PSU Research Proposal

jumentousmanlyInternet and Web Development

Oct 21, 2013 (3 years and 7 months ago)

207 views


0











PSU

Research Proposal









































Title
:

A Toolbox for Arabic
Text Mining



Department:
Computer
Science


PI Name:
Ahmed Sameh


Duration:
1 Year


Budget Est.:
SR 5
5,000


Date:
12/20/2010



1


I
-

PROPOSAL


I
-
1
: PROPOSAL TITLE

(Provide a short descriptive title, give prominence to keywords)



I
-
2
: COMMERCIAL POTENTIAL



Could this project have commercial potential?
(Select one)


Yes


No



If yes, briefly elaborate on the commercial potential





I
-
3
:
CHECK
-
LIST






Have you checked to ensure all questions in the

application form have been answered?


Have you checked to ensure you have included the correct costs in your budget?


The principal investigator and all co
-
principal investigators should sign.



I
-
4
: PERSONNEL AND AUTHORIZATION



PRINCIPAL INVESTIGATOR

[PI]





CO
-

INVESTIGATOR(S
)
[CIs]



A Toolbox for Arabic Text Mining


Academic Rank:


Professor

Full Name:

Ahmed Sameh

College:

CIS

Department:
Computer Science

Telephone:


494
-
8524

Ext:

X8524


Mobile:

0544299846


E
-
Mail:
asameh@cis.psu.edu.sa


Signature: Date:

12/20
/
2010


1)

Full Name:


Mona Diab



Academic Rank:



Assistant
Professor


E
-
Mail:


College:


Department:

Linguistics Department &

Natural Language

Processing Group

Stanford University

Telephone:



Mobile:

Signature: Date:

/ /

2)

Full Name:


NourelDean Soufian

(
non
-
PSU CIs permitted
)


2



II
-

DESCRIPTION


II
-
1
: ABSTRACT

(Provide a statement of the project
-

maximum 200 words)



Academic Rank:

Assistant

Professor






E
-
Mail:

College:


CIS



Department:
Computer Science

Telephone:



Mobile:

Signature: Date:

/ /

3)

Full Name:


Mohamed T
o
unsi



Academic Rank:


Associate Professor



E
-
Mail:

College:
CIS

Department:
Computer Science

Telephone:



Mobile:

Signature: Date:

/ /

4)

Full Name


Academic Rank:




E
-
Mail:

College:

Department:

Telephone:



Mobile:

Signature: Date:

/ /

5)

Full Name:




Academic Rank:




E
-
Mail:

College:

Department:

Telephone:



Mobile:

Signature: Date:

/ /

6)

Full Name:




Academic Rank:




E
-
Mail:

College:

Department:

Telephone:



Mobile:

Signature: Date:

/ /



Text Mining refers to the process of deriving high
-
quality
information

from text. High
-
quality information is
typically derived through the divining of patterns and trends through means such as
statistical pattern

3



II
-
2
: PROJECT GOALS AND OBJECTIVES




The specific

goal
s

of this project are

to demonstrate the
power of Text

mining

within the Arabic language

in
:

-
Concept Mining
:

Concept mining

is an activity that results in the extraction of concepts from set of
documents. Solutions to the task typically involve aspects of
artificial intelligence

and
statistics
,

such as
data
mining

and
text mining
. Because artifacts are typically a loosely structured sequence of words and other
symbols (rather than concepts), the problem is
nontrivial
, but it can provide powerful insights into the
meaning, provenance and similari
ty of documents. Traditionally, the conversion of words to concepts has
been performed using a
thesaurus
, and for computational techniques the tendency is to do the same. The
thesauri used are either specially created for the task, or a pre
-
existing langua
ge model, usually related to
Princeton's

WordNet
.

The mappings of words to concepts are often ambiguous. Typically each word in a given language will relate
to several possible concepts. Humans use context to disambiguate the various meanings of a given pi
ece of
text, where available. Machine translation systems cannot easily infer context.

For the purposes of concept
mining however, these ambiguities tend to be less important than they are with machine translation, for in
large documents the ambiguities
tend to even out, much as is the case with text mining.

There are many techniques for
disambiguation

that may be used. Examples are linguistic analysis of the text
and the use of word and concept association frequency information that may be inferred from
large text
corpora. Recently, techniques that base on
semantic similarity

between the possible concepts and the context
have appeared and gained interest in the scientific community.

learning
. Text mining usually involves the process of structuring the input text (usually parsing, along with
th
e addition of some derived linguistic features and the removal of others, and subsequent insertion into a
database
), deriving patterns within the structured data, and finally evaluation and interpretation of the output.
'High quality' in text mining usuall
y refers to some combination of
relevance
,
novelty
, and interestingness.
Typical text mining tasks include
text categorization
,
text clustering
,
concept/entity extraction
, production of
granular taxonomies,
sentiment analysis
,
document summarization
, and e
ntity relation modeling (
i.e.
,
learning relations between
named entities
).

Natural language processing (NLP) within the Arabic language has been struggling over the years. Very little
has been done in term of producing powerful tools for Arabic processing.

In fact, Arabic is feared to be
recognized as a language of the past, as very many new terms and names in the modern world has no terms
and names in the Arabic language. This problem has developed over the years due to t
he fact that the Arabic
languagi
sti
c researchers are fare away from modern technological tools, and they are not willing to
collaborate with information technology researchers. This lake of communication and collaboration has lead
to the current state of affairs with the NLP of Arabic text.

Arabic text mining is way behind compared to English text mining.
Several English text mining algorithms in
the areas of
text categorization
,
text clustering
,
concept/entity extraction
, production of granular taxonomies,
sentiment analysis
,
document summa
rization
, and entity relation modeling (
i.e.
, learning relations between
named entities

have powerful algorithms and tools.
This research proposal will try to rectify this situation by
developing an Arabic toolbox that will cover basic comparable English algorithms.

In this project we will develop an Arabic toolbox that will contain algorithms for categorization and
classifica
tion, clustering and grouping of related documents, concept extraction algorithms, production of
taxonomies, Wordnet for verbs, nouns, and adjectives, simple dictionary, sentiment analysis algorithms, and
document summarization. The toolbox will be web bas
ed with background database of documents and related
resources. An Arabic stemmer will be developed along with

tagging algorithm. Sample of small
implementations of some of these algorithms are demonstrated in this proposal. These are initial results that
demonstrate the capabilities of the current team.





4


-
Arabic Wordnet:
WordNet

is a
lexical database

for the
Arabic

language
.
It groups Arabic

words

into sets of
synonyms

called
synsets
,

provides short, general definitions, and records the various
semantic

relations
between these
synonym

sets. The purpose is twofol
d: to produce a combination of
dictionary

and
thesaurus

that is more intuitively usable, and to support automatic
text analysis

and
artificial intelligence

applications.
The
database

and
software

tools have been released under a
BSD style license

and can b
e downloaded and
used freely. The database can also be browsed online. WordNet was created and is being maintained at the
Cognitive Science

Laboratory of
Princeton University

under the direction of
psychology

professor

George A.
Miller
. Development began in 1985. Over the years, the project received funding from government agencies
interested in
machine translation
. As of 2011
, the WordNet
does not have an Arabic version. Arabic may be
one of the few languages that does not have WordNet

version. This project will build one only for the verbs.

