Browsing by Author "Pemadasa, M.G.N.M."

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License Detection and Accident Prevention System
(IEEE, 2022-12-09) Dewmin, P.D.R; Yapa, P.S.H; Lokuge, S.D.; Pemadasa, M.G.N.M.; Amarasena, N
According to census and statistics department of Sri Lanka, more than 30,000 accidents occurred from 2013 to 2019. More than two thousand are fatal in this time-period. When considering the top 10 causes of deaths in Sri Lanka, road accidents is at 10th place. Drink and drive, fatigue, drowsiness, distracted driving and driving without a valid license are root causes to these accidents. 20% of the accidents are caused by the drivers without a valid driving license. Currently there is no automated system built using IoT to verify driver’s license. A system to check drowsy driving, distracted driving and driver intoxication is also lacking in the society. By analyzing the data, smart license detection and accident prevention system was proposed to identify and validate driver’s license using RFID. The proposed system also facilitates sub-systems to check driver’s drowsiness, fatigue, alcohol level and driver distracted or not using Raspberry Pi camera module based on computer vision using TensorFlow Lite. An initialized sub-system detects the intensity of the brake pedal being engaged using pressure sensor. The system analyzes the pressure and indicate the intensity level accordingly using the brake light brightness. While these sub systems reduce the probability of occurring an accident, airbag detection sub system reduce the fatality rate of an accident by detecting the deployment of airbags and informing the nearest police station and hospital about an accident using GSM module and SMS Gateway API. The proposed system will reduce the number of accidents occurring throughout the year.
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Mobile Medical Assistant and Analytical System for Dengue Patients
(2021 3rd International Conference on Advancements in Computing (ICAC), SLIIT, 2021-12-09) Jayampathi, K.T.K.; Jananjaya, M.A.C.; Fernando, E.P.C.; Liyanage, Y.A.; Pemadasa, M.G.N.M.; Gunarathne, G.W.D.A.
Dengue fever is a vector-borne viral disease spread by the mosquito Aedes Aegypti. It is a public health problem, with an estimated 50-500 million infections each year and no effective vaccination. People's hectic schedules may not have enough time to see a doctor every time they have a fever. They may overlook their disease, believing it to be a common ailment. Prior medical assistance for dengue patients with fever to check their conditions reliably is a major problem. There is no easily accessible proper system to identify dengue patients at an early stage. This paper presents a mobile medical assistant and analytical system for dengue patients. With a novel approach, using the most appropriate technologies, the mobile application supports identifying dengue patients using the chatbot, analyzing skin conditions, analyzing blood reports, and analyzing dengue-infected areas' functionalities. The registered users can log in to the system and check their dengue condition. The development is carried out with Natural Language Processing, Artificial Neural Network (ANN), Machine Learning, Image Processing, Convolutional Neural Network (CNN), and Android technologies. A mobile application prototype is created and tested, with the possibility of future testing and implementation. The results show effective performances in analyzing dengue conditions.