BACHELOR OF COMPUTER SCIENCE (ARTIFICIAL INTELLIGENCE)

periodicdollsΤεχνίτη Νοημοσύνη και Ρομποτική

17 Ιουλ 2012 (πριν από 4 χρόνια και 11 μήνες)

641 εμφανίσεις


BACHELOR OF COMPUTER SCIENCE (ARTIFICIAL
INTELLIGENCE)






COURSE LEARNING OUTCOMES

Bachelor of Computer Science (Artificial Intelligence) academic program is offered to prepare
graduates with a thorough understanding and superior skills of Computer Science,
particularly in Information & Communication Technology. Graduates will also be equipped
with advance scientific knowledge and engineering skills in Artificial Intelligence to fulfil
industrial needs especially in the field of ICT, robotics and manufacturing.


LEARNING OUTCOMES

The aim of FTMK to conduct the Bachelor of Computer Science (Artificial Intelligence)
programme is to produce students with the following characteristics:

1. Able to obtain and apply knowledge in computer science and information
technology.
2. Able to analyse, design and develop ICT applications.
3. Able to apply artificial intelligent techniques such as searching technique, fuzzy
logic, neural network, evolutionary computing, machine learning, and intelligent
agent when developing a system.
4. Equipped with skills to develop a system individually or in a group based on
artificial intelligence such as intelligent system, expert system, intelligent agent
system and robotic system.
5. Able to conduct research in the fields related and based on artificial intelligence.
6. Able to think creatively and critically in problem solving and able to communicate
effectively to deliver ideas.
7. Able to contribute skills individually or in group in difference disciplines and
domains.
8. Able to present good personality, ethics, leadership and entrepreneurship skills.
9. Able to perform continuous self learning to obtain knowledge and
skills.















CAREER PROSPECTS

A wide range of career opportunities in the field of computer science and information
technology is open to graduates who specialized in artificial intelligence. Graduates
specialized in artificial intelligence can also pursue their postgraduate studies. Among the
career opportunities are listed below.

a. Knowledge Engineer
b. Intelligent Systems or Expert Systems Developer
c. Systems Analyst
d. Systems Programmer
e. Systems Designer
f. Software Developer
g. Software Consultant
h. Computer Scientist
i. Researcher

CURRICULUM STRUCTURE

To be conferred the Bachelor of Computer Science (Artificial Intelligence) with honours, the
student is required to accumulate a minimum of 120 credits from the following course
components:

Components Credit Hours
University Compulsory Subjects 18
Program Core Subjects 72
Course Core Subjects 24
Elective Subjects 6
TOTAL 120








UNIVERSITY COMPULSARY SUBJECTS (18 credits)
BLHC 4042 Entrepreneur Skills and New Business
(Kemahiran Keusahawanan dan Perniagaan Baru)
BLHW 1702 Islamic and Asian Civilizations
(Tamadun Islam dan Tamadun Asia –TITAS)
BLHW 2712 Etnique Relation
(Hubungan Etnik)
BLHW 2402 Technical Communication I
(Komunikasi Teknikal I)
BLHW 3402 Technical Communication II
(Komunikasi Teknikal II)
BLHW 1722 Philosophy of Science and Technology
(Falsafah Sains dan Teknologi)
BLHL 4032 Critical and Creative Thinking
(Pemikiran Kritis dan Kreatif)
BLHW 1012 Foundation English
(Asas Bahasa Inggeris)*
BLHL 1 - - 2 Third Language
(Bahasa Ketiga)
BKK* - - - 1 Co-Curriculum I
(Kokurikulum I)
BKK* - - - 1 Co-Curriculum II
(Kokurikulum II)



PROGRAMME CORE SUBJECTS (72 credits)
BACS 1253 Mathematics for Computer Science I
(Matematik Sains Komputer I)
BACS 1263 Mathematics for Computer Science II
(Matematik Sains Komputer II)
BACS 2213 Statistic and Probability
(Statistik dan Kebarangkalian)
BITP 1113 Programming Technique
(Teknik Pengaturcaraan)
BITP 1123 Data Structure and Algorithm
(Struktur Data dan Algoritma)
BITP 1213 System Development
(Pembangunan Sistem)
BITP 1323 Database
(Pangkalan Data)
BITP 3113 Object Oriented Programming
(Pengaturcaraan Berorientasikan Objek)
BITP 2213 Software Engineering
(Kejuruteraan Perisian)
BITS 1123 Computer Organization and Architecture
(Organisasi dan Senibina Komputer)
BITS 1213 Operating System
(Sistem Pengoperasian)
BITS 1313 Data Communication and Networking
(Komunikasi Data dan Rangkaian)
BITS 2513 Internet Technology
(Teknologi Internet)
BITM 1113 Multimedia System
(Sistem Multimedia)
BITM 2113 Web Application Development
(Pembangunan Aplikasi Web)
BITI 1113 Artificial Intelligence
(Kepintaran Buatan)
BITU 2913 Workshop I
(Bengkel I)
BITU 3923 Workshop II
(Bengkel II)
BITU 3926 Industrial Training
(Latihan Industri)
BITU 3946 Industrial Training Report
(Laporan Latihan Industri)
BITU 3973 Project I
(Projek Sarjana Muda I)
BITU 3983 Project II
(Projek Sarjana Muda II)


COURSE CORE SUBJECTS (24 credits)
BITI 2113 Logic Programming
(Pengaturcaraan Logik)

BITI 2223 Machine Learning
(Pembelajaran Mesin)
BITI 2213 Knowledge Based System
(Sistem Berasaskan Pengetahuan)
BITI 3123 Fuzzy Logic
(Logik Kabur)
BITI 3133 Neural Networks
(Rangkaian Neural)


BITI 3113 Intelligent Agents
(Agen Pintar)
BITI 3143 Evolutionary Computing
(Pengkomputeran Evolusi)
BITS 3423 Information Technology Security
(Keselamatan Teknologi Maklumat)


ELECTIVE SUBJECTS (6 credits)
Choose any two of the following.

BITI 3513 Artificial Intelligence in Manufacturing
(Kepintaran Buatan dalam Pembuatan)
BITI 3523 Artificial Intelligence in Robotics and Automation
(Kepintaran Buatan dalam Robotik & Automasi)
BITI 3413 Natural Language Processing
(Pemprosesan Bahasa Tabi’e)
BITI 3213 Decision Support System
(Sistem Bantuan Keputusan)
BITI 3313 Image Processing and Pattern Recognition
(Pemprosesan & Pengecaman Imej)
BITM 3313 Computer Games Development
(Pembangunan Permainan Komputer)









CURRICULUM STRUCTURE PER SEMESTER

Year One (Semester I)
Code
Subject
Contact Hours
Credit
Pre-requisite
Lecture

Lab

BLHW 1
012

BLHW 1722
BLHW 2712
BLHW 1702
BACS 1253
BITP 1113
BITP 1213
BITS 1123
Foundation English


Philosophy of Science and Technology
Etnique Relation
Islamic and Asian Civilizations
Mathematics for Computer Science I
Programming Technique
System Development
Computer Organization and Architecture
2

2
2
2
2
2
2
2
1

0
0
0
2
2
2
2
2
*

2
2
2
3
3
3
3
*
Exemption for students
with MUET

TOTAL



18



Year One (Semester II)
Code

Subject

Contact Hours

C
redit

Pr
e
-
requisite

Lecture

Lab

BLHW 2402

BKK ---
BACS 1263
BITP 1123
BITP 1323
BITS 1213
BITI 1113
Technical Communication I

