Dr. Reza Ebrahimpour

Professor
Shahid Rajaee Teacher Training University, Iran

Highest Degree
Ph.D. in Cognitive Neurosciences from IPM School of Cognitive Sciences, Iran

My URL
https://livedna.org/98.30505

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Area of Interest:

Computer Sciences
Computational Biology
Neurocomputing
Cognitive computing
Electrical Engineering

Selected Publications

  1. Tohidi-Moghaddam, M., S. Zabbah, F. Olianezhad and R. Ebrahimpour, 2019. Sequence-dependent sensitivity explains the accuracy of decisions when cues are separated with a gap. Attent. Percept. Psychophys., 10.3758%2Fs13414-019-01810-8.
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  2. Shooshtari, S.V., J.E. Sadrabadi, Z. Azizi and R. Ebrahimpour, 2019. Confidence representation of perceptual decision by EEG and eye data in a random dot motion task. Neuroscience, 406: 510-527.
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  3. Rajaei, K., Y. Mohsenzadeh, R. Ebrahimpour and S.M. Khaligh-Razavi, 2019. Beyond core object recognition: Recurrent processes account for object recognition under occlusion. Plos Comput. Biol., Vol. 15. 10.1371/journal.pcbi.1007001.
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  4. Olianezhad, F., S. Zabbah, M. Tohidi-Moghaddam and R. Ebrahimpour, 2019. Residual information of previous decision affects evidence accumulation in current decision. Front. Behav. Neurosci., Vol. 13. 10.3389/fnbeh.2019.00009.
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  5. Karimi-Rouzbahani, H., E. Vahab, R. Ebrahimpour and M.B. Menhaj, 2019. Spatiotemporal analysis of category and target-related information processing in the brain during object detection. Behav. Brain Res., 362: 224-239.
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  6. Arani, S.A.A.A., E. Kabir and R. Ebrahimpour, 2019. Handwritten Farsi word recognition using NN-based fusion of HMM classifiers with different types of features. Int. J. Image Grap., Vol. 19. 10.1142/S0219467819500013.
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  7. Rajaei, K., Y. Mohsenzadeh, R. Ebrahimpour and S.M.K. Razavi, 2018. The essential role of recurrent processing during object recognition under occlusion. J. Vision, 18: 906-906.

  8. Fallah, F. and R. Ebrahimpour, 2018. The time course of visual processing on different levels of object categorization with the same stimulus: A behavioral study. Neurosci. J. Shefaye Khatam, 6: 41-50.
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  9. Ebrahimpour, R., P.P. Ardakani and M.T. Moghadam, 2018. The role of lighting, window views and indoor plants on stress reduction of offices' staffs by psychophysics method. Iran Occupat. Health, 14: 135-147.
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  10. Arani, S.A.A.A., E. Kabir and R. Ebrahimpour, 2018. Combining RtL and LtR HMMs to recognise handwritten Farsi words of small-and medium-sized vocabularies. IET Comput. Vision, 12: 925-932.
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  11. Moini, S., B. Alizadeh, M. Emad and R. Ebrahimpour, 2017. A resource-limited hardware accelerator for convolutional neural networks in embedded vision applications. IEEE Trans. Circuits Syst. II: Express Briefs, 64: 1217-1221.
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  12. Karimi-Rouzbahani, H., N. Bagheri and R. Ebrahimpour, 2017. Invariant object recognition is a personalized selection of invariant features in humans, not simply explained by hierarchical feed-forward vision models. Scient. Rep., Vol. 7. 10.1038/s41598-017-13756-8.
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  13. Karimi-Rouzbahani, H., N. Bagheri and R. Ebrahimpour, 2017. Hard-wired feed-forward visual mechanisms of the brain compensate for affine variations in object recognition. Neuroscience, 349: 48-63.
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  14. Karimi-Rouzbahani, H., N. Bagheri and R. Ebrahimpour, 2017. Average activity, but not variability, is the dominant factor in the representation of object categories in the brain. Neuroscience, 346: 14-28.
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  15. Abbasi, F., R. Ebrahimpour and K. Rajaei, 2017. Computational modeling of figure- ground segregation in object recognition inspired by human visual system. Mach. Vision Image Process., 4: 1-16.
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  16. Olianezhad, F., S. Zabbah and R. Ebrahimpour, 2016. The influence of past decision information on decision making in the present. J. Shefaye Khatam, 4: 1-8.

