIT staff publications presented at external conferences and published in external journals. The materials are organized by faculty to facilitate easy retrieval.

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Now showing 1 - 7 of 7
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    PublicationOpen Access
    A System to Notify Real-Time Radio Signal Failures and Predict the Possibility of Failures - LOST TRANSMISSION
    (University Of Bahrain, 2022-03-31) Sumithraarachchi, G; Ahamed, R; Vithana, N
    The focal point of this work was to build a troubleshooting mobile application, which provides an alert notification when RT (Radio Transmission) failures happen at radio outstations and enables predicting the possibilities of radio signal failures based on weather components. The current radio signal failure notifying process is being done half-manual at most of the radio stations while not providing immediate notifications to the radio station staff. A cloud platform, IoT (Internet of Things) technology, and machine learning technique are combined with the aforementioned system to provide fast service to the radio station end-users. The IoT-based Wi-Fi module distinguishes RT failures of each outstation. When weather data is detected, the predictive model displays the possibilities of radio signal failures. The cloud-based functionalities push instant notifications which make the system highly reliable. A key benefit of this system is that even though the users are out of the radio station, the system will be one notification away from the users to notify sudden RT failures.
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    PublicationOpen Access
    A System to Notify Real-Time Radio Signal Failures and Predict the Possibility of Failures - LOST TRANSMISSION
    (University Of Bahrain, 2022-02-15) Sumithraarachchi, G; Ahamed, R; Vithana, N
    The focal point of this work was to build a troubleshooting mobile application, which provides an alert notification when RT (Radio Transmission) failures happen at radio outstations and enables predicting the possibilities of radio signal failures based on weather components. The current radio signal failure notifying process is being done half-manual at most of the radio stations while not providing immediate notifications to the radio station staff. A cloud platform, IoT (Internet of Things) technology, and machine learning technique are combined with the aforementioned system to provide fast service to the radio station end-users. The IoT-based Wi-Fi module distinguishes RT failures of each outstation. When weather data is detected, the predictive model displays the possibilities of radio signal failures. The cloud-based functionalities push instant notifications which make the system highly reliable. A key benefit of this system is that even though the users are out of the radio station, the system will be one notification away from the users to notify sudden RT failures.
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    PublicationEmbargo
    Machine Learning-Based Skin And Heart Disease Diagnose Mobile App
    (IEEE, 2021-07-01) Tharushika, G. K. A. A; Rasanga, D. M.T; Weerathunge, I; Bandara, P
    This research aims to develop a Mobile app for predicting major diseases we have to face nowadays. These days the heart disease is the main source of death around the world. It is a complex task to predict a heart attack with a doctor because more experience and knowledge are needed. Sometimes it may be gastritis or asthma symptoms. Also, the following most common disease is a skin disease. Most people have some skin disease, and they don’t even have time to check it from a medical centre. These diseases led to deadly cancers kind of things. Implementing the Smart health care application, the skin disease classification and treatment, and the heart disease predictions can be made domestically. The application is taken images of skin disease through the device camera. It classifies the disease with the Keras ResNet trained to classify the accuracy as eighty-seven point eighty-three as a percentage. The heart disease prediction module takes 14 different attributes that can access by the personal and predict the heart disease probability with the model of sklearn KNeighborsClassifier is trained as a percentage with an accuracy of eighty-three point nine. The application was developed on top of the android platform with the SQL Lite database integration.
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    PublicationEmbargo
    Machine Learning-Based Skin And Heart Disease Diagnose Mobile App
    (IEEE, 2021-07-01) Tharushika, G. K. A . A; Rasanga, D. M. T; Weerathunge, I; Bandara, P
    This research aims to develop a Mobile app for predicting major diseases we have to face nowadays. These days the heart disease is the main source of death around the world. It is a complex task to predict a heart attack with a doctor because more experience and knowledge are needed. Sometimes it may be gastritis or asthma symptoms. Also, the following most common disease is a skin disease. Most people have some skin disease, and they don’t even have time to check it from a medical centre. These diseases led to deadly cancers kind of things. Implementing the Smart health care application, the skin disease classification and treatment, and the heart disease predictions can be made domestically. The application is taken images of skin disease through the device camera. It classifies the disease with the Keras ResNet trained to classify the accuracy as eighty-seven point eighty-three as a percentage. The heart disease prediction module takes 14 different attributes that can access by the personal and predict the heart disease probability with the model of sklearn KNeighborsClassifier is trained as a percentage with an accuracy of eighty-three point nine. The application was developed on top of the android platform with the SQL Lite database integration.
