Boosting whale optimization with evolution strategy and Gaussian random walks: an image segmentation method (original) (raw)

References

  1. Simon D (2008) Biogeography-based optimization. IEEE Trans Evol Comput 12(6):702–713
    Google Scholar
  2. Hassanien AE, Emary E (2018) Swarm intelligence: principles, advances, and applications. CRC Press, Boca Raton
    Google Scholar
  3. Abualigah L, Gandomi AH, Elaziz MA, Hussien AG, Khasawneh AM, Alshinwan M, Houssein EH (2020) Nature-inspired optimization algorithms for text document clustering-a comprehensive analysis. Algorithms 13(12):345
    MathSciNet Google Scholar
  4. Holland JH (1992) Genetic algorithms. Sci Am 267(1):66–73
    Google Scholar
  5. Rechenberg I (1978) Evolutionsstrategien. In: Schneider B, Ranft U (eds) Simulationsmethoden in der Medizin und Biologie. Medizinische Informatik und Statistik, vol 8. Springer, Berlin, Heidelberg. Berthold Schneider, Ulrich Ranft. https://doi.org/10.1007/978-3-642-81283-5_8
  6. Koza JR, Koza JR (1992) Genetic programming: on the programming of computers by means of natural selection, vol 1. MIT Press, Cambridge
    MATH Google Scholar
  7. Wang T, Liu W, Zhao J, Guo X, Terzija V (2020) A rough set-based bio-inspired fault diagnosis method for electrical substations. Int J Elec Power Energy Syst 119:105961. https://doi.org/10.1016/j.ijepes.2020.105961
    Article Google Scholar
  8. Kennedy J, Eberhart R (1995) Particle swarm optimization. In: Proceedings of ICNN’95-international conference on neural networks, vol 4. IEEE, pp 1942–1948
  9. Dorigo M, Maniezzo V, Colorni A (1996) Ant system: optimization by a colony of cooperating agents. IEEE Trans Syst Man Cybern Part B (Cybern) 26(1):29–41
    Google Scholar
  10. Heidari AA, Mirjalili S, Faris H, Aljarah I, Mafarja M, Chen H (2019) Harris hawks optimization: algorithm and applications. Future Gener Comput Syst 97:849–872
    Google Scholar
  11. Li MD, Zhao H, Weng XW, Han T (2016) A novel nature-inspired algorithm for optimization: virus colony search. Adv Eng Softw 92:65–88
    Google Scholar
  12. Li S, Chen H, Wang M, Heidari AA, Mirjalili S (2020) Slime mould algorithm: a new method for stochastic optimization. Future Gener Comput Syst 111:300–323
    Google Scholar
  13. Yang Y, Chen H, Heidari AA, Gandomi AH (2021) Hunger games search: visions, conception, implementation, deep analysis, perspectives, and towards performance shifts. Expert Syst Appl 177:114864
    Google Scholar
  14. Ahmadianfar I, Heidari AA, Gandomi AH, Chu X, Chen H (2021) Run beyond the metaphor: an efficient optimization algorithm based on runge kutta method. Expert Syst Appl 181:115079
    Google Scholar
  15. Kirkpatrick S, Gelatt CD, Vecchi MP (1983) Optimization by simulated annealing. Science 220(4598):671–680
    MathSciNet MATH Google Scholar
  16. Rashedi E, Nezamabadi-Pour H, Saryazdi S (2009) GSA: a gravitational search algorithm. Inf Sci 179(13):2232–2248
    MATH Google Scholar
  17. Rao RV, Savsani VJ, Vakharia D (2012) Teaching-learning-based optimization: an optimization method for continuous non-linear large scale problems. Inf Sci 183(1):1–15
    MathSciNet Google Scholar
  18. Glover F (1989) Tabu search-part i. ORSA J Comput 1(3):190–206
    MATH Google Scholar
  19. Ba AF, Huang H, Wang M, Ye X, Gu Z, Chen H, Cai X (2020) Levy-based antlion-inspired optimizers with orthogonal learning scheme. Eng Comput. https://doi.org/10.1007/s00366-020-01042-7
    Article Google Scholar
  20. Liang X, Cai Z, Wang M, Zhao X, Chen H, Li C (2020) Chaotic oppositional sine–cosine method for solving global optimization problems. Eng Comput. https://doi.org/10.1007/s00366-020-01083-y
    Article Google Scholar
