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Browsing by Author "Pannee Suanpang"

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Adaptive Multi-Agent Reinforcement Learning for Optimizing Dynamic Electric Vehicle Charging Networks in Thailand
(Multidisciplinary Digital Publishing Institute (MDPI), 2024) Pitchaya Jamjuntr; Chanchai Techawatcharapaikul; Pannee Suanpang; P. Suanpang; Department of Information Technology, Faculty of Science & Technology, Suan Dusit University, Bangkok, 10300, Thailand; email: pannee_sua@dusit.ac.th
The rapid growth of electric vehicles (EVs) necessitates efficient management of dynamic EV charging networks to optimize resource utilization and enhance service reliability. This paper explores the application of adaptive multi-agent reinforcement learning (MARL) to address the complexities of EV charging infrastructure in Thailand. By employing MARL, multiple autonomous agents learn to optimize charging strategies based on real-time data by adapting to fluctuating demand and varying electricity prices. Building upon previous research that applied MARL to static network configurations, this study extends the application to dynamic and real-world scenarios, integrating real-time data to refine agent learning processes and also evaluating the effectiveness of adaptive MARL in maximizing rewards and improving operational efficiency compared to traditional methods. Experimental results indicate that MARL-based strategies increased efficiency by 20% and reduced energy costs by 15% relative to conventional algorithms. Key findings demonstrate the potential of extending MARL in transforming EV charging network management, highlighting its benefits for stakeholders, including EV owners, operators, and utility providers. This research contributes insights into advancing electric mobility and energy management in Thailand through innovative AI-driven approaches. The implications of this study include significant improvements in the reliability and cost-effectiveness of EV charging networks, fostering greater adoption of electric vehicles and supporting sustainable energy initiatives. Future research directions include enhancing MARL adaptability and scalability as well as integrating predictive analytics for proactive network optimization and sustainability. These advancements promise to further refine the efficacy of EV charging networks, ensuring that they meet the growing demands of ThailandÕs evolving electric mobility landscape. © 2024 by the authors.
An Integration of Big Data and Blockchain for Strategic Analysis of Schools in Thailand
(Springer Science and Business Media Deutschland GmbH, 2023) Pattanaphong Pothipasa; Pannee Suanpang; P. Suanpang; Suan Dusit University, Bangkok, 10300, Thailand; email: pannee_sua@dusit.ac.th
In the era of digital transformation, the information used for strategic management in schools needs to be utilized for maximum benefit, efficiency, and effectiveness. Further, it requires storage in a secure, transparent, and verifiable manner worldwide. Recognizing the importance of this research contribution, the researchers designed a strategic data storage and analysis system to be used in larger secondary schools by integrating big data, artificial intelligence, and blockchain technologies. The integrated system provides a platform to collect and analyze annual operational plans for schools by facilitating compatibility with text analytics and machine learning technologies of large, high-security schools in Thailand. The results showed that the plans and projects for each department in the school were encrypted at the time of login, and the system generated a starting block. Through each step of the corresponding analysis process, new blocks are created continuously, meaning each plan and project will generate a chain of blocks encrypted with the hash function until the plan and project are approved. Furthermore, the system has a high level of security and user satisfaction (X-= 4.00, SD. 0.76)). Thus, the system could be implemented and adopted in schools to support educational management in the 21st century. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
An Intelligent Recommendation for Intelligently Accessible Charging Stations: Electronic Vehicle Charging to Support a Sustainable Smart Tourism City
(MDPI, 2023) Pannee Suanpang; Pitchaya Jamjuntr; Phuripoj Kaewyong; Chawalin Niamsorn; Kittisak Jermsittiparsert; P. Suanpang; Faculty of Science & Technology, Suan Dusit University, Bangkok, 10300, Thailand; email: pannee_sua@dusit.ac.th; K. Jermsittiparsert; Faculty of Education, University of City Island, Famagusta, 9945, Cyprus; email: kittisak.jermsittiparsert@adakent.edu.tr
The world is entering an era of awareness of the preservation of natural energy sustainability. Therefore, electric vehicles (EVs) have become a popular alternative in todayÕs transportation system as they have zero emissions, save energy, and reduce pollution. One of the most significant problems with EVs is an inadequate charging infrastructure and spatially and temporally uneven charging demands. As such, EV drivers in many large cities frequently struggle to find suitable charging locations. Furthermore, the recent emergence of deep reinforcement learning has shown great promise for improving the charging experience in a variety of ways over the long term. In this paper, a Spatio-Temporal Multi-Agent Reinforcement Learning (STMARL) (Master) framework is proposed for intelligently public-accessible charging stations, taking into account several long-term spatio-temporal parameters. When compared to a random selection recommendation system, the experimental results demonstrate that an STMARL (master) framework has a long-term goal of lowering the overall charging wait time (CWT), average charging price (CP), and charging failure rate (CFR) of EVs. © 2022 by the authors.