Co-Curriculum I **
Mathematics for Computer Science II
Data Structure and Algorithm
Database
Operating System
Artificial Intelligence
1

0
2
2
2
2
2
2

3
2
2
2
2
2
2

1
3
3
3
3
3
BLHW 1
012








TOTAL



18



Year Two (Semester I)
Code

Subject

Contact Hours

C
redit

Pr
e
-
req
uisite

Lecture

Lab

BLHW 3402
BKK ----
BACS 2213
BITU 2913
BITP 3113
BITS 2513
BITI 2113
Technical Communication II
Co-Curriculum II **
Statistic and Probability
Workshop 1
Object Oriented Programming
Internet Technology
Logic Programming
1
0
2
0
2
2
2
2
3
2
9
2
2
2
2
1
3
3
3
3
3
BLHW 2402


BITP 1113, BITP 1123
BITP 1113, BITP 1123

BITI 1113

TOTAL



18


**This subject can be taken in any semester.
Year Two (Semester II)
Code

Subject

Contact Hours

C
redit

Pr
e
-
requisite

Lecture

Lab

BLHL 4032
BITP 2213
BITM 1113
BITS 1313
BITI 2223
BITI 2213
Critical and Creative Thinking
Software Engineering
Multimedia System
Data Communication and Networking
Machine Learning
Knowledge Based System
2
2
2
2
2
2
0
2
2
2
2
2
2
3
3
3
3
3




BITP 1323, BITI 1113
BITI 1113

TOTAL


17


Year Three (Semester I)
Code

Subject

Contact Hours

C
redit

Pr
e
-
requisite

Lecture

Lab

BLHL ----
BITU 3913
BITM 1313
BITI 3123
BITI 3113
BITI 3133
Third Language
Workshop II
Web Application Development
Fuzzy Logic
Intelligent Agents
Neural Networks
2
0
2
2
2
2
0
9
2
2
2
2
2
3
3
3
3
3

BITU 2913

BITI 2213, BITP 1113
BITI 1113, BITP 3113
BITI 2223, BACS 1253
BITP 1113

TOTAL


17


Year Three (Semester II)
Code

Subject

Contact Hours

C
redit

Pr
e
-
requisite

Lect
ure

Lab

BLHC 4042
BITU 3973
BITS 3423
BITI 3143
BITI ----
BITI ----
Entrepreneur Skills and New Business
Project I
Information Technology Security
Evolutionary Computing
Elective 1
Elective 2
2
0
2
2
2
2
0
25*
2
2
2
2
2
3
3
3
3
3

BITU 3923
BITS 1213, BITS 1313
BITI 2223, BITP 3113


TOTAL


17


Year Three ( Special Semester)
Code

Subject

Contact Hours

C
redit

Pr
e
-
requisite

Lecture

Lab

BITU 3983 Project II 0 25* 3 BITU 3973

TOTAL


3

* Equivalent to 9 hours of contact if carried out in normal semester.
Year Four (Semester I)
Code

Subject

Contact Hours

C
redit

Pr
e
-
requisite

Lecture

Lab

BITU 3926
BITU 3946
Industrial Training
Industrial Training Report
0
0
24
24
6
6


TOTAL


12



Elective Subjects

Code

Subject

Contact Hours

C
redit

Pr
e
-
requisite

Lecture

Lab

BITI 3513

BITI 3523
BITI 3413
BITI 3213
BITI 3313
BITM 3313

Artificial Intelligence in Manufacturing


Artificial Intelligence in Robotics and Automation
Natural Language Processing
Decision Support System
Image Processing and Pattern Recognition Computer
Games Development
2

2
2
2
2
2
2

2
2
2
2
2
3

3
3
3
3
3
BITI 3123, BITI 3133

BACS 1263
BITI 2113
BITI 2213
BITI 1113, BACS 1253




Third Language

Code

Subject

Contact Hours

C
redit

Pr
e
-
requisite

Lecture

Lab

BLHL 1012

BLHL 1022

BLHL 1112

BLHL 1122

BLHL 1212

BLHL 1222

BLHL 1312
BLHL 1322
BLHL 1412

BLHL 1422

BLHL 1512

BLHL 1522


Bahasa Melayu I
Bahasa Melayu II
Bahasa Arab I
Bahasa Arab II
Bahasa Mandarin I
Bahasa Mandarin II
Bahasa Jepun I
Bahasa Jepun II
Bahasa Jerman I
Bahasa Jerman II
Bahasa Perancis I
Bahasa Perancis II
2
2
2
2
2
2
2
2
2
2
2
2
1
1
1
1
1
1
1
1
1
1
1
1
2
2
2
2
2
2
2
2
2
2
2
2
-
BLHL 1012
-
BLHL 1112
-
BLHL 1212
-
BLHL 1312
-
BLHL 1412
-
BLHL 1512


PROGRAMME CORE SUBJECTS


BACS 1253 Mathematics for Computer Science I (3,3,2)

Learning Outcomes

Upon completing this course, students should be able to:

1. Explain the concepts of fundamental Linear Algebra
and Discrete Mathematic.
2. Solve problems in Computer Science related to
Linear Algebra and Discrete Mathematic theory
using software.
3. Solve application problems using appropriate
techniques.

Synopsis

This course covers two disciplines of mathematics namely
Linear Algebra and Discrete Mathematics. The topics for
Linear Algebra are linear equations, matrices, determinants,
vectors in R
n
, real vector spaces, eigenvalues, eigenvectors,
diagonalization and linear transformation. The topics for
discrete mathematics include logic, sets, function, algorithms,
integers, mathematical reasoning, counting, relations, graphs,
trees and Boolean algebra.

References

1. Kolman, B. and Hill, D.R. Introductory Linear Algebra
with Application, 7th edition. Prentice Hall 2001.
2. H. Anton. Elementary Linear Algebra. 8th edition.
McGraw Hill. 1995.
3. David C.Lay. Linear Algebra and Its Applications 3rd
edition. Addison Wesley 2003.
4. Kenneth H. Rosen. Discrete Mathematics and Its
Applications, 4th edition. McGraw-Hill 1998.
5. Johnsonbaugh, R. Discrete Mathematics. Prentice
Hall 2005.





BACS 1263 Mathematics for Computer Science II (3,3,2)

Learning Outcomes

Upon completing this course, students should be able to:

1. Apply the knowledge and basic concepts of calculus
and numerical analysis.
2. Solve problems in Computer Science related to
calculus and numerical analysis theory using
software.
3. Solve application problems using appropriate
techniques.

Synopsis

This course covers two disciplines of mathematics namely
calculus and numerical analysis. The topics for calculus are
derivatives, function, differentiation techniques, logarithmic
function and exponents as well as its application, integration
techniques, and multivariable functions. The topics for
numerical analysis include Taylor polynomial, numbers, error,
interpolation, numerical differentiation and integration as well
as numercal solution for differential equation.

References

1. Goldstein, L. J., David I. S. (2004). Calculus and Its
Application. Prentice Hall.
2. James Stewart (2003). Calculus. Thomson.
3. Johnston, E.H., Mathews J.C. (2002). Calculus.
Pearson Education. .
4. Atkinson, K. (2004). Elementary Numerical Analysis.
John Wiley & Sons, Inc.
5. Richard L.B., J. Douglas Faires (2004). Numerical
Analysis. Thomson.


BACS 2213 Statistic and Probability (3,2,2)

Learning Outcomes

Upon completing this subject, students should be able to:

1. Explain the concepts of fundamental statistics and
probability.
2. Solve problems in statistic inference related to
hypothesis test using software.
3. Solve application problems using appropriate
statistic techniques.