  21. Hu L, Li H, Cai Z, Lin F, Hong G, Chen H, Lu Z (2017) A new machine-learning method to prognosticate paraquat poisoned patients by combining coagulation, liver, and kidney indices. PLoS One 12(10):e0186427
    Google Scholar
  22. Huang H, Zhou S, Jiang J, Chen H, Li Y, Li C (2019) A new fruit fly optimization algorithm enhanced support vector machine for diagnosis of breast cancer based on high-level features. BMC Bioinform 20(8):1–14
    Google Scholar
  23. Li C, Hou L, Sharma BY, Li H, Chen C, Li Y, Zhao X, Huang H, Cai Z, Chen H (2018) Developing a new intelligent system for the diagnosis of tuberculous pleural effusion. Comput Methods Programs Biomed 153:211–225
    Google Scholar
  24. Zhao X, Zhang X, Cai Z, Tian X, Wang X, Huang Y, Chen H, Hu L (2019) Chaos enhanced grey wolf optimization wrapped elm for diagnosis of paraquat-poisoned patients. Comput Biol Chem 78:481–490
    Google Scholar
  25. Pang J, Zhou H, Tsai Y-C, Chou F-D (2018) A scatter simulated annealing algorithm for the bi-objective scheduling problem for the wet station of semiconductor manufacturing. Comput Ind Eng 123:54–66. https://doi.org/10.1016/j.cie.2018.06.017
    Article Google Scholar
  26. Zhou H, Pang J, Chen P-K, Chou F-D (2018) A modified particle swarm optimization algorithm for a batch-processing machine scheduling problem with arbitrary release times and non-identical job sizes. Comput Ind Eng 123:67–81. https://doi.org/10.1016/j.cie.2018.06.018
    Article Google Scholar
  27. Li Q, Chen H, Huang H, Zhao X, Cai Z, Tong C, Liu W, Tian X (2017) An enhanced grey wolf optimization based feature selection wrapped kernel extreme learning machine for medical diagnosis. Comput Math Methods Med. https://doi.org/10.1155/2017/9512741
    Article Google Scholar
  28. Liu T, Hu L, Ma C, Wang Z-Y, Chen H-L (2015) A fast approach for detection of erythemato-squamous diseases based on extreme learning machine with maximum relevance minimum redundancy feature selection. Int J Syst Sci 46(5):919–931
    MATH Google Scholar
  29. Zhang Y, Liu R, Wang X et al (2021) Boosted binary Harris hawks optimizer and feature selection. Eng Comput 37:3741–3770
    Google Scholar
  30. Chen M, Zeng G, Lu K, Weng J (2019) A two-layer nonlinear combination method for short-term wind speed prediction based on elm, enn, and lstm. IEEE Internet Things J 6(4):6997–7010. https://doi.org/10.1109/JIOT.2019.2913176
    Article Google Scholar
  31. Ba AF, Huang H, Wang M, Ye X, Gu Z, Chen H, Cai X (2020) Levy-based antlion-inspired optimizers with orthogonal learning scheme. Eng Comput 1–22. https://doi.org/10.1007/s00366-020-01042-7
  32. Liang X, Cai Z, Wang M, Zhao X, Chen H, Li C (2020) Chaotic oppositional sine–cosine method for solving global optimization problems. Eng Comput 1–17
  33. Zhang H, Cai Z, Ye X, Wang M, Kuang F, Chen H, Li C, Li Y (2020) A multi-strategy enhanced salp swarm algorithm for global optimization. Eng Comput 1–27
  34. Zeng G-Q, Lu Y-Z, Mao W-J (2011) Modified extremal optimization for the hard maximum satisfiability problem. J Zhejiang Univ Sci C 12(7):589–596
    Google Scholar
  35. Zeng G, Lu Y, Dai Y, Wu Z, Mao W, Zhang Z, Zheng CJIJICIC (2012) Backbone guided extremal optimization for the hard maximum satisfiability problem. Int J Innov Comput Inf Control 8(12):8355–8366
    Google Scholar
  36. Cai Z, Gu J, Luo J, Zhang Q, Chen H, Pan Z, Li Y, Li C (2019) Evolving an optimal kernel extreme learning machine by using an enhanced grey wolf optimization strategy. Expert Syst Appl 138:112814
    Google Scholar
  37. Yu C, Chen M, Cheng K, Zhao X, Ma C, Kuang F, Chen H (2021) SGOA: annealing-behaved grasshopper optimizer for global tasks. Eng Comput. https://doi.org/10.1007/s00366-020-01234-1
    Article Google Scholar
  38. Shen L, Chen H, Yu Z, Kang W, Zhang B, Li H, Yang B, Liu D (2016) Evolving support vector machines using fruit fly optimization for medical data classification. Knowl Based Syst 96:61–75
