Introducing ViewToo v.2.0
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ViewToo is a cloud-based screen sharing and online meeting tool that allows users to easily hold meetings, share screens, files and annotations with multiple attendees via a single meeting link. It has no sign up or registration required, supports file sharing from various cloud services, and works across Android, iOS and other devices. The document provides examples of how a consulting company, university professor and international lawyer have used ViewToo to simplify online meetings and collaboration by letting them invite others and access files from anywhere.
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underpinning
of document
cataloging
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document
steering
and
exemplif
ication
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make the learning task more accurate and competent. One of the
traditional
lazy
text classifier
k
-
Nearest
Neighborhood (
k
NN) has
a
major pitfall in calculating the similarity between
all
the
objects in training and
testing se
t
s,
there by leads to exaggeration of
both
computational complexity
of the algorithm
and
massive
consumption
of
main memory
. To diminish these shortcomings
in
viewpoint
of a
data
-
mining
practitioner
a
n
amalgamati
ve technique is proposed in this paper using
a novel restructured version of
k
NN called
Augmented
k
NN
(AkNN)
and
k
-
Medoids
(kMdd)
clustering.
The proposed work
comprises
preprocesses
on
the
initial training
set
by
imposing
attribute feature selection
for reduc
tion of high dimensionality, also it
detects and excludes the high
-
fliers
samples
in t
he
initial
training set
and
re
structure
s
a
constricted
training
set
.
The kMdd clustering algorithm generates the cluster centers (as interior objects) for each category
and
restructures
the constricted training set
with centroids
. This technique
is
amalgamated with
AkNN
classifier
that
was prearranged with
text mining similarity measure
s.
Eventually, s
ignifican
tweights
and ranks were
assigned to each object in the new
training set based upon the
ir
accessory towards the
object in testing set
.
Experiments
conducted
on Reuters
-
21578 a
UCI benchmark
text mining
data
set
, and
comparisons with
traditional
k
NN
classifier designates
the
referred
method
yield
spreeminentrecital
in b
oth clustering and
classification
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images. Initially the preprocessing step is performed in order to remove noise occurred in input image with
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features from the images. After this process, the relevant geographic images are retrieved from the dataset
by using Euclidean distance. In this, the dataset consists of totally 40 images. From that the images which
are all related to the input image are retrieved by using Euclidean distance. The approach of SIFT is to
perform reliable recognition, it is important that the feature extracted from the training image be
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described by a huge number of attributes, therefore, the feature selection become an important discipline in
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to select the most relevant features to impr
ove
the quality of their studies and to reduce the time processing of their algorithm. In addition to that, the data
used by the applications become richer. They are now represented by a set of complex and structured
objects, instead of simple numerical ma
trixes. The purpose of
our
algorithm is to do feature selection on
rich data,
called Boolean Symbolic Objects
(BSOs)
. These objects are desc
ribed by multivalued features.
The
BSOs
are considered as higher level units which can model complex data, such as c
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reaching the global optimization and to show the efficiency of the GA and AIS th
e application of them in
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-
dimensional spaces in SCOFs the use of the classic optimization methods, is generally non
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efficient
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local optimized solution. A possible solution for SCOFs is to use the EAs which are high in probability of
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Great model a model for the automatic generation of semantic relations betwee...
Great model a model for the automatic generation of semantic relations betwee...ijcsity The
large
a
v
ailable
am
ou
n
t
of
non
-
structured
texts
that
b
e
-
long
to
differe
n
t
domains
su
c
h
as
healthcare
(e.g.
medical
records),
justice
(e.g.
l
a
ws,
declarations),
insurance
(e.g.
declarations),
etc. increases
the
effort
required
for
the
analysis
of
information
in
a
decision making
pro
-
cess.
Differe
n
t
pr
o
jects
and t
o
ols
h
av
e
pro
p
osed
strategies
to
reduce
this
complexi
t
y
b
y
classifying,
summarizing
or
annotating
the
texts.
P
artic
-
ularl
y
,
text
summary
strategies
h
av
e
pr
ov
en
to
b
e
v
ery
useful
to
pr
o
vide
a
compact
view
of
an
original
text.
H
ow
e
v
er,
the
a
v
ailable
strategies
to
generate
these
summaries
do
not
fit
v
ery
w
ell
within
the
domains
that
require
ta
k
e
i
n
to
consideration
the
tem
p
oral
dimension
of
the
text
(e.g.
a
rece
n
t
piece
of
text
in
a
medical
record
is
more
im
p
orta
n
t
than
a
pre
-
vious
one)
and
the
profile
of
the
p
erson
who
requires
the
summary
(e.g
the
medical
s
p
ecialization).
T
o
co
p
e with
these
limitations
this
pa
p
er
prese
n
ts
”GRe
A
T”
a
m
o
del
for
automatic
summary
generation
that
re
-
lies
on
natural
language
pr
o
cessing
and
text
mining
te
c
hniques
to
extract
the
most
rele
v
a
n
t
information
from
narrati
v
e
texts
and
disc
o
v
er
new
in
-
formation
from
the
detection
of
related
information. GRe
A
T
M
o
del
w
as impleme
n
ted
on
sof
tw
are
to
b
e
v
alidated
in
a
health
institution
where
it
has
sh
o
wn
to
b
e
v
ery
useful
to displ
a
y
a
preview
of
the
information
a
b
ou
t
medical
health
records
and
disc
o
v
er
new
facts
and
h
y
p
otheses
within
the
information.
