DATA COMPRESSION AND ENCRYPTION (DCE) DECEMBER 2011 [ELECTIVE] ELECTRONICS AND TELECOMMUNICATION SEMESTER 7

DATA COMPRESSION AND ENCRYPTION (DCE) DECEMBER 2011  [ELECTIVE] ELECTRONICS AND TELECOMMUNICATION SEMESTER 7

Con. 6862-11.                    (REVISED COURSE)                                             MP-5683
                                                   (3 Hours)                                            [Total Marks : 100]

N. B. : (1) Question Number 1 is Compulsory.
           (2) Attempt any four questions out of remaining six questions.

1. (a)	What is the need of compression.	5
(b)	Discuss the application of JPEG 2000.	5
(c)	Compare Cryptography and steganography.	5
(d)	What is Secure Electronic Payment System.	5
2. (a)	A source emits letter from an alphabet A= { a1, a2, a3, a4, a5) with	10
	probabilities P(a1) = 0.15 , P(a2) = 0.04 , P(a3) = 0.26 , P(a4) = 0.05
	and P(a5=0.05.
	a) Calculate the entropy of this source
	b) Find the Huffman code for this source.
	c) Find the average length of the code in
	d) and its redundancy.
(b)	Explain ADPCM Encoder and Decoder for audio compression	10
3. (a)	Describe the features of video compression as compared to image	10
	compression. Explain MPEG industry standards for video compression.
(b)	The o/p of the LZW encoder is the following sequence	5
	Decode the sequence.
(c)	Discuss in brief lossless audio compression	5
4. (a)	Explain the working of DES with block diagram	10
(b)	Discuss and explain security attacks on the system.	10
5. (a)	Explain algorithms for RSA using Chinese Remainder Theorom	10
(b)	Discuss Classical Encryption Techniques.	10
6. (a)	Explain Hash and MAC algorithms used for authentication.	10
(b)	Discuss certificate based and biometric system.	10
7.	Write short notes on any three :-	20
	(a) Approaches to Image Compression.
	(b) S-Box Design.
	(c) Digital signatures.
	(d) Performance and quality measures for compression.

DATA COMPRESSION AND ENCRYPTION (DCE) DECEMBER 2012 [ELECTIVE] ELECTRONICS AND TELECOMMUNICATION SEMESTER 7

DATA COMPRESSION AND ENCRYPTION (DCE) MAY 2012  [ELECTIVE] ELECTRONICS AND TELECOMMUNICATION SEMESTER 7

Con. 8490-12.                                                                                    KR-1980
                                                       (3 Hours)                                              [Total Marks : 100]

N.B. : (1) Question No. 1 is Compulsory.
          (2) Attempt any four questions out of remaining six questions.
          (3) Assume suitable data if needed.
          (4) Figures to be right indicates full marks.

1. (a)	Brief explain Modelling and coding with respect to data compression.	5
(b)	Explain the principle of Public Key Cryptography.	5
(c)	Explain the working principle of Arithmatic Coding with suitable example.	5
(d)	Expalin Chinese remainder theorem.	5
2. (a)	Define the following terms :-	10
	(i) Compression Ratio
	(ii) Distrotion
	(iii) Compression Rate
	(iv) Fidelity and Quality
	(v) Self information.
(b)	Explain Diffie - Hellman key exchange algorithm with example.	10
3. (a)	What is adaptive dictionary based coding. Explain any one method.	10
(b)	What is the signifiance of 'prime number' is public key cryptography ? Explain	10
	RSA algorithm with a suitable example.
4. (a)	Explain DPCM and ADPCM techniques used in audio compression.	10
(b)	What is Malicious Programe ? Explain different types of malicious programmes.	10
5. (a)	Explain audio encoder and decoder used in MPEG.	10
(b)	Explain Digital Immune System.	10
6. (a)	Design Huffman code for a source which generate letters from an alphabet.	10
	Also calculate entrophy of the source.
(b)	What is message digiest ? Explain HMAC algorithm.	10
7.	Write short notes on any two :-	20
	(a) JPEG 2000
	(b) Key Distribution Centre
	(c) Fire wall Design
	(d) Difference between :-
	(i) Lossy and Lossless compression
	(ii) Audio Compression and Image Compression.

