LNCT University Btech CSE VII Semester - Syllabus

LNCT University, Bhopal

Web Engineering (CS-701/AL-701)

Unit –I

INTRODUCTION TO WEB ENGINEERING

Motivation, Categories of Web Applications, Characteristics of Web Applications. Requirements of Engineering in Web Applications- Web Engineering-Components of Web Engineering-Web Engineering Process-Communication-Planning. Overview of static and dynamic web pages with example, difference between web design and web development. Introduction of API and its applications.

Unit –II

WEB APPLICATION DESIGN

Introduction, Web Design from an Evolutionary Perspective, Information Design, Software Design: A Programming Activity, Merging Information Design and Software Design, Problems and Restrictions in Integrated Web Design, A Proposed Structural Approach, Presentation Design, Presentation of Nodes and Meshes. Relation to Content Modeling, Presentation Modeling, Relation to Hypertext Modeling, Customization Modeling, Modeling Framework-Modeling languages- Analysis Modeling for Web Apps-The Content Model-The Interaction Model-Configuration Model.

Unit –III

DEVELOPMENT CONCEPTS

Device-independent Development, Approaches, Inter action Design, User Interaction User Interface Organization, Navigation Design, Designing a Link Representation, Designing Link Internals, Navigation and Orientation, Structured Dialog for Complex Activities, Interplay with Technology and Architecture, Functional Design.

Unit –IV

TESTING WEB APPLICATIONS

Introduction-Fundamentals-Test Specifics in Web Engineering-Test Approaches- Conventional Approaches, Agile Approaches, Testing concepts, Testing Process, Test Scheme, Test Methods and Techniques like Link Testing, Browser Testing, Usability Testing Load, Stress, and Continuous Testing, Testing Security, Test-driven.

Unit –V

PROMOTING WEB APPLICATIONS

Introduction-challenges in launching the web Application-Promoting Web Application- Content Management-Usage Analysis-Web Project Management-Challenges in Web Project Management-Managing Web Team- Managing the Development Process of a Web Application- Risk, Developing a Schedule, Managing Quality, Managing Change, Tracking the Project. Introduction to node JS - web sockets.

References

1. Gerti Kappel, Birgit Proll, “Web Engineering”, John Wiley and Sons Ltd, 2006.
2. Roger S. Pressman, David Lowe, “Web Engineering”, Tata McGraw Hill Publication, 2007.
3. Guy W. Lecky-Thompson, “Web Programming”, Cengage Learning, 2008.

LNCT University, Bhopal

Big Data Analytics(CS-702-A)

Unit –I

INTRODUCTION

Introduction to Big Data and Hadoop: First understand cloud and set up an AWS (Amazon Web Services) account. Types of Digital Data, Introduction to Big Data, Big Data Analytics, History of Hadoop, Apache Hadoop, Analysing Data with Unix tools, Analysing Data with Hadoop, Hadoop Streaming, Hadoop Echo System.

Unit –II

BIG DATA STRATEGY AND DESIGN

IBM Big Data Strategy, Introduction to Infosphere Big Insights and Big Sheets, Apache Spark HDFS(Hadoop Distributed File System) The Design of HDFS, HDFS Concepts, Command Line Interface, Hadoop file system interfaces, Data flow, Data Ingest with Flume and Scoop.

Unit –III

HADOOP INTRODUCTION AND ROLE

Hadoop archives, Hadoop I/O: Compression, Serialization, Avro and File-Based Data structures. Map Reduce Anatomy of a Map Reduce Job Run, Failures, Job Scheduling, Shuffle and Sort, Task Execution, Map Reduce Types and Formats, Map Reduce Features.

Unit –IV

PIG, HIVE AND HIVEQL

Hadoop Eco System Pig : Introduction to PIG, Execution Modes of Pig, Comparison of Pig with Databases, Grunt, Pig Latin, User Defined Functions, Data Processing operators. Hive : Hive Shell, Hive Services, Hive Metastore, Comparison with Traditional Databases, HiveQL, Tables, Querying Data and User Defined Functions.

