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6.3 OUR APPROACH 157

multiscale depiction of the signal. Alexandre Abrahamet et al. [30] illustrated how scikit-learn can be
used to carry out some important analysis steps. Scikit-learn has several supervised and unsupervised
learning algorithms. This work application to neuro-imaging data offers a multipurpose tool to study
the brain. Masaya Misaki et al. [6] compared six multivariate classifiers and examined the response
normalizations for pattern information. These schemes were compared with reaction patterns in human
early visual and inferior temporal cortex and accuracy at deciphering the type of visual objects was
evaluated. Niels VæverHartvig et al. [31] estimated a spatial mixture modeling of fMRI data. The fMRI
data was divided into two components on the basis of activation of voxel. Using this model, the pos-
terior probability for an activated voxel can be effortlessly estimated. It provides a better thresholding
than the statistic image. The method of Everitt and Bullmore [32] was employed for spatial coherency
of activated regions. This was achieved by calculating the posterior probability of an activated voxel
with the use of spatial structure obtained from modeling the activation in a small region. This research
model was functional to synthetic data from statistical image analysis, a synthetic fMRI dataset, and to
data of visual stimulation.
Big data has given us a new method to analyze the huge collection of data stored as health data. A
large amount of research work has been undertaken and their valuable insights help us to make the
relevant changes in the healthcare system. It has decreased the cost of treatment drastically and helps
us to predict the incoming problems in advance using the latest technology [33–36]. To balance the load
of huge health data, some research works [37–40] use the edge computing nodes to make a faster and
more reliable framework. In this works, they have shown the comparative performances of different
smart devices [41–45].

6.3 OUR APPROACH
fMRI is widely used for prediction of the current activity in the human brain and the functional mech-
anism of the brain can be mainly classified into two or more states by analyzing the fMRI data observed
over a single time interval with the assistance of machine learning tools [28]. Therefore, in this chapter
we have trained our fMRI dataset with different machine learning approaches to enhance the precision
of classification.

6.3.1 DATASET
We used preprocessed fMRI data as the input [1], where all the voxel activities were represented after
various experiments consisting of sets of trials.

6.3.2 METHODOLOGY
A series of trials were carried out on six subjects. In half of the trials, subjects were shown a sentence
followed by the picture. While in the rest of the trials the picture is shown first then a sentence. The
proposed flow diagram is depicted in Fig. 6.1A and B and fMRI image sample was shown. The former
is named as an SP dataset and the latter as a PS dataset. In both cases, the first stimulus (either the
picture or sentence) was shown for 4s, then a blank screen for 4s followed by the second stimulus
for 4s (either the picture or the sentence). At the end, the subjects has to press the mouse button in
order to specify whether the sentence correctly matched the picture. Images of the brain were recorded