• International Journal of Advanced Culture Technology
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• v.6 no.4
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• pp.262-265
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• 2018

Deep learning, a sub-field of machine learning changing the prospects of artificial intelligence (AI) because of its recent advancements and application in various field. Deep learning deals with algorithms inspired by the structure and function of the brain called artificial neural networks. This works reviews basic architecture and recent advancement of deep structured learning. It also describes contemporary applications of deep structured learning and its advantages over the treditional learning in artificial interlligence. This study is useful for the general readers and students who are in the early stage of deep learning studies.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.21 no.4
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• pp.57-62
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• 2021

As the parallel computing capability for artificial intelligence improves, the field of artificial intelligence technology is expanding in various industries. In particular, artificial intelligence is being introduced to process data generated from IoT sensors that have enoumous data. However, the limitation exists when applying the AI techniques on IoT network because IoT has time series data, where the importance of data changes over time. In this paper, we propose time-weighted and user-state based artificial intelligence processing techniques to effectively process IoT sensor data. This technique aims to effectively classify IoT sensor data through a data pre-processing process that personalizes time series data and places a weight on the time series data before artificial intelligence learning and use status of personal data. Based on the research, it is possible to propose a method of applying artificial intelligence learning in various fields.

• International Journal of Computer Science & Network Security
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• v.22 no.6
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• pp.25-32
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• 2022

The hypothesis of the study of the article is that the use of elements of artificial intelligence will increase the effectiveness of the educational process of the university if: a set of pedagogical conditions for the construction and use of an expert system with elements of artificial intelligence in the educational process of the university is revealed; a model for preparing a future teacher of vocational training for the use of elements of artificial intelligence has been developed; a special course has been developed that contributes to the implementation of the professional orientation of education. In accordance with this, the following tasks were studied in the article: An analysis of scientific and methodological research in the field of the current state, prospects for the development and use of elements of artificial intelligence in the preparation of a future teacher of vocational training and to determine the dynamics of the introduction of intelligent expert systems in education; A set of pedagogical conditions for the construction and use of an expert system with elements of artificial intelligence in the educational process of a university is revealed; It is substantiated to develop a model for preparing a teacher of vocational training to use elements of artificial intelligence.

• Journal of Digital Convergence
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• v.16 no.1
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• pp.231-242
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• 2018

The efficient management of the Length of Stay(LOS) is important in hospital. It is import to reduce medical cost for patients and increase profitability for hospitals. In order to efficiently manage LOS, it is necessary to develop an artificial intelligence-based prediction model that supports hospitals in benchmarking and reduction ways of LOS. In order to develop a predictive model of LOS for acute stroke patients, acute stroke patients were extracted from 2013 and 2014 discharge injury patient data. The data for analysis was classified as 60% for training and 40% for evaluation. In the model development, we used traditional regression technique such as multiple regression analysis method, artificial intelligence technique such as interactive decision tree, neural network technique, and ensemble technique which integrate all. Model evaluation used Root ASE (Absolute error) index. They were 23.7 by multiple regression, 23.7 by interactive decision tree, 22.7 by neural network and 22.7 by esemble technique. As a result of model evaluation, neural network technique which is artificial intelligence technique was found to be superior. Through this, the utility of artificial intelligence has been proved in the development of the prediction LOS model. In the future, it is necessary to continue research on how to utilize artificial intelligence techniques more effectively in the development of LOS prediction model.

• International Journal of Computer Science & Network Security
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• v.23 no.11
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• pp.99-109
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• 2023

The darknet is frequently referred to as the hub of illicit online activity. In order to keep track of real-time applications and activities taking place on Darknet, traffic on that network must be analysed. It is without a doubt important to recognise network traffic tied to an unused Internet address in order to spot and investigate malicious online activity. Any observed network traffic is the result of mis-configuration from faked source addresses and another methods that monitor the unused space address because there are no genuine devices or hosts in an unused address block. Digital systems can now detect and identify darknet activity on their own thanks to recent advances in artificial intelligence. In this paper, offer a generalised method for deep learning-based detection and classification of darknet traffic. Furthermore, analyse a cutting-edge complicated dataset that contains a lot of information about darknet traffic. Next, examine various feature selection strategies to choose a best attribute for detecting and classifying darknet traffic. For the purpose of identifying threats using network properties acquired from darknet traffic, devised a hybrid deep learning (DL) approach that combines Recurrent Neural Network (RNN) and Bidirectional LSTM (BiLSTM). This probing technique can tell malicious traffic from legitimate traffic. The results show that the suggested strategy works better than the existing ways by producing the highest level of accuracy for categorising darknet traffic using the Black widow optimization algorithm as a feature selection approach and RNN-BiLSTM as a recognition model.

