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Sandip University
School of Engineering and Technology
Second Year of Computer Science and Engineering (2017 Course)
Modern Mathematics
School: Engineering & Technology Programme: B.Tech
Year: Second Year Semester – II
Course: Modern Mathematics Course Code: YCA401
Theory: 04 Hrs/ Week
Credits: 04
End Semester: 50 Marks
CIA: 50 Marks
Prerequisite:- Basic Mathematics
Course Objectives:
To solve order linear differential equations.
To find the roots of polynomial equations by using numerical methods and to learn the
concepts of interpolation.
To understand the basic concepts of probability distributions, correlation, regression and
fitting of curves.
To understand the basic concepts of linear Programing problems.
To find the optimal solution of LPP by using simplex and dual simplex method
Course Outcomes: On completion of the course, student will be able to–
Solve linear differential equation using appropriate techniques.
Apply statistical methods like correlation, regression analysis and probability theory for
analysis and prediction of a given data as applied to machine intelligence.
Solve Linear Programming Problems.
.
Course Contents
Unit I Linear Differential Equations 08 Hours
Linear Differential Equations with constant coefficients, Homogeneous Linear differential equations,
Applications of LDE with constant coefficients to Electrical systems.
Unit II Numerical Methods 08 Hours
Zeroes of transcendental and polynomial equation using Bisection method, Secant method, Regula
falsi method and Newton Raphson method, Rate of convergence of above methods.
Interpolation: Finite differences, difference tables, Newton’s forward and backward interpolation,
Lagrange’s and Newton’s divided difference formula for unequal intervals.
Unit III Probability and Statistics 08 Hours
Probability: Random variable, Binomial, Poisson, and Normal distributions.
Fitting of curves: Coefficient of correlation and lines of regression of bivariate data, Fitting of
Curves by method of Least squares.
Unit IV Linear Programming Problems - I 07 Hours
Formulation of Linear Optimization Problems, constraints, Graphical method to solve LPP, Standard
and Canonical forms, basic solutions and feasible solutions, optimal solutions by simplex method.
Unit V Linear Programming Problems - II
07 Hours
Artificial Variables, Duality concept, formulation of dual problems, duality principle, Relation
between Primal and Dual L.P.P., Dual simplex method.
Books:
Text:
1. B. S. Grewal, Higher Engineering Mathematics, 43rd edition, Khanna Publishers.
2. A text book of Applied Mathematics: Vol. I, II and III by J. N. Wartikar& P. N. Wartikar ,
VidyarthiGrihaPrakashan, Pune.
3. Operations Research by T. A. Taha.
Reference:
1. Ervin Kreyszig, Advanced Engineering Mathematics, 10th edition, John Wiley and Sons.
2. Peter V. O'Neil, Advanced Engineering Mathematics, 7th edition, Cengage Learning.
3. Operations Research by S. D. Sharma.
Sandip University
School of Engineering and Technology
Second Year of Computer Science and Engineering (2017 Course)
Computer Graphics
School: Engineering & Technology Programme: B.Tech
Year: Second Year Semester – II
Course: Computer Graphics Course Code: YCA402
Theory: 04 Hrs/ Week
Credits: 04
End Semester: 50 Marks
CIA: 50 Marks
Prerequisite: -C, C++, Linear algebra, Matrices and Basic Data structures.
Course Objectives:
To acquaint the learner with the basic concepts of Computer Graphics
To learn the various algorithms for generating and rendering graphical figures
To get familiar with mathematics behind the graphical transformations
To understand and apply various methods and techniques regarding projections, animation,
shading, illumination and lighting
Course Outcomes: On completion of the course, student will be able to–
To understand the various computer graphics hardware and display technologies.
Apply mathematics and logic to develop Computer programs for elementary graphic
operations
Develop scientific and strategic approach to solve complex problems in the domain of
Computer Graphics
Develop the competency to understand the concepts related to Computer Vision and Virtual
reality.
Course Contents
Unit I Basic of Computer Graphics & Devices 08 Hours
Introduction to computer graphics, state of art applications of computer graphics, pixel, frame buffer,
resolution, aspect ratio. Video display devices: CRT (Raster scan and random scan displays), flat
panel displays. Interactive devices: joysticks, touch panels, light pens. Data generating devices:
scanners and digitizers.
