IEEE Systems, Man and Cybernetics Magazine - July 2023 - 18

Jingwen Yan (jwyan@stu.edu.cn) is with the School of
Engineering, Shantou University, 515063 Shantou, China.
References
[1] C. M. Michel, M. M. Murray, G. Lantz, S. Gonzalez, L. Spinelli, and R. Grave de Peralta,
" EEG source imaging, " Clin. Neurophysiol., vol. 115, no. 10, pp. 2195-2222, Oct.
2004, doi: 10.1016/j.clinph.2004.06.001.
[2] S. Liu et al., " 3DCANN: A spatio-temporal convolution attention neural network
for EEG emotion recognition, " IEEE J. Biomed. Health Inform., vol. 26, no. 11, pp.
5321-5331, Nov. 2022, doi: 10.1109/JBHI.2021.3083525.
[3] E. Moradi, A. Pepe, C. Gaser, H. Huttunen, and J. Tohka, " Machine learning
framework for early MRI-based Alzheimer's conversion prediction in MCI subjects, "
NeuroImage, vol. 104, pp. 398-412, Jan. 2015, doi: 10.1016/j.neuroimage.
2014.10.002.
[4] S. Liu, C. Zhao, Y. An, P. Li, J. Zhao, and Y. Zhang, " Diffusion tensor imaging
denoising based on Riemannian geometric framework and sparse Bayesian learning, "
J. Med. Imag. Health Inform., vol. 9, no. 9, pp. 1993-2003, Dec. 2019, doi: 10.1166/
jmihi.2019.2832.
[5] S. Liu, L. Zhao, J. Zhao, B. Li, and S.-H. Wang, " Attention deficit/hyperactivity disorder
Classification based on deep spatio-temporal features of functional Magnetic Resonance
Imaging, " Biomed. Signal Process. Control, vol. 71, Jan. 2022, Art. no. 103239,
doi: 10.1016/j.bspc.2021.103239.
[6] A. Kastrup, G. Kruger, G. H. Glover, and M. E. Moseley, " Assessment of cerebral
oxidative metabolism with breath holding and fMRI, " Magn. Reson. Med., vol. 42,
no. 3, pp. 608-611, Sep. 1999, doi: 10.1002/(SICI)1522-2594(199909)42:3<608::AIDMRM26>3.0.CO;2-I.
[7]
E. Kirino, S. Tanaka, Y. Nagai, A. Hattori, and S. Aoki, " S1-3 Functional connectivity
in autism spectrum disorder evaluated using rs-fMRI and DKI, " Clin. Neurophysiol.,
vol. 131, no. 10, pp. e244-e245, Oct. 2020, doi: 10.1016/j.clinph.2020.04.062.
[8] J. F. Agastinose Ronicko, J. Thomas, P. Thangavel, V. Koneru, G. Langs, and
J. Dauwels, " Diagnostic classification of autism using resting-state fMRI data
improves with full correlation functional brain connectivity compared to partial
correlation, " J. Neurosci. Methods, vol. 345, Nov. 2020, Art. no. 108884, doi: 10.1016/
j.jneumeth.2020.108884.
[9] M. Wang, J. Huang, M. Liu, and D. Zhang, " Modeling dynamic characteristics of
brain functional connectivity networks using resting-state functional MRI, " Med.
Image Anal., vol. 71, Jul. 2021, Art. no. 102063, doi: 10.1016/j.media.2021.102063.
[10] T. Iidaka, " Resting state functional magnetic resonance imaging and neural
network classified autism and control, " Cortex, vol. 63, pp. 55-67, Feb. 2015, doi:
10.1016/j.cortex.2014.08.011.
[11] X. Bi, Y. Liu, Q. Jiang, Q. Shu, Q. Sun, and J. Dai, " The diagnosis of autism spectrum
disorder based on the random neural network cluster, " Frontiers Hum. Neurosci.,
vol. 12, Jun. 2018, Art. no. 257, doi: 10.3389/fnhum.2018.00257.
[12] S. Mostafa, L. Tang, and F. X. Wu, " Diagnosis of autism spectrum disorder based
on eigenvalues of brain networks, " IEEE Access, vol. 7, pp. 128,474-128,486, Sep. 2019,
doi: 10.1109/access.2019.2940198.
[13] J. Liu, Y. Sheng, W. Lan, R. Guo, Y. Wang, and J. Wang, " Improved ASD classification
using dynamic functional connectivity and multi-task feature selection, " Pattern
Recognit. Lett., vol. 138, pp. 82-87, Oct. 2020, doi: 10.1016/j.patrec.2020.07.005.
