1. Southwest Airlines has succeeded through difficult times in the airline industry by controlling operating costs. It focuses on high demand routes without connecting flights and turns planes around very quickly to keep costs low.
2. Southwest uses a single aircraft type, the Boeing 737, which standardizes training and lowers maintenance costs. This strengthens Southwest's bargaining power with Boeing and allows renewing the fleet to improve fuel efficiency and reduce maintenance costs.
3. Southwest also benefits from competitive labor costs and efficiency due to its positive employee relations culture. These advantages help Southwest expand easily into new markets and gain market share through its low price strategy.
Mine Blood Donors Information through Improved K-Means Clusteringijcsity
The number of accidents and health diseases which are increasing at an alarming rate are resulting in a huge increase in the demand for blood. There is a necessity for the organized analysis of the blood donor database or blood banks repositories. Clustering analysis is one of the data mining applications and K-means clustering algorithm is the fundamental algorithm for modern clustering techniques. K-means clustering algorithm is traditional approach and iterative algorithm. At every iteration, it attempts to find the distance from the centroid of each cluster to each and every data point. This paper gives the improvement to the original k-means algorithm by improving the initial centroids with distribution of data. Results and discussions show that improved K-means algorithm produces accurate clusters in less computation time to find the donors information
Do you lead a social media team in a government or non-profit organization? If so, you'll want to consider these 10 key points for leading your social media efforts.
Performance evaluation of clustering algorithmsijcsity
This document discusses performance evaluation of clustering algorithms for analyzing sales data of a steel company. It analyzes annual sales data using clustering techniques like K-Means and EM to group sales by products, customers, quantities and reveal patterns. The study finds that partition methods like K-Means and EM are better suited than hierarchical or density-based methods for analyzing the company's sales data. It also discusses various sales analysis dependent issues like product attributes, price fixation, new product development, and key performance indicators that were considered in the clustering analysis.
Personal branding is all the rage. But what does it really mean. And, more importantly, how can you get started? In this presentation I delivered during a lunch session at Atlantic BT, I provided an overview of the topic and explained what I see as the three critical components of a solid personal branding strategy.
Atlantic BT conducts a weekly lunch and learn session for our team members on a variety of topics. The aim is to create a knowledgeable workforce that is up-to-date on the latest topics in our industry.
1. Southwest Airlines has succeeded through difficult times in the airline industry by controlling operating costs. It focuses on high demand routes without connecting flights and turns planes around very quickly to keep costs low.
2. Southwest uses a single aircraft type, the Boeing 737, which standardizes training and lowers maintenance costs. This strengthens Southwest's bargaining power with Boeing and allows renewing the fleet to improve fuel efficiency and reduce maintenance costs.
3. Southwest also benefits from competitive labor costs and efficiency due to its positive employee relations culture. These advantages help Southwest expand easily into new markets and gain market share through its low price strategy.
Mine Blood Donors Information through Improved K-Means Clusteringijcsity
The number of accidents and health diseases which are increasing at an alarming rate are resulting in a huge increase in the demand for blood. There is a necessity for the organized analysis of the blood donor database or blood banks repositories. Clustering analysis is one of the data mining applications and K-means clustering algorithm is the fundamental algorithm for modern clustering techniques. K-means clustering algorithm is traditional approach and iterative algorithm. At every iteration, it attempts to find the distance from the centroid of each cluster to each and every data point. This paper gives the improvement to the original k-means algorithm by improving the initial centroids with distribution of data. Results and discussions show that improved K-means algorithm produces accurate clusters in less computation time to find the donors information
Do you lead a social media team in a government or non-profit organization? If so, you'll want to consider these 10 key points for leading your social media efforts.
Performance evaluation of clustering algorithmsijcsity
This document discusses performance evaluation of clustering algorithms for analyzing sales data of a steel company. It analyzes annual sales data using clustering techniques like K-Means and EM to group sales by products, customers, quantities and reveal patterns. The study finds that partition methods like K-Means and EM are better suited than hierarchical or density-based methods for analyzing the company's sales data. It also discusses various sales analysis dependent issues like product attributes, price fixation, new product development, and key performance indicators that were considered in the clustering analysis.
Personal branding is all the rage. But what does it really mean. And, more importantly, how can you get started? In this presentation I delivered during a lunch session at Atlantic BT, I provided an overview of the topic and explained what I see as the three critical components of a solid personal branding strategy.
Atlantic BT conducts a weekly lunch and learn session for our team members on a variety of topics. The aim is to create a knowledgeable workforce that is up-to-date on the latest topics in our industry.
Feature selection on boolean symbolic objectsijcsity
With the boom in IT technology, the data sets used in
application are more and more larger and are
described by a huge number of attributes, therefore, the feature selection become an important discipline in
Knowle
dge discovery and data mining, allowing the experts
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
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.
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
OfficeBox allows users to store and share files online. Key features include My Folder for personal files, Shared Folders for collaboration, and Guest Folders that allow others limited access to a user's storage space. File links can be created to share individual files or folders without creating a guest account. The user interface provides access to these folders and tools for uploading, downloading, and managing files.
