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Journal articles on the topic 'Quadratic exponential random early detection'

Author: Grafiati

Published: 5 June 2025

Last updated: 15 July 2025

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1

Hassan, Samuel Oluwatosin, Adewole Usman Rufai, Vivian Ogochukwu Nwaocha, et al. "Quadratic exponential random early detection: a new enhanced random early detection-oriented congestion control algorithm for routers." International Journal of Electrical and Computer Engineering (IJECE) 13, no. 1 (2023): 669. http://dx.doi.org/10.11591/ijece.v13i1.pp669-679.

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<span>Network congestion is still a problem on the internet. The random early detection (RED) algorithm being the most notable and widely implemented congestion algorithm in routers faces the problems of queue instability and large delay arising from the presence of an ineffectual singular linear packet dropping function. This research article presents a refinement to RED, named quadratic exponential random early detection (QERED) algorithm, which exploits the advantages of two drop functions, namely quadratic and exponential in order to enhance the performance of RED algorithm. ns-3 sim

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Samuel, Oluwatosin Hassan, Usman Rufai Adewole, Ogochukwu Nwaocha Vivian, et al. "Quadratic exponential random early detection: a new enhanced random early detection-oriented congestion control algorithm for routers." International Journal of Electrical and Computer Engineering (IJECE) 13, no. 1 (2023): 669–79. https://doi.org/10.11591/ijece.v13i1.pp669-679.

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Network congestion is still a problem on the internet. The random early detection (RED) algorithm being the most notable and widely implemented congestion algorithm in routers faces the problems of queue instability and large delay arising from the presence of an ineffectual singular linear packet dropping function. This research article presents a refinement to RED, named quadratic exponential random early detection (QERED) algorithm, which exploits the advantages of two drop functions, namely quadratic and exponential in order to enhance the performance of RED algorithm. ns-3 simulation stud

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3

Hassan, Samuuel Oluwatosin, Vivian Ogochukwu Nwaocha, Adewole Usman Rufai, et al. "Random early detection-quadratic linear: an enhanced active queue management algorithm." Bulletin of Electrical Engineering and Informatics 11, no. 4 (2022): 2262–72. http://dx.doi.org/10.11591/eei.v11i4.3875.

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This paper identifies the lone linear drop function for computing the dropping probability between certain queue threshold values as a major weakness for the random early detection (RED) algorithm as it leads to large delay and queue instability. To address this concern, we propose an enhanced RED-based algorithm called random early detection-quadratic linear (referred to as “RED-QL”) active queue management (AQM) which leveraged the benefit of a quadratic packet drop function for a light-to moderate traffic load conditions together with a linear packet drop function for a heavy traffic load c

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4

Samuel, Oluwatosin Hassan, Ogochukwu Nwaocha Vivian, Usman Rufai Adewole, et al. "Random early detection-quadratic linear: an enhanced active queue management algorithm." Bulletin of Electrical Engineering and Informatics 11, no. 4 (2022): 2262~2272. https://doi.org/10.11591/eei.v11i4.3875.

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This paper identifies the lone linear drop function for computing the dropping probability between certain queue threshold values as a major weakness for the random early detection (RED) algorithm as it leads to large delay and queue instability. To address this concern, we propose an enhanced RED-based algorithm called random early detection-quadratic linear (referred to as “RED-QL”) active queue management (AQM) which leveraged the benefit of a quadratic packet drop function for a light-to moderate traffic load conditions together with a linear packet drop function for a heavy tr

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5

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

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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 (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 e

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Hassan, Samuel O., Adewole U. Rufai, Michael O. Agbaje, Theophilus A. Enem, Lukman A. Ogundele, and Suleiman A. Usman. "Improved random early detection congestion control algorithm for internet routers." Indonesian Journal of Electrical Engineering and Computer Science 28, no. 1 (2022): 384. http://dx.doi.org/10.11591/ijeecs.v28.i1.pp384-395.

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In the internet, router plays a strategic role in the transmission of data packets. Active queue management (AQM) aimed at managing congestion by keeping a reduced average buffer occupancy and hence a minimal delay. The novel random early detection (RED) algorithm suffers from large average buffer occupancy and delay shortcomings. This problem is due in part to the existence of a distinctive linear packet drop function it deploys. In this paper, we present a new version of RED, called improved RED (IMRED). An important strategy of IM-RED is to deploy two dropping functions: i) nonlinear (i.e.

