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Query-Based Machine Learning Model for Data Analysis of Infrasonic Signals in Wireless Sensor Networks

Authors:

Chien-Chang Huang,

Chia-Yun LeeAuthors Info & Claims

ICDSP '18: Proceedings of the 2nd International Conference on Digital Signal Processing

Pages 114 - 118

https://doi.org/10.1145/3193025.3193031

Published: 25 February 2018 Publication History

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

[1]

Kulkarni, R.V., Förster, A., Venayagamoorthy, G. 2011. "Computational intelligence in wireless sensor networks: A survey," IEEE Communications Surveys & Tutorials, 13, 1 (Jan. 2011).

Digital Library

[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).

[4]

Werner-Allen, G., Lorincz, K., Johnson, J., Lees, J., Welsh, M. 2006. "Fidelity and Yield in a Volcano Monitoring Sensor Network", ACM Symp. OSDI (2006), 381--396.

Digital Library

[5]

Lai, L.B., Chang, R.I, Kouh, J.S. 2009. "Detecting Network Intrusions Using Signal Processing with Query-Based Sampling Filter," EURASIP J. on Adv. in Signal Processing, 9 (2009), 3--10.

Digital Library

[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).

[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.

[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]

Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.H. 2009. "The WEKA Data Mining Software: An Update", ACM SIGKDDExplor. Newsl., 11, 1 (2009), 10--18.

Digital Library

[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).

[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.

[16]

Kohavi, R., John, G.H. 1997. "Wrappers for feature subset selection", Artificial Intelligence, 97 (1997), 273--324.

Digital Library

[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]

Hwang, J.N., Choi, J.J., Oh, S., Marks II, R.J. 1991. "Query-based learning applied to partially trained multilayer perceptrons", IEEE Trans. on Neural Networks, 2 (1991), 131--136.

Digital Library

[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]

Lee, C., Landgrebe, D.A. 1993. "Feature Extraction Based on Decision Boundaries", IEEE Trans. on Pattern Analysis and Machine Intelligence, 15, 4 (Apr. 1993), 388--400.

Digital Library

[21]

https://inanalysis.github.io/

Cited By

Iravantchi YAhuja KGoel MHarrison CSample AKitamura YQuigley AIsbister KIgarashi TBjørn PDrucker S(2021)PrivacyMic: Utilizing Inaudible Frequencies for Privacy Preserving Daily Activity RecognitionProceedings of the 2021 CHI Conference on Human Factors in Computing Systems10.1145/3411764.3445169(1-13)Online publication date: 6-May-2021
https://dl.acm.org/doi/10.1145/3411764.3445169
Lin HChiu YLiao WChang R(2019)Service-Oriented Architecture for Intelligent Management with Data Analytics and Visualization2019 IEEE 12th Conference on Service-Oriented Computing and Applications (SOCA)10.1109/SOCA.2019.00018(73-78)Online publication date: Nov-2019
https://doi.org/10.1109/SOCA.2019.00018

Index Terms

Query-Based Machine Learning Model for Data Analysis of Infrasonic Signals in Wireless Sensor Networks
1. Computing methodologies
  1. Machine learning
    1. Machine learning approaches
      1. Neural networks

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cover image ACM Other conferences

ICDSP '18: Proceedings of the 2nd International Conference on Digital Signal Processing

February 2018

198 pages

ISBN:9781450364027

DOI:10.1145/3193025

Copyright © 2018 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 25 February 2018

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ICDSP 2018: 2018 2nd International Conference on Digital Signal Processing

February 25 - 27, 2018

Tokyo, Japan

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Cited By

Iravantchi YAhuja KGoel MHarrison CSample AKitamura YQuigley AIsbister KIgarashi TBjørn PDrucker S(2021)PrivacyMic: Utilizing Inaudible Frequencies for Privacy Preserving Daily Activity RecognitionProceedings of the 2021 CHI Conference on Human Factors in Computing Systems10.1145/3411764.3445169(1-13)Online publication date: 6-May-2021
https://dl.acm.org/doi/10.1145/3411764.3445169
Lin HChiu YLiao WChang R(2019)Service-Oriented Architecture for Intelligent Management with Data Analytics and Visualization2019 IEEE 12th Conference on Service-Oriented Computing and Applications (SOCA)10.1109/SOCA.2019.00018(73-78)Online publication date: Nov-2019
https://doi.org/10.1109/SOCA.2019.00018

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