Syllabus of B. Tech. VI Sem AIDS (RGPV)

• AD-601 Deep Learning

• AD-602 Computer Networks

• AD-603 (A) Data Mining and Warehousing (Departmental Elective)

• AD-603 (B) Digital Image Processing (Departmental Elective)

• AD-603 (C) Information Retrieval (Departmental Elective)

• AD-604 (A) Internet of Things (Open Elective)

• AD-604 (B) Block Chain Technologies (Open Elective)

• AD-604 (C) Compiler Design (Open Elective)

​Syllabus of AD-601 Deep Learning

Source: (rgpv.ac.in)

UNIT-1 : Introduction to Deep Learning

• Introduction to Deep Learning

• Basics: Biological Neuron Idea of computational units

• McCulloch–Pitts Neural Model

• Linear Perceptron Perceptron Learning

• Feed Forward and Back Propagation Networks.

UNIT-2 :

• Feedforward Networks

• Feedforward Networks : Multilayer Perceptron

• Gradient Descent Backpropagation

• Empirical Risk Minimization

• regularization

• auto encoders.

UNIT-3 :

• Convolutional Networks

• Convolutional Networks : The Convolution Operation

• Variants of the Basic Convolution Function

• Structured Outputs Efficient Convolution Algorithms

• Random or Unsupervised Features

• LeNet AlexNet

UNIT-4 : Recurrent Neural Networks

• Recurrent Neural Networks : Bidirectional RNNs Deep Recurrent Networks Recursive Neural Networks

• The Long Short-Term Memory and Other Gated RNNs

UNIT-5 : Deep Generative Models

• Deep Generative Models : Boltzmann Machines

• Restricted Boltzmann Machines

• Introduction to MCMC and Gibbs Sampling

• Gradient computations in RBMs

• Deep Belief Networks

• Deep Boltzmann Machines

APPLICATIONS :

• Image Processing

• Speech Recognition

• Natural Language Processing

LAB Experiments

1. Write a Program to implement Linear Perceptron.
2. Write a Program to implement Multi-Layer Perceptron.
3. Write a Program to implement Autoencoders.
4. Write a Program to implement basic Convolutional Neural Network for Image Classification.
5. Write a Program to implement LeNet for image classification
6. Write a Program to implement AlexNet for image classification
7. Write a Program to implement RNN for text classification
8. Write a Program to implement LSTM for text prediction.
9. Write a Program to implement Boltzmann Machines for any real world classification problem.
10. Write a Program to implement restricted Boltzmann Machines for any real world classification problem

== END OF UNITS==

Syllabus of AD-602 Computer Networks

Source: (rgpv.ac.in)

UNIT-1 :

• Computer Network : Definitions goals components

• Architecture Classifications & Types.

• Layered Architecture : Protocol hierarchy Design Issues

• Interfaces and Services Connection Oriented & Connectionless Services

• Service primitives

• Design issues & its functionality.

• ISOOSI Reference Model : Principle Model

• Descriptions of various layers and its comparison withTCP/IP.

• Principals of physical layer: Media

• Bandwidth

• Data rate and Modulations.

UNIT-2 :

• Data Link Layer : Need Services Provided

• Framing Flow Control Error control.

• Data Link Layer Protocol : Elementary &Sliding Window protocol: 1-bit

• Go-Back-N Selective Repeat H

• ybrid ARQ. MAC Sub layer : MAC Addressing

• Binary Exponential Back-off (BEB) Algorithm

• Distributed Random Access Schemes/Contention Schemes : for Data Services (ALOHA and Slotted ALOHA)

• for Local-Area Networks (CSMA CSMA/CD CSMA/CA).

• IEEE Standards 802 series & their variant.

UNIT-3 :

• Network Layer : Need Services Provided

• Design issues

• Routing algorithms : Least Cost Routing algorithm

• Dijkstra’s algorithm Bellman-ford algorithm

• Hierarchical Routing Broadcast Routing

• Multicast Routing.

• IP Addresses Header format Packet forwarding

• Fragmentation and reassembly ICMP

• Comparative study of IPv4 & IPv6.

