Classification of Different Floral Origin of Honey Using Hybrid Model of Particle Swarm Optimization and Artificial Neural Network

[1]  Riccardo Poli,et al.  Particle swarm optimization , 1995, Swarm Intelligence.

[2]  Elke Anklam,et al.  A review of the analytical methods to determine the geographical and botanical origin of honey , 1998 .

[3]  Danial Jahed Armaghani,et al.  Prediction of uniaxial compressive strength of rock samples using hybrid particle swarm optimization-based artificial neural networks , 2015 .

[4]  Russell C. Eberhart,et al.  A new optimizer using particle swarm theory , 1995, MHS'95. Proceedings of the Sixth International Symposium on Micro Machine and Human Science.

[5]  Edy Tonnizam Mohamad,et al.  Prediction of the unconfined compressive strength of soft rocks: a PSO-based ANN approach , 2015, Bulletin of Engineering Geology and the Environment.

[6]  Zbigniew Brzozka,et al.  Classification of beverages using a reduced sensor array , 2004 .

[7]  Jitendra P. Dave.  Overview of the artificial neural networks and fuzzy logic applications in operational hydrological forecasting systems , 2009 .

[8]  Louis S. Santiago,et al.  A review of volatile analytical methods for determining the botanical origin of honey , 2007 .

[9]  B. Tudu,et al.  Discrimination of monofloral honey using cyclic voltammetry , 2012, 2012 3rd National Conference on Emerging Trends and Applications in Computer Science.

[10]  L. Leija,et al.  Application of the wavelet transform coupled with artificial neural networks for quantification purposes in a voltammetric electronic tongue , 2006 .

[11]  Rafaela Cáceres,et al.  Application of a potentiometric electronic tongue to fertigation strategy in greenhouse cultivation , 2007 .

[12]  D. W. Ball.  The Chemical Composition of Honey. , 2007 .

[13]  A. M. S. Tekanyi,et al.  A Hybrid Model of PSO Algorithm and Artificial Neural Network for Automatic Follicle Classification , 2017 .