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Explains newly found geometric features of Bezier curves and surfaces called "rib and fan.
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2. It proposes representing the curves using spline curves defined by a small number of parameters in order to achieve a simple representation that is adaptive to the data.
3. An expectation-maximization approach is described for estimating the model parameters from the contour data in order to characterize the group or class of curves.
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This document discusses Bézier curves and their properties. It begins by stating that traditional parametric curves are not very geometric and do not provide intuitive shape control. It then outlines desirable properties for curve design systems, including being intuitive, flexible, easy to use, providing a unified approach for different curve types, and producing invariant curves under transformations. The document proceeds to discuss Bézier, B-spline and NURBS curves which address these properties by allowing users to manipulate control points to modify curve shapes. Key properties of Bézier curves are described, including their basis functions and the fact that moving control points modifies the curve smoothly. Cubic Bézier curves are discussed in detail as a common parametric curve type, and
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B-spline surfaces are constructed using a grid of control points and blending functions. The blending functions are polynomials defined over intervals in each parametric direction. This allows a B-spline surface to be defined by blending between control points. Properties include continuity and local influence of control points. A surface can be subdivided by inserting knots or raising the degree. Rational B-spline surfaces use homogeneous coordinates to represent common surfaces like planes and spheres. NURBS are the basis for CAD standards like IGES.
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A Bezier curve is a mathematically defined curve used in graphic applications. It is defined by four points: two anchors at the initial and terminal positions and two handles that control the shape of the curve. The curve can be altered by moving the handles. Bezier curves are commonly used to piece together complex curves from simpler component curves. Matching endpoints ensures continuity, while aligning handles ensures smooth tangency between connecting segments.
Bézier curves are parametric curves defined by control points. The document discusses the De Casteljau algorithm for geometric proof of Bézier equations, and limitations of Bézier curves where a change in one control point affects the global shape. It also mentions B-splines and OpenGL implementation of Bézier curves using evaluators.
This document discusses Bezier curves and their applications. It begins with an introduction to Bezier curves, including their history and properties. It then describes different types of Bezier curves such as classical, rational, and dynamic Bezier curves. The document reviews several applications of Bezier curves in computer graphics, animation, and font design. Finally, it discusses related work using Bezier curves for tasks such as gait recognition, gesture recognition, and facial expression recognition.
The document discusses Bézier curves and provides information about a CS 354 class. It includes details about an in-class quiz, the professor's office hours, and an upcoming lecture on Bézier curves and Project 2, which is due on Friday. The lecture will cover procedural generation of a torus from a 2D grid, GLSL functions needed for the project, normal maps, coordinate spaces, interpolation curves, and Bézier curves.
This document provides an introduction to Bayesian belief networks and naive Bayesian classification. It defines key probability concepts like joint probability, conditional probability, and Bayes' rule. It explains how Bayesian belief networks can represent dependencies between variables and how naive Bayesian classification assumes conditional independence between variables. The document concludes with examples of how to calculate probabilities and classify new examples using a naive Bayesian approach.
Presentation is about genetic algorithms. Also it includes introduction to soft computing and hard computing. Hope it serves the purpose and be useful for reference.
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LAND USE LAND COVER AND NDVI OF MIRZAPUR DISTRICT, UPRAHUL
This Dissertation explores the particular circumstances of Mirzapur, a region located in the
core of India. Mirzapur, with its varied terrains and abundant biodiversity, offers an optimal
environment for investigating the changes in vegetation cover dynamics. Our study utilizes
advanced technologies such as GIS (Geographic Information Systems) and Remote sensing to
analyze the transformations that have taken place over the course of a decade.
The complex relationship between human activities and the environment has been the focus
of extensive research and worry. As the global community grapples with swift urbanization,
population expansion, and economic progress, the effects on natural ecosystems are becoming
more evident. A crucial element of this impact is the alteration of vegetation cover, which plays a
significant role in maintaining the ecological equilibrium of our planet.Land serves as the foundation for all human activities and provides the necessary materials for
these activities. As the most crucial natural resource, its utilization by humans results in different
'Land uses,' which are determined by both human activities and the physical characteristics of the
land.
The utilization of land is impacted by human needs and environmental factors. In countries
like India, rapid population growth and the emphasis on extensive resource exploitation can lead
to significant land degradation, adversely affecting the region's land cover.
