Predicting the Total Workload in Telecommunications by SVMs

Method and application of set pair analysis classified prediction ...

Predicting the Total Workload in Telecommunications by SVMs · Mingfang ZhuChangjie TangShucheng DaiYong XiangShaojie QiaoChen Yu. Computer Science. 2008 3rd ...

Support Vector Machines for predicting protein structural class - PMC

As a result, the overall rate of correct prediction for the four structural classes of 277 domains (the 1 st set) was 220/277 = 79.4%; while the rates of ...

VTGAN: hybrid generative adversarial networks for cloud workload ...

Autoregressive moving average (ARMA), as a traditional time-series forecasting model, is used in [17] to predict cloud workload for resource ...

Estimating distribution shifts for predicting cross-subject ... - Frontiers

Monitoring mental workload in a fast and accurate manner is critical in scenarios where the full attention of an individual is fundamental for the security of ...

Applying self-powered sensor and support vector machine in load ...

SVM is widely adopted for tasks like power load forecasting and constructing ECMs. In summary, current research on analyzing and modeling energy ...

Support Vector Regression for Link Load Prediction - Télécom Paris

prediction of network links load at short time scales. We consider the ... values for each of the SVM parameters, for a total of 1000 combinations. To ...

Support Vector Machine (SVM) Algorithm - GeeksforGeeks

Implementing SVM Algorithm in Python. Predict if cancer is Benign or malignant. Using historical data about patients diagnosed with cancer ...

Detecting and Predicting Pilot Mental Workload Using Heart Rate ...

After carefully reviewing the full texts of these potentially relevant papers, a total of 29 papers were included in this review, and 27 articles were excluded ...

Machine Learning Based Workload Prediction in Cloud Computing

[19], [20] have evaluated the Support. Vector Machine (SVM), NN and Linear Regression machine learning prediction methods. They found that, if the resource.

Reducing Literature Screening Workload With Machine Learning

... support vector machines (SVM) for predicting the inclusion probability of unreviewed references. Simply put, the manually reviewed ...

Support Vector Machines for Binary Classification - MathWorks

Total function evaluations: 30 Total elapsed time: 16.8267 seconds Total ... This example shows how to predict posterior probabilities of SVM models over ...

comparative analysis of predictive models for workload scaling in ...

These models are instrumental in enabling efficient resource allocation and enhancing overall performance. This comparative research focuses on ...

Forecasting of Cloud Computing Services Workload using Machine ...

Abstract: This paper analyses and compares prediction accuracy of different machine learning algorithms intended to forecast the workloads of ...

Predicting New Workload or CPU Performance by Analyzing Public ...

Figure 3 shows the microarchitectures and types of the SKUs in our SPEC and Geekbench datasets. There are 352 SKUs in total for SPEC, and 119 SKUs for Geekbench ...

Machine Learning Based Statistical Prediction Model for Improving ...

These models are tested on real data set of Xen to compute downtime, total number of pages transferred, and total migration time. The ARIMA ...

CAFE and SOUP: Towards Adaptive VDI Workload Prediction - PALM

Moreover, encoding is applied on the multi-grained description to improve the prediction accuracy. Let Sj = {x1,x2,...,xT } be the full-length connection status ...

CLIM: A Cross-level Workload-aware Timing Error Prediction Model ...

as #erroneous cycles/#total cycles. From this point on, we refer the ... On-chip droop-induced circuit delay prediction based on support-vector machines.

SimCost: cost-effective resource provision prediction and ...

Through empirical experiments with 12 benchmark workloads, we show that the cost model yields less than 5% error on average prediction accuracy, ...

Performance Analysis of Machine Learning Centered Workload ... - CS

Many machine learning-based workload forecasting models have been developed by exploiting their computational power and learning capabilities. This paper ...

Support Vector Regression Tutorial for Machine Learning

SVR extends Support Vector Machines (SVM) into regression problems, allowing for the prediction of continuous outcomes rather than classifying ...