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An Improved Brain-Inspired Emotional Learning Algorithm for Fast Classification
2017
Algorithms
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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]
Computer And Computing Technologies In Agriculture, Volume I
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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
2017
World Journal of Engineering and Technology
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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]
2010
Lecture Notes in Computer Science
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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
2021
Journal of multimedia information system
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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
2021
Applied Sciences
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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
2020
Acta Neurochirurgica
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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
2020
International Journal of Advanced Trends in Computer Science and Engineering
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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)
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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!
2015
Frontiers for Young Minds
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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
2021
BioMedical Engineering OnLine
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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
2020
Frontiers in Neuroscience
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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]
2022
arXiv
pre-print
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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
2018
International Journal of Engineering & Technology
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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
2021
International Journal of Scientific Research in Computer Science Engineering and Information Technology
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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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