• International Journal of Computer Science & Network Security
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• v.24 no.7
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• pp.11-23
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• 2024

Triage is a practice of accurately prioritizing patients in emergency department (ED) based on their medical condition to provide them with proper treatment service. The variation in triage assessment among medical staff can cause mis-triage which affect the patients negatively. Developing ED triage system based on machine learning (ML) techniques can lead to accurate and efficient triage outcomes. This study aspires to develop a triage system using machine learning techniques to predict ED triage levels using patients' information. We conducted a retrospective study using Security Forces Hospital ED data, from 2021 through 2023 during Hajj period in Saudia Arabi. Using demographics, vital signs, and chief complaints as predictors, two machine learning models were investigated, naming gradient boosted decision tree (XGB) and deep neural network (DNN). The models were trained to predict ED triage levels and their predictive performance was evaluated using area under the receiver operating characteristic curve (AUC) and confusion matrix. A total of 11,584 ED visits were collected and used in this study. XGB and DNN models exhibit high abilities in the predicting performance with AUC-ROC scores 0.85 and 0.82, respectively. Compared to the traditional approach, our proposed system demonstrated better performance and can be implemented in real-world clinical settings. Utilizing ML applications can power the triage decision-making, clinical care, and resource utilization.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.24-27
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• 2004

This paper describes the method how to control an autonomous robot remotely using Internet. The autonomous robot that has an artificial brain is called "Tarou". (1) It is able to move along the line on the floor based on processing the image data obtained from two CCD cameras. (2) It is able to understand dialogs between human being and it and is able to take actions such as turn right and lefts, go forward 1m and go backward 0.5m, etc. (3) It is able to recognize patterns of objects. (4) It is able to recognize human faces. (5) It is able to communicate human being and to speak according to contents written in the program. We show the techniques to control the autonomous robot "Tarou" remotely by personal computer and/or portable Phone via Internet. The techniques developed in our research could dramatically increase their performance for..the need of artificial life robot as the next generation robot and national homeland security needs.

• Journal of Korea Multimedia Society
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• v.18 no.6
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• pp.710-722
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• 2015

Counterfeit products have been a major concern in global market. With the emergence of RFID systems, to detect counterfeit products in supply chain is relatively easy. Many anti-counterfeiting techniques for products attached by RFID tag are proposed. Most of the previous anti-counterfeiting techniques are not considering the distribution of the counterfeit from a customer to a customer. Using the ownership transfer protocols we can prevent the counterfeit from being distributed on the supply chain as well as between the customers and the customers. The ownership transfer protocols must be modified for anti-counterfeiting because of the usage of the protocol. In this paper, we modify the ownership transfer protocol proposed by G. Kapoor and S. Piramuthu[1] to be able to detect the counterfeit and track and trace the products in the supply chain. Our proposed protocol consists of three phases: the products delivery phase, the products takeover phase, and the products sale phase. We show that our protocol is anti-counterfeiting as well as secure against the security attacks.

• Korean Security Journal
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• no.5
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• pp.109-130
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• 2002

As proceeding Munmin and Kukmin's government, it is to bring in regionalism of genernal public administration and police administration, specialization, efficiency issue, demand inventing of audit technique to meet this trends. Especially, according to supporting qualitative improvement of the audit, its environment faced that orienting performance audit emphasis on not the legality but the efficiency more systematic and scientific theory or technique. In order to attain police audit's efficiency through performance audit, this study discussed that scientific management techniques should be applied police audit. Accordingly, the primary purpose of this study is to apply public audit to scientific management technique, bring to light limits in public sector(especially, police sector). To be efficiency audit(namely, performance audit), 1) OR techniques are explained linear programming, network modeling, PERT/CPM, queuing matrix model, simulation, 2) Statistical analysis methods are argued delphi technique, data envelopment analysis(DEA), analytic hierarchical process(AHP), time series analysis models etc.

• Convergence Security Journal
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• v.9 no.2
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• pp.41-48
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• 2009

Distributed denial of service attack detection for more development and research is underway. The method of using statistical techniques, the normal packets and abnormal packets to identify efficient. In this paper several statistical techniques, using a mix of various offers a way to detect the attack. To verify the effectiveness of the proposed technique, it set packet filtering on router and the proposed DDoS attacks detection method on a Linux router. In result, the proposed technique was detect various attacks and provide normal service mostly.