Synopsis

Students will be introduced to the concept of probability and
inferential statistics. The course starts with Probability followed
by Discrete Random Variables, Continuous Random Variables
and Sampling Distribution. The main topics for Inferential
statististics are Estimation, Hypothesis Testing, Estimation and
Hypothesis Testing: Two Populations, Anova, Simple Linear
Regression and Correlation. This course will also provide the
students with some exposure to statistical software.

References

1. Sh. Sara, Hanissah, Fauziah, Nortazi, Farah Shahnaz,
Introduction to Statistics & Probability A Study Guide
(2008), Pearson – Prentice Hall
2. Douglas C. Montgomery, George C.Runger, Applied
Statistics and Probability for Engineers, 3rd Edition
(2002), John Wiley
3. Richard A. Johnson, Probability and Statistics for
Engineers, 7th Edition (2005), Pearson Prentice Hall
4. Jay L. Devore, Probability and Statistics for Engineering
and the Sciences, 6th Edition (2000), Thomson –
Duxbury
5. David M Levine, Patricia P. Ramsey, Robert K. Smidt ,
Applied Statistics for Engineers and Scientists Using
Microsoft Excel and MINITAB (2001),Prentice Hall


BITP 1113 Programming Technique (3,2,2)

Learning Outcomes

At the end of the lesson, students should be able to:
1. Explain basic principles of problem solving in
Software Engineering.
2. Demonstrate basic principles of programming.
3. Develop basic construction of C++ language in
building program.


Synopsis

This course introduces the students to the basic concepts of
computer and programming techniques that includes program
lifecycle variable, identifier, data type, operator, selection,
repetition, function, array, string, file and pointer.

References

1. D.S Malik (2009), “C++ Programming from Problem
Analysis to Program Design”, Cengage Learning.
2. A.Forouzan, Behrouz, (2000), “A Structured
Programming Approach Using C++”, Brooks/Cole
Thomson Learning.
3. H.M Deitel, P.J Deitel, (2005), “C++ How To
Program”, Prentice Hall.
4. Savitch, Walter, (2006),”Absolute C++”, Addison
Wesley.
5. Bronson, Gary J, (2000), “Program Development and
Design Using C++”, Brooks/Cole Publishing
Company.
6. Knowlton, T, (2000), “Introduction To Computer
Science Using C++”, Thomson Learning.
7. Schildt, H, “The Single Easiest Way To Master C++
Programming”, Mc Graw Hill.



BITP 1123 Data Structure and Algorithm (3,2,2)

Learning Outcomes

At the end of the lesson, students should be able to:
1. Identify suitable data structure for certain
application.
2. Solve problems by applying knowledge in data
structure and algorithm.
3. Analyze the memory and run time efficiency of an
algorithm design.
4. Use and develop data structure based on the current
problem requirement.



Synopsis

This course introduces the students to data structures and
algorithms. The basic concepts in structure, class, array and
pointer are discussed in order to understand the fundamental
of data structures and algorithms. The course focuses on data
structures such as list, stack, queue, tree, searching and hash
while sorting, graph and heaps topics cover the algorithms.
This also includes the algorithm efficiency for run time. Pseudo
code and C++ programming language will be used in algorithm
implementation. Apart from the theory, the students must apply
the data structures and algorithms in the development of small
scale application as a group work.

References

1. Richard F. Gilberg, Behrouz A. Fourouzan, “Data
Structures A Pseudocode Approach with C++”,
Brooks/Cole Thomson Learning, 2001
2. Malik, D. S. “Data Structures Using C++”. Thomson
Course Technolgy, 2005.
3. Michael Main, Walter Savich, “Data Structures &
Other Objects Using C++”, Addison Wesley, 2004.
4. Sartaj, Sahni, “Data Structures, Algorithms and
Applications in C++”, Mc Graw Hill International
Editions, 1998.
5. Berman A., Michael, “Data Structure Via C++ -
Objects by Evolution “, Oxford, 1997.


BITP 1213 System Development (3,2,2)

Learning Outcomes

At the end of the lesson, students should be able to:
1. Identify and explain all the phases in system
development.
2. Follow suitable methodology used in system or
application development.
3. Apply system development life cycle based on the
current problems.

Synopsis

This course introduces the students to the basic system
development concept, analysis, design, modeling,
methodology, technique, tool and other perspectives that are
important to be considered in the development of information
system.

References

1. Valacich, J. S., George, J. F. & Hoffer, J.A. 2006. Modern
Systems Analysis and Design, 5
th
Ed, Pearson Prentic
Hall.
2. Whitten, J., Bentley L. & Dittman, K. 2001. Systems
Analysis and Design Methods, McGraw-Hill.
3. Masrek, M. N., Abdul Rahman, S. & Abdul Jalil, K. 2001.
Analisis & Rekabentuk Sistem Maklumat. McGraw-Hill.
4. Kendall, K. E. & Kendall, J. E. 2002. System Analysis and
Design. Prentice Hall.
5. Shelly, G., Cashman, T. & Rosenblatt, H. 2000. Systems
Analysis and Design, Shelly Cashman Series.
6. Blair, R., Crossland, J., Reynolds, M., Willis, T.
2003. Beginning VB.Net, 2
nd
edition, Wiley Productions.
7. Bradley, J. C. & Millspaugh, A. C. 2005. Programming in
Visual Basic.Net: Visual Basic.NET 2003 Update Edition,
McGraw-Hill International Edition


BITP 1323 Database (3,2,2)

Learning Outcomes

At the end of the lesson, students should be able to:
1. Identify and explain the concept of database, data
modeling (relationship) and SQL statements.
2. Produce data conceptual representation using Entity
Relationship Model.
3. Develop database application based on the current
problem requirement.

Synopsis

This course is an introduction to database and file
management system. It assists the students to form an
understanding of data modeling, file management and
database system functionality in information system. The
students will be introduced to the process of designing,
developing and executing database applications. This course
focuses on practical skills to create, control and execute
statement for database relationship. Exercises based on
various resources will be given in all lab sessions. The
students will submit their exercises at the end of the lab
session. The students must present their database application
project to demonstrate their understanding of the course. This
allows the students to apply their knowledge and the
techniques that they have learnt into the real world database
applications.
References

1. Rob, P. & Coronel, C. (2004) Database Systems:
Design, Implementation, and Management 6th
Edition. Course Technology.
2. Connolly, T., Begg, C. & Strachan, A. (2005)
Database Systems: A Practical Approach to Design,
Implementation, and Management. 4th Edition.
Addison- Wesley.
3. Hoffer, Jeffrey A ., Prescott, Mary B. & McFadden,
Fred R. (2004) Modern Database Management 7th
Edition. Prentice Hall
4. Pratt, P.J. (2004) A Guide to SQL Seventh Edition.
Course Technology
5. Mannino, M.V. (2001) Database Application
Development & Design. McGraw-Hill.


BITP 2213 Software Engineeering (3,2,2)

Learning Outcomes

At the end of the lesson, students should be able to:
1. Explain the concept and importance of requirement
engineering in software development process.
2. Implement software requirement phase and analyze
the requirement engineering specification.
3. Create official documents for software requirement
specification based on the current problems by
following the software requirement engineering
process.
4. Choose a suitable tool to design a case study.

Synopsis

This course introduces the students to system development
and software engineering. The topics includes the software
lifecycle, requirement analysis, software design, processes in
software design, design quality, strategy in design and metric
in software testing. This course also covers software project
management including the budgeting and quality
management.