• Journal of Communications and Networks
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• v.13 no.6
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• pp.591-601
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• 2011

Today's Internet which provides communication channels with best-effort end-to-end performance is rapidly evolving into an autonomic global computing platform. Achieving autonomicity in the Future Internet will require a performance architecture that (a) allows users to request and own 'slices' of geographically-distributed host and network resources, (b) measures and monitors end-to-end host and network status, (c) enables analysis of the measurements within expert systems, and (d) provides performance intelligence in a timely manner for application adaptations to improve performance and scalability. We describe the requirements and design of one such "Future Internet performance architecture" (FIPA), and present our reference implementation of FIPA called 'OnTimeMeasure.' OnTimeMeasure comprises of several measurement-related services that can interact with each other and with existing measurement frameworks to enable performance intelligence. We also explain our OnTimeMeasure deployment in the global environment for network innovations (GENI) infrastructure collaborative research initiative to build a sliceable Future Internet. Further, we present an applicationad-aptation case study in GENI that uses OnTimeMeasure-enabled performance intelligence in the context of dynamic resource allocation within thin-client based virtual desktop clouds. We show how a virtual desktop cloud provider in the Future Internet can use the performance intelligence to increase cloud scalability, while simultaneously delivering satisfactory user quality-of-experience.

• KIEAE Journal
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• v.3 no.2
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• pp.59-66
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• 2003

To be the design method supporting aesthetic ability of human, CAAD system should essentially recognize architectural form in the same way of human. In this study, visual perception process of human was analyzed to search proper computational method performing similar step of perception of it. Through the analysis of visual perception, vision was separated to low-level vision and high-level vision. Edge detection and neural network were selected to model after low-level vision and high-level vision. The 24 images of building, tree and landscape were processed by edge detection and trained by neural network. And 24 new images were used to test trained network. The test shows that trained network gives right perception result toward each images with low error rate. This study is on the meaning of artificial intelligence in design process rather than on the design automation strategy through artificial intelligence.

• Journal of Korea Multimedia Society
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• v.25 no.1
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• pp.52-60
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• 2022

Few-shot learning is a sub-area of machine learning problems, which aims to classify target images that only contain a few labeled samples for training. As a representative few-shot learning method, the Prototypical network has been received much attention due to its simplicity and promising results. However, the Prototypical network uses the sample mean of samples from the same class as the prototypes of that class, which easily results in learning uncharacteristic features in the low-data scenery. In this study, we propose to use local descriptors (i.e., patches along the channel within feature maps) from the same class to explicitly obtain more representative prototypes for Prototypical Network so that significant intra-class feature information can be maintained and thus improving the classification performance on few-shot learning tasks. Experimental results on various benchmark datasets including mini-ImageNet, CUB-200-2011, and tiered-ImageNet show that the proposed method can learn more discriminative intra-class features by the local descriptors and obtain more generic prototype representations under the few-shot setting.

• Journal of Information Processing Systems
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• v.18 no.3
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• pp.311-318
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• 2022

Tobacco sales enterprises often need to summarize and verify the daily sales bills, which may consume substantial manpower, and manual verification is prone to occasional errors. The use of artificial intelligence technology to realize the automatic identification and verification of such bills offers important practical significance. This study presents a novel multi-branch residual network for tobacco sales bills to improve the efficiency and accuracy of tobacco sales. First, geometric correction and edge alignment were performed on the input sales bill image. Second, the multi-branch residual network recognition model is established and trained using the preprocessed data. The comparative experimental results demonstrated that the correct recognition rate of the proposed method reached 98.84% on the China Tobacco Bill Image dataset, which is superior to that of most existing recognition methods.

• Advances in Computational Design
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• v.8 no.1
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• pp.37-59
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• 2023

Squat reinforced concrete (SRC) shear walls are a critical part of the structure for both office/residential buildings and nuclear structures due to their significant role in withstanding seismic loads. Despite this, empirical formulae in current design standards and published studies demonstrate a considerable disparity in predicting SRC wall shear strength. The goal of this research is to develop and evaluate hybrid and ensemble artificial neural network (ANN) models. State-of-the-art population-based algorithms are used in this research for hybrid intelligence algorithms. Six models are developed, including Honey Badger Algorithm (HBA) with ANN (HBA-ANN), Hunger Games Search with ANN (HGS-ANN), fitness-distance balance coyote optimization algorithm (FDB-COA) with ANN (FDB-COA-ANN), Averaging Ensemble (AE) neural network, Snapshot Ensemble (SE) neural network, and Stacked Generalization (SG) ensemble neural network. A total of 434 test results of SRC walls is utilized to train and assess the models. The results reveal that the SG model not only minimizes prediction variance but also produces predictions (with R²= 0.99) that are superior to other models.