Ahmed Al-Ani,Mostefa Mesbah

View Full Paper


  1. [1] D.L. Fisher, M. Rizzo, J.K. Caird, and J.D. Lee, Editors,Driving Simulation for Engineering, Medicine, and Psychol-ogy, CRC Press, Boca Raton, FL, 2011.
  2. [2] A. Varri, K. Hirvonen, J. Hasan, P. Loula, and V. Haikkinen.A computerized analysis system for vigilance studies. Com-puter Methods and Programs in Biomedicine, 39, 1992, 113-124.
  3. [3] M. Nakamura, T. Sugi, A. Ikeda, R. Kakigi, and H. Shibasaki.Clinical application of automatic integrative interpretation ofawake background. EEG: quantitative interpretation, reportmaking, and detection of artifacts and reduced vigilance level.Electroencephalography and clinical Neurophysiology, 98,1996, 103-112.
  4. [4] M.K. Kiymik, M. Akin, and A. Subasi. Automatic recogni-tion of alertness level by using wavelet transform and artificialneural network. Journal of Neuroscience Methods, 139, 2004,231-240.
  5. [5] M.B. Kurt, N. Sezgin, M. Akin, G. Kirbas, and M.Bayram.The ANN-based computing of drowsy level. ExpertSystems with Applications, 36, 2009, 2534-2542.
  6. [6] M.V.M. Yeo, X. Li, K. Shen, and E.P.V. Wilder-Smith. CanSVM be used for automatic EEG detection of drowsiness dur-ing car driving? Safety Science, 47, 2009, 115-124.
  7. [7] E. Vural, M. etin, A. Eril, G. Littlewort, M. Bartlett and J.Movellan, Drowsy driver detection through facial movementanalysis. Proceedings of the 2007 IEEE International Work-shop on Human-computer Interaction, Rio de Janeiro, Brazil,2007.
  8. [8] K.-A. Hwang and C.-H.Yang, Attentiveness assessment inlearning based on fuzzy logic analysis.Expert Systems with Ap-plications, 36, 2009, 6261-6265.
  9. [9] A. Al-Ani, B. Van Dun, H. Dillon, A, Rabie, Analysis ofAlertness Status of Subjects Undergoing The Cortical Audi-tory Evoked Potential Hearing Test, International Conferenceon Neural Information Processing, ICONIP 2012, 2012, 92-99.
  10. [10] A. Al-Ani, M. Mesbah, B. Van Dun, and H. Dillon, FuzzyLogic-Based Automatic Alertness State Classification UsingMulti-channel EEG Data, International Conference on NeuralInformation Processing, ICONIP 2013 2013, 176-183.
  11. [11] A. Al-Ani, A. Alsukker and R.N. Khushaba, Feature subsetselection using differential evolution and a wheel based searchstrategy, Swarm and Evolutionary Computation, 9, 2013, 15-26.
  12. [12] E. Avci, D. Avci, The speaker identification by using ge-netic wavelet adaptive network based fuzzy inference system.Expert Systems with Applications, 36(6), 2009, 9928-9940.
  13. [13] H. Yan, Z. Zou, and H. Wang, Adaptive neuro fuzzy infer-ence system for classification of water quality status, Journalof Environmental Sciences, 22(12), 2010, 1891-1896.
  14. [14] H. Iyatomi and M. Hagiwara, Adaptive fuzzy inference neu-ral network, Pattern Recognition, 37, 2004, 2049-2057.
  15. [15] A. Gonzlez and R. Prez, SLAVE: a genetic learning systembased on an iterative approach, IEEE Transactions on FuzzySystems, 7, 1999, 176-191.152
  16. [16] H. Ishibuchi, T. Yamamoto and T. Nakashima, IEEE Trans-actions on Systems, Man, and Cybernetics-Part B: Cybernet-ics, 35(2), 2005, 359-365.
  17. [17] K.V. Price, R.M. Storn, J.A. Lampinen, Differential Evolu-tion: A Practical Approach to Global Optimization, Springer,2005.
  18. [18] I. Guyon, S. Gunn, M. Nikravesh and L.A. Zadeh FeatureExtraction: Foundations and Applications, Springer-VerlagNew York, 2006.
  19. [19] F.H.L. Da Silva and A. Van Rotterdam, Biophysical As-pects of EEG and Magnetoencephalogram Generation, in D.L.Schomer and F.H.L. Da Silva, Eds., Niedermeyer’s Electroen-cephalography: Basic Principles, Clinical Applications, andRelated Fields, 6th Edition, Lippincott Williams & Wilkins2011

Important Links:

Go Back