2018 QueryBasedMachineLearningModelf: Difference between revisions

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* 1. R. V. Kulkarni, A. Förster, G. K. Venayagamoorthy, Computational Intelligence in Wireless Sensor Networks: A Survey, IEEE Communications Surveys & Tutorials, v.13 n.1, p.68-96, January 2011 [https://dx.doi.org/10.1109/SURv.2011.040310.00002 doi:10.1109/SURv.2011.040310.00002]
* 1. R. V. Kulkarni, A. Förster, G. K. Venayagamoorthy, Computational Intelligence in Wireless Sensor Networks: A Survey, IEEE Communications Surveys & Tutorials, v.13 n.1, p.68-96, January 2011 [https://dx.doi.org/10.1109/SURv.2011.040310.00002 doi:10.1109/SURv.2011.040310.00002]
* 2. Abbasi, M. 2015. "Effect of Wind Turbine Noise on Workers' Sleep Disorder: A Case Study of Manjil Wind Farm in Northern Iran", Fluctuation and Noise Letters, 14, 2 (2015).
* 2. Abbasi, M. 2015. “Effect of Wind Turbine Noise on Workers' Sleep Disorder: A Case Study of Manjil Wind Farm in Northern Iran", Fluctuation and Noise Letters, 14, 2 (2015).
* 3. Shakoor, A. 2010. "Soft Computing based Feature Selection for Environmental Sound Classification", Thesis, Blekinge Institute of Technology (Apr. 2010).
* 3. Shakoor, A. 2010. “Soft Computing based Feature Selection for Environmental Sound Classification", Thesis, Blekinge Institute of Technology (Apr. 2010).
* 4. Geoff Werner-Allen, Konrad Lorincz, Jeff Johnson, Jonathan Lees, Matt Welsh, Fidelity and Yield in a Volcano Monitoring Sensor Network, Proceedings of the 7th Symposium on Operating Systems Design and Implementation, November 06-08, 2006, Seattle, Washington
* 4. Geoff Werner-Allen, Konrad Lorincz, Jeff Johnson, Jonathan Lees, Matt Welsh, Fidelity and Yield in a Volcano Monitoring Sensor Network, Proceedings of the 7th Symposium on Operating Systems Design and Implementation, November 06-08, 2006, Seattle, Washington
* 5. Liang-Bin Lai, Ray-I Chang, Jen-Shiang Kouh, Detecting Network Intrusions Using Signal Processing with Query-based Sampling Filter, EURASIP Journal on Advances in Signal Processing, 2009, p.1-8, January 2009 [https://dx.doi.org/10.1155/2009/735283 doi:10.1155/2009/735283]
* 5. Liang-Bin Lai, Ray-I Chang, Jen-Shiang Kouh, Detecting Network Intrusions Using Signal Processing with Query-based Sampling Filter, EURASIP Journal on Advances in Signal Processing, 2009, p.1-8, January 2009 [https://dx.doi.org/10.1155/2009/735283 doi:10.1155/2009/735283]
* 6. Chang, R.I, Hsu, H.M., Lin, S.Y., Chang, C.C., Ho, J.M. 2017. "Query-Based Learning for Dynamic Particle Swarm Optimization", IEEE Access, 5 (2017), 7648--7658.
* 6. Chang, R.I, Hsu, H.M., Lin, S.Y., Chang, C.C., Ho, J.M. 2017. “Query-Based Learning for Dynamic Particle Swarm Optimization", IEEE Access, 5 (2017), 7648--7658.
* 7. Bulbuller, G. 2006. "Recognition of In-Ear Microphone Speech Data Using Multi-Layer Neural Networks", Thesis, Naval Postgraduate School (2006).
* 7. Bulbuller, G. 2006. “Recognition of In-Ear Microphone Speech Data Using Multi-Layer Neural Networks", Thesis, Naval Postgraduate School (2006).
* 8. Swee, T.T., Salleh, S.H.S., Jamaludin, M.R. 2010. "Speech Pitch Detection Using Short-Time Energy", Int. Conf. on Computer and Communication Engineering (2010), 327--332.
