Journal articles: 'Random early detection (RED)'

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Chen, Jianyong, Cunying Hu, and Zhen Ji. "Self-Tuning Random Early Detection Algorithm to Improve Performance of Network Transmission." Mathematical Problems in Engineering 2011 (2011): 1–17. http://dx.doi.org/10.1155/2011/872347.

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We use a discrete-time dynamical feedback system model of TCP/RED to study the performance of Random Early Detection (RED) for different values of control parameters. Our analysis shows that the queue length is able to keep stable at a given target if the maximum probabilitypmax⁡and exponential averaging weightwsatisfy some conditions. From the mathematical analysis, a new self-tuning RED is proposed to improve the performance of TCP-RED network. The appropriatepmax⁡is dynamically obtained according to history information of bothpmax⁡and the average queue size in a period of time. Andwis properly chosen according to a linear stability condition of the average queue length. From simulations withns-2, it is found that the self-tuning RED is more robust to stabilize queue length in terms of less deviation from the target and smaller fluctuation amplitude, compared to adaptive RED, Random Early Marking (REM), and Proportional-Integral (PI) controller.

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Abdel-Jaber, Hussein, Fadi Thabtah, and Mike Woodward. "Modeling discrete-time analytical models based on random early detection: Exponential and linear." International Journal of Modeling, Simulation, and Scientific Computing 06, no. 03 (September 2015): 1550028. http://dx.doi.org/10.1142/s1793962315500282.

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Congestion control is among primary topics in computer network in which random early detection (RED) method is one of its common techniques. Nevertheless, RED suffers from drawbacks in particular when its "average queue length" is set below the buffer's "minimum threshold" position which makes the router buffer quickly overflow. To deal with this issue, this paper proposes two discrete-time queue analytical models that aim to utilize an instant queue length parameter as a congestion measure. This assigns mean queue length (mql) and average queueing delay smaller values than those for RED and eventually reduces buffers overflow. A comparison between RED and the proposed analytical models was conducted to identify the model that offers better performance. The proposed models outperform the classic RED in regards to mql and average queueing delay measures when congestion exists. This work also compares one of the proposed models (RED-Linear) with another analytical model named threshold-based linear reduction of arrival rate (TLRAR). The results of the mql, average queueing delay and the probability of packet loss for TLRAR are deteriorated when heavy congestion occurs, whereas, the results of our RED-Linear were not impacted and this shows superiority of our model.

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HO, CHARLOTTE YUK-FAN, BINGO WING-KUEN LING, and HERBERT H. C. IU. "SYMBOLIC DYNAMICAL MODEL OF AVERAGE QUEUE SIZE OF RANDOM EARLY DETECTION ALGORITHM." International Journal of Bifurcation and Chaos 20, no. 05 (May 2010): 1415–37. http://dx.doi.org/10.1142/s0218127410026575.

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In this paper, a symbolic dynamical model of the average queue size of the random early detection (RED) algorithm is proposed. The conditions for both the system parameters and the initial conditions that the average queue size of the RED algorithm would converge to a fixed point are derived. These results are useful for network engineers to design both the system parameters and the initial conditions so that internet networks can achieve a good performance.

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Abdel-Jaber, Hussein. "An Exponential Active Queue Management Method Based on Random Early Detection." Journal of Computer Networks and Communications 2020 (May 22, 2020): 1–11. http://dx.doi.org/10.1155/2020/8090468.

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Congestion is a key topic in computer networks that has been studied extensively by scholars due to its direct impact on a network’s performance. One of the extensively investigated congestion control techniques is random early detection (RED). To sustain RED’s performance to obtain the desired results, scholars usually tune the input parameters, especially the maximum packet dropping probability, into specific value(s). Unfortunately, setting up this parameter into these values leads to good, yet biased, performance results. In this paper, the RED-Exponential Technique (RED_E) is proposed to deal with this issue by dropping arriving packets in an exponential manner without utilizing the maximum packet dropping probability. Simulation tests aiming to contrast E_RED with other Active Queue Management (AQM) methods were conducted using different evaluation performance metrics including mean queue length (mql), throughput (T), average queuing delay (D), overflow packet loss probability (PL), and packet dropping probability (DP). The reported results showed that E_RED offered a marginally higher satisfactory performance with reference to mql and D than that found in common AQM methods in cases of heavy congestion. Moreover, RED_E compares well with the considered AQM methods with reference to the above evaluation performance measures using minimum threshold position (min threshold) at a router buffer.

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Bhatnagar, Shalabh, and Rajesh Patro. "A proof of convergence of the B-RED and P-RED algorithms for random early detection." IEEE Communications Letters 13, no. 10 (October 2009): 809–11. http://dx.doi.org/10.1109/lcomm.2009.091276.

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Sharma, Saurabh, Dipti Jindal, and Rashi Agarwal. "Analysing Mobile Random Early Detection for Congestion Control in Mobile Ad-hoc Network." International Journal of Electrical and Computer Engineering (IJECE) 8, no. 3 (June 1, 2018): 1305. http://dx.doi.org/10.11591/ijece.v8i3.pp1305-1314.

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This research paper suggests and analyse a technique for congestion control in mobile ad hoc networks. The technique is based on a new hybrid approach that uses clustering and queuing techniques. In clustering, in general cluster head transfers the data, following a queuing method based on a RED (Random Early Detection), the mobile environment makes it Mobile RED (or MRED), It majorly depends upon mobility of nodes and mobile environments leads to unpredictable queue size. To simulate this technique, the Network Simulator 2 (or NS2) is used for various scenarios. The simulated results are compared with NRED (Neighbourhood Random Early Detection) queuing technique of congestion control. It has been observed that the results are improved using MRED comparatively.

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Jiang, Xianliang, Jiangang Yang, Guang Jin, and Wei Wei. "RED-FT: A Scalable Random Early Detection Scheme with Flow Trust against DoS Attacks." IEEE Communications Letters 17, no. 5 (May 2013): 1032–35. http://dx.doi.org/10.1109/lcomm.2013.022713.122652.

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Mahmood Lafta, Wisam, Saba Qasim Jabbar, and Guangzhi Ma. "Performance Evaluation of Heterogeneous Network Based on RED and WRED." Indonesian Journal of Electrical Engineering and Computer Science 3, no. 3 (September 1, 2016): 540. http://dx.doi.org/10.11591/ijeecs.v3.i3.pp540-545.

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<p>The developing of wireless networks becomes a very important issue nowadays, since it is considered as an easy-using tool without building new infrastructure to cover a wide working area. Applying TCP protocols with the application demands are implemented this work by considering heterogeneous environment of wireless networks connecting with wired networks. The TCP congestion is critical problem is faced heterogeneous environments, this problem is appeared through sending and receiving huge data from wireless networks to wired networks and vice versa. This work is proposed a new approach of using active queue management (AQM) technique with random early detection (RED) and weight random early detection (WRED) strategies to avoid the expected congestion between the heterogeneous environments. Our simulation results show that the quality of service (QoS) is improved by reducing the queue delay and buffer usage, and by increasing the average throughput and utilizationof the system. The simulation is carried out by using OPNET software to test the proposed models for different scenarios.</p>

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Kurdi, Heba, Amal Al-Aldawsari, Isra Al-Turaiki, and Abdulrahman S. Aldawood. "Early Detection of Red Palm Weevil, Rhynchophorus ferrugineus (Olivier), Infestation Using Data Mining." Plants 10, no. 1 (January 6, 2021): 95. http://dx.doi.org/10.3390/plants10010095.