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8

Hassan, Samuel O., Adewole U. Rufai, Michael O. Agbaje, Theophilus A. Enem, Lukman A. Ogundele, and Suleiman A. Usman. "Improved random early detection congestion control algorithm for internet routers." Indonesian Journal of Electrical Engineering and Computer Science 28, no. 1 (2022): 384–95. https://doi.org/10.11591/ijeecs.v28.i1.pp384-395.

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In the internet, router plays a strategic role in the transmission of data packets. Active queue management (AQM) aimed at managing congestion by keeping a reduced average buffer occupancy and hence a minimal delay. The novel random early detection (RED) algorithm suffers from large average buffer occupancy and delay shortcomings. This problem is due in part to the existence of a distinctive linear packet drop function it deploys. In this paper, we present a new version of RED, called improved RED (IMRED). An important strategy of IM-RED is to deploy two dropping functions: i) nonlinear (i.e.

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Orekhov, Andrey V., and Aleksey A. Orekhov. "Network traffic anomalies automatic detection in DDoS attacks." Vestnik of Saint Petersburg University. Applied Mathematics. Computer Science. Control Processes 19, no. 2 (2023): 251–63. http://dx.doi.org/10.21638/11701/spbu10.2023.210.

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Distributed denial-of-service attacks (DDoS attacks) are intrusions into computing systems of the Internet. Their purpose is to make systems of the Internet inaccessible for users. DDoS attack consist of sending many requests to a certain resource at the same time. As a result, the server cannot withstand the network load. In such situation, a provider must determine the moment when attack begins and change the traffic management strategy. Detection of the beginning of a DDoS attack is possible by using unsupervised machine learning methods and sequential statistical analysis of network activi

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Surajo, Yusuf, Aminu Bashir Suleiman, and Usman Yahaya. "EXPONENTIAL-SELF-ADAPTIVE RANDOM EARLY DETECTION SCHEME FOR QUEUE MANAGEMENT IN NEXT GENERATION ROUTERS." FUDMA JOURNAL OF SCIENCES 7, no. 3 (2023): 33–39. http://dx.doi.org/10.33003/fjs-2023-0703-1763.

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ensuring optimal performance in next-generation routers. Active Queue Management (AQM) scheme, has been advocated by the Internet research community for the next generation routers. Random Early Detection (RED) is the most well-known AQM scheme. However, RED lacks self-adaptation mechanism and it is susceptible to parametrization problem. Several variants of RED were developed, however all of them possess a static drop pattern; as such they are severely affected when a traffic load changes. To address the self-adaptation shortcoming of the RED and its variant schemes, Self-Adaptive Random Earl

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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 prope

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Jafri, Syed Talib Abbas, Irfan Ahmed, and Sundus Ali. "Queue-Buffer Optimization Based on Aggressive Random Early Detection in Massive NB-IoT MANET for 5G Applications." Electronics 11, no. 18 (2022): 2955. http://dx.doi.org/10.3390/electronics11182955.

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Elements in massive narrowband Internet of Things (NB-IoT) for 5G networks suffer severely from packet drops due to queue overflow. Active Queue Management (AQM) techniques help in maintaining queue length by dropping packets early, based on certain defined parameters. In this paper, we have proposed an AQM technique, called Aggressive Random Early Detection (AgRED) which, in comparison to previously used Random Early Detection (RED) and exponential RED technique, improves the overall end-to-end delay, throughput, and packet delivery ratio of the massive NB-IoT 5G network while using UDP. This

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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

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Fierens, Pablo I. "Number of sensors versus time to detection in wildfires." International Journal of Wildland Fire 18, no. 7 (2009): 825. http://dx.doi.org/10.1071/wf07137.

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The lack of extensive research in the application of inexpensive wireless sensor nodes for the early detection of wildfires motivated us to investigate the cost of such a network. As a first step, in this paper we present several results that relate the time to detection and the burned area to the number of sensor nodes in the region that is protected. We prove that the probability distribution of the size of the burned area at the moment of detection is approximately exponential, given that some hypotheses hold: the positions of the sensor nodes are independent random variables uniformly dist

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15

SORNETTE, D., and J. V. ANDERSEN. "A NONLINEAR SUPER-EXPONENTIAL RATIONAL MODEL OF SPECULATIVE FINANCIAL BUBBLES." International Journal of Modern Physics C 13, no. 02 (2002): 171–87. http://dx.doi.org/10.1142/s0129183102003085.