-
Arabic Dictionary :
It’s an
on汩l攠in瑥t慣瑩t攠Ar慢楣

d楣瑩in慲y and 瑨es慵rus th慴ahe汰s you find 瑨攠
m敡n楮gs of words and draw conn散瑩tns 瑯 慳ao捩慴cd wordsK 奯u 捡n 敡s楬y s敥 瑨攠m敡n楮g of
敡捨 by
simply p污捩lg th攠mous攠cursor ov敲 楴i

B慳敤 on Ar慢楣itord乥琠w攠wi汬⁤敶敬ep 慮 Ar慢楣id楣瑩in慲yK
併r go慬猠慲攠for 瑨攠d楣瑩in慲y 瑯 b攺e
b慳y t
o us攠d楣瑩in慲y 慮d 瑨敳eurusI
i敡rn how words 慳ao捩慴c 楮

v楳u慬ay in瑥t慣瑩t攠d楳p污yI
d整e楤
敡s 瑯 h敬e wr楴攠捯n瑥t琠for your b汯gI 慲瑩捬攬 瑨es楳 or simply p污y wi瑨
words!

乯 汩m楴n numb敲 of s敡r捨esK iook up 慳am慮y words 慳ayou n敥d 慮y瑩m攮
qh攠us敲 jus琠t
yp攠
words 楮 th攠s敡rch box 慮d 捬楣i 䝯 or simply h楴⁅n瑥tK 佮捥 瑨攠words bra
nch off 瑨攠m慩a qu敲yI you 捡n
doub汥l捬楣c 愠nod攠瑯 f楮d o瑨敲 r敬慴ed wordsK qo 數p汯r攠th攠f敡tur敳e

m污捥 瑨攠mous攠cursor ov敲 愠word
瑯 v楥w 瑨攠m敡n楮g

䑯ub汥lc汩捫 愠nod攠from th攠br慮ch 瑯 v楥w o瑨敲 re
污瑥l wordsI
卣po汬⁴l攠mous攠
wh敥氠lv敲 w
ords 瑯 穯om in
or ou琮tqh楳 h敬es you s敥 mor攠
慳ao捩慴楯ns or v楥w
words and m敡n楮gs mor攠
捬敡rlyI fin慬ayI
C汩lk and dr慧 愠word or br慮捨 瑯 mov攠i琠tround 慮d 數p汯r攠o瑨敲 br慮捨敳e

qhe

tords
楮瑥tf慣攠qu敲楥猠ih攠
Ar慢楣i
tord乥k

汥l楣慬id慴慢慳攠d敶敬ep敤 by mr楮捥瑯n 啮iv敲s楴y 慮d m慤攠慶慩污a汥l
for s瑵den瑳 and 污lguag攠r敳e慲捨敲sK qh楳 d楣瑩in慲y groups synonyms in瑯 syns整猠ehrough 汥x楣慬ir敬慴楯ns
b整w敥n 瑥tmsK qh敳攠m敡n楮gs and sema
n瑩挠r敬慴楯nsh楰s 慲攠r敶敡汥l gr慰h楣慬iy by 瑨攠楮瑥t慣瑩v攠w敢
瑥捨no汯gy m慤攠
慶慩污a汥lby 卮慰py tordsK

J
Ar慢楣i䑯捵m敮ts C污ss楦楣i瑩tn㨠
Document classification/categorization

is a problem in
information
science
. The task is to assign an
electronic
document

to one or more
categories
, based on its contents.
Document classification tasks can be divided into two sorts:
supervised document classification

where
some external mechanism (such as human feedback) provides information on the correct classifica
tion for
documents, and
unsupervised document classification
, where the classification must be done entirely
without reference to external information. There is also a
semi
-
supervised document classification
, where
parts of th
e documents are labeled by the

external mechanism.

-
Arabic Document summarization:
Automatic summarization

is the creation of a shortened version of a
text

by a
computer program
. The product of this procedure still contains the most important points of the original
text.

The phenomenon of
information overload

has meant that access to
coherent

and correctly
-
developed
summaries

is vital. As access to data has increased so has interest in automatic summarization. An example
of the use of summarization technology is
search eng
ines

such as
Google
.

Technologies that can make a
coherent

summary, of any kind of text, need to take into account several variables such as length, writing
-
style and
syntax

to make a useful summary.






III
-

INTRODUCTION


III
-
1
:

REVIEW

AND

ANALYSIS

OF

REL
ATED

WORK




Labor
-
intensive manual text mining approaches first surfaced in the mid
-
1980s, but technological advances

5


have enabled the field to advance during the past decade. Text mining is an interdisciplinary field that draws
on information retrieval,
data mining, machine learning, statistics, and computational linguistics. As most
information (common estimates say over 80%) is currently stored as text, text mining is believed to have a
high commercial potential value. Increasing interest is being paid
to multilingual data mining: the ability to
gain information across languages and cluster similar items from different linguistic sources according to
their meaning.

Currently there is very little previous work done in Arabic Text mining. English text mini
ng on the contrary
has many algorithms and techniques. One of the directions that we will explore in this research is borrow
some ideas from these algorithms and try to develop similar Arabic versions.




III
-
2
:

SIGNIFICANCE

OF

WORK



The Arabic language needs more work from all of us to stand up as a living language and to coop up with
the current advancement in technology. Such Arabic tool box is so much needed at this era of world
globalization.





IV
-

APPROACH AND METHODOLOGY


IV
-
1
: METHODOLOGY



Until recently, websites most often used text
-
based searches, which only found documents containing
specific user
-
defined words or phrases. Now, through use of a semantic web, text mining can find content
based on meaning and context (r
ather than just by a specific word).

Additionally, text mining software can be used to build large dossiers of information about specific people
and events. For example, large datasets based on data extracted from news reports can be built to facilitate
so
cial networks analysis or counter
-
intelligence. In effect, the text mining software may act in a capacity
similar to an intelligence analyst or research librarian, albeit with a more limited scope of analysis.

Text mining is also used in some email spam fi
lters as a way
of determining the characteristics of messages
that are likely to be advertisements or other unwanted material
.
Recently, text mining has received attention
in many areas.

Many text mining software packages are marketed towards security
applications, particularly analysis of
plain text sources such as Internet news.It also involves in the study of text encryption.


One of the directions in this research is
to adapt and modify selected English Text Mining tools (from the
above web site) in

order to produce their equivalent Arabic versions. The cross validation method requires
very accurate English/Arabic translator that will provide input data to the Algorithm/program conversion.

Areas of investigations in this project include:
Arabic Natur
al Language Processing
,
Text Mining of Quran:


The second objective is to strive to improve the quantity and quality of Arabic contents in the area of “Data
and Text Mining” on the Web. All published material from the Hub’s activities will be translated an
d
reviewed by its author(s) to be available in an Arabic Digital Library. A systematic plan to translate many
“data mining” articles and storing them in a searchable Arabic Digital Library will be developed. Text and

6


Multi
-
media mining tools will be used t
o explore this Arabic digital library contents and expose related and
correlated paragraphs and sections for the purpose of developing new Arabic Text mining algorithms and
enhance exiting ones. This brings the other area of focus of the Hub which is the u
nstructured Text mining.


