• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.11
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• pp.1500-1506
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• 2020

In this paper, we study a scheduling problem based on reinforcement learning for overlay device-to-device (D2D) communication networks. Even though various technologies for D2D communication networks using Q-learning, which is one of reinforcement learning models, have been studied, Q-learning causes a tremendous complexity as the number of states and actions increases. In order to solve this problem, D2D communication technologies based on Deep Q Network (DQN) have been studied. In this paper, we thus design a DQN model by considering the characteristics of wireless communication systems, and propose a distributed scheduling scheme based on the DQN model that can reduce feedback and signaling overhead. The proposed model trains all parameters in a centralized manner, and transfers the final trained parameters to all mobiles. All mobiles individually determine their actions by using the transferred parameters. We analyze the performance of the proposed scheme by computer simulation and compare it with optimal scheme, opportunistic selection scheme and full transmission scheme.

• International Journal of Computer Science & Network Security
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• v.22 no.8
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• pp.328-342
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• 2022

The Internet of Things (IoT) is a technology that offers lucrative services in various industries to facilitate human communities. Important information on people and their surroundings has been gathered to ensure the availability of these services. This data is vulnerable to cybersecurity since it is sent over the internet and kept in third-party databases. Implementation of data encryption is an integral approach for IoT device designers to protect IoT data. For a variety of reasons, IoT device designers have been unable to discover appropriate encryption to use. The static support provided by research and concerned organizations to assist designers in picking appropriate encryption costs a significant amount of time and effort. IoTES is a web app that uses machine language to address a lack of support from researchers and organizations, as ML has been shown to improve data-driven human decision-making. IoTES still has some weaknesses, which are highlighted in this research. To improve the support, these shortcomings must be addressed. This study proposes the "IoTES with Security" model by adding support for the security level provided by the encryption algorithm to the traditional IoTES model. We evaluated our technique for encryption algorithms with available security levels and compared the accuracy of our model with traditional IoTES. Our model improves IoTES by helping users make security-oriented decisions while choosing the appropriate algorithm for their IoT data.

• International Journal of Computer Science & Network Security
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• v.21 no.11
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• pp.301-311
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• 2021

The technological advancements of the last two decades directed the era of the Internet of Things (IoT). IoT enables billions of devices to connect through the internet and share their information and resources on a global level. These devices can be anything, from smartphones to embedded sensors. The main purpose of IoT is to make devices capable of achieving the desired goal with minimal to no human intervention. Although it hascome as a social and economic blessing, it still brought forward many security risks. This paper focuses on providing a survey of the most commonly used application layer protocols in the IoT domain, namely, Constrained Application Protocol (CoAP), Message Queuing Telemetry Transport (MQTT), Advanced Message Queuing Protocol (AMQP), and Extensible Messaging and Presence Protocol (XMPP). MQTT, AMQP, and XMPP use TCP for device-to-device communication, while CoAP utilizes UDP to achieve this purpose. MQTT and AMQP are based on a publish/subscribe model, CoAP uses the request/reply model for its structuring. In addition to this, the quality of service provision of MQTT, AMQP, and CoAP is not very high, especially when the deliverance of messages is concerned. The selection of protocols for each application is very a tedious task.This survey discusses the architectures, advantages, disadvantages, and applications of each of these protocols. The main contribution of this work is to describe each of the aforementioned application protocols in detail as well as providing their thorough comparative analysis. This survey will be helpful to the developers in selecting the protocol ideal for their system and/or application.

• The Journal of the Convergence on Culture Technology
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• v.8 no.4
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• pp.375-380
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• 2022

The purpose of this study is to find out consumers' perception and meaning of convenience store convenience food by using big data. For this study, NNAVER and Daum analyzed news, intellectuals, blogs, cafes, intellectuals(tips), and web documents, and used 'convenience store convenience food' as keywords for data search. The data analysis period was selected as 3 years from January 1, 2019 to December 31, 2021. For data collection and analysis, frequency and matrix data were extracted using TEXTOM, and network analysis and visualization analysis were conducted using the NetDraw function of the UCINET 6 program. As a result, convenience store convenience foods were clustered into health, diversity, convenience, and economy according to consumers' selection attributes. It is expected to be the basis for the development of a new convenience menu that pursues convenience and convenience based on consumers' meaning of convenience store convenience foods such as appropriate prices, discount coupons, and events.