  17. Farzmahdi, A., K. Rajaei, M. Ghodrati, R. Ebrahimpour and S.M. Khaligh-Razavi, 2016. A specialized face-processing network consistent with the representational geometry of monkey face patches. Scient. Rep., 6: 1-17.

  18. Emamjomeh, A., B. Goliaei, J. Zahiri and R. Ebrahimpour, 2015. Prediction of gene co-expression by quantifying heterogeneous features. Curr. Bioinfor., 10: 414-424.
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  19. Zahiri, J., M. Mohammad-Noori, R. Ebrahimpour, S. Saadat, J.H. Bozorgmehr, T. Goldberg and A. Masoudi-Nejad, 2014. LocFuse: Human protein-protein interaction prediction via classifier fusion using protein localization information. Genomics, 104: 496-503.
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  20. Zabbah, S., K. Rajaei, A. Mirzaei, R. Ebrahimpour and S.M. Khaligh-Razavi, 2014. The impact of the lateral geniculate nucleus and corticogeniculate interactions on efficient coding and higher-order visual object processing. Vision Res., 101: 82-93.
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  21. Pishgoo, M.R., N. Avanaki, R. Mohammad and R. Ebrahimpour, 2014. The application of multi-layer artificial neural networks in speckle reduction (methodology). J. Elect. Comput. Eng. Innovat., 2: 37-42.
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  22. Olyaee, S., R. Ebrahimpour and S. Esfandeh, 2014. Modeling of measurement error in refractive index determination of fuel cell using neural network and genetic algorithm. Iran. J. Hydrogen Fuel Cell, 1: 95-104.

  23. Masoudnia, S. and R. Ebrahimpour, 2014. Mixture of experts: A literature survey. Artif. Intelli. Rev., 42: 275-293.
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  24. Kheradpisheh, S.R., F. Sharifizadeh, A. Nowzari-Dalini, M. Ganjtabesh and R. Ebrahimpour, 2014. Mixture of feature specified experts. Infor. Fusion, 20: 242-251.
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  25. Kheradpisheh, S.R., A. Nowzari-Dalini, R. Ebrahimpour and M. Ganjtabesh, 2014. An evidence-based combining classifier for brain signal analysis. Plos One, Vol. 9. 10.1371/journal.pone.0084341.
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  26. Gholami-Broujeni, F., R. Ebrahimpour and A.H. Amirifard, 2014. Using artificial neural networks to estimate the return sludge rate: A case study of Torbat Heydarieh. Wastewater Treatment Plant, 25: 99-107.

  27. Ghodrati, M., K. Rajaei and R. Ebrahimpour, 2014. The importance of visual features in generic vs. specialized object recognition: A computational study. Front. Comput. Neurosci., Vol. 8. 10.3389/fncom.2014.00078.
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  28. Ghodrati, M., A. Farzmahdi, K. Rajaei, R. Ebrahimpour and S.M. Khaligh-Razavi, 2014. Feedforward object-vision models only tolerate small image variations compared to human. Front. Comput. Neurosci., Vol. 8. 10.3389/fncom.2014.00074.
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  29. Fateh, D., A.A.M. Birjandi and R. Ebrahimpour, 2014. Enhancement of damping of power system oscillations through UPFC placement based on residue factor and critical modes. Tabriz J. Elect. Eng., 44: 23-31.

