Abstract

A soft methodology study has been applied on tapered plastic multimode sensors. This study basically used tapered plastic multimode fiber [polymethyl methacrylate (PMMA)] optics as a sensor. The tapered PMMA fiber was fabricated using an etching method involving deionized water and acetone to achieve a waist diameter and length of 0.45 and 10 mm, respectively. In addition, a tapered PMMA probe, which was coated by silver film, was fabricated and demonstrated using a calcium hypochlorite (G70) solution. The working mechanism of such a device is based on the observation increment in the transmission of the sensor that is immersed in solutions at high concentrations. As the concentration was varied from 0 to 6 ppm, the output voltage of the sensor increased linearly. The silver film coating increased the sensitivity of the proposed sensor because of the effective cladding refractive index, which increases with the coating and thus allows more light to be transmitted from the tapered fiber. In this study, the polynomial and radial basis function (RBF) were applied as the kernel function of the support vector regression (SVR) to estimate and predict the output voltage response of the sensors with and without silver film according to experimental tests. Instead of minimizing the observed training error, SVR_poly and SVR_rbf were used in an attempt to minimize the generalization error bound so as to achieve generalized performance. An adaptive neuro-fuzzy interference system (ANFIS) approach was also investigated for comparison. The experimental results showed that improvements in the predictive accuracy and capacity for generalization can be achieved by the SVR_poly approach in comparison to the SVR_rbf methodology. The same testing errors were found for the SVR_poly approach and the ANFIS approach.

© 2014 Optical Society of America

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2014

D. Petković, Z. Ćojbašić, V. Nikolić, S. Shamshirband, M. L. Mat Kiah, N. B. Anuar, and A. W. A. Wahab, “Adaptive neuro-fuzzy maximal power extraction of wind turbine with continuously variable transmission,” Energy 64, 868–874 (2014).
[CrossRef]

D. Petković, N. T. Pavlović, S. Shamshirband, M. L. Mat Kiah, N. B. Anuar, and M. Y. Idna Idris, “Adaptive neuro-fuzzy estimation of optimal lens system parameters,” Opt. Lasers Eng. 55, 84–93 (2014).
[CrossRef]

A. Balahura and M. Turchi, “Comparative experiments using supervised learning and machine translation for multilingual sentiment analysis,” Comput. Speech Lang. 28, 56–75 (2014).
[CrossRef]

2013

Z. Wei, T. Tao, D. ZhuoShu, and E. Zio, “A dynamic particle filter-support vector regression method for reliability prediction,” Reliability Eng. Syst. Safety 119, 109–116 (2013).
[CrossRef]

L. Zhang, W.-D. Zhou, P.-C. Chang, J.-W. Yang, and F.-Z. Li, “Iterated time series prediction with multiple support vector regression models,” Neurocomputing 99, 411–422 (2013).
[CrossRef]

Y. Leng, X. Xu, and G. Qi, “Combining active learning and semi-supervised learning to construct SVM classifier,” Knowl.-Based Syst. 44, 121–131 (2013).
[CrossRef]

D. Petković, Z. Ćojbašić, and V. Nikolić, “Adaptive neuro-fuzzy approach for wind turbine power coefficient estimation,” Renew. Sust. Energ. Rev. 28, 191–195 (2013).
[CrossRef]

S. Shamshirband, N. B. Anuar, M. L. Mat Kiah, and A. Patel, “An appraisal and design of a multi-agent system based cooperative wireless intrusion detection computational intelligence technique,” Eng. Appl. Artif. Intell. 26, 2105–2127 (2013).
[CrossRef]

S. Wua, Y. Wanga, and S. Cheng, “Extreme learning machine based wind speed estimation and sensorless control for wind turbine power generation system,” Neurocomputing 102, 163–175 (2013).
[CrossRef]

D. Petković, M. Issa, N. D. Pavlović, and L. Zentner, “Intelligent rotational direction control of passive robotic joint with embedded sensors,” Expert Syst. Appl. 40, 1265–1273 (2013).
[CrossRef]

2012

M. Latino, R. Montanini, N. Donato, and G. Neri, “Ethanol sensing properties of PMMA-coated fiber Bragg grating,” Pro. Eng. 47, 1263–1266 (2012).
[CrossRef]

J. H. Ahn, T. Y. Seong, W. M. Kim, T. S. Lee, I. Kim, and K.-S. Lee, “Fiber-optic waveguide coupled surface plasmon resonance sensor,” Opt. Express 20, 21729–21738 (2012).
[CrossRef]