ViewToo is an online meeting application that allows users to share their screens from cloud storage services with multiple attendees via a single link. It offers simple one-click meetings without registration through customizable meeting codes. Files can be shared and annotated during meetings across devices on Android, iOS, and desktop. Common use cases include online consultations, sharing class presentations, and conducting international meetings from anywhere.
Google Plus and Your Mobile Recruiting StrategyJon Parks
You have a great mobile recruiting strategy. But are you using one of the most powerful social networks available today? Google Plus can provide a powerful boost to your mobile recruiting efforts as long as you know how to make the best use of the platform. In this presentation, I provide an overview of Google Plus, the personal Google Plus profile, Google Plus Communities and Hangouts on Air. The presentation concludes with three key tactics you can begin using today to improve your use of Google Plus in your mobile recruiting strategy. This presentation was delivered at the 2013 Mobile Recruiting Conference in Atlanta, GA on September 24, 2013 (www.mrec.net).
This project is to retrieve the similar geographic images from the dataset based on the features extracted.
Retrieval is the process of collecting the relevant images from the dataset which contains more number of
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
the applications such as detection and classification.
Multi-function toothpastes provide several benefits for oral health beyond just cleaning teeth. They can [1] interact with tooth structure chemically, reduce demineralization, interfere with bacterial adhesion, and provide antibacterial action. Toothpastes also [2] prevent supragingival calculus formation, promote remineralization, and reduce dentinal hypersensitivity. Fluoridated toothpastes are effective at preventing caries when used daily and are now included in over 90% of toothpastes worldwide. Toothpastes also help chemotherapeutically prevent bacterial biofilms and gingivitis, control calculus formation, whiten teeth, and reduce breath malodour. For maximum effectiveness,
Mining academic social network is becoming increasingly necessary with the increasing amount of data. It
is a favorite topic of research for many researchers. The data mining techniques are used for the mining of
academic social networks. In this paper, we are presenting an efficient frequent item set mining technique
for social academic network. The proposed framework first processes the research documents and then the
enhanced frequent item set mining is applied to find the strength of relationship between the researchers.
The proposed method will be fast in comparison to older algorithms. Also it will takes less main memory
space for computation purpose.
An efficient feature extraction method with pseudo zernike moment for facial ...ijcsity
Face recognition is one of the most challenging problems in the domain of image processing and machine
vision. Face recognition system is critical when individuals have very similar biometric signature such as
identical twins. In this paper, new efficient facial-based identical twins recognition is proposed according
to the geometric moment. The utilized geometric moment is Pseudo-Zernike Moment (PZM) as a feature
extractor inside the facial area of identical twins images. Also, the facial area inside an image is detected
using Ada Boost approach. The proposed method is evaluated on two datasets, Twins Days Festival and
Iranian Twin Society which contain scaled, which contain the shifted and rotated facial images of identical
twins in different illuminations. The results prove the ability of proposed method to recognize a pair of
identical twins. Also, results show that the proposed method is robust to rotation, scaling and changing
illumination.
ANALYSE THE PERFORMANCE OF MOBILE PEER TO PEER NETWORK USING ANT COLONY OPTIM...ijcsity
A mobile peer-to-peer computer network is the one in which each computer in the network can act as a
client or server for the other computers in the network. The communication process among the nodes in the
mobile peer to peer network requires more no of messages. Due to this large number of messages passing,
propose an interconnection structure called distributed Spanning Tree (DST) and it improves the efficiency
of the mobile peer to peer network. The proposed method improves the data availability and consistency
across the entire network and also reduces the data latency and the required number of message passes for
any specific application in the network. Further to enhance the effectiveness of the proposed system, the
DST network is optimized with the Ant Colony Optimization method. It gives the optimal solution of the
DST method and increased availability, enhanced consistency and scalability of the network. The
simulation results shows that reduces the number of message sent for any specific application and average
delay and increases the packet delivery ratio in the network.
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.
The document contains a diagram with 16 labeled shapes and two lists. The diagram shows various types of meat arranged in no particular order. The lists contain various types of fruits and vegetables. The document does not provide any additional context or explanation around the content.
Convergence tendency of genetic algorithms and artificial immune system in so...ijcsity
By the advances in the Evolution Algorithms (EAs) and the intelligent optimization metho
ds we witness the
big revolutions in solving the optimization problems. The application of the evolution algorithms are not
only not limited to the combined optimization problems, but also are vast in domain to the continuous
optimization problems. In this
paper we analyze and study the Genetic Algorithm (GA) and the Artificial
Immune System (AIS)
algorithm
which are capable in escaping the local optimization and also fastening
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
AIS in reaching the optimized solution in SCOFs.
Feature selection on boolean symbolic objectsijcsity
With the boom in IT technology, the data sets used in
application are more and more larger and are
described by a huge number of attributes, therefore, the feature selection become an important discipline in
Knowle
dge discovery and data mining, allowing the experts
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
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.
