Neural network configuration for pollen analysis

Palynology is a botanical discipline devoted to the study of pollen and spores [1], focusing mainly on the analysis of the external morphology that presents structural patterns different from those of the variations in the exine, which is the external wall of the pollen grains. The study and microsc...

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Main Authors: amelec, viloria, Mercado, Darwin, Pineda, Omar
Format: Report
Language:English
Published: Corporación Universidad de la Costa 2020
Subjects:
Genetic algorithm
Neural network configuration
Pollen analysis
Arctic
Online Access:https://hdl.handle.net/11323/7261
https://repositorio.cuc.edu.co/
id ftunivcosta:oai:repositorio.cuc.edu.co:11323/7261
record_format openpolar
institution Open Polar
collection REDICUC - Repositorio Universidad de La Costa
op_collection_id ftunivcosta
language English
topic Genetic algorithm
Neural network configuration
Pollen analysis
spellingShingle Genetic algorithm
Neural network configuration
Pollen analysis
amelec, viloria
Mercado, Darwin
Pineda, Omar
Neural network configuration for pollen analysis
topic_facet Genetic algorithm
Neural network configuration
Pollen analysis
description Palynology is a botanical discipline devoted to the study of pollen and spores [1], focusing mainly on the analysis of the external morphology that presents structural patterns different from those of the variations in the exine, which is the external wall of the pollen grains. The study and microscopic analysis of its symmetry, wall opening, contour, shape, size, etc., have a taxonomic value and allows distinguishing different taxa at different levels: family, genera, species. The study of pollen grains is a difficult task, in its different phases, from small microscopic samples. The analysis of these is an important source of information for many scientific and industrial applications, making palynology a valuable tool for various areas of knowledge [1]. In palynology, neural networks have been successfully applied for the classification of pollen grains. For this purpose, RPROP was selected as a neural network training algorithm for the classification of a previously reported dataset.
format Report
author amelec, viloria
Mercado, Darwin
Pineda, Omar
author_facet amelec, viloria
Mercado, Darwin
Pineda, Omar
author_sort amelec, viloria
title Neural network configuration for pollen analysis
title_short Neural network configuration for pollen analysis
title_full Neural network configuration for pollen analysis
title_fullStr Neural network configuration for pollen analysis
title_full_unstemmed Neural network configuration for pollen analysis
title_sort neural network configuration for pollen analysis
publisher Corporación Universidad de la Costa
publishDate 2020
url https://hdl.handle.net/11323/7261
https://repositorio.cuc.edu.co/
genre Arctic
genre_facet Arctic
op_source Advances in Intelligent Systems and Computing
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85089228244&doi=10.1007%2f978-3-030-51859-2_32&partnerID=40&md5=e2878cfc05f98250976ff8e30037d82d
op_relation Rodriguez, I.F., Mégret, R., Acuna, E., Agosto-Rivera, J.L., Giray, T.: Recognition of pollen-bearing bees from video using convolutional neural network. In: 2018 IEEE Winter Conference on Applications of Computer Vision (WACV), pp. 314–322. IEEE, March 2018
Carpenter, G.A.: Neural-network models of learning and memory: leading questions and an emerging framework. Trends Cogn. Sci. 5(3), 114–118 (2001)
Al-Saqer, S.M., Hassan, G.M.: Artificial neural networks based red palm weevil (Rynchophorus Ferrugineous, Olivier) recognition system. Am. J. Agric. Biol. Sci. 6, 356–364 (2011)
Burki, C., Šikoparija, B., Thibaudon, M., Oliver, G., Magyar, D., Udvardy, O., Pauling, A.: Artificial neural networks can be used for Ambrosia pollen emission parameterization in COSMO-ART. Atmos. Environ. 218, 116969 (2019)
