Machine learning-based QOT prediction for self-driven optical networks

Vejdannik, Masoud; Sadr, Ali

doi:10.1007/s00521-020-05123-y

Cited by 8 publications

(4 citation statements)

References 27 publications

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“…ML methods can have more meaningful results for various research topics [3,[22][23][24]. The important thing is to determine the correct algorithms to obtain the relevant results for the problem [9,[25][26][27].…”

Section: Machine Learning Algorithmsmentioning

confidence: 99%

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Estimation of UAV flight time and Battery Consumption for photogrammetric application using multiple machine learning algorithms

2022

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In recent years, important research has been conducted in Machine Learning (ML), especially on Artificial Neural Networks (ANN). Adaptive-Network Based Fuzzy Inference Systems (ANFIS) and Particle Swarm Optimization-Fuzzy Inference System (PSO-FIS) algorithms are popular ML algorithms like ANN. In terms of their working architecture and results, ANN, ANFIS, and PSO-FIS algorithms can obtain useful solutions for different nonlinear problems. This study evaluated the performance of the ANN, ANFIS, and PSO-FIS algorithms and compared the estimation results. Regarding the application, the test and target data was obtained from the flights performed with Unmanned Aerial Vehicles (UAV), including how long the UAV operates (i.e., Flight Time, FT) and how much battery the UAV consumes during the flight (i.e., Battery Consumption, BC). To obtain FT and BC outputs, sixty-five pre- and post-flight data tables were created. The best iterations for estimating the outputs using the three ML algorithms (considering the minimum/maximum values, RMSE, R, and R2) were determined and discussed based on the training, validation, and test estimations.

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Section: Machine Learning Algorithmsmentioning

confidence: 99%

“…In recent years, Machine Learning (ML) methods have been the most crucial research topic in nonlinear problems [1][2][3]. With ML algorithms, unlimited alternative solutions and options can be produced.…”

Section: Introductionmentioning

confidence: 99%

Estimation of UAV flight time and Battery Consumption for photogrammetric application using multiple machine learning algorithms

2022

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“…Support-vector machine and probabilistic neural network (PNN) models are merely used for categorization. In contrast, multilayer perceptron, radial basis function, and generalized regression neural network (GRNN) models are utilized for both regression and classification [11]. The best-transmitted forecasting (categorization methodology) and optical signal-to-noise ratio assessment (regression strategy) precision are achieved by PNN (with an aggregate precision of 99.6 0.5 percent) and GRNN (with an R-squared value of 0.957) [11].…”

Section: Review Of Literaturementioning

confidence: 99%

“…The RMSE and MAE are statistical metrics that distinguish between true and false, whereas R2 depicts the difference between the actual and estimated reaction variance and how well the projected response variance explains the genuine response variance. As a result, a measurement near one is required, showing that the estimated response closely reflects the real reaction and is accurate [11].…”

Section: Prediction Model Usedmentioning

confidence: 99%

Machine Learning Based Network Traffic Predictive Analysis

Srideviponmalar

Kamboj

Shete

2022

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Digital enterprises that use various Internet of Things implementation prototypes, such as cloud, mobile, and edge equipment, are experiencing unprecedented traffic volume and dynamicity. Data center networks (DCN) have faced various issues due to the transient and random nature of traffic created by services and apps. The primary objective of this paper is to predict the network traffic using the machine learning (ML) models before the performance of the network start degrading. Because in the last decade, ML has had a tremendous impact on handling the massive amount of data. With the increase in complexity and traffic, we tried to implement four ML models such as K – Nearest Neighbor (KNN), Random Forest (RF), Gradient Boosting (GB), and Decision Tree (Tree), with tuned sub-parameters to predict the network traffic. We create a matching ML environment based on a sequential database and provide a comparison table of mean square error (MSE), root mean square error (RMSE), mean absolute error (MSE), and coefficient of determination (R2) for each prototype. The simulation results show that the GB with different types is the best-suited model for predicting the network traffic with performance matrix parameters such as MSE 0.001 and RMSE of 0.030. Therefore, the Orange tool is used to stimulate the predictive models.

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