• International Journal of Computer Science & Network Security
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• v.22 no.6
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• pp.390-399
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• 2022

Cyber security and resilience are phrases that describe safeguards of ICTs (information and communication technologies) from cyber-attacks or mitigations of cyber event impacts. The sole purpose of Risk models are detections, analyses, and handling by considering all relevant perceptions of risks. The current research effort has resulted in the development of a new paradigm for safeguarding services offered online which can be utilized by both service providers and users. customers. However, rather of relying on detailed studies, this approach emphasizes task selection and execution that leads to successful risk treatment outcomes. Modelling intelligent CSGs (Cyber Security Games) using MLTs (machine learning techniques) was the focus of this research. By limiting mission risk, CSGs maximize ability of systems to operate unhindered in cyber environments. The suggested framework's main components are the Threat and Risk models. These models are tailored to meet the special characteristics of online services as well as the cyberspace environment. A risk management procedure is included in the framework. Risk scores are computed by combining probabilities of successful attacks with findings of impact models that predict cyber catastrophe consequences. To assess successful attacks, models emulating defense against threats can be used in topologies. CSGs consider widespread interconnectivity of cyber systems which forces defending all multi-step attack paths. In contrast, attackers just need one of the paths to succeed. CSGs are game-theoretic methods for identifying defense measures and reducing risks for systems and probe for maximum cyber risks using game formulations (MiniMax). To detect the impacts, the attacker player creates an attack tree for each state of the game using a modified Extreme Gradient Boosting Decision Tree (that sees numerous compromises ahead). Based on the findings, the proposed model has a high level of security for the web sources used in the experiment.

• International Journal of Computer Science & Network Security
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• v.22 no.6
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• pp.131-138
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• 2022

Novel Coronavirus (COVID-19) is viewed as one of the main general wellbeing theaters on the worldwide level all over the planet. Because of the abrupt idea of the flare-up and the irresistible force of the infection, it causes individuals tension, melancholy, and other pressure responses. The avoidance and control of the novel Covid pneumonia have moved into an imperative stage. It is fundamental to early foresee and figure of infection episode during this troublesome opportunity to control of its grimness and mortality. The entire world is investing unimaginable amounts of energy to fight against the spread of this lethal infection. In this paper, we utilized machine learning and deep learning techniques for analyzing what is going on utilizing countries shared information and for detecting the climate factors that effect on spreading Covid-19, such as humidity, sunny hours, temperature and wind speed for understanding its regular dramatic way of behaving alongside the forecast of future reachability of the COVID-2019 around the world. We utilized data collected and produced by Kaggle and the Johns Hopkins Center for Systems Science. The dataset has 25 attributes and 9566 objects. Our Experiment consists of two phases. In phase one, we preprocessed dataset for DL model and features were decreased to four features humidity, sunny hours, temperature and wind speed by utilized the Pearson Correlation Coefficient technique (correlation attributes feature selection). In phase two, we utilized the traditional famous six machine learning techniques for numerical datasets, and Dense Net deep learning model to predict and detect the climatic factor that aide to disease outbreak. We validated the model by using confusion matrix (CM) and measured the performance by four different metrics: accuracy, f-measure, recall, and precision.

• The korean journal of orthodontics
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• v.52 no.5
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• pp.313-323
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• 2022

Objective: This study aimed to estimate the clinical effects of different types of bone-anchored maxillary protraction devices by using a network meta-analysis. Methods: We searched seven databases for randomized and controlled clinical trials that compared bone-anchored maxillary protraction with tooth-anchored maxillary protraction interventions or untreated groups up to May 2021. After literature selection, data extraction, and quality assessment, we calculated the mean differences, 95% confidence intervals, and surface under the cumulative ranking scores of eleven indicators. Statistical analysis was performed using R statistical software with the GeMTC package based on the Bayesian framework. Results: Six interventions and 667 patients were involved in 18 studies. In comparison with the tooth-anchored groups, the bone-anchored groups showed significantly more increases in Sella-Nasion-Subspinale (°), Subspinale-Nasion-Supramentale(°) and significantly fewer increases in mandibular plane angle and the labial proclination angle of upper incisors. In comparison with the control group, Sella-Nasion-Supramentale(°) decreased without any statistical significance in all treated groups. IMPA (angle of lower incisors and mandibular plane) decreased in groups with facemasks and increased in other groups. Conclusions: Bone-anchored maxillary protraction can promote greater maxillary forward movement and correct the Class III intermaxillary relationship better, in addition to showing less clockwise rotation of mandible and labial proclination of upper incisors. However, strengthening anchorage could not inhibit mandibular growth better and the lingual inclination of lower incisors caused by the treatment is related to the use of a facemask.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.271-271
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• 2023