References

1. Sommerville, I (2007) Perisian Engineering, 8th
Edition, Addison Wesley.
2. Pressman, R.S (2005) Perisian Engineering A
Practitioner’s Approach, 6th Edition. McGraw-Hill.
3. Pfleegar, S.L (2001) Perisian Engineering Theory &
Practice. 2nd Edition. Prentice Hall.
4. Braude J.E, (2001) Perisian Engineering: An Object-
Oriented Perspective, Wiley.
5. Ghezzi C, Jazayeri M, Mandrioli D, (2003)
Fundamentals of Perisian Engineering. 2nd Edition
Prentice Hall.
6. Bern Oestereich,(2002), Developing Perisian with
UML Object oriented Analysis and Design Practice,.
2nd Edition. Addison-Wesley.


BITS 1123 Computer Organization and Architecture (3,2,1)

Learning Outcomes

At the end of the lesson, students should be able to:
1. Define and explain computer architecture and
organization concept including functional
components and their characteristics,
performance and the detailed interactions in
computer system including system bus, different
types of memory and input/output as well as
CPU.
2. Apply computer architecture theory to solve the
basic functional computer problem.
3. Show and assemble basic computer
components.

Synopsis
This course provides detail of computer system’s functional
components, their characteristics, performance and
interactions including system bus, different types of memory
and input/output and CPU, as well as practical
implementations of the components. This course also covers
the architectural issues such as instruction set program and
data types. On top that, the students are also introduced to the
increasingly important area of parallel organization.
References

1. William Stallings, (2007). Computer Organization &
Architecture, 7
th
Edition. Prentice Hall.
2. Carl Hamacher, Zvonko Vranesic, Safwat Zaky,
(2002). Computer Organization, 5
th
Ed. McGraw Hill.
3. Irv Englander, (2003). The Architecture of Computer
Hardware and System Software: An Information
Technology Approach., 3
rd
Ed. John Wiley & Sons.
4. James L. Antonakos, (2004). The 68000
Microprocessor, 5
th
Edition. Prentice Hall.
5. H.Aslinda, R. Marliza, Computer Organization and
Architecture, First Edition.


BITS 1213 Operating System (3,2,2)

Learning Outcomes

At the end of the course, students should be able to:

1. Explain the major components of an operating
system.
2. Elaborate the major operating system
responsibilities or aspects.
3. Explain the differences of the functionality among
various kinds of operating system.

Synopsis

This course gives exposure to the students about the basic
of operating system which comprises process, memory
management, file and I/O and also CPU scheduling. The
introduction part covers the evolution of operating system
followed by the basic concepts, technology and theories
used in operating system such as concurrency, kernel,
deadlock and multithreading.


References

1. William Stallings, Operating Systems: Internals and
Design Principles 6
th

Ed., Prentice Hall
International, Inc.
2. Silberschatz, A (2003). Operating System Concept
6
th
. Ed., John Wiley and Sons, Inc.

3. Nutt, G. (2002), Operating Systems : A modern
Perspective 2
nd
.Ed., Eddison Wesley Longman,
Inc., ISBN 0-201-74196-2
4. Jason W. Eckert, M. John Schitka. Linux Guide to
Certification.
5. Zurina, Fairuz, Zaki, Ariff (2009), Fedora Core 9:
For Beginner and Intermediate, First Edition.


BITS 1313 Data Communication & Networking [3, 2, 2]

Learning Outcomes

At the end of the course, students should be able to:
1. Explain and apply the fundamental concept of data
communication and networking.
2. Differentiate types of media, network topologies
and network technologies.
3. Practice the best technique in developing network
4. Configure and troubleshoot a basic network.

Synopsis

This course introduces the fundamental concepts and
terminology of data communication and networking,
encompassing both technical and managerial aspects. It also
provides an understanding about the challenges and
opportunities faced by the modern businesses. The topics
include: fundamentals of telecommunications, data
transmission mechanisms, telecommunication media and
technologies, considerations for LAN and WAN
implementations, the Internet and intranet applications,
emerging telecommunications technologies, and trends in the
telecommunications industry. Students will also be able to
understand, explain and apply the fundamentals of data
communication and networking as well as skills in network
applications to troubleshoot and configure a basic computer
networks using guided or unguided media.

References

1. Behrouz Forouzan, Data Communications and
Networking, 4
th
Edition, McGraw-Hill, 2007.
2. Andrew S Tanenbaum, Computer Network, Prentice
Hall, 1997.
3. E. Ramos, A. Schoroeder and A. Beheler, Computer
Networking Concepts, McMillan, 1996.
4. Azhar, Haniza and Zakiah, Komunikasi Data dan
Rangkaian (Modul Pengajaran), Edisi Pertama,
2005.
5. B. Nazrulazhar and H. Erman, Data
Communications and Networking: Practical
Approach, 1
st
Edition, Venton, 2008.


BITS 2513 Internet Technology (3,2,2)

Learning Outcomes

Upon completing this subject, students should be able to:
1. Apply the concepts of computer networks, core
components of the Internet infrastructure, protocol
and services.
2. Show the implementation of client and server
application
3. Select the best Internet application according to the
current situation.

Synopsis

Internet has become a major tool in doing business today.
The evolutions of web based knowledge also contribute to
this phenomenon. Hence, this course is purposely designed
to provide an introduction to Internet technologies. This
course covers a wide range of material about the Internet and
the major areas of study including basic concepts of client
and server, networking, Internet Security and its application.

References

1. Douglas E. Comer (2007). The Internet 4th
edition. Pearson Prentice Hall.
2. Behrouz Forouzan, Data Communications and
Networking, 4th Edition, McGraw-Hill, 2007.
3. Fred T. Hofstetter(2005), Internet Technologies
at Work, McGraw Hill Technology Education
4. Douglas E. Comer (2004), Computer Networks
and Internets with Internet Applications, 4th
Edition, Pearson Prentice Hall
5. Preston Gralla (2002). How Internet Works, 6th
edition. Que Publishing


BITM 1113 Multimedia System (3,2,2)

Learning Outcomes

Upon completing this course, students should be able to:
1. Use several media editing software to create original
multimedia content.
2. List down and discuss the software and hardware
components used in multimedia system.
3. Demonstrate life long learning by relating and
describing the fundamental concept of multimedia
systems into other subjects (e.g. Software
Engineering, Internet Technology, PSM etc).
4. Apply problem solving skills by identifying several
different environments in which multimedia might be
used and several different aspects of multimedia
that benefit other forms of information presentation.

Synopsis

This subject prepares the students with the basic concept of
multimedia, technology and the importance of multimedia
application. It covers the introduction to media, multimedia
graphic implementation, 2D/3D graphics and animation,
video, audio, authoring, multimedia integration and
application development. In lab sessions, the students will
be introduced to tools for selected media elements and
authoring software for media integration. Students will be
trained for practical preparation of still image, simple
animation, sound and effectively apply it in a multimedia
project. Students will be exposed to teamwork, leadership,
problem solving and communcation skills while performing
their various tasks and project.




References

1. Norazlin et al. Sistem Multimedia, Venton
Publishing, 2007
2. Todd Perkins. Adobe Flash CS3 Profesional Hans-
on Training, 2008.
3. Tay Vaughan, Multimedia: Making It Work 7th
Edition, McGraw-Hill Osborne Media, 2006.
4. Mark Drew and Ze-Nian Li, Fundamentals of
Multimedia 4th Edition, Prentice Hall, 2004.
5. Nigel Chapman, Digital Multimedia, John Wiley and
Sons, 2004.
6. Ken Abernethy and Tom Allen, Exploring the Digital
Domain: An Introduction to Computing with
Multimedia and Networking, Pws Pub Co, 1999

7. Jamalludin Harun & Zaidatun Tasir, Multimedia:
Konsep & Praktis, Venton Publishing, 2006


BITM 2113 Web Application Development (3,2,2)

Learning Outcomes

Upon completing this course, students should be able to:
1. Explain the concept and the principle of Internet and
WWW based on the latest technologies.
2. Identify and develop important components in Web
applications which comprises client site technology,
server site technology, database server and Web
server.
3. Relate relevant key components in developing Web
applications.

