Argitalpenak (34) MARIA JESUS JIMENEZ COME argitalpenak

2023

  1. Corrosion Behaviour Modelling Using Artificial Neural Networks: A Case Study in Biogas Environment

    Metals, Vol. 13, Núm. 11

2020

  1. The use of artificial neural networks for modelling pitting corrosion behaviour of en 1.4404 stainless steel in marine environment: Data analysis and new developments

    Corrosion Reviews, Vol. 38, Núm. 4, pp. 339-353

2019

  1. A support vector machine-based ensemble algorithm for pitting corrosion modeling of EN 1.4404 stainless steel in sodium chloride solutions

    Materials and Corrosion, Vol. 70, Núm. 1, pp. 19-27

  2. A two-stage forecasting approach for short-term intermodal freight prediction

    International Transactions in Operational Research, Vol. 26, Núm. 2, pp. 642-666

  3. Efficient goods inspection demand at ports: a comparative forecasting approach

    International Transactions in Operational Research, Vol. 26, Núm. 5, pp. 1906-1934

2017

  1. Prediction of carbon monoxide (CO) atmospheric pollution concentrations using meterological variables

    WIT Transactions on Ecology and the Environment, Vol. 211, pp. 137-145

  2. Prediction of pitting corrosion status of EN 1.4404 stainless steel by using a 2-stage procedure based on support vector machines

    Journal of Chemometrics, Vol. 31, Núm. 12

2016

  1. A two-stage model based on artificial neural networks to determine pitting corrosion status of 316L stainless steel

    Corrosion Reviews, Vol. 34, Núm. 1-2, pp. 113-125

  2. Forecasting of short-term flow freight congestion: a study case of Algeciras Bay Port (Spain)

    DYNA: revista de la Facultad de Minas. Universidad Nacional de Colombia. Sede Medellín, Vol. 83, Núm. 195, pp. 163-172

  3. Short-term Forecasting of Intermodal Freight Using ANNs and SVR: Case of the Port of Algeciras Bay

    Transportation Research Procedia

  4. Time Analysis of the Containerized Cargo Flow in the Logistic Chain Using Simulation Tools: The Case of the Port of Seville (Spain)

    Transportation Research Procedia

2015

  1. A novel three-step procedure to forecast the inspection volume

    Transportation Research Part C: Emerging Technologies, Vol. 56, pp. 393-414

  2. A two-stage procedure for forecasting freight inspections at Border Inspection Posts using SOMs and support vector regression

    International Journal of Production Research, Vol. 53, Núm. 7, pp. 2119-2130

  3. Characterization of pitting corrosion of stainless steel using artificial neural networks

    Materials and Corrosion, Vol. 66, Núm. 10, pp. 1084-1091

  4. Pitting corrosion behaviour modelling of stainless steel with support vector machines

    Materials and Corrosion, Vol. 66, Núm. 9, pp. 915-924

2014

  1. A comparison of forecasting methods for Ro-Ro traffic: A case study in the strait of gibraltar

    Advances in Intelligent Systems and Computing

  2. A comprehensive approach based on SVM to model pitting corrosion behaviour of en 1.4404 stainless steel

    Materials and Corrosion, Vol. 65, Núm. 10, pp. 1024-1032

  3. An automatic pitting corrosion detection approach for 316L stainless steel

    Materials and Design, Vol. 56, pp. 642-648

  4. Breakdown potential modelling of austenitic stainless steel

    Journal of Chemometrics, Vol. 28, Núm. 3, pp. 181-191

  5. Hybrid approaches based on SARIMA and artificial neural networks for inspection time series forecasting

    Transportation Research Part E: Logistics and Transportation Review, Vol. 67, pp. 1-13