DATA COMPRESSION AND ENCRYPTION (DCE) MAY 2012 [ELECTIVE] ELECTRONICS AND TELECOMMUNICATION SEMESTER 7

DATA COMPRESSION AND ENCRYPTION (DCE) MAY 2012  [ELECTIVE] ELECTRONICS AND TELECOMMUNICATION SEMESTER 7

Con. 4636.12.                         (REVISED COURSE)                                               GN-8942
                                                     (3 Hours)                                                 [Total Marks : 100]

N. B. : (1) Question No. 1 is Compulsory.
           (2) Attempt any four questions out of remaining six questions.

1. (a)	What are the various models used for data compression?	5
(b)	Compare between A-Law and u-Law compression ?	5
(c)	What are active and passive attacts ?	5
(d)	What are is Biometric Authentication ? Explain with examples.	5
2. (a)	A source A= {a,b,c,d) has probabilities {0.7,0.15,0.1,0.05} respectively.	10
	Generate a tag for the sequence { a b c d a } using arithmetic code.
(b)	Explain working of DES.	10
3. (a)	Take an example and show the difference in the encoding procedures of LZ78	10
	and LZW.
(b)	Explain RSA algorithm with an example.	10
4. (a)	Discuss the various lossless techniques for image compression.	10
(b)	How are the keys distributed in conventional encryption systems ?	10
5. (a)	How is motion compensation used in video compression ?	10
(b)	What are digital signatures and how are they implemented ?	10
6. (a)	Why are Transforms used in compression ? How is JPEG different from	10
	JPEG2000 ?
(b)	What are the various aspects of a firewall design ?	10
7.	Write notes on (any two) :-	20
	(a) Chinese Remainder Theorom
	(b) Elliptic curve cryptography
	(c) MPEG audio standard
	(d) Steganography.

DATA COMPRESSION AND ENCRYPTION (DCE) May 2013 [ELECTIVE] ELECTRONICS AND TELECOMMUNICATION SEMESTER 7

DATA COMPRESSION AND ENCRYPTION (DCE) DECEMBER 2013 [ELECTIVE] ELECTRONICS AND TELECOMMUNICATION SEMESTER 7

Con. 9865-13.                                                                                                   GS-6238
                                                   (3 Hours)                                                  [Total Marks : 100]

  
N.B. : (1) Question No. 1 is Compulsory.
          (2) Attempt any four questions out of remaining six questions.
          (3) Assume suitable data wherever required.
          (4) Figures to be right indicates full marks.
          (5) Answers to the question should be grouped and written together.

1. (a)	What are the different measures of performance of data compression techniques ?	5
(b)	Discuss the applicaltion of JPEG 2000.	5
(c)	Compare public and private key crypto-system.	5
(d)	What is secure electronic payment system ?	5
2. (a)	A source and its letter form an alphabet A = {a, b, c, d, e} with probabilities	10
	P = {0.15, 0.05, 0.25, 0.35, 0.2} respectively. Calculate :-
	(i) Standard Huffman code
	(ii) Minimum variance Huffman code
	(iii) Average length and redundancy of both codes
	(iv) Draw the binary code tree for both the codes.
(b)	Explain the working of DES with block diagram.	10
3. (a)	What are dictionary based compression schemes? Explain LZ77 with suitable examples.	10
(b)	What are various aspects of conventional encryption ? Discuss the types of attacks.	10
4. (a)	How are differential encoding schemes used in audio compression ?	10
(b)	Explain Chinese remainder theorem with an example.	10
5. (a)	Describe various approaches for Image Compression and discuss one of them in	10
	detail.
(b)	Explain Hash and MAC algorithm used for authenication.	10
6. (a)	Describe the features of Video Compression as compared to Image Compression.	10
	Explain MPEG industry standard for video compression.
(b)	Explain Digital Immune System.	10
7.	Write short notes on any two :-	20
	(a) Firewall design
	(b) Elliptic curve cryptography
	(c) MPEG audio standard
	(d) Two dimensional image transform.

