• The Journal of the Korea institute of electronic communication sciences
• /
• v.18 no.5
• /
• pp.999-1008
• /
• 2023

Early prediction of chronic diseases such as diabetes is an important issue, and improving the accuracy of diabetes prediction is especially important. Various machine learning and deep learning-based methodologies are being introduced for diabetes prediction, but these technologies require large amounts of data for better performance than other methodologies, and the learning cost is high due to complex data models. In this study, we aim to verify the claim that DNN using the pima dataset and k-fold cross-validation reduces the efficiency of diabetes diagnosis models. Machine learning classification methods such as decision trees, SVM, random forests, logistic regression, KNN, and various ensemble techniques were used to determine which algorithm produces the best prediction results. After training and testing all classification models, the proposed system provided the best results on XGBoost classifier with ADASYN method, with accuracy of 81%, F1 coefficient of 0.81, and AUC of 0.84. Additionally, a domain adaptation method was implemented to demonstrate the versatility of the proposed system. An explainable AI approach using the LIME and SHAP frameworks was implemented to understand how the model predicts the final outcome.

• Information Systems Review
• /
• v.23 no.4
• /
• pp.69-86
• /
• 2021

As artificial intelligence technology advances, personalized recommendation systems using big data have attracted huge attention. In the case of beauty products, product preferences are clearly divided depending on customers' skin types and sensitivity along with individual tastes, so it is necessary to provide customized recommendation services based on accumulated customer data. Therefore, by employing deep learning methods, this study proposes a neural network-based recommendation model utilizing both product search history and context information such as gender, skin types and skin worries of customers. The results show that our model with context information outperforms collaborative filtering-based recommender system models using customer search history.

• Journal of Korea Society of Industrial Information Systems
• /
• v.29 no.3
• /
• pp.51-65
• /
• 2024

Accurate electrical load forecasting is important to the effective operation of power systems in smart grids. With the recent development in machine learning, artificial intelligence-based models for predicting power demand are being actively researched. However, since existing models get input variables as numerical features, the accuracy of the forecasting model may decrease because they do not reflect the semantic relationship between these features. In this paper, we propose a scheme for short-term load forecasting by using features extracted through the large language models for input data. We firstly convert input variables into a sentence-like prompt format. Then, we use the large language model with frozen weights to derive the embedding vectors that represent the features of the prompt. These vectors are used to train the forecasting model. Experimental results show that the proposed scheme outperformed models based on numerical data, and by visualizing the attention weights in the large language models on the prompts, we identified the information that significantly influences predictions.

• Convergence Security Journal
• /
• v.23 no.4
• /
• pp.122-129
• /
• 2023

The paradigm of warfare is undergoing a revolutionary transformation due to the advancements in technology brought forth by the Fourth Industrial Revolution. Specifically, the U.S. military has introduced the concept of mosaic warfare as a means of military innovation, aiming to integrate diverse resources and capabilities, including various weapons, platforms, information systems, and artificial intelligence. This integration enhances the ability to conduct agile operations and respond effectively to dynamic situations. The incorporation of mosaic warfare could facilitate efficient and rapid command and control by integrating AI staff with human commanders. Ukrainian military operations have already employed mosaic warfare in response to Russian aggression. This paper focuses on the mosaic war fare concept, which is being proposed as a model for future warfare, and suggests the strategy for introducing the Korean mosaic warfare concept in light of the changing battlefield paradigm.

