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Data-driven brain emotional learning-based intelligent controller-PID control of MIMO systems based on a modified safe experimentation dynamics algorithm
Published 2025“…The safe experimentation dynamics algorithm (SEDA) is one such method that optimizes controller parameters using data-driven techniques. …”
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Data-driven brain emotional learning-based intelligent controller-PID control of MIMO systems based on a modified safe experimentation dynamics algorithm
Published 2025“…The safe experimentation dynamics algorithm (SEDA) is one such method that optimizes controller parameters using data-driven techniques. …”
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P300 detection of brain signals using a combination of wavelet transform techniques
Published 2012“…In this research the BCI competition data-set has been processed through 5 optimized detection methods. …”
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Reference-free reduction of ballistocardiogram artifact from EEG data using EMD-PCA
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Computational prediction of changes in brain metabolic fluxes during Parkinson’s disease from mRNA expression
Published 2018“…Here we explore the hypothesis that changes in gene expression for enzymes tend to parallel flux changes in biochemical reaction pathways in the brain metabolic network. This hypothesis is the basis of a computational method to predict metabolic flux changes from post-mortem gene expression measurements in Parkinson’s disease (PD) brain. …”
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Development of a scaled conjugate gradient algorithm for significant RF neural signal processing
Published 2025“…Scale Conjugate Gradient (SCG) algorithm is an efficient training method for ANN that accelerates the learning process and improves output accuracy. …”
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Classification of labour pain using electroencephalogram signal based on wavelet method / Sai Chong Yeh
Published 2020“…The training and parameters selection of the machine learning algorithms are conducted using EEG data collected from ten subjects in the laboratory. …”
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A new hybrid technique for nosologic segmentation of primary brain tumors / Shafaf Ibrahim
Published 2015“…On the other hand, the ANFIS is found to have limited pixel detection in nosologic segmentation of primary brain tumors. The CAPSOCA was proven to be the best algorithm for nosologic segmentation of primary brain tumors from the MRI images data. …”
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Learner’s emotion prediction using production rules classification algorithm through brain computer interface tool
Published 2018“…The use of sensors known as Brain-Computer Interface (BCI) tool can monitor the physical processes and mental states that occur in the human brain. …”
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Brain & heart from computer science perspective
Published 2014“…EEG and ECG have been very useful for capturing the signals of the brain and heart. In computer science, a prime challenge is to interpret these signals into meaningful data and to develop algorithms and applications to establish interface between humans’ bio-signal and computer. …”
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Deep learning segmentation of brain ischemic lesion from magnetic resonance images for three-dimensional modelling
Published 2025“…Automated segmentation is important for early detection and treatments to reduce disability and death risks among brain stroke patients. The existing segmentation algorithm is limited due to its computationally expensiveness in achieving a small accuracy. …”
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Investigation Of Edge Detection Techniques Based On Brain Tumor Images
Published 2018“…However, from visual perspective, Sobel operator produced better edge maps of the brain tumor compared to the Modified Canny algorithm.…”
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Electroencephalogram-based decoding cognitive states using convolutional neural network and likelihood ratio based score fusion
Published 2017“…However, EEG is an important technique, especially for brain-computer interface applications. In this study, a novel algorithm is proposed to decode brain activity associated with different types of images. …”
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Applying SAX-based time series analysis to classify EEG signal using a COTS EEG device
Published 2021“…One of the proposals that could help solve this problem is the use of brain-computer interface (BCI)s. Brain-Computer Interface (BCI) can be used as a direct communication path between the brain and an external device. …”
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Decoding of visual activity patterns from fMRI responses using multivariate pattern analyses and convolutional neural network
Published 2017“…In recent years, Convolutional neural network (CNN) has become a popular method for the extraction of features due to its higher accuracy, however it needs a lot of computation and training data. In this study, an algorithm is developed using Multivariate pattern analysis (MVPA) and modified CNN to decode the behavior of brain for different images with limited data set. …”
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