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Author: search.filters.author.Attanayaka, BSubject: search.filters.subject.Machine learning

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    UrbanGreen - E-Waste Detection and Analysis using YOLOv5
    (Institute of Electrical and Electronics Engineers Inc., 2025) Madusanka A.R.M.S; Nawaratne D.M.R.S.; Gamage, N; Attanayaka, B
    E-waste has become a global concern that challenges environmental sustain ability. The disposal of electronic devices is often poorly managed, especially in urban areas. This research aims to develop an innovative e-waste management system suitable for urban areas, focusing on accurately identifying electronic devices and their harmful components through advanced image processing techniques. (Y olov5) The system identifies various electronic devices, harmful components and materials and assesses their recyclability, improper disposal's environmental and health impacts, empowering users to make informed decisions about disposal and recycling. The system will integrate tools to identify E-waste, promote the reuse of electronic devices, educate the public through interactive educational platforms, and locate nearby e-waste collection centers. By addressing these critical aspects of e-waste management, the project aims to provide a useful platform to manage e-waste effectively in urban areas. This paper was developed to discuss E-waste detection and analysis using YOLOv5 object detection model.
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    E-Learning Education System For Children With Down Syndrome
    (Institute of Electrical and Electronics Engineers, 2022-09-16) Sampath, A.S.T; Vidanapathirana, M.W.; Gunawardana, T.B.A; Sandeepani, P.W.H.; Chandrasiri, L.H.S.S; Attanayaka, B
    The World Health Organization assesses that Down Syndrome (DS) affects about 1 in 1000 births worldwide. Children with DS cannot learn, as usual, instigating numerous inadequacies that lead to formative issues such as trouble encoding information and low intelligence to interpret data for decision-making. As a superior technique for these kids' intercom-municating and logical intellect, free-hand sketch drawing, Voice training, and word prediction activities can be success-fully utilized. As the best way to express the mindset of such chil-dren, introducing an E-Learning system makes a friendlier ac-tivity than learning about the past. Because of the improvement of Artificial intelligence and its encouragement, E-Learning-re-lated exploration and applications are moving at an enormous advancement rate. The main objective of this project is to de-velop a reliable and efficient approach to predicting the devel-opment of DS children. Classifying and identifying those hand-written images and voice samples and those samples are given by children with DS compared to the teacher through the construction of a model structure. This research project specially considered local down syndrome children's hand-drawn images, voice samples, letters, numbers, and words as the input. As a result, it gives accuracy and similarity with the teacher's sam-ples and relates parts in the down syndrome children's samples. The system uses artificial intelligence technologies. Through that, the knowledge capacity of the DS children and their con-veyed articulation of that knowledge can be assessed for additional correlations and investigation.