Phayao is one of the northern provinces in Thailand. It has variety and unique geographical and cultural tourist attractions with few travellers' knowledge of this fact. In 2011, the province came up with such vision intended to develop tourism under the programme "Safety Agriculture and Sustainable Tourism". The local government created a great deal of tourism plans in order to support tourism activities. However, the results were not successful because those tourism plans lacked tourism marketing understanding. The purpose of this research is to develop the better understanding of tourism marketing in term of sustainable tourism development in Phayao province. Mixed methods of both qualitative and quantitative methods were employed as research methodology. Questionnaire surveys were conducted with 400 samples, which were Thai tourists. Furthermore, focus group techniques were employed with 10 experts from related fields, namely tourism, marketing, and policy making fields (from both government and private sectors). The findings of this research are focusing on three major issues: 1) Thai tourist behaviour, 2) tourism marketing mixture in Phayao province, 3) developing of tourism marketing in Phayao province. According to the Thai tourist behaviour, the results revealed the information about the tourists in the following aspects: 1) length of stay (1 day), 2) travelling cost (1700 baht), 3) activities (health tourism and soft adventure), 4) accommodation (hotel), 5) tourism resource (nature and culture). In addition tourism marketing in Phayao province should be developed as follows: 1) planning, 2) product and service development, 3) tourism network, 4) human resource development, 5) price, 6) friend-to-friend, 7) government cooperation.

Background: The world economy was broken by the COVID-19 pandemic, which affected the coffee industry. The COVID-19 pandemic's financial effects might influence equity markets and personal lives. This includes financial commodities like coffee, which the pandemic is predicted to damage. Coffee tourism is an emerging new kind of tourism in Thailand, formed in response to growing demand from visitors with a particular affinity for the beverage. Coffee tourism may contribute considerably to the expansion of Thai tourism if given the proper guidance and assistance. Methods: As part of a coffee tourism experience focusing on first-hand activities and information, tourists can visit neighbouring sites while on a coffee plantation. This research uses a stochastic neuro-fuzzy decision tree (SNF-DT) to analyse coffee tourism in Thailand. The research surveys 400 international and Thai coffee tourists. According to studies, Thai visitors mostly visit coffee tourism locations in Thailand for enjoyment. They also wanted to visit coffee fields in order to get personal knowledge of coffee production and marketing. Based on the comments of Thai visitors, coffee tourism in northern Thailand looks to be highly and effectively handled. Due to the same factor, responses from foreign coffee tourists indicated that many of their journeys to coffee tourism destinations were made entirely for enjoyment rather than the business. They also wanted to meet local tour guides and acquire handmade and locally produced things to understand more about coffee tourism.  Result: According to study results, coffee tourism management in northern Thailand looks well-received by international tourists. We also compare the suggested model to the traditional one to demonstrate its efficacy. The performance metrics are prediction rate, prediction error, and accuracy. The estimated results for our proposed technique are prediction rate (95%), prediction error (97%), and accuracy (94%).

Human trafficking came to the public concern in the 1990s, caused by the movement of people and the operation of organized crime. As the problem grew more complex, collaboration was needed to enhance effective mechanisms to combat human trafficking. However, ASEAN Countries, after more than two decades in fighting against human trafficking issue, efforts in prevention and protection found limited results due to the fluctuating number of trafficked victims in the region. This paper aims to examine the mechanisms and the development of instruments to enhance the efforts in tackling human trafficking problems of ASEAN by employing the descriptive approach of policy analysis, focusing specifically on regional practices and policy development. Data were mainly collected English-based documents; it can be concluded that collaboration among ASEAN countries has been initiated various types of regional instruments. However, the success of practices following the existing regional framework remains murky.

Background: The world economy is affected by the coronavirus disease (COVID-19) pandemic, which affects the coffee industry. Coffee tourism is an emerging new type of tourism in Thailand that is formed in response to the growing demand from visitors with a particular affinity for coffee. Coffee tourism may contribute considerably to the expansion of Thai tourism given proper guidance and assistance. Methods: This study used a stochastic neuro-fuzzy decision tree (SNF-DT) to analyze coffee tourism in Thailand. This research surveyed 400 international and Thai coffee tourists. According to this study, Thai visitors mostly visit coffee tourism locations in Thailand for enjoyment. They also wanted to visit coffee fields to obtain personal knowledge about coffee production and marketing. Responses from foreign coffee tourists indicated that many of their journeys to coffee tourism destinations were entirely for enjoyment rather than business. They also wanted to meet local tour guides and acquire handmade and locally produced things to better understand coffee tourism. Results: According to the study results, coffee tourism management in northern Thailand appears to be well received by international tourists. We also compared the suggested model with the traditional model to demonstrate its efficacy. The performance metrics are the prediction rate, prediction error, and accuracy. The estimated results for our proposed technique are prediction rate (95%), prediction error (97%), and accuracy (94%). Recommendations: Major global businesses such as tourism have been harmed by COVID-19’s unprecedented effects. This study attempts to determine the role of coffee tourism in livelihoods based on real-time data using a machine-learning approach. More research is needed to analyse the factors of the coffee tourism experience using different machine learning approaches.