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