Li, P., Treloar, W.J., Flenley, J.R., Empson, L.: vf: vf towards automation of palynology 2: the use of texture measures and neural network analysis for automated identification of optical images of pollen grains. J. Quat. Sci.: Publ. Quat. Res. Assoc. 19(8), 755–762 (2004)
Li, P., Flenley, J.R.: Pollen texture identification using neural networks. Grana 38(1), 59–64 (1999)
Dewan, P., Ganti, R., Srivatsa, M., Stein, S.: NN-SAR: a neural network approach for spatial autoregression. In 2019 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops), pp. 783–789. IEEE, March 2019
Friedman, M., Kandel, A.: Introduction to Pattern Recognition Statistical, Structural, Neural and Fuzzy Logic Approaches. World Scientific Publishing Company Inc., 2 March 1999
Zewdie, G.K., Lary, D.J., Levetin, E., Garuma, G.F.: Applying deep neural networks and ensemble machine learning methods to forecast airborne ambrosia pollen. Int. J. Environ. Res. Public Health 16(11), 1992 (2019)
Zhao, X., Yue, S.: Analysing and forecasting the security in supply-demand management of Chinese forestry enterprises by linear weighted method and artificial neural network. Enterp. Inf. Syst. 1–18 (2020)
Riedmiller, M., Braun, H.: Direct adaptive method for faster backpropagation learning: the RPROP algorithm. In: Proceedings of the IEEE International Conference on Neural Networks, pp. 586–591 (1993)
Daood, A., Ribeiro, E., Bush, M.: Sequential recognition of pollen grain Z-stacks by combining CNN and RNN. In: The Thirty-First International Flairs Conference, May 2018
Raj, J.S., Ananthi, J.V.: Recurrent neural networks and nonlinear prediction in support vector machines. J. Soft Comput. Paradig. (JSCP) 1(01), 33–40 (2019)
Varela, N., Silva, J., Gonzalez, F.M., Palencia, P., Palma, H.H., Pineda, O.B.: Method for the recovery of images in databases of rice grains from visual content. Procedia Comput. Sci. 170, 983–988 (2020)
Khorissi, N.E., Mellit, A., Guessoum, A., Mesaouer, A.: GA-based feed-forward neural network for image classification: application for the grains of pollen. J. Appl. Comput. Sci. 17(2), 83–96 (2009)
Viloria, A., Acuña, G.C., Franco, D.J.A., Hernández-Palma, H., Fuentes, J.P., Rambal, E.P.: Integration of data mining techniques to PostgreSQL database manager system. Procedia Comput. Sci. 155, 575–580 (2019)
Pentoś, K., Łuczycka, D., Wróbel, R.: The identification of the relationship between chemical and electrical parameters of honeys using artificial neural networks. Comput. Biol. Med. 53, 244–249 (2014)
Al-Mahasneh, M.A., Rababah, T.M., Ma’Abreh, A.S.: Evaluating the combined effect of temperature, shear rate and water content on wild-flower honey viscosity using adaptive neural fuzzy inference system and artificial neural networks. J. Food Process Eng 36(4), 510–520 (2013)
Viloria, A., Lezama, O.B.P.: Improvements for determining the number of clusters in k-Means for innovation databases in SMEs. In: ANT/EDI40, pp. 1201–1206 (2019)
Rashidi, M.M., Galanis, N., Nazari, F., Parsa, A.B., Shamekhi, L.: Parametric analysis and optimization of regenerative Clausius and organic Rankine cycles with two feedwater heaters using artificial bees colony and artificial neural network. Energy 36(9), 5728–5740 (2011)
Pentoś, K., Łuczycka, D., Kapłon, T.: The identification of relationships between selected honey parameters by extracting the contribution of independent variables in a neural network model. Eur. Food Res. Technol. 241(6), 793–801 (2015)
Peyron, O., Vernal, A.D.: Application of artificial neural networks (ANN) to high-latitude dinocyst assemblages for the reconstruction of past sea-surface conditions in Arctic and sub-Arctic seas. J. Quat. Sci.: Publ. Quat. Res. Assoc. 16(7), 699–709 (2001)
Mokin, V.B., Kozachko, O.M., Rodinkova, V.V., Palamarchuk, O.O., Vuzh, T.Y.: The decision support system for the classification of allergenic pollen types based on fuzzy expert data of pollen features on the microscope images. In: 2017 IEEE First Ukraine Conference on Electrical and Computer Engineering (UKRCON), pp. 850–855. IEEE, May 2017
