BCS-601 DESIGN AND ANALYSIS OF ALGORITHMS
Core Course: 13 Marks: 100 Total Credit: 06
Course Outcome: At the end of course, Students will be able to
● Explain various computational problem solving techniques.
● Apply appropriate method to solve a given problem.
● Describe various methods of algorithm analysis.
● Write rigorous correctness proofs for algorithms.
● Demonstrate a familiarity with major algorithms and data structures.
● Apply important algorithmic design paradigms and methods of analysis.
● Synthesize efficient algorithms in common engineering design situations.
UNIT -I
Introduction of Algorithm:
Analysis of algorithms, Time and space complexities, asymptotic notations, Standard notations and common functions, Recurrence solution: Substitution method, iteration method and the master method.
UNIT -II
Divide and Conquer:
Binary search, Min-Max Problem, merge sort, quick sort, and Matrix Multiplication.
Greedy Method:
Knapsack problem, Huffman codes, job sequencing with deadlines, Minimum Spanning Trees: Prim's and Kruskal's algorithms, Single Source Shortest path: Dijkstra's algorithm and Bellman Ford algorithms.
UNIT –III
Dynamic Programming:
O/1 Knapsack problem, all Pair’s shortest paths: Warshal’s and Floyd’s algorithms, Single source shortest paths, Backtracking, Branch and Bound: Travelling Salesman Problem.
UNIT –IV
Graph Algorithms:
Undirected Graph, Directed Graph, Traversing Graphs, Representation of graphs, Breadth-first search, Depth-first search, strongly connected components, topological sort.
String Matching:
Introduction, The naïve string matching algorithm, Rabin-Karp algorithm.
UNIT –V
Introduction to NP-Completeness:
The class P and NP, Polynomial reduction, NP-Completeness Problem, NP-Hard Problems, Reducibility.
TEXT /REFERENCE BOOKS:
1. “Introduction to Algorithms “, Thomas H. Cormen et al., PHI
2. “Fundamentals of computer algorithms”, Ellis Horowitz, SartrajSahni and Rajasekaran, Galgotia
3. “Design Methods and Analysis of Algorithms”, Prof S.K.Basu, BHU, PHI
4. “Data Structures,Algorithms and Applications in C++”, Sahni, TMH
5. “Design and analysis of computer algorithms”, Aho A.V, Hopcroft, J.E. Ullman, Addisionwesley
6. “Fundamentals of Algorithmics”, Brassard and Bratley, PH
BCS 602 COMPUTER GRAPHICS
Core Course:14 Marks: 100 Total Credit: 06
Course Outcome: At the end of course, Students will be able to
Design and implement algorithms for 2D graphics primitives and attributes.
Apply concepts of clipping and visible surface detection in 2D and 3D viewing, and Illumination Models.
Demonstrate Geometric transformations, viewing on both 2D and 3D objects.
Infer the representation of curves, surfaces, Colour and Illumination models
Design and implementation of algorithms for 2D graphics Primitives and attributes.
Explain hardware, software and OpenGL Graphics Primitives.
UNIT-I
Introduction to Computer Graphics:
Application of Graphics, Display Devices: Refresh Cathode-Ray Tubes, Raster Scan Displays, Random Scan Displays, Color CRT Monitors and Flat Panel Displays. Video cards/display cards. Graphic Software, Graphics Software Standard and Software Packages
UNIT-II
Line Generation Algorithms:
DDA algorithm, Bresenham’s algorithm; Circle Generation Algorithms: Midpoint Circle algorithm Polygon filling Algorithms: Scan Line Polygon fill algorithm, Inside - Outside Tests, Boundary-Fill algorithm, Flood - Fill algorithm. Fundamentals of aliasing and Antialiasing Techniques.
UNIT-III
Two Dimensional Viewing:
Window to Viewport coordinates
transformation. Clipping: Clipping operations, Point clipping, Line clipping: Cohen Sutherland Algorithm, Liang Barsky Algorithm, Nicholl-Lee-Nicholl Algorithm,
Polygon clipping: SutherlandHodgeman Algorithm, Weiler Atherton Algorithm, Text clipping, Exterior clipping. Two Dimensional Transformations: Translation, Scaling, Rotation, Reflection, Shear
UNIT-IV
Three Dimensional Viewing:
3D Geometry, 3D display techniques, transformations. Projections: Parallel Projection, Perspective Projection. Orthogonal Projection .
UNIT-V
Color Models and Color Application:
Color Model, Standard Primaries and the Chromaticity Diagram, XYZ Color Model, CIE Chromaticity Diagram. RGB Color Model, YIQ Color Model, CMY Color Model, HSV Color Model. Conversion between HSV and RGB Models.HLS Color Model, Color Selection and Application.
Case study of OpenGL
TEXT/REFERENCE BOOKS:
1. “Principles of Interactive Computer Graphics”, Newman, W. Sproul, R.F., TMH,1980
2. “Fundamentals of Interactive Computer Graphics”, Foley J.D., Van Dome, Addison Wesley,1982
3. “Computer Graphics”, Hearn D., Baker, PHI, 1986
4. “Procedural Elements for Computer Graphics”, Rogers D. F., TMH, 1986
5. “Computer Graphics using OpenGL”, F. S. Hill Jr., Pearson Education, 2003.
BCS-607 SOFT COMPUTING
Discipline Specific Elective DSE:4 (Group-B) Marks: 100 Total Credit: 04
Course Outcome: At the end of course, Students will be able to
Analyze and appreciate the applications which can use fuzzy logic.
