1. Artificial Intelligence & Applications
A. S. Md. Kamruzzaman
Binghamton University
Abstract: When an ordinary person thinks formed on the basis of those programs. The
about a computer, he would immediately say storing of programs allowed the computer to
that computer is a machine with programs change function quickly and easily by running
that cannot act like a human brain. If a a new program. This capability implies that a
computer is asked to make a decision on computer might be able to think and learn by
certain aspects, it gives the result. It should be itself.
impossible for a machine to act like a human AI mostly came from the thinking
intelligence. But in fact, when “Deep Blue” perspective of a computer. A computer can
(International Business Machines [IBM] think of itself and can produce decisions.
software) defeated, Gerry Kesparov, World Dealing with a decision accordingly, a
Champion Chess Grandmaster for six times computer can exhibit behavior similar to a
within ten rounds, it was a big surprise.. The person. The key point is some programs are
idea of acting computers like a human being definitely intelligent. The problem is whether
came from Artificial Intelligence (AI). The a machine can think or a program running on
most common and important areas of AI are a computer can be intelligent. Most programs
searching (for solutions), expert systems, do not perform the same tasks in the same
natural language processing, pattern way that a person does. An intelligent
recognition, Robotics, machine learning logic, program should act like a human being and
uncertainty and “fuzzy logic”. AI provides a exhibit behavior when confronted with a
wide range of ideas of how software can similar problem. It is not necessary in the
simulate human behavior. Artificial same way that the programs actually solve, or
Intelligence deals with a specific kind of attempt to solve problems that a person
software that relates to human activities. This would.
is a branch of Computer Science that is This paper will provide an overview
mostly concerned with the study and creation survey of AI while focusing on areas of
of computer systems that exhibit some forms research and applications. A history of
of intelligence: systems that learn new Artificial Intelligence will be given to readers
concepts and tasks. AI has systems that can to overview ideas. This paper will also provide
reason and draw useful conclusions about the the visual parts of Artificial Intelligence
surroundings, systems that can understand a related to human beings. The paper will
natural language or perceive and comprehend conclude on future perspectives developed by
a visual scene, and systems that perform other AI.
types of feats that require human types of
intelligence. AI has some terms that should be
understood from the Artificial Concepts such I. Introduction
as, intelligence, knowledge, reasoning,
thought, cognition, and learning.
The Turing machine is considered the
first Artificial Intelligence that works on the = = AI
stored program computers. During the early
MACHINE
age of computers, there were actually
machines that literally had to be rewired to
solve different problems. Turing’s recognition
was that those programs could be stored as
data in the computer’s memory and could be
executed later. All modern computers are

2. Figure 1. A robot is taking solution from an it can be explained by a reduction to
AI machine [1] ordinary physical processes.
Philosophers staked out most of the
Humankind has given itself the important ideas of AI, but to make the
scientific name Homo Sapiens which leap to a formal science required a level
means “man the wise”. This is so of mathematical formalization in three
because human mental capacities are so main areas: computation, logic and
important to everyday life. The field of probability. In computation theorem,
AI attempts to understand intelligent intractability, reduction, NP (Non
entities. Thus, one reason to study AI is Probabilistic) completeness and decision
to learn more about humankind. But theory has a great impact on AI which
unlike Philosophy and Psychology, arose from math. Psychology plays
which are also concerned with another major role in AI. Behaviorism
intelligence, AI strives to build started discovering animals’ brain and
intelligent entities as well as understand cognitive psychology started on brain
them. Another reason to study AI is that possesses and processing information.
these constructed intelligent entities are Kenneth Craik [2] made a connection
interesting and useful in their own right. between stimulus and response. The
AI has produced many significant and three key steps of a knowledge based on
impressive results even at this early agent; (1) the stimulus must be translated
stage in its development. Although no into an internal representation; (2) the
one can predict the future in detail, it is representation is manipulated by
clear that computers with the level of cognitive processes to derive new
human intelligence would have a huge internal representations, and (3) these in
impact on the future course of turn are retranslated back into action.
civilization. In Figure 1, this idea is Computer Engineering catalyzed ideas
illustrated visually. of AI. The computer has been
In terms of Philosophy, AI has unanimously acclaimed as the artifact
inherited many ideas, viewpoints, and with the best chance of achieving
techniques from other disciplines. The artificial intelligence. The Turing idea
story of AI began around 450BC [2]. changed the vision of AI. The idea of
"When Plato reported a dialogue in knowledge representation (the study of
which Socrates asks Euthyphro,"I want how to put knowledge into a form that a
to know what is characteristics of piety computer can reason with) was tied to
which makes all actions pious ... that I language and informed by research in
may have it to turn to, and to use as a linguistics that was connected to
standard whereby to judge your actions philosophical analysis language.
