• International Journal of Internet, Broadcasting and Communication
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• 제14권3호
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• pp.285-291
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• 2022

The number of people enrolling in universities is rising due to the simplicity of applying and the benefit of earning a bachelor's degree. However, the on-time graduation rate has declined since plenty of students fail to complete their courses and take longer to get their diplomas. Even though there are various reasons leading to the aforementioned problem, it is crucial to emphasize the cause originating from the management and care of learners. In fact, understanding students' difficult situations and offering timely Number of Test data and advice would help prevent college dropouts or graduate delays. In this study, we present a machine learning-based method for early detection at-risk students, using data obtained from graduates of the Faculty of Information Technology, Dainam University, Vietnam. We experiment with several fundamental machine learning methods before implementing the parameter optimization techniques. In comparison to the other strategies, Random Forest and Grid Search (RF&GS) and Random Forest and Random Search (RF&RS) provided more accurate predictions for identifying at-risk students.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2001년도 추계학술발표논문집 (하)
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• pp.1447-1450
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• 2001

최근 라우터에서는 정체를 회피하고 전송률을 향상시키기 위한 능동적 큐 관리와 패킷 스케줄링에 대한 많은 논의가 이루어지고 있다. 본 논문은 라우터에서의 전송률 향상을 위한 Randrom Early Detectio(RED) 알고리즘과 최근까지 변형된 RED 알고리즘들의 특징을 살펴보고, RED 라우터에 적용하여 실제로 종단 호스트(End-to-end)에서 전송 받는 패킷의 양을 향상하기 위한 선별적 처리 알고리즘을 제안한다.

• 한국정보처리학회논문지
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• 제6권11호
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• pp.3097-3107
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• 1999

Recently many studies have been done on the Random Early Detection(RED) algorithm as an active queue management and congestion avoidance scheme in the Internet. In this paper we first overview the characteristics of RED and the modified RED algorithms in order to understand the current status of these studies. Then we analyze the RED dynamics by investigating how RED parameters affect router queue behavior. We show the cases when RED fails since it cannot react to queue state changes aggressively due to the deterministic use of its parameters. Based on the RED parameter analysis, we propose a self-adaptive algorithm to cope with this RED weakness. In this algorithm we make two parameters be adjusted themselves depending on the queue states. One parameter is the maximum probability to drop or mark the packet at the congestion state. This parameter can be adjusted to react the long burst of traffic, consequently reducing the congestion disaster. The other parameter is the queue weight which is also adjusted aggressively in order for the average queue size to catch up with the current queue size when the queue moves from the congestion state to the stable state.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제10권9호
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• pp.4342-4366
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• 2016

Congestion control in Cluster Wireless Sensor Networks (CWSNs) has drawn widespread attention and research interests. The increasing number of nodes and scale of networks cause more complex congestion control and management. Active Queue Management (AQM) is one of the major congestion control approaches in CWSNs, and Random Early Detection (RED) algorithm is commonly used to achieve high utilization in AQM. However, traditional RED algorithm depends exclusively on source-side control, which is insufficient to maintain efficiency and state stability. Specifically, when congestion occurs, deficiency of feedback will hinder the instability of the system. In this paper, we adopt the Additive-Increase Multiplicative-Decrease (AIMD) adjustment scheme and propose an improved RED algorithm by using neighbor feedback and scheduling scheme. The congestion control model is presented, which is a linear system with a non-linear feedback, and modeled by Lur'e type system. In the context of delayed Lur'e dynamical network, we adopt the concept of cluster synchronization and show that the congestion controlled system is able to achieve cluster synchronization. Sufficient conditions are derived by applying Lyapunov-Krasovskii functionals. Numerical examples are investigated to validate the effectiveness of the congestion control algorithm and the stability of the network.

• 정보처리학회논문지C
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• 제11C권1호
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• pp.89-98
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• 2004

인터넷의 혼잡상황을 해결하기 위하여 제안된 RED(Random Early Detection)와 같은 능동적 큐 관리(Active Queue Management) 알고리즘들은 TCP 데이터에 대하여 우수한 혼잡제어 효과를 나타낸다. 그러나 TCP와 UDP가 병목 링크를 공유하는 경우 불공정성 문제와 큐에서의 지연시간이 길어지는 문제점을 가지고 있다. 본 논문에서는 공정성을 개선함과 동시에 큐 지연시간을 감소할 수 있는 새로운 큐 관리 알고리즘인 PSRED(Protocol Sensitive RED) 알고리즘을 제안하였다. PSRED 알고리즘은 트래픽의 프로토콜 필드를 이용하여 플로우의 종류를 구분하고 각기 다른 패킷폐기함수를 적용함으로써 공정성을 개선하고 평균 큐 길이를 줄일 수 있다.

