• Smart Media Journal
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• v.12 no.4
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• pp.59-59
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• 2023

• Smart Media Journal
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• v.12 no.4
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• pp.58-58
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• 2023

• Smart Media Journal
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• v.12 no.9
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• pp.171-171
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• 2023

• Smart Media Journal
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• v.12 no.9
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• pp.172-172
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• 2023

• Smart Media Journal
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• v.13 no.9
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• pp.38-38
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• 2024

• Smart Media Journal
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• v.12 no.5
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• pp.122-122
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• 2023

• Smart Media Journal
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• v.8 no.3
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• pp.17-22
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• 2019

Signature-based intrusion systems use intrusion detection rules for detecting intrusion. However, writing intrusion detection rules is difficult and requires considerable knowledge of various fields. Attackers may modify previous attempts to escape intrusion detection rules. In this paper, we deal with the problem of detecting modified attacks based on previous intrusion detection rules. We show a simple method of reporting approximate occurrences of at least one of the network intrusion detection rules, based on q-grams and the longest increasing subsequences. Experimental results showed that our approach could detect modified attacks, modeled with edit operations.

• Smart Media Journal
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• v.13 no.1
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• pp.24-35
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• 2024

The combination of Artificial Intelligence and the Internet of Things (AIoT) has gained significant popularity. Deep neural networks (DNNs) have demonstrated remarkable success in various applications. However, deploying complex AI models on embedded boards can pose challenges due to computational limitations and model complexity. This paper presents an AIoT-based system for smart parking lots using edge devices. Our approach involves developing a detection model and a decision tree for occupancy status classification. Specifically, we utilize YOLOv5 for car license plate (LP) detection by verifying the position of the license plate within the parking space.

• Smart Media Journal
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• v.9 no.3
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• pp.59-70
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• 2020

This paper presents a fully automatic tool to recognize the liver region from CT images based on a deep learning model, namely Multiple Filter U-net, MFUnet. The advantages of both U-net and Multiple Filters were utilized to construct an autoencoder model, called MFUnet for segmenting the liver region from computed tomograph. The MFUnet architecture includes the autoencoding model which is used for regenerating the liver region, the backbone model for extracting features which is trained on ImageNet, and the predicting model used for liver segmentation. The LiTS dataset and Chaos dataset were used for the evaluation of our research. This result shows that the integration of Multiple Filter to U-net improves the performance of liver segmentation and it opens up many research directions in medical imaging processing field.

• Smart Media Journal
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• v.9 no.3
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• pp.116-121
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• 2020

Digital Information Display and KIOSK have a problem that initial introduction cost and maintenance cost due to the development cost of dedicated contents and installation cost are high due to the characteristics of the product. In order to solve these problems, We designed and implemented of voice transmission system using Open Source Hardware and Event based Non-Blocking I/O Algorithm.