  30. Emamjomeh, A., B. Goliaei, J. Zahiri and R. Ebrahimpour, 2014. Predicting protein-protein interactions between human and hepatitis C virus via an ensemble learning method. Mol. Biosyst., 10: 3147-3154.
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  31. Mirzaei, A., S.M. Khaligh-Razavi, M. Ghodrati, S. Zabbah and R. Ebrahimpour, 2013. Predicting the human reaction time based on natural image statistics in a rapid categorization task. Vision Res., 81: 36-44.
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  32. Mirnaziri, M., M. Rahimi, S. Alavikakhaki and R. Ebrahimpour, 2013. Using combination of µ, β and γ bands in classification of eeg signals. Basic Clin. Neurosci., 4: 76-87.
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  33. Kheradpisheh, S.R., F. Behjati-Ardakani and R. Ebrahimpour, 2013. Combining classifiers using nearest decision prototypes. Applied Soft Comput., 13: 4570-4578.
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  34. Javadi, M., S.A.A.A. Arani, A. Sajedin and R. Ebrahimpour, 2013. Classification of ECG arrhythmia by a modular neural network based on mixture of experts and negatively correlated learning. Biomed. Signal Process. Control, 8: 289-296.
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  35. Ebrahimpour, R., S.A.A.A. Arani and S. Masoudnia, 2013. Improving combination method of NCL experts using gating network. Neural Comput. Applic., 22: 95-101.
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  36. Ebrahimpour, R., N. Sadeghnejad, A. Sajedin and N. Mohammadi, 2013. Electrocardiogram beat classification via coupled boosting by filtering and preloaded mixture of experts. Neural Comput. Applic., 23: 1169-1178.
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  37. Bagheri, N., R. Ebrahimpour and N. Ghaedi, 2013. New differential fault analysis on PRESENT. EURASIP J. Adv. Signal Process., Vol. 2013. 10.1186/1687-6180-2013-145.
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  38. Bagheri, N., R. Ebrahimpour and N. Ghaedi, 2013. Differential fault analysis on PRINTcipher. IET Networks, 2: 30-36.
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  39. Ahangi, A., M. Karamnejad, N. Mohammadi, R. Ebrahimpour and N. Bagheri, 2013. Multiple classifier system for EEG signal classification with application to brain-computer interfaces. Neural Comput. Applic., 23: 1319-1327.
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  40. Tabassian, M., R. Ghaderi and R. Ebrahimpour, 2012. Combining complementary information sources in the Dempster-Shafer framework for solving classification problems with imperfect labels. Knowledge-Based Syst., 27: 92-102.
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  41. Tabassian, M., R. Ghaderi and R. Ebrahimpour, 2012. Combination of multiple diverse classifiers using belief functions for handling data with imperfect labels. Expert Syst. Applic., 39: 1698-1707.
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  42. Rajaei, K., S.M. Khaligh-Razavi, M. Ghodrati, R. Ebrahimpour and M.E.S.A. Abadi, 2012. A stable biologically motivated learning mechanism for visual feature extraction to handle facial categorization. Plos One, Vol. 7. 10.1371/journal.pone.0038478.
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  43. Masoudnia, S., R. Ebrahimpour and S.A.A.A. Arani, 2012. Incorporation of a regularization term to control negative correlation in mixture of experts. Neural Process. Lett., 36: 31-47.
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  44. Masoudnia, S., R. Ebrahimpour and S.A.A.A. Arani, 2012. Combining features of negative correlation learning with mixture of experts in proposed ensemble methods. Applied Soft Comput., 12: 3539-3551.
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  45. Masoudnia, S., M.J. Abdi and R. Ebrahimpour, 2012. Improving mixture of experts using second order optimization. Int. J. Applied Res. Infor. Technol. Comput., 3: 122-131.
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  46. Javidi, M.M., F. Sharifizadeh and R. Ebrahimpour, 2012. Intelligent contribution to direct experts in mixture of experts for persian handwritten digits recognition. Sci. Ser. Data Rep., 4: 108-124.

