IT 7th Semester Syllabus

1. Unit 1Introduction to Neural Network

   Concept · Biological neural network · Comparison of ANN with biological NN · Evolution of artificial neural network · Basic models · Types of learning · Linear separability · XOR problem · McCulloch-Pitts neuron model · Hebb rule

2. Unit 2Supervised Learning

   Perceptron learning · Single layer/multilayer · Adaline · Madaline · Back propagation network · RBFN · Application of Neural network in forecasting · Data compression · Image compression

3. Unit 3Unsupervised learning

   Introduction · Fixed weight competitive nets · Kohonen SOM · Counter Propagation networks (Theory, Architecture, Flow Chart, Training Algorithm and applications) · Introduction to Convolutional neural networks (CNN) · Recurrent neural networks (RNN)

4. Unit 4Fuzzy Set

   Introduction · Basic Definition and Terminology · Properties and Set‑theoretic Operations · Fuzzy Relations · Membership Functions and their assignment · Fuzzy rules and fuzzy Reasoning · Fuzzy if‑then Rules · Fuzzy Inference Systems · Application of Fuzzy logic in solving engineering problems

5. Unit 5Genetic Algorithm

   Introduction to GA · Simple Genetic Algorithm · Terminology and operators of GA (individual, gene, fitness, population, data structure, encoding, selection, crossover, mutation, convergence criteria) · Reasons for working of GA and Schema theorem · GA optimization problems like TSP (Travelling salesman problem) · Network design routing · Introduction to Ant Colony optimization (ACO) · Particle swarm optimization (PSO)

1. Unit 1Data Science and Big Data Overview

   Types of data · Sources of data · Data collection · Data storage and management · Big Data Overview · Characterization of Big data · Drivers of Big Data · Challenges · Big Data Use Cases · Defining Big Data Analytics and examples of its use cases · Data Analytics Lifecycle: Discovery · Data Analytics Lifecycle: Data Preparation · Data Analytics Lifecycle: Model Planning · Data Analytics Lifecycle: Model Building · Data Analytics Lifecycle: Communicate Results · Data Analytics Lifecycle: Operationalize

2. Unit 2Advanced Analytical Theory and Methods

   Clustering · K-means · Additional Clustering Algorithms · Association Rules · Apriori Algorithm · Applications of Association Rules · Regression · Linear Regression · Logistic Regression · Classification · Decision Trees · Naive Bayes · Additional Classification Methods · Text Analysis · Text Analysis Steps · Determining Sentiments

3. Unit 3Advanced Analytics-Technology and Tools

   Analytics for Unstructured Data Use Cases · MapReduce · Apache Hadoop · Traditional database vs Hadoop · Hadoop Core Components · HDFS · Design of HDFS · HDFS Components · HDFS Architecture · Hadoop 2.0 Architecture · Hadoop-2.0 Resource Management · YARN

4. Unit 4The Hadoop Ecosystem

   Introduction to Hive · HBase · Hive Use Cases: Facebook, Healthcare · Hive Architecture · Hive Components · Integrating Data Sources · Dealing with Real-Time Data Streams and Complex Event Processing · Overview of Pig · Difference between Hive and Pig · Use Cases of Pig · Pig program structure · Pig Components · Pig Execution · Pig data models · Overview of Mahout · Mahout working

5. Unit 5Introduction to R

   Basic Data Analytics Methods Using R · Communicating and Operationalizing an Analytics Project · Creating the Final Deliverables · Data Visualization Basics

1. Unit 1Introduction of Grid and Cloud Computing

   Introduction of Grid and Cloud computing · characteristics · components · business and IT perspective · cloud services requirements · cloud models · Security in public model · public verses private clouds · Cloud computing platforms: Amazon EC2 · Platform as Service: Google App Engine · Microsoft Azure · Utility Computing · Elastic Computing

2. Unit 2Cloud Services and Design

   Cloud services- SAAS · PAAS · IAAS · cloud design and implementation using SOA · conceptual cloud model · cloud stack · computing on demand · Information life cycle management · cloud analytics · information security · virtual desktop infrastructure · storage cloud

3. Unit 3Virtualization Technology

   Virtualization technology: Definition · benefits · sensor virtualization · HVM · study of hypervisor · logical partitioning- LPAR · Storage virtualization · SAN · NAS · cloud server virtualization · virtualized data center

4. Unit 4Cloud Security Fundamentals

   Cloud security fundamentals · Vulnerability assessment tool for cloud · Privacy and Security in cloud · Cloud computing security architecture: Architectural Considerations- General Issues · Trusted Cloud computing · Secure Execution Environments and Communications · Micro-architectures · Identity Management and Access control-Identity management · Access control · Autonomic Security · Cloud computing security challenges: Virtualization security management- virtual threats · VM Security Recommendations · VM-Specific Security techniques · Secure Execution Environments and Communications in cloud

