ASSIGNMENT - 1, MAY M.C.A. THIRD YEAR DEGREE Paper I ARTIFICIAL INTELLIGENCE Maximum : 25 MARKS Answer ALL questions.

Similar documents
1) Explain the following evolutionary process models: a) The spiral model. b) The concurrent development model.

Course Syllabus For Operations Management. Management Information Systems

Federico Rajola. Customer Relationship. Management in the. Financial Industry. Organizational Processes and. Technology Innovation.

Assessment Plan for CS and CIS Degree Programs Computer Science Dept. Texas A&M University - Commerce

M.S. Computer Science Program

NETWORK ADMINISTRATION AND SECURITY

Course DSS. Business Intelligence: Data Warehousing, Data Acquisition, Data Mining, Business Analytics, and Visualization

Table of Contents. Bibliografische Informationen digitalisiert durch

Assessment for Master s Degree Program Fall Spring 2011 Computer Science Dept. Texas A&M University - Commerce

Software Development Training Camp 1 (0-3) Prerequisite : Program development skill enhancement camp, at least 48 person-hours.

Chapter 5 Business Intelligence: Data Warehousing, Data Acquisition, Data Mining, Business Analytics, and Visualization

ANALYTICS CENTER LEARNING PROGRAM

Master of Science in Health Information Technology Degree Curriculum

DSS based on Data Warehouse

CS Master Level Courses and Areas COURSE DESCRIPTIONS. CSCI 521 Real-Time Systems. CSCI 522 High Performance Computing

DATA MINING TECHNOLOGY. Keywords: data mining, data warehouse, knowledge discovery, OLAP, OLAM.

Fluency With Information Technology CSE100/IMT100

Chapter 5. Warehousing, Data Acquisition, Data. Visualization

Proposed Syllabus by C.S.J.M.University,Kanpur. Bachelors of Computer Application

Principles of Data Mining by Hand&Mannila&Smyth

Introduction to Data Mining

USTC Course for students entering Clemson F2013 Equivalent Clemson Course Counts for Clemson MS Core Area. CPSC 822 Case Study in Operating Systems

The programme will be delivered by the faculty of respective NITs with additional support from practicing executives from the IT & ITES industry.

The Masters of Science in Information Systems & Technology

Eastern Washington University Department of Computer Science. Questionnaire for Prospective Masters in Computer Science Students

How to use Big Data in Industry 4.0 implementations. LAURI ILISON, PhD Head of Big Data and Machine Learning

Chapter Managing Knowledge in the Digital Firm

SQL Server 2005 Features Comparison

Introduction to Data Mining and Machine Learning Techniques. Iza Moise, Evangelos Pournaras, Dirk Helbing

Course Outline Department of Computing Science Faculty of Science. COMP Applied Artificial Intelligence (3,1,0) Fall 2015

Microsemi Security Center of Excellence

The University of Jordan

XPROBE. Building Efficient Network Discovery Tools. Fyodor Yarochkin

Study Plan for the Master Degree In Industrial Engineering / Management. (Thesis Track)

MEng, BSc Computer Science with Artificial Intelligence

How To Get A Computer Science Degree At Appalachian State

List of courses MEngg (Computer Systems)

CRYPTOG NETWORK SECURITY

VALLIAMMAI ENGINEERING COLLEGE

Computer Information Systems

How To Get A Computer Science Degree

CRYPTOGRAPHY AND NETWORK SECURITY

Complexity and Scalability in Semantic Graph Analysis Semantic Days 2013

How To Get A Computer Engineering Degree

MEng, BSc Applied Computer Science

Day 7 Business Information Systems-- the portfolio. Today s Learning Objectives

Computer Networks. Network Security 1. Professor Richard Harris School of Engineering and Advanced Technology

Intrusion Detection. Jeffrey J.P. Tsai. Imperial College Press. A Machine Learning Approach. Zhenwei Yu. University of Illinois, Chicago, USA

Business Intelligence. Data Mining and Optimization for Decision Making

Knowledge Management

Textbooks: Matt Bishop, Introduction to Computer Security, Addison-Wesley, November 5, 2004, ISBN

Master s Program in Information Systems

ก ก ก ก ก (3-0-6) ก ก ก (Introduction to Business) (Principles of Marketing)

