Open Access
Issue
E3S Web Conf.
Volume 266, 2021
Topical Issues of Rational Use of Natural Resources 2021
Article Number 02001
Number of page(s) 16
Section Technologies of Complex Processing of Mineral Raw Materials
DOI https://doi.org/10.1051/e3sconf/202126602001
Published online 04 June 2021
  1. A.V. Boikov, R.V. Savelyev, V.A. Payor, O.O. Erokhina, Evaluation of bulk material behavior control method in technological units using DEM. Part 1. CIS Iron and Steel Review, 19:4-7(2020). [Google Scholar]
  2. E.N. Grishchenkova, Development of a Neural Network for Earth Surface Deformation Prediction. Geotech Geol Eng. 36(4): 1953–1957(2018). [Google Scholar]
  3. G.E. Hinton, R.R. Salakhutdinov, Reducing the Dimensionality of Data with Neural Networks. Science. 313(5786): 504–507(2006). [Google Scholar]
  4. S. Hochreiter, J. Schmidhuber. Long Short-Term Memory. Neural Computation. 9(8): 1735–1780(1997). [Google Scholar]
  5. D.T. Jones, Protein Secondary Structure Prediction Based on Position-Specific Scoring Matrices. Journal of Molecular biology. 292(2): 195–202(1999). [Google Scholar]
  6. Y. LeCun, Y. Bengio, G. Hinton, Deep learning. Nature. 521(7553):436–444(2015). [Google Scholar]
  7. R. Milo, S.S. Shen-Orr, S. Itzkovitz, N. Kashtan, D.M. Alon, U. Chklovskii, Network Motifs: Simple Building Blocks of Complex Networks. Science. 298(5594): 824–827(2002). [Google Scholar]
  8. H. Nielsen, J. Engelbrecht, S. Brunak, G. von Heijne, Identification of Prokaryotic and Eukaryotic Signal Peptides and Prediction of Their Cleavage Sites. Protein Engineering Design and Selection. 10(1): pp. 1–6. (1997) [Google Scholar]
  9. J.D. Olden, D.A. Jackson, Illuminating the “Black Box”: a Randomization Approach for Understanding Variable Contributions in Artificial Neural Networks. Ecological Modelling. 154(1-2): 135–150. (2002) [Google Scholar]
  10. M. Reichstein, G. Camps-Valls, B. Stevens, M. Jung, J. Denzler, N. Prabhat Carvalhais, Deep learning and process understanding for data-driven Earth system science. Nature. 566(7743): 195–204(2019). [Google Scholar]
  11. M. Rubinov, O. Sporns, Complex Network Measures of Brain Connectivity: Uses and Interpretations. NeuroImage. 52(3): 1059–1069(2010). [Google Scholar]
  12. J.V. Tu, Advantages and Disadvantages of Using Artificial Neural Networks versus Logistic Regression for Predicting Medical Outcomes. Journal of Clinical Epidemiology. 49(11): 1225–1231(1996). [Google Scholar]
  13. C. Voyant, G. Notton, S. Kalogirou, M.-L. Nivet, C. Paoli, F. Motte, A Fouilloy Machine Learning methods for solar radiation forecasting. A review. Renewable Energy. 105, 569–582 (2017). [Google Scholar]
  14. R. Vaishya, M. Javaid, I.H. Khan, A Haleem Artificial Intelligence (AI) applications for COVID-19 pandemic. Diabetes & metabolic syndrome. 14(4): 337–339(2020). [Google Scholar]
  15. K. Potdar, R. Kinneerkar, A Comparative Study of Machine Learning Algorithms Applied to Predictive Breast Cancer Data. International Journal of Science and Research. 9, 1550–1553 (2016). [Google Scholar]
  16. H. Salehi, R. Burgueno, Emerging artificial intelligence methods in structural engineering. Engineering Structures. 171, 170–189 (2018). [Google Scholar]
  17. D.P. Tabor, L.M. Roch, S.K. Saikin, C. Kreisbeck, D. Sheberla, J.H. Montoya, S. Dwaraknath, M. Aykol, C. Ortiz, H. Tribukait, C. Amador-Bedolla, C.J. Brabec, B. Maruyama, K.A. Persson, A. Aspuru-Guzik, Accelerating the discovery of materials for clean energy in the era of smart automation. Nat Rev Mater. 3(5): 5–20(2018). [Google Scholar]
  18. D. Ali, S. Frimpong, Artificial intelligence, machine learning, and process automation: existing knowledge frontier and way forward for the mining sector. ArtifIntell Rev. 53(8): 6025–6042(2020). [Google Scholar]
  19. R. Berk, H. Heidari, S. Jabbari, M. Kearns, A Roth Fairness in Criminal Justice Risk Assessments. Sociological Methods & Research:004912411878253(2018). [Google Scholar]
  20. A. Chatterjee, U. Gupta, M.K. Chinnakotla, R. Srikanth, M. Galley, P. Agrawal. Understanding Emotions in Text Using Deep Learning and Big Data. Computers in Human Behavior. 93, 309–317 (2019). [Google Scholar]