2011

P. Bhatia and B. D. Gupta, “Surface-plasmon-resonance-based fiber-optic refractive index sensor: sensitivity enhancement,” Appl. Opt. 50, 2032–2036 (2011).
[CrossRef]

R. Y. Shah and Y. K. Agrawal, “Introduction to fiber optics: sensors for biomedical applications,” Indian J. Pharm. Sci. 73, 17–22 (2011).
[CrossRef]

Y. K. Cheong, K. S. Lim, W. H. Lim, W. Y. Chong, R. Zakaria, and H. Ahmad, “Note: fabrication of tapered fibre tip using mechanical polishing method,” Rev. Sci. Instrum. 82, 086115 (2011).
[CrossRef]

J. Xu, K. Feng, and M. Weck, “Free chlorine sensing using an interferometric sensor,” Sens. Actuators B Chem. 156, 812–819 (2011).
[CrossRef]

S. Chakraborty, “Bayesian semi-supervised learning with support vector machine,” Stat. Methodol. 8, 68–82 (2011).
[CrossRef]

2010

L. Ornella and E. Tapia, “Supervised machine learning and heterotic classification of maize (Zea mays L.) using molecular marker data,” Comput. Eletron. Agric. 74, 250–257 (2010).
[CrossRef]

S. Shamshirband, S. Kalantari, Z. S. Daliri, and L. S. Ng, “Expert security system in wireless sensor networks based on fuzzy discussion multi-agent systems,” Sci. Res. Essays 5, 3840–3849 (2010).

R. K. Verma and B. D. Gupta, “Surface plasmon resonance based fiber optic sensor for the IR region using a conducting metal oxide film,” J. Opt. Soc. Am. A 27, 846–851 (2010).
[CrossRef]

S. K. Srivastava and B. D. Gupta, “Simulation of a localized surface-plasmon-resonance-based fiber optic temperature sensor,” J. Opt. Soc. Am. A 27, 1743–1749 (2010).
[CrossRef]

2009

P. Jain, J. M. Garibaldib, and J. D. Hirst, “Supervised machine learning algorithms for protein structure classification,” Comput. Biol. Chem. 33, 216–223 (2009).
[CrossRef]

H. Yang, K. Huang, I. King, and M. R. Lyu, “Localized support vector regression for time series prediction,” Neurocomputing 72, 2659–2669 (2009).
[CrossRef]

2008

S. Rajasekaran, S. Gayathri, and T.-L. Lee, “Support vector regression methodology for storm surge predictions,” Ocean Eng. 35, 1578–1587 (2008).
[CrossRef]

2006

2005

O. S. Wolfbeis, “Materials for fluorescence-based optical chemical sensors,” J. Mater. Chem. 15, 2657–2669 (2005).
[CrossRef]

1998

1993

1985

G. D. Pitt, P. Extance, R. C. Neat, D. N. Batchelder, R. E. Jones, J. A. Barnett, and R. H. Pratt, “Optical-fibre sensors,” IEE Proc. J. Optoelectron. 132, 214–248 (1985).
[CrossRef]

M. Martinelli and A. Barberis, “Interferometric Michelson-type optical-fiber sensor: comparison between phase-modulation and frequency-modulation detection,” J. Opt. Soc. Am. A 2, 603–609 (1985).
[CrossRef]

Agrawal, Y. K.

R. Y. Shah and Y. K. Agrawal, “Introduction to fiber optics: sensors for biomedical applications,” Indian J. Pharm. Sci. 73, 17–22 (2011).
[CrossRef]

Ahmad, H.

Y. K. Cheong, K. S. Lim, W. H. Lim, W. Y. Chong, R. Zakaria, and H. Ahmad, “Note: fabrication of tapered fibre tip using mechanical polishing method,” Rev. Sci. Instrum. 82, 086115 (2011).
[CrossRef]

Ahn, J. H.

Anuar, N. B.

D. Petković, Z. Ćojbašić, V. Nikolić, S. Shamshirband, M. L. Mat Kiah, N. B. Anuar, and A. W. A. Wahab, “Adaptive neuro-fuzzy maximal power extraction of wind turbine with continuously variable transmission,” Energy 64, 868–874 (2014).
[CrossRef]

D. Petković, N. T. Pavlović, S. Shamshirband, M. L. Mat Kiah, N. B. Anuar, and M. Y. Idna Idris, “Adaptive neuro-fuzzy estimation of optimal lens system parameters,” Opt. Lasers Eng. 55, 84–93 (2014).
[CrossRef]

S. Shamshirband, N. B. Anuar, M. L. Mat Kiah, and A. Patel, “An appraisal and design of a multi-agent system based cooperative wireless intrusion detection computational intelligence technique,” Eng. Appl. Artif. Intell. 26, 2105–2127 (2013).
[CrossRef]

Balahura, A.

