• 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.

• Smart Media Journal
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• v.13 no.3
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• pp.36-44
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• 2024

Recently, large-scale artificial intelligence (AI) such as ChatGPT have been developed, and as AI is used across various industrial fields, attention is focused on AI chips (semiconductors). AI chips refer to chips designed for calculations for AI algorithms, and many companies at domestic and abroad, such as NVIDIA, Tesla, and ETRI, are developing AI chips. In this paper, we survey research trends on nine types of AI chips. Currently, many attempts have been made to improve the computational performance of most AI chips, and semiconductors for specific purposes are also being designed. In order to compare various AI semiconductors, each chip is analyzed in terms of operation unit, speed, power, and energy efficiency. We introduce currently existing optimization methodologies for AI computation. Based on this, future research directions for AI semiconductors are presented in this paper.

• Journal of the Korean Society of Radiology
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• v.83 no.6
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• pp.1229-1239
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• 2022

Recently, artificial intelligence (AI) technology has shown potential clinical utility in a wide range of MRI fields. In particular, AI models for improving the efficiency of the image acquisition process and the quality of reconstructed images are being actively developed by the MR research community. AI is expected to further reduce acquisition times in various MRI protocols used in clinical practice when compared to current parallel imaging techniques. Additionally, AI can help with tasks such as planning, parameter optimization, artifact reduction, and quality assessment. Furthermore, AI is being actively applied to automate MR image analysis such as image registration, segmentation, and object detection. For this reason, it is important to consider the effects of protocols or devices in MR image analysis. In this review article, we briefly introduced issues related to AI application of MR image acquisition and reconstruction.

• International Journal of Heart Failure
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• v.6 no.1
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• pp.11-19
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• 2024

The prevalence of heart failure (HF) is increasing, necessitating accurate diagnosis and tailored treatment. The accumulation of clinical information from patients with HF generates big data, which poses challenges for traditional analytical methods. To address this, big data approaches and artificial intelligence (AI) have been developed that can effectively predict future observations and outcomes, enabling precise diagnoses and personalized treatments of patients with HF. Machine learning (ML) is a subfield of AI that allows computers to analyze data, find patterns, and make predictions without explicit instructions. ML can be supervised, unsupervised, or semi-supervised. Deep learning is a branch of ML that uses artificial neural networks with multiple layers to find complex patterns. These AI technologies have shown significant potential in various aspects of HF research, including diagnosis, outcome prediction, classification of HF phenotypes, and optimization of treatment strategies. In addition, integrating multiple data sources, such as electrocardiography, electronic health records, and imaging data, can enhance the diagnostic accuracy of AI algorithms. Currently, wearable devices and remote monitoring aided by AI enable the earlier detection of HF and improved patient care. This review focuses on the rationale behind utilizing AI in HF and explores its various applications.

• Advances in nano research
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• v.17 no.4
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• pp.385-399
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• 2024

NEMS (Nano-Electro-Mechanical Systems) devices play a significant role in the advancement of prosthetic hands due to their unique properties at the nanoscale. Their integration enhances the functionality, sensitivity, and performance of prosthetic limbs. Understanding the electro-thermal buckling behavior of such structures is crucial since they may be subjected to extreme heat. So, in this paper, the two-dimensional hyperbolic differential quadrature method (2D-HDQM) integrated with a four-variable refined quasi-3D tangential shear deformation theory (RQ-3DTSDT) in view of the trace of thickness stretching is extended to study electro-thermal buckling response of three-directional poroelastic FG (3D-PFG) circular sector nanoplate patched with piezoelectric layer. Aimed at discovering the real governing equations, coupled equations with the aid of compatibility conditions are employed. Regarding modeling the size-impacts, nonlocal refined logarithmic strain gradient theory (NRLSGT) with two variables called nonlocal and length scale factors is examined. Numerical experimentation and comparison are used to indicate the precision and proficiency related to the created procedure. After obtaining the outputs of the mathematics, an appropriate dataset is used for testing, training and validating of the artificial intelligence. In the results section will be discussed the trace associated with multiple geometrical and physical factors on the electro-thermal buckling performance of the current nanostructure. These findings are essential for the design and optimization of NEMS applications in various fields, including sensing, actuation, and electronics, where thermal stability is paramount. The study's insights contribute to the development of more reliable and efficient NEMS devices, ensuring their robust performance under varying thermal conditions.

