N. Abdullah, S. A. Aziz, and U. K. Ngah, “Image classification of brain MRI using support vector machine,” in IEEE International Conference on Imaging Systems and Techniques (IEEE, 2011), pp. 242–247.

A. Kharrat, K. Gasmi, M. B. Messaoud, M. Abid, and N. Benamrane, “A hybrid approach for automatic classification of brain MRI using genetic algorithm and support vector machine,” Leonardo J. Sci. 9, 71–82 (2010).

A. Agrawal and G. B. Praveen, “Hybrid approach for brain tumor detection and classification in magnetic resonance images,” in IEEE International Conference on Communication, Control and Intelligent Systems (IEEE, 2015), pp. 162–166.

A. Al-Badarneh, A. M. Alraziqi, and H. Najadat, “A classifier to detect tumor disease in MRI brain images,” in IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (IEEE, 2012), pp. 784–787.

M. H. Sulaiman, Z. Mustaffa, O. Aliman, and M. R. Mohamed, “Using the gray wolf optimizer for solving optimal reactive power dispatch problem,” Appl. Soft Comput. 32, 286–292 (2015).

[Crossref]

A. Al-Badarneh, A. M. Alraziqi, and H. Najadat, “A classifier to detect tumor disease in MRI brain images,” in IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (IEEE, 2012), pp. 784–787.

C. Arizmendi, E. Romero, and A. Vellido, “Binary classification of brain tumours using a discrete wavelet transform and energy criteria,” in IEEE Second Latin American Symposium on Circuits and Systems (LASCAS) (IEEE, 2011).

N. Abdullah, S. A. Aziz, and U. K. Ngah, “Image classification of brain MRI using support vector machine,” in IEEE International Conference on Imaging Systems and Techniques (IEEE, 2011), pp. 242–247.

A. Kharrat, K. Gasmi, M. B. Messaoud, M. Abid, and N. Benamrane, “A hybrid approach for automatic classification of brain MRI using genetic algorithm and support vector machine,” Leonardo J. Sci. 9, 71–82 (2010).

S. A. Medjahed, T. A. Saadi, M. Ouali, and A. Benyettou, “Gray wolf optimizer for hyperspectral band selection,” Appl. Soft Comput. 40, 178–186 (2016).

[Crossref]

H. P. Schwefel and H. G. Beyer, “Evolution strategies—a comprehensive introduction,” Nat. Comput. 1, 3–52 (2002).

[Crossref]

M. Dorigo, T. Stutzle, and M. Birattari, “Ant colony optimization,” IEEE Comput. Intell. Mag. 1, 28–39 (2006).

[Crossref]

D. Whitley, C. Bogart, and T. Starkweather, “Genetic algorithms and neural networks: optimizing connections and connectivity,” Parallel Comput. 14, 347–361 (1990).

[Crossref]

M. Boukadoum and S. Lahmiri, “Classification of brain MRI using the LH and HL wavelet transform sub-bands,” in IEEE International Symposium of Circuits and Systems (IEEE, 2011), pp. 1025–1028.

M. Boukadoum and S. Lahmiri, “Comparison of ANFIS and SVM for the classification of brain MRI pathologies,” in IEEE International Midwest Symposium on Circuits and Systems (IEEE, 2011).

S. Chaplot, N. R. Jagannathan, and L. M. Patnaik, “Classification of magnetic resonance brain images using wavelets as input to support vector machine and neural network,” Biomed. Signal Process. Control 1, 86–92 (2006).

[Crossref]

A. Chatterjee and M. Maitra, “Hybrid multiresolution Slantlet transform and fuzzy c-means clustering approach for normal-pathological brain MR image segregation,” Med. Eng. Phys. 30, 615–623 (2008).

[Crossref]

S. Wang, P. Li, P. Phillips, G. Liu, Y. Zhang, P. Chen, and S. Du, “Pathological brain detection via wavelet packet Tsallis entropy,” Fund. Inform. 151, 275–291 (2017).

