2nd International Conference on Advancements in Computing [ICAC] 2020

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    Predictive Analytics Platform for Airline Industry
    (2020 2nd International Conference on Advancements in Computing (ICAC), SLIIT, 2020-12-10) Tissera, P. H. K.; llwana, A.N.M.R.S.P.; Waduge, K.T.; Perera, M.A.l.; Nawinna, D.P.; Kasthurirathna, D.
    The research is to develop accurate demand forecasting model to control the availability in Airline industry. The primary outcome of the model is that the Airline organization can maximize the revenue by controlling the availability. The product in airline industry is the seat, which is an expensive, unstock able product. The demand for the seats is almost uncertain, the capacity is constraint and difficult to increase and the variable costs are very high. Hence the priority of the expected demand forecast is very high for airline industry. An accurate mechanism to predict the revenue for future months of ODs (Origin destinations) is done using fare and passenger data. The revenue is derived by the number of passengers and the fares they pay which vary for each flight. Airline travel is very susceptible to the social, political and economic changes. Therefore, passenger buying patterns change quite dynamically. Hence, it is challenging to develop an accurate method to project the revenue for each route. To overcome this, we are going to use semi-supervised learning mechanism. We have the current ticketed revenue plus we have the current booked passengers. We also have the ticketed passenger details of previous flights. Hence most of the information is available, however changing market conditions is an unknown variable which can have a significant impact on passenger travel patterns. Through this research We are going to design and develop the best fit model to forecast flight OD level passenger demand based on the historical data.
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    Hand Rehabilitation Using Robot-Assisted Physiotherapy
    (2020 2nd International Conference on Advancements in Computing (ICAC), SLIIT, 2020-12-10) Madhushan, I.H.D.; Charnara, E.B.K.; De Zoysa, A.T.J.; Upeka, G.S.; Abhayasinghe, N.; Abeygunawardhana, P.
    Robotics technology in the modern world is currently being implemented in medical fields to improve the quality of care and patient outcomes. In the proposed system, the robotics technology is used for physiotherapy. In the existing physiotherapy robot devices, there is no feature that provides exercise for every joint of the fingers and the wrist. Therefore, in this system, we used forward kinematics technologies to address each joint of the fingers and wrist thatcan access by the physiotherapist. We have designed the robot hand using the solid work and implemented 3D model then assembled system was tested again using different scenarios. Most existing robotic systems provide finger and wrist exercises separately, but our system can provide all exercises simultaneously. In here, we can predict the next exercises that are given for the patient and the progress of the rehabilitation of the patient. For the prediction, we developed the models using the FB prophet algorithm. When using this device, the patient's hand exercises are monitored in real-time and the physiotherapist can see the angles of the hand movement while controlling the robot device. To control this robot device, we used a mobile application.
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    DenGue CarB: Mosquito Identification and Classification using Machine Learning
    (2020 2nd International Conference on Advancements in Computing (ICAC), SLIIT, 2020-12-10) Mohommed, M.; Rajakaruna, P.; Kehelpannala, N.; Perera, A.; Abeysiri, L.
    This research paper discusses a web-based application that assists Public Health Officers in the dengue identification process. The mosquito classification is done using image processing and machine learning techniques. The training models are developed using Convolutional Neural Networks Algorithm, Support Vector Machine Algorithm, and K-Nearest Neighbors Algorithm to validate the results to determine the most accurate and suitable algorithm. this paper discusses the previous related research work on its significance and drawbacks while highlighting design, methods, and implementation in the solution. We conclude that the CNN algorithm provides the highest accuracy among the machine learning techniques used.