Todd, G.: Fuzzy neural network interface: development and application: a thesis presented in partial fulfilment of the requirements for the degree of Master of Engineering in Information Engineering at Massey University, Palmerston North, New Zealand, (Doctoral dissertation, Massey University (2003)
Cho, H., Berger, B., Peng, J.: Generalizable and scalable visualization of single-cell data using neural networks. Cell Syst. 7(2), 185–191 (2018)
Lehky, S.R., Sejnowski, T.J.: Neural network model of visual cortex for determining surface curvature from images of shaded surfaces. Proc. R. Soc. Lond. B Biol. Sci. 240(1298), 251–278 (1990)
Dell’Anna, R., Lazzeri, P., Frisanco, M., Monti, F., Campeggi, F.M., Gottardini, E., Bersani, M.: Pollen discrimination and classification by Fourier transform infrared (FT-IR) microspectroscopy and machine learning. Anal. Bioanal. Chem. 394(5), 1443–1452 (2009)
Chiroma, H., Khan, A., Abubakar, A.I., Saadi, Y., Hamza, M.F., Shuib, L., Herawan, T.: A new approach for forecasting OPEC petroleum consumption based on neural network train by using flower pollination algorithm. Appl. Soft Comput. 48, 50–58 (2016)
Tomassetti, B., Lombardi, A., Cerasani, E., Di Sabatino, A., Pace, L., Ammazzalorso, D., Verdecchia, M.: Mapping of Alternaria and Pleospora concentrations in Central Italy using meteorological forecast and neural network estimator. Aerobiologia 29(1), 55–70 (2013)
Guyon, V.N., Astwood, J.D., Garner, E.C., Dunker, A.K., Taylor, L.P.: Isolation and characterization of cDNAs expressed in the early stages of flavonol-induced pollen germination in petunia. Plant Physiol. 123(2), 699–710 (2000)
Ramos-Pollán, R., Guevara-López, M.Á., Oliveira, E.: Introducing ROC curves as error measure functions: a new approach to train ANN-based biomedical data classifiers. In: Iberoamerican Congress on Pattern Recognition, pp. 517–524. Springer, Heidelberg, November 2010
Raghu, P.P., Poongodi, R., Yegnanarayana, B.: Unsupervised texture classification using vector quantization and deterministic relaxation neural network. IEEE Trans. Image Process. 6(10), 1376–1387 (1997)
2194-5357
https://hdl.handle.net/11323/7261
Corporación Universidad de la Costa
REDICUC - Repositorio CUC
https://repositorio.cuc.edu.co/
op_rights Attribution-NonCommercial-NoDerivatives 4.0 International
http://creativecommons.org/licenses/by-nc-nd/4.0/
info:eu-repo/semantics/closedAccess
http://purl.org/coar/access_right/c_14cb
_version_ 1788058188126355456
spelling ftunivcosta:oai:repositorio.cuc.edu.co:11323/7261 2024-01-14T10:03:20+01:00 Neural network configuration for pollen analysis amelec, viloria Mercado, Darwin Pineda, Omar 2020 application/pdf https://hdl.handle.net/11323/7261 https://repositorio.cuc.edu.co/ eng eng Corporación Universidad de la Costa Rodriguez, I.F., Mégret, R., Acuna, E., Agosto-Rivera, J.L., Giray, T.: Recognition of pollen-bearing bees from video using convolutional neural network. In: 2018 IEEE Winter Conference on Applications of Computer Vision (WACV), pp. 314–322. IEEE, March 2018 Carpenter, G.A.: Neural-network models of learning and memory: leading questions and an emerging framework. Trends Cogn. Sci. 5(3), 114–118 (2001) Al-Saqer, S.M., Hassan, G.M.: Artificial neural networks based red palm weevil (Rynchophorus Ferrugineous, Olivier) recognition system. Am. J. Agric. Biol. Sci. 6, 356–364 (2011) Burki, C., Šikoparija, B., Thibaudon, M., Oliver, G., Magyar, D., Udvardy, O., Pauling, A.: Artificial neural networks can be used for Ambrosia pollen emission parameterization in COSMO-ART. Atmos. Environ. 218, 116969 (2019) Li, P., Treloar, W.J., Flenley, J.R., Empson, L.: vf: vf towards automation of palynology 2: the use of texture measures and neural network analysis for automated identification of optical images of pollen grains. J. Quat. Sci.: Publ. Quat. Res. Assoc. 