  21. D. Bejou, B. Wray, T.N. Ingram. Determinants of relationship quality: An artificial neural network analysis. Journal of Business Research. 36(2): 137–143(1996). [Google Scholar]
  22. M.Y. Kiang, A. Kumar, An Evaluation of Self-Organizing Map Networks as a Robust Alternative to Factor Analysis in Data Mining Applications. Information Systems Research. 12(2): 177–194(2001). [Google Scholar]
  23. C. Stutzman, D. Cooperstein, C. Munchbach Measure and Manage Brand Health. How To Diagnose And Treat Your Brand's Resilience And Responsiveness (CMO Professionals. Forthcoming. 2012) [Google Scholar]
  24. I. Khajenasiri, A. Estebsari, M. Verhelst, G. Gielen, A Review on Internet of Things Solutions for Intelligent Energy Control in Buildings for Smart City Applications. Energy Procedia. 111, 770–779 (2017). [Google Scholar]
  25. J. Winkowska, D. Szpilko, S. Pejic, Smart city concept in the light of the literature review. Engineering Management in Production and Services. 11(2): 70–86(2019). [Google Scholar]
  26. M. Alaa, A.A. Zaidan, B.B. Zaidan, M. Talal, M. Kiah, A review of smart home applications based on Internet of Things. Journal of Network and Computer Applications. 97, 48–65 (2017). [Google Scholar]
  27. V. Alcacer, V. Cruz-Machado, Scanning the Industry 4.0: A Literature Review on Technologies for Manufacturing Systems. Engineering Science and Technology, an International Journal. 22(3): 899–919(2019). [Google Scholar]
  28. K.-D. Thoben, S. Wiesner, T. Wuest. “Industrie 4.0” and Smart Manufacturing-A Review of Research Issues and Application Examples. Int. J. Automation Technol. 11(1): 4–16(2017). [Google Scholar]
  29. J. Reis, M. Amorim, N. Melao, P. Matos, Digital Transformation: A Literature Review and Guidelines for Future Research. World Conference on Information Systems and Technologies: 411–421. (2018) [Google Scholar]
  30. G. Vial. Understanding digital transformation: A review and a research agenda. The Journal of Strategic Information Systems. 28(2): 118–144(2019). [Google Scholar]
  31. P. Asghari, A.M. Rahmani, H.H.S. Javadi, Internet of Things applications: A systematic review. Computer Networks. 148, 241–261 (2019). [Google Scholar]
  32. A.N. Kolmogorov, On the Representation of Continuous Functions of Many Variables by Superposition of Continuous Functions of One Variable and Addition. Dokl. Akad. Nauk SSSR. 114, 5(1957). [Google Scholar]
  33. K. Hornik, M. Stinchcombe, H. White, Multilayer Feedforward Networks Are Universal Approximators. Neural Networks. 2(5): 359–366(1989). [Google Scholar]
  34. H.O.A. Wold, On Prediction in Stationary Time Series. Annals of Mathematical Statistics. 19(4): 558–567(1948). [Google Scholar]
  35. D. Basak, S. Pal, D.C. Patranabis, Support Vector Regression. Neural Information Processing-Letters and Reviews. 11(10): 203–224(2007). [Google Scholar]
  36. Z. Wang, R.S. Srinivasan. A review of artificial intelligence-based building energy use prediction: Contrasting the capabilities of single and ensemble prediction models. Renewable and Sustainable Energy Reviews. 75, 796–808 (2017). [Google Scholar]
  37. A. Foucquier, S. Robert, F. Suard, L. Stephan, A. Jay, State of the art in building modelling and energy performances prediction: A review. Renewable and Sustainable Energy Reviews. 23, 272–288 (2013). [Google Scholar]
  38. T. Kohonen, The Self-organizing Map. Proceedings of the IEEE. 78(9): 1464–1480 (1990). [Google Scholar]
  39. T. Kohonen, Physiological interpretation of the self-organizing map algorithm. Neural Networks. 6(6): 895–905(1993). [Google Scholar]
  40. A. Saxena, M. Prasad, A. Gupta, N. Bharill, O.P. Patel, A. Tiwari, M.J. Er, W. Ding, C.-T. Lin, A Review of Clustering Techniques and Developments. Neurocomputing. 267, 664–681 (2017). [Google Scholar]
  41. R. Xu, D. Wunsch, Survey of clustering algorithms. IEEE transactions on neural networks. 16(3): 645–678(2005). [Google Scholar]
  42. James B. MacQueen, Some methods for classification and analysis of multivariate observations, 5th Symposium on Mathematical Statistics and Probability, University of California Press, Berkeley(1967). [Google Scholar]