• Cartoon and Animation Studies
• /
• s.48
• /
• pp.85-116
• /
• 2017

The purpose of this study is to analyze conditions that robots depicted in cinematographic works like animations or movies sympathize with and form an attachment with the nuclear person and organize characterizing strategies for emotional sympathetic robots. Along with the development of technology, the areas of artificial intelligence and robots are no longer considered to belong to science fiction but as realistic issues. Therefore, this author assumes that the expressive characteristics of emotional sympathetic robots created by cinematographic works should be used as meaningful factors in expressively embodying human-friendly service robots to be distributed widely afterwards, that is, in establishing the features of characters. To lay the grounds for it, this research has begun. As the subjects of analysis, this researcher has chosen robot characters whose emotional intimacy with the main person is clearly observed among those found in movies and animations produced after the 1920 when robot's contemporary concept was declared. Also, to understand robots' appearance and behavioral tendency, this study (1) has classified robots' external impressions into five types (human-like, cartoon, tool-like, artificial bring, pet or creature) and (2) has classified behavioral tendencies considered to be the outer embodiment of personality by using DiSC, the tool to diagnose behavioral patterns. Meanwhile, it has been observed that robots equipped with high emotional intimacy are all strongly independent about their duties and indicate great emotional acceptance. Therefore, 'influence' and 'Steadiness' types show great emotional acceptance, the influencing type tends to be highly independent, and the 'Conscientiousness' type tends to indicate less emotional acceptance and independency in general. Yet, according to the analysis on external impressions, appearance factors hardly have any significant relationship with emotional sympathy. It implies that regarding the conditions of robots equipped with great emotional sympathy, emotional sympathy grounded on communication exerts more crucial effects than first impression similarly to the process of forming interpersonal relationship in reality. Lastly, to study the characters of robots, it is absolutely needed to have consilient competence embracing different areas widely. This author also has felt that only with design factors or personality factors, it is hard to estimate robot characters and also analyze a vast amount of information demanded in sympathy with humans entirely. However, this researcher will end this thesis as the foundation for it expecting that the general artistic value of animations can be used preciously afterwards in developing robots that have to be studied interdisciplinarily.

• Journal of Korea Technology Innovation Society
• /
• v.10 no.4
• /
• pp.654-686
• /
• 2007

To estimate and analyze an interested science and technology level in any case requires three basic informations: (1) relative positions of our technology level, (2) other relevant technology level of the world best country holding the state of the art technology, and (3) its theoretical or practical maximum level within a certain period of time. Further, additional information from analyzing its respective rate of technology changes is necessary. It seems that most previous empirical or case studies on technology level have not considered third and fourth informations seriously, and thus critically have missed important findings from a dynamic point of view on the matter. A dynamic approach considering types of development processes and paths as well as current position needs an application of a concept of technology development stages and respective growth curves. This paper proposes a new method of approach and application by implementing relatively simple types of the growth curve(S-curve) such as logistic and Comports curves and applying estimation results of these curves to ten core technologies of the growth engines for the next future generation in Korea. The study implies that Korean science and technology level in general clearly gets higher as it approaches to a recent time of period, but relative technology gap from the world best in terms of catching-up period does not get better or narrower in case of at least part of the concerned technologies such as bio new drugs and human organs, and intelligence robots. The possibility does exist that some of our concerned technologies shooting for the next future generation may not come to the world highest level in the near future. The purpose of this study is to propose possibilities of catching-up, if any, by estimating its relevant type of growth pattern by way of measuring and analyzing technology level and by analyzing the technology development process through a position analysis. At this stage this study tries to introduce a new theoretical approach of estimating technology level and its application to existing case study results(data) from Korea Institute of Science and Technology Planning and Evaluation(KISTEP) and Korea Institute of Industrial Technology Evaluation and Planing(ITEP), for years of 2004 and 2006 respectively. The study has some limitations in terms of accuracy of measuring(estimating) a relevant growth curve to a particular technology, feasibility of applying estimated results, accessing and analyzing panel experts opinions. Hence, it is recommended that further study would follow soon enough to verify practical applicability and possible expansion of the study results.