Unit II Scan Conversion 08 Hours
Line and line segments, line and circle drawing algorithms: DDA and Bresenham , Line styles: thick,
dotted and dashed. Antialising and antialising techniques. Character generating methods: stroke and
bitmap method, Multiligual character standards. Display Files: display file structure, algorithms and
display file interpreter. Primitive operations on display file.
Unit III Polygons and Clipping Algorithms 08 Hours
Introduction to polygon, types: convex, concave and complex. Representation of polygon, Inside test,
polygon filling algorithms – flood fill, seed fill, scan line fill and filling with patterns.
Windowing and clipping: viewing transformations, 2-D clipping: Cohen – Sutherland algorithm,
Polygon clipping: Sutherland Hodgeman algorithm, generalized clipping.
Unit IV Geometric Transformations 07 Hours
2-D transformations: Introduction, matrices, Translation, scaling, rotation, homogeneous
coordinates and matrix representation, translation, coordinate transformation, rotation about an
arbitrary point, inverse and shear transformation.
3-D transformations: Introduction, 3-D geometry, primitives, 3-D transformations and matrix
representation, rotation about an arbitrary axis, concept of parallel and perspective projections,
viewing parameters.
Unit V Curves, Fractals, Hidden Surfaces, Light and
Color Models
07 Hours
Hidden surfaces: introduction, back-face removal algorithm: Painter’s algorithm, binary space
partition, Warnock algorithm, Z –buffer.
Light and Color: Introduction, Diffused illumination, point source illumination, Shading
Algorithms, reflections, shadows, ray tracing, Color models and tables: RGB, HIS,
Introduction to curve generation, interpolation, B-splines, Bezier curve, Blending function,
fractals, Fractal lines and surfaces.
Books:
Text:
1. S. Harrington, “Computer Graphics”, 2nd Edition, McGraw-Hill Publications, 1987, ISBN 0
– 07 –100472 – 6.
2. D. Rogers, “Procedural Elements for Computer Graphics”, 2nd Edition, Tata McGraw-Hill
Publication, 2001, ISBN 0 – 07 – 047371 – 4.
Reference: 1. Sinha &Udai , “Computer Graphics”, Tata McGraw-Hill, 2007, ISBN 978-0-07-0634374.
2. J. Foley, V. Dam, S. Feiner, J. Hughes, “Computer Graphics Principles and Practice”, 2nd
Edition,
3. Pearson Education, 2003, ISBN 81 – 7808 – 038 – 9.
4. D. Hearn, M. Baker, “Computer Graphics – C Version”, 2nd Edition, Pearson Education,
2002, ISBN 81 – 7808 – 794 – 4.
5. D. Rogers, J. Adams, “Mathematical Elements for Computer Graphics”, 2nd Edition, Tata
McGraw-Hill Publication, 2002, ISBN 0 – 07 – 048677 – 8.
Sandip University
School of Engineering and Technology
Second Year of Computer Science and Engineering (2017 Course)
Fundamentals of Java Programming
School: Engineering & Technology Programme: B.Tech
Year: Second Year Semester – II
Course: Fundamentals of Java
Programming
Course Code: YCA403
Theory: 04 Hrs/ Week
Credits: 04
End Semester: 50 Marks
CIA: 50 Marks
Prerequisite: - C, C++
Course Objectives:
To understand fundamental concepts of OOP such as data abstraction, encapsulation,
inheritance, dynamic binding and polymorphism.
To understand the implementation of OOP concepts with JAVA.
To learn the features of core java that makes it more popular.
Course Outcomes: On completion of the course, student will be able to–
Implement Object Oriented Programming Concepts
Use and create packages and interfaces in a Java program
Use graphical user interface in Java programs
Create Applets
Implement exception handling in Java
Implement Multithreading
Use Input/output Streams
Course Contents
Unit I Introduction and Basics of OOP 08 Hours
Introduction: Programming language Types and Paradigms, Why Java , Flavors of Java, Features of
Java Language, JVM –The heart of Java, Java’s Magic Bytecode, Java Program Development, Data
types, Loops, Java Source File Structure, Compilation, Executions.
Class & Object: Class Fundamentals,Object & Object reference, Object Life time & Garbage
collection, Creating and Operating Objects, Constructor & initialization code block, Access Control,
Modifiers, Inner Class & Anonymous Classes, Abstract Class & Interfaces, Defining Methods,
Argument Passing Mechanism , Method Overloading, Recursion, Use of “this “ reference, Use of
Modifiers with Classes & Methods.