Analytical Validation and Integration of CIC-Bell-DNS-EXF-2021 Dataset on Security Information and Event Management
(Institute of Electrical and Electronics Engineers Inc., 2024) Gyana Ranjana Panigrahi; Prabira Kumar Sethy; Santi Kumari Behera; Manoj Gupta; Farhan A. Alenizi; Pannee Suanpang; Aziz Nanthaamornphong; P.K. Sethy; Sambalpur University, Department of Electronics, Sambalpur, Odisha, 768019, India; email: prabirsethy.05@gmail.com; A. Nanthaamornphong; Prince of Songkla University, College of Computing, Phuket, 83120, Thailand; email: aziz.n@phuket.psu.ac.th
Contemporary culture presents a substantial obstacle for cyber security experts in the shape of software vulnerabilities, which, if taken advantage of, can jeopardize the Confidentiality, Integrity, and Availability (CIA) of any system. Data-driven and modern threat intelligence tools can enhance cyber security, bolster resilience, and foster innovation across cloud, multi-cloud, and hybrid platforms. As a result, performance evaluation and accuracy verification have become essential for Security Information and Event Management (SIEM) to prevent cyber threats. The SIEM system offers threat intelligence, reporting, and security incident management through the collection and analysis of event logs and other data sources that are specific to events and their context. We propose a hybrid strategy to address threat intelligence, reporting, and security incident management consisting of two layers that utilize a predefined set of characteristics. Here, we use RStudio to assess how well a hybrid intrusion detection system (HIDS) handles the CIC-Bell-DNS-EXF-2021 dataset. Furthermore, we have incorporated our developed model into Multi-Criteria Decision Analysis Methods (MCDM) to enhance the methods' ability to identify complex DNS exfiltration attacks using machine learning algorithms: RF-AHP (RA), KNN-TOPSIS (KT), GBT-VIKOR (GV), and DT-Entropy-TOPSIS (DET). We consider several factors during the work, including accuracy, absolute error, weighted average recall, weighted average precision, kappa value, logistic loss, and root mean square deviation (RMSD). We use the Machine-Automated Model function to integrate and validate the models. According to the findings, GV has the highest level of accuracy, with a rate of 99.52%, while KT has the lowest level of authenticity, with a rate of 93.65%. Furthermore, these findings illustrate enhanced performance metrics for multiclass classification in comparison to previous approaches. © 2013 IEEE.