19(8), 755–762 (2004) Li, P., Flenley, J.R.: Pollen texture identification using neural networks. Grana 38(1), 59–64 (1999) Dewan, P., Ganti, R., Srivatsa, M., Stein, S.: NN-SAR: a neural network approach for spatial autoregression. In 2019 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops), pp. 783–789. IEEE, March 2019 Friedman, M., Kandel, A.: Introduction to Pattern Recognition Statistical, Structural, Neural and Fuzzy Logic Approaches. World Scientific Publishing Company Inc., 2 March 1999 Zewdie, G.K., Lary, D.J., Levetin, E., Garuma, G.F.: Applying deep neural networks and ensemble machine learning methods to forecast airborne ambrosia pollen. Int. J. Environ. Res. Public Health 16(11), 1992 (2019) Zhao, X., Yue, S.: Analysing and forecasting the security in supply-demand management of Chinese forestry enterprises by linear weighted method and artificial neural network. Enterp. Inf. Syst. 1–18 (2020) Riedmiller, M., Braun, H.: Direct adaptive method for faster backpropagation learning: the RPROP algorithm. In: Proceedings of the IEEE International Conference on Neural Networks, pp. 586–591 (1993) Daood, A., Ribeiro, E., Bush, M.: Sequential recognition of pollen grain Z-stacks by combining CNN and RNN. In: The Thirty-First International Flairs Conference, May 2018 Raj, J.S., Ananthi, J.V.: Recurrent neural networks and nonlinear prediction in support vector machines. J. Soft Comput. Paradig. (JSCP) 1(01), 33–40 (2019) Varela, N., Silva, J., Gonzalez, F.M., Palencia, P., Palma, H.H., Pineda, O.B.: Method for the recovery of images in databases of rice grains from visual content. Procedia Comput. Sci. 170, 983–988 (2020) Khorissi, N.E., Mellit, A., Guessoum, A., Mesaouer, A.: GA-based feed-forward neural network for image classification: application for the grains of pollen. J. Appl. Comput. Sci. 17(2), 83–96 (2009) Viloria, A., Acuña, G.C., Franco, D.J.A., Hernández-Palma, H., Fuentes, J.P., Rambal, E.P.: Integration of data mining techniques to PostgreSQL database manager system. Procedia Comput. Sci. 155, 575–580 (2019) Pentoś, K., Łuczycka, D., Wróbel, R.: The identification of the relationship between chemical and electrical parameters of honeys using artificial neural networks. Comput. Biol. Med. 53, 244–249 (2014) Al-Mahasneh, M.A., Rababah, T.M., Ma’Abreh, A.S.: Evaluating the combined effect of temperature, shear rate and water content on wild-flower honey viscosity using adaptive neural fuzzy inference system and artificial neural networks. J. Food Process Eng 36(4), 510–520 (2013) Viloria, A., Lezama, O.B.P.: Improvements for determining the number of clusters in k-Means for innovation databases in SMEs. In: ANT/EDI40, pp. 1201–1206 (2019) Rashidi, M.M., Galanis, N., Nazari, F., Parsa, A.B., Shamekhi, L.: Parametric analysis and optimization of regenerative Clausius and organic Rankine cycles with two feedwater heaters using artificial bees colony and artificial neural network. Energy 36(9), 5728–5740 (2011) Pentoś, K., Łuczycka, D., Kapłon, T.: The identification of relationships between selected honey parameters by extracting the contribution of independent variables in a neural network model. Eur. Food Res. Technol. 241(6), 793–801 (2015) Peyron, O., Vernal, A.D.: Application of artificial neural networks (ANN) to high-latitude dinocyst assemblages for the reconstruction of past sea-surface conditions in Arctic and sub-Arctic seas. J. Quat. Sci.: Publ. Quat. Res. Assoc. 