Extended Abstract : Technology and Computer

Faculty of Science, Thaksin University
Phatthalung (August 26-29, 2022)
Technology and Computer

12th SCiUS Forum

12th SCiUS Forum
August 26 – 29, 2022
at Thaksin University

organized by
Ministry of Higher Education, Science, Research and Innovation

and Thaksin University

12th SCiUS Forum

คำนำ

คณะกรรมการบริหารโครงการห้องเรียนวิทยาศาสตร์ในโรงเรียน โดยการกากับดูแลของมหาวิทยาลัย
(โครงการ วมว.) เห็นชอบให้มีการจัดกิจกรรม 12th SCiUS Forum ในระหว่างวันที่ 26 – 29 สิงหาคม 2565
เพื่อให้นักเรียนโครงการ วมว. ระยะที่ 2 ชั้นมัธยมศึกษาปีที่ 5 ประจาปีการศึกษา 2564 ได้นาเสนอผลงานโครงงาน
วทิ ยาศาสตรแ์ ละแลกเปล่ียนองค์ความรู้กับเพื่อนๆ นกั เรียนตา่ งโรงเรียนในโครงการ วมว. จานวน 19 แหง่ ได้แก่

1. โรงเรยี นสาธิตมหาวทิ ยาลัยเชยี งใหม่ – มหาวิทยาลยั เชียงใหม่
2. โรงเรยี นมธั ยมสาธติ มหาวทิ ยาลยั นเรศวร – มหาวทิ ยาลยั นเรศวร
3. โรงเรียนราชสีมาวิทยาลัย – มหาวิทยาลยั เทคโนโลยีสรุ นารี
4. โรงเรียนสาธิตมหาวิทยาลัยขอนแก่น ฝ่ายมธั ยมศึกษา (ศึกษาศาสตร์) – มหาวทิ ยาลยั ขอนแก่น
5. โรงเรียนสาธติ มหาวทิ ยาลยั มหาสารคาม (ฝา่ ยมธั ยม) – มหาวทิ ยาลยั มหาสารคาม
6. โรงเรยี นดรุณสกิ ขาลยั – มหาวทิ ยาลัยเทคโนโลยพี ระจอมเกล้าธนบุรี
7. โรงเรียนสาธิตแห่งมหาวิทยาลัยเกษตรศาสตร์ วิทยาเขตกาแพงแสน ศูนย์วิจัยและพัฒนาการศึกษา –

มหาวิทยาลัยเกษตรศาสตร์ วิทยาเขตกาแพงแสน
8. โรงเรียนสาธติ "พบิ ูลบาเพ็ญ" มหาวทิ ยาลยั บรู พา – มหาวทิ ยาลยั บูรพา
9. โรงเรยี น มอ.วิทยานสุ รณ์ – มหาวิทยาลยั สงขลานครนิ ทร์ วทิ ยาเขตหาดใหญ่
10. โรงเรยี นสาธติ มหาวทิ ยาลัยสงขลานครนิ ทร์ – มหาวิทยาลัยสงขลานครนิ ทร์ วทิ ยาเขตปตั ตานี
11. โรงเรยี นป่าพะยอมพทิ ยาคม – มหาวิทยาลัยทักษิณ
12. โรงเรียนสาธิตมหาวิทยาลัยพะเยา – มหาวิทยาลัยพะเยา
13. โรงเรยี นลือคาหาญวารินชาราบ – มหาวทิ ยาลยั อบุ ลราชธานี
14. โรงเรยี นสริ นิ ธรราชวทิ ยาลยั – มหาวทิ ยาลัยศลิ ปากร
15. โรงเรยี นสวนกหุ ลาบวิทยาลยั รังสิต – มหาวทิ ยาลัยธรรมศาสตร์
16. โรงเรียนสาธิตมหาวทิ ยาลยั ขอนแกน่ ฝ่ายมธั ยมศกึ ษา (มอดินแดง) – มหาวทิ ยาลัยขอนแกน่
17. โรงเรียน มอ.วิทยานุสรณ์ สุราษฎรธ์ านี – มหาวิทยาลัยสงขลานครนิ ทร์ วิทยาเขตสรุ าษฎร์ธานี
18. โรงเรียนสุรววิ ัฒน์ – มหาวทิ ยาลยั เทคโนโลยสี ุรนารี
19. โรงเรยี นสาธติ อสิ ลามศกึ ษาฯ – มหาวทิ ยาลัยสงขลานครนิ ทร์ วิทยาเขตปตั ตานี

กิจกรรม 12th SCiUS Forum ดาเนินการภายใต้มาตรการป้องกันการระบาดของโรคติดเช้ือไวรัส
โคโรนา 2019 (COVID-19) และแนวปฏิบัติในการเข้าร่วมกิจกรรม 12th SCiUS Forum ซึ่งในการนาเสนอ
ผลงานประกอบด้วยการนาเสนอโครงงานประเภท Oral presentation และ Poster presentation จาแนก
สาขาวิชาโครงงานวิทยาศาสตร์ออกเป็น 7 สาขา ได้แก่ สาขาวชิ าเคมี สาขาวชิ าชีววิทยาและความหลากหลาย
ทางชีวภาพ สาขาวิชาฟิสิกส์และดาราศาสตร์ สาขาวิชาวิทยาศาสตร์ส่ิงแวดล้อมและนิเวศวิทยา สาขาวิชา
คณิตศาสตร์และสถิติ สาขาวิชาเทคโนโลยีและคอมพิวเตอร์ และสาขาวิชาสะเต็มและนวัตกรรม สาหรับ
เอกสารเล่มน้ีเป็นการรวบรวม Extended Abstract ของโครงงำนวิทยำศำสตร์ประเภท Oral
presentation สำขำวชิ ำเทคโนโลยีและคอมพวิ เตอร์

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12th SCiUS Forum

คณะผู้จัดทาหวังเป็นอย่างย่ิงว่า เอกสารฉบับนี้จะเป็นประโยชน์ต่อนักเรียน ครู คณะกรรมการตัดสิน
ผูเ้ ข้าร่วมกิจกรรม รวมถึงคณะทางานจากทุกหน่วยงานที่เกี่ยวข้อง และขอขอบพระคุณผู้เก่ียวข้องทุกท่านที่ได้
ใหค้ วามร่วมมือสนับสนุนการจัดกจิ กรรม 12th SCiUS Forum ในครัง้ น้ี

คณะผู้จดั ทา
กรกฎาคม 2565

-ข-

12th SCiUS Forum

สารบัญ

คานา หนา้
สารบญั ก
รายชอื่ โครงงานวิทยาศาสตร์ ประเภท Oral presentation สาขาเทคโนโลยแี ละคอมพวิ เตอร์ ค

กลุม่ ท่ี 1 วนั ท่ี 27 สิงหาคม 2565 1
กลุ่มที่ 1 วนั ที่ 28 สงิ หาคม 2565 61
กลุ่มท่ี 2 วนั ท่ี 27 สงิ หาคม 2565 94
กลมุ่ ที่ 2 วันที่ 28 สงิ หาคม 2565 156

-ค-

12th SCiUS Forum

List of Science Projects 12thSCiUS Forum
Oral presentation

Technology and Computer Group 1
Saturday August 27, 2022

No. Code Title Author School
Engineering Science
1 OT1_09_03 Development of a Virtual Mr. Panapoj Hongakkaraphan Classrooms
(Darunsikkhalai School)
Reality Tour Application and Miss Naphatsanan Buasombun Princess Sirindhorn's
Website of Darunsikkhalai Mr. Taratorn Assawadejmetakul College
School
PSU.Wittayanusorn
2 OT1_12_01 The Analysis and Data Mr. Pannathorn Laoteng School

Visualization of Thai People Mr. Kulwisit Sakkittiphokin
Affliction Temperature in Mr. Nippich Sangkhapho
COVID-19 Pandemic from

Twitter Messages using

Machine Learning Technique

3 OT1_15_06 Web application for dressing Miss Tadagorn Prateepnatalang

guidelines Miss Aomwara Kornsirilak

4 OT1_09_01 Web Application Mr. Thanin Chantranuwatkul Engineering Science
Classrooms
Development for Learning of Miss Savitree Soontornvivat (Darunsikkhalai School)

Basic Smart Factory Mr. Wiritpol Poonnark Islamic Science
Demonstration School
5 OT1_19_02 Simulation Software for Mr. Nithinan Thammasaro
Piboonbumpen
Proactive Understanding of Mr. Faris Waemama Demonstration School,
COVID-19 Burapha University

6 OT1_13_01 Chrome Extension That Miss Dungwang Srisa-Ard Naresuan University
Secondary
Reduces the Time Spent on Demonstration School
Demonstration School
the YouTube Desktop Prince of Songkla
University, Pattani
Website Campus

7 OT1_03_02 IoT application for smart Miss Panisara Boonmejew

plant production system Miss Kaewta Thanchan

8 OT1_16_02 A development of computer Mr. Naphasadol Dairoop
game to reinforce students’ Mr. Daniel Samae
skills in learning
mathematics.