수문학적 기준지점(HRS, Reference Hydrological Station)은 유량의 변동성의 장기적인 추세를 파악하기 위해 고품질의 자료를 생산하는 관측소를 의미한다. 선진외국의 경우 운영목적과 수문학적 기준지점의 정의는 조금씩 다르지만 유사한 개념의 관측소를 운영하고 있다. 호주의 경우 기후변화에 따른 장기간의 수자원 부존량의 변화를 예측하기 위한 모니터링 지점으로 정의하며, 미국의 경우 시간에 따른 수문학적 특성의 자연적인 변화 및 인간의 활동에 따른 수문환경의 변화에 대한 연구를 위한 기준값을 제공하기 위해 참조지점(HBN, Hydrological Benchmark Network)의 자료를 제공하기 위해 운영한다. 영국은 기후변화에 따른 유역의 수문학적 응답을 조사할 목적으로 참조지점(RHN, Reference Hydrological Network)을 운영하고 있으며, 주로 자연유역에 설치하여 운영하고 있다. WMO는 2006년 '기후연구를 위한 적절한 유량관측소'를 선정해 줄 것을 회원국에 요청하고, 관련 자료의 데이터베이스를 독일의 GRDC(Global Runoff Data Centre)에 수집하고 있다. 국외의 경우 '자연에 가까운 유역특성을 갖는 하천 유량관측망 중 양질의 자료를 보유하고 있는 관측소'를 고려하여 수문학적 기준지점을 선정한다. 하지만 우리나라의 경우 장기간의 유량자료를 보유하고 있는 관측소가 상대적으로 부족하고, 장기간의 유량자료를 보유한 지점 또한 홍수예보, 댐 운영 등 물관리 업무에 직접 활용하기 위해 대하천의 본류 중심으로 자료를 생산하고 있다. 따라서 현재를 기준으로 국제적으로 통용되는 기준에 부합하는 기준관측소를 선정하는 것은 곤란한 상황으로 미래에 수문학적 기준지점이 될 수 있는 관측소를 선정하여 장기간 모니터링을 통해 기준관측소를 확대해 나갈 필요가 있다. 본 연구에서는 국외의 수문학적 기준관측소 선정기준을 비교 검토하여 우리나라 실정에 맞는 기준관측소 선정기준을 개발하였다. 선정 기준은 ① 유역의 개발정도, ② 댐·저수지 등 인위적인 조절 정도, ③ 취수량 또는 방류량 등 유역간의 물 이동, ④ 유량자료의 보유기간 및 정확도 등을 고려하여 기준을 설정하였다. 또한 기준지점의 선정을 위한 절차를 ① 수위관측소 사전목록의 작성, ② 관측소 정보 분석(유역특성, 시계열자료 등), ③ 수문학적 기준관측소 후보 선정, ④ 유관기관 및 전문가 검토를 통한 우선순위 선정 등 4단계로 구분하여 제시하였다.

• Restorative Dentistry and Endodontics
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• v.46 no.3
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• pp.41.1-41.23
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• 2021

Objectives: This study aimed to evaluate the efficacy of various local anesthesia (LA) in vital asymptomatic teeth. Materials and Methods: Randomized controlled trials comparing pulpal anesthesia of various LA on vital asymptomatic teeth were included in this review. Searches were conducted in the Cochrane CENTRAL, MEDLINE (via PubMed), EMBASE, ClinicalTrials.gov, Google Scholar and 3 field-specific journals from inception to May 3, 2019. Study selection, data extraction, and risk of bias assessment using Cochrane Risk of Bias Tool were done by 2 independent reviewers in duplicate. Network meta-analysis (NMA) was performed within the frequentist setting using STATA 15.0. The LA was ranked, and the surface under the cumulative ranking (SUCRA) line was plotted. The confidence of the NMA estimates was assessed using the CINeMA web application. Results: The literature search yielded 1,678 potentially eligible reports, but only 42 were included in this review. For maxillary buccal infiltration, articaine 4% with epinephrine 1:100,000 was more efficacious than lidocaine 2% with epinephrine 1:100,000 (odds ratio, 2.11; 95% confidence interval, 1.14-3.89). For mandibular buccal infiltration, articaine 4% with epinephrine 1:100,000 was more efficacious than various lidocaine solutions. The SUCRA ranking was highest for articaine 4% with epinephrine when used as maxillary and mandibular buccal infiltrations, and lidocaine 2% with epinephrine 1:80,000 when used as inferior alveolar nerve block. Inconsistency and imprecision were detected in some of the NMA estimates. Conclusions: Articaine 4% with epinephrine is superior when maxillary or mandibular infiltration is required in vital asymptomatic teeth.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.11
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• pp.3099-3120
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• 2023

The present fast-moving era brings a serious stress issue that affects elders and youngsters. Everyone has undergone stress factors at least once in their lifetime. Stress is more among youngsters as they are new to the working environment. whereas the stress factors for elders affect the individual and overall performance in an organization. Electroencephalogram (EEG) based stress level classification is one of the widely used methodologies for stress detection. However, the signal processing methods evolved so far have limitations as most of the stress classification models compute the stress level in a predefined environment to detect individual stress factors. Specifically, machine learning based stress classification models requires additional algorithm for feature extraction which increases the computation cost. Also due to the limited feature learning characteristics of machine learning algorithms, the classification performance reduces and inaccurate sometimes. It is evident from numerous research works that deep learning models outperforms machine learning techniques. Thus, to classify all the emotions based on stress level in this research work a hybrid deep learning algorithm is presented. Compared to conventional deep learning models, hybrid models outperforms in feature handing. Better feature extraction and selection can be made through deep learning models. Adding machine learning classifiers in deep learning architecture will enhance the classification performances. Thus, a hybrid convolutional neural network model was presented which extracts the features using CNN and classifies them through machine learning support vector machine. Simulation analysis of benchmark datasets demonstrates the proposed model performances. Finally, existing methods are comparatively analyzed to demonstrate the better performance of the proposed model as a result of the proposed hybrid combination.