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
OfficeBox allows users to store and share files online. Key features include My Folder for personal files, Shared Folders for collaboration, and Guest Folders that allow others limited access to a user's storage space. File links can be created to share individual files or folders without creating a guest account. The user interface provides access to these folders and tools for uploading, downloading, and managing files.
ViewToo is an online meeting application that allows users to share their screens from cloud storage services with multiple attendees via a single link. It offers simple one-click meetings without registration through customizable meeting codes. Files can be shared and annotated during meetings across devices on Android, iOS, and desktop. Common use cases include online consultations, sharing class presentations, and conducting international meetings from anywhere.
Google Plus and Your Mobile Recruiting StrategyJon Parks
You have a great mobile recruiting strategy. But are you using one of the most powerful social networks available today? Google Plus can provide a powerful boost to your mobile recruiting efforts as long as you know how to make the best use of the platform. In this presentation, I provide an overview of Google Plus, the personal Google Plus profile, Google Plus Communities and Hangouts on Air. The presentation concludes with three key tactics you can begin using today to improve your use of Google Plus in your mobile recruiting strategy. This presentation was delivered at the 2013 Mobile Recruiting Conference in Atlanta, GA on September 24, 2013 (www.mrec.net).
This project is to retrieve the similar geographic images from the dataset based on the features extracted.
Retrieval is the process of collecting the relevant images from the dataset which contains more number of
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
the applications such as detection and classification.
Multi-function toothpastes provide several benefits for oral health beyond just cleaning teeth. They can [1] interact with tooth structure chemically, reduce demineralization, interfere with bacterial adhesion, and provide antibacterial action. Toothpastes also [2] prevent supragingival calculus formation, promote remineralization, and reduce dentinal hypersensitivity. Fluoridated toothpastes are effective at preventing caries when used daily and are now included in over 90% of toothpastes worldwide. Toothpastes also help chemotherapeutically prevent bacterial biofilms and gingivitis, control calculus formation, whiten teeth, and reduce breath malodour. For maximum effectiveness,
Mining academic social network is becoming increasingly necessary with the increasing amount of data. It
is a favorite topic of research for many researchers. The data mining techniques are used for the mining of
academic social networks. In this paper, we are presenting an efficient frequent item set mining technique
for social academic network. The proposed framework first processes the research documents and then the
enhanced frequent item set mining is applied to find the strength of relationship between the researchers.
The proposed method will be fast in comparison to older algorithms. Also it will takes less main memory
space for computation purpose.
An efficient feature extraction method with pseudo zernike moment for facial ...ijcsity
Face recognition is one of the most challenging problems in the domain of image processing and machine
vision. Face recognition system is critical when individuals have very similar biometric signature such as
identical twins. In this paper, new efficient facial-based identical twins recognition is proposed according
to the geometric moment. The utilized geometric moment is Pseudo-Zernike Moment (PZM) as a feature
extractor inside the facial area of identical twins images. Also, the facial area inside an image is detected
using Ada Boost approach. The proposed method is evaluated on two datasets, Twins Days Festival and
Iranian Twin Society which contain scaled, which contain the shifted and rotated facial images of identical
twins in different illuminations. The results prove the ability of proposed method to recognize a pair of
identical twins. Also, results show that the proposed method is robust to rotation, scaling and changing
illumination.
ANALYSE THE PERFORMANCE OF MOBILE PEER TO PEER NETWORK USING ANT COLONY OPTIM...ijcsity
A mobile peer-to-peer computer network is the one in which each computer in the network can act as a
client or server for the other computers in the network. The communication process among the nodes in the
mobile peer to peer network requires more no of messages. Due to this large number of messages passing,
propose an interconnection structure called distributed Spanning Tree (DST) and it improves the efficiency
of the mobile peer to peer network. The proposed method improves the data availability and consistency
across the entire network and also reduces the data latency and the required number of message passes for
any specific application in the network. Further to enhance the effectiveness of the proposed system, the
DST network is optimized with the Ant Colony Optimization method. It gives the optimal solution of the
DST method and increased availability, enhanced consistency and scalability of the network. The
simulation results shows that reduces the number of message sent for any specific application and average
delay and increases the packet delivery ratio in the network.
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.
The document contains a diagram with 16 labeled shapes and two lists. The diagram shows various types of meat arranged in no particular order. The lists contain various types of fruits and vegetables. The document does not provide any additional context or explanation around the content.
Convergence tendency of genetic algorithms and artificial immune system in so...ijcsity
By the advances in the Evolution Algorithms (EAs) and the intelligent optimization metho
ds we witness the
big revolutions in solving the optimization problems. The application of the evolution algorithms are not
only not limited to the combined optimization problems, but also are vast in domain to the continuous
optimization problems. In this
paper we analyze and study the Genetic Algorithm (GA) and the Artificial
Immune System (AIS)
algorithm
which are capable in escaping the local optimization and also fastening
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
AIS in reaching the optimized solution in SCOFs.