Hong-Danh Thai

Agricultural mechanization and advanced technology have developed significantly in the coffee industry. However, there are still requirements for human laborers to operate, monitor crop health care, and manage production. The integration of advanced technology can significantly enhance the production efficiency and management practices of agricultural enterprises. This paper aims to address these gaps by proposing and implementing a computer vision-based attendance tracking system on mobile platforms that are suitable for the requirements and limitations of agricultural enterprises. First, the face detection process involves interpreting and locating facial structure. Next, the model transforms a photographic image of a human face into digital data based on the unique features and facial structure. We utilize the InsightFace model with the buffalo_l variant, as well as ArcFace with a ResNet backbone, as a facial recognition algorithm. After capturing a facial image, the system conducts a matching process against the existing database to verify identity. Finally, we implement a mobile application prototype on both iOS and Android platforms, ensuring accessibility for farm workers. As a result, our system achieved 95.2% accuracy on the query set, with an average processing time of <200 ms per image (including face detection, embedding extraction, and database matching). The system performs real-time attendance monitoring, automatically recording the entry and exit times of farm workers using facial recognition technology, and enables quick registration of new workers. Our work is expected to enhance transparency and fairness in the human management process, focusing on the coffee farm use case.

Aquaculture industry is a major contributor to the world’s food supply and therefore provides food security globally. However, many problems exist for the development of the industry; they include poor disease control, lack of sufficient workers, and inefficient use of resources. The advent of big data and Artificial Intelligence (AI) technologies presents an opportunity for the aquaculture industry to utilize these technologies to improve operational practices in the entire industry. A proposed framework for a decision-making support system, utilizing a multi-source data hub will be established to enhance current operational practices in aquaculture. This framework will be designed to collect and integrate multiple types of big data from aquaculture (environmental data, trading data, and data from Internet sources), and then create a common platform for accessing and processing the collected data. Four main modules will be created: monitoring environmental factors for shrimp farming, sharing shrimp farming equipment, calculating costs/profits associated with shrimp farming, and predicting price changes. Experimental prototypes of web-based systems will be built and tested to evaluate their performance. Ultimately, the proposed framework will provide users with a user-friendly platform to access the analysis results and recommendations provided to support decisions made during shrimp aquaculture production. Therefore, our work will serve as a reference point for the adoption of leading-edge technologies into aquaculture production management and contribute to sustainable growth of the aquaculture industry.

The distributed work model, which allows people to work from multiple physical places, has gained significant popularity in recent years, particularly since the outbreak of the COVID-19 epidemic. Despite the potential advantages of cost reductions associated with office space and flexible working environments, this model presents difficulties in effectively controlling and monitoring employee work activities. Applying advanced technologies in human resources can enable organizations to address these challenges effectively. However, SMEs face challenges in adopting advanced technologies like AI and facial recognition due to financial constraints, lack of technical expertise, and cost-effectiveness. The paper focuses on developing a low-cost attendance monitoring mobile application that utilizes real-time facial recognition and location analysis for SMEs. We try to enhance the real-time registration and identification flow with the camera of employees’ mobiles by handling large data streams and integrating the system with a cloud-based solution. First, the AI facial detection module will use the camera on mobile devices as input devices to ensure the flexibility of face recognition data input. The AI Engine layer is built by applying a deep learning-based facial recognition model that was trained using a large dataset of collected employee faces. Employees’ facial identities using the Resnet34 model integrated Additive Angular Margin Loss function to identify employees’ facial identities. The facial recognition model was designed to operate in real-time, with the ability to detect and identify employees as they check in and out of the workplace. Finally, we demonstrate an experimental prototype on a mobile phone application using React Native, NodeJS, and Python. The mobile application was designed to operate on both iOS and Android platforms, ensuring widespread accessibility for SMEs. The experimental findings provide empirical evidence that our system exhibits accuracy and efficiency in the tasks of facial identification and matching of personnel. This paper will serve as a valuable reference for executing strategies that are appropriate for SMEs.

The drone technology field has received attention and witnessed numerous impressive advancements that heralded substantial changes and opened new possibilities in different industries. Several advanced technologies, such as artificial intelligence, computer science, object avoidance technology, and others, have been collaboratively developed to empower drone operations. Drone technology software engineering overview can assist in identifying and prioritizing research gaps that require attention in future research. The main contribution is to investigate the recent development of drone technology by applying the literature review methodology. Our research involves a comprehensive literature review of drone technology software engineering, including worldwide journal papers and reviews published from 2010 to the present. To achieve this goal, we will first research the evolution of drone technology software engineering research trends. Second, we investigate approaches to ensuring the reliability, safety, and security of drone software systems. After that, software testing and validation methodologies have emerged to ensure the robustness and trustworthiness of drone systems are investigated. Finally, the potential of integration of AI/ML capabilities into drone software that has been progressing to enhance autonomous decision-making and adaptation in drone technology is described. As a result, summarized features of a drone system and its increasingly expanding variety of applications are discussed. After that, an analysis to identify the emerging trends, promising research directions, and ongoing challenges in the domain of drone software engineering is presented. These findings are expected to become a valuable reference in this research field.