5. Unit 5Cloud Platforms, Performance and Emerging Issues

   SOA and cloud · SOA and IAAS · cloud infrastructure benchmarks · OLAP · business intelligence · e-Business · ISV · Cloud performance monitoring commands · issues in cloud computing · QOS issues in cloud · mobile cloud computing · Inter cloud issues · Sky computing · Cloud Computing Platform · Xen Cloud Platform · Eucalyptus · OpenNebula · Nimbus · TPlatform · Apache Virtual Computing Lab (VCL) · Anomaly Elastic Computing Platform

1. Unit 1Modeling & Simulation Concepts

   System Concepts · What is a Model? · Type of Models · Modeling & Simulation · Continuous vs. Discrete System Simulation · Numerical Integration vs. Continuous Simulation · Analog vs. Digital Simulation · Simulation vs. Monte‑Carlo Simulation · Nature of Computer Modeling and Simulation · When to Use Simulation? · Limitations of Simulation

2. Unit 2Probability Concepts in Simulation

   Stochastic variables · Random numbers: Pseudo‑random generators · Testing of pseudo‑random number generators · Generation of non‑uniformly distributed random numbers · Discrete and continuous random variables · Density and distributive functions · Study of distributions such as Poisson, Normal, Uniform

3. Unit 3Simulation of Continuous Systems

   Introduction · Differential equations · Pure Pursuit Problem · Simulation of Chemical Reaction · Autopilot Simulation · Simulation of other continuous systems

4. Unit 4Simulation of Discrete Systems

   Arrival patterns and service times · Simulation of Queuing System · Elementary idea about networks of Queuing with particular emphasis to computer system environment

5. Unit 5Verification & Validation

   Design of simulation experiments and validation of simulation experiments · Comparing model data units and real system data · Simulation Language: brief introduction to important discrete and continuous languages such as GPSS (study & use of the language) · Use of database & AI techniques in the area of modeling and simulation

1. Unit 1Introduction to Virtual Reality – Virtual Reality and Virtual Environment

   Introduction · Applications of Virtual Reality · Computer graphics · Real time computer graphics · Flight Simulation · Virtual environment requirement · Benefits of virtual reality · Historical development of VR · Scientific Landmark 3D Computer Graphics: Introduction · The Virtual world space · Positioning the virtual observer · The perspective projection · Human vision · Stereo perspective projection · 3D clipping · Colour theory · Simple 3D modeling · Illumination models · Reflection models · Shading algorithms · Radiosity · Hidden Surface Removal · Realism‑Stereographic image

2. Unit 2Geometric Modeling – Geometric Modeling

   Introduction · From 2D to 3D · 3D space curves · 3D boundary representation · Geometrical Transformations – Introduction · Frames of reference · Modeling transformations · Instances · Picking · Flying · Scaling the VE · Collision detection · Generic VR system – Introduction · Virtual environment · Computer environment · VR technology · Model of interaction · VR Systems

3. Unit 3Virtual Environment – Animating the Virtual Environment

   Introduction · The dynamics of numbers · Linear and Nonlinear interpolation · Animation of objects · Linear and non‑linear translation · Shape & object in‑betweening · Free from deformation · Particle system · Physical Simulation – Introduction · Objects falling in a gravitational field · Rotating wheels · Elastic collisions · Projectiles · Simple pendulum · Springs · Flight dynamics of an aircraft

4. Unit 4VR Hardware and Software – Human Factors

   Introduction · The eye · The ear · The somatic senses · VR Hardware – Introduction · Sensor hardware · Head‑coupled displays · Acoustic hardware · Integrated VR systems · VR Software – Introduction · Modeling virtual world · Physical simulation · VR toolkits · Introduction to VRML

5. Unit 5Augmented and Mixed Reality

   Taxonomy · Technology and features of augmented reality · Difference between AR and VR · Challenges with AR · AR systems and functionality · Augmented reality methods · Visualization techniques for augmented reality · Wireless displays in educational augmented reality applications · Mobile projection interfaces · Marker‑less tracking for augmented reality · Enhancing interactivity in AR environments · Evaluating AR systems

1. Unit 1Introduction to cybercrime

   Definition of cybercrime · Cyber crime and information security · Classification of cybercrimes · Legal perspectives of cybercrime · Indian perspective on cybercrime · Cybercrime and the Indian ITA 2000 · Global perspective on cybercrime · Cyber offences and criminal planning · Tools and methods used in cyber crime · Need for cyber law · The Indian IT Act · Challenges to Indian law and cybercrime scenario in India · Digital signature and Indian IT Act · Amendments in the Indian IT Act · Cybercrime and punishment

2. Unit 2Law and framework for information security

   Law for intellectual property rights (IPR) · Patent law · Copyright law · Indian Copyright Act · Privacy issues and law in Hong Kong, Japan, and Australia · Data Protection Act in Europe · Health Insurance Portability and Accountability Act (HIPAA) 1996 · Gramm‑Leach‑Bliley Act (GLBA) 1999 · Sarbanes‑Oxley Act (SOX) · Legal issues in data mining · Building security into software/system development life cycle