S. H. Wang, S. Du, Y. Zhang, P. Phillips, Y. D. Zhang, X. Q. Chen, and L. N. Wu, “Alzheimer’s disease detection by pseudo Zernike moment and linear regression classification,” CNS Neurol. Disord. Drug Targets 16, 11–15 (2017).

[Crossref]

S. Mirjalili, S. Saremi, L. S. Coelho, and S. M. Mirjalili, “Multi-objective grey wolf optimizer: a novel algorithm for multi-criterion optimization,” Expert Syst. Appl. 47, 106–119 (2016).

[Crossref]

B. A. Devi and S. N. Deepa, “Artificial neural networks design for classification of brain tumour,” in IEEE International Conference on Computer Communication and Informatics (IEEE, 2012).

B. A. Devi and S. N. Deepa, “Artificial neural networks design for classification of brain tumour,” in IEEE International Conference on Computer Communication and Informatics (IEEE, 2012).

S. Goh, Y. Zhang, Z. Dong, S. DiMauro, and B. S. Peterson, “Mitochondrial dysfunction as a neurobiological subtype of autism spectrum disorder evidence from brain imaging,” JAMA Psychiatry 71, 665–671 (2014).

[Crossref]

Y. Zhang, S. Wang, J. Yang, S. Wang, Z. Dong, and P. Phillips, “Pathological brain detection in MRI scanning via Hu moment invariants and machine learning,” J. Exp. Theor. Artif. Intell. 29, 299–312 (2016).

[Crossref]

S. Goh, Y. Zhang, Z. Dong, S. DiMauro, and B. S. Peterson, “Mitochondrial dysfunction as a neurobiological subtype of autism spectrum disorder evidence from brain imaging,” JAMA Psychiatry 71, 665–671 (2014).

[Crossref]

Y. Zhang, S. Wang, Z. Dong, and G. Ji, “An MR brain images classifier system via particle swarm optimization and kernel support vector machine,” Sci. World J. 2013, 130134 (2013).

[Crossref]

M. Dorigo, T. Stutzle, and M. Birattari, “Ant colony optimization,” IEEE Comput. Intell. Mag. 1, 28–39 (2006).

[Crossref]

S. H. Wang, S. Du, Y. Zhang, P. Phillips, Y. D. Zhang, X. Q. Chen, and L. N. Wu, “Alzheimer’s disease detection by pseudo Zernike moment and linear regression classification,” CNS Neurol. Disord. Drug Targets 16, 11–15 (2017).

[Crossref]

S. Wang, P. Li, P. Phillips, G. Liu, Y. Zhang, P. Chen, and S. Du, “Pathological brain detection via wavelet packet Tsallis entropy,” Fund. Inform. 151, 275–291 (2017).

S. Wang, S. Du, M. Yang, J. Yang, B. Liu, J. M. Gorriz, J. Ramirez, T. F. Yuan, and Y. Zhang, “Wavelet entropy and directed acyclic graph support vector machine for detection of patients with unilateral hearing loss in MRI scanning,” Front. Comput. Neurosci. 10, 106 (2016).

J. Kennedy and R. Eberhart, “Particle swarm optimization,” in IEEE International Conference on Neural Networks (IEEE, 1995), pp. 1942–1948.

E. A. El-Dahshan, A. B. M. Salem, and T. Hosny, “Hybrid intelligent techniques for MRI brain images classification,” Digit. Signal Process. 20, 433–441 (2010).

[Crossref]

E. Emary, A. E. Hassanien, and H. M. Zawbaa, “Binary gray wolf optimization approaches for feature selection,” Neurocomputing 172, 371–381 (2016).

[Crossref]

E. Emary, W. Yamany, V. Snasel, and A. E. Hassanien, “Multi-objective gray-wolf optimization for attribute reduction,” Procedia Comput. Sci. 65, 623–632 (2015).

