Outline

SPMP

SRS

SDD

Summary

Prototype

Software System Design Document(General Summary)

Online Course Portal but we can say IOCP.

Classification Technic (Bayesian)

UsingMultimedia

System Overview

Online Course Portal is a WEB based application. The aim is to classify the students according to their success and improve their achievements. Bayesian theorem is used while doing these processes.

System Integration for UserIdentification: Login Module

Type Process, GUI

Purpose Send user to related page. Creating from

System Creator(us)

Function User enters username and password,

system checks authentication and redirect

user to the corresponding page and giving

permission.

Subordinates • Admin

• Student

• Instructor

• Guest User

Module Decomposition

Admin Module

Identification Admin Module

Type Process, GUI

Purpose Having more permission than others and access

specific user and system information.

Function Admin module provide Online Course Portal Structure

and Control Users Information

Subordinates Password

Message

Accept/Reject Users

Content

Logout

Permission

Announcement

Some Instructure ModuleIdentification List of student and grade

Type Process, GUI

Purpose Show classified student and grade

Function Student will classification according to test,technic etc

and Instructor show list.

Subordinates None

Identification Content

Type Process, GUI

Purpose Course Education Documents

Function Instructor can create, update, delete course content in

this module. Instructor created 4 part of document as

video, text, voice, picture and uploaded DB.

Subordinates Add,Delete,Update

Identification Homework

Type Process, GUI

Purpose Give Homework to student

Function Instructor uploads /deletes / updates document in

course page and Instructor must give submission

date.

Subordinates None

Student ModuleIdentification View Content

Type Process, GUI

Purpose Learning Education

Function Before, student selected some course, Student show

course document and education only in related course

page. End of the content , Student direct test question

for first chapter. After some classification technic,

continue related to content like video, picture, text or

voice according to student specific property.

Subordinates None

Identification Solving Test

Type Process, GUI

Purpose Increasing Education Effective

Function System Creator prepare different test. First chapter

applied mix question according to Course Content.

Student direct class according to successful test and

specific property.

Subordinates None

Identificati

on

Show grade

Type Process, GUI

Purpose Learn successful

Function After test result, System sends grade

result in show grade page and other

student average. Student show them.

Subordinat

es

None

Identificati

on

Logout

Type Process, GUI

Purpose Logout the Online Course Portal

Function Instructors can logout the system.

Directing main page.

Subordinat

es

None

Identification Show Content

Type Process, GUI

Purpose Look page

Function Guest look and show main structure, page in Portal

and only read some documents.

Subordinates None

Content Management System

Content Management System

Mysql DB on web based

Method Name DB Connection

Description User Interface (Page) connection to DB and provide interaction

Share type Public

Parameter Mysql ,Php

Processing <?php

$con = mysql_connect("localhost","root");

if (!$con)

{

die('Could not connect: ' . mysql_error());

}

mysql_select_db("ise491", $con);

mysql_close($con)

?>

Relational Database

Classification Technic

Naive Bayesian Classifier for IOCP

• Bayes Theorem takes important place in calculation of probability. Making classification is possible basing on Bayesian Theorem. Bayesian classifiers take place among statistical classification techniques[1].

• After our users log in, firstly, in first chapter, they solve a test. What is more, the test is divided to four different types of questions. They are picture, video, voice, and text. For example, the test consists of twenty questions:

• The questions among from 1st to 5th are in type picture. • The questions among from 6th to 10th are in type text. • The questions among from 11st to 15th are in type video • The questions among from 16th to 20th are in type voice

St_id Class Age Gender St_avg Test_avg Content(type)

1 2 20 Female 2,30 75 Video

2 2 20 Female 2,20 70 Voice

3 2 20 Male 3,35 85 Text

4 4 23 Male 2,40 65 Picture

5 4 23 Male 2,90 80 Picture

6 1 19 Female 3,55 95 Video

7 1 19 Female 1,70 60 Text

8 1 20 Male 3,90 100 Video

9 3 22 Female 3,06 80 Voice

10 3 22 Female 2,30 70 Picture

11 4 23 Male 2,25 70 Text

12 2 21 Male 1,95 65 Video

13 3 23 Female 2,60 75 Voice

14 3 22 Female 2,50 70 Video

15 1 20 Female 2,10 70 Picture

In order to perform Bayesian Classification, Bayes probabilities of each hypothesis are calculated.C1 : Content = PictureC2 : Content = TextC3 : Content = VideoC4 : Content = Voice

• In order to perform Bayesian Classification, Bayes probabilities of each hypothesis are calculated.