As for the Unstructured Text mining: Parallel to the Arabic digital library there will be also an English Data
Mining digital library (having the same contents) that will be developed. Both libraries will have traditional
search
engine beside more elaborated classification and categorization capabilities. Further to this, Text and
Multi
-
media mining tools will be used to explore the two digital libraries contents and expose related and
correlated paragraphs and sections. Text mini
ng is used to find interesting regularities in large textual digital
libraries. Where interesting means: non
-
trivial, hidden, previously unknown and potentially useful. Both
Arabic and English Text mining tools handle digital libraries text at the word lev
el, sentence level, document
level, document
-
collection level, linked
-
document collection level, and at the application level. Most of the
text mining methods reply on the fact that there is usually high redundant data in the documents. Most of the
tools m
ake use of: document summarization techniques, single document graph visualization algorithms,
segmentation algorithms, features selection algorithms, similarity algorithms, clustering, and information
extraction techniques.

They also make use of several
visualization techniques such as: WebSOM, ThemeScape, Graph
-
Based
visualization techniques, and Tiling
-
based visualization techniques.


Statistical tools for text mining include: Yale/Rapid Miner word vector mining, UIMA by IBM, GATE, Aero
Text suite, Att
ensity, Endeca Technologies, Inxight, and Language Ware.


Similar to what we provide for “Data Mining” we also propose the same vertical stacking of text Mining,
s瑡瑩tt楣慬i 慮d visua汩穡瑩ln a汧or楴ims for p敲form楮g 瑥xt m楮楮g 瑯 bo瑨 瑨攠bng汩sh and t
h攠Ar慢楣i d慴愠
mining digital libraries. This will provide an interesting context for researchers in “Text mining” and
“Arabization” fields to investigate how to improve the Arabic text mining algorithms and use a cross
r敦敲敮捥 瑯 瑨攠bng汩lh on敳⸠A v敲
y 楮瑥tes瑩tg r敳敡e捨 dir散瑩tn 捡n b攠d敶敬ep敤 瑨敲攮 䙯r 數慭p汥l 瑨攠
sam攠min楮g qu敳瑩tns 捡n b攠pos敤 瑯 bo瑨 瑨攠bng汩lh 慮d 瑨攠Ar慢楣id楧楴慬i汩lr慲楥猠慮d 瑨攠r敳e汴l 捡n b攠
捯mp慲敤K fn 捡s敳eof d楦f敲敮捥sI 汥慲ning oppor瑵n楴楥i w楬氠b攠d敶敬
oped and algorithms’ modifications
慮d 敮han捥ments 慲攠 瑯 be 楮v敳e楧慴敤K qh攠 two 汩lr慲楥猠 w楬i prov楤攠 sev敲慬a ways and m敡ns for
v敲楦楣i瑩tnI va汩l慴楯nI 慮d 捲oss 捨散king









䑥汩a敲慢汥猠ln
ph慳攠fW

B整愠噥ss楯n f ⬠楴i B敮chm慲k ⬠楴s qun楮g

䑥汩a敲慢汥猠ln mh慳攠ffW

B整愠噥ss楯n ff ⬠楴i B敮捨m慲k ⬠楴i quning

䑥汩a敲慢汥猠ln mh慳攠fffW

B整愠噥ss楯n fff ⬠楴i B敮捨m慲k ⬠楴i qun楮g

䑥汩a敲慢汥猠ln mh慳攠f嘺

䙩na氠l敲s楯n ⬠啳敲 䵡nu慬

qh攠f
o汬lwing is th攠proj散琠pl慮 sch敤u汥l f琠t数r敳敮瑳 thos攠d楦f敲en琠瑡sks wi瑨in th攠r敳敡r捨 慮d
敳瑩m慴敤 dur慴楯n for 敡捨K

bgnin攠

C汩lnt

q數琠

q敳瑩ng


7







IV
-
2
: AVAILABLE RESOURCE
S


Currently there are some o
pen source text

mining algorithm
s that can be used
as tools in some of the above
investigations.


IV
-
3
: EXPECTED RESULTS/O
UTPUTS



The expected output from this project is a Web based Arabic toolbox that will contain basic Arabic
algorithms for Arabic natural language text mining. Some of the algorithms
that will be provided under this
tool are:

text categorization
,
text clustering
,
concept/entity extraction
, production of granular taxonomies,
sentiment analysis
,
document summarization
, and entity relation modeling (
i.e.
, learning relations between
named
entities
).


The following are

some initial results that we have already implemented in the domains of: Arabic Text
categorization/Classification, construction an Arabic Wordnet for Arabic Verbs, and developing an Arabic
Stemmer. In the following paragraphs

we provide short descriptions and some screen shots for the developed
tools.


This tool

present
s

an Arabic Text Mining tool used for classification according to some statistics. As the
number of Arabic documents that are displayed every day on the web or
on other media has grown rapidly,
he need to analyze and classify these documents has become important nowadays.This tool will take as an
input any document that is presented on the web or news papers. The tool will then classify the input
document to one
of a number of categories provided by the tool. These categories are:

o

Economic paper

o

Political paper

o

Medical paper

o

Religion paper

The general idea behind the tool is that it takes a document as an input provided by the user. The tool will
then store all th
e words used in the document without repeating or excluding any word. After that, the tool
will compute the frequency for each stored word which is then used as a statistic to classify the document.
The tool will use a number of databases as training sets
to classify the document. The tool does the following

8


processing:

o

Take the word with the highest frequency.

o

Search for that word in each of the databases given

o

If the word is found in any one of the databases, the tool will stop and classify the
document
as the same type of the database where the word has been found in.

o

If the word is not found in any database, the tool will take the next highest frequency word
and do the same thing done for the previous word.

o

If none of the stored words is found in any of

the databases, the program state that the
document can't be classified.

Thi
s tool as a matter of fact
take
s

all the words in the input document without excluding any of the common
used words in Arabic language. However, we don't need to check for these wo
rds and then remove them
because these words will not be provided in any of the databases for the tool to search in. So, not including
these words in the databases will not force the tool to remove them whenever they are found because the tool
will skip th
em after not finding them in the databases.

All the used databases are in a text file format and they
can easily be updated by the user to increase the size of the training set.

In addition to the classification,
another text file including all the stored
words and their frequency will be added.


Further work:

-

More databases can be added to the tool in order to have bigger training sets which will result in
better results.

-

The output text file including all the words along with their frequency can then be integrated with
other text mining tools for the statistics it provides.

-

The tool can be enhanced by taking the highest two or three words instead of the highest frequency
one to be used to classify the document. This as a matter of fact will result in a better classification.

-

The tool can also be modified by the following. Instead of classifying it as the same type of the
database by the highest frequency word, the tool can

be modified to provide a percentage for each
database for the occurrence of the stored words that are found in each one of them as another
statistical approximation. The user is then required to analyze the resulted percentages to better
assign the docume
nt to one of the categories.

The following are screen shots from the tool:



9








The second tool implemented is a sample Arabic Wordnet dictionary. It deals only with verbs.

This
tool

present
s

an Arabic

Text Mining tool. The tool

provide
s

a Wordnet for Arabic words only. These as

10


a matter of fact can be used to understand the meaning of the words provided which can be used for many
purposes like classification, clustering, and summarization of a text.
This tool will take as an input any
doc
ument that contains Arabic words only. All the words in the input file are nouns and all of them is on the
form of "

لعف "
. 瑨攠ou瑰u琠wil氠瑨敮 b攠慮oth敲 fi汥l捯n瑡tning 瑨攠word 慮d 慬氠of 楴s synonyms.