A. Balahura and M. Turchi, “Comparative experiments using supervised learning and machine translation for multilingual sentiment analysis,” Comput. Speech Lang. 28, 56–75 (2014).
[CrossRef]

Barberis, A.

Barnett, J. A.

G. D. Pitt, P. Extance, R. C. Neat, D. N. Batchelder, R. E. Jones, J. A. Barnett, and R. H. Pratt, “Optical-fibre sensors,” IEE Proc. J. Optoelectron. 132, 214–248 (1985).
[CrossRef]

Batchelder, D. N.

G. D. Pitt, P. Extance, R. C. Neat, D. N. Batchelder, R. E. Jones, J. A. Barnett, and R. H. Pratt, “Optical-fibre sensors,” IEE Proc. J. Optoelectron. 132, 214–248 (1985).
[CrossRef]

Bhatia, P.

Cai, D.

K. Lu, J. Zhao, and D. Cai, “An algorithm for semi-supervised learning in image retrieval,” Pattern Recogn. 39, 717–720 (2006).
[CrossRef]

Chakraborty, S.

S. Chakraborty, “Bayesian semi-supervised learning with support vector machine,” Stat. Methodol. 8, 68–82 (2011).
[CrossRef]

Chang, P.-C.

L. Zhang, W.-D. Zhou, P.-C. Chang, J.-W. Yang, and F.-Z. Li, “Iterated time series prediction with multiple support vector regression models,” Neurocomputing 99, 411–422 (2013).
[CrossRef]

Cheng, S.

S. Wua, Y. Wanga, and S. Cheng, “Extreme learning machine based wind speed estimation and sensorless control for wind turbine power generation system,” Neurocomputing 102, 163–175 (2013).
[CrossRef]

Cheong, Y. K.

Y. K. Cheong, K. S. Lim, W. H. Lim, W. Y. Chong, R. Zakaria, and H. Ahmad, “Note: fabrication of tapered fibre tip using mechanical polishing method,” Rev. Sci. Instrum. 82, 086115 (2011).
[CrossRef]

Chong, W. Y.

Y. K. Cheong, K. S. Lim, W. H. Lim, W. Y. Chong, R. Zakaria, and H. Ahmad, “Note: fabrication of tapered fibre tip using mechanical polishing method,” Rev. Sci. Instrum. 82, 086115 (2011).
[CrossRef]

Cojbašic, Z.

D. Petković, Z. Ćojbašić, V. Nikolić, S. Shamshirband, M. L. Mat Kiah, N. B. Anuar, and A. W. A. Wahab, “Adaptive neuro-fuzzy maximal power extraction of wind turbine with continuously variable transmission,” Energy 64, 868–874 (2014).
[CrossRef]

D. Petković, Z. Ćojbašić, and V. Nikolić, “Adaptive neuro-fuzzy approach for wind turbine power coefficient estimation,” Renew. Sust. Energ. Rev. 28, 191–195 (2013).
[CrossRef]

Daliri, Z. S.

S. Shamshirband, S. Kalantari, Z. S. Daliri, and L. S. Ng, “Expert security system in wireless sensor networks based on fuzzy discussion multi-agent systems,” Sci. Res. Essays 5, 3840–3849 (2010).

Donato, N.

M. Latino, R. Montanini, N. Donato, and G. Neri, “Ethanol sensing properties of PMMA-coated fiber Bragg grating,” Pro. Eng. 47, 1263–1266 (2012).
[CrossRef]

Extance, P.

G. D. Pitt, P. Extance, R. C. Neat, D. N. Batchelder, R. E. Jones, J. A. Barnett, and R. H. Pratt, “Optical-fibre sensors,” IEE Proc. J. Optoelectron. 132, 214–248 (1985).
[CrossRef]

Feng, K.

J. Xu, K. Feng, and M. Weck, “Free chlorine sensing using an interferometric sensor,” Sens. Actuators B Chem. 156, 812–819 (2011).
[CrossRef]

Garibaldib, J. M.

P. Jain, J. M. Garibaldib, and J. D. Hirst, “Supervised machine learning algorithms for protein structure classification,” Comput. Biol. Chem. 33, 216–223 (2009).
[CrossRef]

Gayathri, S.