UNIT-4 :

• Transport Layer : Design Issues

• UDP : Header Format Per-Segment Checksum

• Carrying Unicast/Multicast Real-Time Traffic

• TCP : Connection Management

• Reliability of Data Transfers

• CP Flow Control

• TCP Congestion Control

• TCP Header Format

• TCP Timer Management.

UNIT-5 :

• Application Layer : WWW and HTTP FTP SSH

• Email (SMTP MIME IMAP)DNS Network Management (SNMP).

• Network Security : Introduction to security

• Traditional Ciphers

• Modern Ciphers

• Message Integrity and Authentication.

List of Experiments

1. Study of Different Type of LAN& Network Equipment.
2. Study and Verification of standard Network topologies i.e. Star Bus Ring etc.
3. LAN installations and Configurations.
4. Write a program to implement various types of error correcting techniques.
5. Write a program to implement various types of framing methods.
6. Study of Tool Command Language (TCL).
7. Study and Installation of Standard Network Simulator: N.S-2 N.S3.OpNet QualNetetc .
8. Study & Installation of ONE (Opportunistic Network Environment) Simulator for High Mobility Networks.
9. Configure 802.11 WLAN.
10. Implement &Simulate various types of routing algorithm.
11. Study & Simulation of MAC Protocols like Aloha CSMA CSMA/CD and CSMA/CA using Standard Network Simulators.
12. Study of Application layer protocols-DNS HTTP HTTPS FTP and TelNet.

== END OF UNITS==

Syllabus of AD -603 (A) Data Mining and Warehousing(Departmental Elective )

Source: (rgpv.ac.in)

UNIT-1 :

• Data Warehousing : Introduction Delivery Process

• Data warehouse Architecture

• Data Pre-processing : Data cleaning Data Integration and transformation

• Data reduction.

• Data warehouse Design : Data warehouse schema

• Partitioning strategy Data warehouse Implementation

• Data Marts Meta Data

• Example of a Multidimensional Data model.

• Introduction to Pattern Warehousing.

UNIT-2 :

• OLAP Systems : Basic concepts OLAP queries

• Types of OLAP servers

• OLAP operations etc.

• Data Warehouse Hardware and Operational Design : Security Backup And Recovery.

UNIT-3 :

• Introduction to Data& Data Mining : Data Types Quality of data

• Data Preprocessing Similarity measures

• Summary statistics Data distributions

• Basic data mining tasks

• Data Mining V/s knowledge discovery in databases.

• Issues in Data mining.

• Introduction to Fuzzy sets and fuzzy logic.

UNIT-4 :

• Supervised Learning : Classification: Statistical-based algorithms

• Distance-based algorithms

• Decision tree-based algorithms

• Neural network-based algorithms

• Rule-based algorithms

• Probabilistic Classifiers

UNIT-5 :

• Clustering & Association Rule mining : Hierarchical algorithms

• Partitional algorithms

• Clustering large databases – BIRCH DBSCAN

• CURE algorithms.

• Association rules : Parallel and distributed algorithms such as Apriori and FP growth algorithms.

== END OF UNITS==

Syllabus of AD-603 (B) Digital Image Processing (Departmental Elective )

Source: (rgpv.ac.in)

UNIT-1 :

• Digital Image fundamentals

• A simple image model

• Sampling and Quantization.

• Relationship between pixels.

• Imaging geometry.

• Image acquisition systems

• Different types of digital images

UNIT-2 :

• Image transformations

• Introduction to Fourier transforms

• Discrete Fourier transforms

• Fast Fourier transform

• Walsh transformation

• Hadmord transformation

• Discrete Cosine Transformation.

UNIT-3 :

• Image enhancement

• Filters in spatial and frequency domains

• Histogram based processing.

• Image subtraction Averaging

• Image smoothing

• Nedion filtering

• Low pass filtering

• Image sharpening by High pass filtering.

UNIT-4 :

• Image encoding and segmentation

• Encoding : Mapping Quantizer Coder.

• Error free compression

• Lossy Compression schemes.

• JPEG Compression standard.