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Keeping a basic tenet of economic theory, rational expectations, we model the nonlinear positive feedback between agents in the stock market as an interplay between nonlinearity and multiplicative noise. The derived hyperbolic stochastic finite-time singularity formula transforms a Gaussian white noise into a rich time series possessing all the stylized facts of empirical prices, as well as accelerated speculative bubbles preceding crashes. We use the formula to invert the two years of price history prior to the recent crash on the Nasdaq (April 2000) and prior to the crash in the Hong Kong ma

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Chen, Jianyong, Cunying Hu, and Zhen Ji. "An Improved ARED Algorithm for Congestion Control of Network Transmission." Mathematical Problems in Engineering 2010 (2010): 1–14. http://dx.doi.org/10.1155/2010/329035.

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In order to achieve high throughput and low average delay in computer network, it is necessary to stabilize the queue length and avoid oscillation or chaos phenomenon. In this paper, based on Adaptive Random Early Detection (ARED), an improved algorithm is proposed, which dynamically changes the range of maximum drop probabilitypmaxaccording to different network scenarios and adjustspmaxto limit average queue sizeqavein a steady range. Moreover, exponential averaging weightwis adjusted based on linear stability condition to stabilizeqave. A number of simulations show that the improved ARED alg

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T. SRIKANTH, B. SREYA, B. KEERTHI, and B. SINDHUJA. "DETECTION AND PREDICTION OF FUTURE MENTAL DISORDER FROM SOCIAL MEDIA DATA USING MACHINE LEARNING, ENSEMBLE LEARNING, AND LARGE LANGUAGE MODELS." Journal of Nonlinear Analysis and Optimization 15, no. 02 (2024): 186–91. https://doi.org/10.36893/jnao.2024.v15i12.049.

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The increasing use of social media platforms has led to an exponential rise in data related to individuals' mental health, providing valuable insights for the detection of mental disorders. This project explores the use of Machine Learning (ML) techniques, particularly Random Forest and Decision Tree algorithms, to detect potential mental health issues from social media data. By analyzing the textual content shared by users, the system aims to predict whether a person might be experiencing a mental health disorder based on their posts and interactions. In this study, we preprocess the textual

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18

Gibson, Thomas I., Rebecca S. Millard, Isla MacMillan, Nick Taylor, Mark Thrush, and Hannah Tidbury. "Application of a theoretical simulator to the optimisation of risk-based invasive species surveillance." NeoBiota 97 (January 21, 2025): 19–46. https://doi.org/10.3897/neobiota.97.121188.

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Early detection and rapid response are critical to the successful management of non-indigenous species (NIS) and rely on effective surveillance programmes. Risk-based surveillance, where surveillance targets high risk locations, is the most efficient form of NIS surveillance. However, further research is required on the impact of different levels of emphasis on risk, in sampling designs and on surveillance efficacy. This study implements a theoretical surveillance simulator to model the relative merit of different surveillance strategies with different levels of focus on NIS risk for NIS detec

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Gibson, Thomas I., Rebecca S. Millard, Isla MacMillan, Nick Taylor, Mark Thrush, and Hannah Tidbury. "Application of a theoretical simulator to the optimisation of risk-based invasive species surveillance." NeoBiota 97 (January 21, 2025): 19–46. https://doi.org/10.3897/neobiota.97.121188.

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Early detection and rapid response are critical to the successful management of non-indigenous species (NIS) and rely on effective surveillance programmes. Risk-based surveillance, where surveillance targets high risk locations, is the most efficient form of NIS surveillance. However, further research is required on the impact of different levels of emphasis on risk, in sampling designs and on surveillance efficacy. This study implements a theoretical surveillance simulator to model the relative merit of different surveillance strategies with different levels of focus on NIS risk for NIS detec

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20

Attallah, Sharkas, and Gadelkarim. "Fetal Brain Abnormality Classification from MRI Images of Different Gestational Age." Brain Sciences 9, no. 9 (2019): 231. http://dx.doi.org/10.3390/brainsci9090231.