[Crossref]

A. Faro, D. Giordano, M. Pennisi, and C. Spampinato, “Statistical texture analysis of MRI images to classify patients affected by multiple sclerosis,” in XII Mediterranean Conference on Medical and Biological Engineering and Computing (2010), pp. 272–275.

A. Kharrat, K. Gasmi, M. B. Messaoud, M. Abid, and N. Benamrane, “A hybrid approach for automatic classification of brain MRI using genetic algorithm and support vector machine,” Leonardo J. Sci. 9, 71–82 (2010).

A. Faro, D. Giordano, M. Pennisi, and C. Spampinato, “Statistical texture analysis of MRI images to classify patients affected by multiple sclerosis,” in XII Mediterranean Conference on Medical and Biological Engineering and Computing (2010), pp. 272–275.

S. Goh, Y. Zhang, Z. Dong, S. DiMauro, and B. S. Peterson, “Mitochondrial dysfunction as a neurobiological subtype of autism spectrum disorder evidence from brain imaging,” JAMA Psychiatry 71, 665–671 (2014).

[Crossref]

S. Wang, S. Du, M. Yang, J. Yang, B. Liu, J. M. Gorriz, J. Ramirez, T. F. Yuan, and Y. Zhang, “Wavelet entropy and directed acyclic graph support vector machine for detection of patients with unilateral hearing loss in MRI scanning,” Front. Comput. Neurosci. 10, 106 (2016).

L. Li, L. Sun, J. Guo, J. Qi, S. Li, and B. Xu, “Modified discrete grey wolf optimizer algorithm for multilevel image thresholding,” in Computational Intelligence and Neuroscience (2017).

M. Gupta, V. Rajagopalan, and B. V. V. S. N. P. Rao, “Brain tumor detection in conventional MR images based on statistical texture and morphological features,” in IEEE International Conference on Information Technology (IEEE, 2016), pp. 129–133.

E. Emary, A. E. Hassanien, and H. M. Zawbaa, “Binary gray wolf optimization approaches for feature selection,” Neurocomputing 172, 371–381 (2016).

[Crossref]

E. Emary, W. Yamany, V. Snasel, and A. E. Hassanien, “Multi-objective gray-wolf optimization for attribute reduction,” Procedia Comput. Sci. 65, 623–632 (2015).

[Crossref]

P. S. Hiremath, J. Pujari, and S. Shivashankar, “Wavelet based features for color texture classification with application to CBIR,” IJCSNS Int. J. Comput. Sci. Netw. Security 6, 124–133 (2006).

E. A. El-Dahshan, A. B. M. Salem, and T. Hosny, “Hybrid intelligent techniques for MRI brain images classification,” Digit. Signal Process. 20, 433–441 (2010).

[Crossref]

G. Latif, M. A. Jaffar, S. B. Kazmi, and A. M. Mirza, “Classification and segmentation of brain tumor using texture analysis, Department of Computer Science,” in Recent Advances in Artificial Intelligence, Knowledge Engineering and Data Bases (2010), pp. 147–155.

S. Chaplot, N. R. Jagannathan, and L. M. Patnaik, “Classification of magnetic resonance brain images using wavelets as input to support vector machine and neural network,” Biomed. Signal Process. Control 1, 86–92 (2006).

[Crossref]

S. Kurup and M. S. Jahanavi, “A novel approach to detect brain tumour in MRI images using hybrid technique with SVM classifiers,” in IEEE International Conference on Recent Trends in Electronics, Information & Communication Technology (IEEE, 2016), pp. 546–549.

Y. Zhang, S. Wang, Z. Dong, and G. Ji, “An MR brain images classifier system via particle swarm optimization and kernel support vector machine,” Sci. World J. 2013, 130134 (2013).

[Crossref]

D. M. Joshi, V. M. Misra, and N. K. Rana, “Classification of brain cancer using artificial neural network,” in IEEE International Conference on Electronic Computer Technology (IEEE, 2010), pp. 112–116.