Synopsis

The purpose of this course is to provide the students with a
comprehensive understanding of the tools and problem-
solving techniques related to the development of effective
World Wide Web. It emphasizes on four (4) components of
Web application development which are:
 Client Site Technologies: HTML, XHTML, CSS, XML,
and JavaScript
 Server Site Technologies: PHP
 Database Server: MySQL.
 Web Servers : Apache

References

1. Robert W.Sebesta (2005), Programming The World
Wide Web – 3rd Edition, Addison Wesley,
ISBN: 0-321-31257-0
2. Harvey Deitel, Paul Deitel, Andrew Goldberg (2003),
Internet & Internet & World Wide Web How to
Program - 3rd Edition, Prentice Hall, ISBN:
0131450913
3. Keith Darlington (2005), Effective Website
Development – Tools and Techniques, Addison
Wesley, ISBN: 0-321-18472-6
4. Luke Welling, Laura Thomson (2003), PHP and
MySQL Web Development -Third Edition, Sams
Publishing, ISBN: 0-672-32672-87
5. Bai, Ekedahl, Farrell, Gosselin, Zak, Kaparthi (2003),
The Web Warrior Guide to Web
Programming,Thomson Course Technology, ISBN:
0-619-06458-7


BITP 3113 Object Oriented Programming (3,2,2)

Learning Outcomes

At the end of the lesson, students should be able to:
1. Apply object oriented programming concept and
methods.
2. Build program that implement programming
language syntax and semantic in Java application.
3. Develop object oriented application based on the
current case study.

Synopsis

This course introduces the students to the object oriented
programming methods by using Java programming language.
Student will apply and design the basic object oriented
structure, swing, event handling, interface components,
exception handling, database, multimedia, networking and
threads. Student will also develop a complete Java programs
and applications.




References

1. Liang ,Y .Daniel,(2008) , Introduction Java
Programming , 7
th
Ed.,Prentice Hall.
2. Deitel, H.M . & Deitel ,P.J.,(2006) , Java How to
Program ,7
th
Ed., Pearson Education
International .
3. Bronson ,Gary J.,(2004), Object Oriented
Program Development Using Java –Class
Centered Approach , Thompson Course
Technology .
4. Farrel,Joyce , (2003),Java Programming 2
nd

Ed.,Thomson Course Technology.
5. Doke, E.Reed ,Satzinger,John W.& Williams,
Susan Rebstock , (2002), Object –Oriented
Application Development Using Java. Thomson
Course Technology.


BITI 1113 Artificial Intelligence (3,2,2)

Learning Outcomes

Upon completing this subject, students should be able to:

1. Explain the basic definition of artificial intelligence.
2. Identify the types of artificial intelligence techniques.
3. Use the artificial intelligence techniques in problem
solving.

Synopsis

Students will be exposed to the basic and branches of Artificial
Intelligence (AI) such as various search techniques,
knowledge representation and reasoning, inference
techniques, learning from experience and planning. This
course also covers some applications of AI including game
playing, expert systems, machine learning, and natural
language processing.

References

1. Coppin, B (2004). Artificial Intelligence Illuminated,
Jones and Bartlett.
2. Russel, S & Norvig, P. (2003). Artificial Intelligence: A
Modern Approach, 2nd. Edition, Prentice Hall.
3. Luger, G. F & Stubblefield, W.A. (2002). Artificial
Intelligence: Structures and Strategies for Complex
Problem Solving, 4th. Edition, Addison Wesley.
4. Negnevitsky, M., (2002), Artificial Intelligence: A Guide
to Intelligent System, Addison Wesley.
5. Dean, T, Allen, J & Aloimonos, Y (1995), Artificial
Intelligence Theory and Practice, The Benjamin
Cummings.


BITU 2913 Workshop I (3,0,9)

Learning Outcomes

Upon completing this course, students should be able to:

1. Use the knowledge learnt specifically the
programming techniques to develop a project.
2. Identify and solve problems systematically based on
the information from various resources.
3. Run and produce a project individually.
4. Present and defend the project output.

Synopsis

The aim of Workshop 1 is to provide the students with
experience and skills to develop and present an individual
project. Students must use the knowledge learnt to solve the
problems and think creatively to achieve their projects’
objectives and scopes. Students should be able to apply
programming technique in their projects. The
systems/applications developed must have logic process flow,
robust, consistent, have attractive user interface and are able
to detect errors in input/output data. At the final stage of this
workshop, the students must present and defend their project.
A supervisor will supervise the students for the whole 12
weeks and will evaluate the progress during the
implementation and final presentation. This course is also a
fundamental course to prepare the students for industrial
training.

References

1. Burhanuddin Mohd Aboobaider et. all., Software
Development Using Visual Basic.NET BITU 2913.
2. Julia Case Bradley, Anita C.Millspaugh,
Programming in Visual Basic .NET, McGraw-Hill,
2005 Edition.
3. Jack Koh, Gourab Sen Gupta, Jesicca Goh, Ronnie
Peh, VB.net With Database Access, Prentice Hall,
2002.
4. Dave Grundgeiger, Programming Visual Basic .NET,
O’Reilly, 2002.
5. Francesco Balena, Programming Visual Basic .NET,
Version 2003, Microsoft Press, 2004.


BITU 3923 Workshop II (3,0,9)

Learning Outcomes

Upon completing this course, students should be able to:
1. Analyze and develop a group project.
2. Apply the concept of system design and
development in their projects.
3. Identify, analyze and organize the changes made to
project scope during the project life cycle.
4. Organize a group project with good manner.
5. Present and defend the project output.

Synopsis

This course allows the students to practice their knowledge
and experience gained from the courses taken earlier. This
course builds the students understanding about problem
solving techniques based on their project scopes. The scope
of their projects is based on their programme specializations.
This course requires the project to be developed in a team of
three to five students.

References

1. Schwalbe, K., (2004). Information Technology
Project Management, Thomson.
2. Hughes, B., and Cotterell, M., (2002), Software
Project Management, McGraw-Hill.
3. Gonzalez, A. and Dankel, D., (2004). The
Engineering of Knowledge-Based Systems (Second
Edition), Prentice Hall.
4. Alpaydin, E., (2004). Introduction to Machine
Learning, The MIT Press.
5. Russel, S and Norvig, P., (2003). Artificial
Intelligence: A Modern Approach (Second Edition),
Prentice Hall.


BITU 3926 Industrial Training (6,0,6)

Learning Outcomes

Upon completing this course, students should be able to:
1. Be responsible in performing tasks as an ICT
worker.
2. Apply skills and knowledge learnt in classes.
3. practice discipline and ethique in performing daily
tasks.
4. Use the latest technology in the ICT domains.
5. Interact and communicate with collleagues in a good
manner.

Synopsis

During this course, students will be able to practice the
knowledge that they have learnt in UTeM such as analyzing
and designing, database programming, data structure and
algorithm, operating system, web programming, network and
data communiation etc. It is an opportunity for the students to
gain ICT knowledge as in the industry. The students can
develop soft skills and professionalism through interaction and
communication with colleagues.