Autonomous Energy Management by Applying Deep Q-Learning to Enhance Sustainability in Smart Tourism Cities
(MDPI, 2022) Pannee Suanpang; Pitchaya Jamjuntr; Kittisak Jermsittiparsert; Phuripoj Kaewyong; P. Suanpang; Faculty of Science and Technology, Suan Dusit University, Bangkok, 10300, Thailand; email: pannee_sua@dusit.ac.th
Autonomous energy management is becoming a significant mechanism for attaining sustainability in energy management. This resulted in this research paper, which aimed to apply deep reinforcement learning algorithms for an autonomous energy management system of a microgrid. This paper proposed a novel microgrid model that consisted of a combustion set of a household load, renewable energy, an energy storage system, and a generator, which were connected to the main grid. The proposed autonomous energy management system was designed to cooperate with the various flexible sources and loads by defining the priority resources, loads, and electricity prices. The system was implemented by using deep reinforcement learning algorithms that worked effectively in order to control the power storage, solar panels, generator, and main grid. The system model could achieve the optimal performance with near-optimal policies. As a result, this method could save 13.19% in the cost compared to conducting manual control of energy management. In this study, there was a focus on applying Q-learning for the microgrid in the tourism industry in remote areas which can produce and store energy. Therefore, we proposed an autonomous energy management system for effective energy management. In future work, the system could be improved by applying deep learning to use energy price data to predict the future energy price, when the system could produce more energy than the demand and store it for selling at the most appropriate price; this would make the autonomous energy management system smarter and provide better benefits for the tourism industry. This proposed autonomous energy management could be applied to other industries, for example businesses or factories which need effective energy management to maintain microgrid stability and also save energy. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.
Blockchain of things (BoT) innovation for smart tourism
(John Wiley and Sons Ltd, 2024) Pannee Suanpang; Pattanaphong Pothipassa; Chompunuch Jittithavorn; C. Jittithavorn; College of Management, University of Phayao, Bangkok, 10330, Thailand; email: chompunuch.ji@up.ac.th
This study aims to (a) develop the innovation of BoT prototype; and (b) provide an effective platform to recommend tourists activity, implement and trials blockchain prototype for booking travel activities, whether booking travel programs, air ticket booking hotel stay visits to attractions and payment of goods and services, and evaluate tourist intention to use BoT. The developed architecture enables the integration of blockchain technology capabilities into IoT technology based on high performance of usability, stability, accuracy, and completeness. The BoT prototype is evaluated by 428 users to support smart tourism. This support is significant and the level includes the BoT functional benefit (security, process, and availability) that is positively related to the intention to adopt BoT, and user benefit (trust, usability) is also positive related with intention to adopt BoT. This study significantly contributes to revolutionizing the tourism industry by implementing BOT in smart tourism destinations to gain competitive advantage. © 2024 John Wiley & Sons Ltd.
Brain Cancer Tumor Detection by U-Net Deep Learning Algorithm from MRI Images
(Institute of Electrical and Electronics Engineers Inc., 2024) Utpal Chandra Das; Watit Benjapolakul; Manoj Gupta; Timporn Vitoonpong; Pannee Suanpang; Chanyanan Somthawinpongsai; Sujin Butdisuwan; Aziz Nanthaamornphong; U.C. Das; Center of Excellence in Artificial Intelligence, Machine Learning and Smart Grid Technology, Department of Electrical Engineering, Faculty of Engineering, Chulalongkorn University, Bangkok, 10330, Thailand; email: dasutpal999@gmail.com
This research looks at the genomic subtypes of low-grade glioma tumors and their shape characteristics by deep learning magnetic resonance image (MRI) segmentation. We analyzed preoperative imaging and genetic data from 110 patients with low-grade glioma from the Cancer Genome Atlas. Three shape features were recovered to quantify the two- and three-dimensional aspects of the malignancies. Based on gene expression, DNA copy number, IDH mutation, 1p/19q co-deletion, DNA methylation, and microRNA, previously identified clusters were found in genomic data. We used the exact trait test to investigate the connection between chromosomal clusters and imaging traits. Our findings show a significant correlation between the margin fluctuation-bounding ellipsoid volume ratio and the RNA Seq clusters. Furthermore, a correlation was discovered between RNA-seq clusters and angular standard deviation. The U-net deep learning algorithm demonstrated a test accuracy of 94\% and a mean Dice coefficient of 90\%. These findings suggest that tumor shape characteristics derived from MRI can be projected through genomic subtypes in lower-grade gliomas. © 2024 IEEE.