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12th SCiUS Forum

No. Code Title Author School
Engineering Science
9 OT1_09_02 Web application Sharif-Judge Mr. Natanon Luangvilai Classrooms
(Darunsikkhalai School)
development and Mr. Kaokla Thaikham
Suankularbwittayalai
improvement Mr. Thanatorn Wongthanaporn Rangsit School

10 OT1_11_03 Forced Alignment and Mr. Pasit Chingskol Piboonbumpen
Annotation for Medical Mr. Thares Noonark Demonstration School,
Speech Recognition Mr. Chayaphol Mongkol Burapha University

11 OT1_13_02 Development of mobile Mr. Supakorn Kaewkalong PSU.Wittayanusorn
School
application for drug Mr. Napornprom Sakulmekiat
identification with computer Mr. Akarachai Tongsook Surawiwat School,
vision on Android operating Suranaree University of
Technology
system Mr. Korphong Kaewchoo PSU.Wittayanusorn
12 OT1_15_08 Text Analysis from Drug Mr. Jirath Thongruang School
PSU.Wittayanusorn
Label using Metadata and School
OCR Technique

13 OT1_18_01 The Development of an Miss Kornrawee Kochtat

Application for Cassava Miss Pemika Khakhai
Disease Detection Miss Woraruethai ็hutawattana

14 OT1_15_05 AI Chatbot for Emotional and Miss Chonnipa Choonoi

Mental Health Support for Miss Sirinapat Suttirak

the students

15 OT1_15_11 Application that analyzes Mr. Panapon Thienmontree

people at risk of contracting Mr. Naphat Ampa
COVID-19

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12th SCiUS Forum

Title : Development of a Virtual Reality Tour Application OT1_09_03
and Website of Darunsikkhalai School
Field : Technology and Computer
Author : Mr. Taratorn Assawadejmetakul
Miss. Naphatsanan Buasombun
School : Mr. Panapoj Hongakkaraphan
Advisor : Darunsikkhalai School, Engineering Science Classroom (KMUTT)
Assoc. Prof. Dr. Siam Charoenseang
Mr. Witsanu Supandee
Mr. Nattiwut Teerabut

Abstract
In recent years, the pandemic of coronavirus disease 2019 (COVID-19) is still a prevalent problem in

many countries including Thailand, causing the nation to be under quarantine to prevent the spread of the virus.
Hence, this project aims to design and develop a 3D virtual tour application and website of Darunsikkhalai
Dormitory that allow the users to explore it freely in a 3D environment. Our application is made using the 3D
computer graphic software “Blender” to create 3D models of the building’s exterior, interior ,other models, and
the animations for said models, and the open source game engine “Unity” to develop the application itself which
will then be streamed onto web browsers such as chrome, firefox, edge, and other chromium based browsers for
easy access to our virtual tour. We will also be taking feedback to compare our 3D virtual tour to other panoramic
virtual tours to determine which aspects of the application need future improvements.

Keywords : Virtual Tour / 3D / Application / Website

Introduction
Because of the recent COVID-19 pandemic, people have to take some precautions to prevent the spread

of the virus. The most common way to prevent this spread is by isolating themselves from others, this means that
they won't be able to go outside for a long time. People have began using Virtual Tour to go and experience places
they couldn't physically go. But existing Virtual Tours are quite restrictive, most virtual tours are made up of
panorama images taken with a 360 camera. Because of that, the viewers can only move around in the specific area
that the tour has set up, which means that viewers of the tour cannot fully explore the virtual environment. We
decided to develop a virtual tour desktop application of Darunsikkhalai School Dormitory using the Unity engine,
where the viewers can move and explore wherever they like in a 3D virtual environment. Being in a 3D
environment means that we need 3D models, so we've decided to use the Blender software to create all of our 3D
models. But with this Virtual Tour being a desktop application, people have to download it to their computer to
access the tour. With that in mind, we've decided to also develop a website to stream our application onto the
webpage, using WebRTC and Unity Render Streaming, for better accessibility.

OT1_09_03/1

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12th SCiUS Forum

Methodology Figure 1 : top, front and side view draft of the
1. 3D modeling with Blender building

First, we create a draft that includes the top, front, and side
view of the template structure as shown in Figure 1. Next, create the
3D model of the structure using the draft as template, and create 3D
models for the interior of the structure. Next, we add textures to the
models using textures taken from the pictures of the template structure
or from texture sets on the internet, and add animations to models that
require movements. Finally, we save and export the model files as .fbx
to be used later in Unity.

2. Application development with Unity

We start by creating a landscape from the 3D model, then

write a script to control various functions in the application. We then

add features and user interfaces to the application as shown in Figure

2, Finishing with testing and polishing the application

Figure 2 : user interface text box pop-up in
application

3. Streaming to client devices using Unity Render Streaming

To stream the application to a client’s device, we first need to host a STUN server on the cloud to enable

signaling. Then Host Servers using the amazon web service(AWS) to run unity instances, then use the STUN

server to establish a peer to peer connection between the client and the server via signaling. Finally, we can Stream
the unity instance’s rendered output to the client’s web browser and transmitting inputs from the client to the

server.

Results, Discussion and Conclusion

From our methodology, we used our draft of the building as a reference to create the 3D models in Blender

(Figue 3). In which we created the model of the exterior , interior , dorm rooms as shown in Figure 4, 5, 6, 7

,respectively, and furnitures for the interior and dorm rooms. We then also

created animations for models that require movement. For the application

development section, we were able to import the 3D models of the

Darunsikkhalai School Dormitory from Blender into the Unity engine. We

then wrote the scripts for various functions in the application, as well as

created a UI to give players all kinds of information about our school; and with

the implemented WebRTC technology our application can be streamed on to

the client's devices, bypassing the clientside's hardware limitations allowing

Figure 3 : 3D models in Blender for more accessibility for our application at the expense of higher operation
using draft as reference cost.

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Figure 4 : Comparison between the exterior Figure 5 : Comparison between the 1st floor
3D model and the actual building 3D model and the actual building

Figure 6 : Comparison between the 2nd floor Figure 7 : Comparison between the 3D model
3D model and the actual building of dorm room and the actual room

Acknowledgments
This project was supported by Science Classroom in University Affiliated School (SCiUS). The funding

of SCiUS is provided by Ministry of Higher Education, Science, Research and Innovation. This extended abstract
is not for citation.

References
1. Kersten TP, Tschirschwitz F, Deggim S. DEVELOPMENT OF A VIRTUAL MUSEUM INCLUDING A 4D

PRESENTATION OF BUILDING HISTORY IN VIRTUAL REALITY. Nafplio; 2017. p 361-367.
2. Wessels S, Ruther H, Bhurtha R, Schroeder R. Design and creation of a 3D virtual tour of the

world heritage site of Petra, Jordan. Cape Town: University of Cape Town; 2014. p. 9-10.

OT1_09_03/3
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Title : The Analysis and Data Visualization of Thai People OT1_12_01
Affliction Temperature in COVID-19 Pandemic
from Twitter Messages using Machine Learning Technique

Field : Technology and computing
Author :
Mr. Kulwisi Sakkittiphokin
School : Mr. Pannathorn Laoteng
Mr. Nippich Sangkhapho

Princess Sirindhorn’s College, Silpakorn University.

Advisor : Dr. Sajjaporn Waijanya
(Department of Computing, Faculty of Science, Silpakorn University)

Abstract
The COVID-19 pandemic has had a disastrous impact on populations worldwide and has been a social,

economic, political, and medical calamity in every country. In Thailand, many people are facing financial and
medical problems same as many countries. Social media is the channel for requesting help and informing the
situation government and others. Twitter is a favorite media for Thai people to distribute information during the
critical period. This project collected 16,800 Twitter messages related to the COVID-19 pandemic between
January 2021 to April 2022 by using Twitter API. Those Twitter messages have been classified to be the affliction
message by using the machine learning technique. The experiment was a comparison of the Convolution Neuron
Network (CNN), Long Short-Term Memory (LSTM), and Bidirectional Encoder Representations from
Transformers (BERT). In the data preparation for the visualization on the web, the affliction message was
categorized as the affliction type by the researchers. The web application was developed by using Django
Framework connected with The Machine Learning Model API to get the affliction data and display the affliction
temperature by category and twit area in Thailand in the format of data visualization. The evaluation of this work
uses precision, recall, F-1 score, and accuracy to evaluate the machine learning model. The classification with the
BERT model shows the best performance is a precision of 82.45%, the recall 84.56%, the F1-score 84.56%, and
the accuracy of 83.10%. The web application and visualization are evaluated by using Usability Testing and the
result is good.

Keywords : Machine learning, Model, BERT, Twitter, API

Introduction (อธิบายโดยยอ่ เก่ียวกบั หลกั การและความเป็นมาของการทดลอง)

The spread of the COVID-19 in Thailand had a severe impact on everyone. However, it is almost
impossible to help anyone with their individual affliction. Therefore, this project attempted to illustrate the
affliction of Thai people caused by COVID-19 that evinced on Twitter in terms of temperature separating as
category by using the Machine Learning Technique.

The first part is Machine learning, It is a technology tool based on the idea that a computer can learn
knowledge without the intervention of a person. It examines enormous amounts of information or training data
using algorithms to uncover unique patterns. This system examines these patterns, categorizes them, and makes
predictions. Traditional machine learning teaches the computer how to understand information that has been
tagged by humans; hence, machine learning is a software that learns from a model of human-labeled datasets. In
this part, there are 3 Machine Learning Models: 1) Negative and Positive Messages Classification Model 2)
Affliction Types Classification Model divided into 7 kinds of afflictions: treatment, vaccine, being, situation
report, seeking for help, mentality, and other; 3) Level of Affliction Classification Model that organized into 4
levels from 0 to 3 neutral to high impact for classifying affliction of all data and then storing them in the
database.

The second part is the website developed using Django. This part uses the analyzed data in the database
to represent people's affliction and how hard they felt.

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Methodology
System overview
The system functional is divided into three main parts:
1) The website for the display and user interface is responsible for displaying information users run and sending
data to the model.
2) The data analytics model is responsible for analyzing the imported data, storing in the database and sending to
the website for further display.
3) The API is responsible for receiving requests from the website and transmitting data between the website and
the model.

Operation procedure

Part 1: Data preparation
1.1 Collect data from Twitter using Twitter API as JSON in a CSV file.
1.2 Import CSV file to Microsoft Excel to encode as Unicode (UTF-8).
1.3 Label and categorize data stored as CSV files.

Part 2: Train Models
2.1 Set category.

2.2 Clean data.
2.3 Train and test model.

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2.3.4 Compare results of 3 models (BERT, CNN, LSTM).
The classification with the BERT model shows the best performance is precision is 75%, the recall is 75%, the
F1-score 75%, and the accuracy is 75%. Secondly is the CNN model, which shows performance is precision is
71%, a recall is 70%, the F1-score 70%, and accuracy is 72%. The final is the LSTM model which shows
performance is precision is 71%, a recall is 70%, the F1-score 70%, and accuracy is 72%.

Part 3: Categorizing data.
3.1 Use a model to categorize data.
3.2 Save the results as CSV.
Second part: the website.
Design Website
- Front end

- Home is a section designed to put all text information, shows a graph of the amount of data for in
each month, and the user can select a bar with the commands: Home, All texts, Level of affliction,
and contact.

- All Text is a section in which the user can select a type of information, namely Well-being,
Situation, treatment, uneasy, education, and other aspects that shows a graph of the amount of data
in each type.

- Level of affliction is a section that shows a graph of the amount of data in each level of affliction
which divided into green, yellow, orange and red.

