A robust swarm intelligence-based feature selection model for neuro-fuzzy recognition of mild cognitive impairment from resting-state fMRI
暂无分享,去创建一个
Zening Fu | Yichen Wei | Ahmed M. Anter | Lihua Zhao | Zhiguo Zhang | Jiahui Su | Yueming Yuan | Beiying Lei | Gaoxiong Duan | Wei Mai | Xiucheng Nong | Bihan Yu | Chong Li | Demao Deng | Z. Fu | Zhiguo Zhang | Gaoxiong Duan | Demao Deng | A. Anter | Yueming Yuan | Yichen Wei | Bihan Yu | Wei Mai | Xiucheng Nong | Chong Li | Jiahui Su | Lihua Zhao | W. Mai | D. Deng | G. Duan | J. Su | X. Nong | Beiying Lei
[1] Srikrishna Subramanian,et al. Grey wolf optimization for combined heat and power dispatch with cogeneration systems , 2016 .
[2] Joel J. P. C. Rodrigues,et al. Usability feature extraction using modified crow search algorithm: a novel approach , 2018, Neural Computing and Applications.
[3] Majdi M. Mafarja,et al. S-Shaped vs. V-Shaped Transfer Functions for Ant Lion Optimization Algorithm in Feature Selection Problem , 2017, ICFNDS.
[4] Ayman El-Baz,et al. Alzheimer's disease diagnostics by adaptation of 3D convolutional network , 2016, 2016 IEEE International Conference on Image Processing (ICIP).
[5] Mahdi Hasanipanah,et al. Feasibility of PSO–ANFIS model to estimate rock fragmentation produced by mine blasting , 2016, Neural Computing and Applications.
[6] A. Fagan,et al. Functional connectivity and graph theory in preclinical Alzheimer's disease , 2014, Neurobiology of Aging.
[7] Sanjay Sinha,et al. An iPSC-derived vascular model of Marfan syndrome identifies key mediators of smooth muscle cell death , 2016, Nature Genetics.
[8] Xin-She Yang,et al. A wrapper approach for feature selection based on Bat Algorithm and Optimum-Path Forest , 2014, Expert Syst. Appl..
[9] Anirban Bhowal,et al. Comparative analysis of machine learning algorithms along with classifiers for network intrusion detection , 2015, 2015 International Conference on Smart Technologies and Management for Computing, Communication, Controls, Energy and Materials (ICSTM).
[10] A. Mechelli,et al. Using deep learning to investigate the neuroimaging correlates of psychiatric and neurological disorders: Methods and applications , 2017, Neuroscience & Biobehavioral Reviews.
[11] Bin Hu,et al. A review of structural and functional brain networks: small world and atlas , 2015, Brain Informatics.
[12] Shihui Ying,et al. Multimodal Neuroimaging Feature Learning With Multimodal Stacked Deep Polynomial Networks for Diagnosis of Alzheimer's Disease , 2018, IEEE Journal of Biomedical and Health Informatics.
[13] Jingxin Nie,et al. Different Developmental Pattern of Brain Activities in ADHD: A Study of Resting-State fMRI , 2018, Developmental Neuroscience.
[14] Vince D. Calhoun,et al. Deep neural network with weight sparsity control and pre-training extracts hierarchical features and enhances classification performance: Evidence from whole-brain resting-state functional connectivity patterns of schizophrenia , 2016, NeuroImage.
[15] H. Heinsen,et al. Longitudinal measures of cholinergic forebrain atrophy in the transition from healthy aging to Alzheimer's disease , 2013, Neurobiology of Aging.
[16] Jonathan D. Power,et al. Prediction of Individual Brain Maturity Using fMRI , 2010, Science.
[17] Dinggang Shen,et al. State-space model with deep learning for functional dynamics estimation in resting-state fMRI , 2016, NeuroImage.
[18] D. Bennett,et al. Vitamin E and donepezil for the treatment of mild cognitive impairment. , 2005, The New England journal of medicine.
[19] V. M. Arul Xavier,et al. Chaotic social spider algorithm for load balance aware task scheduling in cloud computing , 2018, Cluster Computing.
[20] Paul S. Addison,et al. The Illustrated Wavelet Transform Handbook Introductory Theory And Applications In Science , 2002 .