S. Rajasekaran, S. Gayathri, and T.-L. Lee, “Support vector regression methodology for storm surge predictions,” Ocean Eng. 35, 1578–1587 (2008).
[CrossRef]

Gupta, B. D.

Hassani, A.

Hirst, J. D.

P. Jain, J. M. Garibaldib, and J. D. Hirst, “Supervised machine learning algorithms for protein structure classification,” Comput. Biol. Chem. 33, 216–223 (2009).
[CrossRef]

Huang, K.

H. Yang, K. Huang, I. King, and M. R. Lyu, “Localized support vector regression for time series prediction,” Neurocomputing 72, 2659–2669 (2009).
[CrossRef]

Idna Idris, M. Y.

D. Petković, N. T. Pavlović, S. Shamshirband, M. L. Mat Kiah, N. B. Anuar, and M. Y. Idna Idris, “Adaptive neuro-fuzzy estimation of optimal lens system parameters,” Opt. Lasers Eng. 55, 84–93 (2014).
[CrossRef]

Issa, M.

D. Petković, M. Issa, N. D. Pavlović, and L. Zentner, “Intelligent rotational direction control of passive robotic joint with embedded sensors,” Expert Syst. Appl. 40, 1265–1273 (2013).
[CrossRef]

Jain, P.

P. Jain, J. M. Garibaldib, and J. D. Hirst, “Supervised machine learning algorithms for protein structure classification,” Comput. Biol. Chem. 33, 216–223 (2009).
[CrossRef]

Jones, R. E.

G. D. Pitt, P. Extance, R. C. Neat, D. N. Batchelder, R. E. Jones, J. A. Barnett, and R. H. Pratt, “Optical-fibre sensors,” IEE Proc. J. Optoelectron. 132, 214–248 (1985).
[CrossRef]

Kalantari, S.

S. Shamshirband, S. Kalantari, Z. S. Daliri, and L. S. Ng, “Expert security system in wireless sensor networks based on fuzzy discussion multi-agent systems,” Sci. Res. Essays 5, 3840–3849 (2010).

Kim, I.

Kim, W. M.

King, I.

H. Yang, K. Huang, I. King, and M. R. Lyu, “Localized support vector regression for time series prediction,” Neurocomputing 72, 2659–2669 (2009).
[CrossRef]

Latino, M.

M. Latino, R. Montanini, N. Donato, and G. Neri, “Ethanol sensing properties of PMMA-coated fiber Bragg grating,” Pro. Eng. 47, 1263–1266 (2012).
[CrossRef]

Lee, K.-S.

Lee, T. S.

Lee, T.-L.

S. Rajasekaran, S. Gayathri, and T.-L. Lee, “Support vector regression methodology for storm surge predictions,” Ocean Eng. 35, 1578–1587 (2008).
[CrossRef]

Leng, Y.

Y. Leng, X. Xu, and G. Qi, “Combining active learning and semi-supervised learning to construct SVM classifier,” Knowl.-Based Syst. 44, 121–131 (2013).
[CrossRef]

Li, F.-Z.

L. Zhang, W.-D. Zhou, P.-C. Chang, J.-W. Yang, and F.-Z. Li, “Iterated time series prediction with multiple support vector regression models,” Neurocomputing 99, 411–422 (2013).
[CrossRef]

Lim, K. S.

Y. K. Cheong, K. S. Lim, W. H. Lim, W. Y. Chong, R. Zakaria, and H. Ahmad, “Note: fabrication of tapered fibre tip using mechanical polishing method,” Rev. Sci. Instrum. 82, 086115 (2011).
[CrossRef]

Lim, W. H.

Y. K. Cheong, K. S. Lim, W. H. Lim, W. Y. Chong, R. Zakaria, and H. Ahmad, “Note: fabrication of tapered fibre tip using mechanical polishing method,” Rev. Sci. Instrum. 82, 086115 (2011).
[CrossRef]

Lu, K.

K. Lu, J. Zhao, and D. Cai, “An algorithm for semi-supervised learning in image retrieval,” Pattern Recogn. 39, 717–720 (2006).
[CrossRef]

Lyu, M. R.

H. Yang, K. Huang, I. King, and M. R. Lyu, “Localized support vector regression for time series prediction,” Neurocomputing 72, 2659–2669 (2009).
[CrossRef]

Martinelli, M.

Mat Kiah, M. L.