Therefore, human intervention has significantly influenced land use patterns over many
centuries, evolving its structure over time and space. In the present era, these changes have
accelerated due to factors such as agriculture and urbanization. Information regarding land use and
cover is essential for various planning and management tasks related to the Earth's surface,
providing crucial environmental data for scientific, resource management, policy purposes, and
diverse human activities.
Accurate understanding of land use and cover is imperative for the development planning
of any area. Consequently, a wide range of professionals, including earth system scientists, land
and water managers, and urban planners, are interested in obtaining data on land use and cover
changes, conversion trends, and other related patterns. The spatial dimensions of land use and
cover support policymakers and scientists in making well-informed decisions, as alterations in
these patterns indicate shifts in economic and social conditions. Monitoring such changes with the
help of Advanced technologies like Remote Sensing and Geographic Information Systems is
crucial for coordinated efforts across different administrative levels. Advanced technologies like
Remote Sensing and Geographic Information Systems
9
Changes in vegetation cover refer to variations in the distribution, composition, and overall
structure of plant communities across different temporal and spatial scales. These changes can
occur natural.
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A Bezier curve is a parametric curve used in computer graphics defined by control points. It was developed by Pierre Bezier in 1962 and uses Bernstein polynomials as the basis. Key properties are that the curve interpolates the first and last control points, lies within the convex hull of the control points, and has its shape determined by all interior points. Higher degree curves are used for more complex shapes by piecing together lower degree Bezier sections.
A Bezier curve is defined by four points that determine its shape and movement. It is commonly used in computer graphics, animation, and fonts to model smooth curves. Bezier curves can be pieced together and their control points adjusted to ensure continuity between sections. They allow complex curves to be generated from simple components. Bezier curves are widely applied in fields like computer graphics, animation, and font design due their ability to efficiently represent smooth curves.
B-spline surfaces are constructed using a grid of control points and blending functions. The blending functions are polynomials defined over intervals in each parametric direction. This allows a B-spline surface to be defined by blending between control points. Properties include continuity and local influence of control points. A surface can be subdivided by inserting knots or raising the degree. Rational B-spline surfaces use homogeneous coordinates to represent common surfaces like planes and spheres. NURBS are the basis for CAD standards like IGES.
The document describes the DeBoor-Cox calculation, which relates the analytical and geometric definitions of B-spline curves. It begins by defining a B-spline curve analytically as a weighted sum of normalized B-spline blending functions and control points. The blending functions are defined recursively. DeBoor and Cox showed that starting from this analytical definition, one can derive the geometric definition of a B-spline curve as a pyramid of control points. Their calculation demonstrated the relationship between the two common definitions of B-splines.
A Bezier curve is a mathematically defined curve used in graphic applications. It is defined by four points: two anchors at the initial and terminal positions and two handles that control the shape of the curve. The curve can be altered by moving the handles. Bezier curves are commonly used to piece together complex curves from simpler component curves. Matching endpoints ensures continuity, while aligning handles ensures smooth tangency between connecting segments.
Bézier curves are parametric curves defined by control points. The document discusses the De Casteljau algorithm for geometric proof of Bézier equations, and limitations of Bézier curves where a change in one control point affects the global shape. It also mentions B-splines and OpenGL implementation of Bézier curves using evaluators.
This document discusses Bezier curves and their applications. It begins with an introduction to Bezier curves, including their history and properties. It then describes different types of Bezier curves such as classical, rational, and dynamic Bezier curves. The document reviews several applications of Bezier curves in computer graphics, animation, and font design. Finally, it discusses related work using Bezier curves for tasks such as gait recognition, gesture recognition, and facial expression recognition.
The document discusses Bézier curves and provides information about a CS 354 class. It includes details about an in-class quiz, the professor's office hours, and an upcoming lecture on Bézier curves and Project 2, which is due on Friday. The lecture will cover procedural generation of a torus from a 2D grid, GLSL functions needed for the project, normal maps, coordinate spaces, interpolation curves, and Bézier curves.
This document provides an introduction to Bayesian belief networks and naive Bayesian classification. It defines key probability concepts like joint probability, conditional probability, and Bayes' rule. It explains how Bayesian belief networks can represent dependencies between variables and how naive Bayesian classification assumes conditional independence between variables. The document concludes with examples of how to calculate probabilities and classify new examples using a naive Bayesian approach.
Presentation is about genetic algorithms. Also it includes introduction to soft computing and hard computing. Hope it serves the purpose and be useful for reference.