[14] F. Zhao, Z. Chen, I. Rekik, S.-W. Lee, and D. Shen, " Diagnosis of autism spectrum
disorder using central-moment features from low- and high-order dynamic restingstate
functional connectivity networks, " Frontiers Neurosci., vol. 14, Apr. 2020, Art. no.
258, doi: 10.3389/fnins.2020.00258.
[15] Y. Wu et al., " JCS: An explainable COVID-19 diagnosis system by joint classification
and segmentation, " IEEE Trans. Image Process., vol. 30, pp. 3113-3126, Feb.
2021, doi: 10.1109/TIP.2021.3058783.
18 IEEE SYSTEMS, MAN, & CYBERNETICS MAGAZINE July 2023
[16] K. Fu, D. Fan, G. Ji, Q. Zhao, J. Shen, and C. Zhu, " Siamese network for RGB-D
salient object detection and beyond, " IEEE Trans. Pattern Anal. Mach. Intell., vol. 44,
no. 9, pp. 5541-5559, Sep. 2022, doi: 10.1109/TPAMI.2021.3073689.
[17] Q. Hu, S. Hu, and S. Liu, " BANet: A balance attention network for anchor-free ship
detection in SAR images, " IEEE Trans. Geosci. Remote Sens., vol. 60, pp. 1-12, Jan.
2022, doi: 10.1109/TGRS.2022.3146027.
[18] Z. Xiao, C. Wang, N. Jia, and J. Wu, " SAE-based classification of school-aged children
with autism spectrum disorders using functional magnetic resonance imaging, "
Multimedia Tools Appl., vol. 77, no. 17, pp. 22,809-22,820, Sep. 2018, doi: 10.1007/
s11042-018-5625-1.
[19] N. Jia, J. Tan, Z. Xiao, Z. Qi, and J. Wu, " Classification of autism spectrum disorder
based on brain functional connectivity and SAE, " J. Nanchang Univ. (Natural Sci.),
vol. 42, no. 4, pp. 399-403, Aug. 2018, doi: 10.13764/j.cnki.ncdl.2018.04.017.
[20] A. Rathore, S. Palande, J. S. Anderson, B. A. Zielinski, P. T. Fletcher, and B. Wang,
" Autism classification using topological features and deep learning: A cautionary
tale, " in Proc. Int. Conf. Med. Image Comput. Comput. Assisted Intervention (MICCAI),
Cham, Switzerland: Springer-Verlag, 2019, pp. 736-744, doi: 10.1007/978-3-030
-32248-9_82.
[21] J. Zhuang, N. C. Dvornek, X. Li, P. Ventola, and J. S. Duncan, " Invertible network
for classification and biomarker selection for ASD, " in Proc. Int. Conf. Med. Image
Comput. Comput. Assisted Intervention (MICCAI), Cham, Switzerland: SpringerVerlag,
2019, pp. 700-708, doi: 10.1007/978-3-030-32248-9_78.
[22] M. Tang, P. Kumar, H. Chen, and A. Shrivastava, " Deep multimodal learning for
the diagnosis of autism spectrum disorder, " J. Imag., vol. 6, no. 6, p. 47, Jun. 2020, doi:
10.3390/jimaging6060047.
[23] L. Shao, C. Fu, Y. You, and D. Fu, " Classification of ASD based on fMRI data with
deep learning, " Cogn. Neurodynamics, vol. 15, no. 6, pp. 961-974, Dec. 2021, doi:
10.1007/s11571-021-09683-0.
[24] W. Yin, S. Mostafa, and F. Wu, " Diagnosis of autism spectrum disorder based on
functional brain networks with deep learning, " J. Comput. Biol., vol. 28, no. 2, pp.
146-165, Feb. 2021, doi: 10.1089/cmb.2020.0252.
[25] B. Lullo. " Autism Brain Imaging Data Exchange I ABIDE I. " ABIDE. Accessed: Jun.
24, 2016. [Online]. Available: https://fcon_1000.projects.nitrc.org/indi/abide/abide_I.html
[26] N. Chaitra, P. A. Vijaya, and G. Deshpande, " Diagnostic prediction of autism spectrum
disorder using complex network measures in a machine learning framework, " Biomed.
Signal Process. Control, vol. 62, Sep. 2020, Art. no. 102099, doi: 10.1016/j.bspc.2020.102099.