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In the past 30 years, the red palm weevil (RPW), Rhynchophorus ferrugineus (Olivier), a pest that is highly destructive to all types of palms, has rapidly spread worldwide. However, detecting infestation with the RPW is highly challenging because symptoms are not visible until the death of the palm tree is inevitable. In addition, the use of automated RPW weevil identification tools to predict infestation is complicated by a lack of RPW datasets. In this study, we assessed the capability of 10 state-of-the-art data mining classification algorithms, Naive Bayes (NB), KSTAR, AdaBoost, bagging, PART, J48 Decision tree, multilayer perceptron (MLP), support vector machine (SVM), random forest, and logistic regression, to use plant-size and temperature measurements collected from individual trees to predict RPW infestation in its early stages before significant damage is caused to the tree. The performance of the classification algorithms was evaluated in terms of accuracy, precision, recall, and F-measure using a real RPW dataset. The experimental results showed that infestations with RPW can be predicted with an accuracy up to 93%, precision above 87%, recall equals 100%, and F-measure greater than 93% using data mining. Additionally, we found that temperature and circumference are the most important features for predicting RPW infestation. However, we strongly call for collecting and aggregating more RPW datasets to run more experiments to validate these results and provide more conclusive findings.

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Abbas, G., A. K. Nagar, H. Tawfik, and J. Y. Goulermas. "Pricing and Unresponsive Flows Purging for Global Rate Enhancement." Journal of Electrical and Computer Engineering 2010 (2010): 1–10. http://dx.doi.org/10.1155/2010/379652.

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Pricing-based Active Queue Management (AQM), such as Random Exponential Marking (REM), outperforms other probabilistic counterpart techniques, like Random Early Detection (RED), in terms of both high utilization and negligible loss and delay. However, the pricing-based protocols do not take account of unresponsive flows that can significantly alter the subsequent rate allocation. This letter presents Purge (Pricing and Un-Responsive flows purging for Global rate Enhancement) that extends the REM framework to regulate unresponsive flows. We show that Purge is effective at providing fairness and requires small memory and low-complexity operations.

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Zhang, Jing Jun, Wen Long Xu, and Li Guo Wang. "An Improved Active Queue Management Algorithm Based on Nonlinear Smoothing." Advanced Materials Research 295-297 (July 2011): 1823–28. http://dx.doi.org/10.4028/www.scientific.net/amr.295-297.1823.

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According to the limitations of calculation of the original random early detection (RED) algorithm in linear packet loss rate. This paper proposes an improved algorithm which imposes nonlinear smooth for packet loss rate function of RED algorithm. The speed of growth of packet loss rate is relatively slow near the minimum threshold, while near the maximum threshold the speed of growth of packet loss rate is relatively faster. In this case, using the trend of the average queue length to dynamically adjust the parameters of the RED algorithm, it reduces the dependence on the parameters of the RED algorithm and enhances the stability of the algorithm. NS simulation shows that this algorithm has been significantly improved for packet loss rate, throughput and other performance.

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CHEN, XI, SIU-CHUNG WONG, CHI K. TSE, and LJILJANA TRAJKOVIĆ. "DETRENDED FLUCTUATION ANALYSIS OF THE TCP-RED ALGORITHM." International Journal of Bifurcation and Chaos 19, no. 12 (December 2009): 4237–45. http://dx.doi.org/10.1142/s0218127409025328.

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It has been observed that Internet gateways employing Transport Control Protocol (TCP) and the Random Early Detection (RED) control algorithm may exhibit instability and oscillatory behavior. Most control methods proposed in the past have been based on analytical models that rely on statistical measurements of network parameters. In this paper, we apply the detrended fluctuation analysis (DFA) method to analyze stability of the TCP-RED system. The DFA is used to analyze time-series data and generate power-law scaling exponents, which indicate the long-range correlations of the time series. We quantify the stability of the TCP-RED system by examining the variation of the DFA power-law scaling exponent when the system parameters are varied. We also study the long-range power-law correlations of TCP window periods.

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Kolhar, Manjur, Mosleh M Abualhaj, and Faiza Rizwan. "QoS Design Consideration for Enterprise and Provider’s Network at Ingress and Egress Router for VoIP protocols." International Journal of Electrical and Computer Engineering (IJECE) 6, no. 1 (February 1, 2016): 235. http://dx.doi.org/10.11591/ijece.v6i1.9013.

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Compliance with the Service Level Agreement (SLA) metric is a major challenge in a Multiprotocol Label Switching Virtual Private Network (MPLS VPN) because mandatory models must be maintained on both sides of the MPLS VPN in order to achieve end-to-end service levels. The end-to-end service of an MPLS VPN can be degraded owing to various issues such as distributed denial of service (DDoS), and Random Early Detection (RED) that prevents congestion and differentiates between legitimate and illegitimate user traffic. In this study, we propose a centralized solution that uses a SLA Violation Detector (SLAVD) and intrusion detection to prevent SLA violation.

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Kolhar, Manjur, Mosleh M Abualhaj, and Faiza Rizwan. "QoS Design Consideration for Enterprise and Provider’s Network at Ingress and Egress Router for VoIP protocols." International Journal of Electrical and Computer Engineering (IJECE) 6, no. 1 (February 1, 2016): 235. http://dx.doi.org/10.11591/ijece.v6i1.pp235-241.

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Compliance with the Service Level Agreement (SLA) metric is a major challenge in a Multiprotocol Label Switching Virtual Private Network (MPLS VPN) because mandatory models must be maintained on both sides of the MPLS VPN in order to achieve end-to-end service levels. The end-to-end service of an MPLS VPN can be degraded owing to various issues such as distributed denial of service (DDoS), and Random Early Detection (RED) that prevents congestion and differentiates between legitimate and illegitimate user traffic. In this study, we propose a centralized solution that uses a SLA Violation Detector (SLAVD) and intrusion detection to prevent SLA violation.

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CHEN, XI, SIU-CHUNG WONG, CHI K. TSE, and FRANCIS C. M. LAU. "OSCILLATION AND PERIOD DOUBLING IN TCP/RED SYSTEM: ANALYSIS AND VERIFICATION." International Journal of Bifurcation and Chaos 18, no. 05 (May 2008): 1459–75. http://dx.doi.org/10.1142/s0218127408021105.

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It has been known that a bottleneck RED (Random Early Detection) gateway can become oscillatory when regulating multiple identical TCP (Transmission Control Protocol) flows. However, a systematic explanation of such oscillatory behavior is not available. In this paper, we first use the fluid-flow model to derive the system characteristic frequency, and then compare with the frequencies of the RED queue length waveforms observed from "ns-2" simulations. The "ns-2" simulator is the only viable simulation tool accepted by industry for verification purposes. Analysis of the TCP source frequency distribution reveals the occurrence of period doubling when the system enters the instability region as the filter resolution varies. Since random events and a large number of TCP flows are involved in the process of generating the average system dynamics, a statistical viewpoint is taken in the analysis. Our results reflect the true system behavior as they are based on data from "ns-2" simulations rather than numerical simulations of analytical models. The physical mechanism of oscillation is explained in terms of the difference in the TCP source frequency and the TCP-RED system characteristic frequency.

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NGA, J. H. C., H. H. C. IU, B. W. K. LING, and H. K. LAM. "ANALYSIS AND CONTROL OF BIFURCATION AND CHAOS IN AVERAGE QUEUE LENGTH IN TCP/RED MODEL." International Journal of Bifurcation and Chaos 18, no. 08 (August 2008): 2449–59. http://dx.doi.org/10.1142/s0218127408021816.