Gender and Mental Health - Counselling and Family Therapy Applications and In...PsychoTech Services
A proprietary approach developed by bringing together the best of learning theories from Psychology, design principles from the world of visualization, and pedagogical methods from over a decade of training experience, that enables you to: Learn better, faster!
LAND USE LAND COVER AND NDVI OF MIRZAPUR DISTRICT, UPRAHUL
This Dissertation explores the particular circumstances of Mirzapur, a region located in the
core of India. Mirzapur, with its varied terrains and abundant biodiversity, offers an optimal
environment for investigating the changes in vegetation cover dynamics. Our study utilizes
advanced technologies such as GIS (Geographic Information Systems) and Remote sensing to
analyze the transformations that have taken place over the course of a decade.
The complex relationship between human activities and the environment has been the focus
of extensive research and worry. As the global community grapples with swift urbanization,
population expansion, and economic progress, the effects on natural ecosystems are becoming
more evident. A crucial element of this impact is the alteration of vegetation cover, which plays a
significant role in maintaining the ecological equilibrium of our planet.Land serves as the foundation for all human activities and provides the necessary materials for
these activities. As the most crucial natural resource, its utilization by humans results in different
'Land uses,' which are determined by both human activities and the physical characteristics of the
land.
The utilization of land is impacted by human needs and environmental factors. In countries
like India, rapid population growth and the emphasis on extensive resource exploitation can lead
to significant land degradation, adversely affecting the region's land cover.
Therefore, human intervention has significantly influenced land use patterns over many
centuries, evolving its structure over time and space. In the present era, these changes have
accelerated due to factors such as agriculture and urbanization. Information regarding land use and
cover is essential for various planning and management tasks related to the Earth's surface,
providing crucial environmental data for scientific, resource management, policy purposes, and
diverse human activities.
Accurate understanding of land use and cover is imperative for the development planning
of any area. Consequently, a wide range of professionals, including earth system scientists, land
and water managers, and urban planners, are interested in obtaining data on land use and cover
changes, conversion trends, and other related patterns. The spatial dimensions of land use and
cover support policymakers and scientists in making well-informed decisions, as alterations in
these patterns indicate shifts in economic and social conditions. Monitoring such changes with the
help of Advanced technologies like Remote Sensing and Geographic Information Systems is
crucial for coordinated efforts across different administrative levels. Advanced technologies like
Remote Sensing and Geographic Information Systems
9
Changes in vegetation cover refer to variations in the distribution, composition, and overall
structure of plant communities across different temporal and spatial scales. These changes can
occur natural.
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An organizational co evolutionary algorithm for classification
1. An Organizational Co-evolutionary
Algorithm For Classification
Developed By: Badar Munir
National University of Computer & Emerging Sciences, Islamabad
2. Index
1. Abstract
2. Introduction
3. Reference Techniques
4. Proposed Technique
5. Results
6. Conclusion
7. Future idea
National University of Computer & Emerging Sciences, Islamabad
3. Abstract
OCEC is inspired from human interacting process.
- It uses the concept of Multi Poulation.
- It evolves individuals of population, individuals
that have same class arranges them in
organization.
Determines the fitness of each organization by
Calculating its
- Significance of each attribute.
- # of attributes in it
National University of Computer & Emerging Sciences, Islamabad
4. Abstract
- Rules are extracted when evolutionary process
ends.
- Generalized rules are by merging rules.
- OCEC performs better than other EA based
classification algorithms and has less
computational complexity.
National University of Computer & Emerging Sciences, Islamabad
5. Co-evolutionary Algorithm
- EA are based on the process Natural Selection.
- When ever it is applied on engineering
problems it gives satisfactory results.
- Co-evolutionary algorithm is Multi-Population.
- In Co-evolutionary algorithms individuals of
species-I competes/ cooperates with species-II.
Best individual from both them is selected and
copied to next generation.
National University of Computer & Emerging Sciences, Islamabad
6. Co-evolutionary Algorithm
Two types of Co-evolutionary algorithms are:
- Competitive
- Cooperative
National University of Computer & Emerging Sciences, Islamabad
7. Classification
Classification is a technique in which
• # possible inputs, #attributes in input,
• Range of attribute values
• Output Classes are already known.
