EAgriculture  A Way to Digitalization 
Author : Shubham Anand , Sukita Shettigar , Suman Goudar , Aditi Ohol , Surekha Janrao 
Abstract  Full Text 
Abstract :— Agricultural sector is the backbone of our
country and it plays a vital role in the overall
economic growth of our nation. India has about 59%
of its total area for agricultural purpose. The
contribution of agricultural sector to our GDP is about
17%. Advanced techniques or the betterment in the
arena of agriculture will as certain to increase the
competence of certain farming activities. In this paper
we introduce a concept for smart farming which
utilizes wireless sensor web technology with a web
based application. This will play a crucial role in
helping farmers. It will aim for the betterment in the
facilities given to the farmers and by focussing on the
measurement of production of the crops. With the
help of data mining techniques and algorithms like Knearest,
decision tree we will gather each and every
data related to the farming and it should be updated
frequently so that farmers and the consumers will get
the right knowledge of the respective crops and about
the suitable equipments related to farming. Existing
system are not so much efficient in displaying such
data characteristics. Our main aim is to enhance the
growth in the agriculture sector and make the existing
system smarter so that the decision maker can define
the expansion of agriculture activities to empower the
different forces in existing agriculture sector 

A Survey on the Analysis of Dissolved Oxygen Level in Water using Data Mining Techniques 
Author : R. Arunkumar and T.Velmurugan 
Abstract  Full Text 
Abstract :Data Mining (DM) is a powerful
and a new field having various techniques to
analyses the recent real world problems. In
DM, environmental mining is one of the
essential and interesting research areas. DM
enables to collect fundamental insights and
knowledge from massive volume of
environmental data. The water quality is
determining the condition of water in the
environment. It represents the concentration
and state (dissolved or particulate) of some or
all the organic and inorganic material present
in the water, together with certain physical
characteristics of the water. The Dissolved
Oxygen (DO) is one of the important aspects
of water quality. The DO is the quantity of
gaseous oxygen (O2) incorporated into the
water. The DO is essential for keeping the
water organisms alive. The amount of DO
level in the water can be detected by various
methods. The data mining techniques are
properly used to find DO Level in the different
types of water. A number of DM methods used
to analyze the DO level such as MultiLayer
Perceptron, Multivariate Linear Regression,
Factor Analysis, and Feed Forward Neural
Network. This survey work discusses about
such type of methods, particularly used for the
analysis of DO level elaborately. Finally, this
research suggests the best DM method to find
DO level in water by means of a comparative
analysis. 

Kidney Failure Due to Diabetics – Detection using Classification Algorithm in Data Mining 
Author : J.Vijayalakshmi 
Abstract  Full Text 
Abstract :: In order to analyse the chosen data from various
points of view, data mining is used as the effective process.
This process is also used to sumup all those views into useful
information. There are several types of algorithms in data
mining such as Classification algorithms, Regression,
Segmentation algorithms, association algorithms, sequence
analysis algorithms, etc.,. The classification algorithm can be
used to bifurcate the data set from the given data set and
foretell one or more discrete variables, based on the other
attributes in the dataset. The ID3 (Iterative Dichotomiser 3)
algorithm is an original data set S as the root node. An
unutilised attribute of the data set S calculates the entropy H(S)
(or Information gain IG (A)) of the attribute. Upon its
selection, the attribute should have the smallest entropy (or
largest information gain) value. The prime objective of this
paper is to analyze the data from a Kidney disorder due to
diabetics by using classification technique to predict class
accurately 

Diagnosis and Prognosis of Oral Cancer using classification algorithm with Data Mining Techniques 
Author : N.Anitha , K.Jamberi 
Abstract  Full Text 
Abstract :Data mining is the process of researching data from
different view points and condensing it into useful information.
There are several types of algorithms in data mining such as
Classification algorithms, Regression, Segmentation
algorithms, association algorithms, sequence analysis
algorithms, etc.,. The classification algorithm can be used to
bifurcate the data set from the given data set and foretell one or
more discrete variables, based on the other attributes in the
dataset. Our method of creating new algorithm GA+ID3 easily
identifies oral cancer data set from the given data set. The
genetic based ID3 classification algorithm diagnosis and
prognosis of oral cancer data set is identified by this paper.


