Content of aliphatic hydrocarbons in limpets as a new way for classification of species using artificial neural networks

被引:20
|
作者
Hernández-Borges, J [1 ]
Corbella-Tena, R [1 ]
Rodríguez-Delgado, MA [1 ]
García-Montelongo, FJ [1 ]
Havel, J [1 ]
机构
[1] Masaryk Univ, Fac Sci, Dept Analyt Chem, CS-61137 Brno, Czech Republic
关键词
limpets; N-alkanes; gas chromatography; artificial neural networks;
D O I
10.1016/j.chemosphere.2003.09.042
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
It is demonstrated that biological species like limpets can be classified according to their level of n-alkanes when artificial neural networks are applied. Marine intertidal and subtidal limpets of the Canary Islands (Spain), Patella piperata, Patella candei crenata and Patella ulyssiponensis aspera were selected as bioindicator organisms. Samples were collected at four stations on the coasts of Fuerteventura. Concentration of n-alkanes in the soft tissues of the limpets has been determined by gas chromatography. Data were treated with artificial neural networks (ANNs) and it was found that using suitable architecture of a supervised artificial neural network, the limpets can be successfully distinguished (classified) up to 98%. (C) 2003 Elsevier Ltd. All rights reserved.
引用
收藏
页码:1059 / 1069
页数:11
相关论文
共 50 条
  • [31] PREDICTION OF THE OPTIMUM ASPHALT CONTENT USING ARTIFICIAL NEURAL NETWORKS
    Othman, Kareem
    Abdelwahab, Hassan
    METALLURGICAL & MATERIALS ENGINEERING, 2021, 27 (02) : 227 - 242
  • [32] Breast Cancer Detection and classification Using Artificial Neural Networks
    Hamad, Yousif A.
    Simonov, Konstantin
    Naeem, Mohammad B.
    2018 1ST ANNUAL INTERNATIONAL CONFERENCE ON INFORMATION AND SCIENCES (AICIS 2018), 2018, : 51 - 57
  • [33] DEFORMATION CLASSIFICATION OF CUTTING DISCS USING ARTIFICIAL NEURAL NETWORKS
    Akarslan, Emre
    Hocaoglu, Fatih Onur
    2014 22ND SIGNAL PROCESSING AND COMMUNICATIONS APPLICATIONS CONFERENCE (SIU), 2014, : 622 - 625
  • [34] Fault Classification and Location in Microgrid Using Artificial Neural Networks
    Kumar, Dharm Dev
    Alam, Mahamad Nabab
    12TH INTERNATIONAL CONFERENCE ON SMART GRID, ICSMARTGRID 2024, 2024, : 395 - 399
  • [35] Classification of the rotating machine condition using artificial neural networks
    McCormick, AC
    Nandi, AK
    PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART C-JOURNAL OF MECHANICAL ENGINEERING SCIENCE, 1997, 211 (06) : 439 - 450
  • [36] Quantitative classification of conversational language using artificial neural networks
    Singh, S
    APHASIOLOGY, 1997, 11 (09) : 829 - 844
  • [37] Aerial Radar Target Classification using Artificial Neural Networks
    Ardon, Guy
    Simko, Or
    Novoselsky, Akiva
    ICPRAM: PROCEEDINGS OF THE 9TH INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION APPLICATIONS AND METHODS, 2020, : 136 - 141
  • [38] Classification of Emotional Valence Dimension Using Artificial Neural Networks
    Ozdemir, Merve Erkmay
    Yildirim, Esen
    Yildirim, Serdar
    2015 23RD SIGNAL PROCESSING AND COMMUNICATIONS APPLICATIONS CONFERENCE (SIU), 2015, : 2549 - 2552
  • [39] Wear particle texture classification using artificial neural networks
    Laghari, MS
    Boujarwah, A
    INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE, 1999, 13 (03) : 415 - 428
  • [40] Classification of Images Acquired with Colposcopy Using Artificial Neural Networks
    Simoes, Priscyla W.
    Izumi, Narjara B.
    Casagrande, Ramon S.
    Venson, Ramon
    Veronezi, Carlos D.
    Moretti, Gustavo P.
    da Rocha, Edroaldo L.
    Cechinel, Cristian
    Ceretta, Luciane B.
    Comunello, Eros
    Martins, Paulo J.
    Casagrande, Rogerio A.
    Snoeyer, Maria L.
    Manenti, Sandra A.
    CANCER INFORMATICS, 2014, 13 : 119 - 124