Method Used:

The general idea behind the tool is
to take a file containing only the words to look for their synonyms. The
tool will then take all these words one by one. When the tool takes a word, it will go and search for that word
in another file containing groups of words where each group contains wo
rds with the same meaning. When
the tool find the target word in one of the groups, it will return that group and store the target word followed
with all of its synonyms in an output file. If the target word is not found in group, the tool will put it also

in
the output file while notifying that it didn't find any related word to it.

Further work:

-

We can expand the training set to have bigger training sets so that we can find a meaning or a
synonym for any input word.

-

We can also make other training sets
for verbs and other non
-
noun or non
-
verbs Arabic words to
enlarge the training sets.

-

The output file is formatted in a way that makes it easy to integrate it with other text mining tools
and using it for other purposes like classification, clustering, and
text summarization.

-

The method used here to search for the word is using sequential search because the training set here
is small. However, it would be better to enhance the tool by using another searching algorithm
which is faster. The need for this will
rise if we enlarge the training set or add another data files for
verbs and other Arabic words types.

The following are screen shots from the tool:





11






The third tool is an Arabic Stemmer. The following is a description with screen shots.

The word Stemming in Data Mining and other fields refers to
the process for reducing inflected (or
sometimes derived) words to their stem, base or root form


g敮敲慬ay 愠wr楴瑥n word formK qh攠s瑥m n敥d
no琠t攠楤敮瑩捡氠瑯 瑨攠morpho汯g楣慬iroo琠tf 瑨攠wo
rd㬠楴Xis usu慬ay suff楣楥i琠瑨慴ar敬慴ed 睯rds m慰 瑯 瑨攠
sam攠s瑥mI ev敮 if th楳 s瑥m 楳 no琠楮 楴s敬e 愠v慬楤 root
K qh攠f楲s琠tv敲 pub汩lh敤 s瑥mm敲 w慳awri瑴敮 by gu汩攠
B整e iov楮s 楮 NVSUK 却pmmers 慲攠捯mmonly us敤 for many purpos敳ek攺efnform慴楯n

o整e楥i慬a慮d in
捯mm敲捩慬cprodu捴献c却pmmers 慲攠捯mmon 敬emen瑳 in
qu敲y sys瑥ms

such 慳a
t敢

s敡r捨 敮gin敳


Description

of The Tool
:

In this report, I will talk about an Arabic word stemmer that is adopted from Arabic Stemmer by Shereen
Khoja by Motaz

K. Saad. The tool will take as an input a file containing Arabic texts and words. The tool
will then perform some operation to store all the words in the input file. While reading the file, the tool will
remove any words that usually can't be stemmed beca
use they are not important like numbers(written in
letters), special characters, or symbols. For each word of intrest, the tool will does the following checks:

o

Check if the word consists of two letters.

o

Check if the word consists of three letters.

o

Check if

the word consists of four letters.

o

Check if the word is a pattern.

o

Check for a definite article.

o

Check for the prefix.

o

Check for suffixes.

-

The tool will use a large database consisting of stems of most of the Arabic used words that can be
found in news
articles, magazines, websites, etc…

-

The tool is implemented using Java language and is integrated with the weka tool in order to stem.


12


-

The output stems will then be stored in a way that can be easily integrated with other tools like
search engines, cluster
ing tools, classification, etc…













V
-

REFERENCES

1
-

Arabic Text Mining Tutorial :
http://textminingthequran.com/tutorial/bismillah.html






VI
-

ROLE
(S)

OF THE INVESTIGATOR(S)

(Attach a brief CV

for each investigator following the format in Appendix
A
)


#

Name of
Investigator

Area of contribution to the project

1


Prof. Ahmed Sameh





System Design & Implementation

2


Asst Prof. Mona Diab




Data Collection & Preparation

3

Dr. Mohamed Tunsi






Data Minin
g Tools



4

Dr.
Noureldean Soufian





System Design & Implementation

5








6




نأ


تيل

لعل

اميسلا

يلاحلا لازيلاو

نمض

لوا

هلو

تاذ

يا

لادب

اهيلا

هنا

نيذلا

هناف


نإ

دعب

دض

يلي

ىلا

ىلإ

يف

يفو

نم



13








VII
-

PROJECT SCHEDULE


PHASES

OF

PROJECT

IMPLEMENTATION

(S
EE
G
ANETT
C
HART ABOVE
)


Steps

Task

Duration
(Months)

1

System requirements specifications: Sameh,
Tunsi


System Architecture

:
Soufian

System Design
: Sameh

Databases Designs
: Sofian

Prototyping of critical sub
-
systems
: Tunsi, Sameh

System Detailed Design
: Sameh, Tunsi

Beta Version Implementation
: Sameh,
Soufian

Testing
: Soufian

Building Deployment Environment
: Sameh

Bench Marking and Collecting Results (First Round)
: Tunsi

System Tuning (Based on First Round Results)
: Sameh

Bench Marking and Collecting Results (Second Round Results)
: Soufian

System Tuning

(Based on Second Round Results)
: Sameh

Bench Marking and Collecting Results (Third Round Results)
: Tunsi

Version 1 Release
:Tunsi

Results Documentation and Analysis with the Performance requirements
:Sameh

Detailed Code Documentation
: Sameh

User and Install
ation Guide (Full How To)
: Soufian




See Gantt
Chart
within this
proposal

Total duration for the proposed project

12 Month





VIII
-

BUDGET OF THE PROPOSED RESEARCH

(
Budget in SAR)


Item

Amount
Requested

(
SAR
)

Priority
1

=

Max;






2

=

Mod;


3

=

Low.


Amount
Approved

(
SAR
)

A.
Personnel
*

(Research Assistant)




1
-

Student Ahmed
Al
-
Jabreen

2
-

Student Kamal

Qarawi

3
-

Student Omar

Al
-
Moughnee

4
-

Student Amr
o Al
-
Munajjed





24
,000


1





For

Official

Use


14


B.
Equipment
*

(List)






Development Server




5
,000


1





C.
Testing and Analysis
*

(Location/Laboratory)




Labtop Computer






5
,000


2






D.
Consumables
*

(List)




Desk Tools




1000


2




E.
Travel
*
(Local
/Internat
)




1
-

Travel for
Mona Diab

(
Stanford
/ Riyadh)




1
0,
000


1



F.
Software* (List)





-
SAS Data Mining Tools


-
Oracle 9i Data Mining


-
Clementines from SPSS


-
Ants Model Builder





1
0,000

1



G. Other Items* (Itemize)








---






Total Amount Requested (
SAR
)

5
5,000



IX
-

JUSTIFICATION OF BUDGET

(Justify each item listed in the budget in the previous section)


Item

Justification

A


Students Research
Assistants



Salary of SR 5
00 for each student for 12 months the duration of
the project.


15






B

Development Server







For developing the proposed experiments.

C

Laptop Computer






For on
-
site data collection and on
-
site testing



D

Desk tools







For general us
e by team members

E

Travel







For the two outside PSU team members.