MOC 20467B: Designing Business Intelligence Solutions with Microsoft SQL Server 2012

OPTIMIZE DMA CONFIGURATION IN ENCRYPTION USE CASE. Guillène Ribière, CEO, System Architect

Eastern Washington University Department of Computer Science. Questionnaire for Prospective Masters in Computer Science Students

ORACLE OLAP. Oracle OLAP is embedded in the Oracle Database kernel and runs in the same database process

Computer Science. Master of Science

Information Technology Career Field Pathways and Course Structure

NEW HORIZON COLLEGE OF ENGINEERING, BANGALORE CLOUD COMPUTING ASSIGNMENT Explain any six benefits of Software as Service in Cloud computing?

IT1104- Information Systems & Technology (Compulsory)

Data Mining + Business Intelligence. Integration, Design and Implementation

Index Contents Page No. Introduction . Data Mining & Knowledge Discovery

This Symposium brought to you by

Core Courses Seminar (0-2) Non-credit Ph.D. Thesis (0-1) Non-credit Special Studies (8-0) Non-credit. Elective Courses

Business Intelligence and Decision Support Systems

The Masters of Science in Information Systems & Technology

Class 10. Data Mining and Artificial Intelligence. Data Mining. We are in the 21 st century So where are the robots?

SQL Server Analysis Services Complete Practical & Real-time Training

Data Warehouse: Introduction

B.Sc (Computer Science) Database Management Systems UNIT-V

Reusable Knowledge-based Components for Building Software. Applications: A Knowledge Modelling Approach

XPROBE-NG. What s new with upcoming version of the tool. Fyodor Yarochkin Armorize Technologies

GUJARAT TECHNOLOGICAL UNIVERSITY, AHMEDABAD, GUJARAT COURSE CURRICULUM COURSE TITLE: ESSENTIALS OF NETWORK SECURITY (COURSE CODE: )

Predictive modelling around the world

PC Business Banking. Technical Requirements

OLAP and Data Mining. Data Warehousing and End-User Access Tools. Introducing OLAP. Introducing OLAP

NEURAL NETWORKS IN DATA MINING

Describe the process of parallelization as it relates to problem solving.

Using reporting and data mining techniques to improve knowledge of subscribers; applications to customer profiling and fraud management

Common Pitfalls in Cryptography for Software Developers. OWASP AppSec Israel July The OWASP Foundation

Network Security Technology Network Management

Would-be system and database administrators. PREREQUISITES: At least 6 months experience with a Windows operating system.

Operationalise Predictive Analytics

Mingyu Web Application Firewall (DAS- WAF) All transparent deployment for Web application gateway

Record Storage and Primary File Organization

1 Data Encryption Algorithm

Improving Decision Making and Managing Knowledge

CORE CLASSES: IS 6410 Information Systems Analysis and Design IS 6420 Database Theory and Design IS 6440 Networking & Servers (3)

SQL Server 2012 End-to-End Business Intelligence Workshop

INFORMATION FILTERS SUPPLYING DATA WAREHOUSES WITH BENCHMARKING INFORMATION 1 Witold Abramowicz,

Course 6234A: Implementing and Maintaining Microsoft SQL Server 2008 Analysis Services

TIM 50 - Business Information Systems

Transcription:

ASSIGNMENT - 1, MAY 2013. Paper I ARTIFICIAL INTELLIGENCE (DMCA 301) 1. (a) Explain about the reasons to study AI. (b) Describe the water jug problem. 2. Differentiate between A* and AO* algorithms. Explain AO*algorithm. 3. Explain about different issues to be considered in Knowledge representation. 4. Discuss in detail about forward versus backward reasoning. 5. What is an expert system? Discuss about the expert system shells.

ASSIGNMENT - 2, MAY 2013. Paper I ARTIFICIAL INTELLIGENCE (DMCA 301) 1. Write a program to play Tic-Tac-Toe. 2. Write an algorithm for simulated annealing. 3. Describe the means-ends analysis method. 4. Explain the resolution procedure for prepositional logic. 5. Write the unification algorithm. 6. Explain the control knowledge search. 7. Discuss about the Dempster-Shafer theory. 8. Write a brief note on commonsense ontologies. 9. What is a production system? 10. What is AI Technique? 11. What is hill climbing? 12. What is natural language processing? 13. What is backward-chaining rule?

