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Prediction of Fine Dust in Gyeonggi-do Industrial Complex using Machine Learning Methods
Dong-Jun Won, Sun-Kyum Kim, Yeonghun Kim, Gyuwon Song
http://doi.org/10.5626/JOK.2021.48.7.764
Recently, research on fine dust has been conducted through various prediction techniques. However, currently the research focused on PM10 concentration prediction, and thus it is necessary to develop a model capable of predicting PM2.5 concentration. In this paper, we have collected air quality, weather, and traffic of the Banwol Shihwa National Industrial Complex in the recent two years. The significance of the variable been identified through correlation analysis and regression analysis among PM2.5 and PM10, SO₂, NO₂, CO, O₃, temperature, humidity, wind direction, wind speed, precipitation, road section vehicle speed for each vehicle. Next, the data has been used to predict PM2.5 concentration based on time in the industrial complex. Through the artificial intelligence techniques, Random Forest, XGBoost, LightGBM, Deep neural network and Voting models, PM2.5 concentration industrial complexes been predicted on an hourly basis, and comparative analysis been conducted based on RMSE. As a result of prediction, RMSE was 6.27, 6.41, 6.22, 6.64, and 6.12, respectively, and each technique showed very high performance compared to 10.77 of the technique predicted by Air Korea.
EFA-DTI: Prediction of Drug-Target Interactions Using Edge Feature Attention
Erkhembayar Jadamba, Sooheon Kim, Hyeonsu Lee, Hwajong Kim
http://doi.org/10.5626/JOK.2021.48.7.825
Drug discovery is a high-level field of research requiring the coordination of disciplines ranging from medicinal chemistry, systems biology, structural biology, and increasingly, artificial intelligence. In particular, drug-target interaction (DTI) prediction is central to the process of screening for and optimizing candidate substances to treat disease from a nearly infinite set of compounds. Recently, as computer performance has developed dramatically, studies using artificial intelligence neural networks have been actively conducted to reduce the cost and increase the efficiency of DTI prediction. This paper proposes a model that predicts an interaction value between a given molecule and protein using a learned molecule representation via Edge Feature Attention-applied Graph Net Embedding with Fixed Fingerprints and a protein representation using pre-trained protein embeddings. The paper describes architectures, experimental methods, and findings. The model demonstrated higher performance than DeepDTA and GraphDTA, which had previously demonstrated the best performance in DTI studies.
Knowledge Completion System through Learning the Relationship between Query and Knowledge Graph
Min-Sung Kim, Min-Ho Lee, Wan-Gon Lee, Young-Tack Park
http://doi.org/10.5626/JOK.2021.48.6.649
The knowledge graph is a network comprising of relationships between the entities. In a knowledge graph, there exists a problem of missing or incorrect relationship connection with the specific entities. Numerous studies have proposed learning methods using artificial neural networks based on natural language embedding to solve the problems of the incomplete knowledge graph. Various knowledge graph completion systems are being studied using these methods. In this paper, a system that infers missing knowledge using specific queries and knowledge graphs is proposed. First, a topic is automatically extracted from a query, and topic embedding is obtained from the knowledge graph embedding module. Next, a new triple is inferred by learning the relationship between the topic from the knowledge graph and the query by using Query embedding and knowledge graph embedding. Through this method, the missing knowledge was inferred and the predicate embedding of the knowledge graph related to a specific query was used for good performance. Also, an experiment was conducted using the MetaQA dataset to prove the better performance of the proposed method compared with the existing methods. For the experiment, we used a knowledge graph having movies as a domain. Based on the assumption of the entire knowledge graph and the missing knowledge graph, we experimented on the knowledge graph in which 50% of the triples were randomly omitted. Apparently, better performance than the existing method was obtained.
Development and Application of Guidelines for Compliance with IEC 62304 International Standards for AI Medical Device Software
DongYeop Kim, Ye-Seul Park, Byungjeong Lee, Jung-Won Lee
http://doi.org/10.5626/JOK.2021.48.1.71
Medical device software developers must implement the processes required by IEC 62304, the international standard for medical device software life-cycle processes, and they must also have a large amount of artifacts to obtain a license. Recently, AI medical device software based on medical images has been actively developed, and since it is treated as standalone software, it must be approved in accordance with IEC 62304 for medical device software. The international standard for AI technology is currently in the discussion stage, and the developer should arbitrarily establish the life-cycle process of AI medical device software, and by matching the specifications of IEC 62304, the performance and safety of AI products will be evaluated. It is unclear which quality management technique should be used to produce the best artifact. This paper provides a quality control technique for fulfilling the scope and requirements of IEC 62304 compliance for AI medical device software in the form of guidelines. These guidelines are also applied to actual AI products to check their potential use in real applications.