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Magnetic resonance imaging (MRI) is a common imaging technique used extensively to study human brain activities. Recently, it has been used for scanning the fetal brain. Amongst 1000 pregnant women, 3 of them have fetuses with brain abnormality. Hence, the primary detection and classification are important. Machine learning techniques have a large potential in aiding the early detection of these abnormalities, which correspondingly could enhance the diagnosis process and follow up plans. Most research focused on the classification of abnormal brains in a primary age has been for newborns and p

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Bindu Sree. "A Comprehensive Machine Learning Approach for Advanced Vehicle Detection and Counting." International Journal of Scientific Research in Computer Science, Engineering and Information Technology 10, no. 4 (2024): 219–24. http://dx.doi.org/10.32628/cseit2410415.

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The exponential rise of urban areas and the associated surge in transportation congestion. Consequently, this study offers a thorough method for vehicle recognition and counting via the use of machine learning, as well as an effective system for real-time traffic monitoring, with the aim of reducing traffic. The first step is to develop a model that can identify and follow moving cars in still photos or video. This research delves into the topic of teaching a computer to count automobiles using machine learning, a kind of artificial intelligence. The purpose of this study is to provide a compu

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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 (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 onse

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23

Zhao, Kaocheng, Ying Ye, Jun Ma, Lifen Huang, and Hengyang Zhuang. "Detection and Dynamic Variation Characteristics of Rice Nitrogen Status after Anthesis Based on the RGB Color Index." Agronomy 11, no. 9 (2021): 1739. http://dx.doi.org/10.3390/agronomy11091739.

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We aimed to elucidate the color changes of rice leaves after anthesis and create an algorithm for monitoring the nitrogen contents of rice leaves and of the whole plant. Hence, we aimed to provide a theoretical basis for the precise management of rice nitrogen fertilizer and the research and development of digital image nutrition monitoring equipment and reference. We selected the leaf colors of the main stems of four major rice varieties promoted in production, including Huaidao 5 (late-maturing medium japonica rice), Yangjing 4227 (early maturing late japonica rice), Changyou 5 (late japonic

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Besteiro, Roberto, Tamara Arango, Manuel R. Rodríguez, and María D. Fernández. "Linear and Nonlinear Mixed Models to Determine the Growth Curves of Weaned Piglets and the Effect of Sex on Growth." Agriculture 14, no. 1 (2023): 79. http://dx.doi.org/10.3390/agriculture14010079.

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This study characterizes the growth of weaned Large White × Landrace hybrid piglets from 6 to 20 kg live body weight (BW) under real farm conditions. Batches of 50 castrated male pigs and 50 gilts were weighed repeatedly over two 6-week breeding cycles. The data was fitted to various linear (quadratic and exponential) and nonlinear (Gompertz, Richards, logistic, Von-Bertalanffy) mixed models to find the best-performing model. During the postweaning phase, animal growth was modelled, and the effect of sex on growth was determined by incorporating the variable, sex, into the mixed models and usi

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Suwannapong and Khunboa. "Congestion Control in CoAP Observe Group Communication." Sensors 19, no. 15 (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 deal

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Aljalal, Majid, Saeed A. Aldosari, Khalil AlSharabi, Akram M. Abdurraqeeb, and Fahd A. Alturki. "Parkinson’s Disease Detection from Resting-State EEG Signals Using Common Spatial Pattern, Entropy, and Machine Learning Techniques." Diagnostics 12, no. 5 (2022): 1033. http://dx.doi.org/10.3390/diagnostics12051033.

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Parkinson’s disease (PD) is a very common brain abnormality that affects people all over the world. Early detection of such abnormality is critical in clinical diagnosis in order to prevent disease progression. Electroencephalography (EEG) is one of the most important PD diagnostic tools since this disease is linked to the brain. In this study, novel efficient common spatial pattern-based approaches for detecting Parkinson’s disease in two cases, off–medication and on–medication, are proposed. First, the EEG signals are preprocessed to remove major artifacts before spatial filtering using a co

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Sugiyanto, Sugiyanto, Gatot Murti Wibowo, and Heru Trisikwanto. "Quantitative Measurement of The Knee Joint Cartilage Signal Intensity (SI) When T2map Sequence Applied to Define a Biomarker for Early Detection of Osteoarthritis (OA) Based on Bmi Groups." Jurnal Riset Kesehatan 3, no. 1 (2015): 467–76. https://doi.org/10.31983/jrk.v3i1.232.