G. Latif, M. A. Jaffar, S. B. Kazmi, and A. M. Mirza, “Classification and segmentation of brain tumor using texture analysis, Department of Computer Science,” in Recent Advances in Artificial Intelligence, Knowledge Engineering and Data Bases (2010), pp. 147–155.

J. Kennedy and R. Eberhart, “Particle swarm optimization,” in IEEE International Conference on Neural Networks (IEEE, 1995), pp. 1942–1948.

A. Kharrat, K. Gasmi, M. B. Messaoud, M. Abid, and N. Benamrane, “A hybrid approach for automatic classification of brain MRI using genetic algorithm and support vector machine,” Leonardo J. Sci. 9, 71–82 (2010).

S. Kurup and M. S. Jahanavi, “A novel approach to detect brain tumour in MRI images using hybrid technique with SVM classifiers,” in IEEE International Conference on Recent Trends in Electronics, Information & Communication Technology (IEEE, 2016), pp. 546–549.

S. Lahmiri, “Glioma detection based on multi-fractal features of segmented brain MRI by particle swarm optimization techniques,” Biomed. Signal Process. Control 31, 148–155 (2017).

[Crossref]

M. Boukadoum and S. Lahmiri, “Comparison of ANFIS and SVM for the classification of brain MRI pathologies,” in IEEE International Midwest Symposium on Circuits and Systems (IEEE, 2011).

M. Boukadoum and S. Lahmiri, “Classification of brain MRI using the LH and HL wavelet transform sub-bands,” in IEEE International Symposium of Circuits and Systems (IEEE, 2011), pp. 1025–1028.

G. Latif, M. A. Jaffar, S. B. Kazmi, and A. M. Mirza, “Classification and segmentation of brain tumor using texture analysis, Department of Computer Science,” in Recent Advances in Artificial Intelligence, Knowledge Engineering and Data Bases (2010), pp. 147–155.

S. Mirjalili, A. Lewis, and S. M. Mirjalili, “Grey wolf optimizer,” Adv. Eng. Softw. 69, 46–61 (2014).

[Crossref]

L. Li, L. Sun, J. Guo, J. Qi, S. Li, and B. Xu, “Modified discrete grey wolf optimizer algorithm for multilevel image thresholding,” in Computational Intelligence and Neuroscience (2017).

S. Wang, P. Li, P. Phillips, G. Liu, Y. Zhang, P. Chen, and S. Du, “Pathological brain detection via wavelet packet Tsallis entropy,” Fund. Inform. 151, 275–291 (2017).

L. Li, L. Sun, J. Guo, J. Qi, S. Li, and B. Xu, “Modified discrete grey wolf optimizer algorithm for multilevel image thresholding,” in Computational Intelligence and Neuroscience (2017).

S. Wang, S. Du, M. Yang, J. Yang, B. Liu, J. M. Gorriz, J. Ramirez, T. F. Yuan, and Y. Zhang, “Wavelet entropy and directed acyclic graph support vector machine for detection of patients with unilateral hearing loss in MRI scanning,” Front. Comput. Neurosci. 10, 106 (2016).

S. Wang, P. Li, P. Phillips, G. Liu, Y. Zhang, P. Chen, and S. Du, “Pathological brain detection via wavelet packet Tsallis entropy,” Fund. Inform. 151, 275–291 (2017).

A. Chatterjee and M. Maitra, “Hybrid multiresolution Slantlet transform and fuzzy c-means clustering approach for normal-pathological brain MR image segregation,” Med. Eng. Phys. 30, 615–623 (2008).

[Crossref]

S. A. Medjahed, T. A. Saadi, M. Ouali, and A. Benyettou, “Gray wolf optimizer for hyperspectral band selection,” Appl. Soft Comput. 40, 178–186 (2016).

[Crossref]

A. Kharrat, K. Gasmi, M. B. Messaoud, M. Abid, and N. Benamrane, “A hybrid approach for automatic classification of brain MRI using genetic algorithm and support vector machine,” Leonardo J. Sci. 9, 71–82 (2010).