• Journal of Korean Society of Forest Science
• /
• v.110 no.4
• /
• pp.610-621
• /
• 2021

The soil creep, primarily caused by earthquakes and torrential rainfall events, has widely occurred across the country. The Korea Forest Service attempted to quantify the soil creep susceptible areas using a discriminant value table to prevent or mitigate casualties and/or property damages in advance. With the advent of advanced computer technologies, machine learning-based classification models have been employed for managing mountainous disasters, such as landslides and debris flows. This study aims to quantify the soil creep susceptibility using several classifiers, namely the k-Nearest Neighbor (k-NN), Naive Bayes (NB), Random Forest (RF), and Support Vector Machine (SVM) models. To develop the classification models, we downscaled 292 data from 4,618 field survey data. About 70% of the selected data were used for training, with the remaining 30% used for model testing. The developed models have the classification accuracy of 0.727 for k-NN, 0.750 for NB, 0.807 for RF, and 0.750 for SVM against test datasets representing 30% of the total data. Furthermore, we estimated Cohen's Kappa index as 0.534, 0.580, 0.673, and 0.585, with AUC values of 0.872, 0.912, 0.943, and 0.834, respectively. The machine learning-based classifications for soil creep susceptibility were RF, NB, SVM, and k-NN in that order. Our findings indicate that the machine learning classifiers can provide valuable information in establishing and implementing natural disaster management plans in mountainous areas.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.23 no.4
• /
• pp.423-430
• /
• 2019

The convergence of the automotive industry and the ICT technology can be broadly divided into the commercial service sector and the Cooperative-ITS (C-ITS) service sector. The C-ITS service sector is using V2X communication technology as a field that aims to provide safer transportation, more green and efficient transportation, and more predictable and productive mobility. The recent convergence of self-driving cars and connected cars requires high data rates, low transmission delays, and low transmission error rates. Interest in comparison of performance between WAVE and C-V2X (LTE-V2X, 5G-V2X) has been amplified and application services by communication technology are being studied. In this paper, we design the application performance evaluation method of Hybrid V2X communication system and confirm that the decrease of packet error rate (PER) performance is caused by the increase of communication distance, not the vehicle speed.

• Education of Primary School Mathematics
• /
• v.27 no.3
• /
• pp.199-226
• /
• 2024

In response to the era of transformation necessitating the introduction of Artificial Intelligence (AI) and digital technologies, educational innovation is undertaken with the implementation of AI digital textbooks in Mathematics, English, and Information subjects by 2025 in Korea. Within this context, this study analyzed the perceptions and needs of elementary school teachers regarding mathematics AI digital textbook. Based on a survey conducted in November 2023, involving 132 elementary school teachers across the country, the analysis revealed that the majority of elementary school teachers had a low perception of the introduction and need for mathematics AI digital textbooks. However, some recognized the potential for personalized learning and effective teaching support. Furthermore, among the core technologies of the AI digital textbook, teachers highly valued the necessity of learning diagnostics and teacher reconfiguration functions and had the most positive perception of their usefulness in math lessons, while their perception of interactivity was relatively low. These findings suggest the need for changing teachers' perceptions through professional development and information provision to ensure the successful adoption and use of mathematics AI digital textbooks. Specifically, providing concrete and practical ways to use the AI digital textbook, exploring alternatives to digital overload, and continuing development and research on core technologies.

• Journal of Korea Society of Industrial Information Systems
• /
• v.16 no.2
• /
• pp.85-97
• /
• 2011

Wireless sensor networks have emerged as one of the key enabling technologies for ubiquitous computing since wireless intelligent sensor nodes connected by short range communication media serve as a smart intermediary between physical objects and people in ubiquitous computing environment. We recognize the wireless sensor network as a massively distributed and deeply embedded system. Such systems require concurrent and asynchronous event handling as a distributed system and resource-consciousness as an embedded system. Since the operating environment and architecture of wireless sensor networks, with the seemingly conflicting requirements, poses unique design challenges and constraints to developers, we propose a very new operating system for sensor nodes based on finite state machine. In this paper, we clarify the design goals reflected from the characteristics of sensor networks, and then present the heart of the design and implementation of a compact and efficient state-driven operating system, SenOS. We describe how SenOS can operate in an extremely resource constrained sensor node while providing the required reactivity and dynamic reconfigurability with low update cost. We also compare our experimental results after executing some benchmark programs on SenOS with those on TinyOS.