Atchariyachanvanich, Kanokwan

Preferred name

Main Affiliation

School of Information Technology

kanokwan.at@kmitl.ac.th

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Bridge Sub Structure Defect Inspection Assistance by using Deep Learning
(2019-10-01)
Kruachottikul, Pravee
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Cooharojananone, Nagul
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Phanomchoeng, Gridsada
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Chavarnakul, Thira
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Kovitanggoon, Kittikul
Road transportation is the most popular transportation in Thailand, which the top two highest traffic are the region-to-region highways; and then inter-city highways. Therefore, the regular maintenance is required to maintain the good condition due to road safety. The most significant process of bridge inspection procedures is sub structure inspection, which requires visual inspection as an initial step. This process is used to quick determine the damage severity i.e. appearance and crack that may cause damage to the structure strength. The current process requires that the experienced maintenance engineer to be on the field in order to visual inspect and estimate whether the maintenance is required. Yet, due to the limitation of number of expert engineers to be on the field, the photo verification is introduced to assist them so that they are no need on every inspection site. However, using human to verify has no standard and uncontrollable. They need to have experience and good knowledge. As well as it is highly depended on individual decision-making skill. Thus, in this paper, the deep learning technique will be presented to assist the expert for quality inspection process of bridge sub structure images. That is using image enhancement and then image splitting and overlapping for image pre-processing. After that applying CNNs for object classification. As a result, the total accuracy is 89% based on 3926 dataset.
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A Data Visualization for Helping Students Decide Which General Education Courses to Enroll: Case of Chulalongkorn University
(2019-01-01)
Cooharojananone, Nagul
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Dilokpabhapbhat, Jidapa
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Rimnong-ang, Thanaporn
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Choosuwan, Manutsaya
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Bunram, Pattamon
Chulalongkorn University has been utilizing information systems for course administration system, named CU-CAS, to help manage the course syllabus, course contents and course satisfaction survey. While current students have been selecting courses based on information from seniors and friends, we recognize that the data from CU-CAS could be useful in selecting course, but have not been fully utilized. Therefore, this project aims to design a data dashboard to help students select courses to register, based on the data from course satisfaction survey by students from the past three years of course offerings. In this work, we developed CU-CAS data visualization using Google Data Studio. Data were analyzed and presented the overall of the evaluation result in term of dashboard. According to our pilot study, students make decisions for enrollment by comparing the evaluation result in the past three years, in the form of different indicators. We also collected and analyzed data from the student blogs that review courses that they took using word cloud and Markov chain. Both data from CU-CAS and blogs will be represented to students to help students make decision in registering courses. This project is one of the e orts to utilize data in a way that is easy to understand to students, allow Chulalongkorn University to understand students learning behavior, and bring back to plan and adjust teaching strategies.
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BMA for the BMA: Boosting Mobility Analysis for the Bangkok Metropolitan Administration via Automated Pedestrian Counting from CCTV
(2024-01-01)
Kujareanpaisal, Poonnaphop
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Mayhasap, Rujira
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Tea-Makorn, Pin Pin
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Jindahra, Pavitra
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Starita, Stefano
The objective of detecting and counting people using the CCTV camera on the footpath is to facilitate and reduce the time required to count the number of people traveling in pedestrian areas without having to actually visit the area. This paper uses the head detection technique to solve the problem of overlapping objects, YOLOv8n for detection and BoT-SORT for object tracking. A program was developed to assist the Bangkok Metropolitan Administration in counting the number of people within the region of interest and visualizing the statistics. Users can view statistics in the form of visual charts to compare the maximum number of people in each period by importing the video into the program. Users can also view historical statistics from previously imported videos. This program enables users to monitor pedestrian traffic in each area, providing valuable insights for urban planning decisions.
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Innovative Mobile Application for Measuring Big Data Maturity: Case of SMEs in Thailand
(2020-01-01)
Limpeeticharoenchot, Santisook
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Cooharojananone, Nagul
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Chavarnakul, Thira
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Tuaycharoen, Nuengwong
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Atchariyachanvanich, Kanokwan
