• Journal of Convergence for Information Technology
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• v.11 no.3
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• pp.7-13
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• 2021

This study develops an artificial intelligence prediction system for Fine particulate Matter(PM2.5) based on the deep learning algorithm GAN model. The experimental data are closely related to the changes in temperature, humidity, wind speed, and atmospheric pressure generated by the time series axis and the concentration of air pollutants such as SO2, CO, O3, NO2, and PM10. Due to the characteristics of the data, since the concentration at the current time is affected by the concentration at the previous time, a predictive model for recursive supervised learning was applied. For comparative analysis of the accuracy of the existing models, CNN and LSTM, the difference between observation value and prediction value was analyzed and visualized. As a result of performance analysis, it was confirmed that the proposed GAN improved to 15.8%, 10.9%, and 5.5% in the evaluation items RMSE, MAPE, and IOA compared to LSTM, respectively.

• Journal of Industrial Convergence
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• v.20 no.2
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• pp.29-36
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• 2022

Computational thinking in the AI and Big Data era for digital society means a series of problem-solving methods that involve expressing problems and their solutions in ways that computers can execute. Computational thinking is an approach to solving problems, designing systems, and understanding human behavior by deriving basic concepts in computer science, and solving difficult problems and elusive puzzles for students. We recently studied 93 pre-service teachers who are currently a freshman at ◯◯ university. The results of the first semester class, the participants created a satisfactory algorithm of the video level. Also, the proposed model was found to contribute greatly to the understanding of the computational thinking of the students participating in the class.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.270-272
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• 2022

Safety is one of the factors that prevent clinical drugs from being distributed on the market. In the case of neurotoxicity, which is the main cause of safety problems caused by drug side effects, risk assessment of drugs and compounds is required in advance. Currently, experiments for testing drug safety are based on animal experimetns, which have the disadvantage of being time-consuming and expensive. Therefore in order to solve the above problem, a neurotoxic prediction model through an in silico experiment was suggested. In this study, the category of neurotoxicity was expanded using a unified medical language system and various related compound data were obtained based on an integrated database. The SMILES (Simplified Molecular Input Line Entry System) of the obtained compounds were converted into fingerprints and it is used as input of machine learning. The model finally predicts the presence or absence of neurotoxicity. The experiment proposed in this study can reduce the time and cost required for the in vivo experiment. Furthermore, it is expected to shorten the research period for new drug development and reduce the burden of suspension of development.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.463-465
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• 2021

Currently, the inspection method printed on food packages and boxes is to sample only a few products and inspect them with human eyes. Such a sampling inspection has the limitation that only a small number of products can be inspected. Therefore, accurate inspection using a camera is required. This paper proposes a deep learning object recognition technology model, which is an artificial intelligence technology, as a method for detecting the defects of expiration date printed on the product packaging. Using the Faster R-CNN (region convolution neural network) model, the color images, converted gray images, and converted binary images of the printed expiration date are trained and then tested, and each detection rates are compared. The detection performance of expiration date printed on the package by the proposed method showed the same detection performance as that of conventional vision-based inspection system.

• Annual Conference of KIPS
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• 2022.11a
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• pp.377-379
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• 2022

POI refers to the point of Interest in Location-Based Social Networks (LBSNs). With the rapid development of mobile devices, GPS, and the Web (web2.0 and 3.0), LBSNs have attracted many users to share their information, physical location (real-time location), and interesting places. The tremendous demand of the user in LBSNs leads the recommendation systems (RSs) to become more widespread attention. Recommendation systems assist users in discovering interesting local attractions or facilities and help social network service (SNS) providers based on user locations. Therefore, it plays a vital role in LBSNs, namely POI recommendation system. In the machine learning model, most of the training data are stored in the centralized data storage, so information that belongs to the user will store in the centralized storage, and users may face privacy issues. Moreover, sharing the information may have safety concerns because of uploading or sharing their real-time location with others through social network media. According to the privacy concern issue, the paper proposes a recommendation model to prevent user privacy and eliminate traditional RS problems such as cold-start and data sparsity.

• Advances in nano research
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• v.17 no.2
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• pp.181-195
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• 2024

This study introduces a novel functionally graded material model, termed the "Coated Functionally Graded Graphene-Reinforced Composite (FG GRC)" model, for investigating the free vibration response of plates, highlighting its potential to advance the understanding and application of material property variations in structural engineering. Two types of coated FG GRC plates are examined: Hardcore and Softcore, and five distribution patterns are proposed, namely FG-A, FG-B, FG-C, FG-D, and FG-E. A modified displacement field is proposed based on the higher-order shear deformation theory, effectively reducing the number of variables from five to four while accurately accounting for shear deformation effects. To solve the equations of motion, an analytical solution based on the Galerkin approach was developed for FG GRC plates resting on a viscoelastic Winkler/Pasternak foundation, applicable to various boundary conditions. A comprehensive parametric analysis elucidates the impact of multiple factors on the fundamental frequencies. These factors encompass the types and distribution patterns of the coated FG GRC plates, gradient material distribution, porosities, nonlocal length scale parameter, gradient material scale parameter, nanoplate geometry, and variations in the elastic foundation. Our theoretical research aims to overcome the inherent challenges in modeling structures, providing a robust alternative to experimental analyses of the mechanical behavior of complex structures.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.24 no.5
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• pp.61-67
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• 2024