and those of other men."[2] Dualism Figure 2 can make the ideas more
which is part of the mind (or soul or clear. It shows how AI could be
spirit), is outside of nature exempts from described in terms of the environment
physical laws. The mind or brain holds and an agent. An agent is anything that
the entire world which operates can be viewed as perceiving the
according to physical laws. It is also environment through sensors and acting
possible to adopt an intermediate upon that environment through effectors.
position in which one accepts that the A human agent has eyes, ears and other
mind has a physical basis, but denies that organs for sensors, and hands, legs,

3. mouth, and other body parts for field of AI. Newell and Simon wrote a
effectors. A robotic agent substitutes reasoning program which is capable of
cameras and infrared range finders for thinking non-numerically, and solved the
the sensors and various motors for the mind-body problem. The name of
effectors. Software agent has encoded bit Artificial Intelligence came from the
strings as percepts and actions. A generic Darmouth Workshop [2].
agent is diagrammed in Figure 2. Early enthusiasm great
expectations (1952 – 1969): Newell and
PERCEPTS
Simon’s [2] early success was followed
SENSORS
up with the General Problem Solver or
(GPS). This was probably the first
ENVIRONMENT
program to embody the “thinking
AGENT humanly” approach. Starting in 1952 [2],
ACTIONS Arthur Samuel wrote a series of
programs for checkers (draughts) that
eventually learned to play tournament
EFFECTORS
level checkers. He discovered the idea
Figure 2. Agents interect with environments
that computers can only do what they are
through sensors and effectors[2] told to do. Early work building on the
neural networks of McCulloch and Pitts
also flourished. The work of Winograd
and Cown (1963) showed how a large
II. History number of elements could collectively
represent an individual concept, with a
It is difficult to pinpoint an exact corresponding increase in robustness and
starting date for the invention of AI. It parallelism.
began to emerge as a separate field of A dose of reality (1966 - 1974):
study during 1940 and 1950s when the Weizenbaum’s ELIZA program (1965)
computer became a commercial reality. [2] which could apparently engage in
Here is the subdivision of some part of serious conversation on any topic,
AI history. actually just borrowed and manipulated
The gestation of AI (1943 - the sentences typed into it by a human.
1956): Warren McCulloch and Walter The illusion of unlimited computational
Pitts (1943)[2] drew on three-source power was not confined to problem-
knowledge of the basic physiology and solving programs. Early experiments in
function of neurons in the brain. They machine evolution (new called genetic
proposed a model of artificial neurons in algorithms) were based on the
which each neuron is characterized as undoubtedly correct belief that by
being “on” or “off”, with a switch to making an appropriate series of small
“on” occurring in response to stimulation mutations to a machine code programs,
by a sufficient number of neighboring one can generate a program with good
neurons. This work was arguably the performance for any particular simple
forerunner of both the logicist tradition task. This idea, then, was to try random
in AI and the connectionist tradition. In mutations and then apply a selection
the early 1950s, the invention of neural process to preserve mutations that
network computer opened another new seemed to improve behavior. During

4. this time there was certain difficulties. framework. There have been a number
The first difficulty was that programs of advances that built upon each other
had insufficient knowledge of their rather than starting from scratch each
subject matter. Secondly the time. Probabilistic Reasoning in
intractability of many of the problems Intelligent Systems marked a new
that AI programs worked by representing acceptance of probability and decision
the basic facts about a problem and theory in AI, following a resurgence of
trying out a series of steps to solve it. interest in formalism was invented to
The theory of NP (Non Probabilistic) allow efficient reasoning about the
completeness brought the problem. This combination of uncertain evidence. This
theory could not able to bring the approach largely overcomes the problem
solutions for problems. Third difficulty with probabilistic reasoning systems of
came from fundamental limitations on the 1960s and 1970s. It also has come to
the basic structures being used to determined AI research on uncertain
generate intelligent behavior. reasoning and expert systems. Similar
Knowledge based systems revolutions have occurred in robotics,
(1969 - 1979): The widespread growth computer vision, machine learning
of applications to real-world problems (including neural networks) and
caused a concomitant increase in the knowledge representation. A better
demands for workable knowledge understanding of the problems and their
representation schemes. A large number complexity properties, combined with
of different representation languages increased mathematical sophistication,
were developed. Some were based on has led to workable research agenda and
logic. For example the Prolog language robust methods perhaps encouraged by
became popular in Europe, and the the progress in solving the sub problems
PLANNER family [2] in the United of AI, researchers have also started to
States. look at the “whole agent” problem again.
