Cost-effective and fault-resilient reusability prediction model by using adaptive genetic algorithm based neural network for web-of-service applications

Padhy, Neelamadhab; Singh, Rajvinder; Satapathy, Suresh Chandra

doi:10.1007/s10586-018-2359-9

Cited by 39 publications

(9 citation statements)

References 24 publications

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“…Once the prediction system is affected by an emergency, the prediction accuracy will be greatly reduced. Padhy et al [ 10 ] analyzed four quantitative prediction models, namely, Markov model, nonlinear regression model, gray model, and neural network model. They pointed out that the gray prediction model and SOM neural network have high short-term prediction accuracy.…”

Section: Related Workmentioning

confidence: 99%

Analysis of Enterprise Human Resources Demand Forecast Model Based on SOM Neural Network

Zheng

2021

Computational Intelligence and Neuroscience

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Human resource planning is the prerequisite of human resource management, and the basic work of human resource planning is to predict human resource demand. Scientific and reasonable human resource demand forecasting results can provide important data support for enterprise human resource planning and strategic decision-making so that human resources management can play a better role in the realization of corporate goals. Because human resource demand is affected by many factors, there is a high degree of nonlinearity and uncertainty between each factor and personnel demand, as well as the incompleteness and inaccuracy of corporate human resource data. In this paper, the self-organizing feature mapping (SOM) artificial neural network prediction model is selected as the prediction model, and the input and output process of sample data is converted into the optimal solution process of the nonlinear function. In the application of the model, the human resource demand prediction index system is used as the input of the SOM neural network and the total number of employees in the enterprise is used as the output so that the problem of nonlinear fitting between human resource demand-influencing factors and human resource demand can be solved. Finally, through the empirical analysis of the enterprise, the model forecasting process is explained and the human resource demand forecast is realized.

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Section: Related Workmentioning

confidence: 99%

Analysis of Enterprise Human Resources Demand Forecast Model Based on SOM Neural Network

Zheng

2021

Computational Intelligence and Neuroscience

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“…Ahmadvand and Goudarzi [6] showed that different data portions, from the same or different sources, have different significance in determining the outcome of the computation, and hence, by prioritizing them and assigning more resources to the processing of more essential data. A cost-effective predictive model using adapting genetic algorithm was adopted by Padhy, Singh and Satpathy [7] to predict the fault tolerance in web service applications.…”

Section: Literature Reviewmentioning

confidence: 99%

Using machine learning techniques to predict the cost of repairing hard failures in underground fiber optics networks

2020

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The two most common outdoor fiber optic cable installations are aerial installation and underground cable installation. Underground cable installation is buried directly underground or placed into a buried duct. Underground burial of fiber cable installations is mostly common for long-distance installations. The cables are usually buried in trenches. Underground duct installation also provides an opportunity for future expansion without the need to dig again. Preparation towards underground cable

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“…Further, the proposed prediction model is validated using K-Fold Cross Validation approach [19] to train and test the model on independent data sets. To validate the prediction model various statistical metrics are used that are described in subsequent section.…”

Section: Execution Time Prediction Modelmentioning

confidence: 99%

“…The proposed prediction model is a function of execution time which includes three parameters-task workload, Central Processing Unit (CPU) speed, and free time of CPU. Various methods are used in prediction models: Linear Regression [17], Support Vector Machine (SVM) [14], genetic algorithms [19] and many other optimization techniques. Here, artificial neural network is used as an optimization method which is inspired by biological neural circuits.…”

Section: Introductionmentioning

confidence: 99%

Untitled

2020

IAJIT

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Cloud computing is on-demand network access model which provides dynamic resource provisioning, selection and scheduling. The performance of these techniques extensively depends on the prediction of various factors e.g., task execution time, resource trust value etc., As the accuracy of prediction model absolutely depends on the input data that are fed into the network, Selection of suitable inputs also plays vital role in predicting the appropriate value. Based on predicted value, Scheduler can choose the suitable resource and perform scheduling for efficient resource utilization and reduced makespan estimates. However, precise prediction of execution time is difficult in cloud environment due to heterogeneous nature of resources and varying input data. As each task has different characteristic and execution criteria, the environment must be intelligent enough to select the suitable resource. To solve these issues, an Artificial Neural Network (ANN) based prediction model is proposed to predict the execution time of tasks. First, input parameters are identified and selected through Interpretive Structural Modeling (ISM) approach. Second, a prediction model is proposed for predicting the task execution time for varying number of inputs. Third, the proposed model is validated and provides 21.72% reduction in mean relative error compared to other state-of-the-art methods.

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Cost-effective and fault-resilient reusability prediction model by using adaptive genetic algorithm based neural network for web-of-service applications

Cited by 39 publications

References 24 publications

Analysis of Enterprise Human Resources Demand Forecast Model Based on SOM Neural Network

Analysis of Enterprise Human Resources Demand Forecast Model Based on SOM Neural Network

Using machine learning techniques to predict the cost of repairing hard failures in underground fiber optics networks

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