  47. Homaeinezhad, M.R., S.A. Atyabi, E. Tavakkoli, H.N. Toosi, A. Ghaffari and R. Ebrahimpour, 2012. ECG arrhythmia recognition via a neuro-SVM-KNN hybrid classifier with virtual QRS image-based geometrical features. Expert Syst. Applic., 39: 2047-2058.
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  48. Ghodrati, M., S.M. Khaligh-Razavi, R. Ebrahimpour, K. Rajaei and M. Pooyan, 2012. How can selection of biologically inspired features improve the performance of a robust object recognition model? Plos One, Vol. 7. 10.1371/journal.pone.0032357.
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  49. Ebrahimpour, R., R. Rasoolinezhad, Z. Hajiabolhasani and M. Ebrahimi, 2012. Vanishing point detection in corridors: Using Hough transform and K-means clustering. IET Comput. Vision, 6: 40-51.
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  50. Ebrahimpour, R., N. Sadeghnejad, S. Masoudnia and S.A.A.A. Arani, 2012. Boosted pre-loaded mixture of experts for low-resolution face recognition. Int. J. Hybrid Intelli. Syst., 9: 145-158.
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  51. Ebrahimpour, R., K. Babakhan, S.A.A.A. Arani and S. Masoudnia, 2012. Epileptic seizure detection using a neural network ensemble method and wavelet transform. Neural Network World, 22: 291-310.
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  52. Ebrahimpour, R., A. Sajedin, S.Z. Moussavi and A.G. Farizhendi, 2012. A bio-inspired computational neural model for illustration face and car expertise effect on the gateway to the right FFA. Int. J. Innov. Comput. Infor. Control, 8: 3451-3466.
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  53. Tabassian, M., R. Ghaderi and R. Ebrahimpour, 2011. Subspace ensembles for face recognition. Aust. J. Intelli. Infor. Process. Syst., 12: 19-24.

  54. Tabassian, M., R. Ghaderi and R. Ebrahimpour, 2011. Knitted fabric defect classification for uncertain labels based on Dempster-Shafer theory of evidence. Expert Syst. Applic., 38: 5259-5267.
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  55. Tabassian, M., R. Ghaderi and R. Ebrahimpour, 2011. Handling classification problems with imperfect labels using an evidence-based neural network ensemble. Int. J. Innovat. Comput. Infor. Control, 7: 7051-7066.
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  56. Javidi, M.M., R. Ebrahimpour and F. Sharifizadeh, 2011. Persian handwritten digits recognition: A divide and conquer approach based on mixture of MLP experts. Int. J. Phys. Sci., 6: 7007-7015.
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  57. Javadi, M., R. Ebrahimpour, A. Sajedin, S. Faridi and S. Zakernejad, 2011. Improving ECG classification accuracy using an ensemble of neural network modules. Plos One, Vol. 6. 10.1371/journal.pone.0024386.
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  58. Homaeinezhad, M.R., E. Tavakkoli, S.A. Atyabi, A. Ghaffari and R. Ebrahimpour, 2011. Synthesis of multiple-type classification algorithms for robust heart rhythm type recognition: Neuro-svm-pnn learning machine with virtual QRS image-based geometrical features. Sci. Iran., 18: 423-431.
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  59. Hatami, N., H. Nirzazadeh and R. Ebrahimpour, 2011. Combine neural network for stock price prediction. J. Econ. Sci., 10: 61-80.
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  60. Farhoudi, Z., S. Faridi, R. Ebrahimpour and S. Setayeshi, 2011. Teacher-directed learning in view-independent face detection with mixture of experts using cortex-like features. Aust. J. Intelli. Infor. Process. Syst., 12: 19-24.
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  61. Ebrahinpour, R., M. Amini and F. Sharifizadehi, 2011. Farsi handwritten recognition using combining neural networks based on stacked generalization. Int. J. Elect. Eng. Infor., 3: 146-164.
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  62. Ebrahimpour, R., S. Sarhangi, M. Javadi and F. Sharifizadeh, 2011. Handwritten word recognition by multiple classifiers: A divide-and-conquer approach. Int. J. Applied Res. Infor. Technol. Comput., 2: 9-21.
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  63. Ebrahimpour, R., S. Sarhangi and F. Sharifizadeh, 2011. Mixture of experts for Persian handwritten word recognition. Iran. J. Elect. Elect. Eng., 7: 217-224.
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  64. Ebrahimpour, R., R.D. Vahid and B.M. Nezhad, 2011. Decision templates with gradient based features for Farsi handwritten word recognition. Int. J. Hybrid Inf. Technol., 4: 1-12.
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  65. Ebrahimpour, R., M. Nazari, M. Azizi and A. Amiri, 2011. Single training sample face recognition using fusion of classifiers. Int. J. Hybrid Infor. Technol., 4: 25-32.
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  66. Ebrahimpour, R., H. Nikoo, S. Masoudnia and M.R. Yousefi, 2011. Mixture of MLP-experts for trend forecasting of time series: A case study of the Tehran stock exchange. Int. J. Forecast., 27: 804-816.
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  67. Ebrahimpour, R., F. Izadi and S. Bahiraei, 2011. Global-based eigenspaces with mixture of experts for view-independent face recognition using different representations. Int. J. Applied Res. Infor. Technol. Comput., 2: 1-13.
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  68. Ebrahimpour, R., E. Kabir and M.R. Yousefi, 2011. Improving mixture of experts for view-independent face recognition using teacher-directed learning. Mach. Vision Applic., 22: 421-432.
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  69. Ebrahimpour, R., A. Jahani, A. Amiri and M. Nazari, 2011. Expression-independent face recognition using biologically inspired features. Indian J. Comput. Sci. Eng., 2: 492-499.
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  70. Tabassian, M., R. Ghaderi and R. Ebrahimpour, 2010. Combining Neural Networks Based on Dempster-Shafer Theory for Classifying Data with Imperfect Labels. In: Mexican International Conference on Artificial Intelligence, Sidorov, G., A.H. Aguirre and C.A.R. Garcia (Eds.)., Springer, Berlin, Heidelberg, pp: 233-244.