[21] Qinghua Zheng,et al. Adaptive Unsupervised Feature Selection With Structure Regularization , 2018, IEEE Transactions on Neural Networks and Learning Systems.
[22] Jin Li,et al. Differentially private Naive Bayes learning over multiple data sources , 2018, Inf. Sci..
[23] Sidong Liu,et al. Multimodal Neuroimaging Feature Learning for Multiclass Diagnosis of Alzheimer's Disease , 2015, IEEE Transactions on Biomedical Engineering.
[24] J. Shimony,et al. Resting-State fMRI: A Review of Methods and Clinical Applications , 2013, American Journal of Neuroradiology.
[25] Gillian Dobbie,et al. Research on particle swarm optimization based clustering: A systematic review of literature and techniques , 2014, Swarm Evol. Comput..
[26] Qiang Li,et al. An Enhanced Grey Wolf Optimization Based Feature Selection Wrapped Kernel Extreme Learning Machine for Medical Diagnosis , 2017, Comput. Math. Methods Medicine.
[27] Andrew Lewis,et al. Grey Wolf Optimizer , 2014, Adv. Eng. Softw..
[28] Jyh-Shing Roger Jang,et al. ANFIS: adaptive-network-based fuzzy inference system , 1993, IEEE Trans. Syst. Man Cybern..
[29] Vladimir Fonov,et al. Standardized evaluation of algorithms for computer-aided diagnosis of dementia based on structural MRI: The CADDementia challenge , 2015, NeuroImage.
[30] M. Milham,et al. The ADHD-200 Consortium: A Model to Advance the Translational Potential of Neuroimaging in Clinical Neuroscience , 2012, Front. Syst. Neurosci..
[31] David M. W. Powers,et al. Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation , 2011, ArXiv.
[32] Bogdan Wilamowski,et al. Fully Connected Cascade Artificial Neural Network Architecture for Attention Deficit Hyperactivity Disorder Classification From Functional Magnetic Resonance Imaging Data , 2015, IEEE Transactions on Cybernetics.
[33] Hao Yang,et al. Novel Effective Connectivity Inference Using Ultra-Group Constrained Orthogonal Forward Regression and Elastic Multilayer Perceptron Classifier for MCI Identification , 2019, IEEE Transactions on Medical Imaging.
[34] Mu-Yen Chen,et al. A hybrid ANFIS model for business failure prediction utilizing particle swarm optimization and subtractive clustering , 2013, Inf. Sci..
[35] Yang Yu,et al. Abnormal Resting-State Functional Connectivity Strength in Mild Cognitive Impairment and Its Conversion to Alzheimer's Disease , 2015, Neural plasticity.
[36] Ahmed M. Anter,et al. Feature selection strategy based on hybrid crow search optimization algorithm integrated with chaos theory and fuzzy c-means algorithm for medical diagnosis problems , 2019, Soft Computing.
[37] Sankalap Arora,et al. Chaotic grey wolf optimization algorithm for constrained optimization problems , 2018, J. Comput. Des. Eng..
[38] Elad Gilboa,et al. Adaptive smoothing based on Gaussian processes regression increases the sensitivity and specificity of fMRI data , 2017, Human brain mapping.
[39] Hossam Faris,et al. Grey wolf optimizer: a review of recent variants and applications , 2017, Neural Computing and Applications.
[40] Seyed Mohammad Mirjalili,et al. Whale optimization approaches for wrapper feature selection , 2018, Appl. Soft Comput..
[41] Mostafa Langarizadeh,et al. Applying Naive Bayesian Networks to Disease Prediction: a Systematic Review , 2016, Acta informatica medica : AIM : journal of the Society for Medical Informatics of Bosnia & Herzegovina : casopis Drustva za medicinsku informatiku BiH.
[42] A. Hao,et al. Discrimination of ADHD children based on Deep Bayesian Network , 2015 .
[43] Karl J. Friston,et al. Movement‐Related effects in fMRI time‐series , 1996, Magnetic resonance in medicine.
[44] Tianzi Jiang,et al. BRANT: A Versatile and Extendable Resting-State fMRI Toolkit , 2018, Front. Neuroinform..
[45] Walter J. Lukiw,et al. Aluminum and its potential contribution to Alzheimer's disease (AD) , 2014, Front. Aging Neurosci..