In recent years, artificial intelligence and deep learning technologies have made significant advances, and the BERT model has been recognized for its excellent contextual understanding in natural language processing based on the transformer architecture. This performance has the potential to take traditional recommendation systems to the next level. In this study, we adopt an approach that combines a collaborative filtering approach with a deep learning model to improve the performance of recommendation systems. Specifically, we implemented a system that uses BERT to analyze the sentiment of user reviews and embed users based on these review sentiments to find and recommend users with similar tastes. In the process, we also utilized Elasticsearch, an open-source search engine, for quick search and retrieval of recommended results. The approach of analyzing users' textual data to increase the accuracy and personalization of recommendations will play an important role in improving the user experience on various online services in the future.

• Steel and Composite Structures
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• v.53 no.1
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• pp.103-114
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• 2024

This study proposes a new hybrid approach that utilizes post-earthquake survey data and numerical analysis results from an evolving finite element routing model to capture vulnerability processes. In order to achieve cost-effective evaluation and optimization, this study introduced an online data evolution data platform. The proposed method consists of four stages: 1) development of diagnostic sensitivity curve; 2) determination of probability distribution parameters of throughput threshold through optimization; 3) update of distribution parameters using smart evolution method; 4) derivation of updated diffusion parameters. Produce a blending curve. The analytical curves were initially obtained based on a finite element model used to represent a similar RC building with an estimated (previous) capacity height in the damaged area. The previous data are updated based on the estimated empirical failure probabilities from the post-earthquake survey data, and the mixed sensitivity curve is constructed using the update (subsequent) that best describes the empirical failure probabilities. The results show that the earthquake rupture estimate is close to the empirical rupture probability and corresponds very accurately to the real engineering online practical analysis. The objectives of this paper are to obtain adequate, safe and affordable housing and basic services, promote inclusive and sustainable urbanization and participation, implement sustainable and disaster-resilient buildings, sustainable human settlement planning and management. Therefore, with the continuous development of artificial intelligence and management strategy, this goal is expected to be achieved in the near future.

• Asia-Pacific Journal of Business
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• v.15 no.3
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• pp.17-45
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• 2024

Purpose - This paper studies the impact of digital transformation on corporate performance based on the stakeholder and dynamic capability theories. Digital transformation is divided into digital technologies (big data, artificial intelligence, blockchain, and cloud computing) and digital technology practical applications. Corporate performance includes financial performance and non-financial performance. The mechanism of dynamic capabilities (innovation capability, absorptive capacity, and adaptive capacity) is further explored. Design/methodology/approach - A fixed-effects model is used to construct a panel data of China's Shanghai and Shenzhen A-share listed companies from 2011 to 2023, and Stata is used for empirical analysis. Findings - In general, digital transformation directly improves corporate performance and indirectly promotes corporate performance through dynamic capabilities (innovation, absorptive capacity, and adaptive capacity). After robustness and endogeneity tests, the conclusion still support. In terms of subdivision, the two dimensions of digital transformation and the practical application of digital technology have different effects on corporate financial performance and non-financial performance. Research implications or Originality - Theoretically, the mechanism of digital transformation on corporate performance is fully and deeply explored, filling the research gap whiting this study. Additionally, the model is constructed using the innovation, absorption and adaptability of dynamic capabilities, providing a different perspective. Practically, it helps to alleviate the current situation of some companies "not wanting to transform" or "not daring to transform", and also clarifies how digital transformation can help companies use dynamic capabilities to improve performance. It provides a decision-making basis for government departments to promote the integration of digital economy and real economy, so that digital transformation can better empower and release corporate performance, thereby promoting the development of China's economy.

• International Journal of Computer Science & Network Security
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• v.24 no.10
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• pp.71-80
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• 2024

The Internet of Things (IoT) is an emerging element that is becoming increasingly indispensable to the Internet and shaping our current understanding of the future of the Internet. IoT continues to extend deeper into the daily lives of people, offering distributed and critical services. In contrast with current Internet, IoT depends on a dynamic architecture where physical objects with embedded sensors will communicate via cloud to send and analyze data [1-3]. Its security troubles will surely impinge all aspects of civilization. Mobile agents are widely used in the context of the IoT and due to the possibility of transmitting their execution status from one device to another in an IoT network, they offer many advantages such as reducing network load, encapsulating protocols, exceeding network latency, etc. Also, cryptographic technologies, like PKI and Blockchain technology, and Artificial Intelligence are growing rapidly allowing the addition of an approved security layer in many areas. Security issues related to mobile agent migration can be resolved with the use of these technologies, thus allowing increased reliability and credibility and ensure information collecting, sharing, and processing in IoT environments, while ensuring maximum autonomy by relying on the AI to allow the agent to choose the most secure and optimal path between the nodes of an IoT environment. This paper aims to present a new model to secure mobile agents in the context of the Internet of Things based on Public Key Infrastructure (PKI), Ethereum Blockchain Technology and Q-learning. The proposed model provides a secure migration of mobile agents to ensure security and protect the IoT application against malevolent nodes that could infiltrate these IoT systems.