Unit II Inheritance, Polymorphism and Packages 08 Hours
Inheritance : Use and Benefits of Inheritance in OOP, Types of Inheritance in Java, Inheriting
Data members and Methods , Role of Constructors in inheritance , Overriding Super Class
Methods, Use of “super”, Polymorphism in inheritance, Type Compatibility and Conversion
Implementing interfaces. Packages: Organizing Classes and Interfaces in Packages , Package as Access Protection , Defining
Package ,CLASSPATH Setting for Packages , Making JAR Files for Library Packages Import and
Static Import Naming Convention For Packages.
Unit III Array, Strings and Thread 08 Hours
Array & String: Defining an Array, Initializing & Accessing Array, Multi –Dimensional Array,
Operation on String, Mutable & Immutable String, Using Collection Bases Loop for String,
Tokenizing a String, Creating Strings using StringBuffer.
Thread: Understanding Threads , Needs of Multi-Threaded Programming ,Thread Life-Cycle,
Thread Priorities ,Synchronizing Threads.
Unit IV Exception Handling and File Handling 07 Hours
Exception Handling: The Idea behind Exception, Exceptions & Errors, Types of Exception, Control
Flow In Exceptions, JVM reaction to Exceptions, Use of try, catch, finally, throw, throws in
Exception Handling, In-built and User Defined Exceptions, Checked and Un-Checked Exceptions.
File Handling: Input/Output Operation in Java (java.io Package), Streams and the new I/O
Capabilities, Understanding Streams, The Classes for Input and Output, The Standard Streams,
Working with File Object, File I/O Basics, Reading and Writing to Files, Buffer and Buffer
Management.
Unit V GUI Programming and Event Handling 07 Hours
GUI Programming: Designing Graphical User Interfaces in Java, Components and Containers,
Basics of Components, Using Containers, Layout Managers, AWT Components, Adding a Menu to
Window, Extending GUI Features Using Swing Components, Java Utilities (java.util Package) The
Collection Framework : Collections of Objects , Collection Types, Sets , Sequence, Map,
Understanding Hashing, Use of ArrayList & Vector.
Event Handling: Event-Driven Programming in Java, Event- Handling Process, Event-Handling
Mechanism, The Delegation Model of Event Handling, Event Classes, Event Sources, Event
Listeners.
Books:
Text:
1. E Balagurusamy "Programming with Java", Fifth Edition, McGraw Hill Education, 2014,
ISBN: 978-9351343202.
2. R. Nageswara Rao, “Core Java: An Integrated Approach”, 1est Edition, DreamtechPress ,
2016, ISBN: 978-9351199250.
3. Herbert Schildt, “Java A Beginner's Guide”, 6th Edition, Tata McGraw-Hill , 2014, ISBN:
9789339213039.
Reference: 1. Herbert Schildt"Java: The Complete Reference"; Ninth Edition, Oracle Press, ISBN 978-0-
07-180855-2.
2. D.T.Editorial Serices "Java 8 programming" Black Book.
3. D.T Editorial Services, R. Nageswara Rao"Core Java: An Integrated Approach"
Sandip University
School of Engineering and Technology
Second Year of Computer Science and Engineering (2017 Course)
Advanced Data Structures
School: Engineering & Technology Programme: B.Tech
Year: Second Year Semester – IV
Course: Advanced Data Structures Course Code: YCA404
Theory: 04 Hrs/ Week
Credits: 04
End Semester: 50 Marks
CIA: 50 Marks
Prerequisite:-
Data Structures and algorithms
Basic Mathematics, Geometry, linear algebra, vectors and matrices.
Course Objectives:
To develop a logic for graphical modelling of the real life problems.
To suggest appropriate data structure and algorithm for graphical solutions of the problems.
To understand advanced data structures to solve complex problems in various domains.
To operate on the various structured data
To build the logic to use appropriate data structure in logical and computational solutions.
Course Outcomes: On completion of the course, student will be able to–
To apply appropriate advanced data structure and efficient algorithms to approach the
problems of various domain.
To design the algorithms to solve the programming problems.
To use effective and efficient data structures in solving various Computer Engineering
domain problems.
To analyze the algorithmic solutions for resource requirements and optimization
Course Contents
Unit I Trees 08 Hours
Tree- basic terminology, General tree and its representation, representation using sequential and
linked organization, Binary tree- properties, converting tree to binary tree, binary tree traversals-
inorder, preorder, post order, level wise -depth first and breadth first, Operations on binary tree.