Can Optimized Genetic Algorithms Improve the Effectiveness of Homestay Recommendation Systems in Smart Villages? A Case of Thailand
(John Wiley and Sons Ltd, 2024) Pannee Suanpang; Pitchaya Jamjuntr; Arunee Lertkornkitja; Chompunuch Jittithavorn; C. Jittithavorn; College of Management, University of Phayao, Bangkok, Thailand; email: chompunuchj@gmail.com
This paper introduces a novel approach to optimize genetic algorithms (GAs) for homestay recommendation systems, specifically designed for smart village tourism destinations. Researchers developed an advanced GA focused on maximizing user satisfaction, the main quality metric. The algorithm was tailored to address the dynamic nature of homestay offerings and the varied preferences of travelers, using users' reviews, listing attributes, and historical booking data. The GA framework included a custom encoding scheme, fitness function, and parameters. Validation occurred through a case study in a smart village, with the algorithm's effectiveness tested via user surveys and ratings. Results showed that GA-driven recommendations surpassed traditional methods, enhancing user satisfaction, trust, and booking rates while benefiting hosts with positive reviews. The optimized GA improved recommendation accuracy and efficiency, boosting economic benefits for local communities and contributing significantly to recommendation system research. © 2024 John Wiley & Sons Ltd.
Development of automatic CNC machine with versatile applications in art, design, and engineering
(Elsevier B.V., 2024) Utpal Chandra Das; Nagoor Basha Shaik; Pannee Suanpang; Rajib Chandra Nath; Kedar Mallik Mantrala; Watit Benjapolakul; Manoj Gupta; Chanyanan Somthawinpongsai; Aziz Nanthaamornphong; U.C. Das; Center of Excellence in Artificial Intelligence, Machine Learning and Smart Grid Technology, Faculty of Engineering, Chulalongkorn University, Bangkok, Pathum Wan District, 10330, Thailand; email: utpal597@gmail.com; A. Nanthaamornphong; College of Computing, Prince of Songkla University, Phuket, Phuket Campus, Thailand; email: aziz.n@phuket.psu.ac.th
The area of computer numerical control (CNC) machines has grown fast, and their use has risen significantly in recent years. This article presents the design and development of a CNC writing machine that uses an Arduino, a motor driver, a stepper motor, and a servo motor. The machine is meant to create 2D designs and write in numerous input languages using 3-axis simultaneous interpolated operations. The suggested machine is low-cost, simple to build, and can be operated with merely G codes. The performance of the CNC writing machine was assessed by testing it on a range of solid surfaces, including paper, cardboard, and wood. The results reveal that the machine can generate high-quality text and images with great accuracy and consistency. The proposed machine's ability to write in several input languages makes it appropriate for various applications, including art, design, and engineering. © 2024 The Author(s)
Development of Healthy Food Recipes from Local Wisdom with An Environmentally Conservative Process to Promote Gastronomy Tourism
(Asian Administration & Management Review, 2023-01-01) Sureeporn Thanyakit; Anong Jainan; Thitiworada Yaisumlee; Pannee Suanpang
This research aimed to study food wisdom and develop healthy food recipes from local wisdom using the environmentally conservative process to promote gastronomy tourism in Ko Koet Community, Ayutthaya Province. The mixed research method was applied using the participatory action and experimental research approaches. Two healthy food recipes to promote gastronomy tourism in Ko Koet Community, Ayutthaya Province were Tom Yum Kung Maenam and Kang Liang Kung, which were the identity of the region. Both recipes were healthy food initiated from local wisdom with the environmentally conservative process accepted by consumers. The level of acceptance was moderate to high. Analysis results indicated that nutritive value of Tom Yum Kung Maenam included 6.81% of protein, 2.46% of carbohydrate, 0.80% of fat, and 0.24% of fiber. Meanwhile, the nutritional value of Kang Liang Kung contained 10.54% of protein, 5.49% of carbohydrates, 1.34% of fat, and 1.54% of fiber. Both menus were healthy food with a nutritional balance created based on local wisdom using the environmentally conservative process and appropriate for promoting the gastronomy tourism of Ko Koet Community, Ayutthaya Province.