Neural Networks (1986 -
present): Some disillusionment was
occurring concerning the applicability
III. Definition of AI
the expert systems technology derived
from MYCIN- type systems [2]. Many AI is a branch of Computer
corporations and research groups found Science concerned with the study and
that building a successful expert system creation of computer systems. AI
involved much more than simply buying exhibits some form of intelligence:
a reasoning system and filling it with systems that learn new concepts and
rules. Some predicted an “AI Winter” in tasks, systems that can reason and draw
which AI funding would be squeezed useful conclusions about the world. AI
severely. systems can understand a natural
Recent events (1987 - present): language or perceive and comprehend a
Speech technology and the related field visual scene, and systems that perform
of level written character recognition are other types of feats that require human
already making the transition to types of intelligence. Intelligence is the
widespread industrial and consumer integrated sum of those feats which
applications. An elegant synthesis of gives us the ability to remember a face
existing planning programs into a simple not seen for thirty or more years, or to

5. build and send rockets to the moon [3]. to try to construct precise and testable
The intelligence requires knowledge. AI theories of the workings of the human
is not the study and creation of mind. The rational thought which
conventional computer systems. govern the operation of the mind, and
From the perspective of initiated the field of logic. Acting
intelligence; AI makes machines rationally is another part of the definition
"intelligent" -- acting, as we would of AI which means acting so as to
expect people to act. The inability to achieve one’s goals, given one’s beliefs.
distinguish computer responses from An agent is something that perceives and
human responses is called the Turing acts. If we look at AI impressive
test. Intelligence requires knowledge achievements, it is still impossible to
Expert problem solving - restricting produce the brain abilities of a three-
domain to allow including significant year-old child. These include the ability
relevant knowledge [4]. to recognize and remember numerous
From a research perspective: diverse objects in a scene, to learn new
artificial intelligence is the study of how sounds and associate them with objects
to make computers do things which, at and concepts and to adopt readily to
the moment, people do better. One way many diverse new situations.
to measure the success of AI within
computers is to interrogate it by a human
via a Teletype. The computer passes the IV. AI Performances
test if the interrogator cannot tell
AI has performed a vital role in
whether there is a computer or a human
so many fields. Researchers are devoting
at the other end. The computer needs to
their time and effort to establish a good
posses the following capabilities [4].
performance on AI. The features of AI
Natural language processing - to enable
can illustrate some ideas of AI
it to communicate successfully in
performance.
English (or some other human
Knowledge representation: It is a
language).
design for knowledge–based agent. A
simple logical language for expressing
1. Knowledge representation- to store
knowledge and showing how it can be
information provided before or during
used to draw conclusions about the
the interrogation.
world and to decide what to do. The
2.Automated reasoning – to use the
language is capable of expressing a wide
stored information to answer questions
variety of knowledge about complex
and to draw new conclusions.
worlds. It could be represented several
3. Machine learning – to adapt to new
ways.
circumstances and to detect and
1.Knowledge Acquisition-
extrapolate patterns.
formalizing knowledge and
4. Computer vision - to perceive objects
implementing knowledge bases are
and robotics to move them about.
major tasks in the construction of large
A computer program has to think like a
AI systems. The hundreds of rules and
human. The interdisciplinary field of
thousand of facts required by many of
cognitive science brings together
these systems are generally obtained by
computer models from AI and
interviewing expert in the domain of
experiment techniques from psychology
application. Representing expert

6. knowledge as facts or rules is typically a to confer the ability (or reasons about
tidious and time-consuming process. one’s own knowledge).
Techniques for automating this Expert systems: these constitute
knowledge acquisition process would most of AI’s commercial success. Expert
constitute a major advance in AI Systems are programs that mimic the
technology. Knowledge acquisition can behavior of a human expert. They use
automate in three ways [5]. Firstly, information that the user supplies to
special-editing systems might be built sender an opinion on a certain subject.
that allow persons who possess expert The expert system asks user questions
knowledge about the domain of until it can identify an object that
application to interact directly with the matches with the answer from the user.
knowledge bases of AI systems.