Unit –V

Clients And Data Analytics With R

Hbase : HBasics, Concepts, Clients, Example, Hbase Versus RDBMS. Big SQL : Introduction Data Analytics with R Machine Learning : Introduction, Supervised Learning, Unsupervised Learning, Collaborative Filtering. Big Data Analytics with Big R.

References

1. Tom White “ Hadoop: The Definitive Guide” Third Edit on, O‟reily Media, 2012.
2. Seema Acharya, Subhasini Chellappan, "Big Data Analytics" Wiley 2015. References
3. Jay Liebowitz, “Big Data and Business Analytics” Auerbach Publications, CRC press (2013)
4. Anand Rajaraman and Jef rey David Ulman, “Mining of Massive Datasets”, Cambridge University Press, 2012.
5. Pete Warden, “Big Data Glossary”, O‟Reily, 2011.
6. Michael Mineli, Michele Chambers, Ambiga Dhiraj, "Big Data, Big Analytics: Emerging Business Intelligence and Analytic Trends for Today's Businesses", Wiley Publications, 2013.

LNCT University, Bhopal

Wireless and Mobile computing (CS-702-B/AL-702-B)

Unit –I

Review of traditional networks: Review of LAN, MAN, WAN, Intranet, Internet, and interconnectivity devices: bridges, Routers etc. Review of TCP/IP Protocol Architecture: ARP/RARP, IP addressing, IP Datagram format and its Delivery, Routing table format, ICMP Messages, Subnetting, Supernetting and CIDR, DNS. NAT: Private addressing and NAT, SNAT, DNAT, NAT and firewalls, VLANS: Concepts, Comparison with Real LANS, Type of VLAN,Tagging, IPV6: address structure, address space and header.

Unit –II

Study of traditional routing and transport: Routing Protocols: BGP- Concept of hidden network and autonomous system, An Exterior gateway protocol, Different messages of BGP. Interior Gateway protocol: RIP, OSPF. Multiplexing and ports, TCP: Segment format, Sockets, Synchronization, Three Way Hand Shaking, Variable window size and Flow control, Timeout and Retransmission algorithms, Connection Control, Silly window Syndrome. Example of TCP: Taho, Reno, Sack etc. UDP: Message Encapsulation, Format and Pseudo header.

Unit –III

Wireless LAN: Transmission Medium For WLANs, MAC problems, Hidden and Exposed terminals, Near and Far terminals, Infrastructure and Ad hoc Networks, IEEE 802.11- System arch, Protocol arch, Physical layer, Concept of spread spectrum, MAC and its management, Power management, Security. Mobile IP: unsuitability of Traditional IP; Goals, Terminology, Agent advertisement and discovery, Registration, Tunneling techniques. Ad hoc network routing: Ad hoc Network routing v/s Traditional IP routing, types of routing protocols, Examples: OADV, DSDV, DSR, ZRP etc.

Unit –IV

Mobile transport layer: unsuitability of Traditional TCP; I-TCP, S-TCP, M-TCP. Wireless Cellular networks: Cellular system, Cellular networks v/s WLAN, GSM – Services, system architecture, Localization and calling, handover and Roaming.

Unit –V

Mobile Device Operating Systems: Special Constraints & Requirements, Commercial Mobile Operating Systems. Software Development Kit: iOS, Android etc.MCommerce : Structure , Pros &Cons, Mobile Payment System ,Security Issues.

References

1. Tom White “ Hadoop: The Definitive Guide” Third Edit on, O‟reily Media, 2012.
2. Seema Acharya, SubhasiniChellappan, "Big Data Analytics" Wiley 2015. References
3. Jay Liebowitz, “Big Data and Business Analytics” Auerbach Publications, CRC press (2013)
4. Michael Mineli, Michele Chambers, AmbigaDhiraj, "Big Data, Big Analytics: Emerging

LNCT University, Bhopal

Deep Learning (CS-702-C/AL-702-C)

Unit –I

History of Deep Learning, McCulloch Pitts Neuron, Thresholding Logic, Activation functions, Gradient Descent (GD), Momentum Based GD, Nesterov Accelerated GD, Stochastic GD, AdaGrad, RMSProp, Adam, Eigenvalue Decomposition. Recurrent Neural Networks,Backpropagation through time (BPTT), Vanishing and Exploding Gradients, Truncated BPTT, GRU, LSTMs, Encoder Decoder Models, Attention Mechanism, Attention overimages.