E-learning in Thailand: An analysis and case study
(International Institute of Informatics and Systemics, IIIS, 2005) Pannee Suanpang; Jarinee Santijanyaporn
This paper presents a discussion of E-Leaming in the context of Thailand using as an example a study carried out in a course in Business Statistics at Suan Dusit Rajabhat University (SDU), Thailand. The online course was a pioneering research project at SDU for studying the efficiency and effectiveness of the online learning system. The research conducted over 16 weeks compared online learning with traditional teaching. Aspects of students' learning outcomes have been analyzed, including quantitative features such as their grades and course evaluations, and this analysis is supported by qualitative features such as results of open-ended questionnaires, interviews and diaries. Results of the analysis show that students' outcomes were more favorable in the online groups than in the traditional groups. The large amount of rich qualitative information obtained highlights a range of reasons for this. The results of this study will be beneficial and useful for further research to develop effective and efficient online learning systems in Thailand, and in other countries with similar educational backgrounds to this country.
Effect of Malva Nut Gel as Fat Replacer on Sponge Cake
(Journal of Food Health and Bioenvironmental Science. 14(3): 28-34., 2023-09-26) Teeranuch Chysirichote; Pannee Suanpang; Piramal Buntham
Malva nut originated in Southeast Asia and is cultivated in the Eastern part of Thailand. Gel prepared from its mature seed coat containing high water-soluble dietary fiber was used as a fat replacer in this research. Part of the butter (25-50%) and milk (10-20%) for the sponge cake were replaced with malva nut gel. Physical, chemical and organoleptic analyses were conducted to evaluate the effects of malva nut gel on the properties of the sponge cake. For specific volume, all samples were not significantly different (p>0.05); moreover, the roughness, lightness (L*) and yellowness (b*) of the crust increased while the values of the crumb decreased. In the final product obtained from this research 10% of the milk and 25% of the butter were replaced with malva nut gel, which was the highest replacement content of butter and milk with a high customer acceptance. Its chemical composition consisted of 22.72% moisture, 6.28% protein, 16.20% fat, 0.90% ash, 53.90% carbohydrate and 3.76% dietary fiber. Cholesterol and total calories were 114.22 mg and 386.52 Kcal, respectively. The content of fat, carbohydrate, cholesterol, and total calories of this product were lower than the basic formula (p ≤ 0.05). In addition, 120 consumers accepted this cake in terms of appearance, color, aroma, taste, softness and rated it overall, as ‘very much liking’ and the aftertaste as ‘moderate liking’.
Effect of Malva Nut Gel as Fat Replacer on Sponge Cake
(Research and Development Institute Suan Dusit University, 2021) Teeranuch Chysirichote; Pannee Suanpang; Piramal Buntham; T. Chysirichote; School of Culinary Arts, Suan Dusit University, Bangkok, 10300, Thailand; email: teeranuch_chy@dusit.ac.th
Malva nut originated in Southeast Asia and is cultivated in the Eastern part of Thailand. Gel prepared from its mature seed coat containing high water-soluble dietary fiber was used as a fat replacer in this research. Part of the butter (25-50%) and milk (10-20%) for the sponge cake were replaced with malva nut gel. Physical, chemical and organoleptic analyses were conducted to evaluate the effects of malva nut gel on the properties of the sponge cake. For specific volume, all samples were not significantly different (p>0.05); moreover, the roughness, lightness (L*) and yellowness (b*) of the crust increased while the values of the crumb decreased. In the final product obtained from this research 10% of the milk and 25% of the butter were replaced with malva nut gel, which was the highest replacement content of butter and milk with a high customer acceptance. Its chemical composition consisted of 22.72% moisture, 6.28% protein, 16.20% fat, 0.90% ash, 53.90% carbohydrate and 3.76% dietary fiber. Cholesterol and total calories were 114.22 mg and 386.52 Kcal, respectively. The content of fat, carbohydrate, cholesterol, and total calories of this product were lower than the basic formula (p ² 0.05). In addition, 120 consumers accepted this cake in terms of appearance, color, aroma, taste, softness and rated it overall, as Ôvery much likingÕ and the aftertaste as Ômoderate likingÕ. © 2021, Research and Development Institute Suan Dusit University. All rights reserved.