* 8. Swee, T.T., Salleh, S.H.S., Jamaludin, M.R. 2010. “Speech Pitch Detection Using Short-Time Energy", Int. Conf. on Computer and Communication Engineering (2010), 327--332.
* 9. Gabarda, S., Cristóbal, G. 2010. "Detection of Events in Seismic Time Series by Time--frequency Methods", IET Signal Processing, 4 (2010), 413--420.
* 9. Gabarda, S., Cristóbal, G. 2010. “Detection of Events in Seismic Time Series by Time--frequency Methods", IET Signal Processing, 4 (2010), 413--420.
* 10. Ramalingam, A., Krishnan, S. 2005. "Gaussian Mixture Modeling Using Short Time Fourier Transform Features for Audio Fingerprinting", IEEE ICME (Jul. 2005), 1146--1149.
* 10. Ramalingam, A., Krishnan, S. 2005. “Gaussian Mixture Modeling Using Short Time Fourier Transform Features for Audio Fingerprinting", IEEE ICME (Jul. 2005), 1146--1149.
* 11. Mark Hall, Eibe Frank, Geoffrey Holmes, Bernhard Pfahringer, Peter Reutemann, Ian H. Witten, The WEKA Data Mining Software: An Update, ACM SIGKDD Explorations Newsletter, v.11 n.1, June 2009 [http://doi.acm.org/10.1145/1656274.1656278 doi:10.1145/1656274.1656278]
* 11. Mark Hall, Eibe Frank, Geoffrey Holmes, Bernhard Pfahringer, Peter Reutemann, Ian H. Witten, The WEKA Data Mining Software: An Update, ACM SIGKDD Explorations Newsletter, v.11 n.1, June 2009 [http://doi.acm.org/10.1145/1656274.1656278 doi:10.1145/1656274.1656278]
* 12. Padmavathi, G., Shanmugapriya, D., Kalaivani, M. 2010. "Acoustic Signal based Feature Extraction for Vehicular Classification", Int. Conf. on Adv. Comput. Theory & Eng., 2 (2010), 11--14.
* 12. Padmavathi, G., Shanmugapriya, D., Kalaivani, M. 2010. “Acoustic Signal based Feature Extraction for Vehicular Classification", Int. Conf. on Adv. Comput. Theory & Eng., 2 (2010), 11--14.
* 13. Gui, Y., Jin, J.S., Zhang, S., Luo, S., Tian, Q. 2010. "Correlation-based Feature Selection and Regression", LNCS, 6297 (2010).
* 13. Gui, Y., Jin, J.S., Zhang, S., Luo, S., Tian, Q. 2010. “Correlation-based Feature Selection and Regression", LNCS, 6297 (2010).
* 14. Lassez, J.L., Rossi, R., Sheel, S., Mukkamala, S. 2008. "Signature Based Intrusion Detection System Using Latent Semantic Analysis", IJCNN (2008), 1068--1074.
* 14. Lassez, J.L., Rossi, R., Sheel, S., Mukkamala, S. 2008. “Signature Based Intrusion Detection System Using Latent Semantic Analysis", IJCNN (2008), 1068--1074.
* 15. Malhi, A., Robert, G.X., 2004. "PCA-based Feature Selection Scheme for Machine Defect Classification", IEEE Trans. Instrumentation and Measurement, 53 (2004), 1517--1525.
* 15. Malhi, A., Robert, G.X., 2004. “PCA-based Feature Selection Scheme for Machine Defect Classification", IEEE Trans. Instrumentation and Measurement, 53 (2004), 1517--1525.
* 16. Ron Kohavi, George H. John, Wrappers for Feature Subset Selection, Artificial Intelligence, v.97 n.1-2, p.273-324, Dec. 1997 [https://dx.doi.org/10.1016/S0004-3702(97)00043-X doi:10.1016/S0004-3702(97)00043-X]
* 16. Ron Kohavi, George H. John, Wrappers for Feature Subset Selection, Artificial Intelligence, v.97 n.1-2, p.273-324, Dec. 1997 [https://dx.doi.org/10.1016/S0004-3702(97)00043-X doi:10.1016/S0004-3702(97)00043-X]