A Big Data maturity model (BDMM) is one of the key tools for Big Data assessment and monitoring, and a guideline for maximizing the usage and opportunity of Big Data in organizations. The development of a BDMM for SMEs is a new concept and is challenging in terms of development, application, and adoption. This article aims to create the novel online adaptive BDMM via responsive web application for SMEs. We develop the BDMM API and a responsive web application for easy access via mobile phone. We developed a model by analyzing the factors impacting the success of implementing Big Data Analytics (BDA) in SMEs based on literature reviews. The model was verified by conducting a survey of 180 SMEs in Thailand, interviewed against four extracted domains. Then, the scoring and classified levels for the model was developed through Latent Class Analysis (LCA) to depict four levels of each domain and four final maturity levels to create an adaptive model. As the experimental results with 33 users including executive officers, managers, IT, and data analytic officers. The user acceptance for our mobile application using TAM indicates that executive officer's group and non-executive group satisfied perceived usefulness, perceived ease of use, and intention to use factor. Use cases of the application include SMEs monitoring for their Big Data Analytics capability for improvement, and the Government Agency providing proper support on SMEs’ level of competency.
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Foot Traffic Analysis Using Wi-Fi Sensor During the Tokyo 2020 Olympics and Paralympics
(2023-01-01)
Traganmaturapot, Peerada
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Sonehara, Noboru
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Hiruma, Nobuharu
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Cooharojananone, Nagul
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Jirapongwanich, Jirakit
Various real-world factors, such as time, weather, distance, environment, the COVID-19 pandemic, or even protests, can all impact human decision-making. However, restrictions and unexpected occurrences may also influence people's decisions regarding their path at any given time. These factors can lead to challenges in managing foot traffic at largescale events. In response to these challenges, this paper proposes a data-driven web-based foot traffic management supporting dashboard for large-scale events based on limited pedestrian count data, consisting of sensor name, latitude, longitude, MAC address, Datetime, and RSSI, collected by Wi-Fi sensors around the Sendagaya area during the Tokyo 2020 Olympics and Paralympics. The results confirmed that our proposed web-based dashboard contributes to human behavior understanding and decision-supporting policymaking for foot traffic management, which improves the design of spectator movement between transportation and venues in large-scale events. Furthermore, the dashboard is valuable from various perspectives, including preventing crowd crushing, redesigning areas to increase engagement in the shopping district, and improving traffic management.
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Exploring User Attitudes and Innovative System Design for Remote Lighting Control Systems in Thailand's Creative Industries
(2024-01-01)
Jeerasottikule, Thammanoon
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Cooharojananone, Nagul
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Atchariyachanvanich, Kanokwan
In this paper, the authors investigate the current state of the lighting design and control sector in Thailand's creative industry. The government aims to promote the creative industry as a key source of income, but there needs more skilled professionals in the industry. The authors have found that successful cases have used the Internet for remote controlling in the creative industry in other countries. Therefore, the paper will explore the potential of using technology to improve professional efficiency and assess the feasibility of implementing remote lighting control systems via the Internet with Thai lighting designers and lighting console operators. Data will be collected through qualitative in-depth interviews and quantitative surveys. The results have shown a promising acceptance rate of wireless devices for lighting control due to their mobility, flexibility, cost-effectiveness, and positive attitudes toward adopting Internet technology. The authors have also proposed a concept design for an internet-based control system tailored to Thai users, focusing on simplicity, ease of connection, and user-friendliness to accommodate those with limited network configuration knowledge. The proposed system aims to reduce professionals, save time, and increase convenience, leveraging Thailand's extensive wireless internet coverage. These systems could significantly benefit Thailand's creative industry by addressing the shortage of skilled professionals and improving efficiency.
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Comparative Foot Traffic Analysis During Normal Periods and Firework Events Using Wi-Fi Sensors
(2024-01-01)
Traganmaturapot, Peerada
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Sonehara, Noboru
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Hiruma, Nobuharu
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Cooharojananone, Nagul
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Kodate, Akihisa