[27] M. GraƱa and M. Silva, " Impact of machine learning pipeline choices in autism
prediction from functional connectivity data, " Int. J. Neural Syst., vol. 31, no. 4,
p. 2,150,009, Apr. 2021, doi: 10.1142/s012906572150009x.
[28] Z. Sherkatghanad, M. Akhondzadeh, S. Salari, M. Zomorodi, and V. Salari, " Automated
detection of autism spectrum disorder using a convolutional neural network, "
Frontiers Neurosci., vol. 13, Jan. 2020, Art. no. 1325, doi: 10.3389/fnins.2019.01325.
[29] T. Eslami, V. Mirjalili, A. Fong, A. R. Laird, and F. Saeed, " ASD-DiagNet: A hybrid
learning approach for detection of autism spectrum disorder using fMRI data, " Frontiers
Neuroinformatics, vol. 13, Nov. 2019, Art. no. 70, doi: 10.3389/fninf.2019.00070.
[30] L. Guo et al., " Classification of the functional magnetic resonance image of autism
based on 4D convolutional neural network, " CAAI Trans. Intell. Syst., vol. 16, no. 6, pp.
1021-1029, Nov. 2021, doi: 10.11992/tis.202009022.
[31] W. Jiang et al., " CNNG: A convolutional neural networks with gated recurrent
units for autism spectrum disorder classification, " Frontiers Aging Neurosci., vol. 14,
Jul. 2022, Art. no. 948704, doi: 10.3389/fnagi.2022.948704.
[32] S. Parisot, S. I. Ktena, E. Ferrante, M. Lee, and D. Rueckert, " Spectral graph
convolutions for population-based disease prediction, " in Proc. Int. Conf. Med. Image
Comput. Comput. Assisted Intervention (MICCAI), Cham, Switzerland: SpringerVerlag,
2017, pp. 177-185, doi: 10.1007/978-3-319-66179-7_21.
https://fcon_1000.projects.nitrc.org/indi/abide/abide_I.html

IEEE Systems, Man and Cybernetics Magazine - July 2023

Table of Contents for the Digital Edition of IEEE Systems, Man and Cybernetics Magazine - July 2023

Contents
IEEE Systems, Man and Cybernetics Magazine - July 2023 - Cover1
IEEE Systems, Man and Cybernetics Magazine - July 2023 - Cover2
IEEE Systems, Man and Cybernetics Magazine - July 2023 - Contents
IEEE Systems, Man and Cybernetics Magazine - July 2023 - 2
IEEE Systems, Man and Cybernetics Magazine - July 2023 - 3
IEEE Systems, Man and Cybernetics Magazine - July 2023 - 4
IEEE Systems, Man and Cybernetics Magazine - July 2023 - 5
IEEE Systems, Man and Cybernetics Magazine - July 2023 - 6
IEEE Systems, Man and Cybernetics Magazine - July 2023 - 7
IEEE Systems, Man and Cybernetics Magazine - July 2023 - 8
IEEE Systems, Man and Cybernetics Magazine - July 2023 - 9
IEEE Systems, Man and Cybernetics Magazine - July 2023 - 10
IEEE Systems, Man and Cybernetics Magazine - July 2023 - 11
IEEE Systems, Man and Cybernetics Magazine - July 2023 - 12
IEEE Systems, Man and Cybernetics Magazine - July 2023 - 13
IEEE Systems, Man and Cybernetics Magazine - July 2023 - 14
IEEE Systems, Man and Cybernetics Magazine - July 2023 - 15
IEEE Systems, Man and Cybernetics Magazine - July 2023 - 16
IEEE Systems, Man and Cybernetics Magazine - July 2023 - 17
IEEE Systems, Man and Cybernetics Magazine - July 2023 - 18
IEEE Systems, Man and Cybernetics Magazine - July 2023 - 19
IEEE Systems, Man and Cybernetics Magazine - July 2023 - 20
IEEE Systems, Man and Cybernetics Magazine - July 2023 - 21
IEEE Systems, Man and Cybernetics Magazine - July 2023 - 22
IEEE Systems, Man and Cybernetics Magazine - July 2023 - 23
IEEE Systems, Man and Cybernetics Magazine - July 2023 - 24
IEEE Systems, Man and Cybernetics Magazine - July 2023 - 25
IEEE Systems, Man and Cybernetics Magazine - July 2023 - 26