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This quantitative study was an experimental design. A total of 18 healthy male and female volunteers, with differnt BMI involved in the study by consecutive random sampling. An expert Radiologist evaluated all the 38 T2map images being studied. The arbitrary SI data were measured using one millimeter ROI of the machine measurement tools and the values of T2map were based on a fitted exponential decay calculation ?generated from the graphs developed by the Window excel. The mean differences amongst T2map values on the sagital plan femoro-tibial cartilage and coronal plan femoro-tibial (medial a

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Abdulridha, Jaafar, An Min, Matthew N. Rouse, Shahryar Kianian, Volkan Isler, and Ce Yang. "Evaluation of Stem Rust Disease in Wheat Fields by Drone Hyperspectral Imaging." Sensors 23, no. 8 (2023): 4154. http://dx.doi.org/10.3390/s23084154.

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Detecting plant disease severity could help growers and researchers study how the disease impacts cereal crops to make timely decisions. Advanced technology is needed to protect cereals that feed the increasing population using fewer chemicals; this may lead to reduced labor usage and cost in the field. Accurate detection of wheat stem rust, an emerging threat to wheat production, could inform growers to make management decisions and assist plant breeders in making line selections. A hyperspectral camera mounted on an unmanned aerial vehicle (UAV) was utilized in this study to evaluate the sev

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Panda, Ashok Kumar, Sonali Pradhan, Chinmayee Pati, Naba Kumar Rath, and Deepak Kumar Baral. "AI-Based Predictive Modeling For Air Quality Assessment And Environmental Risk Forecasting In Urban Ecosystems." International Journal of Environmental Sciences 11, no. 12s (2025): 163–71. https://doi.org/10.64252/n5gynb06.

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The exponential growth of urbanization has intensified environmental degradation, particularly in terms of air pollution, posing severe health and ecological risks to urban populations. Traditional air quality monitoring systems, while effective in data collection, often fall short in predictive capability and real-time responsiveness. In this context, artificial intelligence (AI)-driven predictive modeling emerges as a transformative tool in environmental risk assessment, offering advanced analytical techniques that can not only evaluate current air quality conditions but also forecast future

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Dybkær, Karen, Hanne Due, Rasmus Froberg Brøndum, Ken H. Young, and Martin Bøgsted. "Addition of Drug-Response Specific Micro-RNAs to the International Prognostic Index Improves Prognostic Stratification of GCB-DLBCL Patients Treated with R-CHOP." Blood 134, Supplement_1 (2019): 1623. http://dx.doi.org/10.1182/blood-2019-122351.

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Background: Patients with Diffuse large B-cell lymphoma (DLBCL) in approximately 40% of cases suffer from primary refractory disease and treatment induced immuno-chemotherapy resistance demonstrating that standard provided treatment regimens are not sufficient to cure all patients. Early detection of resistance is of great importance and defining microRNA (miRNA) involvement in resistance could be useful to guide treatment selection and help monitor treatment administration while sparing patients for inefficient, but still toxic therapy. Concept and Aims: With information on drug-response spec

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Hassan, Samuel. "AD-RED: A new variant of random early detection AQM algorithm." Journal of High Speed Networks, October 10, 2023, 1–15. http://dx.doi.org/10.3233/jhs-222055.

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Intensive continuing research has been noticed among scholars in the literature with a particular appreciable interest in developing new enhanced variants for the long-standing Random Early Detection (RED) algorithm. Contemporary trends shows that researchers continue to follow a research line thereby exchanging the linear curve needed in RED with nonlinear curves. Several reports have shown that RED’s sole linear function is insufficiently powered for managing rising degrees of traffic congestion in the network. In this paper, Amended Dropping – Random Early Detection (AD-RED), a revised vers

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Hassan, Samuel O., Chigozirim Ajaegbu, and Olakunle O. Solanke. "Double Function Random Early Detection (DFRED): A Revised RED-Oriented Algorithm." Iraqi Journal of Science, October 30, 2023, 5241–52. http://dx.doi.org/10.24996/ijs.2023.64.10.31.

