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Serenivice: An arduino-driven automated flea repellent device with computer vision integration for cat flea management / Aniciete, Fritz Jannus V.; Genova, Cyreene Lyn A.; Salazar, Ariz Ann C.; Salgado, Darren Jay P. 6

By: te, Fritz Jannus V.; Genova, Cyreene Lyn A.; Salazar, Ariz Ann C.; Salgado, Darren Jay P. 4 0 16, [, ] | [, ] |

Contributor(s): 5 6 [] |

Language: Unknown language code Summary language: Unknown language code Original language: Unknown language code Series: ; January 2024.46Edition: Description: vii, 232 ppContent type: text Media type: unmediated Carrier type: volumeISBN: ISSN: 2Other title: 6 []Uniform titles: | | Related works: 1 40 6 []Subject(s): -- 2 -- 0 -- -- | -- 2 -- 0 -- 6 -- | 2 0 -- | -- -- 20 -- | | -- -- -- -- 20 -- | -- -- -- 20 -- --Genre/Form: -- 2 -- Additional physical formats: DDC classification: | LOC classification: | | 2Other classification:

Contents:

Action note: In: Summary: ABSTRACT: In the domain of feline flea management, the use of traditional repellents can inadvertently expose human to health risks arising from potential or fear responses. These reactions, encompassing injuries, infections, and skin irritations due to direct contact with fleas, underscore the need for a more sophisticated approach. An innovative solution is presented: an automated flea-repellent device designed to administer treatment and attract cats without human intervention. This device is equipped with a sensor that triggers the emission of flea repellent spray, while strategically placed attractants entice feline companions to create a soothing environment. Accompanying this is an intuitive mobile application serving as an alternative control method equipped with a computer vision, which allows remote monitoring of cat and flea presence. Notably, this study assessed the device and application using ISO 25010:2011 software quality standards, focusing specifically on functional suitability, usability, performance efficiency, and portability. The results indicate an overall mean satisfaction rating of 4.53 out of 5.00, signifying its effectiveness and safety. Additionally, the FOMO MobileNet V2 0.35 attains exceptional precision, recall, and a perfect F1 score of 1.00 in identifying background instances. However, challenges in the cat class, with 62.8% accuracy and a 32.8% error rate, suggest opportunities for improvement. Meanwhile, the model excels with 100% accuracy in the flea detection. The nuanced evaluation, reflected in F1 scores, indicates a perfect balance in the background class and moderate performance in the cat and flea classes. Other editions:

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Item type	Current location	Home library	Collection	Call number	Status	Date due	Barcode	Item holds
Book	PLM	PLM Filipiniana Section	Filipiniana-Thesis	T T58.5 .A55 2024 (Browse shelf)	Available		FT7823

Total holds: 0

Undergraduate Thesis: (Bachelor of Science in Information Technology) - Pamantasan ng Lungsod ng Maynila, 2024. 56

ABSTRACT: In the domain of feline flea management, the use of traditional repellents can inadvertently expose human to health risks arising from potential or fear responses. These reactions, encompassing injuries, infections, and skin irritations due to direct contact with fleas, underscore the need for a more sophisticated approach. An innovative solution is presented: an automated flea-repellent device designed to administer treatment and attract cats without human intervention. This device is equipped with a sensor that triggers the emission of flea repellent spray, while strategically placed attractants entice feline companions to create a soothing environment. Accompanying this is an intuitive mobile application serving as an alternative control method equipped with a computer vision, which allows remote monitoring of cat and flea presence. Notably, this study assessed the device and application using ISO 25010:2011 software quality standards, focusing specifically on functional suitability, usability, performance efficiency, and portability. The results indicate an overall mean satisfaction rating of 4.53 out of 5.00, signifying its effectiveness and safety. Additionally, the FOMO MobileNet V2 0.35 attains exceptional precision, recall, and a perfect F1 score of 1.00 in identifying background instances. However, challenges in the cat class, with 62.8% accuracy and a 32.8% error rate, suggest opportunities for improvement. Meanwhile, the model excels with 100% accuracy in the flea detection. The nuanced evaluation, reflected in F1 scores, indicates a perfect balance in the background class and moderate performance in the cat and flea classes.

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