Kidney Failure Due to Diabetics – Detection using Classification Algorithm in Data Mining 
Author : J.Vijayalakshmi 
Abstract  Full Text 
Abstract :: In order to analyse the chosen data from various
points of view, data mining is used as the effective process.
This process is also used to sumup all those views into useful
information. There are several types of algorithms in data
mining such as Classification algorithms, Regression,
Segmentation algorithms, association algorithms, sequence
analysis algorithms, etc.,. The classification algorithm can be
used to bifurcate the data set from the given data set and
foretell one or more discrete variables, based on the other
attributes in the dataset. The ID3 (Iterative Dichotomiser 3)
algorithm is an original data set S as the root node. An
unutilised attribute of the data set S calculates the entropy H(S)
(or Information gain IG (A)) of the attribute. Upon its
selection, the attribute should have the smallest entropy (or
largest information gain) value. The prime objective of this
paper is to analyze the data from a Kidney disorder due to
diabetics by using classification technique to predict class
accurately 

Multilevel Classification Algorithm using Diagnosis and Prognosis of Breast Cancer 
Author : K.Jamberi , E.Ramaraj 
Abstract  Full Text 
Abstract :: In order to analyse the chosen data from various
points of view, data mining is used as the effective process.
This process is also used to sumup all those views into useful
information. There are several types of algorithms in data
mining such as Classification algorithms, Regression,
Segmentation algorithms, association algorithms, sequence
analysis algorithms, etc.,. The classification algorithm can be
used to bifurcate the affected image from the given affected
image and foretell one or more discrete variables, based on the
other attributes in the dataset. The ID3 (Iterative Dichotomiser
3) algorithm is an original affected image S as the root node.
An unutilised attribute of the affected image S calculates the
entropy H(S) (or Information gain IG (A)) of the attribute.
Upon its selection, the attribute should have the smallest
entropy (or largest information gain) value. A genetic
algorithm (GA) is a heuristic quest that imitates the process
of natural selection. Genetic algorithm can easily select cancer
affected image using GA operators, such as
mutation, selection, and crossover. A method existed earlier
(KNN+GA) was not successful for breast cancer and primary
tumor. Our method of creating new algorithm GA and decision
tree algorithm easily identifies breast cancer affected image.
The genetic based classification algorithm diagnosis and
prognosis of breast cancer affected is identified by this paper.


An Efficient Logical Average Distance Measure Algorithm (LADMA) to Analyse MRI Brain Images 
Author : A.Naveen , T.Velmurugan 
Abstract  Full Text 
Abstract : Malignant and benign types of tumor infiltrated in
human brain are diagnosed with the help of an MRI scanner.
With the slice images obtained using an MRI scanner, certain
image processing techniques are utilized to have a clear
anatomy of brain tissues. Some of such data mining technique
is kmeans and fuzzy C means algorithms. This work proposes
a new hybrid algorithm namely LAMDA, which offers
successful identification of tumor and perform well for the
segmentation of tissue regions in brain. Automatic detection of
tumor region in MR (magnetic resonance) brain images has a
high impact in helping the radio surgeons assess the size of the
tumor present inside the tissues of brain and it also supports in
identifying the exact topographical location of tumor region.
Experimental results show that the proposed approach reduces
the number of features and at the same time it achieves high
accuracy level. The observed results to achieve high accuracy
level using minimum number of selected features. 

Kidney Failure Due to Diabetics – Detection using Classification Algorithm in Data Mining 
Author : J.Vijayalakshmi 
Abstract  Full Text 
Abstract :: In order to analyse the chosen data from various
points of view, data mining is used as the effective process.
This process is also used to sumup all those views into useful
information. There are several types of algorithms in data
mining such as Classification algorithms, Regression,
Segmentation algorithms, association algorithms, sequence
analysis algorithms, etc.,. The classification algorithm can be
used to bifurcate the data set from the given data set and
foretell one or more discrete variables, based on the other
attributes in the dataset. The ID3 (Iterative Dichotomiser 3)
algorithm is an original data set S as the root node. An
unutilised attribute of the data set S calculates the entropy H(S)
(or Information gain IG (A)) of the attribute. Upon its
selection, the attribute should have the smallest entropy (or
largest information gain) value. The prime objective of this
paper is to analyze the data from a Kidney disorder due to
diabetics by using classification technique to predict class
accurately 