- Back end
Bring the CSV file obtained from the trained model and open it in Visual Studio Code as a separated tab

by using the command to read the CSV file in view.py.

Result
The result of the 3 models are as 3 following tables:

1. Negative and Positive Messages Classification Model

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2. Affliction Types Classification Model

3. Level of Affliction Classification Model

Discussion
All models in this project were trained using the labeled data, which had been labeled by 2 of 3

authors. So, some models' predictions might not be sensible from some perspectives. Therefore, if the data used
for the training process are labeled by more people, the prediction result would be more sensibly.

Conclusion
The predicted results were compared with the labeled data to train and test the model using 10,000 data

points testing data points after comparing the performance of three models. BERT had the highest overall
accuracy for predicting affliction and not an affliction of the three models tested. The study discovered that the
amount of training data affects accuracy. In this project, there is a limitation that the data used and the types of
distress that are divided still cannot cover all categories of suffering. After selecting the model to use in this
project. The classification with the BERT model shows the best performance is precision is 82%, the recall is
85%, the F1-score is 83%, and the accuracy is 83%.

Acknowledgements
This project was supported by Science Classroom in University Affiliated School (SCiUS). The funding of

SCiUS is provided by Ministry of Higher Education, Science, Research and Innovation. This extended abstract is
not for citation.

References
เขมทดั ศิริขวญั พงคแ์ ละ คณะ. การวิเคราะห์ขอ้ ความจากโซเชียลมเี ดียท่มี ีต่อศลิ ปิ นดว้ ยเทคนิคการเรียนรู้เชิงลกึ รณีตวั อยา่ ง BNK48 [ปริญญานิพนธป์ ริญญา

วิทยาศาสตรมหาบณั ฑิต]. นครปฐม: มหาวิทยาลยั ศิลปากร; 2563

1. เขมทดั ศิริขวญั พงคแ์ ละ คณะ. การวเิ คราะห์ขอ้ ความจากโซเชียลมีเดียท่มี ีต่อศิลปิ นดว้ ยเทคนิคการเรียนรู้เชิงลกึ รณีตวั อยา่ ง BNK48 [ปริญญานิพนธ์
ปริญญาวิทยาศาสตรมหาบณั ฑิต]. นครปฐม: มหาวิทยาลยั ศลิ ปากร; 2563

2. ศุภากร เนียมมาก , สุชานาฏ การประชิต,การวิเคราะหค์ วามรู้สึกของนกั ทอ่ งเที่ยวดว้ ยเทคนิค Long Short Term Memory[ปริญญานิพนธป์ ริญญา
วทิ ยาศาสตรมหาบณั ฑิต]. นครปฐม: มหาวิทยาลยั ศลิ ปากร; 2563

3. วงศธร จยั สิน. ตน้ แบบตวั จาแนกเพลงไทยโดยใช้เทคนิคการเรียนรู้เชิงลกึ [ปริญญานิพนธป์ ริญญาวทิ ยาศาสตรมหาบณั ฑิต]. นครปฐม: มหาวิทยาลยั
ศิลปากร; 2562

4. Nuttachot P,Sajjaporn W. Fundamental of Deep Learning in Practice. 1st ed. Nonthaburi: IDC Premier;
2021

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Title : Web Application For Dressing Guidelines OT1_15_06
Field :
Author : Technology and Computer
Ms.Tadagorn Prateepnatalang
School : Ms.Aomwara Kornsirilak
Advisor : PSU.Wittayanusorn School, Prince of Songkla University
Mr.Winai Rattanapol
Ms.Janya Sainui (Division of Computational Science, Faculty of Science, Prince of
Songkla University)

Abstract
This project aims to create a web application for dressing guidelines that can help people in

dressing problems. By matching categories and colors of clothes that match and are suitable for the user's
body shape. We have used Python in the Flask framework to create web applications.

The result of this project is a web application for dressing guidelines. The operations in the web
application are as follows.

1. Receiving user information such as pictures of clothes, color of clothes.
2. Matching user's clothes by reference from category of clothes and color of clothes.

The web application for dressing guidelines can recommend clothes to suit the body shape of the
user for both categories of clothes and the color of the clothes and can facilitate users.

Keywords : Dressing guideline, Web application, Python, Clothes match, Colors match

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Introduction

Today there are many people that have faced dressing problems. We are aware of that problem and
see that today technology is much more advanced. And from our research the web application is a
technology that people can easily access. So we want to use technology to fix this problem.

This project wants to create the web application for dressing guidelines. The clothes in the web
application are women's clothes. The categories of clothes in the web application are 9 in top and bottom.
Match rules are based on suitability of top and bottom and color of top and bottom.
Methodology

First we assembled the data about the kind of clothes color and clothes that match. Next we
designed web pages and how a web application works. It has two main functions: first is to input data from
users and second to match data that users input. Then we build a web application using Pycharm Flask,
Python, HTML, CSS and JavaScript. Last one we check the functionality and efficiency of the web
application.

Figure 1 : Coding conditions for matching clothes in Python

Figure 2 : Coding conditions for matching clothes in HTML

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Results

This project aims to use technology to solve problems for users who have problems with dressing.
After we build web application we got results as follow

1. Web page shows the top categories.
2. Web page shows the bottom categories.
3. Web page shows form input clothes.
4. Web page shows form input clothes color.
5. Web page shows delete data of clothes.
6. Web page shows matching clothes.

Figure 3 : Web page shows the top categories.

Figure 4 : Web page shows form input clothes color.

Figure 5 : Web page shows matching clothes.
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Conclusion

For the purpose of this science project, We can make a web application for dressing guidelines that
can input the data of clothes pictures in nine kinds of top and bottom and separate them by five colors. Next
it can match the picture of clothes that users input by coding in Pycharm and instructions set from Flask by
Python to make a system to input pictures data from folders, pictures in users computer and clothes data that
users input in a web application and make system for matching data in condition that we make. We use
HTML to build the structure of web applications. We use CSS for decorating a web application and we use
JavaScript to control web pages.

Acknowledgements
This project was supported by Science Classroom in University Affiliated School (SCiUS) under

Prince of Songkla University and PSU.Wittayanusorn School . The funding of SCiUS is provided by the
Ministry of Higher Education, Science, Research and Innovation. This extended abstract is not for citation.

References

ทศั นา ประวิเศษ และ ธนาสยั สุคนธพ์ นั ธุ์. TRYITON:แอปพลิเคชนั หอ้ งลองเส้ือผา้ ออนไลนผ์ า่ นโทรศพั ทม์ อื ถือ. วารสารวิชาการ
วิทยาศาสตร์และเทคโนโลยมี หาวิทยาลยั ราชภฏั สงขลา. 2564;1(2): 59-68

ศิวรี อรัญนารถ และ พดั ชา อุทิศวรรณกลุ . เมอ่ื แฟชนั่ คอื ชีวิต. วารสารมหาวทิ ยาลยั ศิลปากร ฉบบั ภาษาไทย. 2560;37(2): 217-23

นางสาวชุติมา ปาลวิสุทธ์ิ. การพฒั นาเวบ็ แอปพลเิ คชนั เพือ่ ส่งเสริมความสามารถในการใชเ้ ทคโนโลยสี ารสนเทศ สาหรับนกั เรียนช่วงช้นั ท่ี
2 โรงเรียนอนุบาลราชบุรี. 2562;1:28-35

Eve Tokens. 2564. Types Of Clothes: A Guide To Clothing Types;2564 [เขา้ ถงึ เมอื่ 13 ส.ค. 2564]. เขา้ ถงึ ไดจ้ าก:

https://www.thecreativecurator.com/types-of-clothes/

YELLOW SMILE. แชร์คู่มือชาร์ตสีแตง่ ตวั ที่ดูง่ายสุดๆ สอนจบั คสู่ ีเส้ือใหด้ ลู งตวั ใส่ยงั ไงกไ็ ปรอด!;2562 [เขา้ ถึงเมื่อ 13 ส.ค.
2564]. เขา้ ถงึ ไดจ้ าก: https://www.jeab.com/style-beauty/style/color-chart-for-mix-match

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Title : Web Application Development for Learning of Basic OT1_09_01
Smart Factory
Field :
Author : Technology and computer

School : Mr. Thanin Chantranuwatkul
Advisor :
Mr. Wiritpol Poonnark

Miss Savitree Soontornvivat

Darunsikkhalai school Engineering Science Classroom (KMUTT)

Assoc. Prof. Dr. Siam Charoenseang

Mr. Natttiwut Teerabut

Mr. Witsanu Supandee

Abstract :
Nowadays, robots, automatic systems, technologies and smart systems have been adapted into

the industrial field in the name of “Smart Factory”. Due to the potential to work systematically, the ability to
decrease human resources demand and its effectiveness which is better than the traditional factory in terms of
both quality and quantity. The Smart Factory is highly possible to be more widespread in the future.
Consequently, group of researchers aim to develop a web application for learning of basic Smart Factory. The
web application focuses on providing information to improve users’ understanding of basic Smart Factory. The
web application consists of online content part and product customization part. Evaluating the usability of web
application as it contains user satisfaction rating and comprehension testing. The evaluation results will be used
for improvement of the web application in the future. The results have shown that most users can do the
comprehension test correctly after reading online content and most of the users also satisfied with the web
application, so they insist that online content in web application can be a great value in providing information
regarding product customization system.

Keywords : Smart Factory / Web application / Online learning

Introduction
Industry 4.0 refers to The Fourth Industrial Revolution which conceptualizes the rapid changes

in the industrial field. Stepping into industry 4.0 means upgrading the effectiveness of technology in the
industrial field which will cause many benefits, such as decreasing manufacturing duration, increasing both
quantity and quality of products, and the factor that will cause major changes is “Smart Factory”. Smart Factory
is a factory that includes technology AI and IoT to manage the factory's systems. The Smart Factory system
can facilitate the process of the factory and automatize all of the processes. Human resources also will be
reduced due to technologies replacement therefore the Smart Factory is highly possible to be more important
and widespread in the future. However, the information regarding the Smart Factory system has not been
globalized as much as it should be yet, so the group of researchers aims to develop a web application for
learning of basic Smart Factory. The web application focuses on providing information to improve users’
understanding of basic Smart Factory via an internet browser, and users will be able to adapt the knowledge

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practically in the future. With online learning, the web application is available for everyone. Also, users can
easily access the information from anywhere

Methodology
The methods were divided into 3 parts as follows.