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This paper studies the bifurcation and chaos phenomena in average queue length in a developed Transmission Control Protocol (TCP) model with Random Early Detection (RED) mechanism. Bifurcation and chaos phenomena are nonlinear behavior in network systems that lead to degradation of the network performance. The TCP/RED model used is a model validated previously. In our study, only the average queue size [Formula: see text] is considered, and the results are based on analytical model rather than actual measurements. The instabilities in the model are studied numerically using the conventional nonlinear bifurcation analysis. Extending from this bifurcation analysis, a modified RED algorithm is derived to prevent the observed bifurcation and chaos regardless of the selected parameters. Our modification is for the simple scenario of a single RED router carrying only TCP traffic. The algorithm neither compromises the throughput nor the average queuing delay of the system.

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Bisoy, Sukantkishoro, Pradeep Kumar Mallick, and Anjana Mishra. "Fairness analysis of TCP variants in asymmetric network." International Journal of Engineering & Technology 7, no. 2.12 (April 3, 2018): 231. http://dx.doi.org/10.14419/ijet.v7i2.12.11287.

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Conservative nature of Vegas creates less opportunity to get fair share of bandwidth then Reno in wired network. On the other hand, aggressive nature of Reno helps to achieve more share of bandwidth. Both Reno and Vegas assumes that congestion occurs in the forward rather than in reverse path. In asymmetric network the path characteristics of forward and backward is different.In this work, we propose a network model and analyzed the Inter-protocol fairness between TCP Reno and TCP Vegas with some queue management techniques such as Droptail and random early detection (RED) in asymmetric network where the forward and backward path has different characteristics. The simulation experiment results using NS2 indicates that use of RED can achieve better fairness than Droptail in asymmetric network.

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Suwannapong and Khunboa. "Congestion Control in CoAP Observe Group Communication." Sensors 19, no. 15 (August 5, 2019): 3433. http://dx.doi.org/10.3390/s19153433.

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The Constrained Application Protocol (CoAP) is a simple and lightweight machine-to-machine (M2M) protocol for constrained devices for use in lossy networks which offers a small memory capacity and limited processing. Designed and developed by the Internet Engineering Task Force (IETF), it functions as an application layer protocol and benefits from reliable delivery and simple congestion control. It is implemented for request/response message exchanges over the User Datagram Protocol (UDP) to support the Internet of Things (IoT). CoAP also provides a basic congestion control mechanism. In dealing with its own congestion, it relies on a fixed interval retransmission timeout (RTO) and binary exponential backoff (BEB). However, the default CoAP congestion control is considered to be unable to effectively perform group communication and observe resources, and it cannot handle rapid, frequent requests. This results in buffer overflow and packet loss. To overcome these problems, we proposed a new congestion control mechanism for CoAP Observe Group Communication, namely Congestion Control Random Early Detection (CoCo-RED), consisting of (1) determining and calculating an RTO timer, (2) a Revised Random Early Detection (RevRED) algorithm which has recently been developed and primarily based on the buffer management of TCP congestion control, and (3) a Fibonacci Pre-Increment Backoff (FPB) algorithm which waits for backoff time prior to retransmission. All the aforementioned algorithms were therefore implemented instead of the default CoAP mechanism. In this study, evaluations were carried out regarding the efficiency of the developed CoCo-RED using a Cooja simulator. The congestion control mechanism can quickly handle the changing behaviors of network communication, and thus it prevents the buffer overflow that leads to congestions. The results of our experiments indicate that CoCo-RED can control congestion more effectively than the default CoAP in every condition.

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Jiang, Fuchun, Chenwei Feng, Chen Zhu, and Yu Sun. "Performance Analysis of Active Queue Management Algorithm Based on Reinforcement Learning." Journal Européen des Systèmes Automatisés 53, no. 5 (November 15, 2020): 637–44. http://dx.doi.org/10.18280/jesa.530506.

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In the information society, data explosion has led to more congestion in the core network, dampening the network performance. Random early detection (RED) is currently the standard algorithm for active queue management (AQM) recommended by the Internet Engineering Task Force (IETF). However, RED is particularly sensitive to both service load and algorithm parameters. The algorithm cannot fully utilize the bandwidth at a low service load, and might suffer a long delay at a high service load. This paper designs the reinforcement learning AQM (RLAQM), a simple and practical variant of RED, which controls the average queue length to the predictable value under various network loads, such that the queue size is no longer sensitive to the level of congestion. Q-learning was adopted to adjust the maximum discarding probability, and derive the optimal control strategy. Simulation results indicate that RLAQM can effectively overcome the deficiency of RED and achieve better congestion control; RLAQM improves the network stability and performance in complex environment; it is very easy to migrate from RED to RLAQM on Internet routers: the only operation is to adjust the discarding probability.

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Zhu, Hong Gao, and Qi Feng. "Network Video Transmission Quality Assurance Algorithm Based on Congestion Control." Applied Mechanics and Materials 347-350 (August 2013): 849–53. http://dx.doi.org/10.4028/www.scientific.net/amm.347-350.849.

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Time delay and packet loss rate during the network video transmission are the two important factors in network video transmission quality. The purpose of congestion control is to reduce the bad impact of network video transmission quality caused by time delay and packet loss rate. This paper proposed a network video transmission quality assurance algorithm based on unbalanced multiple descriptions coding scheme. The new algorithm predicts the congestion state of network by means of Markov model based on available bandwidth detection, changes video transmission path on the basis of the prediction. Experiments show that compared with RED (Random Early Detection) algorithm, the algorithm is much more effective to estimate network congestionreduce video packet loss rate and time delay, thus the network video transmission quality can be ensured more effectively.

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Amigó, José M., Guillem Duran, Ángel Giménez, José Valero, and Oscar Martinez Bonastre. "Modeling a New AQM Model for Internet Chaotic Behavior Using Petri Nets." Applied Sciences 11, no. 13 (June 24, 2021): 5877. http://dx.doi.org/10.3390/app11135877.

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Formal modeling is considered one of the fundamental phases in the design of network algorithms, including Active Queue Management (AQM) schemes. This article focuses on modeling with Petri nets (PNs) a new scheme of AQM. This innovative AQM is based on a discrete dynamical model of random early detection (RED) for controlling bifurcations and chaos in Internet congestion control. It incorporates new parameters (α,β) that make possible better stability control over oscillations of an average queue length (AQL) at the router. The PN is validated through the matrix equation approach, reachability tree, and invariant analysis. The correctness is validated through the key properties of reachability, boundedness, reversibility, deadlock, and liveness.

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Bujari, Armir, Andrea Marin, Claudio E. Palazzi, and Sabina Rossi. "Smart-RED: A Novel Congestion Control Mechanism for High Throughput and Low Queuing Delay." Wireless Communications and Mobile Computing 2019 (April 4, 2019): 1–10. http://dx.doi.org/10.1155/2019/6941248.

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We consider the scenario in which several TCP connections share the same access point (AP) and a congestion avoidance/control mechanism is adopted with the aim of assigning the available bandwidth to the clients with a certain fairness. When UDP traffic with real-time requirements is present, the problem becomes even more challenging. Very well-known congestion avoidance mechanisms are the Random Early Detection (RED) and the Explicit Congestion Notification (ECN). More recently, the Smart Access Point with Limited Advertised Window (SAP-LAW) has been proposed. Its main idea is that of computing the maximum TCP rate for each connection at the bottleneck, taking into account the UDP traffic to keep a low queue size combined with a reasonable bandwidth utilization. In this paper, we propose a new congestion control mechanism, namely, Smart-RED, inspired by SAP-LAW heuristic formula. We study its performance by using mean field models and compare the behaviours of ECN/RED, SAP-LAW, and Smart-RED under different scenarios. We show that while Smart-RED maintains some of the desirable properties of the SAP-LAW, it solves the problems it may have in case of bursty UDP traffic or TCP connections with very different needs of bandwidth.