    Google Scholar
  39. Wang M, Chen H (2020) Chaotic multi-swarm whale optimizer boosted support vector machine for medical diagnosis. Appl Soft Comput 88:105946
    Google Scholar
  40. Wang M, Chen H, Yang B, Zhao X, Hu L, Cai Z, Huang H, Tong C (2017) Toward an optimal kernel extreme learning machine using a chaotic moth-flame optimization strategy with applications in medical diagnoses. Neurocomputing 267:69–84
    Google Scholar
  41. Zeng G-Q, Chen J, Dai Y-X, Li L-M, Zheng C-W, Chen M-RJN (2015) Design of fractional order pid controller for automatic regulator voltage system based on multi-objective extremal optimization. Neurocomputing 160:173–184
    Google Scholar
  42. Zeng G-Q, Lu K-D, Dai Y-X, Zhang Z-J, Chen M-R, Zheng C-W, Wu D, Peng W-WJN (2014) Binary-coded extremal optimization for the design of pid controllers. Neurocomputing 138:180–188
    Google Scholar
  43. Zeng G-Q, Xie X-Q, Chen M-R, Weng J (2019) Adaptive population extremal optimization-based pid neural network for multivariable nonlinear control systems. Swarm Evol Comput 44:320–334. https://doi.org/10.1016/j.swevo.2018.04.008
    Article Google Scholar
  44. Zhao X, Li D, Yang B, Chen H, Yang X, Yu C, Liu S (2015) A two-stage feature selection method with its application. Comput Electr Eng 47:114–125
    Google Scholar
  45. Zhao X, Li D, Yang B, Ma C, Zhu Y, Chen H (2014) Feature selection based on improved ant colony optimization for online detection of foreign fiber in cotton. Appl Soft Comput 24:585–596
    Google Scholar
  46. Pei H, Yang B, Liu J, Chang K (2020) Active surveillance via group sparse Bayesian learning. IEEE Trans Pattern Anal Mach Intell. https://doi.org/10.1109/TPAMI.2020.3023092
    Article Google Scholar
  47. Xue X, Chen Z, Wang S, Feng Z, Duan Y, Zhou Z (2020) Value entropy: a systematic evaluation model of service ecosystem evolution. IEEE Trans Serv Comput. https://doi.org/10.1109/TSC.2020.3016660
    Article Google Scholar
  48. Xue X, Wang SF, Zhan LJ, Feng ZY, Guo YD (2019) Social learning evolution (sle): computational experiment-based modeling framework of social manufacturing. IEEE Trans Ind Inform 15(6):3343–3355. https://doi.org/10.1109/tii.2018.2871167
    Article Google Scholar
  49. Li J, Soladie C, Seguier R (2020) Local temporal pattern and data augmentation for micro-expression spotting. IEEE Trans Affect Comput. https://doi.org/10.1109/TAFFC.2020.3023821
    Article Google Scholar
  50. Wang S-J, He Y, Li J, Fu X (2011) Mesnet: a convolutional neural network for spotting multi-scale micro-expression intervals in long videos. IEEE Trans Image Process. https://doi.org/10.1109/TIP.2021.3064258
    Article Google Scholar
  51. Tu J, Lin A, Chen H, Li Y, Li C (2019) Predict the entrepreneurial intention of fresh graduate students based on an adaptive support vector machine framework. Math Probl Eng 2019:1–16
    Google Scholar
  52. Wei Y, Ni N, Liu D, Chen H, Wang M, Li Q, Cui X, Ye H (2017) An improved grey wolf optimization strategy enhanced svm and its application in predicting the second major. Math Probl Eng 2017:1–12
    Google Scholar
  53. Mirjalili S, Lewis A (2016) The whale optimization algorithm. Adv Eng Softw 95:51–67
    Google Scholar
  54. Hussien AG, Hassanien AE, Houssein EH, Amin M, Azar AT (2019) New binary whale optimization algorithm for discrete optimization problems. Eng Optim 1–15
  55. Aljarah I, Faris H, Mirjalili S (2018) Optimizing connection weights in neural networks using the whale optimization algorithm. Soft Comput 22(1):1–15
    Google Scholar
  56. Elaziz MA, Mirjalili S (2019) A hyper-heuristic for improving the initial population of whale optimization algorithm. Knowl Based Syst 172:42–63
    Google Scholar
