P. Srinivasa Pai

Orcid: 0000-0002-3858-6014

According to our database1, P. Srinivasa Pai authored at least 12 papers between 2010 and 2023.

Collaborative distances:
  • Dijkstra number2 of five.
  • Erdős number3 of four.

Timeline

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Bibliography

2023
Artificial intelligence and machine learning as a viable solution for hip implant failure diagnosis - Review of literature and in vitro case study.
Medical Biol. Eng. Comput., June, 2023

A Machine Learning Technique for Intelligent Manufacturing: Integrating Wavelet Packet Transform (WPT) Into Neural Network Modeling.
Proceedings of the IEEE International Conference on Engineering, Technology and Innovation, 2023

2022
Total hip replacement monitoring: numerical models for the acoustic emission technique.
Medical Biol. Eng. Comput., 2022

A Statistical Correlation Analysis of Big Data in Machining.
Proceedings of the IEEE International Conference on Big Data, 2022

2021
An efficient online sequential extreme learning machine model based on feature selection and parameter optimization using cuckoo search algorithm for multi-step wind speed forecasting.
Soft Comput., 2021

2020
Hip implant performance prediction by acoustic emission techniques: a review.
Medical Biol. Eng. Comput., 2020

2017
Optimization studies in high speed turning of Ti-6Al-4V.
Appl. Soft Comput., 2017

2016
Prediction of performance and emission characteristics in a biodiesel engine using WCO ester: a comparative study of neural networks.
Soft Comput., 2016

Comparison of denoising schemes and dimensionality reduction techniques for fault diagnosis of rolling element bearing using wavelet transform.
Int. J. Manuf. Res., 2016

2012
Evaluation of Effectiveness of Wavelet Based Denoising Schemes Using ANN and SVM for Bearing Condition Classification.
Comput. Intell. Neurosci., 2012

Radial-Basis-Function-Network-Based Prediction of Performance and Emission Characteristics in a Bio Diesel Engine Run on WCO Ester.
Adv. Artif. Intell., 2012

2010
Prediction of built-up edge formation in machining with round edge and sharp tools using a neural network approach.
Int. J. Comput. Integr. Manuf., 2010


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