Part 1: Web application development
1.1 Designing system structure
Creating the system scenario diagram according to the project scope. Secondly, designing a state

diagram by using Lucidchart in the scope of system scenario diagram, the state diagram depicts the user flow
and overall processes which are the guideline for the next step by using Adobe XD.

1.2 Front-end development
After designing the user interface, this part was about developing the base of the web application
via Codeigniter for applying MVC web development and Bootstrap frameworks for frontend development’s
convenience. Front-end development of the web application was done by using the designs from the previous
method.
1.3 Prepearing the details in the web application
The details in the web application were developed individually depending on each part of the
web application, and it can break down into 2 parts which are Online content part and the Product customization
part

Part 2: Connecting system (Back-end)
This process is mainly about connecting the web application to the demonstration site by developing an API,
Node-RED, and MQTT. The system overview (figure 1) illustrates all connection in web application.
2.1 API

Developing an API for connecting from Web application and demonstration site, and can breaks
down into 2 parts which is API-1 and API-2
(API-1 is used for sending the data from UI to the database and sending the updated data to UI, API-2 is used
for connecting the database with Node-RED in the next part.)

2.2 Node-RED
Node-RED was installed in both Computer-1 and Computer-2. Computer-1 is used for
requesting the command from API-2, and if API-2 sends back the command, Node-RED will pass it to

Computer-2 sequentially. After Computer-2 receives the command from Computer-1, it will require Python in
the coding process for sending the command to robots. Then, Computer-2 will send the status (success or fail)
to Computer-2 through Node-RED

2.3 MQTT
In this project, the MQTT protocol was used in 2 parts :
- Communication between Computer-1 and Computer-2
- Sending command from the computer which is used for controlling robots.

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Figure 1: System overview
Part 3: Evaluating web application
Evaluating web applications consists of 2 aspects.

3.1 Satisfaction
To evaluate user satisfaction, the group of researchers provided the google form with questions
asked on a scale of 1-5. Then, calculated the satisfaction score by dividing the sum of all scores by the number
of respondents.
3.2 Comprehension
After users finish reading online content and using product customization, users must take a
comprehension test to evaluate their comprehension level. The test consists of 5 multiple choice questions and
all of the questions involve information regarding the product customization system.
Results
There are 70 respondents that evaluated satisfaction and the comprehension of web application
through google forms. First, the satisfaction part contains an average score of 4.88 from 5, the median is 5, and
the standard deviation is 0.32 points. Next, the comprehension part, the range of the points is from 2 to 5 and
the number of respondents who got each point is 2, 6, 20, and 42 users respectively. After statistically analyse
data, it was found that the mean of the result is 4.46 points, the median is 5 and the standard deviation is 0.77
points.
Conclusion
Most of users are satisfied with the usability and the information of the web application. The
online content can provide product customization system information regarding evaluating users’
comprehension effectively. The evaluating results show that most of the users can answer the comprehension

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test correctly. Moreover, 42 of 70 respondents which is equal to 60% of respondents answered all questions
correctly, so it shows that users will be able to practically apply the knowledge gained from reading the online
content in the future.
Acknowledgments

This project was supported by Science Classroom in University Affiliated School (SCiUS) under
the King Mongkut's University of Technology Thonburi and Darunsikkhalai school Engineering Science
Classroom. The funding of SCiUS is provided by the Ministry of Higher Education, Science, Research, and
Innovation, which is highly appreciated. This extended abstract is not for citation.
References
Atmoko R.A. et al. IoT real time data acquisition using MQTT protocol Journal of Physics: Conference Series
2017;853:1-3
Barenji A.V, Değirmenci C. Robot Control System based on Web Application and RFID Technology. MATEC
Web of Conferences 2015;28:1-3.
Chen B et al. Smart Factory of Industry 4.0: Key Technologies, Application Case, and Challenges. IEEE
Access 2017;6:6505 - 6519.

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Title: OT1_19_0Simulation Software for Proactive Understanding of COVID-19
Field:
Author: Technology and Computer 2
Faris Waemama
School:
Advisor: Nithinan Thammasaro

Islamic Sciences Demonstration School, Prince of Songkla University

Asst. Prof. Dr. Somporn Chuai-Aree (Prince of Songkla University, Pattani Campus)

Abstract

Coronavirus disease (COVID-19) is a contagious disease. This contagious disease causes many
problems for people today. This proactive understanding of COVID-19 simulation software project has been
developed with the aim of proactively providing knowledge and understanding to those interested in studying
the pattern of the COVID-19 outbreak. There are four mathematical models of infectious diseases, namely
SIR, reverse SIR, SIRD and SIQRD were studied to create variable-based simulation software for each model
to find the appropriate management approach and suitable regulation to prepare and solve the problem of this
disease. A new model namely, SIRCVD model had been investigated for complicated situation the result can
be used to explain for proactive preparation of people.

Keywords: Infectious, COVID-19, Mathematical model, Software

Introduction

The COVID-19 problem has had a tremendous impact all over the world. As a result, the organizers
have devised a plan to solve people's concerns in order to improve their quality of life. By giving the most
comprehensive understanding of germ transmission by using computer simulation software.

Methodology

In this research mathematical models based on four models consist of the following parameters

S is the amount of susceptible, I is the amount of infected people, R is the amount of Recovered

people (or the number of people immune and the number of deaths in the SIR model), D is the amount of

deaths, Q is the amount of quarantine people, C is the amount of carrier, V is amount of vaccinated people
and β, γ, α, δ, ω is hypothetical.

SIR model is the model to describe the relationship between S, I, R Variables.

the model can be described by three equations.

+ 1 − = − = −
≈


+ 1 − = − = −
≈

+1 − = =
≈

reverse SIR model is a model to describe the relationship between S, I, R with feedback loop control

reverse SIR model can be reported as follow.

+ 1 − − +
≈ = = − +


+ 1 − = − = −
≈

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+ 1 − = − = −
≈


SIRD model is a model to describe the relationship between S, I, R, D. It can be modeled as follow.

+ 1 − − = − +
≈ = +


+ 1 − = − – = − −
≈

+ 1 − =
≈ − = −


+ 1 − =
≈ =


SIQRD model is a model to describe the relationship between S, I, Q, R, D. It can be modeled as follow.

+ 1 − − +
≈ = = − +


+ 1 − = – = −
≈

+ 1 – = – – = − −
≈

+ 1 − = −
≈ = −


+ 1 − =
≈ =


SIRCVD model is a model to describe the relationship between S, I, R, C, V, D. It can be modeled as follow.

+ 1 − = − + − − + = − + − − +
≈

+ 1 − = − − + = − − +
≈

+ 1 − = − = −
≈


+ 1 − = =
≈

+ 1 − = − − = − −
≈

+ 1 − − + = − +
≈ =

+ 1 − = − = −
≈

Results

1. mathematical model

1.1 variables

Represent the number of: susceptible people with S, infected people with I, quarantined
people0020with Q, recovered people with R, deaths with D, Carrier with C, Vaccinated with V
Hypothetical: infectious is β, recover is γ, susceptible is α, die is δ, quarantine is ω.

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1.2 Use values to create 4 models: SIR, SIR reverse, SIRD and SIQRD models.

1.3 Rewrite an approximation

1.3.1 Rewrite SIR model approximation for use in program by
+1 = + ∆ (− )
+1 = + ∆ ( − )
+1 = + ∆ ( )

1.3.2 Rewrite SIR reverse model approximation for use in program by
+1 = + ∆ (− + )
+1 = + ∆ ( − )
+1 = + ∆ ( − )

1.3.3 Rewrite SIRD model approximation for use in program by
+1 = + ∆ (− + )
+1 = + ∆ ( − − )
+1 = + ∆ ( − )
+ 1 = + ∆ ( )

1.3.4 Rewrite SIQRD model approximation for use in program by
+1 = + ∆ (− + )
+1 = + ∆ ( − )
+1 = + ( − − )
+1 = + ∆ ( − )
+ 1 = + ∆ ( )

1.3.5 Rewrite SIRCVD model approximation for use in program by
+1 = + ( − + − + )
+1 = + ( − − + )
+1 = + ( − )
+1 = + ( )
+1 = + ( − − )
+1 = + ( − + )
+1 = + ( − )

2. Program(software)

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2.1 Program function
The display and control window on the left side has three primary tabs:
"Models" for math graphs, "Simulation" for particle outbreak simulation, and "Graph" for graphs generated
from the simulation tab (second bar)
The start and control windows are on the right side:
"Intro" is the default program guide, "Tab1" is the simulation control page with population display, and
"Tab2" is the second control page.
2.2 How to use graph

1. Edit value
2. Click “Plot Graph”
2.3 How to simulate
1. Click “Start COVID”
1.1. Select the Graph tab sheet on the left to view the

simulation graph
1.2. Select Tab1 for control simulation
1.3. before change Simulation Click “Clear Sim”

Ex. SIR Graph Ex. SIR reverse Graph

Conclusion

We obtain a simulation program about pandemic and understand the relationship between susceptible,
infected, quarantine, recovered, carrier, vaccinated and dead people. We obtain our investigated model
namely SIRCVD based on SIR, SIR reverse, SIRD and SIQRD models.

Acknowledgements

This project was supported by Science Classroom in University Affiliated School (SCiUS). The
funding of SCiUS is provided by Ministry of Higher Education, Science, Research and Innovation. This
extended abstract is not for citation.

Reference

1. Singh, H., Srivastava, H. M., Hammouch, Z., & Nisar, K. S. (2021). Numerical simulation and stability
analysis for the fractional-order dynamics of COVID-19. Results in physics, 20, 103722.

2. Seligman, B., Ferranna, M., & Bloom, D. E. (2021). Social determinants of mortality from COVID-19: A
simulation study using NHANES. PLoS medicine, 18(1), e1003490.