• Detection of discontinuation by point detection

• Line detection edge detection

• Edge linking and boundary detection

• Local analysis

• Global processing via Hough transforms and graph theoretic techniques

UNIT-5 :

• Mathematical morphology- Binary

• Dilation crosses Opening and closing

• Simple methods of representation

• Signatures Boundary segments Skeleton of a region

• Polynomial approximation

• Recent advancement in DIP

• Machine learning for image processing application

== END OF UNITS==

Syllabus of AD-603 (C) Information Retrieval (Departmental Elective)

Source: (rgpv.ac.in)

UNIT-1 :

• Introduction – History of IR- Components of IR –

• Issues –

• Open source Search engine.

• Frameworks –

• The Impact of the web on IR –

• The role of artificial intelligence (AI) in IR –

• IR Versus Web Search –

• Components of a search engine

• characterizing the web.

UNIT- 2 :

• Boolean and Vector space retrieval models-

• Term weighting –

• TF-IDF weighting cosine similarity –

• Pre-processing –

• Inverted indices –

• efficient processing with sparse vectors Language Model based IR –

• Probabilistic IR –

• Latent Semantic indexing –

• Relevance feedback and query expansion.

UNIT-3 :

• Web search overview

• web structure the user paid placement search engine optimization

• Web Search Architectures –

• crawling – meta-crawlers

• Focused Crawling –

• web indexes –

• Near duplicate detection –

• Index Compression –

• XML retrieval.

UNIT-4 :

• Link Analysis -hubs and authorities –

• Page Rank and HITS algorithms –

• Searching and Ranking Relevance Scoring and ranking for Web –

• Similarity –

• Hadoop & Map Reduce –

• Evaluation –

• Personalized search –

• Collaborative filtering and content-based recommendation of documents And products – handling invisible Web –

• Snippet generation

• Summarization.

• Question Answering

• Cross-Lingual Retrieval.

UNIT-5 :

• Information filtering : organization and relevance feedback –

• Text Mining-

• Text classification and clustering –

• Categorization algorithms

• naive Bayes

• decision trees and nearest neighbor –

• Clustering algorithms : agglomerative clustering

• k-means

• expectation maximization (EM).

== END OF UNITS==

Syllabus of AD-604 (A) Internet of Things (Open Elective)

Source: (rgpv.ac.in)

UNIT-1 :

• IoT definition Characteristics

• IoT conceptual and architectural framework

• Components of IoT ecosystems

• Physical and logical design of IoT

• IoT enablers Modern day IoT applications

• M2M communications IoT vs M2M IoT vs WoT

• IoT reference architecture

• IoT Network configurations

• IoT LAN IoT WAN IoT Node

• IoT Gateway IoT Proxy

• Review of Basic Microcontrollers and interfacing.

UNIT-2 :

• Define Sensor Basic components and challenges of a sensor node

• Sensor features Sensor resolution;

• Sensor classes: Analog Digital

• Scalar Vector Sensors;

• Sensor Types bias drift

• Hysteresis error quantization error; Actuator;

• Actuator types : Hydraulic Pneumatic electrical

• thermal/magnetic

• mechanical actuators

• soft actuators.

UNIT-3 :

• Basics of IoT Networking

• IoT Components

• Functional components of IoT

• IoT service oriented architecture

• IoT challenges 6LowPAN

• IEEE 802.15.4 ZigBee and its types

• RFID Features RFID working principle and applications

• NFC (Near Field communication)Bluetooth

• Wireless Sensor Networks and its Applications

UNIT-4 :

• MQTT MQTT methods and components

• MQTT communication topics and applications SMQTT

• CoAP CoAP message types

• CoAP Request-Response model XMPP

• AMQP features and components

• AMQP frame types.

UNIT-5 :

• IoT Platforms Arduino Raspberry Pi Board

• Other IoT Platforms; Data Analytics for IoT

• Cloud for IoT Cloud storage models & communication APIs

• Attacks in IoT system vulnerability analysis in IoT

• IoT case studies : Smart Home Smart framing etc.