F

Software







Data Mining Tools Software

G













X

-

RELEASE TIME FOR RESEARCH TEAM MEMBERS



RELEASE TIME FROM

TEACHING LOAD



#

Team Member

Time

Commit
ment

(hrs/w
eek
s/terms
)

Teaching
Load Max

PI



Ahmed Sameh

4 h/w

e.g.

1 course

FA11


16


CI1




Noureldean Soufian


2h/w


CI2



Mohamed T
o
unsi

2h/w


CI3




Mona Diab


1h/w


CI4




1h/
w


CI5









XI
-

EXTERNAL
FUNDING


#

Source of Funds

Amount (
SAR
)

Used for

……

costs

1



None







2









3












Appendix
A: CV

Format for Principal Investigator and Co
-
Investigators

(Two pages maximum, material should be related to submitted project)


Title and Name:
Professor

Ahmed Sameh



Specialty:

Artificial Intelligence,

Modeling and Information Systems



Department and College:
Computer Science



Summary of Experience/Achievements Related to Research Proposal:



1
-

Ahmed Sameh,

Ayman Kassem, “Lumbar Spine: Parameter Estimation for Realistic Modelling”, WSEAS
Transactions

on Applied and Theoretical Mechanics, ISSN:1991
-
8747,
Issue 5, Volume 2, May 2008


2
-

Ahmed Sameh, Ayman Kassem, “A General Framework for Lumbar Spine Modelling

and Simulation”,
International

Journal

of Human Factors in Modelling and Simulation, IJHFMS, The North American Spine
Society, Volume 1, Issue 2, January 2008


3
-

Dalia El
-
Mansy, Ahmed Sameh, “A Collaborative Inter
-
Data Grid Strong Semantic Model with
Hybrid
Namespaces”,

Journal

of Software (JSW), Academic Publisher, Volume 3, Issue 1, January 2008



4
-

Ahmed Sameh, “Simulating Lumbar Spine Motion”, Research in Computing Science (RCS)
Journal
,

17


National Polytechnic Institute of Mexico, ISSN 1665
-
9899, Vo
lume 18, Issue 4, June 2007



5
-

Ahmed Sameh, and Ayman Kassem, “3D Modeling and Simulation of Lumbar Spine Dynamics”, in the
fn瑥tn慴楯n慬a
Journal

of Human Factors Modelling and Simulation , Volume IJHFMS
-
942, 2007



6
-
Adhami Louai, Abdel
-
Malek Karim, McG
owan Dennis, Mohamed A. Sameh, "A Partial Surface/Volume
Match for High Accuracy Object Localization", International
Journal

of Machine Graphics and Vision, vol
10, no. 2, 2001


7
-
Mohamed A. Sameh, “Interactive Learning in Artificial Neural Networks Throu
gh Visualization”, The
fn瑥tn慴楯n慬a
Journa
l of Computers and Applications (IJCA), Vol. 20, #2, 1998


8
-

Mohamed A. Sameh and Attia E. Emad, "Parallel 1D and 2D Vector Quantizers Using Kohonen Self
-
Organizing Neural Network",

in the International
Journal
of the Neural Computing and Applications, V.
(4), no. 2, Springer Verlag, London, 1996


9
-

Ahmed Sameh, Amgad Madkour, “Intelligent open Spaces: Learning User History Using Neural Network
for Future Prediction of Requested Resources”,
mro捥敤楮gs
fbbb C卅pMUI NN瑨 fbbb fn瑥tn慴楯n慬a
Conf敲敮捥 on Compu瑡瑩tn慬a 卣楥p捥 慮d bng楮敥r楮gI NS
J
NU gu汹 OMMUI 口p m慵汯I 卐I Br慺楬⸠ fbbb
Compu瑥t 卯捩整c OMMUI f卂丠VTU
J
M
J
㜶㤵
J
㌱㤳
J
V




J

Ahmed Sameh, Ayman Kaseem, “Modelling and Simulation of Human Lumbar
Spine”,
Proceedings of
the

2008 International
Conference
on Modelling, Simulation, and Visualization, MSV 2008, Las Vegas,
Nevada, July 14
-
17, 2008,

CSREA Press 2008, ISBN 1
-
60132
-
081
-
7


11
-

Ahmed Sameh, Dalia El
-
Mansy, “A Collaborative Inter
J
䑡瑡 䝲楤s 䵯
del with Hybrid Namespace”, 14
th

IEEE International
Conference

on Availability, Reliability, and Security, (DAWAM


Aob匠OMMTFI 噩敮n愬
Aus瑲楡i Apr楬iNM
J
NPI OMMT



J

Ahmed Sameh, “Simulating Lumbar Spine Motion: Parameter Estimation for Realistic Modelli
ng”, The
S
th

Mexican International

Conference

on Artificial Intelligence (MICAI07), Aguascalientes, Mexico,
November 4
-
10, 2007


13
-

Sherif Akoush, Ahmed Sameh, “Bayesian Learning of Neural Networks for Mobile User Position
Prediction”, The International W
orkshop on m敲form慮捥 䵯d敬汩eg 慮d bv慬a慴楯n 楮 Compu瑥ts 慮d
瑥汥tommun楣慴楯n 乥瑷orks Em䵅CqMTF
J

p慲琠 of 瑨攠 fbbb NS
th

International
Conference

on Computer
Communications and Networks, ICCCN 2007, Honolulu, Hawaii, August 13
-
16, 2007


14
-

Ahmed Sameh
, “The Schlumberger High Performance Cluster at AUC”,
Proceedings

of the 13
th

International
Conference

on Artificial Intelligence Applications, Cairo, February 4
-
6, 2005


15
-
Mohamed A. Sameh, Rehab El
-
Kharboutly, "Modeling a Service Discovery Bridge Using Rapide
Architecture Description Language",
Proceedings

of the 18th European Simulation
Multiconference

(ESM
2004), Magdeburg, Germany, June 13
-
16, 2004


16
-
Mohamed A. Sameh, Rehab El
-
Kharboutly, and Hazem Al
-
Ashmawy, "Modeling Wireless Discovery
and Deployment of Hybrid Multimedia N/W
-
Web Services Using Rapide ADL",
Proceedings

of the 7th
IEEE International
Conference
on High Speed N/Ws amd Multimedia Commun
ications (HSNMC04),
Toulouse, France, June 30
-

July 2nd, 2004


17
-
Mohamed A. Sameh, Rhab El
-
Kharboutly, "Modeling Jini
-
UpnP Using Rapide ADL",
Proceedings

of the
10th EUROMEDIA
Conference

(EUROMEDIA 2004), Hasselt, Belgium, April 19
-
21, 2004


18
-
Mohamed A.

