Thailand is becoming one of the top strategic gateways for trade, investment, and logistics in the region. This includes improving connectivity between areas domestically and internationally[1], as well as developing digital and logistical infrastructure to facilitate trade and investment. By doing so, Thailand aims to be a key player in the global marketplace[2] and attract more international business.[1] Customs brokers act as intermediaries[3] between businesses and customs to perform customs clearance. They can be a great asset when it comes to international trade. These professionals are experts in navigating Customs' and other government agencies' requirements, which can ensure that any shipments reach their destination smoothly and efficiently. Dealing with complex processes and handling documents with many details is a great challenge that customs brokers face. However, the ability to adapt to these challenges and have even expanded their role in the supply chain by adopting new technologies like digitalization and advanced innovation of customs declaration systems. By doing so, they have been able to make the clearance process more efficient and modern, while still ensuring compliance with customs duties and other laws. It is impressive to see how customs brokers have been able to keep up with industry changes and remain an integral part of the supply chain. The customs brokerage industry plays a pivotal role in international trade by facilitating the seamless movement of goods across borders while ensuring compliance with ever-evolving regulations. This paper introduces a transformative vision for the customs brokerage business, leveraging modern technology to enhance efficiency, accuracy, and customer experience [4]. Modern technology in the “Easy Paste” model marks a paradigm shift in the customs brokerage industry. This visionary approach streamlines operations and positions customs brokers as crucial partners in the global trade ecosystem, fostering economic growth, sustainability, and innovation.
The k-means clustering algorithm calculates the distances between data points within each cluster and determines new centroids for each cluster until the data points remain unchanged within their respective groups. This process affects the iteration of the k-means algorithm, leading to an increase in computation time. Consequently, the overall execution time of the algorithm is prolonged. This research presents a process that utilizes the farthest first and canopy algorithms to reduce the number of iterations in the k-means clustering. This improvement focuses on enhancing the centroid initialization method for each cluster. The research study utilized ten datasets from the UCI machine learning repository to test the effectiveness of improving the k-means clustering algorithm. It compares the number of iterations and the sum of squares error as performance metrics. The results showed that the farthest first and canopy algorithms significantly reduce the number of iterations in the k-means clustering when compared to random centroid initialization. These research findings confirm the hypothesis that the method of centroid initialization for each cluster impacts the iteration process of k-means clustering.
In order to detect defects on semiconductor components, machine learning (ML) and deep learning (DL) approach are introduced into vision inspection system where these ML/DL approach capable to identify object with complex appearance and eliminate the task requirement of placing PCB or electronic component on special fixture and jig. However, there is a small dataset issue occur when implementing ML/DL approach into vision inspection application due to collecting such data is difficult and expensive in industry application. A novel defect classification system using extremely small datasets is proposed, named GENSS, where the GEometric variant property is used to geNerate synthetic data, and then uses Structural Similarity and image hashing approaches to automatically select pre-trained models. Early experiments showed that the suggested method is a practical approach that can achieve high accuracy, at 85.71 %.
Predicting volatility in the Foreign Exchange (FOREX) market is of paramount importance for stakeholders, including portfolio managers, risk managers, and central banks. It significantly impacts risk management, trading strategies, and monetary policy formulation. Traditional finance models, such as Autoregressive Conditional Heteroskedasticity (ARCH) and Generalized Autoregressive Conditional Heteroskedasticity (GARCH), have been extensively employed for modeling and forecasting financial market volatility. In recent years, machine learning (ML) algorithms, including Support Vector Machines (SVM), Neural Networks (NN), and Deep Learning (DL), have emerged as promising approaches for predicting complex financial time series data. This research presents a systematic and performance-based evaluation of traditional finance models and ML algorithms for predicting FOREX volatility, with a specific focus on the EUR/USD currency pair using daily close prices. The comparative analysis assesses the predictive performance of ARCH, GARCH, SVM, NN, and DL models, utilizing the Root Mean Square Error (RMSE) as the primary performance metric. The findings of this study indicated that traditional finance models, including ARCH and GARCH, exhibited reasonable performance in capturing certain aspects of FOREX volatility. However, ML algorithms surpassed these models, with the SVM, NN, and DL models demonstrating superior predictive capabilities. The insights derived from this analysis hold significant value as a guide for decision-making processes for various stakeholders. Additionally, this research contributed to the existing body of knowledge on FOREX volatility prediction, paving the way for future research in this domain.
Due to the digitization of healthcare, EHR systems are commonly used to maintain vast amounts of personally identifiable information and protected health information. There are, however, significant concerns regarding the security and privacy of EHR systems due to the dangers of unauthorized access, data intrusions, and identity theft. This study endeavors to identify methods and techniques that can be utilized to safeguard patient information contained in electronic health records. Examining the current security measures for EHR systems, highlighting the issues they cause, and proposing novel countermeasures against these attacks. Our research concentrates primarily on EHR system encryption techniques, authentication processes, and auditing procedures. In addition, the study also discusses the challenges associated with deployment and regulatory requirements, as well as the impending developments in EHR system security. By prioritizing the security and privacy of their electronic health record systems, healthcare providers can protect patient data, maintain regulatory compliance, and earn the trust of patients and other stakeholders.