  57. Emary E, Zawbaa HM, Sharawi M (2019) Impact of lèvy flight on modern meta-heuristic optimizers. Appl Soft Comput 75:775–789
    Google Scholar
  58. Oliva D, El Aziz MA, Hassanien AE (2017) Parameter estimation of photovoltaic cells using an improved chaotic whale optimization algorithm. Appl Energy 200:141–154
    Google Scholar
  59. Xiong G, Zhang J, Shi D, He Y (2018) Parameter extraction of solar photovoltaic models using an improved whale optimization algorithm. Energy Convers Manag 174:388–405
    Google Scholar
  60. Chen H, Xu Y, Wang M, Zhao X (2019) A balanced whale optimization algorithm for constrained engineering design problems. Appl Math Model 71:45–59
    MathSciNet MATH Google Scholar
  61. Mafarja MM, Mirjalili S (2017) Hybrid whale optimization algorithm with simulated annealing for feature selection. Neurocomputing 260:302–312
    Google Scholar
  62. Abdel-Basset M, El-Shahat D, El-Henawy I, Sangaiah AK, Ahmed SH (2018) A novel whale optimization algorithm for cryptanalysis in Merkle–Hellman cryptosystem. Mob Netw Appl 23(4):723–733
    Google Scholar
  63. Jadhav AN, Gomathi N (2018) Wgc: hybridization of exponential grey wolf optimizer with whale optimization for data clustering. Alex Eng J 57(3):1569–1584
    Google Scholar
  64. Agrawal R, Kaur B, Sharma S (2020) Quantum based whale optimization algorithm for wrapper feature selection. Appl Soft Comput 89:106092
    Google Scholar
  65. Salgotra R, Singh U, Saha S (2019) On some improved versions of whale optimization algorithm. Arabian J Sci Eng 44(11):9653–9691
    Google Scholar
  66. Hussien AG, Houssein EH, Hassanien AE (2017) A binary whale optimization algorithm with hyperbolic tangent fitness function for feature selection. In: 2017 Eighth international conference on intelligent computing and information systems (ICICIS). IEEE, pp 166–172
  67. Hussien AG, Hassanien AE, Houssein EH, Bhattacharyya S, Amin M (2019) S-shaped binary whale optimization algorithm for feature selection. In: Recent trends in signal and image processing. Springer, pp 79–87
  68. Hemasian-Etefagh F, Safi-Esfahani F (2019) Group-based whale optimization algorithm. Soft Comput 1–27
  69. Hassib EM, El-Desouky AI, Labib LM, El-kenawy E-SM (2019) Woa+ brnn: an imbalanced big data classification framework using whale optimization and deep neural network. Soft Comput 1–20
  70. Liu M, Yao X, Li Y (2020) Hybrid whale optimization algorithm enhanced with lévy flight and differential evolution for job shop scheduling problems. Appl Soft Comput 87:105954
    Google Scholar
  71. Jiang R, Yang M, Wang S, Chao T (2020) An improved whale optimization algorithm with armed force program and strategic adjustment. Appl Math Model 81:603–623
    MathSciNet MATH Google Scholar
  72. Guo W, Liu T, Dai F, Xu P (2020) An improved whale optimization algorithm for forecasting water resources demand. Appl Soft Comput 86:105925
    Google Scholar
  73. Got A, Moussaoui A, Zouache D (2020) A guided population archive whale optimization algorithm for solving multiobjective optimization problems. Expert Syst Appl 141:112972
    Google Scholar
  74. Abdel-Basset M, Manogaran G, El-Shahat D, Mirjalili S (2018) Integrating the whale algorithm with tabu search for quadratic assignment problem: a new approach for locating hospital departments. Appl Soft Comput 73:530–546
    Google Scholar
  75. Tharwat A, Moemen YS, Hassanien AE (2017) Classification of toxicity effects of biotransformed hepatic drugs using whale optimized support vector machines. J Biomed Inform 68:132–149
    Google Scholar
  76. Zhao D, Liu H, Zheng Y, He Y, Lu D, Lyu C (2019) Whale optimized mixed kernel function of support vector machine for colorectal cancer diagnosis. J Biomed Inform 92:103124
    Google Scholar
