Faculty of Computing

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Subject: search.filters.subject.Convolutional Neural Networks

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Now showing 1 - 3 of 3
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    Segmentation and significance of herniation measurement using Lumbar Intervertebral Discs from the Axial View
    (IEEE, 2022-10-04) Siriwardhana, Y; Karunarathna, D; Ekanayake, I. U
    According to statistics, more than 60% of people suffer lower back pain at a certain time in their lives. Disc hernias are the most common cause of lower back pain, and the lumbar spine is responsible for more than 95% of all herniated discs. Generally, radiologists study the MRI during the clinical phase to detect a disc hernia. There could be several cases to evaluate, leaving the doctors to cogitate and envisage. Medical image segmentation aids in the diagnosis of spinal pathology, studying the anatomical structures, surgical procedures, and the evaluation of various treatments. However, manual segmentation of medical images necessitates a significant amount of time, effort, and discipline on the part of domain experts. This research study describes a framework that automates the segmentation of lumbar intervertebral discs using MRI images. Through this system, we can detect minor changes at the pixel level that are impossible to identify with the naked eye. We used convolutional neural networks with the UNet architecture to achieve the semantic segmentation process. The segmentations were evaluated using the Jacquard index and the dice coefficient.
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    Smart Attendance and Progress Management System
    (Springer, Singapore, 2021) Krishnapillai, L; Veluppillai, S; Akilan, A; Saumika, V. N; De Silva, K. P; Gamage, M. P. A. W
    Management of attendance may be a great burden on lecturers if done manually. This study focuses on finding an automated solution for taking attendance and keeping track of progress of a student in a smart way. The smart attendance system is generally using biometrics for identifying individuals. In this study, face recognition was considered for identification. The student's face is recognized and attendance is taken using face biometrics based on high-definition monitor camera. The images of the student are given as an input and image classification was done using CNN algorithm preventing duplicate entries for attendance. For tracking the progress of the student, the factors affecting the GPA are trained using Machine Learning algorithms. This research also aims to examine the effective progress of undergraduate students by taking past year records and find out the factors for their high and low output which will be helpful to improve their performance.
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    Computer-Vision Enabled Waste Management System for Green Environment
    (2021 3rd International Conference on Advancements in Computing (ICAC), SLIIT, 2021-12-09) Hewagamage, P.; Mihiranga, A.; Perera, D.; Fernando, R.; Thilakarathna, T.; Kasthurirathna, D.
    Waste management has become a critical requirement to maintain a green environment in Sri Lanka as well as other countries. Town councils have to regularly collect different types of wastes to clean cities/towns. Hence managing the waste of the cities is a challenging task. However, most of the urban councils currently use a manual approach to managing waste. However, it results in many difficulties for the people and cleaning staff who involve in the process by following strict guidelines. Issues due to waste contamination, no proper information management of waste collection, and no punctuality in removing waste from the garbage bins are some of the significant issues arising from the manual process. Due to the drawbacks of the manual approach, social issues, environmental issues, health issues can occur easily. This paper proposes a better solution to replace this manual system with an automated system to overcome these issues. Hence, the main objective of this research is to introduce an ICT-based innovative design that can be used to develop an effective waste management system in town councils. In the proposed model, we will introduce a Computer Vision-based smart waste bin system with real-time monitoring that incorporates various technologies such as computer vision, sensor-based IoT devices, and geographical information system (GIS) related technologies. Our proposed solution consists of a waste bin system, which is capable of automated waste segregation. Our design facilitates the admin users to expand the waste bin kit by adding more waste categories in a user-friendly manner, making our product adaptive in any environment. At the same time, waste bins can notify the real-time waste status. Our system generates the optimum collection routing path and displays it in a mobile app using those real-time status details. We also demonstrate a lowcost prototype.