D. Petković, N. T. Pavlović, S. Shamshirband, M. L. Mat Kiah, N. B. Anuar, and M. Y. Idna Idris, “Adaptive neuro-fuzzy estimation of optimal lens system parameters,” Opt. Lasers Eng. 55, 84–93 (2014).
[CrossRef]

D. Petković, Z. Ćojbašić, V. Nikolić, S. Shamshirband, M. L. Mat Kiah, N. B. Anuar, and A. W. A. Wahab, “Adaptive neuro-fuzzy maximal power extraction of wind turbine with continuously variable transmission,” Energy 64, 868–874 (2014).
[CrossRef]

S. Shamshirband, N. B. Anuar, M. L. Mat Kiah, and A. Patel, “An appraisal and design of a multi-agent system based cooperative wireless intrusion detection computational intelligence technique,” Eng. Appl. Artif. Intell. 26, 2105–2127 (2013).
[CrossRef]

Montanini, R.

M. Latino, R. Montanini, N. Donato, and G. Neri, “Ethanol sensing properties of PMMA-coated fiber Bragg grating,” Pro. Eng. 47, 1263–1266 (2012).
[CrossRef]

Neat, R. C.

G. D. Pitt, P. Extance, R. C. Neat, D. N. Batchelder, R. E. Jones, J. A. Barnett, and R. H. Pratt, “Optical-fibre sensors,” IEE Proc. J. Optoelectron. 132, 214–248 (1985).
[CrossRef]

Neri, G.

M. Latino, R. Montanini, N. Donato, and G. Neri, “Ethanol sensing properties of PMMA-coated fiber Bragg grating,” Pro. Eng. 47, 1263–1266 (2012).
[CrossRef]

Ng, L. S.

S. Shamshirband, S. Kalantari, Z. S. Daliri, and L. S. Ng, “Expert security system in wireless sensor networks based on fuzzy discussion multi-agent systems,” Sci. Res. Essays 5, 3840–3849 (2010).

Nikolic, V.

D. Petković, Z. Ćojbašić, V. Nikolić, S. Shamshirband, M. L. Mat Kiah, N. B. Anuar, and A. W. A. Wahab, “Adaptive neuro-fuzzy maximal power extraction of wind turbine with continuously variable transmission,” Energy 64, 868–874 (2014).
[CrossRef]

D. Petković, Z. Ćojbašić, and V. Nikolić, “Adaptive neuro-fuzzy approach for wind turbine power coefficient estimation,” Renew. Sust. Energ. Rev. 28, 191–195 (2013).
[CrossRef]

Ornella, L.

L. Ornella and E. Tapia, “Supervised machine learning and heterotic classification of maize (Zea mays L.) using molecular marker data,” Comput. Eletron. Agric. 74, 250–257 (2010).
[CrossRef]

Patel, A.

S. Shamshirband, N. B. Anuar, M. L. Mat Kiah, and A. Patel, “An appraisal and design of a multi-agent system based cooperative wireless intrusion detection computational intelligence technique,” Eng. Appl. Artif. Intell. 26, 2105–2127 (2013).
[CrossRef]

Pavlovic, N. D.

D. Petković, M. Issa, N. D. Pavlović, and L. Zentner, “Intelligent rotational direction control of passive robotic joint with embedded sensors,” Expert Syst. Appl. 40, 1265–1273 (2013).
[CrossRef]

Pavlovic, N. T.

D. Petković, N. T. Pavlović, S. Shamshirband, M. L. Mat Kiah, N. B. Anuar, and M. Y. Idna Idris, “Adaptive neuro-fuzzy estimation of optimal lens system parameters,” Opt. Lasers Eng. 55, 84–93 (2014).
[CrossRef]

Petkovic, D.

D. Petković, N. T. Pavlović, S. Shamshirband, M. L. Mat Kiah, N. B. Anuar, and M. Y. Idna Idris, “Adaptive neuro-fuzzy estimation of optimal lens system parameters,” Opt. Lasers Eng. 55, 84–93 (2014).
[CrossRef]

D. Petković, Z. Ćojbašić, V. Nikolić, S. Shamshirband, M. L. Mat Kiah, N. B. Anuar, and A. W. A. Wahab, “Adaptive neuro-fuzzy maximal power extraction of wind turbine with continuously variable transmission,” Energy 64, 868–874 (2014).
[CrossRef]

D. Petković, M. Issa, N. D. Pavlović, and L. Zentner, “Intelligent rotational direction control of passive robotic joint with embedded sensors,” Expert Syst. Appl. 40, 1265–1273 (2013).
[CrossRef]

D. Petković, Z. Ćojbašić, and V. Nikolić, “Adaptive neuro-fuzzy approach for wind turbine power coefficient estimation,” Renew. Sust. Energ. Rev. 28, 191–195 (2013).
[CrossRef]

Pierscionek, B. K.