S. Mirjalili, S. Saremi, L. S. Coelho, and S. M. Mirjalili, “Multi-objective grey wolf optimizer: a novel algorithm for multi-criterion optimization,” Expert Syst. Appl. 47, 106–119 (2016).

[Crossref]

S. Mirjalili, “How effective is the grey wolf optimizer in training multi-layer perceptrons,” Appl. Intell. 43, 150–161 (2015).

[Crossref]

S. Mirjalili, A. Lewis, and S. M. Mirjalili, “Grey wolf optimizer,” Adv. Eng. Softw. 69, 46–61 (2014).

[Crossref]

S. Mirjalili, S. Saremi, L. S. Coelho, and S. M. Mirjalili, “Multi-objective grey wolf optimizer: a novel algorithm for multi-criterion optimization,” Expert Syst. Appl. 47, 106–119 (2016).

[Crossref]

S. Saremi, S. M. Mirjalili, and S. Z. Mirjalili, “Evolutionary population dynamics and grey wolf optimizer,” Neural Comput. Appl. 26, 1257–1263 (2015).

[Crossref]

S. Mirjalili, A. Lewis, and S. M. Mirjalili, “Grey wolf optimizer,” Adv. Eng. Softw. 69, 46–61 (2014).

[Crossref]

S. Saremi, S. M. Mirjalili, and S. Z. Mirjalili, “Evolutionary population dynamics and grey wolf optimizer,” Neural Comput. Appl. 26, 1257–1263 (2015).

[Crossref]

G. Latif, M. A. Jaffar, S. B. Kazmi, and A. M. Mirza, “Classification and segmentation of brain tumor using texture analysis, Department of Computer Science,” in Recent Advances in Artificial Intelligence, Knowledge Engineering and Data Bases (2010), pp. 147–155.

D. M. Joshi, V. M. Misra, and N. K. Rana, “Classification of brain cancer using artificial neural network,” in IEEE International Conference on Electronic Computer Technology (IEEE, 2010), pp. 112–116.

M. H. Sulaiman, Z. Mustaffa, O. Aliman, and M. R. Mohamed, “Using the gray wolf optimizer for solving optimal reactive power dispatch problem,” Appl. Soft Comput. 32, 286–292 (2015).

[Crossref]

M. H. Sulaiman, Z. Mustaffa, O. Aliman, and M. R. Mohamed, “Using the gray wolf optimizer for solving optimal reactive power dispatch problem,” Appl. Soft Comput. 32, 286–292 (2015).

[Crossref]

A. Al-Badarneh, A. M. Alraziqi, and H. Najadat, “A classifier to detect tumor disease in MRI brain images,” in IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (IEEE, 2012), pp. 784–787.

N. Abdullah, S. A. Aziz, and U. K. Ngah, “Image classification of brain MRI using support vector machine,” in IEEE International Conference on Imaging Systems and Techniques (IEEE, 2011), pp. 242–247.

S. A. Medjahed, T. A. Saadi, M. Ouali, and A. Benyettou, “Gray wolf optimizer for hyperspectral band selection,” Appl. Soft Comput. 40, 178–186 (2016).

[Crossref]

M. Pradhan, T. Pal, and P. K. Roy, “Grey wolf optimization applied to economic load dispatch problems,” Electr. Power Energy Syst. 83, 325–334 (2016).

[Crossref]

S. Chaplot, N. R. Jagannathan, and L. M. Patnaik, “Classification of magnetic resonance brain images using wavelets as input to support vector machine and neural network,” Biomed. Signal Process. Control 1, 86–92 (2006).

[Crossref]

A. Faro, D. Giordano, M. Pennisi, and C. Spampinato, “Statistical texture analysis of MRI images to classify patients affected by multiple sclerosis,” in XII Mediterranean Conference on Medical and Biological Engineering and Computing (2010), pp. 272–275.

S. Goh, Y. Zhang, Z. Dong, S. DiMauro, and B. S. Peterson, “Mitochondrial dysfunction as a neurobiological subtype of autism spectrum disorder evidence from brain imaging,” JAMA Psychiatry 71, 665–671 (2014).

