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    Standalone Application and Chromium Browser Extension-based System for Online Examination Cheating Detection
    (2021 3rd International Conference on Advancements in Computing (ICAC), SLIIT, 2021-12-09) Kariyawasam, S.; Lakshan, A.; Liyanage, A.; Gimhana, K.; Piyawardana, V.; Mallawarachchi, Y.
    Educational organizations and institutes that provide services to the public use e-learning frequently than before. The incapacity to evaluate the knowledge acquired is a flaw in education. Due to the current situation, traditional evaluation and examinations are not possible. In a developing country like Sri Lanka, the conduct of online examinations has not been efficient, resulting in cheating at examinations due to vulnerabilities resulting from organizational policies and the difficulty to track down candidates who are prone to cheating, therefore use of facial features for candidate verification and to monitor the background interactions the use of audio and video is taken into consideration with the aid of two cameras; the system mounted camera and a wearable camera containing a microphone allowing audio detection. In this research, we suggest using the training data set generated from individuals to undertake a training approach to improve the robustness for background interactions through audio and video to detect the level of cheating of candidates.
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    Fully Automatic Hydroponic Cultivation Growth System
    (2021 3rd International Conference on Advancements in Computing (ICAC), SLIIT, 2021-12-09) Thalwatte, A. M.; Ranasinghe, U. G. K. L.P. S.; Sakee, M. F. A.; Ikram, M. M. M.; Mahaadikara, H.; Wellalage, S.
    Fully automated hydroponic system with monitoring and controlling components. This technique of developing plants can be used to develop plants in the flats the improvement is focused on the deployment of agricultural greenhouses into small-scale stages reworking it into a smart greenhouse. The identification system successfully identified the stage of plants well into the sprout stage and primary stage. Using an automated system and assembling the sensors and actuators considered about four factors which mainly impact the plant growing, light intensity level measurement, Temperature level, water level land co2 supply Also implement a pre-harvest disease detection using image processing and machine learning and alert the user regarding the prevention methods. The system counts infected disease plant and gets percentage then graphically represent a comparison of the yield production. Then forecast yield production. Another important component is to identify the Leaf Disease That Has Affected the Plant, Design database to record all data and provide a report for each disease.