  71. Shams, A.V., S. Faridi, S.M.K. Razavi, R. Ebrahimpour and M. Javadi, 2010. Expression invariant face recognition using biologically inspired features from visual cortex mechanism. Aust. J. Intelli. Infor. Process. Syst., 12: 25-29.

  72. Shahrabadi, A., R. Ebrahimpour and H. Nikoo, 2010. Using a modified trainable neural network ensemble for trend prediction of Tehran Stock Exchange (Case study: Kharg petrochemical company). J. Manage. Res., 21: 159-174, (In Persian).
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  73. Rasoolinezhad, R., R. Ebrahimpour and M.B. Menhaj, 2010. Clustering based world modelling in highways. Int. Rev. Model. Simul., 3: 997-1001.

  74. Rasoolinezhad, R., M. Ebrahimi and R. Ebrahimpour, 2010. Monocular mapless navigation in streets and highways. Int. Rev. Automat. Control, 3: 545-549.

  75. Olyaee, S., R. Ebrahimpour and S. Hamedi, 2010. Modeling and compensation of periodic nonlinearity in two-mode interferometer using neural networks. IETE J. Res., 56: 102-110.
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  76. Olyaee, S., M.S.E. Abadi, R. Ebrahimpour and M.R. Moradian, 2010. A comparative study of two blind source separation approaches to resolve the multi-source limitation of the nutating rising-sun reticle based optical Trackers. Int. J. Comput. Electrical Electron. Eng., 2: 283-291.
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  77. Niknafs, S., R. Ebrahimpour and S. Amiri, 2010. Combined neural network for power peak factor estimation. Aust. J. Basic Applied Sci., 4: 3404-3410.

  78. Moussavi, S.Z., K. Zarei and R. Ebrahimpour, 2010. Improving Classification Performance with Focus on the Complex Areas. In: International Conference on Current Trends in Theory and Practice of Computer Science, Van Leeuwen, J., A. Muscholl, D. Peleg, J. Pokorny and B. Rumpe (Eds.)., Springer, Berlin, Heidelberg, pp: 612-626.