Effect of Malva Nut Gel as Fat Replacer on Sponge Cake
(2023-09-26) Teeranuch Chysirichote; Pannee Suanpang; Piramal Buntham
Malva nut originated in Southeast Asia and is cultivated in the Eastern part of Thailand. Gel prepared from its mature seed coat containing high water-soluble dietary fiber was used as a fat replacer in this research. Part of the butter (25-50%) and milk (10-20%) for the sponge cake were replaced with malva nut gel. Physical, chemical and organoleptic analyses were conducted to evaluate the effects of malva nut gel on the properties of the sponge cake. For specific volume, all samples were not significantly different (p>0.05); moreover, the roughness, lightness (L*) and yellowness (b*) of the crust increased while the values of the crumb decreased. In the final product obtained from this research 10% of the milk and 25% of the butter were replaced with malva nut gel, which was the highest replacement content of butter and milk with a high customer acceptance. Its chemical composition consisted of 22.72% moisture, 6.28% protein, 16.20% fat, 0.90% ash, 53.90% carbohydrate and 3.76% dietary fiber. Cholesterol and total calories were 114.22 mg and 386.52 Kcal, respectively. The content of fat, carbohydrate, cholesterol, and total calories of this product were lower than the basic formula (p ≤ 0.05). In addition, 120 consumers accepted this cake in terms of appearance, color, aroma, taste, softness and rated it overall, as ‘very much liking’ and the aftertaste as ‘moderate liking’.
Enhanced Decision Making in Smart Grid Management by Optimizing Adaptive Multi-Agent Reinforcement Learning with Vehicle-to-Grid Systems
(Regional Association for Security and crisis management, 2024) Pannee Suanpang; Pitchaya Jamjuntr; P. Suanpang; Department of Information Technology, Faculty of Science & Technology, Suan Dusit University, Bangkok, Thailand; email: pannee_sua@dusit.ac.th
This research proposes a decision-making framework in which the Adaptive Multi-Agent Reinforcement Learning (MARL) model and the concept of Vehicle-to-Grid (V2G) interactivity are employed to improve the effective management of smart grids. The research hypothesis introduces innovations for improving the efficiency and security of power systems in the global south, primarily by controlling the net energy transmission between the defined electric vehicles (EVs) and the grid. Other issues that require attention to ensure the proper functioning of smart grids include demand response, load management, and energy storage optimization. In this instance, these gaps are filled by the systemÕs proposed framework. With the help of MARL, the system dynamics' autonomous learning aspects allow the system to adapt to the capacity of renewable energy sources and electricity demand, which is also time-dependent. Because of the MARL, the autonomous coordination of decision-making has resulted in very positive changes in the system's effectiveness. In particular, this framework permitted an increase of 13.6% in the total energy exchange between EVs and the grid, and the grid stability index improved from 0.84 to 0.87 compared to what would have been achieved with the conventional methods. Enhanced energy management and pricing rehabs added another 22% to net savings. Further, it is stated that deploying MARL-based V2G systems in developing areas has many benefits, including more robust grid reliability and energy security and better integration of renewable energy resources. Such changes aid in reducing fossil fuel use and greenhouse gas emissions. © 2024 Regional Association for Security and crisis management. All rights reserved.