  77. Gharehchopogh FS, Gholizadeh H (2019) A comprehensive survey: Whale optimization algorithm and its applications. Swarm Evol Comput 48:1–24
    Google Scholar
  78. Shahinzadeh H, Gharehpetian GB, Moazzami M, Moradi J, Hosseinian SH (2017) Unit commitment in smart grids with wind farms using virus colony search algorithm and considering adopted bidding strategy. In: 2017 Smart Grid Conference (SGC). IEEE, pp 1–9
  79. Jayasena KPN, Li L, Elaziz MA, Xiong S (2018) Multi-objective energy efficient resource allocation using virus colony search (vcs) algorithm. In: 2018 IEEE 20th international conference on high performance computing and communications; IEEE 16th international conference on smart city; IEEE 4th international conference on data science and systems (HPCC/SmartCity/DSS). IEEE, pp 766–773
  80. Hosseini S, Moradian M, Shahinzadeh H, Ahmadi S (2018) Optimal placement of distributed generators with regard to reliability assessment using virus colony search algorithm. Int J Renew Energy Res (IJRER) 8(2):714–723
    Google Scholar
  81. Yousri D, Allam D, Eteiba M (2019) Chaotic whale optimizer variants for parameters estimation of the chaotic behavior in permanent magnet synchronous motor. Appl Soft Comput 74:479–503
    Google Scholar
  82. Elaziz MA, Oliva D (2018) Parameter estimation of solar cells diode models by an improved opposition-based whale optimization algorithm. Energy Convers Manag 171:1843–1859
    Google Scholar
  83. Elhosseini MA, Haikal AY, Badawy M, Khashan N (2019) Biped robot stability based on an a-c parametric whale optimization algorithm. J Comput Sci 31:17–32
    MathSciNet Google Scholar
  84. Tubishat M, Abushariah MA, Idris N, Aljarah I (2019) Improved whale optimization algorithm for feature selection in arabic sentiment analysis. Appl Intell 49(5):1688–1707
    Google Scholar
  85. He Y, Dai L, Zhang H (2020) Multi-branch deep residual learning for clustering and beamforming in user-centric network. IEEE Commun Lett 24(10):2221–2225. https://doi.org/10.1109/LCOMM.2020.3005947
    Article Google Scholar
  86. Yan J, Meng Y, Yang X, Luo X, Guan X (2021) Privacy-preserving localization for underwater sensor networks via deep reinforcement learning. IEEE Trans Inform Forensics Secur 16:1880–1895. https://doi.org/10.1109/TIFS.2020.3045320
    Article Google Scholar
  87. García S, Molina D, Lozano M, Herrera F (2009) A study on the use of non-parametric tests for analyzing the evolutionary algorithms’ behaviour: a case study on the cec’2005 special session on real parameter optimization. J Heuristics 15(6):617
    MATH Google Scholar
  88. Hussien AG, Oliva D, Houssein EH, Juan AA, Yu X (2020) Binary whale optimization algorithm for dimensionality reduction. Mathematics 8(10):1821
    Google Scholar
  89. Mirjalili S, Mirjalili SM, Lewis A (2014) Grey wolf optimizer. Adv Eng Softw 69:46–61
    Google Scholar
  90. Hussien AG, Amin M, Abd El Aziz M (2020) A comprehensive review of moth-flame optimisation: variants, hybrids, and applications. J Exp Theor Artif Intell 1–21
  91. Kaveh A, Talatahari S (2010) An improved ant colony optimization for constrained engineering design problems. Eng Comput. https://doi.org/10.1108/02644401011008577
    Article MATH Google Scholar
  92. He Q, Wang L (2007) A hybrid particle swarm optimization with a feasibility-based rule for constrained optimization. Appl Math Comput 186(2):1407–1422
    MathSciNet MATH Google Scholar
  93. Gandomi AH, Yang X-S, Alavi AH, Talatahari S (2013) Bat algorithm for constrained optimization tasks. Neural Comput Appl 22(6):1239–1255
    Google Scholar
  94. Hussien AG (2021) An enhanced opposition-based salp swarm algorithm for global optimization and engineering problems. J Ambient Intell Humaniz Comput 1–22
  95. Liu Y, Zhang Z, Liu X, Wang L, Xia X (2021) Efficient image segmentation based on deep learning for mineral image classification. Adv Powder Technol 32(10):3885–3903