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    PublicationEmbargo
    HemoSmart: A Non-invasive, Machine Learning Based Device and Mobile App for Anemia Detection
    (IEEE, 2020-12-22) Jayakody, A; Edirisinghe, E. A. G. A.
    This paper presents a non-invasive method to detect Anemia (a low level of Hemoglobin) easily. The Hemoglobin concentration in human blood is an important substance to health condition determination. With the results which are obtained from Hemoglobin test, a condition which is called as Anemia can be revealed. Traditionally the Hemoglobin test is done using blood samples which are taken using needles. The non-invasive Hemoglobin measurement system, discussed in this paper, describes a better idea about the hemoglobin concentration in the human blood. The images of the finger- tip of the different hemoglobin level patients which are taken using a camera is used to develop the neural network-based algorithm. The pre-mentioned algorithm is used in the developed noninvasive device to display the Hemoglobin level. Before doing the above procedure, an account is created in the mobile app and a questionnaire is given to answer by the patient. Finally, both the results which are obtained from the mobile app and the device are run through a machine learning algorithm to get the final output. According to the result patient would be able to detect anemia at an early stage.
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    PublicationEmbargo
    Mobile App to Support People with Dyslexia and Dysgraphia
    (IEEE, 2018-12-21) Avishka, I; Kumarawadu, K; Kudagama, A; Weerathunga, M; Thelijjagoda, S
    Computers and technology play a very important role in human lives. Even though this statement is true, one can come in to a conclusion that there are many parts in the medical factor which are left untouched. The reason behind this is that each day various types of diseases that affect the human are found out. Dyslexia and dysgraphia have become trending disorders these days. It was found that 1 out of 10 people are having dyslexia adding up to 700 million people worldwide. 5-20 percent of people are facing problems with their handwriting mainly due to dysgraphia. People also have primary dyslexia which is caused from inheritance from their parents. The only treatment found out for patients diagnosed with dyslexia and dysgraphia are making them practice reading and writing until they reach a level fluency and accuracy. These patients face a lot of difficulties and obstacles such as channeling health professionals which can be very costly. Introducing a mobile application known as “The CURE” which helps the patients to practice reading and writing on their own. This paper describes about the system which helps to identify and evaluate whether a normal person is having dyslexia and dysgraphia and if so to help them to practice reading and letter writing. Two machine learning models are trained for two different data sets to increase the accuracy and reliability of the system. One model will be used to identify the words that are spelled out by the dyslexic patient and the other to identify the characters written by the patient diagnosed with dysgraphia. The application is built in an interactive manner to keep the user interested with the system. With implementation of “The CURE” the number of patients diagnosed with dyslexia and dysgraphia will reduce drastically with minimum assistance from health professionals.
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    PublicationEmbargo
    Guided Vision: A High Efficient And Low Latent Mobile App For Visually Impaired
    (IEEE, 2021-12-09) Rizan, T; Siriwardena, V; Raleen, M; Perera, L; Kasthurirathna, D
    This paper presents a novel solution for visually impaired individuals. A mobile app is connected to an ESP32CAM and a remote server to help visually impaired individuals to navigate around their environment safely. A deep learning model is deployed in the mobile app to detect obstacles in real-time without connecting to the internet. Other tasks such as reading texts, recognizing people, and describing objects are done in the remote server. We managed to connect the mobile app to the ESP32CAM and the remote server simultaneously. This was possible because the ESP32CAM is connected to the mobile app through Bluetooth. This gave the mobile the ability to connect to the remote server via the internet. To the best of our knowledge, no research has been done using Bluetooth to stream images to do object detection in a mobile app locally. Hence, our solution can detect obstacles locally and do other tasks mentioned previously in the remote server. This paper discusses how the ESP32CAM, obstacle detection module, face recognition module, text reading module, and object description module was implemented such that a low latent and highly efficient mobile app is created using minimal resources.