• C1 : Content = PictureC2 : Content = TextC3 : Content = VideoC4 : Content = Voice

• • • It is necessary to calculate expressions below:• P(X | C1) * P(C1) • P(X | C2) * P(C2) • P(X | C3) * P(C3)• P(X | C4) * P(C4)

• • a) Calculating P(X | C1 ) * P(C1)• P(X1 | C1) = P(Class=2 | Content=Picture) = 0/4

P(X1 | C1) = P(Age=20 | Content= Picture) = 1/4P(X1 | C1) = P(Gender=Female | Content= Picture) = 2/4P(X1 | C1)=P(St_avg=2,30 | Content= Picture) = 1/4P(X1 | C1)=P(Test_avg=100 | Content= Picture) = 0/4

• P(X | C1) = P(X | Content=Picture) = 0/4 * 1/4 * 2/4 * 1/4 * 0/4 = 0• P(C1) = P(Content=Picture) = 4/15• As a result of all operations above;

P(X | C1) * P(C1) = 0 * 4/15 = 0• • b) Calculating P(X | C2) * P(C2)• P(X1 | C2) = P(Class=2 | Content=Text) = 1/3

P(X1 | C2) = P(Age=20 | Content= Text) = 1/3P(X1 | C2) = P(Gender=Female | Content= Text) = 1/3P(X1 | C2)=P(St_avg=2,30 | Content= Text) = 0/3P(X1 | C2)=P(Test_avg=100 | Content= Text) = 0/3

• P(X | C2) = P(X | Content=Text) = 1/3 * 1/3 * 1/3 * 0/3 * 0/3 = 0• P(C2) = P(Content =Text) = 3/15As a result of all operations above;

P(X | C2) * P(C2) = 0 * 3/15 = 0•

• c) Calculating P(X | C3) * P(C3) • P(X1 | C3) = P(Class=2 | Content=Video) = 2/5

P(X1 | C3) = P(Age=20 | Content=Video) = 2/5P(X1 | C3) = P(Gender=Female | Content=Video) = 3/5P(X1 | C3)=P(St_avg=2,30 | Content=Video) = 1/5P(X1 | C3)=P(Test_avg=100 | Content=Video) = 1/5

• P(X | C3) = P(X | Content=Video) = 2/5 * 2/5 * 3/5 * 1/5 * 1/5 = 12/3125

P(C3) = P(Content=Video) = 5/15• As a result of all operations above;

P(X | C3) * P(C3) = 12/3125 * 5/15 = 0.00128• • d) Calculating P(X | C4) * P(C4)• P(X1 | C4) = P(Class=2 | Content=Voice) = 1/3

P(X1 | C4) = P(Age=20 | Content= Voice) = 1/3P(X1 | C4) = P(Gender=Female | Content= Voice) = 3/3P(X1 | C4)=P(St_avg=2,30 | Content= Voice) = 0/3P(X1 | C4)=P(Test_avg=100 | Content= Voice) = 0/3

• P(X | C4) = P(X | Content=Voice ) = 1/3 * 1/3 * 3/3 * 0/3 * 0/3 = 0

• P(C4) = P(Content=Voice)=3/15• • As a result of all operations above;

P(X | C4) * P(C4) = 0 * 3/15 = 0• e) Result

arg max{ (X | Ci) P(Ci)} = max{0, 0, 0.00128, 0} = 0.00128Hence, It is clearly understood that the example which is given belongs to content “Video”.

Final Data Decomposition and System

Referance

[1] Pressman, Roger S., Software Engineering, 4th edition, McGraw-Hill, 1997 S.86-Bayesian

[2]Fairley, R. E., Workbreakdown Structure, Software Engineering Project Management, IEEE CS Press, 1997

[3]Php and JS,Css codes get www.w3school.com

[4]Project content was created by SE346 lesson notes.A.Akca Okan

[5] Database information get Compe 341 Lecture notes.D.Mishra

Thank You for Listening