2 research outputs found
Classification and Recognition of Encrypted EEG Data Neural Network
With the rapid development of Machine Learning technology applied in
electroencephalography (EEG) signals, Brain-Computer Interface (BCI) has
emerged as a novel and convenient human-computer interaction for smart home,
intelligent medical and other Internet of Things (IoT) scenarios. However,
security issues such as sensitive information disclosure and unauthorized
operations have not received sufficient concerns. There are still some defects
with the existing solutions to encrypted EEG data such as low accuracy, high
time complexity or slow processing speed. For this reason, a classification and
recognition method of encrypted EEG data based on neural network is proposed,
which adopts Paillier encryption algorithm to encrypt EEG data and meanwhile
resolves the problem of floating point operations. In addition, it improves
traditional feed-forward neural network (FNN) by using the approximate function
instead of activation function and realizes multi-classification of encrypted
EEG data. Extensive experiments are conducted to explore the effect of several
metrics (such as the hidden neuron size and the learning rate updated by
improved simulated annealing algorithm) on the recognition results. Followed by
security and time cost analysis, the proposed model and approach are validated
and evaluated on public EEG datasets provided by PhysioNet, BCI Competition IV
and EPILEPSIAE. The experimental results show that our proposal has the
satisfactory accuracy, efficiency and feasibility compared with other
solutions
Embedded Chaotic Whale Survival Algorithm for Filter-Wrapper Feature Selection
Classification accuracy provided by a machine learning model depends a lot on
the feature set used in the learning process. Feature Selection (FS) is an
important and challenging pre-processing technique which helps to identify only
the relevant features from a dataset thereby reducing the feature dimension as
well as improving the classification accuracy at the same time. The binary
version of Whale Optimization Algorithm (WOA) is a popular FS technique which
is inspired from the foraging behavior of humpback whales. In this paper, an
embedded version of WOA called Embedded Chaotic Whale Survival Algorithm
(ECWSA) has been proposed which uses its wrapper process to achieve high
classification accuracy and a filter approach to further refine the selected
subset with low computation cost. Chaos has been introduced in the ECWSA to
guide selection of the type of movement followed by the whales while searching
for prey. A fitness-dependent death mechanism has also been introduced in the
system of whales which is inspired from the real-life scenario in which whales
die if they are unable to catch their prey. The proposed method has been
evaluated on 18 well-known UCI datasets and compared with its predecessors as
well as some other popular FS methods.Comment: 28 pages, 6 figures, submitted a minor revision to Soft Computing,
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