This study uses the API of Upbit, one of Korea’s cryptocurrency exchanges, to predict continuous time series for a limited period and cryptocurrencies using LSTM, a machine learning technique. The trading (buying and selling) point algorithm presented in this study was used to conduct experimental research on efficient profit creation for cryptocurrency investment. Several related studies have shown the results of time series prediction for long-term forecasts, such as a week or several months. Still, they have not attempted to make an ultra-short-term prediction in units of one minute. This paper attempts such a 1 min prediction. This is an experiment to create efficient profits by setting efficient trading (buying and selling) points using machine learning techniques and repeating these operations by an algorithm. Applying it to cryptocurrency shows the possibility of time series prediction.

Despite the increasing attention on smart farms as a solution to rural issues such as aging agricultural populations, a shortage of young farmers, decreased production area, and reduced investment leading to stagnant income, exports, and growth rates, many farms still rely on traditional methods like cultivating tangerines in open fields. Despite this, increasing farm income requires producing high-quality tangerines and selling them at premium prices, with fruit sweetness being a crucial factor. Therefore, there is a need to examine the close correlation between tangerine quality and sweetness. In this paper, we use deep learning with the PyCaret library to predict and analyze tangerine sweetness using data from seven regions in Jeju and 13 comprehensive factors influencing sweetness, including terrain, temperature, humidity, precipitation, sunlight, wind speed, acidity, sugar-acid ratio, and others. Although applying all 13 factors could achieve over 90% accuracy, our study, limited to seven factors, still achieves a respectable 82.4% prediction accuracy, demonstrating the significant impact of weather data on sweetness. Moreover, these optimistic predictions enable the estimation of tangerine quality and price formation in the market for the coming year, allowing tangerine farmers and related agencies to respond to market conditions proactively. Furthermore, by applying these data to smart farms to control factors influencing tangerine sweetness, it is anticipated that high-quality tangerine production and increased farm income can be achieved.

The rapid development of information and communication technology has fostered a natural integration of technology and design. As a result, there is increasing interest in Augmented Reality (AR) business card systems that leverage digital media. This research aims to advance the design of an AR-based participatory business card information system in line with contemporary trends. Key aspects of this study include applying technology to acquire contextual information from paper business cards, transmitting it to a server, and delivering it to mobile devices; facilitating interactivity between users and content through a screen interface; providing multimedia business content (video, image, text, 3D elements) via image markers recognized by users on mobile devices, while also adapting the type and method of content delivery. The AR business card system designed in this research enhances traditional paper business cards by incorporating visual information and interactive elements and automatically generating buttons linked to phone numbers, location information, and homepages. This innovative approach enables users to interact and enriches their overall experience while adhering to strict quality control measures.

Climate change is accelerating the introduction of new fruit varieties worldwide, yet machine learning models for agricultural quality prediction require years of historical data to be reliable. This bottleneck severely limits AI deployment in smart farming: a new variety planted today may lack sufficient quality records for a decade. Transfer learning from established crops offers a promising shortcut, but our investigation reveals a critical pitfall: naively combining data from multiple established crops is highly unreliable—improving results in some target domains (+23.7%) while causing catastrophic performance degradation in others (R² drops from 0.553 to −0.494). We term this the source overwhelming problem, where abundant source data drowns the signal from scarce target observations. We present Balanced Multi-Source Transfer Learning (BMTL), a principled framework that controls source-target balance to enable effective cross-crop knowledge transfer, operating in a few-shot regime where a small target validation set (n^val_T ≥ 10) is available. We establish a counterintuitive finding: source-target balance matters more than domain similarity for successful transfer. On fruit quality prediction spanning 4 crops (apple, citrus, grape, pear; 3,721 quality measurements with 20,896 integrated weather records from the Korea Meteorological Administration), BMTL improves R2 by +0.143 points (from 0.393 to 0.536, a +36.4% relative gain) over single-crop training in 7 of 8 tested scenarios, and up to +117.8% when target data is extremely scarce (n_T =50). On UCI benchmarks, BMTL is the only transfer-based method avoiding catastrophic negative transfer on high-shift domains. The proposed method reduces the data collection burden for new crop varieties, enabling agricultural AI systems to become operational years earlier—with direct benefits for food security and supply chain management.