  43. T. Kohonen, The self-organizing map. Neurocomputing. 21(1-3): 1–6. (1998) [Google Scholar]
  44. J. Vesanto, E. Alhoniemi, Clustering of the self-organizing map. IEEE transactions on neural networks. 11(3): 586–600(2000). [Google Scholar]
  45. D.E. Goldberg, Genetic algorithms in search, optimization, and machine learning (Addison-Wesley, Reading, Mass., Wokingham, 1989). [Google Scholar]
  46. L. Kaufman, P.J. Rousseeuw.Inding groups in data. An introduction to cluster analysis (Wiley, New York, 1989). [Google Scholar]
  47. P.H.A. Sneath, R.R. Sokal, Numerical taxonomy. The principles and practice of numerical classification. (Forthcoming, 1973) [Google Scholar]
  48. B. King, Step-Wise Clustering Procedures. Journal of the American Statistical Association. 62(317): 86 (1967) [Google Scholar]
  49. T. Zhang, R. Ramakrishnan, M. Linvy, BIRCH: an efficient data clustering method for very large databases. ACMSIGMOD Record. 25(2): 103–114(1996). [Google Scholar]
  50. S. Guha, R. Rastogi, K. Shim, CURE. An efficient clustering algorithm for large databases. ACMSIGMOD Record. 27(2): 73–84(1998). [Google Scholar]
  51. S. Guha, R. Rastogi, K. Shim, Rock: A robust clustering algorithm for categorical attributes. Information Systems Research. 25(5): 345–366(2000). [Google Scholar]
  52. Martin Ester, Hans-Peter Kriegel, Jorg Sander, Xiaowei Xu, A density-based algorithm for discovering clusters in large spatial databases with noise. Proceedings of the Second International Conference on Knowledge Discovery and Data Mining: 226–231(1996). [Google Scholar]
  53. M. Ankerst, M.M. Breunig, H.-P. Kriegel, J. Sander. OPTICS: ordering points to identify the clustering structure. ACMSIGMOD Record. 28(2): 49–60(1999). [Google Scholar]
  54. R.J. Campello, D. Moulavi, A. Zimek, J. Sander, Hierarchical Density Estimates for Data Clustering, Visualization, and Outlier Detection. ACM Transactions on Knowledge Discovery from Data. 10(1): 1–15(2015). [Google Scholar]
  55. Y. Cheng, Mean shift, mode seeking, and clustering. IEEE Transactions on Pattern Analysis and Machine Intelligence. 17(8): 790–799(1995). [Google Scholar]
  56. B.J. Frey, D. Dueck, Clustering by passing messages between data points. Science. 315(5814): 972–976(2007). [Google Scholar]
  57. G.J. McLachlan, E.B. Kaye, Mixture models: Inference and applications to clustering. Vol. 38. (New York: M. Dekker, 1988). [Google Scholar]
  58. J.A. Hartigan, M.A. Wong, Means Clustering Algorithm. Applied Statistics. 28(1): 100–108 (1979) [Google Scholar]
  59. N. Vasquez, C. Magan, J. Oblitas, T. Chuquizuta, H. Avila-George, W. Castro, Comparison between artificial neural network and partial least squares regression models for hardness modeling during the ripening process of Swiss-type cheese using spectral profiles. Journal of Food Engineering. 219, 8–15 (2018). [Google Scholar]
  60. I.M. Yusri, Majeed A. Abdul, R., Mamat, M.F. Ghazali, O.I. Awad, W.H., A Review on the Application of Response Surface Method and Artificial Neural Network in Engine Performance and Exhaust Emissions characteristics in alternative fuel. Renewable and Sustainable Energy Reviews. 90, 665–686 (2018). [Google Scholar]
  61. R.H. Myers, Classical and Modern Regression With Application (Duxbury Press, Boston, MA, 1986). [Google Scholar]
  62. G.E. Hinton, How Neural Networks Learn from Experience. Scientific American. 267(3): 144–151(1992). [Google Scholar]
  63. H. White, Learning in Artificial Neural Networks: A Statistical Perspective. Neural Computation. 1(4): 425–464(1989). [Google Scholar]
  64. G.D. Garson. Interpreting Neural-network Connection Weights. Artificial Intelligence Expert. 6(4): 46–51(1991). [Google Scholar]
  65. J.D. Olden, M.K. Joy, R.G. Death, An Accurate Comparison of Methods for Quantifying Variable Importance in Artificial Neural Networks Using Simulated Data. Ecological Modelling. 178(3-4): 389–397. (2004) [Google Scholar]
  66. J.D. Olden, D.A. Jackson, Illuminating the “Black Box”: A Randomization Approach For Understanding Variable Contributions in Artificial Neural Networks. Ecological Modelling, 154, 135–150 (2002). [Google Scholar]