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Dropping packets with a linear function between two configured queue thresholds in Random Early Detection (RED) model is incapable of yielding satisfactory network performance. In this article, a new enhanced and effective active queue management algorithm, termed Double Function RED (DFRED in short) is developed to further curtail network delay. Specifically, DFRED algorithm amends the packet dropping probability approach of RED by dividing it into two sub-segments. The first and second partitions utilizes and implements a quadratic and linear increase respectively in the packet dropping prob

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Si, Yuanyuan, Hongjun Liu, and Yuehui Chen. "Constructing a 3D Exponential Hyperchaotic Map with Application to PRNG." International Journal of Bifurcation and Chaos 32, no. 07 (2022). http://dx.doi.org/10.1142/s021812742250095x.

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Some weaknesses of 1D chaotic maps, such as lacking of ergodicity, multiple bifurcations, dense periodic windows, and short iteration period, limit their practical applications in cryptography. A higher-dimensional chaotic map with ergodicity can solve these problems. Based on 1D quadratic map, a 3D exponential hyperchaotic map (3D-EHCM) is constructed, and its dynamic behaviors, such as phase diagram, Lyapunov exponent spectrum, Kolmogorov entropy (KE), correlation dimension, approximate entropy and randomness, are analyzed and tested. The results demonstrate that the 3D-EHCM has ergodicity i

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"A Brief Review on Queue Management Systems for different Applications." Transaction on Biomedical Engineering Applications and Healthcare 4, no. 1 (2023). http://dx.doi.org/10.36647/tbeah/04.01.a003.

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Queue management technology helps to reduce actual and predicted customer wait times, improve customer satisfaction, and provide the data to your managers needed to further optimize service, Queue Management System (QMS)presents a viable solution for different applications. It is employed to manage lines in a queue area in a variety of circumstances and locales. The article discusses the concepts of Queue Management Systems for Hospitals, Satellite Networks Based on Traffic Prediction, using Deep Neural Networks (DNN). Managing high patient loads in tertiary care hospitals represents a signifi

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Xu, Xiangyang, Haotian Wang, Xihui Liang, Chuan Zhao, and Ziyuan Ren. "Rolling Bearing Early Fault Detection Method Based on Feature Clustering Fusion Degradation Index." Chinese Journal of Mechanical Engineering 38, no. 1 (2025). https://doi.org/10.1186/s10033-025-01263-1.

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Abstract The research on rolling bearing early fault detection is mainly focused on degradation index extraction and adaptive setting of alarm threshold. The mainstream methods are to extract degradation indicators based on adaptive features and set adaptive alarm thresholds based on the Shewhart control chart. However, the adaptive feature extraction method does not consider the correlation between features, and the Shewhart control chart is not sensitive to small fluctuations caused by early faults. In this study, a rolling bearing early fault detection method based on a feature clustering f

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Boualleg, Yaakoub, Kheir Eddine Daouadi, Oussama Guehairia, et al. "Deep multi-view feature fusion with data augmentation for improved diabetic retinopathy classification." Journal of Intelligent Systems 34, no. 1 (2025). https://doi.org/10.1515/jisys-2024-0374.

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Abstract Diabetic retinopathy (DR) is a leading cause of blindness worldwide, necessitating early detection to prevent severe visual impairment. Despite numerous proposed classification techniques, challenges persist due to the high parameter count of deep learning algorithms, imbalanced datasets, and limited performance. This study introduces a novel framework for DR classification that leverages multi-view deep features, multilinear whitened principal component analysis, tensor exponential discriminant analysis, synthetic minority oversampling technique, and deep random forest. We evaluated

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Haider, Aun, and Richard Harris. "A Hybrid Random Early Detection Algorithm for Improving End-to-End Congestion Control in TCP/IP Networks." African Journal of Information & Communication Technology 4, no. 1 (2008). http://dx.doi.org/10.5130/ajict.v4i1.418.

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The successful operation of the present Internet depends mainly upon TCP/IP which employs end-to-end congestion control mechanisms built in the end hosts. In order to further enhance this paradigm of end-to-end control the Random Early Detection algorithm (RED) has been proposed, which starts to mark or drop packets at the onset of congestion.  The paper addresses issues related to the choice of queue length  indication parameters for packet marking/dropping decisions  in RED-type algorithms under varying traffic conditions. Two modifications to RED are prop

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"Active Queue Management Techniques for Congestion Control in TCP Communication Networks: New Prospective." International Journal of Innovative Technology and Exploring Engineering 9, no. 2S5 (2020): 73–77. http://dx.doi.org/10.35940/ijitee.b1016.1292s519.