3. Unit 3Digital Forensics Science

   Need for computer forensics · Understanding computer forensics · Computer forensics vs. related disciplines · Brief history of computer forensics · Cyber forensics and digital evidence · Digital forensics lifecycle · Chain of custody concept · Network forensics · Approaching a computer forensics investigation · Setting up a computer forensics laboratory · Forensics and social networking sites · Computer forensics from compliance perspective · Challenges in computer forensics · Forensics auditing · Anti‑forensics

4. Unit 4Current Computer Forensics Tools

   Evaluating computer forensics tool needs · Types of computer forensics tools · Tasks performed by computer forensics tools · Tool comparisons · Other considerations for tools · Computer forensics software tools · Command‑line forensics tools · UNIX/Linux forensics tools · GUI forensics tools · Computer forensics hardware tools · Forensic workstations

5. Unit 5Forensics of hand‑held devices and network forensics

   Forensics of hand‑held devices · Investigating network intrusions and cyber crime · Network forensics and investigating logs · Investigating network traffic · Investigating web attacks · Router forensics · Cyber forensics tools and case studies

1. Unit 1Unit I

   IoT definition · Characteristics · IoT conceptual and architectural framework · 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 · IPv4 vs IPV6

2. Unit 2Unit II

   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

3. Unit 3Unit III

   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

4. Unit 4Unit IV

   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

5. Unit 5Unit V

   IoT Platforms · Arduino · Raspberry Pi Board · Other IoT Platforms · Data Analytics for IoT · Cloud for IoT · Cloud storage models & communication APIs · IoT case studies

1. Unit 1Introduction

   Introduction to Semantic Web: Limitations of current Web · Development of Semantic Web · Emergence of the Social Web · Social Network analysis: Development of Social Network Analysis · Key concepts and measures in network analysis · Electronic sources for network analysis: Electronic discussion networks, blogs and online communities · Web‑based networks · Applications of Social Network Analysis

2. Unit 2Modelling, Aggregating and Knowledge Representation

   Ontology and their role in the Semantic Web: Ontology‑based knowledge representation · Ontology languages for the Semantic Web: Resource Description Framework (RDF) · Web Ontology Language (OWL) · Modelling and aggregating social network data: State‑of‑the‑art in network data representation · Ontological representation of social individuals · Ontological representation of social relationships · Aggregating and reasoning with social network data · Advanced representations

3. Unit 3Extraction and Mining Communities in Web Social Networks

   Extracting evolution of Web community from a series of Web archives · Detecting communities in social networks · Definition of community · Evaluating communities · Methods for community detection and mining · Applications of community‑mining algorithms · Tools for detecting communities, social network infrastructures and communities · Decentralized online social networks · Multi‑relational characterization of dynamic social network communities

4. Unit 4Predicting Human Behaviour and Privacy Issues

   Understanding and predicting human behaviour for social communities · User data management · Inference and distribution · Enabling new human experiences · Reality mining · Context awareness · Privacy in online social networks · Trust in online environment · Trust models based on subjective logic · Trust network analysis · Trust transitivity analysis · Combining trust and reputation · Trust derivation based on trust comparisons · Attack spectrum and countermeasures

5. Unit 5Visualization and Applications of Social Networks

   Graph theory · Centrality measures · Clustering techniques · Node‑Edge diagrams · Matrix representation of networks · Visualizing online social networks · Visualizing social networks with matrix‑based representations · Matrix and node‑link diagrams · Hybrid representations · Applications of network visualisation · Cover networks · Community welfare networks · Collaboration networks · Co‑citation networks

1. Unit 1Introduction and Digital Image Fundamentals

   Origins of Digital Image Processing · Applications of Digital Image Processing · Fundamental steps in image processing · Elements of digital image processing systems · Image sampling and quantization · Neighbourhood relationships and connectivity · Distance measures between pixels · Linear and non‑linear operations

2. Unit 2Image Enhancement in the Spatial Domain

   Gray level transformations · Histogram processing · Enhancement using arithmetic and logical operations · Spatial filtering fundamentals · Smoothing spatial filters · Sharpening spatial filters · Combined spatial enhancement techniques

3. Unit 3Image Enhancement in the Frequency Domain and Image Restoration

   Fourier Transform and frequency‑domain representation · Smoothing filters in the frequency domain · Sharpening filters in the frequency domain · Homomorphic filtering · Image degradation and restoration model · Noise models · Restoration using spatial filtering · Periodic noise reduction via frequency‑domain filtering · Linear shift‑invariant degradations · Estimation of degradation function · Inverse filtering · Wiener filtering · Constrained least‑squares filtering · Geometric mean filtering · Geometric transformations

4. Unit 4Image Compression and Image Segmentation

   Coding and redundancy (interpixel, psychovisual) · Image compression models · Elements of information theory · Error‑free compression comparison · Lossy compression techniques · Standard image compression formats · Edge detection and discontinuity detection · Edge linking and boundary detection · Thresholding methods · Region‑oriented segmentation · Motion‑based segmentation

5. Unit 5Representation, Description and Object Recognition

   Image representation schemes · Boundary descriptors · Regional descriptors · Principal component analysis for description · Morphology basics · Fundamental morphological algorithms · Pattern classes and recognition · Decision‑theoretic recognition methods · Structural recognition methods

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