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Hamadneh, Nabhan, Mamoon Obiedat, Ahmad Qawasmeh, and Mohammad Bsoul. "HRED, An Active Queue Management Algorithm for TCP Congestion Control." Recent Patents on Computer Science 12, no. 3 (May 8, 2019): 212–17. http://dx.doi.org/10.2174/2213275912666181205155828.

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Background: Active Queue Management (AQM) is a TCP congestion avoidance approach that predicts congestion before sources overwhelm the buffers of routers. Random Early Detection (RED) is an AQM strategy that keeps history of queue dynamics by estimating an average queue size parameter avg and drops packets when this average exceeds preset thresholds. The parameter configuration in RED is problematic and the performance of the whole network could be reduced due to wrong setup of these parameters. Drop probability is another parameter calculated by RED to tune the drop rate with the aggressiveness of arriving packets. Objective: In this article, we propose an enhancement to the drop probability calculation to increase the performance of RED. Methods: This article studies the drop rate when the average queue size is at the midpoint between the minimum and maximum thresholds. The proposal suggests a nonlinear adjustment for the drop rate in this area. Hence, we call this strategy as the Half-Way RED (HRED). Results: Our strategy is tested using the NS2 simulator and compared with some queue management strategies including RED, TD and Gentle-RED. The calculated parameters are: throughput, link utilization and packet drop rate. Conclusion: Each performance parameter has been plotted in a separate figure; then the robustness of each strategy has been evaluated against these parameters. The results suggest that this function has enhanced the performance of RED-like strategies in controlling congestion. HRED has outperformed the strategies included in this article in terms of throughput, link utilization and packet loss rate.

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XIAO, MIN, WEI XING ZHENG, and JINDE CAO. "BIFURCATION CONTROL OF A CONGESTION CONTROL MODEL VIA STATE FEEDBACK." International Journal of Bifurcation and Chaos 23, no. 06 (June 2013): 1330018. http://dx.doi.org/10.1142/s0218127413300188.

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This paper proposes to use a state feedback method to control the Hopf bifurcation for a novel congestion control model, i.e. the exponential random early detection (RED) algorithm with a single link and a single source. The gain parameter of the congestion control model is chosen as the bifurcation parameter. The analysis shows that in the absence of the state feedback controller, the model loses stability via the Hopf bifurcation early, and can maintain a stationary sending rate only in a certain domain of the gain parameter. When applying the state feedback controller to the model, the onset of the undesirable Hopf bifurcation is postponed. Thus, the stability domain is extended, and the model possesses a stable sending rate in a larger parameter range. Furthermore, explicit formulae to determine the properties of the Hopf bifurcation are obtained. Numerical simulations are given to justify the validity of the state feedback controller in bifurcation control.

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Pavlyukovich, Nataliia, Oleksandr Pavlyukovich, Marta Garazdiuk, Oleg Wanchuliak, and Oleksandr Garazdiuk. "LASER STOKES-POLARIMETRY OF THE ERYTHROCYTES SUSPENSION AT COMORBID COURSE OF CORONARY ARTERY DISEASE, DIABETES MELLITUS TYPE 2 AND ANEMIA." CBU International Conference Proceedings 5 (September 24, 2017): 998–1004. http://dx.doi.org/10.12955/cbup.v5.1059.

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Introduction: Investigation of the rheological properties of the erythrocytes is one of the crucial issues in the pathogenesis of most diseases of internal organs. Changes in the morphological structure of the red blood cell membrane serve as an early diagnostic criterion of coronary artery disease, diabetes mellitus type 2 and anaemia, thus a search for modern methods of investigation which can be used for early detection of erythrocytes membrane disorders is of great importance. Methods of laser polarimetry of the erythrocytes suspension smear are crucial in this area.Objectives: Investigation of the possible structural changes of the erythrocytes membranes in patients with coronary artery disease, diabetes mellitus type 2 and anaemia of different degrees of severity.Methods: For the objective assessment of the structural state of erythrocytes membrane laser polarimetry of the red cell suspension smear was applied. Set of the statistical points of the 1st-4th grades was calculated and their effectiveness in the early pre-clinical detection of the erythrocytes membrane disorder was established.Results: Images of the erythrocytes suspension samples, registered in the co-axial and crossed planes of the polarizer and analyser transmissions, regardless of the investigated group, are coordinating heterogeneous. However, obvious signs of the changes of the images structure depending on the presence and type of pathology are not visually detected. As the basis of an objective approach, a statistical analysis with histograms of the coordinate distributions of the random variables and a statistical calculation of the statistical points of the 1st-4th grades should be used. All statistical points, which describe the coordinate distributions of the various parameters of the Stokes vector, have individual sets of values and may be used for the differential diagnosis of the coronary artery disease, diabetes mellitus type 2 and anaemia.Conclusions: Interconnections between the values of the sets of the statistical points of the 1st-4th grades, which characterized coordinate distributions of the intensity, azimuth and ellipticity of the polarization of the erythrocytes suspension laser images of the elderly and senile patients with comorbidity were established by means of laser polarimetry. The most sensitive were asymmetry and kurtosis, which can be used for early detection of the erythrocytes membrane disorder when routine methods of its visualization and estimation still are not able to be applied.

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Kennedy, Okokpujie, Emmanuel Chukwu, Olamilekan Shobayo, Etinosa Noma-Osaghae, Imhade Okokpujie, and Modupe Odusami. "Comparative analysis of the performance of various active queue management techniques to varying wireless network conditions." International Journal of Electrical and Computer Engineering (IJECE) 9, no. 1 (February 1, 2019): 359. http://dx.doi.org/10.11591/ijece.v9i1.pp359-368.

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This paper demonstrates the robustness of active queue management techniques to varying load, link capacity and propagation delay in a wireless environment. The performances of four standard controllers used in Transmission Control Protocol/Active Queue Management (TCP/AQM) systems were compared. The active queue management controllers were the Fixed-Parameter Proportional Integral (PI), Random Early Detection (RED), Self-Tuning Regulator (STR) and the Model Predictive Control (MPC). The robustness of the congestion control algorithm of each technique was documented by simulating the varying conditions using MATLAB® and Simulink® software. From the results obtained, the MPC controller gives the best result in terms of response time and controllability in a wireless network with varying link capacity and propagation delay. Thus, the MPC controller is the best bet when adaptive algorithms are to be employed in a wireless network environment. The MPC controller can also be recommended for heterogeneous networks where the network load cannot be estimated.

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Ardiansyah, Rizka. "Pengukuran Performa Algoritma Kendali Kongesti Active Queue Manajemen (AQM) Pada Jaringan Backbone Multicast Berbasis Protocol Multicast PIM-DM." ScientiCO : Computer Science and Informatics Journal 1, no. 2 (March 7, 2019): 71. http://dx.doi.org/10.22487/j26204118.2018.v1.i2.12061.