    Google Scholar
  96. Liu Y, Zhang Z, Liu X, Wang L, Xia X (2021) Ore image classification based on small deep learning model: Evaluation and optimization of model depth, model structure and data size. Miner Eng 172:107020. https://doi.org/10.1016/j.mineng.2021.107020
    Article Google Scholar
  97. Otsu N (1979) A threshold selection method from gray-level histograms. IEEE Trans Syst Man Cybern 9(1):62–66
    MathSciNet Google Scholar
  98. Kapur JN, Sahoo PK, Wong AK (1985) A new method for gray-level picture thresholding using the entropy of the histogram. Comput Vis Graph Image Process 29(3):273–285
    Google Scholar
  99. Huynh-Thu Q, Ghanbari M (2008) Scope of validity of psnr in image/video quality assessment. Electron Lett 44(13):800–801
    Google Scholar
  100. Wang Z, Bovik AC, Sheikh HR, Simoncelli EP (2004) Image quality assessment: from error visibility to structural similarity. IEEE Trans Image Process 13(4):600–612
    Google Scholar
  101. Zhang L, Zhang L, Mou X, Zhang D (2011) Fsim: a feature similarity index for image quality assessment. IEEE Trans Image Process 20(8):2378–2386
    MathSciNet MATH Google Scholar
  102. Qiu S, Wang Z, Zhao H, Hu H (2016) Using distributed wearable sensors to measure and evaluate human lower limb motions. IEEE Tran Instrum Meas 65(4):939–950
    Google Scholar
  103. Yang C, Zhao H, Bruzzone L, Benediktsson JA, Liang Y, Liu B, Zeng X, Guan R, Li C, Ouyang Z (2020) Lunar impact crater identification and age estimation with Chang’e data by deep and transfer learning. Nat Commun 11(1):6358. https://doi.org/10.1038/s41467-020-20215-y
    Article Google Scholar
  104. Li J, Chen C, Chen H, Tong C (2017) Towards context-aware social recommendation via individual trust. Knowl Based Syst 127:58–66. https://doi.org/10.1016/j.knosys.2017.02.032
    Article Google Scholar
  105. Li J, Lin J (2020) A probability distribution detection based hybrid ensemble qos prediction approach. Inf Sci 519:289–305. https://doi.org/10.1016/j.ins.2020.01.046
    Article MathSciNet Google Scholar
  106. Li J, Zheng X-L, Chen S-T, Song W-W, Chen D-R (2014) An efficient and reliable approach for quality-of-service-aware service composition. Inf Sci 269:238–254. https://doi.org/10.1016/j.ins.2013.12.015
    Article Google Scholar
  107. Jin L, Wen Z, Hu Z (2020) Topology-preserving nonlinear shape registration on the shape manifold. Multimed Tools Appl 1–13
  108. Wu X, Xu X, Liu J, Wang H, Hu B, Nie FJ (2020) Supervised feature selection with orthogonal regression and feature weighting. IEEE Trans Neural Netw Learn Syst. https://doi.org/10.1109/TNNLS.2020.2991336
    Article Google Scholar
  109. Deng W, Xu J, Zhao H, Song Y (2020) A novel gate resource allocation method using improved pso-based qea. IEEE Trans Intell Transp Syst. https://doi.org/10.1109/TITS.2020.3025796
    Article Google Scholar
  110. W D, JJ X, YJ S, HM Z (2020) An effective improved co-evolution ant colony optimization algorithm with multi-strategies and its application. Int J Bioinspired Comput 16(3):158–170
    Google Scholar
  111. Wang X, Bennamoun M, Sohel F, Lei H (2021) Diffusion geometry derived keypoints and local descriptors for 3d deformable shape analysis. J Circuits Syst Comput 30(01):2150016
    Google Scholar
  112. Wang X, Sohel F, Bennamoun M, Guo Y, Lei H (2017) Scale space clustering evolution for salient region detection on 3d deformable shapes. Pattern Recognit 71:414–427
    Google Scholar
  113. Feng C, Zhu Z, Cui Z, Ushakov V, Dreher J, Luo W, Gu R, Wu X, Krueger F (2021) Prediction of trust propensity from intrinsic brain morphology and functional connectome. Hum Brain Mapp 42(1):175–191
    Google Scholar