Unit –II

Autoencoders and relation to PCA, Regularization in autoencoders, Denoisingautoencoders, Sparse autoencoders, Contractive autoencoders, Regularization: Bias Variance Tradeoff, L2 regularization, Early stopping, Dataset augmentation, Parameter sharing and tying, Injecting noise at input, Ensemble methods, Dropout, Batch Normalization, Instance Normalization, Group Normalization.

Unit –III

Greedy Layerwise Pre-training, Better activation functions, Better weight initialization methods, Learning Vectorial Representations Of Words, Convolutional Neural Networks, LeNet, AlexNet, ZF-Net, VGGNet, GoogLeNet, ResNet, Visualizing Convolutional Neural Networks, Guided Backpropagation, Deep Dream, Deep Art, Recent Trends in Deep Learning Architectures.

Unit –IV

Introduction to reinforcement learning(RL), Bandit algorithms – UCB, PAC,Median Elimination, Policy Gradient, Full RL & MDPs, Bellman Optimality, Dynamic Programming - Value iteration, Policy iteration, and Q-learning & Temporal Difference Methods, Temporal- Difference Learning, Eligibility Traces, Function Approximation, Least Squares Methods

Unit –V

Fitted Q, Deep Q-Learning , Advanced Q-learning algorithms , Learning policies by imitating optimal controllers , DQN & Policy Gradient, Policy Gradient Algorithms for Full RL, Hierarchical RL,POMDPs, Actor-Critic Method, Inverse reinforcement learning, Maximum Entropy Deep Inverse Reinforcement Learning, Generative Adversarial Imitation Learning,Recent Trends in RL.

References

1. Deep Learning, An MIT Press book, Ian Goodfellow and YoshuaBengio and Aaron Courville
2. Pattern Classification- Richard O. Duda, Peter E. Hart, David G. Stork, John Wiley & Sons Inc.
3. Reinforcement Learning: An Introduction, Sutton and Barto, 2nd Edition.
4. Reinforcement Learning: State-of-the-Art, Marco Wiering and Martijn van OtterlAnandRajaraman and Jefrey David Ulman, “Mining of Massive Datasets”, Cambridge University Press, 2012.

LNCT University, Bhopal

Data Visualization (AL-702-A)

Unit –I

INTRODUCTION TO DATA HANDLING Overview of Data analysis, Introduction to Data visualization, Working with statistical formulas - Logical and financial functions , Data Validation & data models, Power Map for visualize data , Power BI-Business Intelligence , Data Analysis using statistical methods, Dashboard designing.

Unit –II

INTRODUCTION TO DATA MANIPULATION USING FUNCTION: Heat Map, Tree Map, Smart Chart, Azure Machine learning , Column Chart, Line Chart , Pie,Bar, Area, Scatter Chart, Data Series, Axes , Chart Sheet , Trendline , Error Bars, Sparklines, Combination Chart, Gauge, Thermometer Chart , Gantt Chart , Pareto Chart etc , Frequency Distribution, Pivot Chart, Slicers , Tables: Structured References, Table Styles , What-If Analysis: Data Tables, Goal Seek, Quadratic Equation , Transportation Problem, Maximum Flow Problem, Sensitivity Analysis, Histogram, Descriptive, Statistics, Anova, F-Test, t-Test, Moving, Average, Exponential Smoothing | Correlation model | Regression model, Practical Lab.

Unit –III

Data Strategy & Consumer behaviour Analytics: Understanding Product & Category, Competitive Analysis, Market Share understanding- Market potential Index, Seasonality-Sales Trending, Consumer behaviour Analytics-MIND AND MARKET FACTORS, Budget planning & Execution- MIMI, Regression & Correlation Analysis for Sales trending, Forecasting method with predictive investment modelling, Cohort Analysis, Google Analytics(GA), Case Studies-Assignments.