Se
v
eral
tests
w
ere
executed
su
c
h
as
F
unctional
-
i
t
y
,
Usabili
t
y
and
P
erformance
regarding
to
the
impleme
n
ted
sof
t
w
are.
In
addition,
precision
and
recall
measures
w
ere
applied
on
the
results
ob
-
tained
through
the
impleme
n
ted
t
o
ol,
as
w
ell
as
on
the
loss
of
information
obtained
b
y
pr
o
viding
a
text
more
shorter than
the
original
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computational complexity, we employ an entropy-based geneselection approach to select relevant
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an average accuracy of 98.9% in predicting cancer recurrence over 3 datasets. The experimental results
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set
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imposing
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for reduc
tion of high dimensionality, also it
detects and excludes the high
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.
The kMdd clustering algorithm generates the cluster centers (as interior objects) for each category
and
restructures
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is
amalgamated with
AkNN
classifier
that
was prearranged with
text mining similarity measure
s.
Eventually, s
ignifican
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assigned to each object in the new
training set based upon the
ir
accessory towards the
object in testing set
.
Experiments
conducted
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-
21578 a
UCI benchmark
text mining
data
set
, and
comparisons with
traditional
k
NN
classifier designates
the
referred
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in b
oth clustering and
classification
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style of writing. Some of the challengeable problems in character identification are changing in the style of
writing, font and turns of words and etc. In this paper, the goal is Persian character identification using
independent orthogonal moment as the feature extraction technique.The proposed feature extraction
method is the combination of Pseudo-Zernike Moment and Fourier-Mellin Moment called Pseudo-Zernike-
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images. Initially the preprocessing step is performed in order to remove noise occurred in input image with
the help of Gaussian filter. As the second step, Gray Level Co-occurrence Matrix (GLCM), Scale Invariant
Feature Transform (SIFT), and Moment Invariant Feature algorithms are implemented to extract the
features from the images. After this process, the relevant geographic images are retrieved from the dataset
by using Euclidean distance. In this, the dataset consists of totally 40 images. From that the images which
are all related to the input image are retrieved by using Euclidean distance. The approach of SIFT is to
perform reliable recognition, it is important that the feature extracted from the training image be
detectable even under changes in image scale, noise and illumination. The GLCM calculates how often a
pixel with gray level value occurs. While the focus is on image retrieval, our project is effectively used in
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application are more and more larger and are
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dge discovery and data mining, allowing the experts
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ove
the quality of their studies and to reduce the time processing of their algorithm. In addition to that, the data
used by the applications become richer. They are now represented by a set of complex and structured
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algorithm is to do feature selection on
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The
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are considered as higher level units which can model complex data, such as c
luster of
individuals, aggregated data or taxonomies. In this paper we will introduce a new feature selection
criterion for
BSOs
, and we will explain
how we improved its complexity.
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optimization problems. In this
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reaching the global optimization and to show the efficiency of the GA and AIS th
e application of them in
Solving Continuous Optimization Functions (SCOFs) are studied. Because of the multi variables and the
multi
-
dimensional spaces in SCOFs the use of the classic optimization methods, is generally non
-
efficient
and high cost. In other
words the use of the classic optimization methods for SCOFs generally leads to a
local optimized solution. A possible solution for SCOFs is to use the EAs which are high in probability of
succeeding reaching the local optimized solution. The results in pa
per show that GA is more efficient than
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Great model a model for the automatic generation of semantic relations betwee...
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large
a
v
ailable
am
ou
n
t
of
non
-
structured
texts
that
b
e
-
long
to
differe
n
t
domains
su
c
h
as
healthcare
(e.g.
medical
records),
justice
(e.g.
l
a
ws,
declarations),
insurance
(e.g.
declarations),
etc. increases
the
effort
required
for
the
analysis
of
information
in
a
decision making
pro
-
cess.
Differe
n
t
pr
o
jects
and t
o
ols
h
av
e
pro
p
osed
strategies
to
reduce
this
complexi
t
y
b
y
classifying,
summarizing
or
annotating
the
texts.
P
artic
-
ularl
y
,
text
summary
strategies
h
av
e
pr
ov
en
to
b
e
v
ery
useful
to
pr
o
vide
a
compact
view
of
an
original
text.
H
ow
e
v
er,
the
a
v
ailable
strategies
to
generate
these
summaries
do
not
fit
v
ery
w
ell
within
the
domains
that
require
ta
k
e
i
n
to
consideration
the
tem
p
oral
dimension
of
the
text
(e.g.
a
rece
n
t
piece
of
text
in
a
medical
record
is
more
im
p
orta
n
t
than
a
pre
-
vious
one)
and
the
profile
of
the
p
erson
who
requires
the
summary
(e.g
the
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