MARC details
| 000 -LEADER |
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| fixed length control field |
03468nam a22003257a 4500 |
| 003 - CONTROL NUMBER IDENTIFIER |
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| control field |
OSt |
| 005 - DATE AND TIME OF LATEST TRANSACTION |
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| control field |
20250710103308.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
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| fixed length control field |
250710b |||||||| |||| 00| 0 eng d |
| 040 ## - CATALOGING SOURCE |
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| Original cataloging agency |
TUPM |
| Language of cataloging |
English |
| Transcribing agency |
TUPM |
| Modifying agency |
TUPM |
| Description conventions |
rda |
| 050 ## - LIBRARY OF CONGRESS CALL NUMBER |
|---|
| Classification number |
BTH QA 76.9 |
| Item number |
A45 2024 |
| 100 ## - MAIN ENTRY--PERSONAL NAME |
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| Personal name |
Alicante, Ace Jester. |
| Relator term |
author |
| 245 ## - TITLE STATEMENT |
|---|
| Title |
Smart segregation system: |
| Remainder of title |
discrimination of degradable and non-degradable waste using deep learning towards environmental sustainability/ |
| Statement of responsibility, etc. |
Ace Jester Alicante, Paulo R. Bendijo, Kenneth I. Lopena, Kaye Charm Chiara Malubay, and Maureen E. Ramirez.-- |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. |
|---|
| Place of publication, distribution, etc. |
Manila: |
| Name of publisher, distributor, etc. |
Technological University of the Philippines, |
| Date of publication, distribution, etc. |
2024. |
| 300 ## - PHYSICAL DESCRIPTION |
|---|
| Extent |
xii, 150pages: |
| Dimensions |
29cm. |
| 336 ## - CONTENT TYPE |
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| Source |
rdacontent |
| 337 ## - MEDIA TYPE |
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| Source |
rdamedia |
| 338 ## - CARRIER TYPE |
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| Source |
rdacarrier |
| 500 ## - GENERAL NOTE |
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| General note |
Bachelor's thesis |
| 502 ## - DISSERTATION NOTE |
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| Dissertation note |
College of Industrial Technology.-- |
| Degree type |
Bachelor of engineering technology major in computer engineering technology: |
| Name of granting institution |
Technological University of the Philippines,<br/> |
| Year degree granted |
2024. |
| 504 ## - BIBLIOGRAPHY, ETC. NOTE |
|---|
| Bibliography, etc. note |
Includes bibliographic references and index. |
| 520 ## - SUMMARY, ETC. |
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| Summary, etc. |
Solid waste management continues to encounter significant challenges, particularly in<br/>waste segregation, where manual sorting is often inefficient and obsolete. With the aim of<br/>resolving this serious concern, an eco-friendly, low-cost, and automated waste sorting<br/>system was designed to detect, sort, and classify waste into four different categories:<br/>biodegradable, non-biodegradable, hazardous, and unsorted. The system integrates<br/>hardware components such as a camera module, servo motors, ultrasonic sensors, and a<br/>Raspberry Pi, combined with advance image processing techniques supported by machine<br/>learning and deep learning algorithms. Real-time system status is displayed on an LCD<br/>display, while bin capacity levels are visually and audibly indicated using LEDs and<br/>buzzers. The entire system was developed using an iterative prototyping approach,<br/>allowing for continuous improvement to enhance its performance. You Only Look Once<br/>version 8 deep learning model, also known as YOLOv8, was trained on a custom waste<br/>dataset for precise object detection and identification, with TensorFlow Lite seamlessly<br/>integrated to optimize efficient real-time processing. Smooth coordination was achieved<br/>between the camera module, servo motors, sorting mechanism, ultrasonic sensors, and the<br/>Raspberry Pi acting as the main controller. Extensive testing was conducted to assess<br/>classification accuracy, response time, system efficiency, processor performance, and<br/>detection reliability. Black-box testing confirmed the system functionality, while evaluated<br/>using ISO/IEC 25010 standards focusing on functionality, performance efficiency,<br/>usability, reliability, and maintainability through survey questionnaire with 10 waste<br/>management experts as respondents. The system achieved a user satisfaction score of 4.5<br/>out of 5 on the Likert scale, which falls under the 'Very Good' category. This study<br/>establishes the vast potential of combining hardware with machine learning and deep<br/>learning algorithms to design innovative and environmentally friendly solutions for waste<br/>management automation. |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name entry element |
Solid waste management |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name entry element |
Image processing |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name entry element |
YOLOv8 |
| 700 ## - ADDED ENTRY--PERSONAL NAME |
|---|
| Personal name |
Bendijo, Paulo R. |
| Relator term |
author |
| 700 ## - ADDED ENTRY--PERSONAL NAME |
|---|
| Personal name |
Lopena, Kenneth I. |
| Relator term |
author |
| 700 ## - ADDED ENTRY--PERSONAL NAME |
|---|
| Personal name |
Malubay, Kaye Charm Chiara. |
| Relator term |
author |
| 700 ## - ADDED ENTRY--PERSONAL NAME |
|---|
| Personal name |
Ramirez, Maureen E. |
| Relator term |
author |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
|---|
| Source of classification or shelving scheme |
Library of Congress Classification |
| Koha item type |
Bachelor's Thesis CIT |
| Suppress in OPAC |
No |