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02305nam a22002417a 4500 |
| 003 - CONTROL NUMBER IDENTIFIER |
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ft8839 |
| 005 - DATE AND TIME OF LATEST TRANSACTION |
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20251128101648.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
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251128b ||||| |||| 00| 0 eng d |
| 041 ## - LANGUAGE CODE |
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| Language code of text/sound track or separate title |
engtag |
| 050 ## - LIBRARY OF CONGRESS CALL NUMBER |
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| Classification number |
T58 C78 2025 |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER |
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| Classification number |
. |
| 100 1# - MAIN ENTRY--PERSONAL NAME |
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| Personal name |
Cruz, Ken Josh S.; Enano Jr., Alberto L.; Kharylle S. Sumabat |
| 245 ## - TITLE STATEMENT |
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| Title |
Paradapp: An intelligent car parking assist and using convolutional neural network for student and novice drivers |
| 264 #1 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE |
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. |
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. |
| Date of production, publication, distribution, manufacture, or copyright notice |
c2025 |
| 300 ## - PHYSICAL DESCRIPTION |
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Capstone Project: (Bachelor of Science in Information Technology) - Pamantasan ng Lungsod ng Maynila, 2025 |
| 336 ## - CONTENT TYPE |
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text |
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text |
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text |
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unmediated |
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unmediated |
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unmediated |
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volume |
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volume |
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volume |
| 505 ## - FORMATTED CONTENTS NOTE |
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ABSTRACT: Parking is a significant challenge for many drivers, often leading to stress and accidents. This paper presents Paradapp, an intelligent car parking assistant designed to aid both student and novice drivers. Utilizing Convolutional Neural Networks (CNN), specifically the YOLO object detection module, Paradapp leverages vehicle’s car cameras to detect obstacles and estimate distances in real-time. The application provides voice-prompted instructions to guide drivers through the parking process, enhancing safety and efficiency. The study aims to reduce parking related stress and accidents, offering a smart solution accessible via mobile devices. Paradapp’s development follows the AGILE methodology, ensuring interactive improvements and user-centric design. To train the model, a total of 14,000 images were collected from the KTTU and COCO repository to form a dataset related to parking and driving scenarios. The mean average precisions of the YOLOv8 model is at 74.3% while the F1 is at 73% while the F1 score is at 73% and the Recall score is at 87%. This research contributes to the growing field of intelligent driving system, providing valuable insights for future advancements in driver-assistance technologies. The system’s effectiveness is evaluated based on ISO 25010:2011 software quality standards, focusing on functional suitability, performance efficiency, usability, reliability, and safety. The overall acceptance of the system is 4.49, interpreted as “Agree”. |
| 526 ## - STUDY PROGRAM INFORMATION NOTE |
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Filipiniana |
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academic writing |
| 942 ## - ADDED ENTRY ELEMENTS |
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| Item type |
Thesis/Dissertation |