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R. Y. Shah and Y. K. Agrawal, “Introduction to fiber optics: sensors for biomedical applications,” Indian J. Pharm. Sci. 73, 17–22 (2011).
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D. Petković, Z. Ćojbašić, V. Nikolić, S. Shamshirband, M. L. Mat Kiah, N. B. Anuar, and A. W. A. Wahab, “Adaptive neuro-fuzzy maximal power extraction of wind turbine with continuously variable transmission,” Energy 64, 868–874 (2014).
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S. Shamshirband, N. B. Anuar, M. L. Mat Kiah, and A. Patel, “An appraisal and design of a multi-agent system based cooperative wireless intrusion detection computational intelligence technique,” Eng. Appl. Artif. Intell. 26, 2105–2127 (2013).
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Z. Wei, T. Tao, D. ZhuoShu, and E. Zio, “A dynamic particle filter-support vector regression method for reliability prediction,” Reliability Eng. Syst. Safety 119, 109–116 (2013).
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L. Ornella and E. Tapia, “Supervised machine learning and heterotic classification of maize (Zea mays L.) using molecular marker data,” Comput. Eletron. Agric. 74, 250–257 (2010).
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Wahab, A. W. A.

D. Petković, Z. Ćojbašić, V. Nikolić, S. Shamshirband, M. L. Mat Kiah, N. B. Anuar, and A. W. A. Wahab, “Adaptive neuro-fuzzy maximal power extraction of wind turbine with continuously variable transmission,” Energy 64, 868–874 (2014).
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S. Wua, Y. Wanga, and S. Cheng, “Extreme learning machine based wind speed estimation and sensorless control for wind turbine power generation system,” Neurocomputing 102, 163–175 (2013).
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J. Xu, K. Feng, and M. Weck, “Free chlorine sensing using an interferometric sensor,” Sens. Actuators B Chem. 156, 812–819 (2011).
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Z. Wei, T. Tao, D. ZhuoShu, and E. Zio, “A dynamic particle filter-support vector regression method for reliability prediction,” Reliability Eng. Syst. Safety 119, 109–116 (2013).
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O. S. Wolfbeis, “Materials for fluorescence-based optical chemical sensors,” J. Mater. Chem. 15, 2657–2669 (2005).
[CrossRef]

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S. Wua, Y. Wanga, and S. Cheng, “Extreme learning machine based wind speed estimation and sensorless control for wind turbine power generation system,” Neurocomputing 102, 163–175 (2013).
[CrossRef]

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J. Xu, K. Feng, and M. Weck, “Free chlorine sensing using an interferometric sensor,” Sens. Actuators B Chem. 156, 812–819 (2011).
[CrossRef]

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Y. Leng, X. Xu, and G. Qi, “Combining active learning and semi-supervised learning to construct SVM classifier,” Knowl.-Based Syst. 44, 121–131 (2013).
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H. Yang, K. Huang, I. King, and M. R. Lyu, “Localized support vector regression for time series prediction,” Neurocomputing 72, 2659–2669 (2009).
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L. Zhang, W.-D. Zhou, P.-C. Chang, J.-W. Yang, and F.-Z. Li, “Iterated time series prediction with multiple support vector regression models,” Neurocomputing 99, 411–422 (2013).
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Yang, N.

Zakaria, R.

Y. K. Cheong, K. S. Lim, W. H. Lim, W. Y. Chong, R. Zakaria, and H. Ahmad, “Note: fabrication of tapered fibre tip using mechanical polishing method,” Rev. Sci. Instrum. 82, 086115 (2011).
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D. Petković, M. Issa, N. D. Pavlović, and L. Zentner, “Intelligent rotational direction control of passive robotic joint with embedded sensors,” Expert Syst. Appl. 40, 1265–1273 (2013).
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L. Zhang, W.-D. Zhou, P.-C. Chang, J.-W. Yang, and F.-Z. Li, “Iterated time series prediction with multiple support vector regression models,” Neurocomputing 99, 411–422 (2013).
[CrossRef]

Zhao, J.