[Crossref]

M. Siavash, C. Pfeifer, B. Vahidi, and A. Rahiminejad, “An application of grey wolf optimizer for optimal power flow of wind integrated power systems,” in International Scientific Conference on Electric Power Engineering (2017).

S. Wang, P. Li, P. Phillips, G. Liu, Y. Zhang, P. Chen, and S. Du, “Pathological brain detection via wavelet packet Tsallis entropy,” Fund. Inform. 151, 275–291 (2017).

S. H. Wang, S. Du, Y. Zhang, P. Phillips, Y. D. Zhang, X. Q. Chen, and L. N. Wu, “Alzheimer’s disease detection by pseudo Zernike moment and linear regression classification,” CNS Neurol. Disord. Drug Targets 16, 11–15 (2017).

[Crossref]

Y. Zhang, S. Wang, J. Yang, S. Wang, Z. Dong, and P. Phillips, “Pathological brain detection in MRI scanning via Hu moment invariants and machine learning,” J. Exp. Theor. Artif. Intell. 29, 299–312 (2016).

[Crossref]

M. Pradhan, T. Pal, and P. K. Roy, “Grey wolf optimization applied to economic load dispatch problems,” Electr. Power Energy Syst. 83, 325–334 (2016).

[Crossref]

A. Agrawal and G. B. Praveen, “Hybrid approach for brain tumor detection and classification in magnetic resonance images,” in IEEE International Conference on Communication, Control and Intelligent Systems (IEEE, 2015), pp. 162–166.

P. S. Hiremath, J. Pujari, and S. Shivashankar, “Wavelet based features for color texture classification with application to CBIR,” IJCSNS Int. J. Comput. Sci. Netw. Security 6, 124–133 (2006).

L. Li, L. Sun, J. Guo, J. Qi, S. Li, and B. Xu, “Modified discrete grey wolf optimizer algorithm for multilevel image thresholding,” in Computational Intelligence and Neuroscience (2017).

R. Qu and H. Xing, “A population based incremental learning for network coding resources minimization,” IEEE Commun. Lett. 15, 698–700 (2011).

[Crossref]

M. Siavash, C. Pfeifer, B. Vahidi, and A. Rahiminejad, “An application of grey wolf optimizer for optimal power flow of wind integrated power systems,” in International Scientific Conference on Electric Power Engineering (2017).

M. Gupta, V. Rajagopalan, and B. V. V. S. N. P. Rao, “Brain tumor detection in conventional MR images based on statistical texture and morphological features,” in IEEE International Conference on Information Technology (IEEE, 2016), pp. 129–133.

S. Wang, S. Du, M. Yang, J. Yang, B. Liu, J. M. Gorriz, J. Ramirez, T. F. Yuan, and Y. Zhang, “Wavelet entropy and directed acyclic graph support vector machine for detection of patients with unilateral hearing loss in MRI scanning,” Front. Comput. Neurosci. 10, 106 (2016).

D. M. Joshi, V. M. Misra, and N. K. Rana, “Classification of brain cancer using artificial neural network,” in IEEE International Conference on Electronic Computer Technology (IEEE, 2010), pp. 112–116.

M. Gupta, V. Rajagopalan, and B. V. V. S. N. P. Rao, “Brain tumor detection in conventional MR images based on statistical texture and morphological features,” in IEEE International Conference on Information Technology (IEEE, 2016), pp. 129–133.

M. Torabi, S. Razavian, B. Vosoughi-Vahdat, and R. Vaziri, “A wavelet-packet-based approach for breast cancer classification,” in Annual International Conference of the IEEE Engineering in Medicine and Biology Society (IEEE, 2011), pp. 5100–5103.

C. Arizmendi, E. Romero, and A. Vellido, “Binary classification of brain tumours using a discrete wavelet transform and energy criteria,” in IEEE Second Latin American Symposium on Circuits and Systems (LASCAS) (IEEE, 2011).