Exploring the causal relationship model of foreign tourists' behavior and soft power creation in gastronomy tourism: A case study in Thailand
(IGI Global, 2023) Pannee Suanpang; Sippanan Nuanla-ong; Sureeporn Thanyaki
This study aimed to explore the causal relationship model between foreign tourists' behavior and the creation of soft power in gastronomy tourism with a focus on a case study of Ayutthaya World Heritage, Thailand. The research investigates the factors that would influence foreign tourists' behavior and their impact on soft power creation in the context of gastronomy tourism. Using quantitative data collected through surveys of 400 samples and analysis relationship between international tourists' behavior and the creation of soft power, the findings revealed that international tourists' behavior was influenced by various factors, such as travel motivation, cultural interest, Thai cuisine, and destination image. These factors significantly affected the tourists' decision-making process. This research provides insights into the causal relationship model between foreign tourists' behavior and the creation of soft power in gastronomy tourism. © 2023, IGI Global. All rights reserved.
Extensible Metaverse Implication for a Smart Tourism City
(MDPI, 2022) Pannee Suanpang; Chawalin Niamsorn; Pattanaphong Pothipassa; Thinnagorn Chunhapataragul; Titiya Netwong; Kittisak Jermsittiparsert; P. Suanpang; Faculty of Science & Technology, Suan Dusit University, Bangkok, 10300, Thailand; email: pannee_sua@dusit.ac.th
The metaverse is an innovation that has created the recent phenomenon of new tourism experiences from a virtual reality of a smart tourism destination. However, the existing metaverse platform demonstrated that the technology is still difficult to develop, as the service provider did not disclose the internal mechanisms to developers, and it was a closed system, which could not use or share the userÕs data across platforms. The aim of this paper was to design and develop an open metaverse platform called the Òextensible metaverseÓ, which would allow new developers to independently develop the capabilities of the metaverse system. The acquisition of this new technology was conducted through requirements analysis, then the analysis and design of the new system architecture, followed by the implementation, and the evaluation of the system by the users. The results found that the extended metaverse was divided into three tiers that created labels, characters, and virtual objects. Furthermore, the linking tier combined the 3D elements, and the deployment tier compiled the results of the link to use all three parts by using the Blender program, Godot Engine, and PHP + WebGL as their respective key mechanisms. This system was tested in Suphan Buri province, Thailand, which was evaluated by 428 users. The results of the metaverse satisfaction, created tourism experience, and overall satisfaction of the variation of the satisfaction of using the metaverse were 86.0%, 79.7%, and 92.9%, respectively. The relative Chi-square (_2/df) of 1.253 indicated that the model was suitable. The comparative fit index (CFI) was 0.984, the goodness-of-fit index (GFI) was 0.998, and the model based on the research hypothesis was consistent with the empirical data. The root mean square error of approximation (RMSEA) was 0.024. In conclusion, the extended metaverse is more flexible than other platforms and also creates the userÕs satisfaction and tourism experience in the smart destination to support sustainable tourism. © 2022 by the authors.
Gastronomy Tourism in Ayutthaya World Heritage from TouristÕs Perspective
(Research and Development Institute Suan Dusit University, 2022) Pannee Suanpang; Jiranuch Sopa; Apiradee Arnmanee; Jatupon Dongjit; P. Suanpang; Faculty of Science & Technology, Suan Dusit University, 10700, Thailand; email: pannee_sua@dusit.ac.th
Gastronomy tourism is typically characterized as the pursuit of a unique experience of eating and drinking in the originality of a dish and is indigenous to a place, region, or country, covers the basic themes of local dishes. The gastronomy tourism in Ayutthaya, Thailand has high potential especially in the many activities that link to World Heritage site, therefore it requires a study of the tourist Ôperception and motivation for travel. The aim of this study consist of the following: (1) study touristÕs motivation for gastronomy tourism in Ayutthaya World Heritage and (2) Conduct a factor analysis of the touristÕs motivation for gastronomy tourism in Ayutthaya World Heritage. A quantitative research approach was conducted by collecting data from 385 questionnaires. The results found that (1) touristÕs motivation to visit Ayutthaya was to make merit and pay respect to the Buddha followed by gastronomy tourism and their search for food information from food review pages. The gastronomy activities that tourists are interested in consisted of eating by the rivers site and visiting historical sites. Factors affecting the decision to travel to Ayutthaya for gastronomy tourism is a restaurant with good atmosphere and average food cost per meal of 1,000-1,500 baht. (2) The factor affecting motivation in gastronomy tourism in Ayutthaya World Heritage consisted of 1 latent variable and 7 observable variables. The relationship was in the range [0.223, 0.490] and the CFA affecting gastronomy tourism motivation showed that the gastronomy tourism product aspect was the factor affecting motivation has a maximum component weight followed by physical appearance and price, 77.30%, 75.40% and 61.70%, respectively. © 2022, Research and Development Institute Suan Dusit University. All rights reserved.