K. Lu, J. Zhao, and D. Cai, “An algorithm for semi-supervised learning in image retrieval,” Pattern Recogn. 39, 717–720 (2006).
[CrossRef]

Zhou, W.-D.

L. Zhang, W.-D. Zhou, P.-C. Chang, J.-W. Yang, and F.-Z. Li, “Iterated time series prediction with multiple support vector regression models,” Neurocomputing 99, 411–422 (2013).
[CrossRef]

ZhuoShu, D.

Z. Wei, T. Tao, D. ZhuoShu, and E. Zio, “A dynamic particle filter-support vector regression method for reliability prediction,” Reliability Eng. Syst. Safety 119, 109–116 (2013).
[CrossRef]

Zio, E.

Z. Wei, T. Tao, D. ZhuoShu, and E. Zio, “A dynamic particle filter-support vector regression method for reliability prediction,” Reliability Eng. Syst. Safety 119, 109–116 (2013).
[CrossRef]

Appl. Opt.

Comput. Biol. Chem.

P. Jain, J. M. Garibaldib, and J. D. Hirst, “Supervised machine learning algorithms for protein structure classification,” Comput. Biol. Chem. 33, 216–223 (2009).
[CrossRef]

Comput. Eletron. Agric.

L. Ornella and E. Tapia, “Supervised machine learning and heterotic classification of maize (Zea mays L.) using molecular marker data,” Comput. Eletron. Agric. 74, 250–257 (2010).
[CrossRef]

Comput. Speech Lang.

A. Balahura and M. Turchi, “Comparative experiments using supervised learning and machine translation for multilingual sentiment analysis,” Comput. Speech Lang. 28, 56–75 (2014).
[CrossRef]

Energy

D. Petković, Z. Ćojbašić, V. Nikolić, S. Shamshirband, M. L. Mat Kiah, N. B. Anuar, and A. W. A. Wahab, “Adaptive neuro-fuzzy maximal power extraction of wind turbine with continuously variable transmission,” Energy 64, 868–874 (2014).
[CrossRef]

Eng. Appl. Artif. Intell.

S. Shamshirband, N. B. Anuar, M. L. Mat Kiah, and A. Patel, “An appraisal and design of a multi-agent system based cooperative wireless intrusion detection computational intelligence technique,” Eng. Appl. Artif. Intell. 26, 2105–2127 (2013).
[CrossRef]

Expert Syst. Appl.

D. Petković, M. Issa, N. D. Pavlović, and L. Zentner, “Intelligent rotational direction control of passive robotic joint with embedded sensors,” Expert Syst. Appl. 40, 1265–1273 (2013).
[CrossRef]

IEE Proc. J. Optoelectron.

G. D. Pitt, P. Extance, R. C. Neat, D. N. Batchelder, R. E. Jones, J. A. Barnett, and R. H. Pratt, “Optical-fibre sensors,” IEE Proc. J. Optoelectron. 132, 214–248 (1985).
[CrossRef]

Indian J. Pharm. Sci.

R. Y. Shah and Y. K. Agrawal, “Introduction to fiber optics: sensors for biomedical applications,” Indian J. Pharm. Sci. 73, 17–22 (2011).
[CrossRef]

J. Mater. Chem.

O. S. Wolfbeis, “Materials for fluorescence-based optical chemical sensors,” J. Mater. Chem. 15, 2657–2669 (2005).
[CrossRef]

J. Opt. Soc. Am. A

Knowl.-Based Syst.

Y. Leng, X. Xu, and G. Qi, “Combining active learning and semi-supervised learning to construct SVM classifier,” Knowl.-Based Syst. 44, 121–131 (2013).
[CrossRef]

Neurocomputing

H. Yang, K. Huang, I. King, and M. R. Lyu, “Localized support vector regression for time series prediction,” Neurocomputing 72, 2659–2669 (2009).
[CrossRef]

L. Zhang, W.-D. Zhou, P.-C. Chang, J.-W. Yang, and F.-Z. Li, “Iterated time series prediction with multiple support vector regression models,” Neurocomputing 99, 411–422 (2013).
[CrossRef]

S. Wua, Y. Wanga, and S. Cheng, “Extreme learning machine based wind speed estimation and sensorless control for wind turbine power generation system,” Neurocomputing 102, 163–175 (2013).
[CrossRef]

Ocean Eng.

S. Rajasekaran, S. Gayathri, and T.-L. Lee, “Support vector regression methodology for storm surge predictions,” Ocean Eng. 35, 1578–1587 (2008).
[CrossRef]

Opt. Express

Opt. Lasers Eng.