DATA COMPRESSION AND ENCRYPTION (DCE) DECEMBER 2013 [ELECTIVE] ELECTRONICS AND TELECOMMUNICATION SEMESTER 7

DATA COMPRESSION AND ENCRYPTION (DCE) [ELECTIVE] ELECTRONICS AND TELECOMMUNICATION SEMESTER 7

Con. 8446-13.                                                                                                     LJ-14002
                                                        (3 Hours)                                            [Total Marks : 100]


N. B. : (1) Question No. 1 is Compulsory.
           (2) Attempt any four questions out of remaining six questions.

1. (a)	How are DPCM and ADPCM used for audio compression?	5
(b)	What is the difference between Shannon Fano coding and Huffman coding?	5
(c)	Discuss the major steps in S-box design.	5
(d)	What are digital signatures and how are they used?	5
2. (a)	A source A= {w,x,y,z) has probabilities {0.15,0.1,0.7,0.05} respectively. Generate	10
	and decipher the tag for the sequence [x,y,z,w,z] using arithmetic code.
(b)	Explain how triples DES works with two and three keys.	10
3. (a)	Take an alphabet string and code it using LZ78. what are the limitations and how can	10
	they be overcome with LZW?
(b)	Classify the different types of attacks and explain them with examples.	10
4. (a)	How is the frequency domain coding useful in audio compression?	10
(b)	Discuss the various principle involved in private and public key cryptography.	10
5. (a)	Explain one lossless technique of image compression.	10
(b)	What are message authentication and Hash function and how are they used?	10
6. (a)	What are the various steps involved in video compression?	10
(b)	Explain the various certificate based on Biometric authentication methods.	10
7.	Write short notes on (any two) :-	20
	(a) JPEG 2000.
	(b) Chinese Remainder Theorom.
	(c) Digital Immune Systems.
	(d) Success Electronic Payment Systems.

COMPUTER VISION (CV) [ELECTIVE], Semester 8, B.E. Computer Science (CS), May 2013.

COMPUTER VISION (CV) [ELECTIVE], Semester 8,

B.E. Computer Science (CS), May 2013.

Con. 8527-13

GS-3577

(3 Hours)

[Total Mark: 100]

N.B. (1) Question No 1 is compulsory.

(2) Attempt any four questions out of remaining six questions.

(3) Assume suitable data whenever necessary and justify the same.

(4) Figures to the right indicate full marks.

1. (a) Give all the steps involved in recognition methodology and briefly explain each. ---- (10 Marks)

(b) Explain ‘opening’ and ‘closing’ with example. ---- (10 Marks)

2. (a) Explain Hough transform with example. Mention all its merits and demerits. ---- (10 Marks)

(b) What is knowledge based vision? Explain different of knowledge representation used in computer vision. ---- (10 Marks)

3. (a) Explain Border tracking algorithm with suitable example. ---- (10 Marks)

(b) Explain inverse perspective projection. ---- (10 Marks)

4. (a) Explain intensity matching of 1 dimensional signals. ---- (10 Marks)

(b) Explain back – tracking algorithm with suitable example. ---- (10 Marks)

5. (a) Apply ‘iterative’ and ‘classical’ connected component labelling algorithms on following image: ---- (10 Marks)

0	0	0	0	0	0	0	1	1	0
0	1	1	0	0	0	1	1	1	0
0	1	1	1	0	1	1	1	1	0
0	0	1	1	0	0	0	1	1	0

(b) Explain boundary descriptors. --- (10 Marks)

6. (a) Explain ‘thinning’ and ‘thickening’ with the help of examples. ---- (10 Marks)

(b) Explain mixed spatial gray – level moments. ---- (10 Marks)

7. Write short note on: ---

(a) External points. ---- (5 Marks)

(b) Principal component analysis. --- (5 Marks)

(c) View class matching. ---- (5 Marks)

(d) Global V/S local features. --- (5 Marks)

COMPUTER VISION (CV) [ELECTIVE], Semester 8, B.E. Computer Science (CS), May 2011.

COMPUTER VISION (CV) [ELECTIVE], Semester 8,

B.E. Computer Science (CS), May 2011.

(3 Hours)

[Total Mark: 100]

1. (a) Define Vanishing point and Vanishing line. ----- (5 Marks)

(b) What is significance of shape no. in region analysis? ---- (5 Marks)

(c) What is zero crossing edge detectors. ----- (5 Marks)

(d) Explain Least square method of curve fitting algorithm. ---- (5 Marks)

2. (a) Give all the steps involved in recognition methodology and briefly explain each. ---- (10 Marks)

(b) Explain Run length implementation of algorithm with suitable example. ---- (10 Marks)

3. (a) Consider elongated region whose external points are given by ----- (10 Marks)

(r1, c1) = (24, 137)
(r2, c2) = (24, 163)
(r3, c3) = (30, 181)
(r4, c4) = (32, 181)
(r5, c5) = (39, 155)
(r6, c6) = (39,145)
(r7, c7) = (32, 119)
(r8, c8) = (30, 119)

Determine M1, M2, M3, M4, ϕ1, ϕ2, ϕ3, ϕ4. If this region is considered to be rectangle, what would be Computed orientation, length and width of rectangle?