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UDP is a data transmission protocol on the Internet that allows data to be sent in realtime and become a development base of various services such as IPTV, VOIP, VOD, and video conferencing. The multicast network was a solution to provide better service to many public needs with the concept of simplicity and efficiency. Dense traffic situation triggering a packet loss and delay that not tolerated by some services, especially multimedia streaming service, while UDP does not guarantee the quality of service so that the necessary traffic management method that serves as a traffic controller to the congestion in the network. This study analyzes the performance of technology based on Active Queue Management (AQM) congestion control in a multicast backbone network based on Protocol Independent Multicast – Dense Multicast using Droptail, Deficit Round Robin (DRR), and Random Early Detection (RED) algorithm, which is simulated using NS2. The simulation results show that the congestion control algorithm, DRR provides the best performance with a balanced value of mean delay, mean throughput, and the mean loss. RED is a congestion control algorithm which has the best queue management mechanism but not able to suppress the mean loss in each scenario tested because RED has a small amount of buffer size, Droptail not an appropriate algorithm for multicast networks implemented in, the absence of an interrupt mechanism trigger data services will have a tendency towards a particular data packet.

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Mahapatro, Susrita, Prabhat Kumar sahu, and Pradeep Kumar Dash. "A hybrid active queue management technique for QoS based unicasting protocols in MANETs." International Journal of Engineering & Technology 7, no. 2.6 (March 11, 2018): 58. http://dx.doi.org/10.14419/ijet.v7i2.6.10068.

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Mobile Ad hoc Networks (MANETs) is the interconnection of several self-energy dependent mobile nodes. It is one of the self-configured and infrastructure less network. In MANET the issues are security, Quality Of Services, Congestion etc. The major issue of MANET is to control the congestion. Congestion means the network capacity is larger than the no of packets transmitted across the network. In this paper, an effort has been made to evaluate and analyze the performance of the network by means of comparing two on-demand reactive routing protocols such as AODV [Ad-hoc On-demand Distance Vector] and DSR [Dynamic Source Routing] with each other along with also by comparing with DSDV [Destination Sequenced Distance Vector], which is a proactive routing protocol. The comparison is also being made under the light of two queue management techniques such as RED (Random Early Detection) and Droptail. A gateway discovery algorithm is being used by both AODV and DSR routing protocols, which uses the mechanism of connecting mobile nodes through gateway, however Bellman-Ford algorithm is being used by DSDV which uses the concept of including freedom from loops in the routing table. By means of simulation through NS-2 network simulator by varying the number of nodes, we observe that the performance differentials of AODV, DSR, and DSDV routing protocols are directed to foremost performance differentials to guarantee QoS for both of these protocols in RED and Droptail queue management techniques. There is a lot of queue management approach to minimize the congestion rate and transfer the data successfully to the destination. Two queue management techniques popularly used to manage queues in MANets are Active queue management and Passive queue management. Active queue management is widely used and RED is the most popular approach which gives low average delay, high throughput and lower packet drop rate. But RED algorithms have some drawback. So here we are using a hybrid approach, named as MRED by means of combining the features of both RED and ECN to overcome the drawbacks of RED.

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Ni, Hao, Peng Gong, and Xuecao Li. "Extraction of Old Towns in Hangzhou (2000–2018) from Landsat Time Series Image Stacks." Remote Sensing 13, no. 13 (June 22, 2021): 2438. http://dx.doi.org/10.3390/rs13132438.

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With rapid urbanization in recent decades, more and more urban renewal has taken place in China. Meanwhile, the early developed areas without change have become old towns, which need special attention in future city planning. However, other than field surveys, there is no specific method to identify old towns. To fill this gap, we used time-series image stacks established from Landsat Surface Reflectance Tier 1 data on the Google Earth Engine (GEE) platform, facilitated by Global Urban Boundary (GUB), Essential Urban Land Use Categories (EULUC) and Global Artificial Impervious Area (GAIA) data. The LandTrendr change detection algorithm was applied to extract detailed information from 14 band/index trajectories. These features were then used as inputs to two methods of old town identification: statistical thresholding and random forest classification. We assessed these two methods in a rapidly developing large city, Hangzhou, and subsequently obtained overall accuracies of 81.33% and 90.67%, respectively. Red band, NIR band and related indices show higher importance in random forest classification, and the magnitude feature plays an outstanding role. The final map of Hangzhou during the 2000–2018 period shows that the old towns were concentrated in the downtown region near West Lake within the urban boundaries in 2000, and far fewer than the renewed areas. The results could serve as references in the provincial and national planning of future urban developments.

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Sari, Indah Anita, Bayu Setyawan, Marcelinus A. S. Adhiwibawa, and Agung Wahyu Susilo. "Chromatographic identification of leaf color characteristics on fine-flavor and bulk cacao as selection indicator." Pelita Perkebunan (a Coffee and Cocoa Research Journal) 32, no. 1 (April 30, 2016): 1–9. http://dx.doi.org/10.22302/iccri.jur.pelitaperkebunan.v32i1.210.

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A problem encountered in plant breeding process to determine bean color quality of fine-flavor cocoa is a long selection period. Preliminary results indicatedthat the fine-flavor cocoa has a low color reflectance than bulk cocoa. The objectiveof this study is to find more applicable and easier method to do the early detectionof fine-flavor cocoa on the breeding population. Detection of the leaf color haracteristics was done by chromatographic and spectrophotometry analysis. hromatographic analysis was carried out in Kaliwining Experimental Station, IndonesianCoffee and Cocoa Research Institute using complete random design consistedof two types of cocoa; fine-flavor (10 clones) and bulk cocoa (10 clones). pectrophotometric analysis was conducted at Ma Chung University, Malang, East Java.Four clones of bulk cocoa and six clones of fine flavor cocoa were used in thisanalysis. The results of the study showed that the flush color characteristics of thefine-flavor cocoa were different from the bulk cocoa characteristics as shown bythe differences in the value of L*, b* and anthocyanin content. Flush characteristic s ofthe fine-flavor cocoa had brighter color, more toward green with higher yellowlevel, while the bulk cocoa had darker flush, red and lower yellow level. Thecolor parameter of L* and b* had a higher genetic variation compared with theenvironment variation. However, the parameter a* had a higher environmental variation compared with genetic variation. Grouping of fine-flavor cocoa and bulk cocoacould be done through the detection of flush anthocyanin content, when tendencyof flush anthocyanin content on fine-flavor cocoa was lower than bulk cocoa.The detection of flush color particularly L* and b* parameters through hromatographic analysis and anthocyanin content through spectrophotometric analysismay be used as a criteria for selection the fine flavor cocoa on seedling phase.

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WANG, LAN, GEYONG MIN, and IRFAN AWAN. "STOCHASTIC MODELING AND ANALYSIS OF AN ACTIVE CONGESTION CONTROL PROTOCOL UNDER DIFFERENTIATED BURSTY TRAFFIC." Journal of Interconnection Networks 08, no. 04 (December 2007): 369–85. http://dx.doi.org/10.1142/s0219265907002089.

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Traffic congestion degrades not only the user-perceived Quality-of-Service (QoS), such as leading to high packet loss rates, low throughput, and increased delays, but also causes excessive energy consumption in energy-sensitive systems (e.g., wireless sensor networks). A simple way to detect congestion is to monitor and measure queue length in network nodes or routers. This paper develops an analytical performance model for a finite capacity queueing system with an enhanced Random Early Detection (RED) congestion control scheme based on the instantaneous queue length in the presence of differentiated classes of bursty traffic. The aggregate traffic is captured by the superposition of 2-state Markov Modulated Poisson Processes (MMPP). The individual threshold is assigned to each traffic class in order to differentially control traffic injection rate. The accuracy of this model is verified by comparing the analytical results against those obtained from simulation experiments. The model is adopted to investigate the effects of traffic burstiness and system capacity on the performance of the congestion control scheme.