Binary Search Tree (BST), BST operations, Threaded binary tree- concepts, threading, insertion and
deletion of nodes in in-order threaded binary tree.
Case Study- Use of binary tree in expression tree-evaluation and Huffman's coding
Unit II Graphs 07 Hours
Graphs- Basic Concepts, Storage representation, Adjacency matrix, adjacency list, adjacency multi
list, inverse adjacency list. Traversals-depth first and breadth first, Minimum spanning Tree, Greedy
algorithms for computing minimum spanning tree- Prims and Kruskal Algorithms, Dikjtra's Single
source shortest path, Topological ordering.
Unit III Hashing 08 Hours
Hash Table- Concepts-hash table, hash function, bucket, collision, probe, synonym, overflow, open
hashing, closed hashing, perfect hash function, load density, full table, load factor, rehashing, issues
in hashing, hash functions- properties of good hash function, division, multiplication, extraction,
mid-square, folding and universal, Collision resolution strategies- open addressing and chaining,
Hash table overflow- open addressing and chaining, extendible hashing.
Heap-Basic concepts, realization of heap and operations
Unit IV Search Trees, Indexing and Multiway Trees 07 Hours
Symbol Table-Representation of Symbol Tables- Static tree table and Dynamic tree table, Height
Balanced Tree- AVL tree.
Indexing and Multiway Trees- Indexing, indexing techniques, Types of search tree- Multiway
search tree, B-Tree, B+Tree
Unit V File Organization 08 Hours
Sequential file organization- concept and primitive operations, Direct Access File- Concepts and
Primitive operations, Indexed sequential file organization-concept, types of indices, structure of
index sequential file, Linked Organization- multi list files, coral rings, inverted files and cellular
partitions.
Books:
Text:
1. Horowitz, Sahani, Dinesh Mehata, “Fundamentals of Data Structures in C++”, Galgotia
Publisher, ISBN: 8175152788, 9788175152786.
2. M Folk, B Zoellick, G. Riccardi, “File Structures, Pearson Education”, ISBN:81-7758-37-5.
3. Peter Brass, “Advanced Data Structures‖”, Cambridge University Press, ISBN:978-1-107-
43982-5
Reference: 1. Aho, J. Hopcroft, J. Ulman, ―Data Structures and Algorithms‖, Pearson Education, 1998,
ISBN-0-201-43578-0.
2. Michael J Folk, ―File Structures an Object Oriented Approach with C++‖, Pearson
Education, ISBN: 81-7758-373-5.
3. SartajSahani, ―Data Structures, Algorithms and Applications in C++‖, Second Edition,
University Press, ISBN:81-7371522 X.
4. G A V Pai, ―Data Structures and Algorithms‖, The McGraw-Hill Companies, ISBN -
9780070667266.
Goodrich, Tamassia, Goldwasser, ―Data Structures and Algorithms in Java‖, Wiley
Publication, ISBN: 9788126551903.
Sandip University
School of Engineering and Technology
Second Year of Computer Science and Engineering (2017 Course)
Applied Statistical Analysis
School: Engineering & Technology Programme: B.Tech
Year: Second Year Semester – II
Course: Applied Statistical Analysis Course Code: YCA405
Theory: 03Hrs/ Week
Practical: 02 Hrs/Week
Credits: 03
End Semester: 50 Marks
CIA: 100 Marks
Prerequisite:-
Data Structures and algorithms
Basic Mathematics, Geometry, linear algebra, vectors and matrices.
Course Objectives: The course enables students to:
Learn how to analyze statistical data properly.
Understand the role of formal statistical theory and informal data analytic methods.
Course Outcomes: On completion of the course, student will be able to–
Gain an understanding of statistical methods relevant to upper division interdisciplinary
courses.
Sharpen students’ statistical intuition and abstract reasoning as well as their reasoning
from numerical data throughcommunity-based and other research.
Course Contents
Unit I Introduction to Statistical Analysis 08 Hours
Introduction, Meaning of Statistics, The Scientific Method, Basic Steps of the Research Process,
Experimental Data and Survey Data, Populations and Samples, Census and Sampling Method,
Parameter and Statistic, Independent and Dependent Variables, Examining Relationships,
Introduction to SPSS Statistics.
Unit II Describing Data 07 Hours
Introduction, Types of Data, Data Transformation, and Summarizing Data: Graphical Methods,
Summarizing Data: Measures of Central Tendency, Summarizing Data: Measures of Dispersion,
Levels of Measurement, Random Variables and Probability Distributions, Discrete and Continuous
Random Variable, Making Inferences about Populations from samples, Estimator and Estimate,
Confidence Interval for Population Mean (Large Sample).