References

Industrial Training Committee ”Industrial Training Guidelines”,
UNIC, Universiti Teknikal Malaysia Melaka.


BITU 3946 Industrial Training Report (6,0,6)

Learning Outcomes

Upon completing this course, students should be able to:

1. Apply the skills and knowledge learnt
2. Use the latest technlogy in the ICT domain.
3. Organize information to produce a formal report.

Synopsis

This course requires the students to produce a report while
undergoing the industrial training. The students should be able
to apply the courses that they have learnt at UTeM such as to
analyze and design, database programming, data structure
and algorithm, operating system, web programming, network
and data communication etc. It is an opportunity for them to
gain industrial ICT knowledge.

References

Industrial Training Committee ”Industrial Training Guidelines”,
UNIC, Universiti Teknikal Malaysia Melaka.

BITU 3973 Project I [3,0,9]
Learning Outcomes

Upon completing this course, students should be able to:

1. Run testing and validate their systems based on the
projects’ timeline.
2. Solve problems related to the industrial need in the
ICT domain.
3. Complete the project output that has the commercial
value.
4. Present and defend the output.
5. Organize information to produce a formal report.

Synopsis

This course joins together all the subjects learnt from year one
of the studies including to analyze and to design a specific
system, the application of database, algorithm and data
structure, web programming, data communication etc. It is
compulsory to the final year students to develop a Final Project
and to attend the offered courses.

References

1. Bachelor Degree Project and Diploma Project
Committee, PSM Report Guideline, FTMK, Universiti
Teknikal Malaysia Melaka.

2. Bachelor Degree Project and Diploma Project
Committee, PSM Report Guideline Book, FTMK,
Universiti Teknikal Malaysia Melaka .
3. Bachelor Degree Project and Diploma Project Committee,
PSM Report Guideline Reference, FTMK, Universiti
Teknikal Malaysia Melaka.


BITU 3983 Project II [3,0,9]

Learning Outcomes

Upon completing this subject, students should be able to:

1. Run testing and validate their system based on the
project timeline.
2. Solve problems related to the industrial need in the ICT
domain.
3. Complete the project output that has the commercial
value.
4. Present and defend the output.
5. Organize information to produce a formal report.


Synopsis

This course joins together all the subjects learnt from year one
of the studies including to analyze and to design a specific
system, the application of database, algorithm and data
structure, web programming, data communication etc. It is
compulsory to the final year students to develop a Final Project
and to attend the offered courses.

References

1. Bachelor Degree Project and Diploma Project
Committee, PSM Report Guideline, FTMK, Universiti
Teknikal Malaysia Melaka.

2. Bachelor Degree Project and Diploma Project
Committee, PSM Report Guideline Book, FTMK,
Universiti Teknikal Malaysia Melaka .
3. Bachelor Degree Project and Diploma Project Committee,
PSM Report Guideline Reference, FTMK, Universiti
Teknikal Malaysia Melaka.
COURSE CORE SUBJECTS


BITI 2113 - Logic Programming (3,2,2)


Learning Outcomes

Upon completing this subject, students should be able to:
1. Identify the elements and concepts of logic and
procedural programming.
2. Produce the Prolog algorithm for solving logic
programming problems.
3. Design and implement basic program using logic
programming structures.

Synopsis

Students are exposed to the basic concepts of logic
programming such as Prolog syntax and semantic including
predicate logic, facts, rules, query, recursive rules,
backtracking control, input output and unification. This course
is also aimed to prepare the students who will take other
Artificial Intelligence subjects.

References

1. Bratko, Ivan, (2001). Prolog: Programming for
Artificial Intelligence, 3rd. Edition, Addison Wesley.
2. Mellish, C.S & Clocksin W.F(2003), Programming in
Prolog, Springler Verlag.
3. Luger, G. F & Stubblefield, W.A. (2002). Artificial
Intelligence: Structures and Strategies for Complex
Problem Solving, 4th. Edition, Addison Wesley.
4. Mellish, C.S. and Clocksin, W.F.. (2003).
Programming in PROLOG: Using the ISO Standard.
5th Edition. Springer-Verlag Berlin and Heidelberg
GmbH & Co.
5. Bramer, M. (2005). Logic Programming with Prolog.
Springer-Verlag London Ltd. ISBN: 1852339381.







BITI 2213 - Knowledge Based System (3,2,2)


Learning Outcomes

Upon completing this subject, students should be able to:
1. Explain by relating and describing the fundamental
concept of knowledge based system and their
components.
2. Assess and identify appropriate concept and
components in knowledge based system problem
solving.
3. Develop a basic knowledge based system based on
appropriate concept and component.

Synopsis

This course involves introduction to knowledge based system,
phases in developing the system, types of knowledge
representations, knowledge acquisitions and types of inference
techniques and reasoning. Besides, students are exposed to
Expert Systems as one of the knowledge based system.

References

1. Gonzalez and D. Dankel (2004). The Engineering of
Knowledge-Based Systems (2
nd
Edition), Prentice
Hall.
2. J. Giarratano and G. Riley (2004). Expert Systems-
Principles and Programming (4
th
Edition),
Thomson/PWS Publishing Company.
3. Efraim Turban & Jay E. Aronson (2005), Decision
support systems and intelligent systems, Prentice
Hall.
4. Negnevitsky, M., (2002), Artificial Intelligence: A
Guide to Intelligent System, Addison Wesley.
5. Russel, S & Norvig, P. (2003). Artificial Intelligence:
A Modern Approach, 2nd. Edition, Prentice Hall.









BITI 2223 - Machine Learning (3, 2, 2)

Learning Outcomes

Upon completing this subject, students should be able to:
1. Explain by relating the fundamental concept of
machine learning theory.
2. Assess and identify the appropriate techniques in
machine learning problem solving.
3. Demonstrate machine learning algorithm based on
machine learning concepts.


Synopsis

The course aims to provide exposure on the foundation of
machine learning, which is the study of how to build a
computer system that learns from experience. The course
starts with an overview of Data Mining for a background study.
Main topics that will be covered are such as concept learning,
decision tree learning, Bayesian learning, instance-based
learning, learning sets of rules, and reinforcement learning.
Besides, some applications of machine learning including
robotic control, autonomous navigation, bioinformatics, speech
recognition, and web data processing will be introduced.

References

1. Mitchell, T.M., (1997), Machine Learning, McGraw
Hill.
2. Witten, I.A., Frank, E., (2005), Data Mining: Practical
Machine Learning and Techniques (Second
Edition),Morgan Kaufmann.
3. E.N Richard (2003), Learning Bayesian Networks
(Hardcover), Prentice Hall.
4. Alpaydin, E., (2004), Introduction to Machine
Learning, The MIT Press.
5. Han, J. and Kambel, M. (2000), Data Mining:
Concepts and Techniques. Morgan Kaufman.


BITI 3123 - Fuzzy Logic (3,2,2)


Learning Outcomes

Upon completing this subject, students should be able to:
1. Explain and analyse the fundamental concept of fuzzy
logic.
2. Investigate and identify the appropriate techniques in
fuzzy logic problem solving.
3. Manipulate computer programme based on fundamental
techniques of fuzzy logic for problem solving.

Synopsis

The course aims to provide exposure on the foundation of
fuzzy logic as one of the soft computing techniques. The
course starts with an overview on the concept of fuzziness.
The main topics will cover the algebra, quantities and the
logical aspect of fuzzy sets, fuzzy operations, fuzzification, de-
fuzzification and fuzzy control. Various applications of fuzzy
control such as the rule-based system, PI type, supervisory
and adaptive controllers will be included in the discussion.

