• International Journal of Computer Science & Network Security
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• v.21 no.3
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• pp.177-184
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• 2021

All over the world, people are affected by many chronic diseases and medical practitioners are working hard to find out the symptoms and remedies for the diseases. Many researchers focus on the feature detection of the disease and trying to get a better health recommendation system. It is necessary to detect the features automatically to provide the most relevant solution for the disease. This research gives the framework of Health Recommendation System (HRS) for identification of relevant and non-redundant features in the dataset for prediction and recommendation of diseases. This system consists of three phases such as Pre-processing, Feature Selection and Performance evaluation. It supports for handling of missing and noisy data using the proposed Imputation of missing data and noise detection based Pre-processing algorithm (IMDNDP). The selection of features from the pre-processed dataset is performed by proposed ensemble-based feature selection using an expert's knowledge (EFS-EK). It is very difficult to detect and monitor the diseases manually and also needs the expertise in the field so that process becomes time consuming. Finally, the prediction and recommendation can be done using Support Vector Machine (SVM) and rule-based approaches.

• The Journal of the Korea institute of electronic communication sciences
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• v.17 no.6
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• pp.1283-1292
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• 2022

At present, several artificial intelligence projects in the healthcare and medical field are competing with each other, and the interfaces between the systems lack unified specifications. Thus, this study presents an artificial intelligence platform for healthcare and medical fields which adopts the deep learning technology to provide algorithms, models and service support for the health and medical enterprise applications. The suggested platform can provide a large number of heterogeneous data processing, intelligent services, model managements, typical application scenarios, and other services for different types of business. In connection with the suggested platform application, we represents a medical service which is corresponding to the trusted and comprehensible tracking and analyzing patient behavior system for Health and Medical treatment using Internet of Behavior concept.

• International Journal of Computer Science & Network Security
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• v.22 no.10
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• pp.237-245
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• 2022

The prominence of IoTs (Internet of Things) and exponential advancement of computer networks has resulted in massive essential applications. Recognizing various cyber-attacks or anomalies in networks and establishing effective intrusion recognition systems are becoming increasingly vital to current security. MLTs (Machine Learning Techniques) can be developed for such data-driven intelligent recognition systems. Researchers have employed a TFDNNs (Tensor Flow Deep Neural Networks) and DCNNs (Deep Convolution Neural Networks) to recognize pirated software and malwares efficiently. However, tuning the amount of neurons in multiple layers with activation functions leads to learning error rates, degrading classifier's reliability. HTFDNNs ( Hybrid tensor flow DNNs) and MRNs (Modified Residual Networks) or Resnet CNNs were presented to recognize software piracy and malwares. This study proposes HTFDNNs to identify stolen software starting with plagiarized source codes. This work uses Tokens and weights for filtering noises while focusing on token's for identifying source code thefts. DLTs (Deep learning techniques) are then used to detect plagiarized sources. Data from Google Code Jam is used for finding software piracy. MRNs visualize colour images for identifying harms in networks using IoTs. Malware samples of Maling dataset is used for tests in this work.

• International conference on construction engineering and project management
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• 2009.05a
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• pp.230-236
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• 2009

Double-front construction machinery, which was designed for complicated tasks, requires intelligent systems that can provide the quantitative work analysis needed to determine effective work procedures and that can provide operational and cognitive support for operators. Construction work environments are extremely complicated, however, and this makes state identification difficult. We therefore defined primitive static states (PSS) that are determined using on-off data for the lever inputs and manipulator loads for each part of the grapple and front and that are completely independent of the various environmental conditions and operator skill levels. To confirm the usefulness of PSS, we performed experiments with a demolition task by using our virtual reality simulator. We confirmed that PSS could robustly and accurately identify the work states and that untrained skills could be easily inferred from the PSS-based work analysis. We also confirmed in skill-training experiments that advice information using PSS-based skill analysis greatly improved work performance. We thus confirmed that PSS can adequately identify work states and are useful for work analysis and skill improvement.

• Proceedings of the Korea Information Processing Society Conference
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• 2020.11a
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• pp.673-676
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• 2020

In 21 century, we entered the multiculture society. Multi cultural society became a trend. It is known that 65.5 percents of Koreans find difficuty in using foreign languages. The need for intellignet and machine translation is increasing rapidly. This paper suggests a translation system which is mainly using in MICE industry. There are many applications provide translation, however, using multuple translayion appication is rare. We provide untact service which can import many application tools to provide a better service.