Secondly, advances in natural language For example:
processing techniques will allow humans Expert: Is it green?
to instruct and teach computer systems Users: No.
through ordinary conversations. Thirdly, Expert: Is it red?
AI systems might learn important Users: Yes.
knowledge from their experiences in Expert: Does it grow on a tree?
their problem domains. User: No.
Representational Formalisms; Expert: Does it grow on a cane?
2. Commonsense reasoning- Many of User: Yes.
the existing ideas about AI techniques Expert: Does the cane have thorns?
have been refined on “toy” problems, User: Yes.
such as problems in the ‘block worlds’, Expert: It is a raspberry.
in which the necessary knowledge is Every expert system has two parts
reasonably easy to formalize. [6]: the knowledge base and the
Representing Prepositional Attitudes reference engine. The knowledge base is
[5]- a database that holds specific
information and rules about a certain
San knows that Pete is a lawyer. subject. The inference engine is the
San does not believe that John is a information that the user supplies to find
doctor. an object that matches. It has two
Pete wants it to rain. branches; 1.Deterministic, and
John fears that Sam believes that the 2.probabilistic.
morning star is not Venus. The interface engine can also be
The underlined portions of these defined as the forward-chaining method
sentences are propositions, and the and the backward–chaining method.
relations know, believe etc. refer to The Forward-Chaining Method:
attitudes of agents toward these Forward-chaining is sometimes called
propositions. A logical formalization for “data-driven”[6] because the inference
expressing the appropriate relations engine uses information that the user
between agents and attitudes. provides to move through a network of
3. Meta–Knowledge – A good logical AND and OR until it reaches a
solution to the problem of reasoning terminal point, which is the object.
about the knowledge of others ought also In Figure 3, a fruit knowledge base
creates a Forward-chaining interface

7. engine. The engine would arrive at the Understanding commands written in
object apple, when it is given the proper standard human languages. NL processor
attributes as shown in Figure 3. A extract information from any given
input. The core of any NLP system is the
Round Grows on trees parser. The parser is the section of code
that reads each sentence, word by word
Does not grow in Deep South
and decide what is what. The example of
a parser.
Red or yellow The State Machine Parser: The state-
machine parser uses the current state of
the sentence to predict what type of word
Apple may legally follow. Figure 5 shows the
state machine that is a directed graph
Figure 3. Forward-chaining to the object that shows the valid transitions from one
apple[9]
state to another. For example, a noun can
be followed by a verb or a preposition. A
Forward-chaining system essentially state machine is shown in Figure 5.
builds a tree from the leaves down to the
root.
The Backward-Chaining Method: Noun
Backward-chaining is the reverse of
forward-chaining. A backward-chaining Preposition
inference engine starts with a hypothesis Adjective Verb
(an object) and request information to
confirm or deny it. In Figure 4, the fruit Adverb
Try apple Figure 5. The state-machine of the restricted
grammar [9]
Grows on trees
Grows on vine
Is round
Red or yellow
Vision and Pattern Recognition:
Vision systems can be
Does not grow in Deep South implemented in two ways. First method
Is orange
Apple tries to reduce an image to the lines that
form the outline of each object. This
Figure 4. Backward-chaining to the object apple method uses various filters to remove
[9] information from the image, and contrast
enhances to make all parts of the image
either black or white. They are called
Question is an apple, applying
binary image because every paint in the
backward-chaining inferences to the fruit
image is either black or white. Second
knowledge base. As the diagram shows,
method attempts to give the computer a
backward-chaining prunes a tree.
more humanlike view of the image. This
Natural Language processing:
method gives to the computer
Natural Language Processing (NLP)
information about the brightness of the
tries to make the computer capable of
part of the image. It allows the computer

8. to derive two important features from the information in a database. Role does not
image that are not possible with a light require any generalization to be derived
contrast image because of surfaces and or any high level thinking.
shadows. It is easy to interpret an image Cognitive learning – This form of
but correctly identifying the objects or learning requires analyzing, organizing,
features that make up the image. There and correlating specific pieces of
are several ways to do it. Firstly, knowledge. The product of this mental
computer can do it by controlled effort is the creation of class
hallucination [6]. Recognition of object descriptions. The ability to learn class
is also another issue in the pattern description is fundamental to the
recognition. creation of a computer that thinks the
Robotics: There are two types of way that human does.
robots. Industrial assembly robots are Logic and uncertainty: Logic lies
used in a controlled environment. It can at the heart of computer programming. A
perform only programmed task. There programming language is simply an
are two ways to teach a new task to a implementation of a special form of
robot: knowledge. One of the most pleasing
aspects of logic is that it is certain.