Sameh, "E
-
Access Custom Webber: A Multi
-
Protocol Stream Controller",
Proceedings

of
the IADIS International Conference on Applied Computing, Lisbon, Portugal, March 23
-
26, 2004


19
-

Ayman Kassem, A. Sameh, and Tony Keller, “Modeling and Simulation of Lumb
ar Spine Dynamics”,
Proceedings

of the 15
th

IASTED International Conference on Modeling and Simulation and Optimization
(MSO 2004), Marina Del Rey, California, March 2004


18



20
-
Mohamed A. Sameh, and Shenouda S., "Tera
-
Scale High Performance Distributed and P
arallel Super
-
Computing at AUC",
Proceedings

of the 12th International Conference on Artificial Intelligence, Cairo, Feb.
18
-
20, 2004


21
-
Shenouda S., Mohamed L., and Mohamed A. Sameh, "AUC Cluster Participation in Global Grid
Communities",
Proceedings

of
the 12th International Conference on Artificial Intelligence, Cairo, Feb. 18
-
20, 2004


22
-
El
-
Ashmawi Hazem, and Mohamed A. Sameh, “XML
J
卯捫整ei慮gu慧e
J
fnd数敮dent 䑩a瑲楢u瑥t 佢j散琠
Computing Model”,
Proceedings

of the 15
th

International Conference on Para
llel and Distributed
Computing Systems, Louisville, Kentucky, September, 2002


23
-
Mohamed Karasha, Greenshields Ian, and Mohamed A. Sameh, “HUSKY: A Multi
J
䅧en琠Ar捨楴散iure
for Adaptive Scheduling of Grid Aware Applications”,
Proceedings

of the High Performance Computing
Symposium with the 2002 Advanced Simulation Technologies Conference (ASTC 2002), San Diego,
California, April 14
-
18, 2002


24
-
Atef Rania, Mohamed A. Sameh,and Abdel
-
Malek Karim, "Three Dimensional Deformable Modeling of
the Spinal Lumbar Region",
Proceedings

of the 11
th

International Conference on Intelligent Systems on
Emerging Technologies (ICIS
-
2002), Boston, July 18
-
20, 2002



25
-
Kassem Ayman, Mohamed A. Sameh, and Abdel
-
Malek Karim, "A Spring
-
Dashpot
-
String Element for
Modeling Spinal Column Dynamics",
Proceedings

of the International Workshop on Growth and Motion in
3D Medical Images, Copenhagen, Denmark, May 28
-

June 1, 2002


26
-
Kassem Ayman, and Mohamed A. Sameh, “A Fast Technique for model
楮g 慮d Con瑲o氠of 䑹nam楣
System”,
Proceedings

of the 11
th

International Conference on Intelligent Systems on Emerging Technologies
(ICIS
-
2002), Boston, July 18
-
20, 2002


27
-
Mohamed A. Sameh, and Kaptan Noha, "Anytime Algorithms for Maximal Constraint Sat
isfaction",
Proceedings

of the ISCA 14th International Conference on Computer Applications in Industry and
Engineering (CAINE' 2001), Nov. 27
-

29, at Las Vegas, Nevada, 2001



28
-
Mohamed A. Sameh, and Mansour Marwa "Enhancing Partitionable Group Membership Service in
Asynchronous Distributed Systems",
Proceedings

the ISCA 14th International Conference on Computer
Applications in Industry and Engineering (CAINE' 2001), Nov. 27
-

29,

at Las Vegas, Nevada, 2001


29
-
Abdalla Mahmoud, Mohamed A. Sameh, Harras Khalid, Darwich Tarek, "Optimizing TCP in a Cluster of
Low
-
End Linux Machines",
Proceedings

of the 3
rd

WSEAS Symposium on Mathematical Methods and
Computational Techniques in Electri
cal Engineering, Athens, Greece, Dec. 29
-
31, 2001


30
-
Rania Abdel Hamid, and Mohamed A. Sameh, “Visual Constraint Programming Environment for
Configuration Problems”,
Proceedings

of the 15
th

International Conference on Computers and their
Applications, New

Orleans, Louisiana, March 2000


31
-
Essam A. Lotfy, and Mohamed A. Sameh, “Applying Neural Networks in Case
J
B慳敤 o敡soning
Adaptation for Cost Assessment of Steel Buildings”,
Proceedings

of the 10
th

International Conference on
Computing and Information, I
CCI
-
2000, Kuwait, Nov. 18
-
21, 2000


32
-
Ghada A. Nasr, and Mohamed A. Sameh, “ Evolution of Recurrent Cascade Correlation Networks with a
Distributed Collaborative Species”,
Proceedings

of the IEEE Symposium on Computations of Evolutionary
Computation and N
eural Networks, San Antonio, TX, May 2000


33
-
El
-
Beltagy S., Rafea A., and Mohamed A. Sameh, “An Agent Based Approach to Expert System
Explanation”,
Proceedings

of the 12
th

International FLAIRS Conference, Orlando, Florida, 1999


34
-

Mohamed A. Sameh, Botros A. Kamal, "2D and 3D Fractal Rendering and Animation",
Proceedings

of
the Seventh Eurographics Workshop on Computer Animation and Simulation, Aug. 31st
-

Sept. 2nd, in

19


Poitiers, France, 1996


35
-
Mohamed A. Sameh, "A Robust Vision System for three Dimensional Facial Shape Acquisition,
Recognition, and Understanding",
Proceedings

of the 1st Golden West International Conference on
Intelligent Systems, Reno, Nevada, 1991


36
-
Mohamed A. Sameh, "A Neur
al Trees Architecture for Fast Control of Motion",
Proceedings

of the
FLAIRS Artificial Intelligence Conference, Cocoa Beach, Florida, 1991


37
-
Mohamed A. Sameh, Armstrong W.W., "Towards a Computational Theory for Motion Understanding:
The Expert Animator
Model",
Proceedings

of the 4th International Conference on Artificial Intelligence for
Space Applications, Nasa, Huntsville, Alabama, 1988


CV of
Mona Diab
:

I am a scholar at Stanford University in the
li
nguistics department
working with
Daniel Jurafsky
and also part
of the
Natural language Processing lab
.

I finished my PhD in the University of Maryland, College Par
k, where I was in the
linguistics department
and
was part of the
CLIP lab

in the
University of Maryland Institute of Advanced Computer Studies
. I worked
under the supervision of a great advisor
Philip Resnik
. My thesis, defended in May 2003, is titled
Word
Sense Disambiguation within a Multilingual Framework
.

Earlier on, 1995
-
1997, I earned an MSc. degree in Artificial Intelligence (Machine Learning) from the
George Washington Univ
ersity

under the supervision of
Professor Peter Bock
.

I worked in the
Center for Spoken Language Research (CSLR)

at the University of Colorado at Boulder for
five mon
ths as a research associate after graduation, then I moved to Stanford, California in January of 2004.

Here is my
CV
.


Research Interests

My main research area is statistical natural language pr
ocessing. I am specifically involved
in computational semantics, Arabic computational linguistics, semantic processing and
machine learning.

I am interested in cross linguistic similarities and divergences in language use and how these types of
relations c
an be exploited to solve some of the language processing problems.

The NLaSP coll maybe checked
here
.

Publications



Diab, Mona.

Relieving the data acquisition bottleneck for Word Sense Disambiguation.
Proceedings of ACL 2004.[
pdf
].



Diab, Mona,

Kadri Hacioglu and Daniel Jurafsky.
Automatic Tagging of Arabic Text:

From raw
text to Base Phrase Chunks.

Proceedings of HLT
-
NAACL 2004.[
pdf
].



Diab, Mona.

An Unsupervised Approach for bootstrapping Arabic Sense Tagging.
Proceedings of
Arabic Script
Based Languages Workshop, Coling 2004.[
pdf
].



Diab, Mona

and Philip Resnik,
An Unsupervised Method for Word Sense Tagging using Parallel
Corpora,

Proceedings of ACL, 2002.[
ps
].