Gastronomy Tourism in Ayutthaya World Heritage from Tourist’s Perspective
(Graphicsite, 2023-05-08) Pannee Suanpang; Jiranuch Sopa; Apiradee Arnmanee; Jatupon Dongjit
Gastronomy tourism is typically characterized as the pursuit of a unique experience of eating and drinking in the originality of a dish and is indigenous to a place, region, or country, covers the basic themes of local dishes. The gastronomy tourism in Ayutthaya, Thailand has high potential especially in the many activities that link to World Heritage site, therefore it requires a study of the tourist ‘perception and motivation for travel. The aim of this study consist of the following: (1) study tourist’s motivation for gastronomy tourism in Ayutthaya World Heritage and (2) Conduct a factor analysis of the tourist’s motivation for gastronomy tourism in Ayutthaya World Heritage. A quantitative research approach was conducted by collecting data from 385 questionnaires. The results found that (1) tourist’s motivation to visit Ayutthaya was to make merit and pay respect to the Buddha followed by gastronomy tourism and their search for food information from food review pages. The gastronomy activities that tourists are interested in consisted of eating by the rivers site and visiting historical sites. Factors affecting the decision to travel to Ayutthaya for gastronomy tourism is a restaurant with good atmosphere and average food cost per meal of 1,000-1,500 baht. (2) The factor affecting motivation in gastronomy tourism in Ayutthaya World Heritage consisted of 1 latent variable and 7 observable variables. The relationship was in the range [0.223, 0.490] and the CFA affecting gastronomy tourism motivation showed that the gastronomy tourism product aspect was the factor affecting motivation has a maximum component weight followed by physical appearance and price, 77.30%, 75.40% and 61.70%, respectively.
GIS BASE SUPPORTING MUAY THAI IN CREATIVE TOURISM ON ANDAMAN COAST THAILAND
(Allied Business Academies, 2021) Nattada Srimuk; Pannee Suanpang; Titiya Netwong
Mauy Thai is becoming the new phenomenal in new normal tourism due to new face of traveling to Thailand that emphasizes the healthy and wellness to strong and creative tourism experience of travel in the local. The aims of this paper were (1) study the guideline for developing Geographic Information System (GIS) of Muay Thai gym creative tourism and (2) study the using of GIS of Muay Thai gym creative tourism. The research design using mixed methods for research methodology, it used both quantitative and qualitative approaches. The result found that: the behavior of tourists according to the study (1) Guideline for developing GIS of Muay Thai gym creative tourism include international tourist context and user's requirement 1) accommodation, 2) travelling modes, 3) form of service provided, 4) the expenses 5) the attitude of tourists, 6) social media such as website, Instagram and 7) understanding about Muay Thai and expectation to win the championship of Muay Thai (2) The QGIS software used to develop geographic information for tourism including a bels and graphic, symbols categorization, identification, and visualization. GIS provide information about roads, location, operator, number of service personnel, business products, teaching course, the creative work of Muay Thai gym. The overview of opinions to using GIS of Muay Thai gym creative tourism is at a high level. Result of ensuring the suitability of geographic information of Muay Thai gym creative tourism from experts, the overall view is at a high level ((Formula presented) =3.44, SD. =0.50). © 2021. All Rights Reserved.

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