16(7), 699–709 (2001) Mokin, V.B., Kozachko, O.M., Rodinkova, V.V., Palamarchuk, O.O., Vuzh, T.Y.: The decision support system for the classification of allergenic pollen types based on fuzzy expert data of pollen features on the microscope images. In: 2017 IEEE First Ukraine Conference on Electrical and Computer Engineering (UKRCON), pp. 850–855. IEEE, May 2017 Todd, G.: Fuzzy neural network interface: development and application: a thesis presented in partial fulfilment of the requirements for the degree of Master of Engineering in Information Engineering at Massey University, Palmerston North, New Zealand, (Doctoral dissertation, Massey University (2003) Cho, H., Berger, B., Peng, J.: Generalizable and scalable visualization of single-cell data using neural networks. Cell Syst. 7(2), 185–191 (2018) Lehky, S.R., Sejnowski, T.J.: Neural network model of visual cortex for determining surface curvature from images of shaded surfaces. Proc. R. Soc. Lond. B Biol. Sci. 240(1298), 251–278 (1990) Dell’Anna, R., Lazzeri, P., Frisanco, M., Monti, F., Campeggi, F.M., Gottardini, E., Bersani, M.: Pollen discrimination and classification by Fourier transform infrared (FT-IR) microspectroscopy and machine learning. Anal. Bioanal. Chem. 394(5), 1443–1452 (2009) Chiroma, H., Khan, A., Abubakar, A.I., Saadi, Y., Hamza, M.F., Shuib, L., Herawan, T.: A new approach for forecasting OPEC petroleum consumption based on neural network train by using flower pollination algorithm. Appl. Soft Comput. 48, 50–58 (2016) Tomassetti, B., Lombardi, A., Cerasani, E., Di Sabatino, A., Pace, L., Ammazzalorso, D., Verdecchia, M.: Mapping of Alternaria and Pleospora concentrations in Central Italy using meteorological forecast and neural network estimator. Aerobiologia 29(1), 55–70 (2013) Guyon, V.N., Astwood, J.D., Garner, E.C., Dunker, A.K., Taylor, L.P.: Isolation and characterization of cDNAs expressed in the early stages of flavonol-induced pollen germination in petunia. Plant Physiol. 123(2), 699–710 (2000) Ramos-Pollán, R., Guevara-López, M.Á., Oliveira, E.: Introducing ROC curves as error measure functions: a new approach to train ANN-based biomedical data classifiers. In: Iberoamerican Congress on Pattern Recognition, pp. 517–524. Springer, Heidelberg, November 2010 Raghu, P.P., Poongodi, R., Yegnanarayana, B.: Unsupervised texture classification using vector quantization and deterministic relaxation neural network. IEEE Trans. Image Process. 6(10), 1376–1387 (1997) 2194-5357 https://hdl.handle.net/11323/7261 Corporación Universidad de la Costa REDICUC - Repositorio CUC https://repositorio.cuc.edu.co/ Attribution-NonCommercial-NoDerivatives 4.0 International http://creativecommons.org/licenses/by-nc-nd/4.0/ info:eu-repo/semantics/closedAccess http://purl.org/coar/access_right/c_14cb Advances in Intelligent Systems and Computing https://www.scopus.com/inward/record.uri?eid=2-s2.0-85089228244&doi=10.1007%2f978-3-030-51859-2_32&partnerID=40&md5=e2878cfc05f98250976ff8e30037d82d Genetic algorithm Neural network configuration Pollen analysis Pre-Publicación http://purl.org/coar/resource_type/c_816b Text info:eu-repo/semantics/preprint http://purl.org/redcol/resource_type/ARTOTR info:eu-repo/semantics/acceptedVersion http://purl.org/coar/version/c_ab4af688f83e57aa 2020 ftunivcosta 2023-12-17T19:22:17Z Palynology is a botanical discipline devoted to the study of pollen and spores [1], focusing mainly on the analysis of the external morphology that presents structural patterns different from those of the variations in the exine, which is the external wall of the pollen grains. The study and microscopic analysis of its symmetry, wall opening, contour, shape, size, etc., have a taxonomic value and allows distinguishing different taxa at different levels: family, genera, species. The study of pollen grains is a difficult task, in its different phases, from small microscopic samples. The analysis of these is an important source of information for many scientific and industrial applications, making palynology a valuable tool for various areas of knowledge [1]. In palynology, neural networks have been successfully applied for the classification of pollen grains. For this purpose, RPROP was selected as a neural network training algorithm for the classification of a previously reported dataset. Report Arctic REDICUC - Repositorio Universidad de La Costa

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