Anomaly Detection by a Surveillance System through the Combination of C3D and Object-centric Motion Information
Seulgi Park, Myungduk Hong, Geunsik Jo
http://doi.org/10.5626/JOK.2021.48.1.91
In the existing closed-circuit television (CCTV) videos, the deep learning-based anomaly detection reported in the literature detected anomalies using only the object"s action value. For this reason, it is difficult to extract the action value of an object depending upon the situation, and there is a problem that information is reduced over time. Since the cause of abnormalities in CCTV videos involves several factors such as frame complexity and information according to time series analysis, there is a limit to detecting an abnormality using only the action value of the object. To solve this problem, in this paper, we designed a new deep learning-based anomaly detection model that combined optical flow with C3D to use various feature values centered on the objects. The proposed anomaly detection model used the UCF-Crime dataset, and the experimental results achieved an area under the curve (AUC) of 76.44. Compared to previous studies, this study worked more effectively in fast-moving videos such as explosions. Finally, we concluded that it was appropriate to use the information according to different feature values and time series analysis considering various aspects of the behavior of an object when designing an anomaly detection model.
Survey on Feature Attribution Methods in Explainable AI
Gihyuk Ko, Gyumin Lim, Homook Cho
http://doi.org/10.5626/JOK.2020.47.12.1181
As artificial intelligence (AI)-based technologies are increasingly being used in areas that can have big socioeconomic effects, there is a growing effort to explain decisions made by AI models. One important direction in such eXplainable AI (XAI) is the ‘feature attribution’ method, which explains AI models by assigning a contribution score to each input feature. In this work, we surveyed nine recently developed feature attribution methods and categorized them using four different criteria. Based on the categorizations, we found that the current methods focused only on specific settings such as generating local, white-box explanations of neural networks and lacked theoretical foundations such as axiomatic definitions. We suggest future research directions toward a unified feature attribution method based on our findings.
LEXAI : Legal Document Similarity Analysis Service using Explainable AI
http://doi.org/10.5626/JOK.2020.47.11.1061
Recently, in keeping with the improvement of deep learning, studies on using deep learning a specialized field have diversified. Semantic searching for legal documents is an essential part of the legal field. However, it is difficult to function outside of the service using the expert system because it requires professional knowledge in the relevant field. It is also challenging to establish an automated, semantically similar legal document retrieval environment because the cost of hiring professional human resources is high. While existing retrieval services provide an environment based on expert systems and statistical systems, the proposed method adopts the deep learning method with a classification task. We propose a database system structure that provides searching for legal documents with high semantic similarity using an explainable neural network. The features of these proposed methods show the performance of developing and verifying visual similarity assessment methods for semantic relevance among similar documents.
A Comparative Study of Machine Learning Algorithms for Diagnosis of Ischemic Heart Disease
Pyoung-Woo Park, Min-Koo Kim, Hong-Seok Lim, Duk-Yong Yoon, Seok-Won Lee
http://doi.org/10.5626/JOK.2018.45.4.376
In recent years, studies on artificial intelligence have been actively conducted, and artificial intelligence technology supports accurate and efficient decision-making for mankind. Also, the accumulation of medical knowledge and related data is accelerating, and studies on diagnosis of diseases through artificial intelligence technology are being carried out briskly. In this study, I chose a representative cardiovascular disease, specifically ischemic heart disease, as a research domain, and analyzed the available algorithms comparing effective approaches in the medical expert system for diagnosis of the disease. Concretely, the purpose of the study is to assist medical experts and physicians based on the initial patient record data, help them to explain the cause of ischemic heart disease, and minimize unnecessary related tests. In addition, the experimental data can be configured so that medical professionals can use them as learning models, thereby maximizing their experience and knowledge efficiently.
Question Answering Optimization via Temporal Representation and Data Augmentation of Dynamic Memory Networks
Dong-Sig Han, Chung-Yeon Lee, Byoung-Tak Zhang
The research area for solving question answering (QA) problems using artificial intelligence models is in a methodological transition period, and one such architecture, the dynamic memory network (DMN), is drawing attention for two key attributes: its attention mechanism defined by neural network operations and its modular architecture imitating cognition processes during QA of human. In this paper, we increased accuracy of the inferred answers, by adapting an automatic data augmentation method for lacking amount of training data, and by improving the ability of time perception. The experimental results showed that in the 1K-bAbI tasks, the modified DMN achieves 89.21% accuracy and passes twelve tasks which is 13.58% higher with passing four more tasks, as compared with one implementation of DMN. Additionally, DMN’s word embedding vectors form strong clusters after training. Moreover, the number of episodic passes and that of supporting facts shows direct correlation, which affects the performance significantly.
The Present and Perspective of Quantum Machine Learning
This paper presents an overview of the emerging field of quantum machine learning which promises an innovative expedited performance of current classical machine learning algorithms by applying quantum theory. The approaches and technical details of recently developed quantum machine learning algorithms that have been able to substantially accelerate existing classical machine learning algorithms are presented. In addition, the quantum annealing algorithm behind the first commercial quantum computer is also discussed.
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