(b) Develop the algorithm for automatically calculating threshold value by minimizing within group variance. --- (10 Marks)

4. (a) Explain the following region growing algorithm with suitable example. ---- (10 Marks)

(i) Centroid linkage

(ii) Hybrid linkage

(iii) Single linkage

(b) What is signature segmentation? Obtain the horizontal, vertical and diagonal signature of the binary image segment shown below: ---- (10 Marks)

			1	1	1	1	1
		1	1	1
			1	1	1
				1	1	1				1	1
1										1	1
1	1									1	1
	1
			1	1	1	1
			1	1	1	1

5. (a) Explain border tracking algorithm with suitable example. ---- (10 Marks)

(b) Using Hough transform show that the points (0,1), (1,4) and (-1,-2) are collinear and hence find the equation of line. ---- (10 Marks)

6. (a) What is knowledge based vision? Explain different forms of knowledge representation used in Computer vision. ---- (10 Marks)

(b) What are control strategies? Discuss the two major form of control Hierarchical and Hierarchical. ---- (10 Marks)

7. (a) Explain Information Integration ----- (5 Marks)

(b) What is facet model recognition ------ (5 Marks)

(c) Explain view class matching. ----- (5 Marks)

(d) Explain Intensity matching for one dimensional signal. ---- (5 Marks)

SOFT COMPUTING (SC) [ELECTIVE], Semester 7, B.E. Computer Science (CS), December 2013.

SOFT COMPUTING (SC) [ELECTIVE], Semester 7,

B.E. Computer Science (CS), December 2013.

Con.7749-13

(REVISED COURSE)

LJ- 13927

(3 Hours)

[Total Mark: 100]

N. B.: (1) Question no 1 is compulsory.

(2) Attempt any four questions out of the remaining questions.

(3) Figures to the right indicate full marks.

1. (a) Model the following as a fuzzy set using suitable membership function – “Numbers close to 6”. ------ (8 Marks)

(b) Explain standard fuzzy membership functions. ---- (6 Marks)

(c) Determine all α – level sets as and strong α – level sets for the following fuzzy set. ---- (6 Marks)

A = { (1, 0.2) (2, 0.5) (3, 0.8) (4, 1) (5, 0.7) (6, 0.3) }

2. Design a fuzzy Controller to determine the wash time of a domestic washing machine. Assume that the inputs are dirt and grease on the clothes. Use three descriptors for each input variable and five descriptors for out put variable. Derive a set of rules for control action and defuzzification. The design should be supported by figures wherever possible. Clearly indicate that if the clothes are soiled to a larger degree the wash time required will be more. --- (20 Marks)

3. (a) Determine the weights after four steps of training for Perceptron learning rule of a single neuron network starting with initial weights: --- (10 Marks)

W = [0 0]^t , inputs as X1= [2 2]^t ,
X2 = [1 - 2]^t , X3 = [-2 2]^t , X4 = [-1 1]^t ,
d1 = 0, d2 = 1, d3 = 0, d4 = 1, and c = 1.

(b) Explain Mamdani type of Fuzzy Inference system in detail. ---- (10 Marks)

4. (a) Prove the following identities : - --- (10 Marks)

(i) For unipolar continuous activation function: -
f¹ (net) = 0 (1- 0)

(ii) For bipolar continuous activation function: -
f¹ (net) = 1/2(1- 0¹)

(b) Explain error back propagation training algorithm with the help of a flowchart. --- (10 Marks)

5. (a) Explain RBF network and give the comparison between RBF and MLP. --- (10 Marks)

(b) Explain with examples linearly and non – linearly separable pattern classification. --- (10 Marks)

6. (a) What is learning in neural network ? Differentiate between Supervised and Unsupervised Learning. ---- (10 Marks)

(b) Explain Travelling salesperson problem using simulated annealing. ---- (10 Marks)

7. Write notes on any two of the following: ---- (20 Marks)

(a) Learning Vector Quantization.

(b) Derivative Free Optimization.

(c) Winner takes all learning rule.