Diab, Mona.

An Unsupervised Method for Word Sense Tagging using Parallel Corpora: A
Preliminary Investigation.

Special Interest Group in Lexical Semantics (SIGLEX) Workshop,
Association for C
omputational Linguistics, 2000.[
pdf
].



Diab, Mona

and Steven Finch.
A Statistical Word
-
Level Translation Model for Comparable
Corpora.

Proc. of Conference on Content
-
based Multimedia
Information Access (RIAO2000),
2000.[
ps
].


20




Resnik, Philip and
Mona Diab
,
Measuring Verb Similarity
, Cognitive Science Society
(CogSci2000), 2000.[
pdf
].



Dorr, Bonnie, Gina Levow, Douglas Oard, Philip Resnik, Amy Weinberg,
Mona Diab
, Maria
Katsova.
MADLIBS: An Event Translingual Lexical Conceptual Structure Based Information
Retrieval System.

North American Association for Compu
tational Linguistics, NAACL 2000.



Resnik, Philip, Mari B. Olsen and
Mona Diab
,
The Bible as a Parallel Corpus: Annotating the
`Book of 2000 Tongues'
, Computers and the Humanities, 33(1
-
2), 1999.



Diab, Mona,

John Schuster and Peter Bock.

A Preliminary Sta
tistical Investigation into the impact
of an N
-
Gram Analysis Approach based on Word Syntactic Categories toward Text Author
Classification,

Proc. of 6th International Conference on Artificial Intelligence & Applications,
Egypt 1998 [
ps
].



Riopka, Terry,
Mona Diab

and Peter Bock.
Quantifying and Interpreting the Effect of Intelligent
Information.

Proc. of 6th International Conference on Artificial Intelligence & Applications, Egypt
1998 [
ps
].


Software

o

We have developed a set of Arabic processing tools in conjunction with our NAACL'04
[
paper
].

o

The tools utili
ze the Yamcha SVM tools to tokenize, POS tag and Base Phrase Chunk
Arabic text.

o

You may download our tarred and compressed (55mb) [
package
].

o

The tools are compiled for a linux
platform. For questions or comments contact [
me
].


CV of Noureldean Soufian


Publication









Book:


S. Noureddine: Conceptual Development and Quantitative Analysis of an Availability
Enhancing Middleware for Distributed Applications, Mensch & Buch Verlag, Berlin, 2002, ISBN 3
-
89820
-
347
-
6.









S. Noureddine: A Geometric Programming App
roach for the Satisfiability Problem, submitted to
Comp. Intel. Studies,


August, 2009.









S. Noureddine: Some Aspects of Islamic Logic, submitted to Applied Computing and Informatics,
KSA, August, 2009.











A Geometric Programming Approach fo
r the Satisfiability Problem, submitted to Comp. Intel.
Studies,


April, 2009.









M. Madi, S. Noureddine, A. Fellah: On Cryptology: Origin, Science, and Novel Techniques to
Interactive Data Decryption, First International Conference on Arab's & Musli
m's History of
Sciences, UAE, 2008.









Fellah, S. Noureddine: Deterministic Timed AFA: A New Class of Timed Automata, Journal of
Computer Science, Science Publications, 2007.









S. Noureddine: Analysis of a New Reduction Calculus for the Satisf
iability Problem, Proceedings
of the 9th ALC conference, 2006.









Fellah, S. Noureddine: Some Succinctness Properties of O
-
DTAFA, WSEAS Transactions on
Computers, 5(3), March, 2006.









Y. Chali, S. Noureddine: Document Clustering with Grouping
and Chaining Algorithms, In Proc.
of the 2nd International Joint Conference on Natural Language Processing, South Korea, 2005.









Y. Chali, S. Noureddine: Text Clustering for Natural Language Applications, Journal of Computer
Science, Science Publica
tions, 2005.


21










S. Noureddine: A Simple Reduction Calculus for Propositional Logic Formulas, 9th Asian Logic
Conference, Russia, August, 2005.


Publication









Book:


S. Noureddine: Conceptual Development and Quantitative Analysis of an Availabi
lity
Enhancing Middleware for Distributed Applications, Mensch & Buch Verlag, Berlin, 2002, ISBN 3
-
89820
-
347
-
6.









S. Noureddine: A Geometric Programming Approach for the Satisfiability Problem, submitted to
Comp. Intel. Studies,


August, 2009.









S. Noureddine: Some Aspects of Islamic Logic, submitted to Applied Computing and Informatics,
KSA, August, 2009.











A Geometric Programming Approach for the Satisfiability Problem, submitted to Comp. Intel.
Studies,


April, 2009.









M. Ma
di, S. Noureddine, A. Fellah: On Cryptology: Origin, Science, and Novel Techniques to
Interactive Data Decryption, First International Conference on Arab's & Muslim's History of
Sciences, UAE, 2008.









Fellah, S. Noureddine: Deterministic Timed AFA:
A New Class of Timed Automata, Journal of
Computer Science, Science Publications, 2007.









S. Noureddine: Analysis of a New Reduction Calculus for the Satisfiability Problem, Proceedings
of the 9th ALC conference, 2006.









Fellah, S. Noureddine: Some Succinctness Properties of O
-
DTAFA, WSEAS Transactions on
Computers, 5(3), March, 2006.









Y. Chali, S. Noureddine: Document Clustering with Grouping and Chaining Algorithms, In Proc.
of the 2nd International Joint

Conference on Natural Language Processing, South Korea, 2005.









Y. Chali, S. Noureddine: Text Clustering for Natural Language Applications, Journal of Computer
Science, Science Publications, 2005.









S. Noureddine: A Simple Reduction Calculus

for Propositional Logic Formulas, 9th Asian Logic
Conference, Russia, August, 2005.

CV

Of Mohamed Tounsi

Dr. Mohamed Tounsi

Associate Professor in Computer Science

Specialization
: Artificial Intelligence


Short Bio:


22


I.

Research interests



Constraint Programming and constraint satis
faction problems, Local Search Methods and


Hybrid Methods, Data Mining, Combinatorial Optimization, Multi
-
Objective Optimization,


Multi
-
Criteria Decision Making, Multi
-
agent modeling and parallel solving, Fuzzy Set


Theory,





II.

Current Projects



Data Mining applications in social networks


Data Mining applications in healthcare


Mining Arabic text


Small world based algorithms for optimization problems


Swarm intellig
ence for solving unconstrained optimization problems



III.

Publications


Recent Publications


1.

Mohamed Tounsi, (2010)

An intelligent bank assessment system: Preliminary Results”
International Journal of Electronic Finance, volume: 4 number: 03 Inderscience
Publishing.


2.

Mohamed Tounsi (2010)

“TTGENERATOR: An Intelligent Solver for Timetabling System”
Journal of Applied Soft Computing, Elsevier publishing (Accepted)


3.

Mohamed Tounsi (2010)

“New Swarm Intelligence Based Heuristics”
Journal of Applied Soft
Comput
ing, Elsevier publishing (Accepted)


4.