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Title: Chrome Extension That Reduces the Time Spent on the OT1_13_01
YouTube Desktop Website

Field: Technology and Computer
Author: Miss Dungwang Srisa-ard
School: Piboonbumpen Demonstration School of Burapha University
Advisor: Dr. Thanongsak Thepsonthi, Burapha University

Abstract
YouTube addiction has become an increasing phenomenon with the rise of social media and big

tech. YouTube is a free video streaming platform that enables users to upload and watch videos online. Over
the years, the site has implemented several features to the platform that increase the time users spend on the
site. We hypothesized that removing these novelty features without obscuring too much of the user
experience would reduce the time users spent on the website. To test this hypothesis, a Chrome extension
that removed the features deemed addictive was created and tested on participants with a self-reported
YouTube addiction. In the first week, the time participants normally spent on the site was recorded, and in
the second week, the amount of time spent on the site while using the Chrome extension was recorded.
Using the paired t-test, it was found that there was a significant decrease in the amount of time spent on
YouTube while using the Chrome extension.

Keywords : Chrome, Browser, Extension, YouTube

Introduction
When trying to focus on work on desktop computers or laptops, people sometimes adopt strategies

such as blocking distracting websites to help them focus. However, websites such as YouTube are a grey
area. While YouTube is mainly used for entertainment, there are times when it is used for education or
troubleshooting. Since YouTube is designed to maximize the time users spend on the platform to generate
revenue from advertising, it is often hard to just watch one video. Oftentimes, people go in to just watch one
video, and the next thing they know, an entire afternoon is wasted. Unfortunately, some people believe that
our lack of self-control leads us to spend too much time on YouTube. However, it is normal to be addicted to
things that are designed to be addictive. So in order to reduce the hours spent on YouTube, instead of having
self-control, users could try to remove the additive elements. This can be done via a browser extension. This
paper goes into how to block and turn off features on YouTube using a Chrome extension. Then it is tested
on 9 participants. The study revealed that less time was spent on YouTube while using the Chrome
extension.

Methodology
Building the Chrome extension
The first objective is to build a Chrome extension that would alter the appearance of the YouTube

website. Chrome extensions are written in web development languages such as JavaScript, CSS, and HTML.
There is an official documentation for Chrome extensions available at https://developer.chrome.com/docs/

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extensions/ from Google. All extensions require a manifest file. The manifest file is a JSON file that contains
metadata for a group of files. It describes the essential information about the Chrome extension, such as the
name, description, version, file paths, and permissions. Two main methods were employed to alter the
appearance and functionality of the YouTube website. They are DOM manipulation and the declarative net
request API.

DOM manipulation
Blocking elements in the DOM is done in the content script. The element can be selected using a
variety of methods, such as querySelector or getElementById. Then a function checks whether this element
exists. If it does, it removes the element using the remove method. Disabling autoplay is done in a similar
fashion. This is done by selecting the autoplay button element and creating a function that checks whether
the button exists and if it is turned on. If the conditions are met, the click method is used on the button,
otherwise do nothing. In cases where the element appears after the content script load, the function is
wrapped inside the setInterval method, which periodically calls the function.
Declarative net request API
Images on the site are requested from ytimg.com and ggpht.com. The declarative net request API
was introduced with the manifest version 3 update. It replaces the web request API which was used in the
manifest version 2. Instead of using JavaScript, the conditions are written in JSON files. In order to block
these images, the server has to be blocked at the onBeforeRequest stage of the web request life cycle. This
event is triggered when the request is about to be made.

Figure 1: A picture on www.youtube.com before and after installing the extension. This extension blocks the thumbnail, channel profile picture, channel
banner, shorts page, YouTube originals page, subscription page, home page, explore page, related video side bar, end screen video tile, and disables
autoplay. For the sake of consistency in testing, there were no customization options to block or disable blocking for each individual element. The
extension can be found here: https://chrome.google.com/webstore/detail/distraction-free-youtube/cjfomfbgjppmmipmjdfpcjlilpjbahli/.

Study design
A poster (https://bit.ly/3sYDEmJ) to recruit participants for the experiment was created.
Participants were required to be 16 years old or older, use a desktop computer or laptop daily, and think they
spent too much time on YouTube. The poster included a QR code to the application form. The questions on
the application form include age, gender, occupation, and contact information (Line ID, or email). A PDF
instructional manual was included for the data collection process. Participants were asked to install an
extension which would track the time spent on every website for a week. In the first week, the time normally
spent on www.youtube.com was recorded. After the first week, participants were asked to install the

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YouTube blocking extension. Both the extensions were enabled in private browsing mode. After 2 weeks,
they were asked to export and send back the CSV files from the time tracking extension.

Data processing
There were a total of 22 responses to our application form. However, only 11 sent back data, and of
those datasets, only 9 were viable. The CSV files sent from the participants contained the domain names of
all the visited websites during the two-week period. The time spent on www.youtube.com was extracted and
the average time (minutes/day) spent on the website in the first and second weeks were calculated. If the
time spent on the website on a given day was equal to 0 the data point was excluded from the average. A
dataset will be rejected if there are more than five days where the time spent is equal to 0 minutes. The data
was analyzed using the paired t-test.
Results
Using the paired t-test at the significance level of 0.05, the null hypothesis H0: µ ≤ 0 was rejected.
The test concluded that the time participants spent on YouTube while using the extension was significantly
lower than the time they spent on YouTube without the extension.

Participant no.

Table 1: The average time spent on YouTube without using the extension and when using the extension The recruitment poster was shared in group chats
via the Line messaging app. There were also participants who were recruited offline. A pilot study with two users was carried out to calibrate the length of
data collection and ensure that the study design was clear. Our participants were between the ages of 16 and 52. 6 were office workers, and 3 were students.
Of the participants, 5 were male and 4 were female.

Discussion
In the following, some of the limitations in scope, methodology, other external factors, and possible

improvements to the study are discussed.
Scope limitation
The scope of the study limits us to only working with laptops and desktop computers using the

Chrome browser, even though a majority of YouTube users are on mobile devices.2 In addition, Chrome was
the only browser that data was collected from. Although users were instructed to use the Chrome browser,
participants were not asked to omit the use of other browsers. It could be possible that participants became
frustrated with the drastic change in the appearance of the website and opted to use other browsers or the
mobile app instead.

Limitations of the methodology
The extension may have unnecessarily blocked too many elements. For some of the elements, there
is insubstantial evidence to support that the blocked element would reduce the time spent. This may lead the
user to feel frustrated. Since all the features were blocked at once, there is no way to know which feature has
the most impact on user retention. It would be interesting to test each individual feature to find the optimal
number of features to eliminate without compromising the user experience.

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Another limitation to the study is improper data collection. Of the 9 viable datasets, 5 of the
participants informed us that they had installed the YouTube blocking extension in the first week and
removed it in the second. Since participants were instructed to install the timer extension in the first week, it
can be inferred that some participants did not read the instructions properly. There was also no way to
prevent the user from turning off the extension. Even though the results suggested that the extension was
effective, the time users spent before using the extension, excluding the outlier (114), was 10 minutes/day.
The average time spent per visit on www.youtube.com in March 2021 was 29 minutes and 37 seconds,
which is higher than our user average.1 This suggests that our participants are either not actually addicted to
YouTube or they were not using the web app on Chrome. These flaws could have been mitigated by
improving the recruitment method by sending out a survey on YouTube usage, then sending out invites to
participants that were classified as addicted. It is also important to consider that the users were made aware
that their browsing habits were being tracked. According to the Hawthorn effect, the behavior of the subject
of a study is altered due to their awareness of being observed. This could have been prevented by framing
the experiment as something else different from its actual purpose.

External factors
In this study, a third-party tool was used to collect our usage data. The timer extension only counts
time when the tab is active. An active tab is the tab that is currently being used. If a user is multitasking, the
time will not be counted. The timer extension displays a badge which shows the time spent on each website.
The time badge is only visible to the user when the extension is pinned to the address bar. There were no
instructions given to the users regarding whether to keep the extension pinned or unpinned. Users who have
the extension pinned may become aware of the time spent, which could impact their behavior. This could be
prevented by creating our own timer functionality that counts the time when the page is visible on the screen
regardless of the active status. The timer functionality could be integrated into the extension, and
automatically send back usage data. This might help to increase the uniformity of the result.
Conclusion
In this study, a Chrome extension that blocks elements and disables features on www.youtube.com
was created, which aims to reduce the time spent on the site. The extension was tested, and it was found that
the difference in the time spent before and after using the extension was statistically significant. There was a
significant decrease in the time spent on the YouTube website while using the extension compared to the
time spent while not using the extension.

Acknowledgements
This project was supported by Science Classroom in University Affiliated School (SCiUS). The

funding for SCiUS is provided by the Ministry of Higher Education, Science, Research, and Innovation. This
extended abstract is not for citation.

References
1. L. Ceci. Global time spent on youtube.com 2021 [Internet]. Statista. 2022 [cited 2022May27]. Available

from: https://www.statista.com/statistics/1257254/youtubecom-time-spent-per-visit/
2. YouTube statistics 2022 [Internet]. Official GMI Blog. [cited 2022 May 27) Available from: https://

www.globalmediainsight.com/blog/youtube-users-statistics

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Title : OT1_03_02IoT applications for smart plant production system

Field : Technology and computer
Author : Miss. Kaewta Thanchan

Miss. Panisara Boonmejew

School : Naresuan University Secondary Demonstration School
Advisor : Assoc. Prof. Dr. Mathanee Sanguansermsri (Naresuan University)

Asst. Prof. Choopong Chuaypen (Naresuan University)

Asst. Prof. Dr. Kwanchai Kraitong (Naresuan University)

Abstract
The objective of this project is to develop a monitoring and control system for environment control

in plant cultivation houses, namely a water and nutrient circulation system, air circulation system and an
artificial lighting system. It was developed in the form of an IoT platform with the application Blynk and
conducted studies and experiments with a closed plant production system in order to demonstrate precise
control of environmental factors.

From the results of the operation of the developed system on an IoT platform with the Blynk
application that was installed to a closed plant production system, it could be concluded that the developed
system was able to measure and display the light intensity, the amount of carbon dioxide in the air, water
temperature, air temperature, air humidity and the flow rate of water in the planting trough on mobile phone
including command on-off function to control the operation of various devices such as the ventilation fan in
the air circulation system, light lamps in artificial lighting system and water pumps in circulating water and
nutrients system. Moreover, it might be utilized as a prototype for further development.

Keywords: application Blynk, IoT, closed plant production system

Introduction
As a result, the world's food demand will increase by 60%. On the other hand, agricultural land will

decrease due to population expansion, and climate change. These factors affect the production of food
resources. Smart farming is one approach to solving problems that has received a lot of attention.