• Journal of Communications and Networks
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• 제5권3호
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• pp.275-281
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• 2003

Random Early Detection (RED) [1] is an active queue management scheme which has been deployed extensively to reduce packet loss during congestion. Although RED can improve loss rates, its performance depends severely on the tuning of its operating parameters. The idea of adaptively varying RED parameters to suit the network conditions has been investigated in [2], where the maximum packet dropping probability $max_p$ has been varied. This paper focuses on adaptively varying the queue weight $\omega_q$ in conjunction with $max_p$ to improve the performance. We propose two algorithms viz., $\omega_q$-thresh and $\omega_q$-ewma to adaptively vary $\omega_q$. The performance is measured in terms of the packet loss percentage, link utilization and stability of the instantaneous queue length. We demonstrate that varying $\omega_q$ and $max_p$ together results in an overall improvement in loss percentage and queue stability, while maintaining the same link utilization. We also show that $max_p$ has a greater influence on loss percentage and queue stability as compared to $\omega_q$, and that varying $\omega_q$ has a positive influence on link utilization.

• 한국통신학회논문지
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• 제34권11B호
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• pp.1254-1260
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• 2009

본 논문에서는 다수의 병렬 TCP flow들을 가진 소수의 스테이션들이 링크 대역폭을 독점하는 불공평성 문제 에 대해 randam early detection (RED)을 수정한 history-aware RED (HRED)를 제안한다. BitTorrent와 같은 peer-to-peer방식의 파일 공유 애플리케이션들은 파일 다운로드를 위해 다수의 병렬 TCP flow들을 이용한다. 만약 파일 공유 애플리케이션을 수행하는 스테이션들이 다른 스테이션들과 링크를 공유할 경우 대역폭을 독점하는 문제가 발생한다. 이 경우 개별 TCP flow들 간의 공평성 지원을 위해 개발된 RED를 적용하더라도 불공평성은 개선되지 않는다. 제안하는 HRED는 RED와 유사하게 도착하는 패킷들에 대해 확률적으로 drop여부를 결정하되, 스테이션의 링크 점유율에 따라 drop 확률을 조정할 수 있어, 대역폭을 독점하는 스테이션들의 패킷들에 drop 패널티를 부과할 수 있다. 여러 가지 상황을 가정한 시뮬레이션을 통해 HRED가 RED에 비해 스테이션 차원에서의 throughput 공평성 지원 측면에서 최소 60%이상, 전송 효율성 측면에서 4%이상 개선되었음을 확인하였다.

• 한국정보처리학회논문지
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• 제6권11S호
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• pp.3269-3277
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• 1999

To promote the inclusion of end-to-end congestion control in the design of future protocols using best-effort traffic, we propose a router mechanism, Extended FRED(ex-FRED). In this paper, we catagorize the TCP controlled traffics into robust and fragile traffic and discuss several unfairness conditions between them caused by the diverse applications. For example, fragile traffic from bursty application cannot use its fair share due to their slow adaptation. Ex-FRED modifies the FRED(Fair Random Early Drop), which can show wrong information due to the narrow view of actual buffer. Therefore, Ex-FRED uses per-flow accounting in larger virtual buffer to impose an each flow a loss rate that depends on the virtual buffer use of a flow. The simulation results show that Ex-FRED uses fair share and has good throughput.

• ETRI Journal
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• 제45권6호
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• pp.1007-1021
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• 2023

Stroke is the leading cause of permanent disability in adults, and it can cause permanent brain damage. According to the World Health Organization, 795 000 Americans experience a new or recurrent stroke each year. Early detection of medical disorders, for example, strokes, can minimize the disabling effects. Thus, in this paper, we consider various risk factors that contribute to the occurrence of stoke and machine learning algorithms, for example, the decision tree, random forest, and naive Bayes algorithms, on patient characteristics survey data to achieve high prediction accuracy. We also consider the semisupervised self-training technique to predict the risk of stroke. We then consider the near-miss undersampling technique, which can select only instances in larger classes with the smaller class instances. Experimental results demonstrate that the proposed method obtains an accuracy of approximately 98.83% at low cost, which is significantly higher and more reliable compared with the compared techniques.

• Asian Pacific Journal of Cancer Prevention
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• 제16권6호
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• pp.2547-2553
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• 2015

Purpose: The present study concerns the effectiveness of an educational intervention for improving knowledge, attitudes and practices (KAP) of breast cancer early detection among target group women (TGW) in the district of Gampaha, Sri Lanka. Materials and Methods: The study was a community-based intervention. Two medical officer of health areas in Gampaha district were selected using random sampling as intervention (IA) and control (CA). Public health midwives (PHMs) in the IA were exposed to the educational intervention first, conducted the same among the TGW through PHMs. KAP was assessed using an interviewer- administrated questionnaire among 260 TGW from each area selected using cluster sampling before and six months after the intervention. Results: The overall median scores for KAP among TGW in IG increased significantly from pre intervention level of 54% (IQR: 46-59%), 50% (IQR: 41-59%), and 0% (IQR: 0-20%) to post intervention level of 77% (IQR: 72-82%), 68% (IQR: 59- 76 %) and 40% (IQR: 20-60%) respectively. In CG, overall median scores for KAP remained almost the same at pre intervention 54% (IQR:44-59%), 50% (IQR:36-59%) and 0% (IQR: 0-20%) and post intervention 54% (IQR:46-59%), 50% (IQR:36-64%) and 0% (IQR: 0-20%) respectively. Conclusions: The educational intervention was found to be effective.