Temperature and humidity excursions during transport accelerate quality loss in fresh produce. This study develops and validates a self-contained Internet of Things (IoT) platform for in-transit monitoring and energy-aware operation. The battery-powered device operates independently of vehicle power and continuously logs temperature, relative humidity, GPS position, and onboard power draw. Power budgeting combines firmware-level deep-sleep scheduling with a LiFePO₄ battery pack, enabling uninterrupted operation for up to four days. Using ∼10,000 time-stamped observations collected over four consecutive days in a standard dry truck (non-commercial validation), we trained and compared Gradient Boosting Machine (GBM), Random Forest (RF), and Linear Regression (LR) models to predict energy consumption under varying environmental and routing conditions. GBM and LR achieved high explanatory power (R2≈0.88) with a mean absolute error of 0.77 A·h, while RF provided interpretable feature importance data, identifying temperature as the dominant driver, followed by trip duration and humidity. The end-to-end system integrates an EMQX MQTT broker, a Laravel web application, MongoDB storage, and Node-RED flows for real-time dashboards and multi-day historical analytics. The proposed platform supports proactive decision-making in perishable logistics, with the AI analysis validating that the collected time-aligned on-route data can configure sampling/transmit cadence to preserve autonomy under stressful conditions.

Sentiment analysis is a task in natural language processing that focuses on identifying and categorizing emotions expressed in text. Despite the remarkable predictive performance achieved by deep learning models in this domain, their limited interpretability posed a significant challenge. Furthermore, the development of interpretable sentiment analysis models for the Thai language had received inadequate attention. To address this gap, this study proposed a Zero-shot Interpretable Sentiment Analysis Framework, integrating sentiment polarity extraction and leveraging the zero-shot learning with the powerful WangchanBERTa model. Our framework utilized the word selection method from the feeling wheel to represent six primary feelings as sentiment polarities, effectively capturing the subtle emotions expressed in the text. These sentiment polarities played a crucial role as features in training our model, enhancing its interpretability for sentiment analysis tasks. Through the evaluation of three Thai sentiment analysis datasets, we compared the sentiment polarity extraction with two traditional feature extraction methods and ten classification algorithms. The results showed the superiority of the sentiment polarity extraction over Bag of words and its competitive performance compared to TF-IDF in terms of accuracy. To gain insights into the model’s decision-making process, SHAP (SHapley Additive exPlanations) was employed to analyze feature importance. Our findings highlighted the alignment of influential features with the sentiment polarities of the text, providing a crucial understanding of the model’s functionality. Notably, we uncovered a clear relationship between specific feeling features and their corresponding sentiment classes, deepening our comprehension of the model’s performance in sentiment analysis. This study not only contributed to the advancement of sentiment analysis in the Thai language but also bridged the gap between predictive performance and model transparency, yielding a novel and interpretable approach for sentiment analysis.

Temperature and humidity excursions during transport accelerate quality loss in fresh produce. This study develops and validates a self-contained Internet of Things (IoT) platform for in-transit monitoring and energy-aware operation. The battery-powered device operates independently of vehicle power and continuously logs temperature, relative humidity, GPS position, and onboard power draw. Power budgeting combines firmware-level deep-sleep scheduling with a LiFePO₄ battery pack, enabling uninterrupted operation for up to four days. Using ∼10,000 time-stamped observations collected over four consecutive days in a standard dry truck (non-commercial validation), we trained and compared Gradient Boosting Machine (GBM), Random Forest (RF), and Linear Regression (LR) models to predict energy consumption under varying environmental and routing conditions. GBM and LR achieved high explanatory power (R2≈0.88) with a mean absolute error of 0.77 A·h, while RF provided interpretable feature importance data, identifying temperature as the dominant driver, followed by trip duration and humidity. The end-to-end system integrates an EMQX MQTT broker, a Laravel web application, MongoDB storage, and Node-RED flows for real-time dashboards and multi-day historical analytics. The proposed platform supports proactive decision-making in perishable logistics, with the AI analysis validating that the collected time-aligned on-route data can configure sampling/transmit cadence to preserve autonomy under stressful conditions.

Sentiment analysis is a task in natural language processing that focuses on identifying and categorizing emotions expressed in text. Despite the remarkable predictive performance achieved by deep learning models in this domain, their limited interpretability posed a significant challenge. Furthermore, the development of interpretable sentiment analysis models for the Thai language had received inadequate attention. To address this gap, this study proposed a Zero-shot Interpretable Sentiment Analysis Framework, integrating sentiment polarity extraction and leveraging the zero-shot learning with the powerful WangchanBERTa model. Our framework utilized the word selection method from the feeling wheel to represent six primary feelings as sentiment polarities, effectively capturing the subtle emotions expressed in the text. These sentiment polarities played a crucial role as features in training our model, enhancing its interpretability for sentiment analysis tasks. Through the evaluation of three Thai sentiment analysis datasets, we compared the sentiment polarity extraction with two traditional feature extraction methods and ten classification algorithms. The results showed the superiority of the sentiment polarity extraction over Bag of words and its competitive performance compared to TF-IDF in terms of accuracy. To gain insights into the model’s decision-making process, SHAP (SHapley Additive exPlanations) was employed to analyze feature importance. Our findings highlighted the alignment of influential features with the sentiment polarities of the text, providing a crucial understanding of the model’s functionality. Notably, we uncovered a clear relationship between specific feeling features and their corresponding sentiment classes, deepening our comprehension of the model’s performance in sentiment analysis. This study not only contributed to the advancement of sentiment analysis in the Thai language but also bridged the gap between predictive performance and model transparency, yielding a novel and interpretable approach for sentiment analysis.