ASSIGNMENT - 1, MAY 2013. Paper II CRYPTOGRAPHY AND NETWORKS SECURITY (DMCA 302) 1. Explain the following substitution techniques : (a) (b) Caesar Cipher Hill Cipher. 2. Explain in detail the AES key expansion algorithm. 3. Discuss about double DES and triple DES. 4. Explain about the principles of public-key cryptosysetms. 5. What is DSS approach? Explain the digital signature algorithm.

ASSIGNMENT - 2, MAY 2013. Paper II CRYPTOGRAPHY AND NETWORKS SECURITY (DMCA 302) 1. Explain the difference between differential and linear cryptanalysis. 2. Write the Euclidean algorithm for polynomials and use it to find ( a ( x), b( x) ) 6 5 4 3 2 4 2 a ( x) = x + x + x + x + x + x + 1, b ( x) = x + x + x + 1. 3. Briefly describe the key expansion algorithm. 4. Explain the difference between a session key and a master key. 5. Explain the factoring problem. 6. What is the difference between direct and arbitrated digital signature? 7. Briefly write about password management. 8. What are the design goals for a firewall? 9. What is one-time pad? 10. What is a meet-in-the-middle attack? 11. What is Euler s totient function. 12. What is a honeyport? gcd where 13. What is a circuit-level gateway?

ASSIGNMENT - 1, MAY 2013. Paper III EMBEDDED SYSTEMS (DMCA 303) 1. Explain about various cache mapping Technologies. 2. Explain about various embedded processor Technologies. 3. Explain Data flow models. 4. Explain control process models. 5. Explain single purpose processors.

ASSIGNMENT - 2, MAY 2013. Paper III EMBEDDED SYSTEMS (DMCA 303) 1. Explain about priority arbiter. 2. Explain stepper motor controllers. 3. Discuss how to select a micro processor for an embedded system design. 4. Explain the architecture of a general purpose processor. 5. Explain elevator controller using PSM. 6. Explain about Interrupt. 7. Explain about Memory. 8. Explain the terms (a) (b) Software and Hardware 9. Define port. 10. Define Cross complier. 11. What is meant by Real time systems? 12. What is CMOS? 13. What is FPGA?

ASSIGNMENT - 1, MAY 2013. Paper IV DATA MINING TECHNIQUES (DMCA 304) 1. Define a random variable and discuss its relationships between multiple random variables. 2. What is dimensional modeling? Discuss in the detail various schemes for modeling multidimensional data for data warehouse development. 3. Explain about various partitional algorithms used for data clustering. 4. Discuss various optimization methods for continuous parameter spaces. 5. What is SQL? Discuss different ways of evaluating a query with examples.

ASSIGNMENT - 2, MAY 2013. Paper IV DATA MINING TECHNIQUES (DMCA 304) 1. Contrast the differences between data mining versus knowledge discovery in databases. 2. Describe the significance of neural networks. Also explain their advantages. 3. What are the predictive models for classification? 4. What is association rule learning? Explain. 5. What is uncertanity? Explain how fuzzy theory can quantify uncertanity. 6. Discuss about models and patterns. 7. Discuss about search and optimization methods. 8. Describe predictive modeling for regression. 9. What is score function? 10. What is descriptive modeling? 11. What is OLAP? 12. What is Data organization? 13. What is multidimensional scaling?

ASSIGNMENT - 1, MAY 2013. Paper V SYSTEMS AUDITING (DMCA 305) 1. Explain programming Management Controls. 2. Differentiate between Application Controls and Management Control of Information systems. 3. Explain effectiveness of system evaluation process. 4. Explain Concurrent Audit Techniques. 5. Explain Quality Assurance Management Control.

ASSIGNMENT - 2, MAY 2013. Paper V SYSTEMS AUDITING (DMCA 305) 1. Explain Database Controls. 2. Discuss Expert system. 3. Explain about output controls. 4. What is distributed database environment? 5. Explain Public key Cryptography. 6. Explain test Data and code comparision. 7. How does the real memory errors are detected? 8. Explain Evaluation system Efficiency. 9. Define Auditing. 10. What is sandwitch testing? 11. What is meant by crypto system? 12. Define Data Integrity. 13. Why Information controls are used?