• Journal of Intelligence and Information Systems
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• v.29 no.1
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• pp.107-119
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• 2023

The technology that recognizes a soldier's motion and movement status has recently attracted large attention as a combination of wearable technology and artificial intelligence, which is expected to upend the paradigm of troop management. The accuracy of state determination should be maintained at a high-end level to make sure of the expected vital functions both in a training situation; an evaluation and solution provision for each individual's motion, and in a combat situation; overall enhancement in managing troops. However, when input data is given as a timer series or sequence, existing feedforward networks would show overt limitations in maximizing classification performance. Since human behavior data (3-axis accelerations and 3-axis angular velocities) handled for military motion recognition requires the process of analyzing its time-dependent characteristics, this study proposes a high-performance data-driven classifier which utilizes the long-short term memory to identify the order dependence of acquired data, learning to classify eight representative military operations (Sitting, Standing, Walking, Running, Ascending, Descending, Low Crawl, and High Crawl). Since the accuracy is highly dependent on a network's learning conditions and variables, manual adjustment may neither be cost-effective nor guarantee optimal results during learning. Therefore, in this study, we optimized hyperparameters using Bayesian optimization for maximized generalization performance. As a result, the final architecture could reduce the error rate by 62.56% compared to the existing network with a similar number of learnable parameters, with the final accuracy of 98.39% for various military operations.

• Journal of Intelligence and Information Systems
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• v.22 no.4
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• pp.139-155
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• 2016

The purpose of this study is to find the research identity of the Korea Intelligent Information Systems Society through the profiling methods and co-word analysis in the most recent three-year('2014~'2016) study to collect keyword. In order to understand the research identity for intelligence information system, we need that the relative position of the study will be to compare identity by collecting keyword and research methodology of The korea Society of Management Information Systems and Korea Association of Information Systems, as well as Korea Intelligent Information Systems Society for the similar. Also, Korea Intelligent Information Systems Society is focusing on the four research areas such as artificial intelligence/data mining, Intelligent Internet, knowledge management and optimization techniques. So, we analyze research trends with a representative journals for the focusing on the four research areas. A journal of the data-related will be investigated with the keyword and research methodology in Korean Society for Big Data Service and the Korean Journal of Big Data. Through this research, we will find to research trends with research keyword in recent years and compare against the study methodology and analysis tools. Finally, it is possible to know the position and orientation of the current research trends in Korea Intelligent Information Systems Society. As a result, this study revealed a study area that Korea Intelligent Information Systems Society only be pursued through a unique reveal its legitimacy and identity. So, this research can suggest future research areas to intelligent information systems specifically. Furthermore, we will predict convergence possibility of the similar research areas and Korea Intelligent Information Systems Society in overall ecosystem perspectives.

• The Journal of the Acoustical Society of Korea
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• v.28 no.3E
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• pp.109-117
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• 2009

Identity recognition in real environment with a reliable mode is a key issue in human computer interaction (HCI). In this paper, we present a robust person identification system considering score-based optimal reliability measure of audio-visual modalities. We propose an extension of the modified reliability function by introducing optimizing parameters for both of audio and visual modalities. For degradation of visual signals, we have applied JPEG compression to test images. In addition, for creating mismatch in between enrollment and test session, acoustic Babble noises and artificial illumination have been added to test audio and visual signals, respectively. Local PCA has been used on both modalities to reduce the dimension of feature vector. We have applied a swarm intelligence algorithm, i.e., particle swarm optimization for optimizing the modified convection function's optimizing parameters. The overall person identification experiments are performed using VidTimit DB. Experimental results show that our proposed optimal reliability measures have effectively enhanced the identification accuracy of 7.73% and 8.18% at different illumination direction to visual signal and consequent Babble noises to audio signal, respectively, in comparison with the best classifier system in the fusion system and maintained the modality reliability statistics in terms of its performance; it thus verified the consistency of the proposed extension.

• Industrial Engineering and Management Systems
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• v.6 no.2
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• pp.94-105
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• 2007

Measurement technology plays an important role in discrete manufacturing industry. Probe-type coordinate measuring machines (CMMs) are normally used to capture the geometry of part features. The measured points are then fit to verify a specified geometry by using the least squares method (LSQ). However, it occasionally overestimates the tolerance zone, which leads to the rejection of some good parts. To overcome this drawback, minimum zone approaches defined by the ANSI Y14.5M-1994 standard have been extensively pursued for zone fitting in coordinate form literature for such basic features as plane, circle, cylinder and sphere. Meanwhile, complex features such as torus have been left to be dealt-with by the use of profile tolerance definition. This may be impractical when accuracy of the whole profile is desired. Hence, the true deviation model of torus is developed and then formulated as a minimax problem. Next, a relatively new and simple population based evolutionary approach, particle swarm optimization (PSO), is applied by imitating the social behavior of animals to find the minimum tolerance zone torusity. Simulated data with specified torusity zones are used to validate the deviation model. The torusity results are in close agreement with the actual torusity zones and also confirm the effectiveness of the proposed PSO when compared to those of the LSQ.

• Journal of Intelligence and Information Systems
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• v.6 no.2
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• pp.41-47
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• 2000

Ideograph is a truly unifies data and procedural dependencies. Ideograph can be used to assist various program optimization, such as common expression elimination, code motion, constant folding etc. In this paper, we propose an improved representation of the data and control flow dependencies information for the efficient program execution. In pursuing this goal, we propose a model and in particularly implement a dependency information extractor and information table, which contains data and control flow information per a basic block And then we design and implementation of the optimized abstract syntax tree using Ideograph which has a control flow information and data flow information for source program.