Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: EMAP: A Cloud-Edge Hybrid Framework for EEG Monitoring and
  Cross-Correlation Based Real-time Anomaly Prediction   State-of-the-art techniques for detecting, or predicting, neurological
disorders (1) focus on predicting each disorder individually, and are (2)
computationally expensive, leading to a delay that can potentially render the
prediction useless, especially in critical events. Towards this, we present a
real-time two-tiered framework called EMAP, which cross-correlates the input
with all the EEG signals in our mega-database (a combination of multiple EEG
datasets) at the cloud, while tracking the signal in real-time at the edge, to
predict the occurrence of a neurological anomaly. Using the proposed framework,
we have demonstrated a prediction accuracy of up to 94% for the three different
anomalies that we have tested.

13
The categories of this paper are: quant-ph
27
The categories of this paper are: comp-gas
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The categories of this paper are: hep-ex
0
The categories of this paper are: hep-ph
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The categories of this paper are: nucl-th
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The categories of this paper are: patt-sol
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The categories of this paper are: mtrl-th
5
The categories of this paper are: gr-qc
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The categories of this paper are: dg-ga
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The categories of this paper are: funct-an
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The categories of this paper are: hep-th
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The categories of this paper are: q-alg
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The categories of this paper are: adap-org
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The categories of this paper are: q-bio
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The categories of this paper are: chao-dyn
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The categories of this paper are: solv-int