References

1. Nguyen, H. T., Walker, E. A. (1999). A First Course
in Fuzzy Logic. 2
nd
Edition, CRC Press.
2. Ross, T. J. (2004). Fuzzy Logic with Engineering
Applications, 2
nd
Edition, John Wiley.
3. Chen, G., Pham, Trung Tat (2000). Introduction to
Fuzzy Sets, Fuzzy Logic, and Fuzzy Control System.
CRC Pr I llc.
4. James, J.B. (2002). An introduction to fuzzy logic
and fuzzy sets. CRC Press.
5. McNeill, Martin, Ellen. T. (1994). Fuzzy Logic: A
Practical Approach, Academic Press Professional.


BITI 3133 - Neural Networks (3,2,2)

Learning Outcomes

Upon completing this subject, students should be able to:
1. Explain and analyze the fundamental concept of
neural network.
2. Investigate and identify the appropriate techniques
in neural network problem solving.
3. Manipulate computer programme based on
fundamental techniques of neural network for
problem solving.

Synopsis

This course introduces a soft computing technique that is
neural network. . Few fundamental theories in neural network
will be introduced including biological neural network, artificial
statistic neural network, Hebbian learning and rivalry strength
learning. Besides, brief introduction to information theory,
application and practice of neural network in relevant domains
will be discussed.

References

1. Andries, E (2002). Computational Intelligence. An
Introduction, John Wiley & Sons.
2. Zilouchian, Jamshidi. (2001). Intelligent Control
Systems Using Soft Computing Methodologies.
CRC Press, Inc.
3. Kumar, S. (2004). Neural networks : a classroom
approach. Mc Graw Hill, New Delhi.
4. Perlovsky, L.I (2001). Neural networks and intellect :
using model - based concepts. Oxford University
Press, New York.
5. Smith, K. A. (2002) Neural networks in business :
techniques and applications. Idea Group
Publications. Hershey, P.A.
6. Haykin, S. (1999). Neural Networks. A
Comprehensive Foundation. Prentice Hall, New
Jersey.
7. Fausett, L. (1994). Fundamentals of Neural
Networks. Prentice Hall.


8. Bose, N. K and Liang, P. (1996). Neural Network
Fundamentals with Graphs, Algorithms, and
Applications. McGraw-Hill.



BITI 3113 Intelligent Agents (3,2,2)

Learning Outcomes

Upon completing this subject, students should be able to:
1. Explain and analyze the fundamental concept of
intelligent agents.
2. Investigate and identify the appropriate techniques
in intelligent agents problem solving.
3. Manipulate computer programme based on
fundamental techniques of intelligent agents for
problem solving.

Synopsis

This course covers the underlying theory of agents, the
common agent architectures, methods of cooperation and
communication, and the potential applications for agents.
Students will be exposed to the concept of intelligent agent
and multi agent systems. Students will also construct their own
agents for solving different types of problems. The potential
applications of agents are numerous including web search
assistants, travel advisors, electronic secretaries, bidders in
on-line auctions, tutoring systems, and actors in games or
simulations. Some of the tools to be used are J2SE and Zeus
agent building toolkit
.
References

1. Michael Wooldridge (2002). An Introduction to
MultiAgent Systems. Chichester: England, John
Wiley and Sons.
2. Gerhard Weiss (2000). Multiagent Systems: A
modern approach to Distributed Artificial
Intelligence. The MIT Press.
3. Jacques Ferber (1999). Multi-Agent Systems: An
Introduction to Distributed Artificial Intelligence.
Addison-Wesley Professional.
4. Joseph P. Bigus & Jennifer Bigus (2001).
Constructing Intelligent Agents Using Java:
Professional Developer's Guide, 2nd Edition. Wiley.
5. Lin Padgham & Michael Winikoff (2004). Developing
IntelligentAgent Systems: A Practical Guide. John
Wiley & Sons.


BITI 3143 - Evolutionary Computing (3,2,2)

Learning Outcomes

Upon completing this subject, students should be able to:
1. Explain and analyse the fundamental concept of
intelligent agents.
2. Investigate and identify the appropriate techniques
in intelligent agents problem solving.
3. Manipulate computer programme based on
fundamental techniques of intelligent agents for
problem solving.

Synopsis

This course introduce problem-solving technique in
evolutionary computing. Evolutionary computing uses
algorithms which are inspired by mechanisms of biological
evolution. These search-algorithms apply the concepts of
genetic recombination, mutation, and natural selection in
producing the potential solutions. A number of evolutionary
computing techniques will be taught, and this course puts
greater emphasis on Genetic Algorithms. Others techniques
such as Simulated Annealing, Ant Colony Optimization and
Memetic Algorithm are also covered in this course.

References

1. Haupt, R.L, Haupt, S.E, (2004) Practical Genetic
Algorithms, Wiley-Interscience
2. Eiben, A.E., Smith, J.E., (2003) Introduction to
Evolutionary Computing, Springer
3. B. Eric, D. Marco, T. Guy, (2001), Swarm
Intelligence: From Natural to Artificial Systems,
Oxford University Press.
4. Mitchell, M. (1998) An Introduction to Genetic
Algorithms (Complex Adaptive Systems), The MIT
Press.
5. Drechsler, R. (EDT), Drechsler, N. (EDT) (2002)
Evolutionary Algorithms for Embedded System
Design (Genetic Algorithms and Evolutionary
Computation), Kluwer Academic


BITS 3423 Information Technology Security (3,2,2)

Learning Outcomes

At the end of the course, students should be able to:
1. Explain and elaborate the concept of computer
security theories and related items.
2. Study and identify the concept and the suitable
components in providing service and security
mechanism in computer software, operating system,
database, network system and computer security
management.
3. Produce the appropriate security system mechanism
for computer software and computer network.
4. Analyze issues that are related to the law and ethics
in computer security as well as identify the cyber law
associated with computer security issues.

Synopsis

Security in Information Technology is a very important issue.
It is an area that deserves study by computer professionals,
students, and even many computer users. Through this
course, student will learn how to control failures of
confidentiality, integrity and availability in applications,
databases, operating systems and networks alike. Student
will also learn on how to plan the recovery solution if any
disaster happens to the computing environment.

References

1. 1. Siti Rahayu, Robiah, Mohd Faizal and
Nazrulazhar (2006), Information Technology
Security, Pearson.
2. W. Stallings (2003). Network Security Essentials:
Applications and Standards, 2
nd
edition, Prentice
Hall, Inc.
3. C.P. Pfleeger, S. L. Pfleeger (2003). Security in
computing 3
rd
Ed., Prentice Hall International, Inc.
4. D. Gollmann (2005). 2
nd
Edition, Computer
Security, John Wiley & Sons, Inc.
5. B. Schneier (1996). Applied Cryptography:
Protocols, Algorithms and Source Code in C 2
nd

Ed, John Wiley & Sons, Inc.










ELECTIVE SUBJECTS



BITI 3213 –

Decision Support System (3,2,2)

Learning Outcomes

Upon completing this subject, students should be able to:
1. Explain and analyse the fundamental concept of
decision support system.
2. Investigate and identify the appropriate techniques
in decision support system problem solving.
3. Manipulate computer programme based on
fundamental techniques of decision support system
for building intelligent system.

Synopsis

The course aims to provide students with knowledge of various
decision support systems and artificial intelligence systems
and the ways in which they support effective decision making
in organisations. Topics covered are introduction to DSS,
decision makers, types of DSS, development of DSS,
modelling and optimization, group DSS, executive ESS, and
intelligent DSS.