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the computer network area has grown very fast from previous years, as a result of which the control of traffic load in the network is at a higher priority. In network, congestion occurs if numbers of coming packets exceed, like bandwidth allocation along with buffer space. This might be due to poor network performance in terms of throughput, packet loss rate, and average packet queuing delay. For enhancing the overall performance when this network will become congested, numerous exclusive aqm (active queue management) techniques were proposed and few are discussed in this research paper. Parti

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Hassan, S. O., A. O. Oluwatope, C. Ajaegbu, K.-K. A. Abdullah, and A. O. Olasupo. "QLREDActive Queue Management Algorithm." Journal of Computer Science and Its Application 28, no. 1 (2021). http://dx.doi.org/10.4314/jcsia.v28i1.8.

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The Random Early Detection (RED) algorithm has not been successful in keeping the average queue size low. In this paper, we an improved RED-based algorithm called QLRED which divides the dropping probability function of the RED algorithm into two equal segments. The first segment utilises a quadratic packet dropping function while the second segment deploys a linear packet dropping function respectively so as to distinguish between light and high traffic loads. The ns-3 simulation performance evaluations clearly showed that QLRED algorithm effectively controls the average queue size under vari

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Shin, Hye-Su, Vinayakumar Gedi, Joon-Ki Kim, and Dong-ki Lee. "Detection of Gram-negative bacterial outer membrane vesicles using DNA aptamers." Scientific Reports 9, no. 1 (2019). http://dx.doi.org/10.1038/s41598-019-49755-0.

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Abstract Infection of various pathogenic bacteria causes severe illness to human beings. Despite the research advances, current identification tools still exhibit limitations in detecting Gram-negative bacteria with high accuracy. In this study, we isolated single-stranded DNA aptamers against multiple Gram-negative bacterial species using Toggle-cell-SELEX (systemic evolution of ligands by exponential enrichment) and constructed an aptamer-based detection tool towards bacterial secretory cargo released from outer membranes of Gram-negative bacteria. Three Gram-negative bacteria, Escherichia c

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Eledkawy, Amr, Taher Hamza, and Sara El-Metwally. "Towards precision oncology: a multi-level cancer classification system integrating liquid biopsy and machine learning." BioData Mining 18, no. 1 (2025). https://doi.org/10.1186/s13040-025-00439-8.

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Abstract Background Millions of people die from cancer every year. Early cancer detection is crucial for ensuring higher survival rates, as it provides an opportunity for timely medical interventions. This paper proposes a multi-level cancer classification system that uses plasma cfDNA/ctDNA mutations and protein biomarkers to identify seven distinct cancer types: colorectal, breast, upper gastrointestinal, lung, pancreas, ovarian, and liver. Results The proposed system employs a multi-stage binary classification framework where each stage is customized for a specific cancer type. A majority v

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Halder, Sayanti, Abhishek Thakur, Supriya Suman Keshry, et al. "SELEX based aptamers with diagnostic and entry inhibitor therapeutic potential for SARS-CoV-2." Scientific Reports 13, no. 1 (2023). http://dx.doi.org/10.1038/s41598-023-41885-w.

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AbstractFrequent mutation and variable immunological protection against vaccination is a common feature for COVID-19 pandemic. Early detection and confinement remain key to controlling further spread of infection. In response, we have developed an aptamer-based system that possesses both diagnostic and therapeutic potential towards the virus. A random aptamer library (~ 1017 molecules) was screened using systematic evolution of ligands by exponential enrichment (SELEX) and aptamer R was identified as a potent binder for the SARS-CoV-2 spike receptor binding domain (RBD) using in vitro binding

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Matboli, Marwa, Abdelrahman Khaled, Manar Fouad Ahmed, et al. "Machine learning-based stratification of prediabetes and type 2 diabetes progression." Diabetology & Metabolic Syndrome 17, no. 1 (2025). https://doi.org/10.1186/s13098-025-01786-6.