M. Pradhan, T. Pal, and P. K. Roy, “Grey wolf optimization applied to economic load dispatch problems,” Electr. Power Energy Syst. 83, 325–334 (2016).

[Crossref]

S. A. Medjahed, T. A. Saadi, M. Ouali, and A. Benyettou, “Gray wolf optimizer for hyperspectral band selection,” Appl. Soft Comput. 40, 178–186 (2016).

[Crossref]

E. A. El-Dahshan, A. B. M. Salem, and T. Hosny, “Hybrid intelligent techniques for MRI brain images classification,” Digit. Signal Process. 20, 433–441 (2010).

[Crossref]

S. Mirjalili, S. Saremi, L. S. Coelho, and S. M. Mirjalili, “Multi-objective grey wolf optimizer: a novel algorithm for multi-criterion optimization,” Expert Syst. Appl. 47, 106–119 (2016).

[Crossref]

S. Saremi, S. M. Mirjalili, and S. Z. Mirjalili, “Evolutionary population dynamics and grey wolf optimizer,” Neural Comput. Appl. 26, 1257–1263 (2015).

[Crossref]

H. P. Schwefel and H. G. Beyer, “Evolution strategies—a comprehensive introduction,” Nat. Comput. 1, 3–52 (2002).

[Crossref]

P. S. Hiremath, J. Pujari, and S. Shivashankar, “Wavelet based features for color texture classification with application to CBIR,” IJCSNS Int. J. Comput. Sci. Netw. Security 6, 124–133 (2006).

M. Siavash, C. Pfeifer, B. Vahidi, and A. Rahiminejad, “An application of grey wolf optimizer for optimal power flow of wind integrated power systems,” in International Scientific Conference on Electric Power Engineering (2017).

E. Emary, W. Yamany, V. Snasel, and A. E. Hassanien, “Multi-objective gray-wolf optimization for attribute reduction,” Procedia Comput. Sci. 65, 623–632 (2015).

[Crossref]

A. Faro, D. Giordano, M. Pennisi, and C. Spampinato, “Statistical texture analysis of MRI images to classify patients affected by multiple sclerosis,” in XII Mediterranean Conference on Medical and Biological Engineering and Computing (2010), pp. 272–275.

D. Whitley, C. Bogart, and T. Starkweather, “Genetic algorithms and neural networks: optimizing connections and connectivity,” Parallel Comput. 14, 347–361 (1990).

[Crossref]

M. Dorigo, T. Stutzle, and M. Birattari, “Ant colony optimization,” IEEE Comput. Intell. Mag. 1, 28–39 (2006).

[Crossref]

M. H. Sulaiman, Z. Mustaffa, O. Aliman, and M. R. Mohamed, “Using the gray wolf optimizer for solving optimal reactive power dispatch problem,” Appl. Soft Comput. 32, 286–292 (2015).

[Crossref]

L. Li, L. Sun, J. Guo, J. Qi, S. Li, and B. Xu, “Modified discrete grey wolf optimizer algorithm for multilevel image thresholding,” in Computational Intelligence and Neuroscience (2017).

M. Torabi, S. Razavian, B. Vosoughi-Vahdat, and R. Vaziri, “A wavelet-packet-based approach for breast cancer classification,” in Annual International Conference of the IEEE Engineering in Medicine and Biology Society (IEEE, 2011), pp. 5100–5103.

M. Siavash, C. Pfeifer, B. Vahidi, and A. Rahiminejad, “An application of grey wolf optimizer for optimal power flow of wind integrated power systems,” in International Scientific Conference on Electric Power Engineering (2017).

M. Torabi, S. Razavian, B. Vosoughi-Vahdat, and R. Vaziri, “A wavelet-packet-based approach for breast cancer classification,” in Annual International Conference of the IEEE Engineering in Medicine and Biology Society (IEEE, 2011), pp. 5100–5103.