References

1. Cylde W. Holsapple dan Andrew B. Whinston.
(1996), Decision Support Systems: A Knowledge-
Based Approach, Singapore: International Thomson
Publishing (ITP) ISBN 0314065105
2. Efraim Turban & Jay E. Aronson (2005), Decision
Support Systems and Intelligent Systems, Prentice
Hall. ISBN 0130894656
3. George M. Marakas (2003), Decision Support
Systems in the 21st Century, Prentice Hall. ISBN
013122848X
4. Matthew Liberatore, Robert Nydick (2002) Decision
5. Technology: Modeling, Software, and Applications
ISBN 0471417122
6. Srinivasan, Ananth.(2000) Implementing
DecisionSupport Systems : Methods, Techniques,
and Tools McGraw-Hill, ISBN 0077095081



BITI 3513 - Artificial Intelligence in Manufacturing

(3,2,2)

Learning Outcomes

Upon completing this subject, students should be able to:
1. Analyse the situation of manufacturing operation and
how does artificial intelligent technique improve
manufacturing operation performance.
2. Research in artificial intelligent techniques which is
appropriate for producing intelligent manufacturing
environment.
3. Manipulate computer programme based on
fundamental techniques of artificial intelligence in
manufacturing for problem solving.

Sinopsis

Students are exposed to manufacturing operations in several
areas/domain such as system design, planning, scheduling,
monitoring and control. The theory and principles
accompanied by the real world problem in each area will be
studied. It will then be extended with the applications of AI
techniques such as knowledge-based system, Neural Network
and other that the students already learn from previous AI
subjects. At the end of the course, students will involve in the
development of intelligence manufacturing module system by
using appropriate artificial intelligence techniques.
References

1. Kusiak, A., (2000), Computational Intelligence in
Design and Manufacturing, John Wiley & Sons
2. Wang, A., Kusiak, A.(2000) Computational
Intelligence in Design and Manufacturing Handbook.
CRC Press
3. Rusell, S. & Norvig, P (2003), Artificial Intelligence a
Modern Approach 2 ed. Prentice Hall.
4. Poole D., Mackworth A., & Goebel, R. (1998)
Computational Intelligence. Oxford University Press.
5. Bourbakis, N.G., (1998), Artificial Intelligence and
Automation, World Scientific

BITI 3523 Artificial Intelligence in Robotics and
Automation (3,2,2)

Learning Outcomes

Upon completing this subject, students should be able to:
1. Explain and analyze fundamental concepts related to
robotic such as direct kinematic and inverse
kinematic of manipulators.
2. Research in programming techniques of robot
manipulator’s dynamic equations.
3. Model and stimulate robotic programming for human
function simulation.

Synopsis

This course covers introduction of robotics, which includes
mechanical structure of robot systems, mechanics of robot
manipulators and control systems. The students will be
exposed to the fundamental of automation and robotic
programming.

References

1. John, J.C. (2005), Introduction to Robotics
Mechanics and Control. Prentice Hall, Pearson
Education, Inc.
2. Predco, M. (2003), Programming Robot Controllers.
McGraw-Hill.
3. Law, K.H. (2002), Robotics Principles and System
Modeling. Prentice Hall, Pearson Education
Asia Pte. Ltd.
4. Joseph, L.J. (2003), Robot Programming: A Practical
Guide to Behavior-Based Robotics. McGraw-
Hill.
5. Jones, J. and Roth, D. (2004), Robot Programming.
McGraw-Hill.
6. Dusko, K., Miomir, V. (2003), Intelligent control of
robotic systems. Kluwer academic Publisher.







BITI 3413 - Natural Language Processing (3,2,2)


Learning Outcomes

Upon completing this subject, students should be able to:
1. Explain and analyze the fundamental concept of
natural language processing.
2. Investigate and identify the appropriate techniques
in natural language processing problem solving.
3. Manipulate computer programme based on
fundamental techniques of natural language
processing for building intelligent system.

Synopsis

This course provides knowledge to students about natural
language processing (NLP). Topics covered: English grammar,
grammar representations, NLP tasks including syntactic
analysis (grammars and parsing), semantic analysis (word and
sentence meaning), and discourse analysis (pronoun
resolution and text structure) and its applications such as
machine translation, information retrieval and search,
information filtering and text categorization, information
extraction, spell checking, dictation, command interfaces,
question-answering systems, and other dialog systems.

References

1. Allen, J. (1995). Natural Language Understanding.
Benjamin/Cummins Publishing.

2. Jurafsky, D. & Martin, J. (2000). Speech and
Language Processing: An Introduction to Natural
Language Processing, Computational Linguistics
and Speech. Prentice-Hall.
3. Manning, C. D. and Schütze, (1999). H. Foundations
of Statistical Natural Language Processing. The MIT
Press.
4. Gal, A., Lapalme, G., Saint-Dizier, P., & Somers, H.
(1991). Prolog for Natural Language Processing.
John Wiley & Sons.
5. Rusell, S. & Norvig, P (2003), Artificial Intelligence A
Modern Approach, 2nd edition. Prentice Hall.



BITI 3313 Image Processing and Pattern Recognition
(3,2,2)


Learning Outcomes

Upon completing this subject, students should be able to:

1. Explain and and analyse the fundamental concept of
image processing and pattern recognition.
2. Investigate and identify the appropriate techniques
in image processing and pattern recognition problem
solving.
3. Manipulate computer programme based on
fundamental techniques of image processing and
pattern recognition for building intelligent system.

Synopsis

This course provides basic image processing techniques to
students, such as sampling, digitization, preprocessing,
segmentation, feature extraction, and transformation. The
course emphasize on object recognition of computer vision
systems. The students will also be exposed in pattern
recognition and their application in other fields such as
robotics, medical and remote sensing.



References

1. Gonzalez, R. C. & Woods, R. E. (2007) Digital
Image Processing, 3nd Edition, Prentice Hall.
2. Gonzalez, R. C. & Woods, R. E. & Eddins, S. L.
(2004) Digital Image Processing Using MATLAB,
Prentice Hall.
3. McAndrew, A. (2004) An Introduction to Digital
Image Processing with MATLAB, Course
Technology.
4. Shapiro, L.G. & Stockman, G.C. (2001) Computer
Vision, Prentice Hall.






BITM 3133 Computer Games Development (3,2,2)

Learning Outcomes

At the end of the course, students should be able to:

1. Explain and report the principles, basic of interface
design and technologies behind the rules to play the
games.
2. Show how the functions of computer games can be
used to create experience, including rules design,
game mechanic, game balancing, social game
integration and the integration of visual, audio, tactile
and textual elements into the game experience.
3. Describe and construct how characters, plots and
dialogues are developed in interactive story telling.
4. Construct text based and graphical computer
games’ prototypes.


Synopsis

Electronic game is one of the most popular forms of
entertainment that we need to understand from the
perspectives of commercial products, cultural phenomena
and computer technology particularly computer graphics.
An understanding of software technologies such as
graphics, networks, software design and artificial
intelligence as well as the cultural context is necessary in
designing and developing computer games. This subject
focuses on the design of computer games and how
different technologies can be adopted in practical
projects.











References

1. Gary R (2007), ActionScript 3.0 Game Programming
University.
2. Breackeen D., Barker B. & Vanheluwe (2004) Developing
Games In Java, New Riders.
3. Crawford C.(2003) Chris Crawford on Game Design.
Prentice Hall.
4. Crawford C. (2003) The Art of Interactive Design. No
Strach Press.
5. Rollings A & Adams E. (2003) Game Architecture and
Design. New Riders.