  114. Li Q, Wu X, Liu T (2021) Differentiable neural architecture search for optimal spatial/temporal brain function network decomposition. Med Image Anal 69:101974. https://doi.org/10.1016/j.media.2021.101974
    Article Google Scholar
  115. Zhang L, Zhang Z, Wang W, Jin Z, Su Y, Chen H (2021) Research on a covert communication model realized by using smart contracts in blockchain environment. IEEE Syst J. https://doi.org/10.1109/JSYST.2021.3057333
    Article Google Scholar
  116. Zhang L, Zhang Z, Wang W, Waqas R, Zhao C, Kim S, Chen H (2020) A covert communication method using special bitcoin addresses generated by vanitygen. Comput Mater Continua 65(1):597–616 http://www.techscience.com/cmc/v65n1/39585
  117. Zhang L, Zou Y, Wang W, Jin Z, Su Y, Chen H (2021) Resource allocation and trust computing for blockchain-enabled edge computing system. Comput Secur. https://doi.org/10.1016/j.cose.2021.102249
    Article Google Scholar
  118. Chen H, Yang B, Liu J, Zhou X-N, Philip SY (2019) Mining spatiotemporal diffusion network: a new framework of active surveillance planning. IEEE Access 7:108458–108473
    Google Scholar
  119. Luo J, Li M, Liu X, Tian W, Zhong S,... Shi K (2020) Stabilization analysis for fuzzy systems with a switched sampled-data control. J Franklin Inst 357(1):39–58. https://doi.org/10.1016/j.jfranklin.2019.09.029
  120. Liu X, Yang B, Chen H, Musial K, Chen H, Li Y, Zuo W (2021) A scalable redefined stochastic blockmodel. ACM Trans Knowl Discov Data (TKDD) 15(3):1–28
    Google Scholar
  121. Cao X, Cao T, Gao F, Guan X (2021) Risk-averse storage planning for improving res hosting capacity under uncertain siting choice. IEEE Trans Sustain Energy. https://doi.org/10.1109/TSTE.2021.3075615
    Article Google Scholar
  122. Fei X, Wang J, Ying S, Hu Z, Shi J (2020) Projective parameter transfer based sparse multiple empirical kernel learning machine for diagnosis of brain disease. Neurocomputing 413:271–283. https://doi.org/10.1016/j.neucom.2020.07.008
    Article Google Scholar
  123. Hu Z, Wang J, Zhang C, Luo Z, Luo X, Xiao L, Shi J, Uncertainty modeling for multi center autism spectrum disorder classification using takagi-sugeno-kang fuzzy systems. IEEE Trans Cogn Dev Syst
  124. Saber A, Sakr M, Abo-Seida OM, Keshk A, Chen H (2021) A novel deep-learning model for automatic detection and classification of breast cancer using the transfer-learning technique. IEEE Access 9:71194–71209. https://doi.org/10.1109/ACCESS.2021.3079204
    Article Google Scholar
  125. Qiu S, Wang Z, Zhao H, Qin K, Li Z, Hu H (2018) Inertial/magnetic sensors based pedestrian dead reckoning by means of multi-sensor fusion. Inf Fusion 39:108–119
    Google Scholar
  126. Huang P, Zhao L, Jiang R, Wang T, Zhang X (2021) Self-filtering image dehazing with self-supporting module. Neurocomputing 432:57–69
    Google Scholar
  127. Wang T, Zhao L, Huang P, Zhang X, Xu J (2021) Haze concentration adaptive network for image dehazing. Neurocomputing 439:75–85
    Google Scholar
  128. Zhang X, Wang T, Wang J, Tang G, Zhao L (2020) Pyramid channel-based feature attention network for image dehazing. Comput Vis Image Underst 197–198:103003. https://doi.org/10.1016/j.cviu.2020.103003
    Article Google Scholar
  129. Zhou W, Yu L, Zhou Y, Qiu W, Wu M,... Luo T (2018) Local and Global Feature Learning for Blind Quality Evaluation of Screen Content and Natural Scene Images. IEEE Trans Image Process 27(5):2086–2095. https://doi.org/10.1109/TIP.2018.2794207
  130. Zhang X, Fan M, Wang D, Zhou P, Tao D Top-k feature selection framework using robust 0-1 integer programming. IEEE Trans Neural Netw Learn Syst
  131. Zhang X, Li W, Ye X, Maybank S (2015) Robust hand tracking via novel multi-cue integration. Neurocomputing 157:296–305
    Google Scholar

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