COURSE OBJECTIVES:
•
To understand the concept of computational intelligence paradigms and practice
assignments
•
To understand and apply optimization based on swarm intelligence on various
applications
•
To develop skills to both design and critique visualizations.
•
To understand technological advancements of data visualization and various data
visualization
techniques
•
To understand the methodologies used to visualize large data sets
UNIT
I COMPUTATIONAL INTELLIGENCE PARADIGMS 9
Artificial
Neural Networks – Artificial Neuron- Supervised Learning Neural Networks – Unsupervised
Learning Neural Networks – Reinforcement Learning - Evolutionary Computation Introduction
– Genetic Algorithms - Genetic Programming - Swarm Intelligence - Artificial
Immune Systems - Fuzzy Systems - Assignments
UNIT
II COMPUTATIONAL SWARM INTELLIGENCE 9
Particle
Swarm Optimization- Basic Particle Swarm Optimization-Social Network Structures
– Basic Variations - Basic PSO Parameters - Single-Solution Particle Swarm
Optimization-Advanced Topics - Applications - Ant Algorithms - Ant Colony
Optimization- Meta-Heuristic- Cemetery Organization and Brood Care- Division of
Labor - Advanced Topics – Applications- Traveling Salesman Problem
UNIT
III INTRODUCTION AND FOUNDATION OF DATA VISUALIZATION 9
Basics
- Relationship between Visualization and Other Fields -The Visualization
Process – Pseudo code Conventions - The Scatter plot. Data Foundation - Types
of Data - Structure within and between Records - Data Preprocessing - Data Sets
Visualization stages - Semiology of Graphical Symbols - The Eight Visual
Variables – Historical Perspective - Taxonomies - Experimental Semiotics based
on Perception Gibson‘s Affordance theory – A Model of Perceptual Processing.
UNIT
IV VISUALIZATION TECHNIQUES 9
Spatial
Data: One-Dimensional Data - Two-Dimensional Data – Three Dimensional Data –
Dynamic Data - Combining Techniques. Geospatial Data : Visualizing Spatial Data
- Visualization of Point Data -Visualization of Line Data - Visualization of
Area Data – Other Issues in Geospatial Data Visualization Multivariate Data :
Point-Based Techniques – Line-Based Techniques - Region-Based Techniques -
Combinations of Techniques – Trees Displaying Hierarchical Structures –
Graphics and Networks- Displaying Arbitrary Graphs/Networks.
UNIT
V INTERACTION CONCEPTS AND TECHNIQUES 9
Text
and Document Visualization: Introduction - Levels of Text Representations - The
Vector Space Model - Single Document Visualizations -Document Collection
Visualizations – Extended Text Visualizations Interaction Concepts: Interaction
Operators - Interaction Operands and Spaces – A Unified Framework. Interaction
Techniques: Screen Space - Object-Space –Data Space – Attribute Space- Data
Structure Space - Visualization Structure – Animating Transformations -
Interaction
Control.
TOTAL : 45 PERIODS
COURSE
OUTCOMES:
At
the end of the course, students will be able to
CO1:
Understand the concept of computational intelligence paradigms through
practicing
assignments
CO2:
Apply optimization based on swarm intelligence on various applications
CO3:
Visualize the objects in different dimensions and design and process the data
for
Visualization.
CO4:
Apply the visualization techniques in physical sciences, computer science,
applied
mathematics
and medical sciences.
CO5:
Apply the virtualization techniques for research projects.
CO6:
Design and Implement Advanced Text Visualization Techniques
REFERENCE
BOOKS:
1.
Computational Intelligence An Introduction Andries P. Engelbrecht University of
Pretoria South Africa
2.
Matthew Ward, Georges Grinstein and Daniel Keim, “Interactive Data
Visualization Foundations, Techniques, Applications”, 2010.
3.
Colin Ware, “Information Visualization Perception for Design”, 4th edition,
Morgan Kaufmann Publishers, 2021.
4.
Robert Spence “Information visualization – Design for interaction”, Pearson
Education, 2nd Edition, 2007.
5.
Alexandru C. Telea, “Data Visualization: Principles and Practice,” A. K. Peters
Ltd, 2008.
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