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Abstract Background Diabetes mellitus, a global health concern with severe complications, demands early detection and precise staging for effective management. Machine learning approaches, combined with bioinformatics, offer promising avenues for enhancing diagnostic accuracy and identifying key biomarkers. Methods This study employed a multi-class classification framework to classify patients across four health states: healthy, prediabetes, type 2 Diabetes Mellitus (T2DM) without complications, and T2DM with complications. Three models were developed using molecular markers, biochemical marke

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Dan, Guangyu, Cui Feng, Zheng Zhong, et al. "Tissue classification from raw diffusion‐weighted images using machine learning." Medical Physics, April 8, 2025. https://doi.org/10.1002/mp.17810.

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AbstractBackgroundIn diffusion‐weighted imaging (DWI), a large collection of diffusion models is available to provide insights into tissue characteristics. However, these models are limited by predefined assumptions and computational challenges, potentially hindering the full extraction of information from the diffusion MR signal.PurposeThis study aimed at developing a MOdel‐free Diffusion‐wEighted MRI (MODEM) method for tissue differentiation by using a machine learning (ML) algorithm based on raw diffusion images without relying on any specific diffusion model. MODEM has been applied to both

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Madhab, Paul Choudhury, and Paul Choudhury J. "A Comparative performance of Machine Learning models with cross validation techniques for the prediction lever disease." June 7, 2025. https://doi.org/10.5281/zenodo.15335413.

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Artificial Intelligence (AI) has changed something so that it is much better in various aspects of our lives, offering solutions to numerous problems and bridging gaps between reality and business. Within the domain of AI, emerging technologies such as machine learning and deep learning models have played an important role in transforming the way so that one can analyze data, make decisions, and can take action or give attention to difficult situations or problems, with an objective to understand them to find solutions. With the exponential growth of data usage an

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Kiran, Mahreen, Ying Xie, Nasreen Anjum, Graham Ball, Barbara Pierscionek, and Duncan Russell. "Machine learning and artificial intelligence in type 2 diabetes prediction: a comprehensive 33-year bibliometric and literature analysis." Frontiers in Digital Health 7 (March 27, 2025). https://doi.org/10.3389/fdgth.2025.1557467.

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BackgroundType 2 Diabetes Mellitus (T2DM) remains a critical global health challenge, necessitating robust predictive models to enable early detection and personalized interventions. This study presents a comprehensive bibliometric and systematic review of 33 years (1991-2024) of research on machine learning (ML) and artificial intelligence (AI) applications in T2DM prediction. It highlights the growing complexity of the field and identifies key trends, methodologies, and research gaps.MethodsA systematic methodology guided the literature selection process, starting with keyword identification

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Abiy, Saron Abeje, Yaregal Animut, Worku Mequannt Ambaw, Getie Mihret Aragaw, and Bayew Kelkay Rade. "Incidence of death and its predictors among neonates admitted with sepsis in referral hospitals, northwest Ethiopia, a prospective cohort study." Frontiers in Pediatrics 11 (April 13, 2023). http://dx.doi.org/10.3389/fped.2023.1129924.

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BackgroundEach year, approximately 2.7 million neonates die in their first month of life worldwide, and the majority of these deaths occur in low-income countries. According to the Global Burden of Disease estimation, 1.3 million annual incident cases of neonatal sepsis were reported worldwide, resulting in 203,000 sepsis-attributable deaths. Little is known about the time to death of neonates and predictors after admission with a diagnosis of sepsis. This study aimed to assess the incidence and predictors of death among neonates admitted to the neonatal intensive care unit with a diagnosis of

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Puttegowda, Kiran, Anil Kumar D, Vinaykumar Ravi, et al. "Advanced Machine Learning Techniques for Prognostic Analysis in Breast Cancer." Open Bioinformatics Journal 18, no. 1 (2025). https://doi.org/10.2174/0118750362356119250121072106.

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Aims The aim of this research is mainly to use machine learning methods for forecasting significant characteristics related to breast cancer using the data to facilitate diagnosis and treatment accordingly. Such factors include the progesterone receptor status (PR+), a biomarker that helps in the understanding of the hormone receptor status of breast cancer cells, and PR status has specific prognostic value for the effectiveness of hormone therapies. Also, in the study, it is essential to predict a tumor stage, which is one of the more significant factors to determine cancer progression and tr

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