Understand the difference between learning and programming and explore practical applications of Neural Networks (NN).
Students would understand the efficiency of a hybrid system and how Neural Network and fuzzy logic can be hybridized to form a Neuro-fuzzy network and its various applications
Ability to appreciate the importance of optimizations and its use in computer engineering fields and other domains.
To introduce the ideas of fuzzy sets, fuzzy logic and use of heuristics based on human experience.
UNIT–I
Introduction :
What is soft computing? Different tools of soft computing and its comparison, Area of application.
UNIT-II
Artificial Neural Network(ANN):
Architecture, Introduction, Evolution of Neural Network, Biological Neural Network Vs ANN, Basic Model of ANN, Different types of ANN, Single layer Perceptron, Solving XOR problem, Activation function, Linear severability, Supervised and unsupervised learning, perceptron learning, delta learning, Feed-forward and Feedback networks, Error Back Propagation Network (EBPN), Associative memories and its types, Hopefield Network, Kohenenself-organizing Map.
UNIT-III
Fuzzy Logic:
Introduction to Classical Sets and Fuzzy Sets, Membership Function, properties and operations of classical set and Fuzzy set, a-cuts, Properties of a-cuts, Linguistic Variables, Membership function, Classical relation and Fuzzy Relation and its properties and operations, Defuzzification and its methods, Fuzzy rule base.
UNIT–IV
Genetic Algorithm :
What is Optimization?, Introduction, Application, GA operators: selection, crossover and mutation ,different techniques of selection ,crossover and mutation, different types of chromosomes, Application of GA.
UNIT-V
Hybrid Soft Commuting:
Design of Neuro-Fuzzy model like ANFIS ,Neuro-Genetic, Fuzzy-Genetic Neuro-Fuzzy-Genetic model, MATLAB environment for soft computing.
TEXT/REFERENCE BOOKS:
1. Principles of soft computing , S.N. Shivanandan and S.N Deepa , Wiley publication, Wiley India Edition.
2. Neural network and Learning Machines, Simon Haykin, Pearson Education, 2011.
3. Artificial Neural Networks, Robert J. Scholkoff, McGraw Hill Education( India) Pvt. Limited,1997.
3. Neural Networks and Fuzzy Systems, A dynamical Systems Approach to Machine Learning, Bart Kosko, PHI learning private limited.
4. Neural Networks, Fuzzy Logic and Genetic Algorithm: Synthesis and Applications, S. Rakasekaran, G.A. VijayalakshmiPai, PHI learning private limited, 14th Edition. 2003.
5. Neural Networks and Fuzzy Logic, K. Vinoth Kumar, R. Saravana Kumar, S. K. Kataraia and Sons publication.
6. Artificial Neural Networks, B.Yegnanarayana Prentice Halll of India (P) Limited.
7. Introduction to Artificial Neural Systems, Jacek M. Zurada, Jaico Publication House.
8. Fuzzy Sets, Uncertainty and Information, G. J. Klir and T.A. Folger, PHI learning private limited. Publisher– Pearson 3Edition 199
BCS-605 BIG DATA ANALYTICS
Discipline Specific Elective DSE:4 (Group-A) Marks: 100 Total Credit: 04
Course Outcome: At the end of course, Students will be able to
Understand fundamentals of Big Data analytics.Investigate Hadoop framework and Hadoop Distributed File system.
Illustrate the concepts of NoSQL using MongoDB and Cassandra for Big Data.
Demonstrate the MapReduce programming model to process the big data along with Hadoop tools.
Analyze web contents and Social Networks to provide analytics with relevant visualization tools
Interpret business models and scientific computing paradigms, and apply software tools for big data analytics.
UNIT-I
Introduction To Big Data:
Big Data and its importance, Characteristics of Big Data, What Comes Under Big Data, Who’s Generating Big Data, Challenges in Handling Big Data, How Big Data Impact on IT, Big Data Analytics, Big data applications, Future of Big Data, Risks of Big Data.
UNIT-II
Introduction To Hadoop:
Introduction to Hadoop, Hadoop Architecture, Design Principles of Hadoop, Advantages of Hadoop, Hadoop Storage: Hadoop Distributed File System (HDFS), Properties of HDFS, NameNode, Secondary NameNode, DataNode, Goals of HDFS, Hadoop vs. Other Systems.
UNIT-III
Hadoop MapReduce: Hadoop MapReduce, MapReduce paradigm, Resource manager, Node manager, Partitioner, combiner.
UNIT-IV
Yarn: Introduction to YARN, YARN Framework, Classic MapReduceVs YARN, Schedulers: FIFO, Fair, Capacity.
UNIT-V
HADOOP Ecosystem: Spark, Hive, HBase, Pig, Sqoop, Oozie.
TEXT/ REFERENCE BOOKS
1. “Professional Hadoop Solutions”, Boris lublinsky, Kevin t. Smith, Alexey Yakubovich, Wiley, 2015.
2. “Understanding Big data”, Chris Eaton, Dirk deroos et al., McGraw Hill, 2012.
3. “HADOOP: The definitive Guide” , Tom White, O Reilly 2012.
4. “Big Data Analytics with R and Haoop”, VigneshPrajapati, Packet Publishing 2013.
5. “Oracle Big Data Handbook”, Tom Plunkett, Brian Macdonald et al , Oracle Press, 2014.
6. http://www.bigdatauniversity.com/
7. “Big Data and Business analytics”, JyLiebowitz, CRC press, 2013