Extract Pages From Syllabus_CSE

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DISTRIBUTED OPERATING SYSTEMS

(Departmental Elective-IV)

CSE-440

L T P Theory: 75 Marks

3 1 - Sessional: 50 Marks

Unit-1

Architecture of distributed operating system: Introduction, motivation, system architecture type, issues in distributed

operating system, communication primitive.

Unit-2

Distributed mutual exclusion: Introduction, classification, preliminaries, simple solution, non token based

algorithm, Lamport algorithm, Ricart algorithm, Mackawas algorithm, A generalized non token based algorithm,

Token based algorithm, Broadcast algorithm, Heuristic algorithm, Tree based algorithm, comparative performance

analysis.

Unit-3Distributed deadlock detection: Introduction, deadlock handling, strategies, issues in deadlock detection and

resolution, Control organization, centralized, distributed and hierarchical detection algorithm.

Unit-4

Distributed file system: Introduction,architecture mechanism for building, design issues, log structured file system.

Distributed scheduling: Introduction, motivation, issues in load distribution, component of load algorithm,

stabilizing, load distribution algorithm, performance comparasion, selection of a suitable load sharing algorithm,

requirement for load distribution, task migration, issues in task migration.
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CSE-476

DATA WAREHOUSING AND DATA MINING

(Departmental Elective-V)

L T P Theory: 75 Marks

3 1 - Practical: 25 Marks

UNIT- 1

Data Warehousing:: Definition, Scope, Practical implications, Structures and functions.

Data Mining: Process, Technologies and Rules, Platform tools and tool characteristics, operational vs. information

systems.

UNIT- 2Types of data warehouses: Host based, single stage, LAN based, Multistage, stationary distributed and virtual data-

warehouses.

UNIT-3Data Warehouses architecture: Metadata, operational data and operational data bases. Data warehouse architecture

model, 2-tier, 3-tier and 4-tier data warehouses.

OLAP and DSS support in data warehouses.

UNIT- 4Data Mining: Knowledge discovery through statistical techniques, Knowledge discovery through neural networks,

Fuzzy tech. and genetic algorithms.


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NEURAL NETWORK & FUZZY LOGIC

CSE-402

Theory: 100 Marks

L T P Sessional: 50 Marks

4 1 -

Unit 1

Introduction:-Concepts of neural networks, Characteristics of neural networks, Historical Perspective,

Applications of neural networks.

Fundamental of Neural Networks: The biological prototype, Neuron concept, Single Layer neural network, Multi

layer neural networks, terminology, Notation and representation of neural networks, Training of artificial neural

networks.

Representation of perceptron and issues, perceptron learning and training, Classification, linear separability.

Unit 2

Hopfield nets: Structure, training and applications, Stability.

Backpropagation:- Concept, Applications and back propagation training algorithm.

Counter Propagation Networks: Kohonen Network, Grossberg Layer &Training, Applications of counter

propagation, Image classification.

Unit 3

Bi-directional Associative memories: Structure, retriving a stored association, encoding associations, memory

capacity.

ART: ART architecture, ART classification operation, ART implementation and characterstics of ART.

Image compression using ART.

UNIT 4

Optical Neural Network: Vector multipliers Hopfield net using Electro optical matrix multipliers, Holographic

correlator, Optical Hopfield net using volume holograms.

The Cognitrons and Neocognitrons: Their structure and training.

Genetic Algorithms: Elements, A simple genetic algorithm, Working of genetic algorithm evolving neural

networks.


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Extract Pages From Syllabus_CSE