IEEE Systems, Man and Cybernetics Magazine - July 2023 - 27
IEEE Systems, Man and Cybernetics Magazine - July 2023 - 28
IEEE Systems, Man and Cybernetics Magazine - July 2023 - 29
IEEE Systems, Man and Cybernetics Magazine - July 2023 - 30
IEEE Systems, Man and Cybernetics Magazine - July 2023 - 31
IEEE Systems, Man and Cybernetics Magazine - July 2023 - 32
IEEE Systems, Man and Cybernetics Magazine - July 2023 - 33
IEEE Systems, Man and Cybernetics Magazine - July 2023 - 34
IEEE Systems, Man and Cybernetics Magazine - July 2023 - 35
IEEE Systems, Man and Cybernetics Magazine - July 2023 - 36
IEEE Systems, Man and Cybernetics Magazine - July 2023 - 37
IEEE Systems, Man and Cybernetics Magazine - July 2023 - 38
IEEE Systems, Man and Cybernetics Magazine - July 2023 - 39
IEEE Systems, Man and Cybernetics Magazine - July 2023 - 40
IEEE Systems, Man and Cybernetics Magazine - July 2023 - 41
IEEE Systems, Man and Cybernetics Magazine - July 2023 - 42
IEEE Systems, Man and Cybernetics Magazine - July 2023 - 43
IEEE Systems, Man and Cybernetics Magazine - July 2023 - 44
IEEE Systems, Man and Cybernetics Magazine - July 2023 - 45
IEEE Systems, Man and Cybernetics Magazine - July 2023 - 46
IEEE Systems, Man and Cybernetics Magazine - July 2023 - 47
IEEE Systems, Man and Cybernetics Magazine - July 2023 - 48
IEEE Systems, Man and Cybernetics Magazine - July 2023 - Cover3
IEEE Systems, Man and Cybernetics Magazine - July 2023 - Cover4
https://www.nxtbook.com/nxtbooks/ieee/smc_202310
https://www.nxtbook.com/nxtbooks/ieee/smc_202307
https://www.nxtbook.com/nxtbooks/ieee/smc_202304
https://www.nxtbook.com/nxtbooks/ieee/smc_202301
https://www.nxtbook.com/nxtbooks/ieee/smc_202210
https://www.nxtbook.com/nxtbooks/ieee/smc_202207
https://www.nxtbook.com/nxtbooks/ieee/smc_202204
https://www.nxtbook.com/nxtbooks/ieee/smc_202201
https://www.nxtbook.com/nxtbooks/ieee/smc_202110
https://www.nxtbook.com/nxtbooks/ieee/smc_202107
https://www.nxtbook.com/nxtbooks/ieee/smc_202104
https://www.nxtbook.com/nxtbooks/ieee/smc_202101
https://www.nxtbook.com/nxtbooks/ieee/smc_202010
https://www.nxtbook.com/nxtbooks/ieee/smc_202007
https://www.nxtbook.com/nxtbooks/ieee/smc_202004
https://www.nxtbook.com/nxtbooks/ieee/smc_202001
https://www.nxtbook.com/nxtbooks/ieee/smc_201910
https://www.nxtbook.com/nxtbooks/ieee/smc_201907
https://www.nxtbook.com/nxtbooks/ieee/smc_201904
https://www.nxtbook.com/nxtbooks/ieee/smc_201901
https://www.nxtbook.com/nxtbooks/ieee/smc_201810
https://www.nxtbook.com/nxtbooks/ieee/smc_201807
https://www.nxtbook.com/nxtbooks/ieee/smc_201804
https://www.nxtbook.com/nxtbooks/ieee/smc_201801
https://www.nxtbook.com/nxtbooks/ieee/systems_man_cybernetics_1017
https://www.nxtbook.com/nxtbooks/ieee/systems_man_cybernetics_0717
https://www.nxtbook.com/nxtbooks/ieee/systems_man_cybernetics_0417
https://www.nxtbook.com/nxtbooks/ieee/systems_man_cybernetics_0117
https://www.nxtbook.com/nxtbooks/ieee/systems_man_cybernetics_1016
https://www.nxtbook.com/nxtbooks/ieee/systems_man_cybernetics_0716
https://www.nxtbook.com/nxtbooks/ieee/systems_man_cybernetics_0416
https://www.nxtbook.com/nxtbooks/ieee/systems_man_cybernetics_0116
https://www.nxtbook.com/nxtbooks/ieee/systems_man_cybernetics_1015
https://www.nxtbook.com/nxtbooks/ieee/systems_man_cybernetics_0715
https://www.nxtbook.com/nxtbooks/ieee/systems_man_cybernetics_0415
https://www.nxtbook.com/nxtbooks/ieee/systems_man_cybernetics_0115
https://www.nxtbookmedia.com