1.by using teach pendant, or Things that are represented by logic are
2.programmed by using a Robotic- ‘true’ or ‘false’. Resolving or dealing
control language. with uncertainty is critical to machine
Teach pendant is a hand-held control intelligence because it is required for
box that allows an operator to move the successful interfacing with the real
various joints of the robot. It is linked world. Fuzzy logic deals with the
through the robot’s main control evaluation of logical expression that
computer. Moving each joint can do it contain uncertain values probabilistic
and computer records each position [6]. systems utilizes the probability of the
For complex jobs Robotic Control occurrence of various events in order to
Language s are used. It is a computer arrive at an answer [6].
program used to control a robot. Appearing human: The idea is to
Autonomous Robots: It is much more make a computer appear to be like a
complex than industrial robots. It sensors person. It is completely integrated
that allows to hear and see and program which appears to be human.
understand natural language and what The name of this program is ELIZA. If a
the language means. It can also solve human is compared with a computer
problems. It can be implemented by (program), the human has the emotions
parallel processing but it is still under and the personality. In terms of
research. computer, a machine can not act like a
Machine learning: Two types of human. The program which is created by
learning: role learning and cognitive the human being, can do whatever the
learning. programmer wants. It can appear to have
Role learning is something from emotions and personality because the
memorization. In terms of computer, it is programmer built the program with those
a set of instruction programmed in a functions in it. This way the computer
database, which can easily follow a shows that it has emotions and
procedure or store some item of personality.

9. AI is the field where human brain
V. Application of AI and machine talks together. The
importance of AI is very wide. Human
The distinction between a brain can be transformed into a machine
computer “user” and a computer format and all the research is done
“programmer” is that the user provides through AI. Cognitive Psychology and
new input, or data (words or numbers), AI are very related. Cognitive
while the programmer defines new Psychology discusses on human
operations, or programs, as well as new behavior and AI deals how to transform
types of data [7]. machine close to human.
The GPS developed in 1957 by Alan The invention of supercomputers
Newell and Hervert Simon, embodied a is one of the great inventions of AI
grandiose vision. A single computer which changed the view of AI. With a
program that could solve any problem, combined budget of about one billion
given a suitable description of the dollars [3], the Japanese are determined
problem GPS caused quite a stir when it to realize many of their goals, namely, to
was introduced and some people in AI produce systems that can converse in a
felt it would sweep in a grand new era of natural language, understand speech and
intelligent machines. It is much easier to visual scenes, learn and refine their
implement a GPS in steps. There are few knowledge, make decisions and exhibit
steps which are [7] other human traits. The Defense
Advanced Research Projects Agency
a. Describe the problem in vague term (DARPA) has increased it’s funding for
b. Specify the problem in algorithmic research in AI. In addition, most of the
terms. larger high-tech companies such as IBM,
c. Implement the problem in a DEC, AT&T have their own research
programming language. programs.
d. Test the program on representative
examples. VII. Future Perspective
e. Debug and analyze the resulting
program and Lots of plans have taken to
f. Repeat the process. improve the research of AI. At the same
The main programming languages used time, funding is increased to improve its
in AI are Lisp and Prolog. Both have standing. Researchers are trying to get
features which make them suitable for the Autonomous Robots which will
AI programming, such as support for list change the entire AI field.
processing, pattern matching and : (1) Reducing the time and cost of
exploratory programming. Both are also development is a big plan for AI.
widely used -Prolog especially in Europe (2) Allowing students to work
and Japan, and Lisp in the US. This wide collaboratively is another plan from
use within the field is another reason to researchers.
choose Lisp or Prolog for AI One important research issue is
implementation [8]. reducing the time and cost in order to
develop such systems. Current strategies
for doing this include the development
VI. Importance of AI of authoring tools and creating systems
in a modular fashion. Solving this

10. problem will be an enormous environment but also on the actions of
breakthrough in ITS research, since other agents. The standard scenario
more systems could be constructed and involves a set of agents who make their
thus more research into the effectiveness decisions simultaneously, without
of computer based instruction could be knowledge of the decisions of the other
performed [9]. agent.