The coffee industry contributes to the economic restructuring of many countries, often associated with a closed process from production to consumption. The green coffee bean grading standard provided by the Specialty Coffee Association (SCA) is one of the best methods for grading coffee beans. Traditionally, the assessment of quality and classification of coffee beans relies on visual examination, which demands significant time and effort and is easily inaccurate. Deep learning technology, characterized by precision, velocity, and veracity, can be adopted to empower the reduction of human labor and improve the productivity, quality, and efficiency of these tasks. Therefore, this paper aims to address these issues by implementing deep learning to classify coffee bean quality in real time by integrating the system with a cloud-based solution. First, image processing and data augmentation techniques are employed to handle the coffee bean image data. Subsequently, the model is trained using YOLOv8, a framework for object recognition, and OpenCV, an open-source image processing technology, to classify coffee beans. Finally, an application is developed for real-time video and image-streaming coffee bean recognition using React Native, NodeJS, and Python. The experimental results provide empirical evidence that our system enhances accuracy and efficiency in the tasks of classifying coffee bean quality in nine distinct varieties of coffee beans, with the time required reduced to a mere 1 to 3 seconds. Our system can be a useful solution for coffee producers, processors, and traders without relying on stationary equipment, especially in large farms or warehouses.

Electrical fires frequently occur due to the ignition generated by electrical gadgets, wiring, cables, and multi-taps. These fires can cause numerous human casualties and property damage every year. The mechanisms that cause electrical fires are generally unique and require specialist research to understand their features fully. Due to the rising number of electrical fires, IT-based assessment models for electrical fire causes assessment have been proposed and experimented with to improve efficiency and accuracy. This survey aims to thoroughly examine and analyze the current advanced practices in detecting and evaluating electrical fire causes, focusing explicitly on IT-based assessment methods and simulations to provide a full review. This is achieved by comprehensively surveying the techniques and procedures utilized in prior works and research. Firstly, we introduce the survey of factors contributing to electrical fires. Next, electrical fire simulations to determine the causes and behavior of electrical fires are investigated. The assessment of electrical techniques to understand electrical fire causes has been emphasized. Lastly, the summaries and proposed enhancements to advance the progress in this domain are examined. To the best of our knowledge, the survey of electrical fire causes assessment IT-based methodology available has been reviewed and analyzed for the first time. These findings are expected to become a valuable reference in this domain.

Foreign Direct Investment (FDI) is an important resource that helps accelerate the development of the country’s economy, add substantial funding to growth and facilitate technology transfer. Republic of Korea (ROK) is one of the world’s developed countries with dynamic economy, advanced science and technology. In recent years, the Korean government has continuously formulated tax policies, policies to support the business economy and import policies to support foreign businesses in Korea. The Pangyo Valley Creative Economy Valley is being groomed as a global startup hub in Asia. Small and medium enterprises (SMEs) in foreign countries are increasingly interested and eager to seek investment opportunities in the Korean market. Nonetheless, for these companies, language barriers and cultural and institutional differences make it more difficult and time-consuming to learn about the Korean market (such as investment trends, laws, visa policies, taxes and business establishment issues in Korea, etc.). In this study, we explored the process of searching information and seeking investment opportunities and built a business consulting and support application in the first stages of starting a business in ROK to increase effectiveness and save time, which is also an innovative business practice in Use-case ROK. We designed our Virtual Assistant system that can crawl and analyze data on foreign investments in ROK from open data resource websites (data.co.kr) and used analytic and aggregation techniques to explore trends in investments of foreign enterprises. We also researched the process of searching information and seeking investment opportunities for SMEs when investing in ROK, government support policies, laws and taxes as well as a number of other related issues. We built datasets and used Natural Language Processing (NLP) together with Natural Language Understanding (NLU) algorithms to build chatbot applications. Friendly framework for new developers to add and build up the dataset of AI Assistant is built by providing input intent data function, input Entity data function, input utterance data function as well as training and test function. In addition, we built a web-app connected to the server to visualize all the results of research so that SMEs owners can easily use and look for information on investments. Based on the research results, we can make recommendations to SMEs in keeping with the changing investment trends in ROK.