D. Petković, N. T. Pavlović, S. Shamshirband, M. L. Mat Kiah, N. B. Anuar, and M. Y. Idna Idris, “Adaptive neuro-fuzzy estimation of optimal lens system parameters,” Opt. Lasers Eng. 55, 84–93 (2014).
[CrossRef]

Pattern Recogn.

K. Lu, J. Zhao, and D. Cai, “An algorithm for semi-supervised learning in image retrieval,” Pattern Recogn. 39, 717–720 (2006).
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M. Latino, R. Montanini, N. Donato, and G. Neri, “Ethanol sensing properties of PMMA-coated fiber Bragg grating,” Pro. Eng. 47, 1263–1266 (2012).
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Z. Wei, T. Tao, D. ZhuoShu, and E. Zio, “A dynamic particle filter-support vector regression method for reliability prediction,” Reliability Eng. Syst. Safety 119, 109–116 (2013).
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D. Petković, Z. Ćojbašić, and V. Nikolić, “Adaptive neuro-fuzzy approach for wind turbine power coefficient estimation,” Renew. Sust. Energ. Rev. 28, 191–195 (2013).
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Y. K. Cheong, K. S. Lim, W. H. Lim, W. Y. Chong, R. Zakaria, and H. Ahmad, “Note: fabrication of tapered fibre tip using mechanical polishing method,” Rev. Sci. Instrum. 82, 086115 (2011).
[CrossRef]

Sci. Res. Essays

S. Shamshirband, S. Kalantari, Z. S. Daliri, and L. S. Ng, “Expert security system in wireless sensor networks based on fuzzy discussion multi-agent systems,” Sci. Res. Essays 5, 3840–3849 (2010).

Sens. Actuators B Chem.

J. Xu, K. Feng, and M. Weck, “Free chlorine sensing using an interferometric sensor,” Sens. Actuators B Chem. 156, 812–819 (2011).
[CrossRef]

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S. Chakraborty, “Bayesian semi-supervised learning with support vector machine,” Stat. Methodol. 8, 68–82 (2011).
[CrossRef]

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Figures (8)

Fig. 1.
Fig. 1.

Experimental setup for the proposed concentration sensor based on ion detection using a tapered PMMA fiber.

Fig. 2.
Fig. 2.

Output voltage versus the calcium hypochlorite (G70) concentration for the proposed tapered PMMA-based sensor with and without silver thin film.

Fig. 3.
Fig. 3.

Relationship between the concentration of calcium hypochlorite and sensor voltage output without silver coating for all experimental tests.

Fig. 4.
Fig. 4.

Relationship between the concentration of calcium hypochlorite and sensor voltage output with silver coating for all experimental tests.

Fig. 5.
Fig. 5.

Plot of observed and predicted sensor output for silver coatings with the original dataset using SVR_rbf, SVR_poly, and ANFIS models during (a) training and (b) testing.

Fig. 6.
Fig. 6.

Plot of observed and predicted sensor output for silver coatings without the original dataset using SVR_rbf, SVR_poly, and ANFIS models during (a) training and (b) testing.

Fig. 7.
Fig. 7.

Plot of ANFIS predicted sensor output with silver coating.

Fig. 8.
Fig. 8.

Plot of ANFIS predicted sensor output without silver coating.

Tables (5)

Tables Icon

Table 1. Statistical Properties of Experimental Data for Calcium Hypochlorite

Tables Icon

Table 2. Performance Criteria

Tables Icon

Table 3. User-Defined Parameters for Support Vector Regressions SVR_rbf and SVR_poly for Parameter F1 and F2 Predictions

Tables Icon

Table 4. Performance Indices of Various Approaches for Sensor Output Predictions with Silver Coatings

Tables Icon

Table 5. Performance Indices of Various Approaches for Sensor Output Predictions without Silver Coatings

Equations (10)

Equations on this page are rendered with MathJax. Learn more.

y=wTφ(x)+b,
minJ(w,e)=12wTw+γ12i=1Nei2,
yi=wT(xi)+b+ei,i=1,,N,
L(w,b,e;α)=J(w,e)i=1Nαi{wT(xi)+b+eiyi}.
Lw=0w=i=1Nαi(xi),
Lb=0b=i=1Nαi=0,
Lei=0αi=γei,i=1,,N,
Lxi=0wT(xi)+b+eiyi=0,i=1,,N.
y=f(x)=i=1Nα^iK(x,xi)+b^,
K(x,xi)=exp(1σ2xxi2),

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