AI is trying to discover some
desirable property P; There are number VIII. Conclusion
of choices for p which are,
Perfect rationality: the classical A computer is a game device to a
notion of rationality in decision theory. child, but it can be used in different ways
A perfectly rational agent acts at every depending on the user’s needs.
instant in such a way as to maximize its Programmers build all kinds of programs
expected utility, given the information it to satisfy the needs of the growing
has acquired from the environment. number of users. It will not be surprising
Calculate rationality: the notion of to utilize computers with highly
rationality that has used implicitly in developed artificial intelligence
designing logical and decision capabilities a few years from now to do
theoretical agents. A calculatively thee tasks. Computers will have the
rational agent eventually returns what ability to create a program that can
would have been the rational choice at create another program and thus simulate
the beginning of its deliberation. This is the behavior of the human brain. The
an interesting property for a system to best example thus far of these
exhibit because it continues an “in- capabilities were documented by Deep
principle” capacity to do the right thing. Blue in a chess game that serves as a
Bounded optimality: A bounded technological landmark for the future.
optimal agent behaves as well as The shear knowledge of a computer
possible given its computational adapting and functioning faster than the
resources. That is, the expected utility of human mind though programmed to do
the agent program for a bounded optimal so by humans is in essence a frightening
agent is at least as high as the expected reality that needs to be confronted.
utility of any other agent program Though AI just finished with its period
running on the same machine. of infancy, it has ramifications that yet
Philosophy has also seen a remain unknown. to everyone. The effort
gradual evolution in the definition of and research can bring the surprising
rationality. There has been a shift from innovations that the majority crave and
consideration of act utilitarianism – the desire but there are also results which
rationality of individual acts – to rule cannot be forseen when the computer
utilitarianism, or the rationality of begins to think for itself.
general policies for acting.
Another area is game theory, a
branch of economics that began References
widespread study of decision theory.
[1] My own Creative picture.
Game theory studies decision problems [2] Norvis, Peter &Russel, Stuart Artificial
in which the utility of a given action Intelligence: A modern Approach, Prentice Hall,
depends not only on chance events in the NJ, 1995

11. [3] Patterson, Dan W. Introduction to Artificial
Intelligence and Expert Systems, Prentice Hall of
India Private Limited New Delhi, 1998
[4] Brown, Carol E. and O'Leary, Daniel E.
“INTRODUCTION TO ARTIFICIAL
INTELLIGENCE AND EXPERT SYSTEMS”
Artificial Intelligence / Expert Systems Section of
the American Accounting Association ,
http://www.bus.orst.edu/faculty/brownc/es_tutor/
es_tutor.htm#1-AI
01/03/2000
[5] Nilsson, Nils J. Principles of Artificial
Intelligence, Narosa Publishing House New
Delhi, 1998
[6] Schildt, Herbert Artificial Intelligence
Using C, Osborne McGraw Hill Berkeley,
California,, 1987
[7] Norvig, Peter Paradigms of Artificial
Intelligence Programming Morgan Kaufmann
Publishers, San Mateo, California 1992
[8] Cawsey, Alison Databases and Artificial
Intelligence 3 Artificial Intelligence Segment,
http://www.cee.hw.ac.uk/~alison/ai3notes/all.ht
ml 08/ 19/1994
[9] Beck, Joseph, Stem, Mia and Haugsiaa
“Applications of AI in Education” The ACM's
First Electronic Publication
http://www.acm.org/crossroads/xrds3-1/aied.htm
l 02/13/ 2000
A. S. MD.
KAMRUZZAMAN;
received an Associate in
Science from LaGuardia C.
College/CUNY in Computer
Science in August 1999
CAREER OBJECTIVE:
Webmaster ORIGIN: Bangladesh HONORS
from CUNY: National & College Dean's List ‘97
– ‘99 Vice-president-Phi Theta Kappa
International Honor Society ‘98 – ‘99. College
Senator, Student Govt. Association ‘98 – ‘99.
Student Advisory Council - Foreign Student
Club, Asian Club, Alpha Theta Phi ‘97 – ‘99.
AWARDS: Leadership Award’98 Honors Award
for Academia from the Dept. of Student
Affairs’98 & ‘99 Student Govt. Association
Award’98. My Bio and Web Creations -- http://
www.york.cuny.edu/~kzaman/newpage.html