Fundamental speech recognition technologies for high-resourced languages are currently successful to build high-quality applications with the use of deep learning models. However, the problem of... Show More

Fundamental speech recognition technologies for high-resourced languages are currently successful to build high-quality applications with the use of deep learning models. However, the problem of “borrowing” these speech recognition technologies for under-resourced languages like Vietnamese still has challenges. This study reviews fundamental studies on speech recognition in general as well as speech recognition in Vietnamese, an under-resourced language in particular. Then, it specifies the urgent issues that need current research attention to build Vietnamese speech recognition applications in practice, especially the need to build an open large sentence-labeled speech corpus and open platform for related research, which mostly benefits small individuals/organizations who do not have enough resources.

This article presents a market basket analysis of a health food store in Thailand. The analysis identifies data attributes that frequently occur together in the dataset. Frequent occurrences of data... Show More

This article presents a market basket analysis of a health food store in Thailand. The analysis identifies data attributes that frequently occur together in the dataset. Frequent occurrences of data attributes representing customer purchasing behaviors are extracted as association rules using the frequent pattern growth algorithm. The generated associations are evaluated using standard measures based on occurrence counts and an additional financial measure. Marketing strategies in the form of cross-selling pairs of specific products are then designed based on the data attributes appearing in the significant associations. The cross-selling products are offered at discounted prices and promoted in marketing campaigns. A break-even analysis is performed to estimate the required number of additional sales volumes from each marketing campaign to compensate for the discounted prices. The presented use case demonstrates the effectiveness of extending the market basket analysis to include a financial measure that can lead to practical marketing campaigns.

This study focuses on the impact of the Covid-19 crisis on low-cost carriers (LCCs), particularly in comparison to full-service airlines (FSAs). With a target segment of price-sensitive leisure... Show More

This study focuses on the impact of the Covid-19 crisis on low-cost carriers (LCCs), particularly in comparison to full-service airlines (FSAs). With a target segment of price-sensitive leisure travelers, LCCs have been significantly affected. The objective of this paper is to analyze customer requirements for Thai LCCs during the Covid-19 recovery period and identify strategic improvement decisions accordingly. Nok Air, a well-established LCC in Thailand, is used as a case study. The proposed fuzzy QFD approach is employed to prioritize customer requirements, suggest strategic decisions, and enhance operational practices for the airline's recovery. Key findings include positioning as a premium LCC, offering premium services, increasing ancillary revenue, and improving aircraft utilization. This research is the first to apply fuzzy QFD to prioritize strategic decisions for managing LCCs during the Covid-19 recovery, aiming to enhance customer satisfaction and performance ratings set by the management team.

This article presents a non-linear multi-objective optimization model with four different objectives for manual rice seed harvesting, aiming to ensure members' fairness and mutual benefits for a... Show More

This article presents a non-linear multi-objective optimization model with four different objectives for manual rice seed harvesting, aiming to ensure members' fairness and mutual benefits for a group of rice field owners responsible for seed planting and a group of workers tasked with harvesting rice seeds. The harvesting plan primarily focuses on minimizing the average injury risk to workers and secondarily balances this risk among workers. Simultaneously, the model seeks to minimize and equitably allocate wage costs for rice field owners. Worker characteristics, including age, gender, and body mass index are considered to influence injury risk differentially. The optimal solution involves rotating workers to different rice stalk types in several fields, all within appropriate work and rest periods. This approach serves to prevent musculoskeletal disorders and fatigue among the workers while helping rice field owners reduce their costs. This collaborative planning has the potential to enhance sustainability within the farming community.

Identifying the primary factors of teaching quality remains a pivotal agenda for informed decision making, strategic planning, and resource allocation. This study builds upon ten key factors derived... Show More

Identifying the primary factors of teaching quality remains a pivotal agenda for informed decision making, strategic planning, and resource allocation. This study builds upon ten key factors derived from previous research and recognizes the inherent complexity within their relationships. Emphasizing the necessity for a structured model, this work employs an interpretive structural modelling (ISM) approach and Matrice d'impacts croisés multiplication appliquée á un classment (MICMAC) analysis for constructing a hierarchical model that delineates the interrelationships among the factors influencing teaching quality. The findings indicate the substantial impact of intrinsic factors, particularly teachers' individual and psychological characteristics, on other factors. Additionally, our analysis highlights the critical role of student composition in enhancing overall teaching quality. These insights significantly contribute to the literature by offering valuable guidance to decision makers for maintaining teaching quality within higher education institutions.