== END OF UNITS==

Syllabus of AD-604 (B) Block Chain Technologies (Open Elective)

Source: (rgpv.ac.in)

UNIT-1 :

• Introduction : Overview of Block chain

• Public Ledgers Bit coin

• Smart Contracts Block in a Block chain

• Transactions Distributed Consensus

• Public vs Private Block chain

• Understanding Crypto currency to Block chain

• Permissioned Model of Block chain

• Overview of Security aspects of Block chain;

• Basic Crypto Primitives : Cryptographic Hash Function

• Properties of a hash function

• Hash pointer and Merkle tree

• Digital Signature Public Key Cryptography

• A basic crypto currency

UNIT-2 :

• Understanding Block chain with Crypto currency : Bit coin and Block chain: Creation of coins Payments and double spending

• Bit coin Scripts Bit coin P2P Network

• Transaction in Bit coin Network

• Block Mining Block propagation and block relay.

• Working with Consensus in Bit coin : Distributed consensus in open environments

• Consensus in a Bitcoin network

• Proof of Work (PoW) – basic introduction

• Hash Cash PoW Bit coin PoW

• Attackson PoW and the monopoly problem

• Proof of Stake Proof of Burn and Proof of Elapsed Time

• The life of a Bitcoin Miner

• Mining Difficulty Mining Pool.

UNIT-3 :

• Understanding Block chain for Enterprises : Permissioned Block chain: Permissioned model and use cases

• Design issues for Permissioned block chains

• Execute contracts State machine replication

• Overview of Consensus models for permissioned block chain Distribute dconsensus in closed environment Paxos

• RAFT Consensus Byzantine general problem

• Byzantine fault tolerant system

• Lamport-Shostak-Pease BFT Algorithm

• BFT over Asynchronous systems.

UNIT-4 :

• Enterprise application of Block chain :

• Cross border payments

• Know Your Customer (KYC)

• Food Security Mortgage over Block chain

• Block chain enabled Trade

• We Trade – Trade Finance Network

• Supply Chain Financing and Identity on Block chain

UNIT-5 :

• Block chain application development :

• Hyper ledger Fabric- Architecture

• Identities and Policies Membership and Access Control

• Channels Transaction Validation

• Writing smart contract using Hyper ledger Fabric

• Writing smart contract using Ethereum

• Overview of Ripple and Corda

== END OF UNITS==

Syllabus of AD-604 (C) Compiler Design (Open Elective)

Source: (rgpv.ac.in)

UNIT-1 : Introduction to compiling & Lexical Analysis

• Introduction of Compiler

• Major data Structure in compiler

• types of Compiler Front-end and Back-end of compiler

• Compiler structure : analysis-synthesis model of compilation

• various phases of a compiler

• Lexical analysis : Input buffering

• Specification & Recognition of Tokens

• Design of a Lexical Analyzer Generator LEX.

UNIT-2 : Syntax Analysis &Syntax Directed Translation

• Syntax analysis: CFGs Top down parsing

• Brute force approach recursive descent parsing

• transformation on the grammars

• predictive parsing bottom up parsing

• operator precedence parsing

• LRparsers (SLR LALR LR)Parser generation.

• Syntax directed definitions : Construction of Syntax trees

• Bottom up evaluation of S-attributed definition

• Lattribute definition Top down translation

• Bottom Up evaluation of inherited attributes Recursive Evaluation

• Analysis of Syntax directed definition

UNIT-3 : Type Checking & Run Time Environment

• Type checking : type system specification of simple type checker

• equivalence of expression

• types type conversion

• overloading of functions and operations

• polymorphic functions.

• Run time Environment : storage organization

• Storage allocation strategies

• parameter passing dynamic storage allocation

• Symbol table Error Detection & Recovery

• Ad-Hoc and Systematic Methods.

UNIT-4 : Code Generation

• Intermediate code generation : Declarations Assignment statements

• Boolean expressions Case statements

• Back patching

• Procedure calls Code Generation : Issues in the design of code generator

• Basic block and flow graphs

• Register allocation and assignment

• DAG representation of basic blocks

• peephole optimization

• generating code from DAG.

UNIT-5 : Code Optimization

• Introduction to Code optimization: sources of optimization of basic blocks

• loops in flow graphs dead code elimination

• loop optimization Introduction to global data flow analysis

• Code Improving transformations

• Data flow analysis of structure flow graph Symbolic debugging of optimized code.

== END OF UNITS==

==End of Syllabus==