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Author: search.filters.author.Narabin, AkanHas files: No

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    AHP approach for employee recruitment with COVID-19 situation in Thailand
    (2026-01-01)
    Narabin, Akan
    ;
    Boonjing, Veera
    ;
    Wuttanachamsri, Kanognudge
    With the COVID-19 situation being unlike the usual, selecting an appropriate person for a job position requires the criteria and weight of each criterion be determined and adjusted to suit the crisis. The criteria chosen in this work are emphasised on selecting applicants who, while studying, have been in the midst of the COVID-19 situation. In this research, we employ analytic hierarchy process to assist the committee to have an agreement. In this study, each person in the committee can have his/her own pairwise comparison matrix of the criteria with a three-level hierarchical model. Some ambiguity may occur when initiating a hierarchical model; therefore, in this work, the criteria used in the three-level model are properly adjusted to create a four-level hierarchical model. The comparison inspected, provide some guidance in both theoretical manner and applications.
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    Leveraging Race Prediction Algorithms to Enhance Team Composition in Big Data Science Teams
    (2024-01-01)
    Chumthong, Thanathip
    ;
    Jitkajornwanich, Kulsawasd
    ;
    Kraishan, Obada
    ;
    Kee, Kerk F.
    ;
    Narabin, Akan
    As big data science projects scale in complexity, optimizing team composition has become vital for improving creativity, productivity, and project success. We explore the possibility of incorporating race prediction algorithms for enhancing racial diversity in team composition in big data science projects. This paper evaluates five race prediction algorithms - wru, ethnicolr, ethnicolr2, pyethnicity, and rethnicity - and then discuss their potential in supporting racially diverse team assembly in big data projects. Utilizing three datasets, we assess algorithm performance and applicability, emphasizing their role in building balanced teams that enhance agility, inclusivity, and bias mitigation. We present an actionable methodology for integrating demographic insights into team management. In addition, we propose ethical safeguards to ensure responsible race prediction use, recommending data privacy measures, aggregate-only data handling, and transparency in communication. We argue that when used within ethical constraints, race prediction can support robust team processes, reduce reliance on less diverse teams, and ultimately facilitate more creative and equitable big data project outcomes.
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    Teaching assistant selection in Thailand by using an extended VIKOR based on piecewise linear approximation of fuzzy numbers
    (2023-01-01)
    Narabin, Akan
    ;
    Samutrak, Phairoj
    Because of the COVID-19 situation, selection for a teaching assistant position to get a TA scholarship in a university in Thailand needs to be performed online by the formed committee. Due to the online process and the limited number of scholarships offered by the university, beyond the face-to-face interview, multiple-criteria decision analysis can help to select a proper student. In this study, we use the extended VIKOR method with fuzzy numbers to help committees to select the students from the applicants. The criteria and the weights of the criteria are provided with the help of committees. Both trapezoidal and triangular linguistic variables are used to find the solution and to observe the range of the possible result. The different weights supporting the strategy of maximum group utility are varied to detect the potential alternatives. The ranking results are also compared with the one obtained from the TODIM approach to illustrate the appropriate alternative.
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    A Problem-Based Learning (PBL) and Teaching Model using a Cloud-Based Constructivist Learning Environment to Enhance Thai Undergraduate Creative Thinking and Digital Media Skills
    (2021-01-01)
    Srikan, Parawee
    ;
    Pimdee, Paitoon
    ;
    Leekitchwatana, Punnee
    ;
    Narabin, Akan
    The objective of this research was to develop a Problem-Based Learning (PBL) Model which used a cloud-based constructivist learning environment to enhance Thai undergraduate creative thinking and digital media skills. Initially using a mixed-methods approach, a five-step model was conceptualized. Thereafter, a panel of five academic experts gave input into the model’s design from which the model was expanded to include six related learning environments. The instrument used in the research was a problem-based assessment form. Data collection was carried out utilizing group chats and analyzed using descriptive statistics including the mean and standard deviation. The results of the study revealed that the initial model contained five steps including (1) problem identification, (2) problem analysis, (3) research, (4) presentations, and (5) summary and evaluation, which is integrated into the model’s additional six learning environment elements. These six learning environments were (1) problem-based, (2) resources, (3) cognitive tools, (4) collaboration, (5) scaffolding, and finally, (6) coaching. When applying the proposed model and related environments, there was a consensus from the experts that the model had excellent suitability and can be used as a model for teaching and learning at the bachelor’s degree level.
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    Clustering Mutual Funds by Net Asset Value Change Ratios
    (2020-12-29)
    Boongasame, Laor
    ;
    Narabin, Akan
    The traditional factors of the clustering mutual fund (such as Net Asset Value (NAV)) are not always an efficient measure in both maximizing returns and minimizing portfolio risk. This research presents a novel measure, Net Asset Value Change Ratios for some of time durations N (NAVCR-N), to assist the mutual fund clustering. We proved the usage of the NAVCR-N as mutual fund LTF similarity measures and LTF are then selected from differing clusters to create a diversified mutual fund portfolio. Approximately a hundred mutual fund data different times from the set for the fiscal year 2010-2018 are applied in the experiment to evaluate the effectiveness of the random approach and our diverse approaches.