The current problem with the adoption of the smart farming system in Thailand is that it often
encounters errors caused by processing. The effects of IoT devices and command system complexity make it
unable to meet the needs of the Thai population in the agricultural sector.

We have seen the benefits and problems that arise with the Smart Farming system and therefore have
an idea to develop a command-and-control system for various systems in the form of an IoT Platform with
Blynk, which is a less complicated system, in order to get a command system that is more convenient to use
and meets the needs of farming.Recently, research has shown that some species of the genus Streptomyces
combat against some plant pathogenic microorganisms, including Xoo (Chithrashree et al., 2011). Moreover,
using actinomycete strains and their bioactive compounds to control bacterial blight in rice appears to be a
cost-effective and environmentally friendly strategy (Gnanamanickam, 2009).

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Methodology
The experiments were divided into 4 parts as follows,
Part 1: Prepare the materials and equipment
Part 2: Design and develop a smart control system

2.1 Design the layout of the smart control system

From the layout of the smart control system, the system can be divided into 2 parts:
Part 1: devices and the Arduino part

2.2 Assembly the devices and sensors to the various boards that are prepared, which are
divided into 2 sections.
2.2.1 section 1 is the section that connects the sensors to the Arduino ESP32
board supporting data transmission via Wi-Fi. Therefore, the measured data
from each sensor can be sent to the Arduino platform and transmitted to
the Blynk cloud. The application then retrieves the values of each sensor
from the cloud and displays the measured values in real-time.
2.2.2 section 2 is the section of the connecting devices. It will be used to control
smart farm systems such as air circulation fans, water circulation pumps,
and the board ESP8266 supporting Wi-Fi connection.
Therefore, various devices can be controlled via Wi-Fi.

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part 2 is the program development part
2.3 Develop a program and check the accuracy of the sensor
2.3.1 The program development part is divided into 3 sections.
2.3.1.1 A set of codes is created in this section for sending data from
the Arduino platform to the cloud of the Blynk application
in order to store the measured value of each sensor.
2.3.1.2 The application section is built to display the sensor value data.
2.3.1.3 The final application section is used to send system control
commands back through the application.

Part 3: Test the developed smart control system, record the results, discussion, and conclusions
3.1 Implement the developed smart control system into a closed plant production
system located in Faculty of Engineering, Naresuan University, which divided into 3 parts:
3.1.1 The air circulation system part
3.1.2 The artificial lighting
and nutrient supply system
3.1.3 The control box part
3.3.2 Record the results
3.3.3 Discussion and conclusions on the obtained experimental results are performed in detail.

Result
When the system starts the measured data from each sensor is transmitted to the Arduino program

and sent to the Blynk Cloud. The application then retrieves the values of each sensor from the cloud and
displays them in real-time.

Conclusion
From the performance, it can be discussed whether the system was designed and developed. can

measure various values such as water temperature, flow rate, and moisture content of planting material. The
air temperature and humidity and the amount of carbon dioxide and can be used for controlling equipment
such as LED lamps, water pumps, and fans in a closed plant system at Naresuan University. It is written
correctly and can be used as a model for further study and development.

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Acknowledgments
This project was supported by Science Classroom in University Affiliated School (SCiUS). The

funding of SCiUS is provided by Ministry of Higher Education, Science, Research and Innovation. This
extended abstract is not for citation.

References
1. Kaewphaitoon, S. and Lekcharoen, S.. The Smart Classroom System with Internet
of Thing (IoT) Through Android and IOS. Proceeding of The 13th RSU National Graduate Research
Conference; 2018 Oct 16; Bangkok. Bangkok: Rangsit University;2018.
2. Kozai, T., Niu, G. and Takagaki, M.. Plant Factory: An Indoor Vertical Farming System for Efficient
Quality Food Production. USA: Nikki Levy; 2016. 405 p.
3. Kum-on, N., Noinuan, A. and Nim-on, O. Internet of things application
for closed system plant factory [Senior project report, B.ENG.]. Phitsanulok :Naresuan University;
2019. 110 p.
4. Pothong, T., Mekarun, P. &Choosumrong, S. Development of Smart
Farming Service System for Smart Farmer using FOSS4G and IoT. Naresuan Agriculture Journal.
2019 Jul -Dec;16(2): 10-17.
5. Sriamnuay, B., Prangson, S., Piticharoenporn, W. and Sihanam, P. The Design of the Smart Farm
System using the Internet of Things Technology for Lime Farms in Phetchaburi Province. Poster
session presented at: The 6th Nation Conference Nakhonratchasima College [Internet]; 2019 March
30; Nakhonratchasima, Thailand.
Available from: http://journal.nmc.ac.th/th/admin/Journal/2562Vol9No1_89.
6. Srisongmuang, P., Srisongmuang, C., Busabok, S., Homsub, C., and Kongsomsawang, S..
Development of smart farm management system model in plant house by embedded computer.
Research Journal Rajamangala University of Technology Thanyaburi. 2021 Feb 15;20(1): 21-29.

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Title: A development of computer game to reinforce students’ skills in learning mathematics

Field: Technology and Computer OT1_16_02
Author: Mr. Daniel Samae

Mr. Naphasadol Dairoop

School: Demonstration School Prince of Songkla University, Prince of Songkla University, Pattani Campus

Advisor: Asst. Prof. Dr.Kriangsak Damchoom, Department of Mathematics and Computer Science, Faculty
Science and Technology, Prince of Songkla University, Pattani campus

Abstract

This project is aimed at developing a computer game as a media for learning mathematics. To carry
out the project, Unity and C# were used as a tool and language for developing the game. The scope of
mathematics contents used in this game covers the contents of Matthayom 4. The game developed is a role-
playing game [RPG] game with 2 modes. The first mode is a tutorial mode. It is the mode of providing
mathematics knowledge to the players who want to learn or to review contents before playing the game. The
second mode is the playing mode. It is the mode that allows players to experience the game and solve
mathematics problems during the game. The playing mode is divided into 10 sub-stages/levels. Each sub-stage
is composed of getting mission dialogs, fighting monsters, collecting items, upgrading weapon, and answering
the questions to complete the level. Each stage will have different contents with 5 random-questions to assess
the performance of the players, in order to proceed to the higher stages. Players can register to play, save or
resume the game at any time, and can view their scores on the ranking board. To evaluate the game, an online
questionnaire was used as our tool for assessment. The evaluation is composed of 7 aspects with 5 levels
satisfaction. The results obtained from 30 responses showed (1) the content consistency was 4.10; (2) the
development of knowledge and skills in mathematics was 3.93; (3) the appropriate of content sorting was 4.16;
(4) the accuracy and coverage of contents was 4.10 (5) the attractiveness of game and contents was 3.66; (6)
the attractiveness of the graphics used was 3.36; and (7) the level of difficulty of the exam as 3.50. The overall
mean of game performance was 3.83.

Keywords: mathematics game, computer game, learning median, Unity

Introduction
Because nowadays technology has become a part of human life. The technology has helped in terms

of facilitating. and create fun at present, the game has become a part of the lives of children, youth and even
people of working age. In addition, the game will provide fun. enjoy Games also play a role in enhancing
learning and developing skills, but playing inappropriate games can also have an impact. The main impact will
be game addiction. This affects and creates problems for children in many areas, such as physical health,
schooling and behaviour, which is an important impact. to hinder the child's development plus problems with

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studying Thai society today Most students are not fond of math due to many factors such as being boring,
difficult to understand, lacking motivation and the problem of asking questions in class The authors, therefore,
want students to see the importance of mathematics, where mathematics is very important to the development
of human thinking. Because it will allow humans to have creative ideas. Think rationally, think systematically,
and change the minds of many that games will affect children's bad behaviour. That is not every game that has
a bad impact. Some games are intended to be fun, and entertaining, or some games are intended to help promote
and develop knowledge and abilities in various fields, so the publisher wishes to create a game to promote
learning in mathematics. In the content of the Matthayom 4 so that players can have fun and enjoy playing the
game. Along with gaining knowledge in mathematics in various subjects as well, allowing you to learn math
in a new, not boring way, and can also make playing the game more useful.

Methodology
1. Population determination and sample selection
Population and sample used in the study The population used in this research were people of

all ages and genders.
The sample group used in the study used in this research is a player who has entered the

game, a total of 30 students who have been random with a simple random method
2. Tools used the instrument used in this study was Unity.
3. Creation and preparation of educational games
4. Data Collection
5. Analyze and summarize the results of the experiment.

Results
Design and development Developing computer games to enhance student’s math learning skills

with Unity. That can create a game that inserts learning mathematical thinking skills in the content of
Matthayom 4, which the assessment results were better than what we had expected.

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Table 1 Satisfaction level from game satisfaction questionnaire Satisfaction level Mean ( ̅)
Assessment list 1
0 4.10
5432 1 3.93

1. Content consistency 12 10 7 1 0 4.16
1 4.10
2. Develop knowledge and skills in 10 12 5 2 2 3.66
mathematics 2 3.36
2 3.50
3. Content sorting is appropriate 11 14 4 1

4. Accuracy and content coverage 12 12 4 1

5. Interesting content 8 10 8 2

6. The attractiveness of the graphics used 5 9 10 4

7. The difficulty level of the exam 7993

From Table 1, it can be concluded that the overall game performance is moderate with an average of 3.83

The game is a role-playing game [RPG] with 2 modes. The first mode is a tutorial mode. It is for the
player to learning mathematics before playing the game. The second mode is the playing mode. The playing
mode is divided into 10 sub-stages/levels. Each sub-stage is composed of getting mission dialogs, fighting
monsters, collecting items, upgrading weapon, and answering the questions to complete the level (see Figure
2).

Figure 1 : Example stage in the game Figure 2 : Example quiz system in the game

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Conclusion
Based on the results of evaluating the performance of the game by using the game satisfaction

questionnaire. There are 7 aspects in total. Overall game performance is moderate. The mean was 3.83. The
assessment was divided into 7 areas as follows: (1) the content consistency. The mean was 4.10 (2) the
development of knowledge and skills in mathematics. The mean was 3.93 (3) In terms of content sorting
appropriately. The mean was 4.16 (4) In terms of accuracy and content coverage with an average of 4.10 (5)
In terms of content attractiveness with an average of 3.66 (6) in terms of the attractiveness of the graphics
used.The average was 3.36 (7) on the level of difficulty of the exam. has an average of 3.50

Acknowledgements
This project was supported by Science Classroom in University Affiliated School (SCiUS). The

funding of SCiUS is provided by Ministry of Higher Education, Science, Research and Innovation. This
extended abstract is not for citation.