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YAR, ASFAND-E., I. U. AWAN, and M. E. WOODWARD. "PERFORMANCE MODELLING OF TRAFFIC CONGESTION IN WIRELESS NETWORKS." Journal of Interconnection Networks 07, no. 01 (March 2006): 163–77. http://dx.doi.org/10.1142/s021926590600165x.

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Evolution in Wireless Technologies and Networks imposes a greater need for network support as current congestion control and avoidance techniques are mainly designed for wired networks. The current performance evaluation techniques proposed for wireless networks are not able to achieve optimal performance to guarantee desired Quality of Service (QoS) standards. Thus, the new schemes such as Active Queue Management (AQM) are needed to be adaptive to dynamic wireless networks and bursty traffic conditions to help in avoiding severe performance degradation in wireless environment. Thus, in this paper we developed and validated a novel approximate analytical performance model of a multiple threshold Random Early Detection (RED) congestion control mechanism based on the principle of Maximum Entropy (ME). It can be employed at the wireless gateways/base stations to regulate the buffer management and bandwidth allocation. Closed form expressions for the state and blocking probabilities have also been characterized. Numerical examples have been presented for aggregate and marginal QoS measures, which show the credibility of the ME solution and its validation against simulation.

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Baer, Constance Regina, Niroshan Nadarajah, Claudia Haferlach, Wolfgang Kern, and Torsten Haferlach. "The Use of Unique Molecular Identifiers (UMIs) Strongly Improves Sequencing Detection Limits Allowing Earlier Detection of Small TP53 Mutated Clones in Leukemias." Blood 128, no. 22 (December 2, 2016): 2027. http://dx.doi.org/10.1182/blood.v128.22.2027.2027.

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Abstract Background: TP53 mutations arise in a broad set of hematologic diseases and are associated with poor prognosis and therapy failure. Even small TP53 mutated clones were demonstrated to be of clinical relevance and therefore their early detection is mandatory. Mutations can occur throughout the entire gene (mainly exons 4-10) and include base exchanges, deletions and insertions. Next generation sequencing (NGS) generally detects mutations, which are present in at least 3% of sequences. The detection of mutations at burdens below 3% is still hampered by polymerase and sequencing errors. Especially true mutations caused by single base exchanges (missense-, nonsense- and splice site mutations) are difficult to distinguish from non-specific background. To overcome this limitation, individual molecules can be tagged by unique molecular identifiers (UMIs). UMIs are small random sequences added to each individual molecule before amplification. Building a consensus sequence out of all amplified products derived from one original DNA template, allows reconstructing the sequence of the initial molecule and thereby eliminates amplification errors in silico. Aim: 1) To evaluate reduction of non-specific background in NGS assays after using UMIs to build consensus sequences. 2) To use consensus sequencing to identify early subclones by backtracking of known TP53 mutations in CLL patients. Methods: We added eight random nucleotides as UMI to all reverse primers (design adapted from Peng, et al. 2015) and performed an initial primer extension step. In a subsequent PCR, regions of interest including UMIs were amplified and adapters were added for MiSeq sequencing (Illumina, San Diego, CA). We performed 119 sequencing assays, and analyzed results with a 1% detection limit. Consensus sequences were built using SeqNext 4.3 (JSI Medical Systems, Kippenheim, Germany). A median number of 6 (range: 2-115) reads/consensus read was obtained. Samples contained 66 previously identified mutations (47 single base exchanges; 14 small deletions; 5 small insertions), of which 29 had a burden of 3% or lower. These had been confirmed to be specific by independently repeated sequencing analysis or known in patients at multiple time points during follow-up. Results: We evaluated background signal at each position before consensus read building. On average 97.5% of bases had low-level non-specific background. At each individual position, 0.01-0.87% of reads deviated from the reference sequence (Figure A). Building consensus reads by combining all sequences derived from one original DNA molecule reduced the number of bases with low-level non-specific signal to an average of 18% (Figure B). We performed 119 sequencing analysis with a 1% detection limit, aiming to identify 66 known mutations and no non-specific deviations from the reference sequence. With both approaches, 63/66 mutations were detected and correlation of mutation burdens was high (R2=0.99; P<0.001, calculated by linear regression). Two mutations (1% and 2%) only reached the detection cutoff without consensus read building and one mutation only after consensus read building. Importantly, without using UMIs for consensus reads, in sum 88 non-specific deviations from the reference sequence (likely artifacts) were detectable with the 1% cutoff, while only two likely artifacts remained after consensus sequences building. Thus, the reduction of sequencing artifacts should allow using a 1% cutoff for mutation detection in future routine settings. Therefore, we conducted a backtracking using 31 of the above identified TP53 mutations detected in 15 CLL patients. Samples covered a median timeframe of 26 (range: 2-68) months. Using the previous 3% cutoff for mutation burden, we identified 8/31 (26%) mutations 2-55 months earlier (median: 4 months). Even more, using the adjusted 1% cutoff with UMIs allowed detecting 18/31 (58%) TP53 mutations earlier (2-55 months; median: 18 months). Conclusion: Including UMIs and building consensus sequences, 1) reduced background signal in silico and 2) allowed improving NGS detection limits. This is crucial for the identification of low burden mutations in TP53 and other genes, where small subclones can rapidly expand and have been shown to require early treatment intervention. Disclosures Baer: MLL Munich Leukemia Laboratory: Employment. Nadarajah:MLL Munich Leukemia Laboratory: Employment. Haferlach:MLL Munich Leukemia Laboratory: Employment, Equity Ownership. Kern:MLL Munich Leukemia Laboratory: Employment, Equity Ownership. Haferlach:MLL Munich Leukemia Laboratory: Employment, Equity Ownership.

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Azam, Bakht, Sami Ur Rahman, Muhammad Irfan, Muhammad Awais, Osama Mohammed Alshehri, Ahmed Saif, Mohammed Hassan Nahari, and Mater H. Mahnashi. "A Reliable Auto-Robust Analysis of Blood Smear Images for Classification of Microcytic Hypochromic Anemia Using Gray Level Matrices and Gabor Feature Bank." Entropy 22, no. 9 (September 17, 2020): 1040. http://dx.doi.org/10.3390/e22091040.

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Accurate blood smear quantification with various blood cell samples is of great clinical importance. The conventional manual process of blood smear quantification is quite time consuming and is prone to errors. Therefore, this paper presents automatic detection of the most frequently occurring condition in human blood—microcytic hyperchromic anemia—which is the cause of various life-threatening diseases. This task has been done with segmentation of blood contents, i.e., Red Blood Cells (RBCs), White Blood Cells (WBCs), and platelets, in the first step. Then, the most influential features like geometric shape descriptors, Gray Level Co-occurrence Matrix (GLCM), Gray Level Run Length Matrix (GLRLM), and Gabor features (mean squared energy and mean amplitude) are extracted from each of the RBCs. To discriminate the cells as hypochromic microcytes among other RBC classes, scanning is done at angles (0∘, 45∘, 90∘, and 135∘). To achieve high-level accuracy, Adaptive Synthetic (AdaSyn) sampling for imbalance learning is used to balance the datasets and locality sensitive discriminant analysis (LSDA) technique is used for feature reduction. Finally, upon using these features, classification of blood cells is done using the multilayer perceptual model and random forest learning algorithms. Performance in terms of accuracy was 96%, which is better than the performance of existing techniques. The final outcome of this work may be useful in the efforts to produce a cost-effective screening scheme that could make inexpensive screening for blood smear analysis available globally, thus providing early detection of these diseases.