Recent observations indicate an increase in the frequency of crowd crush incidents, highlighting the urgent need for effective mitigation strategies. Addressing this issue necessitates a comprehensive understanding of the factors influencing visitor decision-making to prevent adverse outcomes such as crowd crushes. This study introduces a Streamlit dashboard designed to visualize foot traffic data in the Sendagaya area and integrates contextual data from 10 key factors, including events and locations, points of interest (POIs), periods of time, online search activity, height of buildings, temperature and weather conditions, currency exchange rates, earthquakes, number of international flight arrivals, and hotel room rates. This integration facilitates comparative analysis of foot traffic patterns during standard periods versus periods coinciding with significant events, such as the 2023 Jingu Gaien Fireworks Festival, to assess their impact on congestion levels. Conducted exclusively in Sendagaya, the study utilized 8 Wi-Fi sensors throughout August 2023, encompassing 3 key stages: data collection, preprocessing, and dashboard development. The analysis revealed significant determinants-including events, points of interest, time periods, and online activity-that influence visitor foot traffic, while other factors exhibited no discernible impact. These findings have important implications for enhancing decision-making processes, preparedness measures, risk management strategies, and data-driven policymaking for sustainable tourism development.
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Improvement of a Machine Learning Model Using a Sentiment Analysis Algorithm to Detect Fake News: A Case Study of Health and Medical Articles on Thai Language Websites
(2024-01-01)
Atchariyachanvanich, Kanokwan
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Saengkhunthod, Chotipong
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Kerdnoonwong, Parischaya
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Chanlekha, Hutchatai
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Cooharojananone, Nagul
These days, the problem of fake news has grown to be a major social and personal concern. With the amount of information generated through social media, it is very crucial to be able to detect and properly take care of that fake information. Previous studies proposed a machine learning model to detect fake news in online Thai health and medical articles. Still, the problem of detecting fake news with similar content but different objectives exists, and the accuracy of the model needs improvement. Therefore, this study aims to solve these problems by adding 33 new features, including textual features, sentiment-based features, and lexicon features, i.e., herbs, fruits, and vegetables, to identify the objective of an article. We trained and tested the model’s prediction accuracy on a new dataset containing 582 reliable and 435 unreliable (fake news) articles from eight Thai websites. Our improved classification model using XGBoost with Lasso, the best feature selection method, achieved an accuracy of 97.76% without over-fitting, reflecting a 7.16% improvement over our earlier model.
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The study of the local community products (OTOP) website characteristics toward buyer decision using eye tracking
(2018-05-08)
Cooharojananone, Nagul
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Akasarakul, Krittika
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Wongkhamdi, Thipsuda
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Pruetthiwongwanich, Phonkornkrit
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Atchariyachanvanich, Kanokwan
Thai government has launched a policy to encourage local community people to sell their products (OTOP) through e-commerce. One Tambon One Product or OTOP was derived from the concept of One Village One Product (OVOP) in Japan. Currently, people sell their products through the e-commerce web portal such as Facebook rather than having their own OTOP official websites. Therefore, this study aims to study and develop the prototype of the official website. The results show that social influence, perceived ease of use, reliability of website and reliability of product have a positive relationship to the intention to buy a product. Researchers further investigated using eye tracking device to analyze the participants' purchasing behavior. The results show that participants spent time looking at images on the center of the homepage, the product menu bar at the bottom of the center of the homepage, and the components from the product page, respectively. Moreover, participants did not spend time looking or even clicking the social network icon such as Facebook. The results of this study and implications are further discussed.
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An exploratory study on users' intention to purchase items from mobile applications in Thailand
(2015-10-05)
Atchariyachanvanich, Kanokwan
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Nararatwong, Rungsiman
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Cooharojananone, Nagul
In this paper, we study the relationship between continuation of use (COU) factor and the users' intention to purchase items in smartphone applications. We also proposed the four factors: trust, diversity of in- app items, social norm and resource usage, specifically affect the COU of the smartphone applications. Structural equation model (SEM) analysis of a questionnaire based survey collected from 226 respondents in Thailand revealed that the continual use of an application is the key factor affecting the intention of users to make in-app purchases. This factor is affected by the diversity of in-app items, trust and social norms. However, the cost of using the application does not significantly affect the continuation of use. This study contributes to m-commerce as we proposed the analysis model, which specifically adapted to smartphone applications targeting consumers in Thailand. The result helps developers to focus on crucial factors which will enhance the possibility of users' continuous use and decision to purchase in-app items.

Atchariyachanvanich, Kanokwan

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