Unit III Testing Hypothesis 08 Hours
Introduction, Null and Alternative Hypothesis, Type I and Type II Error, The Procedure of
Hypothesis Testing, Hypothesis Testing of a Population Mean: Large Sample, Hypothesis Testing of
a Population Mean: Small Sample, Hypothesis Test of a Proportion (One Sample), Hypothesis Test
of Population Variance, Hypothesis Test of Population Mean: Two Independent Samples(),
Hypothesis Test of Population Mean: Dependent Samples (Paired Samples), Hypothesis Test about
Two Population Proportion, Hypothesis Test about Two Population Variances, Analysis of Variance
(ANOVA), Nonparametric Test, Sign Test for Paired Data, Wilcoxon Matched Pairs Signed Ranks
Test (for n>10 pairs), Mann-Whitney U Test, Kruskal-wallis Tests (H Test).
Unit IV Examining Relationships 07 Hours
Introduction, Types of Correlation, Karl Pearson Coefficient Correlation, Spearman’s Rank Order
Correlation, Partial Correlation, Residuals and Plots, Simple Linear Regression, Multiple Regression
Model, Repeated Measures, Non-linear Regression, Polynomial Regression Models, Weighted Least
Squares, Two Stage Least Squares 1, Structural Equation Modeling.
Unit V Advanced Techniques 08 Hours
Identifying Groups: Classification, Profit Analysis, Discriminant Function Analysis, Proportional
Odds Models, Decision Trees, Neural Networks, Cluster Analysis, Factor Analysis,
Multidimensional Scaling.
Advanced Statistical Analysis Lab Exercises
Using the preexistingDrinks.sav data file
Exercise 1 : to create standardized (Z-) scores for several variables
Using the preexistingCensus.sav data file
Exercise 2 : To run Frequencies to explore the distributions of several variables.
Using the preexistingDrinks.sav data file
Exercise 3 : To obtain summary statistics for scale variables
Using the preexistingCensus.sav data file
Exercise 4 : To create two and three-way cross tabulations to explore the relationship between
several variables and touse the Chart Builder to visualize the relationship.
Using the preexistingCensus.sav data file
Exercise 5 : To run the Independent-Samples T Test, to interpret the output and visualize the
results with an error barchart.
Using the preexisting data file Census.sav.
Exercise 6 : To use One-Way ANOVA with post hoc tests to explore the relationship between
several variables. You willuse the PASW Statistics.
Using the preexisting data file Bank.sav.
Exercise 7 : To visualize the relationship between two scale variables creating scatterplots
and to quantify thisrelationship with the correlation coefficient.
Using the preexisting PASW Statistics data file Census.sav.
Exercise 8 : To run linear regressions and to interpret the output
Using the preexisting data file SPSS_CUST.SAV
Exercise 9 : To use nonparametric tests to explore the relationship between several
Books:
Text:
1. Applied Statistical Analysis (IBM ICE Publication)
Reference:
2. Statistical Data Analysis (Oxford Science Publications) by Glen Cowen
3. Statistical Analysis : an Introduction using R.Wikibooks
4. Multivariate Statistical Analysis A Conceptual Introduction, 2nd edition by Sam
KashKachigan
5. Handbook of Statistical Analysis and Data Mining Application by Robert Nisbet,
John, IV Elder, Gary Miner
Sandip University
School of Engineering and Technology
Second Year of Computer Science and Engineering (2017 Course)
Java Programming Laboratory
School: Engineering & Technology Programme: B.Tech
Year: Second Year Semester – II
Course: Java Programming Laboratory Course Code: YCA411
Theory: --
PR: 04 Hours/Week
Credits: 02
Practical : 25 Marks
Term Work : 25 Marks
Guidelines for Assessment
Continuous assessment of laboratory work is done based on overall performance and lab assignments
performance of student. Each lab assignment assessment will assign grade/marks based on
parameters with appropriate weightage. Suggested parameters for overall assessment as well as each
lab assignment assessment include- timely completion, performance, innovation, efficient codes,
punctuality and neatness.
Guidelines for Laboratory Conduction
The instructor is expected to frame the assignments by understanding the prerequisites, technological
aspects, utility and recent trends related to the topic. The assignment framing policy need to address
the average students and inclusive of an element to attract and promote the intelligent students. The
instructor may set multiple sets of assignments and distribute among batches of students. It is
appreciated if the assignments are based on real world problems/applications. Encourage students for
appropriate use of Hungarian notation, Indentation and comments. Use of open source software is
encouraged.
