Atchariyachanvanich, Kanokwan

Loading...
Thumbnail Image
Preferred name
Atchariyachanvanich, Kanokwan
Main Affiliation
Email
kanokwan.at@kmitl.ac.th

Settings

Now showing 1 - 4 of 4
  • Loading...
    Thumbnail Image
    Some of the metrics are blocked by your 
    consent settings
    Item type:Publication,
    Bridge Sub Structure Defect Inspection Assistance by using Deep Learning
    (2019-10-01)
    Kruachottikul, Pravee
    ;
    Cooharojananone, Nagul
    ;
    Phanomchoeng, Gridsada
    ;
    Chavarnakul, Thira
    ;
    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.
  • Loading...
    Thumbnail Image
    Some of the metrics are blocked by your 
    consent settings
    Item type:Publication,
    Innovative Mobile Application for Measuring Big Data Maturity: Case of SMEs in Thailand
    (2020-01-01)
    Limpeeticharoenchot, Santisook
    ;
    Cooharojananone, Nagul
    ;
    Chavarnakul, Thira
    ;
    Tuaycharoen, Nuengwong
    ;
    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.
  • Loading...
    Thumbnail Image
    Some of the metrics are blocked by your 
    consent settings
    Item type:Publication,
    Adaptive big data maturity model using latent class analysis for small and medium businesses in Thailand
    (2022-11-15)
    Limpeeticharoenchot, Santisook
    ;
    Cooharojananone, Nagul
    ;
    Chavarnakul, Thira
    ;
    Charoenruk, Nuttirudee
    ;
    Atchariyachanvanich, Kanokwan
    Big data analytics (BDA) is widely adopted in large enterprises. However, very few small- and medium-sized enterprises (SMEs) have adopted BDA because they lack the relevant knowledge, which makes BDA development expensive and unsuitable. A big data maturity model (BDMM) is a tool for assessing the stage for using big data in a company, and it acts as a guide for improvement. However, most BDMMs are designed for large enterprises using rule-based scoring, which is static over time. Developing a suitable BDMM for SMEs is a challenging task for professionals in terms of acquiring small-scale expertise owing to the lack of case studies for verifying the maturity level. This study proposes a new BDMM for Thai SMEs and a new methodology for developing a dynamic model using latent class analysis (LCA), which explains the behaviour of each latent class and provides non-rule-based scoring. We define four types of capabilities in SMEs: organizational and attitude factors, information technology, technology, and people readiness. Data are collected from 135 SMEs in Thailand. We introduce a methodology for developing multiple building stages of the BDMM. Further, we experiment with several clusters suitable for SMEs using statistic-based and data visualization approaches. The proposed BDMM is validated via a secondary evaluation of 11 firms, nine months after the initial evaluation. Further, we introduce a web-based application for respondents to obtain their firm's assessment results. The visualization-based result helps the respondents compare their business with other companies at the same or higher maturity level. In summary, SMEs can use the proposed BDMM to plan for continuous self-improvement and thus optimize their business using BDA to maximize its value to the organization.
  • Loading...
    Thumbnail Image
    Some of the metrics are blocked by your 
    consent settings
    Item type:Publication,
    Classifying attitudes of thai business organizations toward the perceived benefit of customer predictive analytics
    (2018-07-02)
    Limpeeticharoenchot, Santisook
    ;
    Cooharojananone, Nagul
    ;
    Chavarnakul, Thira
    ;
    Atchariyachanvanich, Kanokwan
    In this paper, we applied K-means++, Agglomerative and Decision Tree techniques to classify characteristics of Thai business organizations toward the perceived benefit of predictive analytics. We believe that different characteristics of business organizations require different solution approaches to overcome challenges and gain business value. In the exploratory survey, we collected 84 respondents, who are data analysts, business analysts, business owners, management, and IT managers. The respondents' size of organization varies from small firms to large firms. The result from three techniques of classification shows that Thai business organizations perceiving the benefit of predictive analytics could be divided into two groups. We further confirmed that with the analysis of variance technique to identify the difference of means in each parameter. We also found that the most important descriptive profile is led by customer-related factors such as a change in the percentage of customers, the number of direct customers, and number of sales and marketing staff. These are followed by technology factors, which are the number of external and internal data sources and number of analytics and technologies currently used for data analytics.