We propose a multi-threaded algorithm that can improve the performance of geometric acoustic (GA)-based sound propagation algorithms in mobile devices. In general, sound propagation algorithms require high computational cost because they perform based on ray tracing algorithms. For this reason, it is difficult to operate sound propagation algorithms in mobile environments. To solve this problem, we processed the early reflection and late reverberation steps in parallel and verified the performance in four scenes based on eight sound sources. The experimental results showed that the performance of the proposed method was on average 1.77 times better than that of the single-threaded method, demonstrating that our algorithm can improve the performance of mobile devices.

In the realm of Generative AI, where various models are introduced, prompt engineering emerges as a significant technique within natural language processing-based Generative AI. Its primary function lies in effectively enhancing the results of sentence generation by large language models (LLMs). Notably, prompt engineering has gained attention as a method capable of improving LLM performance by modifying the structure of input prompts alone. In this study, we apply prompt engineering to Korean-based LLMs, presenting an efficient approach for generating specific conversational responses with less data. We achieve this through the utilization of the query transformation module (QTM). Our proposed QTM transforms input prompt sentences into three distinct query methods, breaking them down into objectives and key points, making them more comprehensible for LLMs. For performance validation, we employ Korean versions of LLMs, specifically SKT GPT-2 and Kakaobrain KoGPT-3. We compare four different query methods, including the original unmodified query, using Google SSA to assess the naturalness and specificity of generated sentences. The results demonstrate an average improvement of 11.46% when compared to the unmodified query, underscoring the efficacy of the proposed QTM in achieving enhanced performance.

Recently, the demand for remote counseling has been on the rise using conversational agents with Large Language Models (LLM) in many areas. This LLM trend is expanding beyond generating simple words to constructing complex sentences, showing progress in various fields. The significant development of LLM is attributed to the progress in Natural Language Processing (NLP) technology, built upon extensive language data. This study introduces a client technology of Open API System based conversational agent using LLM. The target agent is a Chatbot which can generate health counseling messages including empathetic conversations for users with caring his chronic conditions requiring ongoing health management. The overall system can be linked with the community care system through user APPs and APIs for digital healthcare context management. The advantage with the client with REST API is that APIs make it easy to integrate new applications with existing software systems, allowing target systems to meet requirements across a variety of platforms.

In this paper, the production process of holographic stereograms for digital human portraits, which are increasing interest in the field of holographic art, was shown. The digital human face model was produced using real-object based 3D modeling. After taking a human portrait image with 7 multi-view cameras, it was replaced with a digital human image through post processing. In order to produce digital holographic stereograms, more viewpoint images must be extracted from the face of the digital human. 768 viewpoint images were generated using the neural network multi-view interpolation method from 7 multi-view images. Finally, the process of manufacturing digital holographic stereogram of a digital human was explained, and the results of the experiment were shown.

Recently, demand for telemedicine counseling has been on the rise. Although telemedicine is legally prohibited in Korea, the number of healthcare-related questions on online question and answer platforms is steadily increasing. However, doctors have to manually write answers to similar questions, which is time-consuming and inefficient. This study implemented a healthcare information provision question and answer system using an algorithm that combines BERT and GPT-2 structures. Intellectual Q&A pairs were used as training data, and the BERT-GPT algorithm experimented with creating sentences that provide healthcare information such as related medical subjects and suspected diseases as answers to healthcare-related questions. As a result of BERT-GPT2 algorithm gets worse PPL score and loss than the baseline model, but better score in the qualitative assessment. This result means BERT-GPT2 understands the conversation intention better.

The expansion of the 4th Industrial Revolution is making it easier to collect and analyze big data through cloud technology. In addition, advances in semiconductor design and manufacturing technology are accelerating the development of artificial intelligence due to improvements in computer processing speed and performance. These developments in artificial intelligence technology are expected to be applied to the healthcare industry and create new value. Recently, ChatGPT, which has evolved one step further, has emerged, bringing about the development of a new technology called ‘Generative AI’. Research is being actively conducted on how AI can replace fields that were considered the exclusive domain of humans with specialized knowledge, such as medicine and law. In this study, we aim to take the opportunity to identify trends in the healthcare field of generative AI.