  79. Makhsoos, N.T., R. Ebrahimpour and A. Hajiany, 2010. Teacher-directed learning based on image-contrast for face recognition with mixture of experts. Int. J. Comput. Sci. Eng. Syst., 4: 1-13.

  80. Ebrahimpour, R., E.K. Abharian, S.Z. Moussavi and A.A.M. Birjandi, 2010. Transient stability assessment of a power system by mixture of experts. Int. J. Eng., 4: 93-104.
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  81. Ebrahimpour, R., E.K. Abharian, A.A.M. Birjandi and S.Z. Moussavi, 2010. Transient stability assessment of a powersystem with a UPFC by mixture of experts. Int. J. Comput. Elect. Eng., 2: 643-648.
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  82. Ebrahimpour, R., A. Esmkhani and S. Faridi, 2010. Farsi handwritten digit recognition based on mixture of RBF experts. IEICE. Electron. Express, 7: 1014-1019.
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  83. Ebrahimpour, R., A. Amiri, M. Nazari and A. Hajiany, 2010. Robust model for signature recognition based on biological inspired features. Int. J. Comput. Elect. Eng., 2: 622-626.
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  84. Ebrahimpour, R. and F.M. Jafarlou, 2010. View-independent face recognition with hierarchical mixture of experts using global eigenspaces. J. Commun. Comput., 7: 39-45.
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  85. Abbasi, R., T.K. Rasoulzade, E. Kabir, R. Ebrahimpour and Z. Abbasi, 2010. Prediction of marital satisfaction based on personality factors: Comparison of artificial neural network and regression method. J. Behav. Sci., 4: 171-175.
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  86. Makhsoos, N.T., R. Ebrahimpour and A. Hajiany, 2009. Face recognition based on neuro-fuzzy system. Int. J. Comput. Sci. Network Secur., 9: 319-326.

  87. Hamedi, S., S. Olyaee and R. Ebrahimpour, 2009. Modeling of nonlinearity in nanometrology interferometric system using neural networks ensemble. Proceedings of the 1st National Conference on Optics and Laser Engineering, May 20-21, 2009, Isfahan, Iran, pp: 262-267.

  88. Ebrahimpour, R., N.T. Makhsoos and A. Hajiany, 2009. Face recognition by combining neural network based on mixture of experts. J. Technol. Edu., 3: 125-136.
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  89. Ebrahimpour, R., M.R. Moradian, A. Esmkhani and F.M. Jafarlou, 2009. Recognition of Persian handwritten digits using characterization Loci and mixture of experts. Int. J. Digital Content Technol. Applic., 3: 42-46.
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  90. Ebrahimpour, R. and S. Hamedi, 2009. Hand written digit recognition by multiple classifier fusion based on decision templates approach. World Acad. Sci. Eng. Technol., 57: 560-565.

  91. Ebrahimpour, R. and H. Sarmadi, 2009. A new approach to combine classifiers trained by NCL. Int. J. Digital Content Technol. Applic., 3: 80-84.

  92. Ebrahimpour, R. and E.K. Abharian, 2009. An improved method in transient stability assessment of a power system using committee neural networks. Int. J. Comput. Sci. Network Security, 9: 119-124.

  93. Avanaki, M.R., H. Ahmadinejad, R. Ebrahimpour and A.N. Avanaki, 2009. Evaluation of pure-fractal and wavelet-fractal compression techniques. ICGST-GVIP J., 9: 41-47.

  94. Ebrahimpour, R., E. Kabir, H. Esteky and M.R. Yousefi, 2008. View-independent face recognition with mixture of experts. Neurocomputing, 71: 1103-1107.
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  95. Ebrahimpour, R., E. Kabir and M.R. Yousefi, 2008. Teacher-directed learning in view-independent face recognition with mixture of experts using single-view eigenspaces. J. Franklin Inst., 345: 87-101.
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