MARC details
| 000 -LEADER |
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| fixed length control field |
02777ntm a2200337 i 4500 |
| 003 - CONTROL NUMBER IDENTIFIER |
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| control field |
MY-KuUP |
| 005 - DATE AND TIME OF LATEST TRANSACTION |
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| control field |
20251125111038.0 |
| 006 - FIXED-LENGTH DATA ELEMENTS--ADDITIONAL MATERIAL CHARACTERISTICS |
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| fixed length control field |
t||||fs|||| 000 0 |
| 007 - PHYSICAL DESCRIPTION FIXED FIELD--GENERAL INFORMATION |
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| fixed length control field |
ta |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
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| fixed length control field |
250506t20232023my a|||fs|||| 000 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
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| International Standard Book Number |
THE0010030 (Local) |
| Qualifying information |
Hardback |
| 040 ## - CATALOGING SOURCE |
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| Original cataloging agency |
UMPSA |
| Language of cataloging |
eng |
| Transcribing agency |
UMPSA |
| Description conventions |
rda |
| 090 ## - LOCALLY ASSIGNED LC-TYPE CALL NUMBER (OCLC); LOCAL CALL NUMBER (RLIN) |
|---|
| Classification number (OCLC) (R) ; Classification number, CALL (RLIN) (NR) |
PSM .N33 2023 r Bc. |
| 100 0# - MAIN ENTRY--PERSONAL NAME |
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| Personal name |
Syarifah Nabilah Syahirah Syed Abd Wahab, |
| Relator term |
author. |
| 245 10 - TITLE STATEMENT |
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| Title |
Distance and speed predictive analytics based on dashcam video / |
| Statement of responsibility, etc. |
Syarifah Nabilah Syahirah Binti Syed Abd Wahab |
| 264 #1 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE |
|---|
| Place of production, publication, distribution, manufacture |
Kuantan Pahang : |
| Name of producer, publisher, distributor, manufacturer |
UMPSA, |
| Date of production, publication, distribution, manufacture, or copyright notice |
2023 |
| 264 #4 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE |
|---|
| Date of production, publication, distribution, manufacture, or copyright notice |
© 2023 |
| 300 ## - PHYSICAL DESCRIPTION |
|---|
| Extent |
xv, 41 pages : |
| Other physical details |
illustrations ; |
| Accompanying material |
1 CD-ROM. |
| 336 ## - CONTENT TYPE |
|---|
| Source |
rdacontent |
| Content type term |
text |
| 337 ## - MEDIA TYPE |
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| Source |
rdamedia |
| Media type term |
unmediated |
| 338 ## - CARRIER TYPE |
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| Source |
rdacarrier |
| Carrier type term |
volume |
| 347 ## - DIGITAL FILE CHARACTERISTICS |
|---|
| Source |
rda |
| File type |
text file |
| Encoding format |
PDF |
| 500 ## - GENERAL NOTE |
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| General note |
Center for Mathematical Sciences |
| 502 ## - DISSERTATION NOTE |
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| Dissertation note |
Bachelor of Applied Science In Data Analytics With Honour -- Universiti Malaysia Pahang – 2023 |
| 504 ## - BIBLIOGRAPHY, ETC. NOTE |
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| Bibliography, etc. note |
Includes bibliographical references |
| 520 3# - SUMMARY, ETC. |
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| Summary, etc. |
Dashcam is a device that capture the surroundings of a vehicle and is commonly utilised as evidence in road crimes and accidents. Malaysian drivers who have difficulty estimating the distance and speed between vehicles due to human error might be prevented by utilising dashcam data, preventing an accident. The purpose of this research is to develop a predictive modelling for estimating the distance and speed of vehicles on the road. The goal is to reduce the incidence of accidents and crimes that occur on the road. The study was conducted using deep learning techniques such as Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN) methods. The dataset used in this study was provided by Cybersecurity Malaysia and it consists of dashcam video footage captured by Malaysian drivers. The dashcam footage was used to analysed the real-life distance and speed between vehicles, which be used to make more accurate predictions. Based on the research conducted using deep learning techniques such as LSTM and CNN models on the provided dashcam video footage, the evaluation results revealed moderate accuracy indicating room for improvement. The study provided insights into vehicle speed distributions and distances, paving the way for future advancements in distance and speed predictive analytics. The findings from this study will help to enhance road safety in Malaysia by providing valuable insights and improving drivers’ ability to estimate distance and speed, potentially reducing accidents and crimes on the road. |
| 610 20 - SUBJECT ADDED ENTRY--CORPORATE NAME |
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| Corporate name or jurisdiction name as entry element |
Center for Mathematical Sciences |
| General subdivision |
Dissertations |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| Topical term or geographic name entry element |
Universities and colleges |
| General subdivision |
Dissertations |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name entry element |
Final Year Report |
| General subdivision |
Dissertations |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
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| Source of classification or shelving scheme |
Library of Congress Classification |
| Koha item type |
Final Year Report |