HUMAN COMPUTING INTERACTION (HCI) [ELECTIVE] MAY 2012 COMPUTER SCIENCE SEMESTER 8

HUMAN COMPUTING INTERACTION (HCI) [ELECTIVE] MAY 2012 COMPUTER SCIENCE SEMESTER 8

Con.3983-12                                     (REVISED COURSE)                        GN-7572

                                                                (3 Hours)                           [Total Marks: -100]


N.B: (1) Question No.1 is compulsory.
        (2) Attempt any four questions out of remaining.
        (3) Figures to the right indicate full marks.
        (4) Assume suitable data wherever required and justify the same.

1. (a) Explain principles of user interface. [5 Marks]
    (b) Explain various types of statistical graphics in brief. [5 Marks]
    (c) Explain influence on icon usability. [5 Marks]
    (d) Explain various types of messages. [5 Marks]

2. (a) Explain the difference between proper menu design and poor menu design with
        diagrams. [10 Marks] 
    (b) Explain following techniques in determining requirements. Categorize them as direct or
         indirect method. [10 Marks]
         (i) Electronic Survey
        (ii) Card sorting
       (iii) Facilitated Team Workshop
       (iv) Trade Show

3. (a) Explain various New and Navigation schemes selection of window with example. [10 Marks]
    (b) What are the mental model and why are they important in interface design? [10 Marks]

4. (a) Explain various colar models. Explain various considerations for choosing background and
         foreground color web page. [10 Marks]
    (b) Explain proper device based and screen based controls. [10 Marks]

5. (a) What are pointing devices? What tasks and explain about direct control pointing
          devices. [10 Marks] 
    (b) What are human considerations in source design? Also explain organizing screen elements,
          ordering of screen data and contents. [10 Marks]   
 
6. (a) Explain various interface building tools. [10 Marks]
    (b) Differentiate between Graphical Users Interface and Web Page design. [10 Marks]

7. Write short notes on :- (Any Two) [20 Marks]
     (a) Speech Recognition digitization and generation.
     (b) Designing for visually impaired.
     (c) Software tools and specification methods.
     (d) Designing process to understand business junctions. 

SOFT COMPUTING (SC) [ELECTIVE], Semester 7, B.E. Computer Science (CS), May 2013.

SOFT COMPUTING (SC) [ELECTIVE], Semester 7,

B.E. Computer Science (CS), May 2013.

Con.9787-13

(REVISED COURSE)

GS- 6157

(3 Hours)

[Total Mark: 100]

N. B.: (1) Question no 1 is compulsory.

(2) Attempt any four questions out of the remaining questions.

(3) Figures to the right indicate full marks.

1. (a) Explain cylindrical extension and projection operations on fuzzy relation with example. ---- (5 Marks)

(b) A neuron with 3 inputs has the weight vector w = [0.1 0.3 -0.2]. The activation function is binary sigmoidal activation function. If input vector is [0.8 0.6 0.4] then find the output of neuron. --- (5 Marks)

(c) What are the various types of Mutation techniques? ---- (5 Marks)

(d) Model the following as fuzzy set using trapezoidal membership function. --- (5 Marks)
“Middle age”

2.(a) What is supervised and unsupervised learning ? Compare different learning rules. ---- (10 Marks)

(b) Explain the Radial Basis function neural network for the solution of XOR. --- (10 Marks)

3. (a) Explain Kohonen’s self organizing neural network. --- (10 Marks)

(b) For the given membership function as shown in figure below, determine the defuzzified output value by 2 methods.---- (10 Marks)

4. (a) Explain the operations of genetic programming with help of flowchart . ---- (10 Marks)

(b) Compare Mamdani and Sugeno Fuzzy models. ---- (10 Marks)

5. Design fuzzy logic controller for water purification plant. Assume the grade of water and temperature of waters as the inputs and the required amount of purifier as the output. Use three descriptor for input and output variables. Derive set of rules for control the action and defuzzification. The design should be supported by figures. Clearly indicate that if water temperature is low and grade of water is low, then amount of purifier required is large. ---- (20 Marks)

6. (a) Explain Travelling salesperson problem using simulated Annealing. --- (10 Marks)

(b) Explain Error back propagation training Algorithm (EBPTA). ---- (10 Marks)

7. Write short notes on any two: --- (20 Marks)

(a) Character recognition using neural network

(b) Learning vector quantization

(c) Derivative based optimization.

SOFT COMPUTING (SC) [ELECTIVE], Semester 7, B.E. Computer Science (CS), December 2012.