Mohamed Tounsi et al.(2010)


A mu汴i
J
捲楴敲楡i慰pro慣h for 橯b pr敦敲敮捥s

International
Journal of Data Analysis Techniques and Strategies (IJDATS)

Mohamed Tounsi received his PhD in C
omputer Science specialization in artificial intelligence from
University of Nantes, FRANCE in 2002. He was the chairman of computer science department and
Assistant Professor at the Department of Computer Science, Prince Sultan University, KSA. His
curren
t research interest includes constraint programming, meta
-
heuristics, bioinformatics,
intelligent argent and optimization algorithm. Previously, Dr. Tounsi received his master of science
from Paris 9, Dauphine University, Paris, FRANCE. Dr. Tounsi publishe
d several journals
publication is different international journals (see Research section). He is currently an editorial
member of various journals in the field of computing and he is a board member of Saudi Computer
Society.


Degrees




PhD in Computer Science, specialization in Artificial Intelligence,
University of Nantes, France

2002



M.S. in Computer Science, specialization in Operational Research,
University of Paris Dauphine,
France

1998



Engineer in Operation Research,
University of Science and Technology Houari Boumedine,
Algiers
, 1995


23



5.

Mohamed Tounsi (2010)

“a Multi
J
佢j散瑩t攠䡥ur楳瑩ts B慳敤 for 佰瑩m
ization Problems”
International Journal of Artificial Intelligence and Soft Computing, Inderscience
Publishing.(Accepted)


6.

Mohamed Tounsi et al. (2009)

“The Role of BPR in the implementation of ERP Systems”,
International Journal of Business Process Manage
ment journal. Vol. 15 No. 5. pp.:653
-
668.
Emerald Publishing.


7.

Mohamed Tounsi (2008)
, “An explanation
J
b慳敤 瑯o汳 for d敢ugging 捯ns瑲慩a琠s慴楳f慣瑩on
problems”.
Journal of Applied Soft Computing, Elsevier publishing (Accepted)
. 8(4): 1400
-
1406
(2008)


8.

Mohamed Tounsi et al. (2008)
“An Iterative local
J
s敡r捨 framework for solv楮g 捯ns瑲慩n琠
satisfaction problem”.

Journal of Applied Soft Computing, Elsevier publishing (Accepted)

8(4):
1530
-
1535 (2008)


9.

Mohamed Tounsi et al.
(2008)

“A Bluetooth intelligent e
J
h敡汴l捡r攠sys瑥m㨠Wn慬ysis 慮d d敳楧n
issues”.
International Journal of Mobile Computing (IJMC) 6(6): 683
-
695 (2008).

Inderscience
Publishing.


10.

Mohamed Tounsi (2008)

“Toward a General Model for Local Search Technique”
Jo
urnal of
Applied Computer Informatics. Vol 7 No 1. 2008.Elsevier Publishing.


11.

Mohamed Tounsi et al. (2008)

“The development of an intelligent Agent Prototype for Mutual
Fund Investment”
International Journal of Electronic Finance. Vol 2 No. 3 pp.300
-
313. 2
008.
Inderscience Publishing.


12.

Mohamed Tounsi (2008)

“An overview of ILOG Optimization Suite”, Journal of Applied
Compu瑥t fnform慴楣献a噯氠l 乯 OK OMMUK


13.

Mohamed Tounsi et al.(2008)

“Greedy
J
B慳敤 Appro慣h for 卯汶楮g 䑡瑡 A汬l捡瑩tn mrob汥m 楮 愠
䑩a瑲楢ut
ed Environment”
. Proceedings of the International Conference on Parallel and
Distributed Processing Techniques and Applications, PDPTA 2008, Las Vegas, USA, July 14
-
17,
2008, 975
-
980.


14.

Mohamed Tounsi (2008)

“Intelligent System for Bank Assessment: A Preliminary Results”
The
19
th

Saudi National Computer Conference (NCC19) .December 2008.




IV.

Research Activities



Member of Editorial Board for International Journal:


International Journal of Electronic
Healthcare (IJEH), Inderscience Eds.

Business Process Management (BPMJ), Emerald Eds.

Applied Computing and Informatics (ACI), SCS Eds.


Reviewers of International Journals:



Applied Artificial Intelligence Journal

Applied Soft Computing Journal


24


Supercomputing Journal

Business Process Management Journal

New Mathematics and Natural Computation Journal (NMCJ),

Applied of Computer and Informatics


Reviewers of different International and National conferences


International Conference on Artificial
Intelligence conference

International Conference on Parallel and Distributed Processing Techniques and

Applications PDPTA conference

IBAMA conference

IASTED Conferences (AIA, MSO)

ROADEF Conference (French conference of operational Research)

JNPC
Conference (French conference of solving NP
-
complete problems)


Member of Scientific Committee of different conferences:


IASTED Conference, Artificial Intelligence and Applications (AIA), 2009

International Conference on Artificial Intelligence ICAI’200
VI i慳⁖agasI 啓A

International Conference on Artificial Intelligence ICAI’2008, Las Vegas, USA

f千䅌 OMMT Conf敲敮捥K

fA協b䐠Conf敲敮捥I Ar瑩f楣i慬afn瑥汬tg敮捥 and App汩捡瑩lns EAfAFI fnnsbruckI 䅵s瑲楡i

tb卅p匠Conf敲敮捥I 䑩s瑡n捥 i敡rning 慮d t敢 bng
in敥r楮g E䑉tbBDOMMSFI i楳bonI mor瑵g慬a

乃CNU ENU
th

National Conference on Computer) Riyadh, Saudi Arabia.

International Conference on Artificial Intelligence ICAI’2006, Las Vegas, USA

fA協b䐠Conf敲敮捥

fn瑥tn慴楯n慬aConf敲en捥 on m䅒䅌ibi A乄k䑉協ofB啔b
䐠C位mrqf乇kA乄k久qt佒䭓
J
㈰〵O


Workshop Organized


Workshop at the 7
th

World Multi Conference on Systemic , Cybernetics and

Informatics (SCI 2003), Florida, USA





Member of Scientific Associations:



Board member of Saudi Computer Society (SCS)

French
Association of Operations Research









Appendix
B
: Evaluations and Approvals



COLLEG
E

REVIEW

COMMITTEE

Evaluation and Recommendation


Item/ Evaluation

Excel
-
lent

Very

G
ood

Good

Weak

Research methodology





Research objectives






25


Research originality





Research contribution





Research applicability and relevance





Overall evaluation





Recommendations of
College

Committee




Approved

Disapproved

Amount of Budget Approved by
College

Committee:



(
SAR
)


Chair

College
Committee
-

Title and Full Name:




Signature:

Date:


/ /

Recommendations of the
College

Council



Approved

Disapproved


Dean of the College Council
-

Title and Full Name



Signature: Date:


/ /




PSU
INSTITUTIONAL
RESEARCH COMMITTEE

(IRC)

Recommendation



Recommendation of the
PSU
IRC





Approved

Disapproved

Chair

IRC

Committee
-

Title and Full Name:


Signature:




Date:


/ /






26


PSU EXTERNAL REVIEW
PANEL FOR RESEARCH P
ROPOSALS

Recommendation


Recommendation of the
Eternal Review

Committee
.



Approved: Amount of grant approved: (
SAR
)




Disapproved:





Postponed:




Directed to:


Chair

of

External Review

Panel

-

Title and Full Name:





Signature:


Date:



/ /

Recommendation of
University Council








Approved



Disapproved

Signature:


Date:



/ /