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Fauzi, M. Ali, and Anny Yuniarti. "Ensemble Method for Indonesian Twitter Hate Speech Detection." Indonesian Journal of Electrical Engineering and Computer Science 11, no. 1 (July 1, 2018): 294. http://dx.doi.org/10.11591/ijeecs.v11.i1.pp294-299.

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Due to the massive increase of user-generated web content, in particular on social media networks where anyone can give a statement freely without any limitations, the amount of hateful activities is also increasing. Social media and microblogging web services, such as Twitter, allowing to read and analyze user tweets in near real time. Twitter is a logical source of data for hate speech analysis since users of twitter are more likely to express their emotions of an event by posting some tweet. This analysis can help for early identification of hate speech so it can be prevented to be spread widely. The manual way of classifying out hateful contents in twitter is costly and not scalable. Therefore, the automatic way of hate speech detection is needed to be developed for tweets in Indonesian language. In this study, we used ensemble method for hate speech detection in Indonesian language. We employed five stand-alone classification algorithms, including Naïve Bayes, K-Nearest Neighbours, Maximum Entropy, Random Forest, and Support Vector Machines, and two ensemble methods, hard voting and soft voting, on Twitter hate speech dataset. The experiment results showed that using ensemble method can improve the classification performance. The best result is achieved when using soft voting with F1 measure 79.8% on unbalance dataset and 84.7% on balanced dataset. Although the improvement is not truly remarkable, using ensemble method can reduce the jeopardy of choosing a poor classifier to be used for detecting new tweets as hate speech or not.

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Reddy*, Kota Akshith, Sharmila Banu K, Sai Kanishka Ippagunta, Chandra Havish Siddareddi, and Jahnavi Polsani. "Performance Comparison of Deep CNN Models for Disease Diagnosis on Apple Leaves." International Journal of Engineering and Advanced Technology 10, no. 6 (August 30, 2021): 79–91. http://dx.doi.org/10.35940/ijeat.f3040.0810621.

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The apple is one of the most cultivated fruits in the world. They are round in shape and their color varies from green to red. Apple Orchards face constant threats from a large number of insects and pathogens and the early detection of these diseases can help in mitigating these harmful effects. An apple tree takes around six to ten years to mature and produce fruit and therefore, the production costs are high and there is no room for such diseases to get a healthy fruit and a profitable yield. Delayed or incorrect diagnosis of these diseases can lead to using either inadequate or more than required chemicals or using a wrong chemical altogether to treat the plant. Historically, this problem was solved using conventional machine learning algorithms like SVMs, Decision Trees and Random Forests. However, in recent times, the approach to solve this problem has shifted to deep learning, specifically Convolutional Neural Networks. CNN’s are powerful tools that can be used for image classification. We can get state-ofthe-art results even by using small amounts of data and little to no data preprocessing. In this work, we are going to compare some of the state of the art CNN architectures on the task of accurately classifying a given image into different categories of diseases or as a healthy leaf. Finally, experimental results are conveyed and performance analysis of these various architectures has been done.

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Gao, Y., A. Quevedo, and J. Loya. "FOREST DISTURBANCE DETECTION BY LANDSAT-BASED NDVI TIME SERIES FOR AYUQUILA RIVER BASIN, JALISCO, MEXICO." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-3/W12-2020 (November 4, 2020): 31–35. http://dx.doi.org/10.5194/isprs-archives-xlii-3-w12-2020-31-2020.

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Abstract. Time series data have been applied for forest disturbance detection. The validation of detected changes is challenging partially because the validation data are often not readily available. Unlike multi-temporal change analysis, time series analysis not only detects areas of change but also reports time of change. Both spatial and temporal accuracy are therefore important for the accuracy assessment. Ayuquila River Basin (ARB) is one of the early action areas in Mexico for the implementation of REDD+ initiatives under UNFCCC. In ARB, shifting cultivation and cattle grazing often take place, resulting in degraded forestland. Sub-annual forest disturbance detection and estimation contribution to the improved local forest management and REDD+ implementation. Landsat-based NDVI time series data covering 1999–2018 were analysed using linear regression and the breakpoints of change and the magnitude of change were detected. Breakpoints with magnitude of change ranging from (−0.05) to (−0.2) were verified during a field campaign in October 2018. Here the magnitude of change is related with NDVI differences. Areas with magnitude of change higher than (−0.2) were identified as false changes. Verification data were generated by visually interpreting time series Landsat images of 2016–2018. In this way, areas with forest loss were identified. By stratified random sampling, 683 points were applied for the verification including 511 points of forests and 172 points of forest loss. It yields 75.84% for the overall accuracy of the change detection; for the detected forest loss as a category, the user’s accuracy is 88.89% and the producer’s accuracy is 0.46%. A possible reason for the very low producer’s accuracy is that the selected magnitude value (−0.2) is too low and some of the detected changes were filtered out.

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Karam, Zeyad A. "Hybrid Fuzzy Congestion Controllers for Computer Networks Tuned by Modified Particle Swarm Optimization." International Journal of Advances in Telecommunications, Electrotechnics, Signals and Systems 7, no. 2 (June 1, 2018): 17. http://dx.doi.org/10.11601/ijates.v7i2.250.

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One of the most debated issues nowadays is the quality of computer network service. The best internet service must provide a fast processing of the traffic. Each router has a queue of packets that provides a buffer space, where the packets wait for processing. Transmission Control Protocol (TCP) is a packets congestion control theory. Active Queue Management (AQM) is a mechanisms proposed to employ at gateways to improve the performance of TCP congestion control. AQM mechanisms aim to provide high link utilization with low loss rate and low queuing delay while reacting to load changes quickly. Random Early Detection (RED) is an extensively studied AQM algorithm that can detect congestion by dropping packets randomly with certain probability that serves as the function of the average queue size. In this work, hybrids Fuzzy Logic Controllers (FLC) are proposed to measure the router queue size directly by use them as a congestion controllers. A multiple hybrid fuzzy controllers are proposed, where (Proportional Integral Derivative controller (PID) -like FLC-Particle Swarm Optimization (PSO) Based, Proportional Derivative (PD)-like FLC with conventional I-PSO Based and PID tuned by Fuzzy Logic-PSO Based), which is provided to regulate the queue length, round trip time and packet loss. The Particle Swarmed Optimization (PSO) algorithm is used for tuning the gains of hybrid fuzzy logic controller which helps in reducing the error of the queue size. This is achieved through minimizing the rise time, peak time, settling time and overshoot of the AQM response. The empirical results revealed a high-performance improvement regarding the proposed method in comparison to previous works of other researchers.

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H.Ismail, Alshimaa, Ayman El-Sayed, Zeiad Elsaghir, and Ibrahim Z. Morsi. "Enhanced Random Early Detection (ENRED)." International Journal of Computer Applications 92, no. 9 (April 18, 2014): 25–28. http://dx.doi.org/10.5120/16039-5015.

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Lin, Dong, and Robert Morris. "Dynamics of random early detection." ACM SIGCOMM Computer Communication Review 27, no. 4 (October 1997): 127–37. http://dx.doi.org/10.1145/263109.263154.

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Sumarno, Gano, Mesa Rahmi Stephani, and Ricky Wibowo. "Keterampilan Motorik Kasar dan Halus Anak Usia Dini Usia 4-5 Tahun pada Kelompok Sosial Ekonomi Status Orang Tua Tingkat Menengah." TEGAR: Journal of Teaching Physical Education in Elementary School 2, no. 1 (October 3, 2018): 60. http://dx.doi.org/10.17509/tegar.v2i1.16273.