C. Arizmendi, E. Romero, and A. Vellido, “Binary classification of brain tumours using a discrete wavelet transform and energy criteria,” in IEEE Second Latin American Symposium on Circuits and Systems (LASCAS) (IEEE, 2011).

M. Torabi, S. Razavian, B. Vosoughi-Vahdat, and R. Vaziri, “A wavelet-packet-based approach for breast cancer classification,” in Annual International Conference of the IEEE Engineering in Medicine and Biology Society (IEEE, 2011), pp. 5100–5103.

S. Wang, P. Li, P. Phillips, G. Liu, Y. Zhang, P. Chen, and S. Du, “Pathological brain detection via wavelet packet Tsallis entropy,” Fund. Inform. 151, 275–291 (2017).

Y. Zhang, S. Wang, J. Yang, S. Wang, Z. Dong, and P. Phillips, “Pathological brain detection in MRI scanning via Hu moment invariants and machine learning,” J. Exp. Theor. Artif. Intell. 29, 299–312 (2016).

[Crossref]

Y. Zhang, S. Wang, J. Yang, S. Wang, Z. Dong, and P. Phillips, “Pathological brain detection in MRI scanning via Hu moment invariants and machine learning,” J. Exp. Theor. Artif. Intell. 29, 299–312 (2016).

[Crossref]

S. Wang, S. Du, M. Yang, J. Yang, B. Liu, J. M. Gorriz, J. Ramirez, T. F. Yuan, and Y. Zhang, “Wavelet entropy and directed acyclic graph support vector machine for detection of patients with unilateral hearing loss in MRI scanning,” Front. Comput. Neurosci. 10, 106 (2016).

Y. Zhang, S. Wang, Z. Dong, and G. Ji, “An MR brain images classifier system via particle swarm optimization and kernel support vector machine,” Sci. World J. 2013, 130134 (2013).

[Crossref]

Y. Zhang, L. Wu, and S. Wang, “A novel method for magnetic resonance brain image classification based on adaptive chaotic PSO,” Prog. Electromagn. Res. 109, 325–343 (2010).

[Crossref]

S. H. Wang, S. Du, Y. Zhang, P. Phillips, Y. D. Zhang, X. Q. Chen, and L. N. Wu, “Alzheimer’s disease detection by pseudo Zernike moment and linear regression classification,” CNS Neurol. Disord. Drug Targets 16, 11–15 (2017).

[Crossref]

D. Whitley, C. Bogart, and T. Starkweather, “Genetic algorithms and neural networks: optimizing connections and connectivity,” Parallel Comput. 14, 347–361 (1990).

[Crossref]

Y. Zhang, L. Wu, and S. Wang, “A novel method for magnetic resonance brain image classification based on adaptive chaotic PSO,” Prog. Electromagn. Res. 109, 325–343 (2010).

[Crossref]

S. H. Wang, S. Du, Y. Zhang, P. Phillips, Y. D. Zhang, X. Q. Chen, and L. N. Wu, “Alzheimer’s disease detection by pseudo Zernike moment and linear regression classification,” CNS Neurol. Disord. Drug Targets 16, 11–15 (2017).

[Crossref]

R. Qu and H. Xing, “A population based incremental learning for network coding resources minimization,” IEEE Commun. Lett. 15, 698–700 (2011).

[Crossref]

L. Li, L. Sun, J. Guo, J. Qi, S. Li, and B. Xu, “Modified discrete grey wolf optimizer algorithm for multilevel image thresholding,” in Computational Intelligence and Neuroscience (2017).

E. Emary, W. Yamany, V. Snasel, and A. E. Hassanien, “Multi-objective gray-wolf optimization for attribute reduction,” Procedia Comput. Sci. 65, 623–632 (2015).

[Crossref]

Y. Zhang, S. Wang, J. Yang, S. Wang, Z. Dong, and P. Phillips, “Pathological brain detection in MRI scanning via Hu moment invariants and machine learning,” J. Exp. Theor. Artif. Intell. 29, 299–312 (2016).

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