NAME RANK YEARS TENURED
Mike Assistant Prof 3 no
Mary Assistant Prof 7 yes
Bill Professor 2 yes
Jim Associate Prof 7 yes
Dave Assistant Prof 6 no
Anne Associate Prof 3 no
National University of Computer & Emerging Sciences, Islamabad
8. Classification
- We divide the dataset into
Training Test
Data Data
Input Data
National University of Computer & Emerging Sciences, Islamabad
9. Classification
Classification
Algorithms
Training
Data
NAME RANK YEARS TENURED Classifier
(Model)
Mike Assistant Prof 3 no
Mary Assistant Prof 7 yes
Bill Professor 2 yes
Jim Associate Prof 7 yes
IF rank = ‘professor’
Dave Assistant Prof 6 no
OR years > 6
Anne Associate Prof 3 no THEN tenured = ‘yes’
National University of Computer & Emerging Sciences, Islamabad
13. Michigan Approach
-Maintains a population of individual rules
which compete with each other for space and
priority in a population.
- It is not a good approach because it cannot
find best solution in complex problems instead
it converges rapidly.
National University of Computer & Emerging Sciences, Islamabad
14. Pittsburgh Approach
-Maintains a population of variable-length rule
set which compete with each other with respect
to performance on a domain task.
- computational cost for complex problems is
too high.
National University of Computer & Emerging Sciences, Islamabad
15. GABIL Approach
- GABIL continuously learns and refines
classification rules by interacting with
environment.
- For rules refinement it uses Genetic Algorithm
National University of Computer & Emerging Sciences, Islamabad
16. COGIN Approach
- CONGIN is a inductive approach that uses GA.
- It promotes Competitive or Predator type COE
between classification nichie’s.
National University of Computer & Emerging Sciences, Islamabad
17. JOINGA Approach
- CONGIN is a inductive approach that uses GA.
- It uses Cooperative or Symbiotic type COE
between classification nichie’s.
- It is used for Multi-Model classification.
National University of Computer & Emerging Sciences, Islamabad
18. REGAL Approach
- It is a distributed GA based approach designed
for learning first-order logic concepts
description from examples.
National University of Computer & Emerging Sciences, Islamabad
19. G-NET Approach
-G-NET is a descendant of REGAL that
consistently achieves better performance.
National University of Computer & Emerging Sciences, Islamabad
20. Organizational co-evolutionary (OCEC)
- OCEC copies COE model of Multiple
Populations
- It organizes the individuals in a sets called
organizations.
- Focusing on extracting rules from individuals &
organization.
- It does not focus on making organizations but
it focus on simulating interacting process among
organization.
- It is bottom-up approach.
National University of Computer & Emerging Sciences, Islamabad
21. Organizational co-evolutionary (OCEC)
- OCEC is based on organizations.
• Organization 1 • Organization 2
• Organization3 • Organization 4
National University of Computer & Emerging Sciences, Islamabad
22. Organization?
- An organization is a set of instances that have
same class
- Intersection between organizations is empty.
Org1 Π Org2 = Ø
Outlook Temp Humidity Wind Play
Sunny Hot High False No
Sunny Hot High True No
Overcast Hot High False Yes
Rainy Mild High False Yes
Rainy Cool Normal False Yes
* Each instance of an org is called Member of org.
National University of Computer & Emerging Sciences, Islamabad
23. Organization?
- If all members of org have the same value for
attribute A , then A is a Fixed-Value Attribute.
Suppose A’ is a fixed-value attribute that satisfy
the conditions required for rule extraction, then
A’ is a Useful Attribute. The fixed-value attribute
set of org is labeled as Forg, and the useful
attribute set is labeled as Uorg
- Useful attribute is significant because it
extracts rule.
National University of Computer & Emerging Sciences, Islamabad
24. Organization?
Wind Forg1 & Uorg1 (Org2)
Outlook Uorg2 (Org2)
Temp Forg2 & Uorg2
Humidity Forg2 & Uorg2
Outlook Temp Humidity Wind Play
Sunny Hot High False No
Sunny Hot High True No
Overcast Hot High False Yes
Rainy Mild High False Yes
Rainy Cool Normal false Yes
National University of Computer & Emerging Sciences, Islamabad
25. Classification of Organizations
Classification of organizations are:
- Normal organization
- Trivial Organization
- Abnormal organization
National University of Computer & Emerging Sciences, Islamabad
26. Normal Organization
- It has more than one members
- Has non-empty useful attributes set
Outlook Temp Humidity Wind Play
Sunny Hot High False No
Sunny Hot High True No
Overcast Hot High False Yes
Rainy Mild High False Yes
Rainy Cool Normal False Yes
- It is denoted as ORGN
National University of Computer & Emerging Sciences, Islamabad
27. Trivial Organization
- It has only one members &
- All attributes of a member are useful.