References
Gafoor KA, Kurukkan A. Why High School Students Feel Mathematics Difficult? An Exploration of Affective
Beliefs. Online Submission. 2015 Aug.
Pivec M, Kearney P. Games for learning and learning from games. Informatica. 2007;31(4).

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Title : Web application Sharif-Judge development
and improvement
OT1_09_02

Field : Technology and Computer

Authors : Mr. Kaokla Thaikham

Mr. Natanon Luangvilai

Mr. Thanatorn Wongthanaporn

School : Darunsikkhalai School (SCiUS project), KMUTT

Advisor : Mr. Jiravatt Rewrujirek, Office of Engineering Science Classroom, KMUTT

Abstract

Over the last decade, the popularity in using digital systems has been grown dramatically.
Consequently, the demand for programmers and software developers in labor market was increased.
However, a basic knowledge is needed for working as a developer. There are many ways to learn about
computer programming nowadays. A web application named “Sharif-Judge”, a platform designed for
using in computer programming course, is one of them. It has a lot of features that supports teachers
in computer programming class: user roles, assignment management, scoreboard etc. Although it has
been packed with many useful features, some features and modifications should be added or made to
make the web application becomes more user-friendly. Therefore, we try to improve its user interface
(UI) and develop new features in Sharif Judge to refine its functionality and make the application
more convenient by using the application development cycle which consists of 5 steps: planning
and problem analysis, solutions design, program development, system testing, and deployment. The
authors divided the web application development and improvement into periods (sprints) to meet
user’s needs and solve problems on the spot. In addition, we have created a questionnaire to assess
satisfaction of user after testing the improved version of the web application and use them as a guide
for further improvement to the Sharif-Judge web application.

Keywords: application development, code inspector, coding judge, MVC, Sharif Judge

Introduction

Popularity in using digital systems has grown dramatically. Consequently, the demand for
programmers and software developers in the labor market is also increased. However, basic knowl-
edge is needed for working as a developer. There are many ways to learn about programming nowa-
days. One of them is the web application called “W3Schools” which is a popular choice for many
programmers since they offer tutorials and references in many languages categorized in an orderly

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manner and has questions to check learner’s comprehension.

However, W3Schools is not only a solution for learning how to code, or even the website like
programming.in.th, which contains the collection of problems in competitive programming written
in Thai language, may not be appropriate to use as a primary platform for teaching programming in
the classroom because of inability to follow the development of learners conveniently. Additionally,
teachers cannot create and assign their problems to the students. Therefore, the authors are interested
in a web application that can grade the program submitted by the students and inform the results to
them. It should also allow teachers to create and assign problems to the students themselves. This
will facilitate both teachers and students and bring great benefits to a programming class.

The web application that the authors were interested in was “Sharif Judge”. It is a platform
designed to be used as a teaching tool in programming. Sharif Judge has various features that can
facilitate teaching and learning in the classroom, including a role system that can be assigned to group
of users and an assignment system that can set time intervals and a group of students.

However, Sharif Judge still has some problems and inconveniences in usage. The authors
decided to develop and improve Sharif Judge to be more convenient to use, and this will be useful for
further teaching in the programming course.

Methodology

There are three phases (sprints) in the development and improvement of Sharif Judge web
application. Each sprint consists of five phases: Planning & analysis, System design, Program de-
velopment, Integration & Testing and Deployment.

First, in planning & analysis phase, we use Sharif Judge web application to determine if
there are any improvements that can be made. It was found that there are still 3 main parts that can
be improved: code submission system, assignment import & export system, and user interface (UI).
These parts will be developed in each sprint appropriately.

Next, for system design phase, we brainstorm for the solution which satisfies or solve the
problem mentioned earlier. After that, in phase of program development, we turn the solution into a
source code that can be put into the Sharif Judge platform. Then we test it to see if the code can be
run smoothly and works according to our designed solution. Finally, if there isn’t any bugs or errors
in our program, we push these changes and modifications to the repository and production server
respectively.

These 5 phases have been completed in each sprint’s development. In this work, we have
developed a Sharif Judge in 3 sprints.

In the first sprint, we have added code submission into the Problems page to let users type and
submit their code directly from this page without switching to the Code Submit page. Additionally,

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we have changed the main theme of Sharif Judge from light theme to dark theme. In the second sprint,
we have improved the assignment import & export function so that user can import the assignment
along with the settings exported from the system. Moreover, we will create a theme switching button
that can toggle between light theme and dark theme. In the last sprint, we have added the notification
to notify user when the assignment is imported either successfully or unsuccessfully. Furthermore, a
bug involved theme switching button is also fixed.

After the 3rd sprint is ended, the authors conduct the survey to collect user’s response about
the satisfaction in using improved version of Sharif Judge in 3 aspects: convenience, user-friendliness,
and user satisfaction. User can give a rating on each aspect in a scale of ten. We can conclude that
user has high satisfaction in each criterion if the average of overall rating in that criteria is more than
8.0.

Results
Web application development

1. Theme switcher between light mode and dark mode, Code submission section in a Problems
page (See figure 1 below)

2. Assignment import & export system with notifications (See figure 2 on the next page)
Survey’s results

According to the survey, dark mode has an average score of 9.55 in terms of convenience,
9.82 in terms of user-friendliness, and 9.73 in terms of user satisfaction. Code submit has an average

Figure 1: Problems page with Code submission section in Dark theme

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Figure 2: Assignment and its settings are imported successfully on Assignment page with theme
switcher on the top-right
score of 9.55 in terms of convenience, 9.55 in terms of user-friendliness, and 9.45 in terms of user
satisfaction. Lastly, assignment import & export function has an average score of 9.45 in terms of
convenience, 9.55 in terms of user-friendliness, and 9.45 in terms of user satisfaction.
Conclusions

According to the results, the developed sections have an average score above 8 in three as-
pects. In summary, the improved part contributes to the user experience, which corresponds to the
objectives of this project.
Acknowledgements

This project was supported by Science Classroom in University Affiliated School (SCiUS)
under the King Mongkut’s University of Technology Thonburi and Darunsikkhalai school Engineer-
ing Science Classroom. The funding of SCiUS is provided by the Ministry of Higher Education,
Science, Research, and Innovation, which is highly appreciated. This extended abstract is not for
citation.
References
Leff A, Rayfield J. Web-application development using the Model/View/Controller design pattern.
Proceedings Fifth IEEE International Enterprise Distributed Object Computing Conference. :118-
127.

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Title: Forced Alignment and Annotation for Medical Speech OT1_11_03
Recognition

Field: Technology and Computer
Author: Mr. Thares Noonark

Mr. Chayaphol Mongkol
Mr. Pasit Chingskol
School: Suankularbwittayalai Rangsit School and Thammasat University
Advisor: Assoc.Prof.Dr. Charturong Tantibundhit, Thammasat University

Abstract

Speech-to-text alignment for Automatic Speech Recognition (ASR) has many applications in speech research.
Even though interest in natural language processing and the audio field has skyrocketed in the past few years,
only a handful of studies are focused on improving natural language processing (NLP) systems. Speech-to-
text alignment is a technique for automatically generating phone-level segmentation by aligning orthographic
transcriptions to audio recordings. Aeneas, a tool for speech-to-tech alignment systems or synchronization
maps between a list of text fragments and an audio file, was employed in the early stage of this research since
the Thai language was not supported by the Aeneas system. Subsequently, PyThaiNLP, a Thai linguistic
library, was used to map Thai text to English phone-level transcription in the Aeneas model. As a result, the
model's accuracy was approximately 6%, which is considerably low. To increase the system’s accuracy, we
used a transformer, which achieved state-of-the-art performance in a variety of audio, text, and image tasks,
as well as in ASR systems. The use of a transformation model “wav2vec2” based on fine-tuning Thai corpora
for force alignment in the future direction sheds light on how to improve the system’s accuracy. The accuracy
rate is 62.70% with an average of 140 audio files from various speakers. However, when using files with
minimum noise, the result accuracy increases to 89.17%, although the majority of the audio files in the dataset
do not meet this requirement.

Keywords: Forced Alignment, Natural Language Processing (NLP), Text annotation, Transformer,
Wav2Vec2

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Introduction
Forced alignment refers to the process by which transcriptions are aligned to audio recordings to

automatically generate phone-level segmentation. While automatic alignment does not yet rival manual
alignment, the amount of time gained through forced alignment is often worth the slight decrease in accuracy
for countless projects, especially for Thai linguistics which has just a few studies. We developed this model
for a replacement of a manual aligner with an automatic aligner which can improve the accuracy from human
error and minimize the length of the working process. This research is created to provide timestamps for
outputs from the medical ASR model called Udonthani Cancer Hospital (UDCH) Doctor & Nurse Note
(Sujittra Puangarom & Sirikorn Sangchocanonta, 2020). We introduced the Transformer (Thomas Wolf et
al,.2020), a dominant architecture for natural language processing as an automatically forced aligner. This
model utilizes an open-source processor (ASR), including Thai NLP ASR, that others would not.

Methodology
This experiment was divided into 3 parts consisting of

Part 1: Preparing data for training and testing set
500 hours of doctor and nurse note audio files were collected from Udon Thani Cancer Hospital. It

is later being preprocessed in mainly 2 steps. Firstly, annotating the audio files under both Thai and English
contexts by a linguistic expert. Including indexing each annotated transcription into shorter utterances for the
purpose of training the model. Secondly, defining the segmentation of each utterance into timestamps, this
process is being completed on the Praat, a computer software package for speech analysis in phonetics.
Part 2: Model

Wav2vec2, a pre-trained model for Automatic Speech Recognition released in September 2020, is
able to master speech representations from approximately 50 hours of unlabeled speech in a way of randomly
masking feature vectors before passing them to a transformer network. It assists in the exploitation of vast
amounts of unlabeled data in order to generate a model that can be easily converted later as from what has
been shown that pre-training followed by fine-tuning on relatively minimal labeled speech data produces
competitive performance. Using the multilingual pretrained wav2vec 2.0 models, XLSR-wav2vec2, which
learns a single model from the raw waveforms of speech in various languages to learn cross-lingual speech
representations The model is then fine-tuned using labeled data, and the results show that cross-lingual
pretraining exceeds monolingual pretraining by a significant margin, performance-wise. We have
implemented the model called wav2vec2-large-xlsr-53-th which is the wav2vec2-large-xlsr-53-based model
that is being fine-tuned on Thai Common voice 7.0, using the transformer architecture (cite).
Part 3: How to calculate the percentage accuracy

Our transcripts come in 2 different languages, Thai and English, but the forced alignment model
required a general-known language so we have to convert the transcript to the ARPABET, a set of phonetic
transcriptions representing phonemes and allophones of general American English with distinct sequences of
ASCII characters. Although English transcripts can be converted directly to the ARPABET, Thai transcripts
need to transform into the International Phonetics Alphabet (IPA) language, an alphabetic ARPABET of
phonetic notation based primarily on the Latin script, but several characters are not included in the ARPABET
library so the general IPA which have all alphabets in the ARPABET library is necessitated.