In addition to these, instructor may assign one real life application in the form of a mini-project based
on the concepts learned. Instructor may also set one assignment or mini-project that is suitable to
respective branch beyond the scope of syllabus.
Set of suggested assignment list is provided in three groups. Each student must perform at least 13
assignments as at 05 compulsory from group A, 07 from group B and 01 from group C
Operating System recommended: 64-bit Open source Linux or its derivative.
Programming tools recommended: JDK.
Suggested List of Laboratory Assignments
Group A (Compulsory Assignments)
1. Write a java program for employee class to display basic information.
2. Design a class in java to perform various mathematical operations on given numbers.
3. Write a java program for calculating area of circle.
4. Write a program for implementing single inheritance for student class.
5. Write a program for implementing multilevel inheritance for employee class.
Group B (Any 7)
1. Implement java program to display content of array.
2. Write a java program to find the prime number from 1 to 20.
3. Write a java program to display Fibonacci series of any number.
4. Implement a java program to perform addition of two numbers, accept numbers form user.
5. Write a java program to find.
a. Length of given string.
b. Reverse the string.
c. Palindrome.
6. Implement a java program to count number of vowels from given string.
7. Design an applet program to perform addition of two numbers.
8. Write an exception handling program to handle divide by zero and Array Index OutOfBounds
errors.
9. Implement AWT program to design student admission form.
10. Write a java program to read and write the content of given "example.txt" file.
Group C (Any 1)
1. Design a calculator using AWT.
2. Implement Tic Tac Toe using AWT.
Sandip University
School of Engineering and Technology
Second Year of Computer Science and Engineering (2017 Course)
Advanced Data Structures Laboratory
School: Engineering & Technology Programme: B.Tech
Year: Second Year Semester – II
Course: Advanced Data Structures Laboratory Course Code: YCA412
Theory: --
PR: 04 Hours/Week
Credits: 02
Practical : 25 Marks
Term Work : 25 Marks
Guidelines for Assessment
Continuous assessment of laboratory work is done based on overall performance and lab assignments
performance of student. Each lab assignment assessment will assign grade/marks based on
parameters with appropriate weightage. Suggested parameters for overall assessment as well as each
lab assignment assessment include- timely completion, performance, innovation, efficient codes,
punctuality and neatness.
Guidelines for Laboratory Conduction
The instructor is expected to frame the assignments by understanding the prerequisites, technological
aspects, utility and recent trends related to the topic. The assignment framing policy need to address
the average students and inclusive of an element to attract and promote the intelligent students. The
instructor may set multiple sets of assignments and distribute among batches of students. It is
appreciated if the assignments are based on real world problems/applications. Encourage students for
appropriate use of Hungarian notation, Indentation and comments. Use of open source software is
encouraged.
In addition to these, instructor may assign one real life application in the form of a mini-project based
on the concepts learned. Instructor may also set one assignment or mini-project that is suitable to
respective branch beyond the scope of syllabus.
Set of suggested assignment list is provided in three groups. Each student must perform at least 13
assignments as at 05 compulsory from group A, 07 from group B and 01 from group C
Operating System recommended: 64-bit Open source Linux or its derivative.
Programming tools recommended: gcc /g++ compiler
Suggested List of Laboratory Assignments 1. C Program to Construct a Tree & Perform Insertion, Deletion, Display 2. In-order, Pre-order and Post-order Tree Traversal using C Programming
3. C Program to Implement Binary Tree using Linked List
4. C program to implement Depth First Search(DFS) 5. C program to implement breadth First Search(BFS)
6. A C / C++ program for Prim's Minimum Spanning Tree (MST) algorithm. The program is for
adjacency matrix representation of the graph.
7. C++ program for Kruskal's algorithm to find Minimum Spanning Tree of a given connected, undirected and weighted graph
8. A C++ program for Dijkstra's single source shortest path algorithm. The program is for adjacency matrix representation of the graph
9. C Program To Perform Insertion, Deletion and Traversal In B-Tree.
10. C++ Program to Implement B+ Tree
11. Write a C Program to implement hashing. 12. C Program to Implement Hash Tables chaining with Singly Linked Lists.
13. C Program for the Implementation of a Symbol Table with functions to create, insert, modify, search
and display