Outlook Temp Humidity Wind Play
Sunny Hot High True No
Overcast Hot High False Yes
- It is denoted as ORGT
National University of Computer & Emerging Sciences, Islamabad
28. Abnormal Classification
- It is an organization with empty useful
attributes.
Outlook Temp Humidity Wind Play
Sunny Hot High False No
Sunny Hot High True No
Overcast Hot High False Yes
Rainy Mild High True Yes
Rainy Cool Normal False Yes
- It is denoted as ORGA
National University of Computer & Emerging Sciences, Islamabad
29. Organization Records
Organization keeps record of
- Member list
- Attribute type
- Organization type
- Member class
- Fitness of organization
National University of Computer & Emerging Sciences, Islamabad
30. Fitness of Organization
Fitness of an organization is calculated as:
- # of members
- # of useful attributes
-
National University of Computer & Emerging Sciences, Islamabad
31. Data Representation
OCEC can handle both
- Nominal &
- Continuous data
Outlook Temp Humidity Wind Play
Sunny Hot High False No
Sunny Hot High True No
Overcast Hot High False Yes
Rainy Mild High False Yes
Rainy Cool Normal false Yes
National University of Computer & Emerging Sciences, Islamabad
32. Knowledge Representation
- A is a set of attributes
- Each attribute has range of values.
Outlook Temp Humidity Wind Play
Sunny Hot High False No
Sunny Hot High True No
Overcast Hot High False Yes
Rainy Mild High False Yes
Rainy Cool Normal false Yes
National University of Computer & Emerging Sciences, Islamabad
33. Knowledge Representation
- Instance Space I is the cartesian product of set
of attributes
Outlook Temp Humidity Wind Play
Sunny Hot High False No
Sunny Hot High True No
Overcast Hot High False Yes
Rainy Mild High False Yes
Rainy Cool Normal false Yes
National University of Computer & Emerging Sciences, Islamabad
34. Knowledge Representation
- C is a set of classes
- Each member is
Outlook Temp Humidity Wind Play
Sunny Hot High False No
Sunny Hot High True No
Overcast Hot High False Yes
Rainy Mild High False Yes
Rainy Cool Normal false Yes
National University of Computer & Emerging Sciences, Islamabad
35. Rule Representation
Rules are represented in
IF <condition> THEN <class>
Each term in condition is triple:
Attribute, operator, value
* Rules are extracted when evolutionary process Ends
National University of Computer & Emerging Sciences, Islamabad
36. Working of (OCEC)
- OCEC during COE process generates a of set of
examples and at the end of COE it generates set
of rules.
if Temp = Mild and Outlook= Sunny
then Class = Play Tennis
National University of Computer & Emerging Sciences, Islamabad
37. Working of (OCEC)
- Inclusion or exclusion of attribute from a rule
depends upon the Significance of the attribute.
- EA Method is devised for determining the
Significance of the attribute.
- on the basis of attribute significance Fitness
function of organization is defined.
National University of Computer & Emerging Sciences, Islamabad
38. Working of (OCEC)
- EA Method is devised for determining the
Significance of the attribute.
- On the basis of attribute significance Fitness
function of organization is defined.
National University of Computer & Emerging Sciences, Islamabad
39. Evolutionary Operators (OCEC)
- Migrating Operator
- Exchanging Operator
- Merging Operator
Traditional operators such as mutation and
crossover are not used.
National University of Computer & Emerging Sciences, Islamabad
40. Migrating Operators (OCEC)
- 2 parent organizations are selected
- n members are selected from either parent
and are migrated to child’s
1 2 3 4 5 6 7 8
1 2 3 4 5 1 2 3
National University of Computer & Emerging Sciences, Islamabad
41. Exchanging Operators (OCEC)
- 2 org’s are randomly selected from Population
org1 & org2
Parent Parent
ORG1 ORG2
Child- Off-
ORGc1 ORGc2
National University of Computer & Emerging Sciences, Islamabad
42. Exchanging Operators (OCEC)
- n members from each parent org1 are
randomly selected and exchanged
- Two child organization orgc1 & orgc2
1 2 3 4 5 6 7 8
1 6 7 8 5 1 2 3
National University of Computer & Emerging Sciences, Islamabad
43. Exchanging Operators (OCEC)
- Two child organization orgc1 & orgc2
- Precondition is:
|orgp1|>1 and |orgp2|>1
1 ≤ n < MIN{|orgp1|, |orgp2|}
National University of Computer & Emerging Sciences, Islamabad
44. Merging Operators
- 2 org’s are randomly selected from Population
orgp1 & orgp2
Parent Parent
ORG1 ORG2
Child-
ORGc1
National University of Computer & Emerging Sciences, Islamabad
45. Merging Operators (OCEC)
- n members from each org1 are randomly
selected and merged.