* 17. Vasantha, M., Bharathy, V.S. 2010. "Evaluation of Attribute Selection Methods with Tree based Supervised Classification", Int. J. of Computer Applications, 8, 12 (Oct. 2010).
* 17. Vasantha, M., Bharathy, V.S. 2010. “Evaluation of Attribute Selection Methods with Tree based Supervised Classification", Int. J. of Computer Applications, 8, 12 (Oct. 2010).
* 18. J. -N. Hwang, J. J. Choi, S. Oh, R. J. Marks, II, Query-based Learning Applied to Partially Trained Multilayer Perceptrons, IEEE Transactions on Neural Networks, v.2 n.1, p.131-136, January 1991 [https://dx.doi.org/10.1109/72.80299 doi:10.1109/72.80299]
* 18. J. -N. Hwang, J. J. Choi, S. Oh, R. J. Marks, II, Query-based Learning Applied to Partially Trained Multilayer Perceptrons, IEEE Transactions on Neural Networks, v.2 n.1, p.131-136, January 1991 [https://dx.doi.org/10.1109/72.80299 doi:10.1109/72.80299]
* 19. Chang, R.I, Lai, L.B., Kouh, J.S. 2007. "Intrusion Detection by Backpropagation Neural Networks with Sample-Query and Attribute-Query," Int. J. of Comput. Intelligence Research, 3, 1 (2007), 6--10.
* 19. Chang, R.I, Lai, L.B., Kouh, J.S. 2007. “Intrusion Detection by Backpropagation Neural Networks with Sample-Query and Attribute-Query," Int. J. of Comput. Intelligence Research, 3, 1 (2007), 6--10.
* 20. C. Lee, D. A. Landgrebe, Feature Extraction Based on Decision Boundaries, IEEE Transactions on Pattern Analysis and Machine Intelligence, v.15 n.4, p.388-400, April 1993 [https://dx.doi.org/10.1109/34.206958 doi:10.1109/34.206958]
* 20. C. Lee, D. A. Landgrebe, Feature Extraction Based on Decision Boundaries, IEEE Transactions on Pattern Analysis and Machine Intelligence, v.15 n.4, p.388-400, April 1993 [https://dx.doi.org/10.1109/34.206958 doi:10.1109/34.206958]
* 21. https://inanalysis.github.io/
* 21. https://inanalysis.github.io/

Latest revision as of 04:37, 8 May 2024

Subject Headings: Query-Based Learning System.

Notes

Cited By

Quotes

Abstract

As infrasonic signals can through objects and propagate at a long distance, infrasound sensors are widely applied in wireless sensor networks to monitor environment events of a large area. The signal conditions are usually complex and have various characteristics while monitoring the large area. Different features in both time and frequency domains should be extracted and considered. Big data increases the computation complexity, and the wrong selection of features may decreases the accuracy in event prediction. To overcome this problem, a query-based-learning method is applied to select the proper features for smart edge computing in machine learning. Experimental results show that the proposed method provides good performance when comparing with previous feature selection methods.

References

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 AuthorvolumeDate ValuetitletypejournaltitleUrldoinoteyear
2018 QueryBasedMachineLearningModelfRay-I Chang
Chien-Chang Huang
Liang-Bin Lai
Chia-Yun Lee
Query-Based Machine Learning Model for Data Analysis of Infrasonic Signals in Wireless Sensor Networks10.1145/3193025.31930312018