SOFT COMPUTING (SC) [ELECTIVE], Semester 7,

B.E. Computer Science (CS), December 2012.

Con.10900-12

(REVISED COURSE)

KR- 1899

(3 Hours)

[Total Mark: 100]

N. B.: (1) Question no 1 is compulsory.

(2) Attempt any four questions out of the remaining questions.

1. (a) Model the following as Fuzzy set using suitable membership function “number close to 6”. ---- (5 Marks)

(b) Draw CANFIS architecture for Sugeno Fuzzy Model. ---- (5 Marks)

(c) Explain the different Fuzzy membership function. ---- (5 Marks)

(d) Explain Single Continues perceptron training Algorithm (SCPTA). ---- (5 Marks)

2. Design a Fuzzy controller to determine the wash time of domestic washing. Assume that input is dirt and grease on clothes. Use three descriptor for input variables. Derive set of rules for control the action and defuzzification. The design should be supported by the figures. Show that if the clothes are soiled to a larger degree the wash time will be more and vice- versa. ---- (20 Marks)

3. (a) Prove the following identities: ---- (10 Marks)

(i) For unipolar continuous activation function f ‘ (net) = O(1 - O)

(ii) For bipolar continuous activation function f ‘ (net) = O(1- O²) / 2
Where O is out.

(b) Explain Genetic Algorithm with the help of example. --- (10 Marks)

4. (a) Explain Error back propagation training algorithm with the help of a flowchart. ---- (10 Marks)

(b) Explain any four defuzzification methods with suitable Example. ---- (10 Marks)

5. (a) Explain with an example McCulloch – pitts neuron model. --- (6 Marks)

(b) Explain with neat diagram supervised and unsupervised learning. --- (6 Marks)

(c) State the different properties of Fuzzy set. ---- (8 Marks)

6. (a) Describe the basic Hopfield model and give the theory of energy Minimization in auto- associative Hopfield network. ---- (10 Marks)

(b) Explain the architecture of Bidirectional associative memory. Hoe is stored and retrieval performed in BAM? ---- (10 Marks)

7. Write short notes on any two of the following: ----- (20 Marks)

(a) TSP using simulated Annealing

(b) RBF network

(c) Multicategory perception training algorithm. (MCTPA).

SOFT COMPUTING (SC) [ELECTIVE], Semester 7, B.E. Computer Science (CS), May 2012.

SOFT COMPUTING (SC) [ELECTIVE], Semester 7,

B.E. Computer Science (CS), May 2012.

Con.4679-12

GN- 9047

(3 Hours)

[Total Mark: 100]

N.B. 1. Question No. 1 is compulsory.

2. Attempt any Four questions out of the remaining.

3. Figures to the right indicate full marks

1. (a) Explain fuzzy extension principle with the help of an example. --- (6 Marks)

(b) Explain Mc Culloch Pitts Neuron Model with help of an example. --- (6 Marks)

(c) Explain standard fuzzy membership functions. --- (8 Marks)

2. Design a Fuzzy logic controller for a domestic washing machine with two Inputs dirtiness of the load and weight of the laundry and output as amount of detergent used. Use five descriptors for each linguistic variable. Generate a set of rules for control action and defuzzification. ---- (20 Marks)

3. (a) What is learning in Neural Networks? Compare different learning rules. ---- (10 Marks)

(b) Explain error back propagation training algorithm with the help of a flowchart. ---- (10 Marks)

4. (a) Determine the weights after one iteration for hebbian learning of a single neuron network starting with initial weights W = [1, -1], inputs as ----- (12 Marks)

X1 = [1, -2], X2 = [2, 3], X3 = [1, -1] and c=1.

Use (i) Bipolar binary activation function

(ii) Bipolar continuous activation function

(b) Explain perceptron Learning with the help of an example. --- (8 Marks)

5. (a) Explain with examples linearly separable and non – linearly separable pattern classification. --- (10 Marks)

(b) Explain the three types of fuzzy Interface Systems in detail. --- (10 Marks)

6. (a) Explain Travelling Salesperson Problem using Simulated Annealing. ---- (10 Marks)

(b) Explain RBF network and give the comparison between RBF and MLP. --- (10 Marks)

7. Write notes on any two of the following ----- (20 Marks)

(a) Derivative based Optimization method of steepest descent.

(b) Learning Vector Quantization

(c) ANFIS Application – Printed Character Recognition

(d) Kohonen’s Self Organizing Network.