  67. M. Smith, Neural networks for statistical modeling (Van Nostrand Reinhold, New York, 1993). [Google Scholar]
  68. J.-G. Lee, S. Jun, Y.-W. Cho, H. Lee, G.B. Kim, J.B. Seo, N. Kim, Deep Learning in Medical Imaging: General Overview. Korean journal of radiology. 18(4):570–584(2017). [Google Scholar]
  69. K. Suzuki, Overview of deep learning in medical imaging. Radiological physics and technology. 10(3): 257–273(2017). [Google Scholar]
  70. S. Eckart, C. Penke, S. Voss, H. Krause, Laminar burning velocities of low calorific and hydrogen containing fuel blends. Energy Procedia, 120, 149–156 (2017). [Google Scholar]
  71. I. Antonopoulos, V. Robu, B. Couraud, D. Kirli, S. Norbu, A. Kiprakis, D. Flynn, S. Elizondo-Gonzalez, S. Wattam, Artificial intelligence and machine learning approaches to energy demand-side response: A systematic review. Renewable and Sustainable Energy Reviews. 130: 109899(2020). [Google Scholar]
  72. F. Kujur, S. Singh, Emotions as a predictor for consumer engagement in YouTube advertisement. JAMR, 15(2): 184–197(2018). [Google Scholar]
  73. C. Yoo, K.C. Cha, S.-H. Kim, A quantile regression approach to gaining insights for reacquisition of defected customers. Journal of Business Research. 120, 443–452 (2020). [Google Scholar]
  74. J.M. Wandeto, B. Dresp-Langley, The quantization error in a Self-Organizing Map as contrast and color-specific indicator of single-pixel change in large random patterns. Neural networks: the official journal of the International Neural Network Society. 119, 273–285 (2019). [Google Scholar]
  75. A. Delbimbo, L. Landi, S. Santini Three-dimensional planar-faced object classification with Kohonen maps. Opt. Eng. 32(6): 1222–1234(1993). [Google Scholar]
  76. H. Jangid, S. Jain, B. Teka, R. Raja, A. Dutta, Kinematics-based end-effector path control of a mobile manipulator system on an uneven terrain using a two-stage Support Vector Machine. Robotica. 38(8): 1415–1433(2020). [Google Scholar]
  77. S. Kuramoto, H. Sawada, P. Hartono, Visualization of the topographical internal representation of learning robots. International Joint Conference on Neural Networks: 1–7 (2020). [Google Scholar]
  78. L. Leinonen, T. Hiltunen, K. Torkkola, J. Kangas Self-organized acoustic feature map in the detection of fricative-vowel coarticulation. The Journal of the Acoustical Society of America. 93(6): 3468–3474(1993). [Google Scholar]
  79. J.A. Walter, K.I. Schulten, Implementation of self-organizing neural networks for visuomotor control of an industrial robot. IEEE transactions on neural networks. 4(1): 86–96(1993). [Google Scholar]
  80. Y. Wei, X. Zhang, Y. Shi, L. Xia, S. Pan, J. Wu, M. Han, X. Zhao, A review of data-driven approaches for prediction and classification of building energy consumption. Renewable and Sustainable Energy Reviews, 82, 1027–1047 (2018). [Google Scholar]
  81. G.W. Milligan, M.C. Cooper. A study of standardization of variables in cluster analysis. Journal of Classification. 5(2): 181–204(1988). [Google Scholar]
  82. P. Mangiameli, S.K. Chen, D. West. A comparison of SOM neural network and hierarchical clustering methods. European Journal of Operational Research. 93(2): 402–417(1996). [Google Scholar]
  83. N.G. Waller, H.A. Kaiser, J.B. Illian, M. Manry. A comparison of the classification capabilities of the 1-dimensional Kohonen neural network with two partitioning and three hierarchical cluster analysis algorithms. Psychometrika, 63(1): 5–22(1998). [Google Scholar]
  84. F. Bacao. V. Lobo, M. Painho, Self-organizing Maps as Substitutes for K-Means Clustering. Computational Science-ICCS, 476–483 (2005). [Google Scholar]
  85. Q.F. Magoule, Data Mining and Machine Learning in Building Energy Analysis. Towards High-Performance Computing. (Wiley-ISTE, UNITED STATES, 2014). [Google Scholar]
  86. I.P. Panapakidis, T.A. Papadopoulos, G.C. Christoforidis, G.K. Papagiannis, Pattern recognition algorithms for electricity load curve analysis of buildings, Energy and Buildings, 73, 137–145 (2014). [Google Scholar]

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