Unit –IV

TABLEAU SOFTWARE: GETTING STARTED WITH TABLEAU SOFTWARE: What is Tableau? What does the Tableau product suite comprise of? How Does Tableau Work? Tableau Architecture, What is My Tableau Repository? Connecting to Data & Introduction to data source concepts, Understanding the Tableau workspace, Dimensions and Measures, Data Types & Default Properties, Building basic views, Saving and Sharing your work-overview.

References

1. "Information Dashboard Design: Displaying Data for At-a-glance Monitoring” by Stephen Few.
2. "Beautiful Visualization, Looking at Data Through the Eyes of Experts by Julie Steele, Noah Iliinsky".

LNCT University, Bhopal

Cryptography and Information Security (CS-703-A)

Unit –I

Mathematical Background for Cryptography: Abstract Algebra, Number Theory, Modular Inverse,Extended Euclid Algorithm, Fermat's Little Theorem, Euler Phi-Function, Euler's theorem.Introduction to Cryptography: Principles of Cryptography, Classical Cryptosystem, Cryptanalysis onSubstitution Cipher (Frequency Analysis), Play Fair Cipher, Block Cipher. Data Encryption Standard(DES), Triple DES, Modes of Operation, Stream Cipher.

Unit –II

Advanced Encryption Standard (AES), Introduction to Public Key Cryptosystem, Discrete LogarithmicProblem, Diffie-Hellman Key Exchange Computational & Decisional Diffie-Hellman Problem, RSAAssumptions &Cryptosystem,RSA Signatures &Schnorr Identification Schemes, Primarily Testing,Elliptic Curve over the Reals, Elliptic curve Modulo a Prime., Chinese Remainder Theorem.

Unit –III

Message Authentication, Digital Signature, Key Management, Key Exchange, Hash Function. Universal Hashing, Cryptographic Hash Function, MD, Secure Hash Algorithm (SHA), Digital Signature Standard (DSS), Cryptanalysis: Time-Memory Trade-off Attack, Differential Cryptanalysis. Secure channel and authentication system like Kerberos.

Unit –IV

Information Security: Threats in Networks, Network Security Controls–Architecture, Wireless Security, Honey pots, Traffic Flow Security, Firewalls – Design and Types of Firewalls, Personal Firewalls,IDS, Email Security: Services Security for Email Attacks Through Emails, Privacy-Authentication ofSource Message, Pretty Good Privacy(PGP), S-MIME. IP Security: Overview of IPSec, IP& IP version 6Authentication, Encapsulation Security Payload ESP, Internet Key Exchange IKE, WebSecurity: SSL/TLS, Basic protocols of security. Encoding –Secure Electronic Transaction SET.

Unit –V

Cryptography and Information Security Tools: Spoofing tools: like Arping etc., Foot printing Tools (ex-nslookup, dig, Whois,etc..), Vulnerabilities Scanning Tools (i.e. Angry IP, HPing2, IP Scanner, Global Network Inventory Scanner, Net Tools Suite Pack.), NetBIOS Enumeration Using NetView Tool, Steganography Merge Streams, Image Hide, Stealth Files, Blindsideusing:STools, Steghide, Steganos.Stegdetect, Steganalysis - Stego Watch- Stego Detection Tool, StegSpy.Trojans Detection Tools( i.e. Netstat, fPort, TCPView, CurrPorts Tool, Process Viewer), Lan Scanner Tools (i.e.look@LAN, Wireshark, Tcpdump). DoS Attack Understanding Tools- Jolt2, Bubonic.c, Land and LaTierra, Targa, Nemesy Blast, Panther2, Crazy Pinger, Some Trouble, UDP Flood, FSMax.

References

1. Cryptography and Network Security Principles and Practice Fourth Edition,William Stallings, Pearson Education.
2. Network Security Essentials: Applications and Standards, by William Stallings.Prentice Hall.
3. Behrouz A Ferouzan, “Cryptography and NetworkSecurity” Tata Mc Graw Hills, 2007
4. Charles PPfleeger, Shari Lawrence Pfleeger “Security in Computing”, 4thEdition Prentice Hall of India, 2006.
5. Introduction to Modern Cryptography by Jonathan Katz and Yehuda Lindell, Chapman and Hall/CRC