- One child organization orgc1 & orgc2
1 2 3 4 5 6 7 8
1 2 7 8
National University of Computer & Emerging Sciences, Islamabad
46. Selection Operators (OCEC)
- Tournament Selection Mechanism is used.
National University of Computer & Emerging Sciences, Islamabad
47. Rule Extraction From Organization
-Rules are extracted from organizations when
Evolutionary process ends.
- Rules are extracted on the basis useful
attributes.
- Each useful attribute becomes TERM (part of
condition).
if temp=hot then play = yes
National University of Computer & Emerging Sciences, Islamabad
48. Performance Evaluation of OCEC
-Multiplexer problem
- Radar Target Recognition Problem.
-All results shows that OCEC has
- Higher prediction accuracy
- Low computational cost.
National University of Computer & Emerging Sciences, Islamabad
49. Scalability Evaluation of OCEC
-Scalability of OCEC is evaluated on synthetic
sets.
- trainging exampels increases from 1lac to 10
Million
- attributes are increases from 9 to 400.
- results shows that I achieves good scalability.
National University of Computer & Emerging Sciences, Islamabad
50. EVALUATION OF OCEC’S EFFECTIVENESS
A. Multiplexer Problems
o Multiplexer problems were introduced to the
machine learning community by Wilson in
1987, and have often been used to evaluate
the performance of learning classifier
systems
National University of Computer & Emerging Sciences, Islamabad
51. EVALUATION OF OCEC’S EFFECTIVENESS
B. Experimental Results
o The 20- and 37-multiplexer problems are used
o The training set of the 20-multiplexer
problem has 3000 examples, and that of the
37-multiplexer problem has 15 000 examples
o The test set of each problem has 100 000
examples
o The parameter N is set to 10% of the number
of the training set, and n
National University of Computer & Emerging Sciences, Islamabad
52. EVALUATION OF OCEC’S EFFECTIVENESS
The evolutionary process of OCEC for the 20-multiplexer
problem
National University of Computer & Emerging Sciences, Islamabad
53. EVALUATION OF OCEC’S EFFECTIVENESS
The evolutionary process of OCEC for the 37-multiplexer
problem
National University of Computer & Emerging Sciences, Islamabad
54. Coding Output
The evolutionary process of OCEC for the 37-multiplexer
problem
National University of Computer & Emerging Sciences, Islamabad
55. Coding Output
National University of Computer & Emerging Sciences, Islamabad
56. Coding Output
National University of Computer & Emerging Sciences, Islamabad
57. Comparison between OCEC & EA
- OCEC is based on organization while
traditional EA are based in individuals.
-OCEC has bottom-up searching mechanism
while EA has top-down searching mechanism
- the benefit of using organization is that I does
not generate meaningless rules.
- OCEC has higher prediction accuracy and low
computational cost.
National University of Computer & Emerging Sciences, Islamabad
58. Conclusion
- It is best tool for data mining.
- It has low computational cost
- It performs well in a complex, huge dataset of
individuals.
- On high scalability it performs better than
other techniques.
National University of Computer & Emerging Sciences, Islamabad
59. Future IDEA
-If we use a Floating Point Fitness Function then
it will give us better result in Scientific
applications.
National University of Computer & Emerging Sciences, Islamabad
Editor's Notes
evolutionary and selection operators are used to simulate the interacting process among organization.
evolutionary and selection operators are used to simulate the interacting process among organization.
evolutionary and selection operators are used to simulate the interacting process among organization.
evolutionary and selection operators are used to simulate the interacting process among organization.
evolutionary and selection operators are used to simulate the interacting process among organization.
evolutionary and selection operators are used to simulate the interacting process among organization.
evolutionary and selection operators are used to simulate the interacting process among organization.