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An Improved Brain-Inspired Emotional Learning Algorithm for Fast Classification

Ying Mei, Guanzheng Tan, Zhentao Liu
2017 Algorithms  
In this paper, an improved brain-inspired emotional learning (BEL) algorithm is proposed for fast classification.  ...  The BEL algorithm was put forward to mimic the high speed of the emotional learning mechanism in mammalian brain, which has the superior features of fast learning and low computational complexity.  ...  Author Contributions: Ying Mei is responsible for the research work related to brain-inspired emotional learning algorithm, performed all of the simulations and did all of the write-up.  ... 
doi:10.3390/a10020070 fatcat:sh6dqkfzqvdwvgrihacj4uc2tu

An Improved Fast Brain Learning Algorithm [chapter]

Shuo Xu, Xin An, Lan Tao
Computer And Computing Technologies In Agriculture, Volume I  
The improved fast BRAIN learning algorithm is also given.  ...  In this paper, an underlying problem on the fast BRAIN learning algorithm is pointed out, which is avoided by introducing the quantity count (·, ·).  ...  In what follows, the improved fast BRAIN learning algorithm can be sketched: Improved Fast BRAIN Learning Algorithm Input: { } CONCLUSION In this paper, we analyze the reasons that an underlying computational  ... 
doi:10.1007/978-0-387-77251-6_37 dblp:conf/ifip12/XuAT07 fatcat:t4qjzcrboffbxckk2t57zfwura

Medical Image Segmentation of Improved Genetic Algorithm Research Based on Dictionary Learning

Xianqi Cao, Jiaqing Miao, Yu Xiao
2017 World Journal of Engineering and Technology  
that the algorithm in brain MRI image segmentation has fast calculation speed and the advantage of accurate segmentation.  ...  An alternate iterative algorithm of sparse encoding, sample dictionary and dictionary based on atomic update process is K-SVD decomposition.  ...  Numerical experiments show that the algorithm proposed in the brain MRI medical image segmentation application has fast calculation speed and accurate segmentation characteristics.  ... 
doi:10.4236/wjet.2017.51008 fatcat:5jzia7d44jcrnh6nasxjljrjsu

Standing on the Shoulders of Giants: Improving Medical Image Segmentation via Bias Correction [chapter]

Hongzhi Wang, Sandhitsu Das, John Pluta, Caryne Craige, Murat Altinay, Brian Avants, Michael Weiner, Susanne Mueller, Paul Yushkevich
2010 Lecture Notes in Computer Science  
We formulate the calibration process as a bias correction problem, which is addressed by machine learning using training data.  ...  We propose a simple strategy to improve automatic medical image segmentation.  ...  Out of the average brain volume, 9.7 × 10 5 voxels, the FAST algorithm produces 8.9 × 10 4 mislabeled voxels.  ... 
doi:10.1007/978-3-642-15711-0_14 fatcat:tu6q5x7ckrawnb3vpvcm6qoxve

Comparison of Pre-processed Brain Tumor MR Images Using Deep Learning Detection Algorithms

Hee Jae Kwon, Gi Pyo Lee, Young Jae Kim, Kwang Gi Kim
2021 Journal of multimedia information system  
The RetinaNet model for detecting brain tumors through deep learning algorithms demonstrated satisfactory performance in finding lesions.  ...  Detecting brain tumors of different sizes is a challenging task. This study aimed to identify brain tumors using detection algorithms.  ...  This pre-processing technique is an improvement over histogram equalization, which reduces the noise.  ... 
doi:10.33851/jmis.2021.8.2.79 fatcat:ovllg34wznawtjf7sf3x5hn2am

Research Progress and Prospects of Agricultural Aero-Bionic Technology in China

Yali Zhang, Haoxin Tian, Xinrong Huang, Chenyang Ma, Linlin Wang, Hanchao Liu, Yubin Lan
2021 Applied Sciences  
Bionic technology has received more and more attention in recent years, and breakthroughs have been made in the fields of biomedicine and health, military, brain science and brain-like navigation, and  ...  Finally, prospects of agricultural aero-bionic technology were also discussed from multiple bionic target fusion, three-dimensional spatial information exploration, sensors, and animal brain system mechanism  ...  In order to improve the convergence effect of the algorithm, an asymmetric mapping crossover operator was proposed.  ... 
doi:10.3390/app112110435 fatcat:66bjym3hszbtdla7ikw3lodony

Semantic segmentation of cerebrospinal fluid and brain volume with a convolutional neural network in pediatric hydrocephalus—transfer learning from existing algorithms

Florian Grimm, Florian Edl, Susanne R. Kerscher, Kay Nieselt, Isabel Gugel, Martin U. Schuhmann
2020 Acta Neurochirurgica  
This study aims to investigate whether these established segmentation algorithms can be transferred to new, more generalizable deep learning algorithms employing an extended transfer learning procedure  ...  In previous studies, we investigated the possibility of segmenting MRI data to determine cerebrospinal fluid and brain volume using a classical machine learning algorithm.  ...  Classical algorithms developed prior to the era of deep learning provide valid segmentation by filtering algorithms or individually adapting machine learning algorithms to address very specific questions  ... 
doi:10.1007/s00701-020-04447-x pmid:32583085 fatcat:2ikjcmlsbzhhlhax5fzkkyst4i

Machine Learning Algorithms for the diagnosis of Alzheimer's and Parkinson's Disease