• Proceedings of the Korean Information Science Society Conference
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• 2005.07a
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• pp.412-414
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• 2005

Because a sensor node must operate on a tiny battery, the goal to eliminate energy inefficiencies leads the current researchers excavating for new techniques to advocate. As sensor networks edge closer towards wide spread deployment, security issues become a central concern. So far much research has focused on making sensor networks feasible and useful, and has not concentrated much on security issues especially computationally inexpensive techniques. In this paper we introduce a simple scheme relying on one-way hash-functions that greatly enhances location privacy by changing traceable identifiers on every read getting by with only a single, unreliable message exchange. Thereby the scheme is safe from many threats like eavesdropping, message interception, spoofing, and replay attacks.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.18 no.10
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• pp.2579-2585
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• 2014

Location-Based Services can be defined as services that integrated a mobile network device's position with other information so as to provide added value to users. One of the most important elements in the LBS is the ability to locate objects. In this paper, the positioning techniques using radio signal were introduced, and the positioning principles and accuracies for LBS of smart phone were analyzed. As a result, the some techniques for improving user security were suggested.

• International Journal of Computer Science & Network Security
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• v.21 no.9
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• pp.155-162
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• 2021

The expansion of the world wide web has led to a huge amount of user generated content over different forums and social media platforms, these rich data resources offer the opportunity to reflect, and track changing public sentiments and help to develop proactive reactions strategies for decision and policy makers. Analysis of public emotions and opinions towards events and sentimental trends can help to address unforeseen areas of public concerns. The need of developing systems to analyze these sentiments and the topics behind them has emerged tremendously. While most existing works reported in the literature have been carried out in English, this paper, in contrast, aims to review recent research works in Arabic language in the field of thematic sentiment analysis and which techniques they have utilized to accomplish this task. The findings show that the prevailing techniques in Arabic topic-based sentiment analysis are based on traditional approaches and machine learning methods. In addition, it has been found that considerably limited recent studies have utilized deep learning approaches to build high performance models.

• International Journal of Computer Science & Network Security
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• v.21 no.6
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• pp.77-88
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• 2021

Heart Failure represents a critical pathological case that is challenging to predict and discover at an early age, with a notable increase in morbidity and mortality. Machine Learning and Neural Network techniques play a crucial role in predicting heart attacks, diseases and more. These techniques give valuable perspectives for clinicians who may then adjust their diagnosis for each individual patient. This paper evaluated neural network models for heart attacks predictions. Several online learning methods were investigated to automatically and accurately predict heart attacks. The UCI dataset was used in this work to train and evaluate First Order and Second Order Online Learning methods; namely Backpropagation, Delta bar Delta, Levenberg Marquardt and QuickProp learning methods. An optimizer technique was also used to minimize the random noise in the database. A regularization concept was employed to further improve the generalization of the model. Results show that a three layers' NN model with a Backpropagation algorithm and Nadam optimizer achieved a promising accuracy for the heart attach prediction tasks.

• International Journal of Computer Science & Network Security
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• v.21 no.1
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• pp.1-5
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• 2021

Cyberbullying is a problem that is faced in many cultures. Due to their popularity and interactive nature, social media platforms have also been affected by cyberbullying. Social media users from Arab countries have also reported being a target of cyberbullying. Machine learning techniques have been a prominent approach used by scientists to detect and battle this phenomenon. In this paper, we compare different machine learning algorithms for their performance in cyberbullying detection based on a labeled dataset of Arabic YouTube comments. Three machine learning models are considered, namely: Multinomial Naïve Bayes (MNB), Complement Naïve Bayes (CNB), and Linear Regression (LR). In addition, we experiment with two feature extraction methods, namely: Count Vectorizer and Tfidf Vectorizer. Our results show that, using count vectroizer feature extraction, the Logistic Regression model can outperform both Multinomial and Complement Naïve Bayes models. However, when using Tfidf vectorizer feature extraction, Complement Naive Bayes model can outperform the other two models.