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Early childhood motor development is important to know, as an effort to identify children's talents and abilities. Early detection also needs to be known to determine what efforts can be taken to optimize child development. This study aims to provide a description of gross and fine motor development in children aged 4-5 years who are associated with parents' socio-economic status (SES). The research method used descriptive quantitative method, with Ex-Post Facto design. The study population was children 4-5 years old Kindergarten and Raudhatul Athfal in urban areas with Intermediate (Middle and Upper and Lower Middle) SES levels. The sample was chosen by cluster random sampling. Participating samples have received parental approval. SES is measured using the SES Questioner. Motor skills were measured using ASQ 3rd Edition. Efforts to increase active lifestyles in early childhood require the role of a fairly large parent. Good gross motoric development is the child's initial capital to be able to carry out more complex motion tasks. Fine motoric is no less important to continue stimulated both in the home and school environment. Because this plays an important role as a basis for children to be able to write and read, as well as doing simple motor tasks. AbstrakPerkembangan motorik anak sejak dini penting untuk diketahui, sebagai upaya identifikasi bakat dan kemampuan anak. Deteksi dini pun perlu diketahui untuk menentukan upaya apa saja yang bisa ditempuh untuk mengoptimalkan perkembangan anak. Penelitian ini bertujuan untuk memberikan gambaran perkembangan motorik kasar dan halus pada anak usia 4-5 tahun yang dihubungkan dengan latar belakang sosial ekonomi status (SES) orang tua. Metode penelitian menggunakan metode deskriptif kuantitatif, dengan desain Ex-Post Facto. Populasi penelitian adalah anak Taman Kanak-kanak dan Raudhatul Athfal berusia 4-5 tahun di wilayah perkotaan dengan tingkat SES Menengah (Menengah Atas maupun Menengah Bawah). Sampel dipilih dengan cluster random sampling. Sampel yang berpartisipasi telah mendapatkan persetujuan orang tua. SES diukur menggunakan SES Questioner. Keterampilan motorik diukur menggunakan ASQ 3rd Edition. Upaya peningkatan gaya hidup aktif pada anak usia dini memerlukan peran orang tua yang cukup besar. Perkembangan motorik kasar yang baik merupakan modal awal anak untuk mampu melakukan tugas gerak yang kian lama kian kompleks. Motorik halus pun tidak kalah penting untuk terus distimulasi baik di lingkungan rumah maupun sekolah. Karena hal ini sangat berperan penting sebagai dasar bagi anak untuk mampu menulis dan membaca, juga mengerjakan tugas motorik yang sederhana.

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Kim, Sunhae, Hye-Kyung Lee, and Kounseok Lee. "Screening of Mood Symptoms Using MMPI-2-RF Scales: An Application of Machine Learning Techniques." Journal of Personalized Medicine 11, no. 8 (August 20, 2021): 812. http://dx.doi.org/10.3390/jpm11080812.

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(1) Background: The MMPI-2-RF is the most widely used and most researched test among the tools for assessing psychopathology, and previous studies have established its validity. Mood disorders are the most common mental disorders worldwide; they present difficulties in early detection, go undiagnosed in many cases, and have a poor prognosis. (2) Methods: We analyzed a total of 8645 participants. We used the PHQ-9 to evaluate depressive symptoms and the MDQ to evaluate hypomanic symptoms. We used the 10 MMPI-2 Restructured Form scales and 23 Specific Problems scales for the MMPI-2-RF as predictors. We performed machine learning analysis using the k-nearest neighbor classification, linear discriminant analysis, and random forest classification. (3) Results: Through the machine learning technique, depressive symptoms were predicted with an AUC of 0.634–0.767, and the corresponding value range for hypomanic symptoms was 0.770–0.840. When using RCd to predict depressive symptoms, the AUC was 0.807, but this value was 0.840 when using linear discriminant classification. When predicting hypomanic symptoms with RC9, the AUC was 0.704, but this value was 0.767 when using the linear discriminant method. (4) Conclusions: Using machine learning analysis, we defined that participants’ mood symptoms could be classified and predicted better than when using the Restructured Clinical scales.

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ZHANG, Yan-ping, Jun MA, Yong-cheng WANG, and Xiao-yan CHEN. "Improved nonlinear random early detection algorithm." Journal of Computer Applications 31, no. 4 (June 7, 2011): 890–92. http://dx.doi.org/10.3724/sp.j.1087.2011.00890.

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Vaidya, Rahul, and Shalabh Bhatnagar. "Robust optimization of Random Early Detection." Telecommunication Systems 33, no. 4 (December 2, 2006): 291–316. http://dx.doi.org/10.1007/s11235-006-9020-2.

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Almutairi, Sami A. "Knowledge of kindergarten and elementary school teachers regarding attention deficit hyperactivity disorder at Qassim region." International Journal of Growth and Development 1, no. 1 (December 7, 2017): 8. http://dx.doi.org/10.25081/ijgd.2017.v1i1.9.

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Background: Attention Deficit Hyperactivity Disorder (ADHD) is one of the most common mental disorders that manifest among children. Despite the fact that the teacher’s role is essential in the assessment and management of pupils with ADHD and the recommendation of participation of teachers for the success and efficiency of diagnoses and treatment, the vast majority of teachers have neither understanding nor knowledge of ADHD. Objectives: The current study explores the kindergarten and elementary school teachers’ knowledge regarding early detection and management of ADHD. Methods: Cross sectional survey using stratified random sampling technique was carried out in governmental and private elementary and Kindergarten schools in four cities of Qassim region, Saudi Arabia. Results: The study included 1095 teachers, 711 (59.3%) did not get information about ADHD during undergraduate studies. Teachers’ overall ADHD knowledge mean was 21.7+5.5 out of 38 marks. Teachers with high qualification degree and kindergarten specialty scored 56.4% and 60.2% respectively. Teachers who attended conferences related to ADHD scored 64.5%, while teachers who read about ADHD or have been ever asked to diagnose/teach an ADHD student had 59.4% and 62.1% respectively. The level of ADHD knowledge perception showed that 76% of teachers were knowledgeable in relation to overall perception about ADHD. Conclusion: Teachers who have higher qualifications or training in identifying ADHD children scored higher in our study.

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Floyd, S., and V. Jacobson. "Random early detection gateways for congestion avoidance." IEEE/ACM Transactions on Networking 1, no. 4 (1993): 397–413. http://dx.doi.org/10.1109/90.251892.

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Hosamo, Mohsen, S. P. Singh, and Anand Mohan. "Random Early Detection method for ABR service." Computers & Electrical Engineering 34, no. 4 (July 2008): 290–308. http://dx.doi.org/10.1016/j.compeleceng.2007.03.005.

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Abdulkareem, Muntadher, Kassem Akil, Ali Kalakech, and Seifedine Kadry. "EFRED: Enhancement of Fair Random Early Detection Algorithm." International Journal of Communications, Network and System Sciences 08, no. 07 (2015): 282–94. http://dx.doi.org/10.4236/ijcns.2015.87028.

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Aweya, J., M. Ouellette, and D. Y. Montuno. "An optimization-oriented view of random early detection." Computer Communications 24, no. 12 (July 2001): 1170–87. http://dx.doi.org/10.1016/s0140-3664(00)00365-0.

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Pippas, J. B., and I. S. Venieris. "Applying delay random early detection to IP gateways." Computer Communications 24, no. 14 (September 2001): 1370–79. http://dx.doi.org/10.1016/s0140-3664(01)00289-4.

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Journal articles on the topic 'Random early detection (RED)'

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