• Proceedings of the Korea Information Processing Society Conference
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• 2020.11a
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• pp.80-82
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• 2020

전통적으로 기초 과학 분야의 대규모 워크로드 작업들은 슈퍼컴퓨터와 같은 대용량 클러스터 시스템을 이용하여 수행해왔다. 그러나 최근 빅데이터 및 머신 러닝과 같은 새로운 분야에서의 컴퓨팅 자원 요구가 증가하고 기존 사용자의 요구 사항도 다양해짐에 따라 기존의 클러스터 시스템 운영 환경에서는 많은 어려움이 나타나고 있다. 이러한 문제를 해결하기 위해 한국과학기술정보연구원(KISTI)에서는 지난 3 월부터 KI (KISTI Intelligent) Cloud 서비스를 개발하여 서비스를 제공하고 있다. KI Cloud 서비스는 다음과 같은 특징이 있다. 첫째, Jupyter 과 RStudio 와 같은 대화형 개발 환경을 웹을 통해 제공함으로써 사용자는 언제, 어디서나 손쉽게 서비스를 활용할 수 있다. 둘째, 컨테이너 기술을 활용하여 사용자가 요구하는 개발 및 실행 환경을 실시간으로 구성하여 제공한다. 셋째, 사용자의 서비스 환경을 동적으로 구성하여 제공함으로써 컴퓨팅 자원의 효율성을 높일 수 있다.

• International Journal of Computer Science & Network Security
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• v.23 no.7
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• pp.131-140
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• 2023

Network security situational awareness systems helps in better managing the security concerns of a network, by monitoring for any anomalies in the network connections and recommending remedial actions upon detecting an attack. An Intrusion Detection System helps in identifying the security concerns of a network, by monitoring for any anomalies in the network connections. We have proposed a CRF based IDS system using genetic search feature selection algorithm for network security situational awareness to detect any anomalies in the network. The conditional random fields being discriminative models are capable of directly modeling the conditional probabilities rather than joint probabilities there by achieving better classification accuracy. The genetic search feature selection algorithm is capable of identifying the optimal subset among the features based on the best population of features associated with the target class. The proposed system, when trained and tested on the bench mark NSL-KDD dataset exhibited higher accuracy in identifying an attack and also classifying the attack category.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.33 no.6
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• pp.1067-1076
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• 2023

Recently, as variant malware has increased, the scale of cyber hacking incidents is expanding. To respond to intelligent cyberhacking attack, machine learning-based research is actively underway to effectively classify malware families. However, existing classification models have problems where performance deteriorates when the dataset is obfuscated or sparse. In this paper, we propose a hybrid dataset that combines features extracted from ASM files and BYTES files, and evaluate classification performance using FNN. As a result of the experiment, the proposed method showed performance improvement of about 4% compared to a single dataset, and in particular, performance improvement of about 30% for rare families.

• International Journal of Computer Science & Network Security
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• v.23 no.12
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• pp.199-203
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• 2023

In recent decades, the growth of communication technology has resulted in an explosion of data-related information. Ontology perception is being used as a growing requirement to integrate data and unique functionalities. Ontologies are not only critical for transforming the traditional web into the semantic web but also for the development of intelligent applications that use semantic enrichment and machine learning to transform data into smart data. To address these unclear facts, several researchers have been focused on expanding ontologies and semantic web technologies. Due to the lack of clear-cut limitations, ontologies would not suffice to deliver uncertain information among domain ideas, conceptual formalism supplied by traditional. To deal with this ambiguity, it is suggested that fuzzy ontologies should be used. It employs Ontology to introduce fuzzy logical policies for ambiguous area concepts such as darkness, heat, thickness, creaminess, and so on in a device-readable and compatible format. This survey efforts to provide a brief and conveniently understandable study of the research directions taken in the domain of ontology to deal with fuzzy information; reconcile various definitions observed in scientific literature, and identify some of the domain's future research-challenging scenarios. This work is hoping that this evaluation can be treasured by fuzzy ontology scholars. This paper concludes by the way of reviewing present research and stating research gaps for buddy researchers.

• The Journal of the Korea Contents Association
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• v.20 no.7
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• pp.618-628
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• 2020

The purpose of this paper is to propose a new security orchestration service model by combining various security solutions that have been introduced and operated individually as a basis for cyber security framework. At present, in order to respond to various and intelligent cyber attacks, various single security devices and SIEM and AI solutions that integrate and manage them have been built. In addition, a cyber security framework and a security control center were opened for systematic prevention and response. However, due to the document-oriented cybersecurity framework and limited security personnel, the reality is that it is difficult to escape from the control form of fragmentary infringement response of important detection events of TMS / IPS. To improve these problems, based on the model of this paper, select the targets to be protected through work characteristics and vulnerable asset identification, and then collect logs with SIEM. Based on asset information, we established proactive methods and three detection strategies through threat information. AI and SIEM are used to quickly determine whether an attack has occurred, and an automatic blocking function is linked to the firewall and IPS. In addition, through the automatic learning of TMS / IPS detection events through machine learning supervised learning, we improved the efficiency of control work and established a threat hunting work system centered on big data analysis through machine learning unsupervised learning results.