Nancy Noella R S
2020 International Journal of Advanced Trends in Computer Science and Engineering  
On comparison of trained samples with the input image for the PET images, bagged ensemble learning classifier worked better than the other classification algorithms and yields an accuracy of 90.3%.  ...  The PET image dataset used in this work consists of 1050 images with AD, PD and Healthy Brain images.  ...  They used different machine learning algorithms for the detection of Parkinson disease and recorded that Random forest algorithm performs well with an accuracy of 90.26%.  ... 
doi:10.30534/ijatcse/2020/252942020 fatcat:c5gfyv7ztbfxtffajgawioo3lu

VCIP 2020 Index

2020 2020 IEEE International Conference on Visual Communications and Image Processing (VCIP)  
Infrared Colorization with Semantic Segmentation and Transfer Learning Liu, Meng-Hsuan Application of Brain-Computer Interface and Virtual Reality in Advancing Cultural Experienc Liu, Pengyu Fast  ...  An Optimized Video Encoder Implementation w Screen Content Coding Tools Li, Yuan A Novel Quality Enhanced Low Complexity Ra Control Algorithm for HEVC Li, Yunsong Deep Convolutional Neural Network  ... 
doi:10.1109/vcip49819.2020.9301896 fatcat:bdh7cuvstzgrbaztnahjdp5s5y

Blocking Our Brain: How We Can Avoid Repetitive Mistakes!

Lorie-Marlène Brault Foisy, Emmanuel Ahr, Steve Masson, Grégoire Borst, Olivier Houdé
2015 Frontiers for Young Minds  
From reading this article and learning more about positive inhibition, you might have realized one really important thing: our brains continuously adapt and improve as we learn.  ...  If you recognize these cases, you can learn to inhibit the tricky heuristic and replace it with an algorithm that will give you the correct answer for sure.  ...  With a good education, you can make the best choices to improve your life and the world. But as a schoolboy, I found it so hard to understand how I can be an efficient learner!  ... 
doi:10.3389/frym.2015.00017 fatcat:lqhyiv7z4jcixmqjxov463b4qq

Brain CT registration using hybrid supervised convolutional neural network

Hongmei Yuan, Minglei Yang, Shan Qian, Wenxin Wang, Xiaotian Jia, Feng Huang
2021 BioMedical Engineering OnLine  
To this end, the HSCN-Net, a hybrid supervised convolutional neural network, was developed for precise and fast brain CT registration.  ...  Background Image registration is an essential step in the automated interpretation of the brain computed tomography (CT) images of patients with acute cerebrovascular disease (ACVD).  ...  HSCN-Net could achieve accurate and fast brain CT image registration, and addresses the scarcity of excellent algorithms for brain CT image registration.  ... 
doi:10.1186/s12938-021-00971-8 pmid:34965854 pmcid:PMC8715595 fatcat:d6nfayyd6zg5zoq6bi3n4grrw4

An Intelligent EEG Classification Methodology Based on Sparse Representation Enhanced Deep Learning Networks

Jing-Shan Huang, Yang Li, Bin-Qiang Chen, Chuang Lin, Bin Yao
2020 Frontiers in Neuroscience  
The classification of electroencephalogram (EEG) signals is of significant importance in brain-computer interface (BCI) systems.  ...  The datasets from BCI Competition 2005 (dataset IVa) and BCI Competition 2003 (dataset III) were used to test the performance of the proposed deep learning classifier.  ...  In this article,we propose an intelligent EEG classification method based on sparse representation and enhanced deep learning networks.The features of the EEG signal are obtained through the CSP algorithm  ... 
doi:10.3389/fnins.2020.00808 pmid:33177970 pmcid:PMC7596898 fatcat:ic5fnvfoknb3lhgiabysldk3h4

Improving Across-Dataset Brain Tissue Segmentation Using Transformer [article]

Vishwanatha M. Rao, Zihan Wan, David J. Ma, Pin-Yu Lee, Ye Tian, Andrew F. Laine, Jia Guo
2022 arXiv   pre-print
brain.  ...  However, manual segmentation is highly labor-intensive, and automated approaches have struggled due to properties inherent to MRI acquisition, leaving a great need for an effective segmentation tool.  ...  CNNs have been found to outperform machine learning algorithms such as random forest and SVM specifically for brain tissue segmentation (Zhang et al., 2015) .  ... 
arXiv:2201.08741v1 fatcat:ncmo4p3qxzdelgsbjnzgnfynku

Acute Stage of Brain Stroke Diagnosis Using Hybrid Genetic Algorithm for Optimization of Feature Selection and Classifier

C Amuthadevi, K Meena, K Arthi
2018 International Journal of Engineering & Technology  
Brain Stroke is the third leading reason of death or major disabilities and needs computer guided assistance to diagnose at an earliest stage of disease.  ...  MRI of brain is mainly used for accurate diagnosis even though its cost is high.  ...  To evaluate the proposed technique, an image data base of 20 Brain Tumor images was used. The proposed method gave fast and better recognition rate when compared with conventional classifiers.  ... 
doi:10.14419/ijet.v7i2.4.11168 fatcat:5cpxqvnvxrbhlib7h3fgrej7ti

Heart Stroke Diagnosis using AI Model

Prathamesh D. Mane, Dr. Surabhi Thorat
2021 International Journal of Scientific Research in Computer Science Engineering and Information Technology  
The results indicated that Reinforcement Learning is an optimal algorithm for diagnosing complex problems. [1]  ...  Two data sets were then created and analyzed using machine learning algorithms.  ...  A stroke, or brain attack, happens when blood flow to your brain is stopped. It is an emergency situation. The brain needs a constant supply of oxygen and nutrients in order to work well.  ... 
doi:10.32628/cseit217652 fatcat:44fmp56iofc6xhjzomwydxntdy
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