000			02158nam a2200301Ia 4500
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008			240716n 000 0 eng d
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041			_aengtag
050			_aQA76.87 D45 2024
082			_a.
100			_aRick Armand De Leon, Zymon Gabriel Guevarra.
245		0	_aEnchancement of convolutional neural network through augmentation in zoo animal image recognition for toddlers: a hybrid approach using teachable machine.
264			_aManila: _bPLM, _c2024
300			_bUndergraduate Thesis : (Bachelor of Science major in Computer Science) - Pamantasan ng Lungsod ng Maynila, 2024.
336			_btext _atext _2text
337			_30 _bunmediated _aunmediated _2unmediated
338			_bvolume _avolume _2volume
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505			_aABSTRACT: Young children are exposed to technology more and more in the modern digital age, which has a big impact on how they learn. As mobile devices and interactive applications become more common, there is an increasing demand for educational resources that are especially tailored to the developmental needs of young children. Artificial intelligence (AI) and early childhood education have a lot to offer when it comes to utilizing cutting-edge technology to improve learning in a fun and interactive way. In order to improve Convolutional Neural Networks (CNNs) ability to identify photos of zoo animals for younger learners, this thesis investigates the integration of image augmentation techniques. The work uses augmentation techniques to increase the model’s generalization across various image kinds and situations and enrich the training dataset using the Teachable Machine platform. A comparison of the enhanced prototype and the current system showed notable advancements. Through three testing, the augmented prototype’s accuracy range from 80% to 83%, proving the usefulness of augmentation techniques in improving model performance-particularly in identifying animals from different perspectives.
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655			_aacademic writing
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