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On the other hand, we use the Levenshtein distance to compare the actual word (from the inputted
transcript) and the predicted word (from the ASR). It works by comparing two different measured sequences
using string metric, this means that the predicted word that has the distance closer to 0 is more likely to be
the actual word than the distance that has more value.

Fig 1 IOU comparison between a ground-truth transcript (red color) and
a predicted transcript (blue color) with the same dataset

Results
The accuracy percentage is about 62.70% with an average of 140 audio files from different speakers.

However, with files that provide minimal noise, the result accuracy rises to 89.17% but still, most of the audio
files in the dataset don’t possess the minimal noise condition.

Conclusion
From the aforementioned statements using the IOU as a calculating method, our objectives came out

to be 62.70% which we expected to be more efficient but there are several obstacles. For instance, Firstly,
noise, accent, conversation speed, and pronunciation are uncontrolled variants that come from different
speakers. Secondly, despite actual transcripts that we aligned by ourselves; the timestamp is not 100%
accurate. Using forced alignments, the timestamp is annotated by the phoneme level of each word but by the
Praat method, even though the timestamp is dependent on how we would like to annotate it such as using this
method, the end-time of the previous word can also be a start-time of a present word which would lead to a
decrease in a percentage so, predicted words in the boundary are the same as the actual word.

Our assumption as to why noise affects the percentage of accuracy is due to the fact that we used
Intersection Over Union (IOU) as the metric for calculating. IOU is a term used to describe the extent of
overlap of two boxes. The greater the region of overlap, the greater the IOU. The rule that we used to segment
the audio files is if the end time of the transcription line is met, the end time of the utterance will be
immediately determined while the model rule is different; determining both the start and end time when the
start and end of the transcript are met, respectively, as provided by the visualizer.

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Acknowledgments

This project was supported by Science Classroom in University Affiliated School (SCiUS) under
Thammasat University, Suankularb Wittayalai Rangsit School, Center of Excellence in Intelligent Informatics,
Speech and Language Technology, and Service Innovation (CILS) under Thammasat University, Faculty of
Liberal Art under Thammasat University and Udon Thani Cancer Hospital (UDCH). The funding of SCiUS
is provided by the Ministry of Higher Education, Science, Research, and Innovation, which is highly
appreciated. This extended abstract is not for citation.
References
Wolf T, Debut L, Sanh V, et al. 2020. Transformers: State-of-the-Art Natural Language Processing.
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System
Demonstrations, [online] Available at: <https://aclanthology.org/2020.emnlp-demos.6.pdf> [Accessed 3
April 2022].
Hugging Face.co. 2022. airesearch/wav2vec2-large-xlsr-53-th · Hugging Face. [online] Available at:
<https://huggingface.co/airesearch/wav2vec2-large-xlsr-53-th> [Accessed 3 April 2022].
GitHub. 2022. GitHub - menelik3/cmudict-ipa: The CMU Pronouncing Dictionary converted to IPA.
[online] Available at: <https://github.com/menelik3/cmudict-ipa> [Accessed 6 May 2022].
GitHub. 2022. GitHub - attapol/tltk: Thai Language Toolkit. [online] Available at:
<https://github.com/attapol/tltk> [Accessed 6 May 2022].
En.wikipedia.org. 2022. Levenshtein distance - Wikipedia. [online] Available at:
<https://en.wikipedia.org/wiki/Levenshtein_distance> [Accessed 8 May 2022].

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Title: Development of mobile application for drug identification with 12th SCIUS Forum
computer vision on android operating system
Field: OT1_13_02
Author:
School: Mr. Supakorn Kaewkalong, Mr. Napornprom Sakulmekiat
Advisor: Piboonbumpen Demonstration School, Burapha university
Assoc. Prof. Dr. Norrarat Wattanamongkhol, Faculty of Engineering, Burapha University

Abstract: This study is about the development of mobile application for drug identification by using

computer vision based on Android operating system. The propose of this study is to decrease the number of the

patients from drugs errors. In the application, the user can either take a photo of drugs or upload from the gallery.

The images will be sent to the server and be identified by using Image Processing (IP) combine with Machine

Leaning (ML) algorithm. Then the result of the identification will be shown on the application along with drug

instructions. After we test the accuracy of the Image Processing by using three different home remedies. We found

that the application is easy to use and user-friendly and the application can correctly classify the drug. By giving a

high percentage of accuracy

Keyword: Drugs, Image Processing (IP), Machine Learning (ML), Application

Introduction

Medicine is necessary for living things due the sickness that very common in living things but using
without knowledge can lead to death. According from research, 2% of the patient is death from the overdose of the
self-medication is mostly elderly who use the specific drug that might forgotten or miscommunication that can lead
to medication error.

Refer to mentioned problem, project aim to design and coding the application to decrease the problem by
using the image processing to identify and can show the information of the medication and design the application to
friendly for all ages.

Methodology

This project can be split in to 3 main part coding unit, user unit(application), server unit. Users have to run
the application on android operation system to scan the medication and the application will send the image from the
scanner to the server to run the image processing from coding unit and return the result to the user

Part 1: coding unit

Coding unit has received image from server in specific folder and Image Processing has two main
classification criteria which can be classify by shape of capsule and color. Coding unit must run the image
processing to select the object in image by function in OpenCV library to crop the drug and compare to the 3,600
images in the database by Machine Learning and to return the information of the drug to the user through email by
using the external developer API from Google

Part 2: user unit

The application was coding to run on android operation system by JAVA script to scan the image by using
the phone camera to capture the image by using android API and send to server and it was designed to simple but
look good by using the circle and pastel color as the main component like other famous application and have less
button to minimize the step that user need to perform.

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Part 3: server unit
Server was assigned to receive the image from the android application by using the third party library to
access the server. Server is run on the personal computer by MySQL library on Xampp program and access
the internet by PHP library and the coding unit will use the image from the server folder to process and
send data of the medication to server through PHP library and it will send back to user unit to inform the
user

Result and discussion
The result can be split in to 2-part application and image processing

Part 1: application

Figure 1: application user interface

From the inquiry form can be concluded that the application is designed to suit everyone with 70% or 35
user from the 50 user is satisfied with design and have great user experience from the application, but the
application isn’t compatible with the android operation system 7 or lower due the library compatible and
the version of the JAVA script.
Part 2: image processing

Figure 2: Image processing track the paracetamol

The Image Processing can select the drug and crop the image to compare in database and we found that the
Image Processing can predict the drug as percentage, by coming out as a number greater than 70% which is
a acceptable number

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Table 1: Percentage of the prediction

Paracetamol-tylenol Paracetamol-bakamol Fa Ta Lai Jone capsule
74% 82% 76%

Conclusions

The application is user-friendly by simple and colorful user-interface and image processing is
accurate to be reliable and able to update the database and kind of medicine

Acknowledgement

This project was supported by Science Classroom in University Affiliated School (SCiUS) under
Burapha University and Piboonbumpen demonstration School. The funding of SCiUS is provided by Ministry of
Higher Education, Science, Research0 and Innovation, which is highly appreciated. This extended abstract is not for
citation

References

• Hsien-Wei Ting, A drug identification model developed using deep learning technologies: A Review, BMC
Health Services Research ; 2020

• Zhong-Qiu Zhao, Object Detection With Deep Learning: A Review, IEEE TRANSACTIONS ON
NEURAL NETWORKS AND LEARNING SYSTEMS ; 2019

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Title : Text Analysis from Drug Label OT1_15_08

Field : using Metadata and OCR Technique
Authors : Technology and Computer
Mr. Jirath Thongruang
School : Mr. Korphong Kaewchoo
Advisor : PSU.Wittayanusorn School, Prince of Songkhla University
Prof. Dr. Chinnapong Angsuchotmethee, Prince of Songkhla University
Mr.Weerawat Wongmek, PSU.Wittayanusorn School

Abstract :

Still can't deny that Medicine is also one of the most important human factors. Because the drug has
properties to eliminate ailments and relieve ailments for users. The best medicines will have clear labels on them.
The label of this drug consists of 4 main parts: the name of the drug, its properties, the method of use of the drug,
and the expiration date of the drug. The authors foresee that the OCR readings on the drug labels are highly
anticipated. Therefore, the authors have brought OCR to compare which may make the results of reading more
accurate. Therefore, the concept of metadata and substring may help OCR read more accurately. Metadata is
detailed information that shows the origin of the data, but metadata is used with information technology.

By being able to apply this research and think further until it becomes a system for reading drug names
from labels and other tasks that may be useful in theory and practice in the future.

Keywords : OCR, Metadata, Substring

Introduction

It can't deny that Medicine is also one of the most important human factors. Because the drug has
properties to eliminate ailments and relieve ailments for users. The best medicines will have clear labels on them.
The label of this drug consists of 4 main parts: the name of the drug, its properties, the method of use of the drug,
and the expiration date of the drug. Incorrect drug readings have consequences, citing 2016 research that said drug
misuse has long been a serious global problem. found that less than half of Patients were Treated to Standard
Treatment And more than half of the patients were unable to use the drugs that were given by the prescriber,
resulting in adverse reactions. drug resistance problem But in Thailand, at least 50 percent of the drug was used,
which was unreasonable drug use. Thus, the authors realized that OCR readings on drug labels were highly
predictable. Let the authors bring the concept of metadata and substring may help OCR read more accurately.

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