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Description : It seems that very early on, scientists and doctors alike were captivated by the potential such a technology might have in medical. With intelligent computers able to store and process vast stores of knowledge, the hope was that they would become as a perfect doctor, assisting or surpassing clinicians with tasks like diagnosis. Medical artificial intelligence is primarily concerned with the construction of AI programs that perform diagnosis and make therapy recommendations

Problem Domain : medical

Develop By :Amani Al-Ajlan 427220111 KING SAUD UNIVERSITY COLLEGE OF COMPUTER & INFORMATION SCIENCES DEPARTMENT OF COMPUTER SCIENCE Second Semester 1427/1428 AH

by : Samuna a/p manokaran 2190

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INMAMUSYS : Intelligent multiagent music system

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INMAMUSYS : Intelligent multiagent music system

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Description :Music generation is a complex task even for human beings. This paper describes a two level competitive/ collaborative multiagent approach for autonomous, non-deterministic, computer music composition. Our aim is to build a high modular system that composes music on its own by using Experts Systems technology and rule-based systems principles. To do that, rules issued from musical knowledge are used and emotional inputs from the users are introduced. In fact, users are not allowed to directly control the composition process. Two main goals are sought after: investigating relationships between computers and emotions and how the latter can be represented into the former, and developing a framework for music composition that can be useful for future experiments. The system has been successfully tested by asking several people to match compositions with suggested emotions.

Problem Domain : Computer Music

Develop By : Department of Computer Science and Artificial Intelligence, Universidad de Granada, 18071 Granada, Spain E.T.S.I. Informática y de Telecomunicación, CnPeriodista Daniel Saucedo Aranda, 18071 Granada, Spain

By : Sivashankari a/p Sivanantham 220215

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mMES : A MOBILE MEDICAL EXPERT SYSTEM FOR HEALTH INSTITUTION IN GHANA 

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mMES : A MOBILE MEDICAL EXPERT SYSTEM FOR HEALTH INSTITUTION IN GHANA 

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Description :

The importance of health and being medically fit is goal for every human being. Unhealthy situations of human beings are of course very disadvantageous and disastrous to our society. Some public hospitals in Ghana comprising of Korle Bu Teaching Hospital (KBTH), Komfo Anokye Teaching Hospital (KATH), 37 Military Hospital, Ridge Hospital as well as Tema General Hospital are notable hospitals that do their maximum best to provide health needs to Ghanaians. The facilities of these hospitals however, find it difficult to meet the enormous requirements of patients that daily attend hospitals for minor or major treatments as well as check-ups and birth delivery. Various private hospitals have sprung up in Ghana to support and relieve pressure of enormous patient numbers the public hospitals. In addition to limited health facilities, Medical Doctors in both private and public hospitals in Ghana are always under a lot of stress and intense pressure to perform accurate diagnosis and prescriptions for so many daily patients through medical consultancy. Through Artificial Intelligence (AI) techniques, literature review and design-based research methodologies, this paper focuses on how Medical Doctors in Ghana can use a designed Expert System through mobile technology to speed up diagnosis, confirm their own diagnosis, provide advice on found diagnosis and provide advice on certain diseases when diagnosed on a patient. The study is underpinned to Korle Bu Teaching Hospital, which is currently the third largest hospital in Africa and the leading national referral Hospital Centre in Ghana.

Problem Domain: Medical Field

Develop by: Nana Yaw Asabere School of Software, DUT, P.R. China / Lecturer, Computer Science Dept. Accra Polytechnic, Ghana

By: eswaari a/p k.bhaskaran 219865

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Geographic Information Systems (GIS) And Simulation Models For Water
        Resources Management

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Geographic Information Systems (GIS) And Simulation Models For Water
        Resources Management

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Description :
The development of the system was derived from the major problem occur in Kelantan, specifically the Kelantan river which drains the province of Kelantan in north-eastern peninsular Malaysia. A catchment of about 12,000 km2 (upstream of Guillemard bridge) and an altitude difference of more than 2100 m generates an average runoff of about 500 m3/sec, with the variations of the local Monsoon climate. The variability of rainfall with extreme monthly values between 0 and 1750 mm in dry and wet months, respectively, already suggest the main problem: reliability of water resources for the rice paddies that supply about 12 % of national production. Droughts and floods that affect the efficiency of the irrigation system, continuing changes in land use, and the potential of water pollution from intensive agriculture pose a range of problems that require innovative tools for their solution (Fedra, 2002). So this system was then proposed and eventually developed in the meant of managing the water resources properly, in order to prevent those problems derived from the past situations from happening again.

Problem Domain :
Environmental Management.

Knowledge Domain :
WaterWare system.

Inference engine :
uses a combination of both forward and backward chaining.

Develop by :

K. Fedra in 2002.

By :

MOHD NORSHAZNI BIN ABD MUTTALIB (220454)

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puff is a Medical system expert system for diagnosis of respiratory condition.

Puff was built to run on hospital microcomputers

PUFF consist of the EMYCIN programs plus a pulmonary disease knowledge base.

Problem domain: Medical.

Knowledge domain: Pulmonary physiology

Language: Basic language ( Coded in LISP)

inference engine: Backward chaining. (Goal directed control structure)

Developed by: Computer scientist from Stanford university and researcher from institute of medical science in san fransisco and physician from pacific medical center.

Ajibola Victor

218785.

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Reactor is an EXPERT SYSTEM FOR DIAGNOSING AND TREATMENT OF NUCLEAR REACTOR ACCIDENTS .

The purpose of REACTOR is to monitor a nuclear reactor facility, detect deviations from normal operating conditions, determine the significance of the situation, and recommend an appropriate response.

It has a very large knowledge base with a procedure that reasons both forward and backward.

The system uses the situation rules and reasons forward from known facts until a conclusion can be reached. If not enough information is available to reach a conclusion, REACTOR reasons backward to gather additional information.

PROBLEM DOMAIN: NUCLEAR PLANT.

KNOWLEDGE DOMAIN: DETECTION OF NUCLEAR REACTOR ACCIDENTS AND TREATMENT.

INFERENCE ENGINE: forward chaining and backward chaining

LANGUAGE: UNKNOWN.

DEVELOPED BY: EG&G Idaho Inc.

WRITTEN MY AJIBOLA VICTOR

218785

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VM is an expert monitoring and controling system.

its purpose was to provide diagnostic and therapeutic advice in a hospital's Intensive Care Unit.

Vm can provide a summary of a patient's physiological status appropriate for the clinician,

Problem Domain: Medical

Knowledge domain: diagnostic and therapeutic conditions of hospital's Intensive Care Unit.

Language: unknown.

inference engine: forward chaining.

developed by: unknown.

Ajibola victor 218785.

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ITS was developed to help first year engineering student gain deep knowledge and understanding of the fundamentals.

ITS is very useful in motivating student and enhansing thier performance

ITS uses adaptation techniques to personalize with the environment.

ITS is available to student 24*7. and student can learn anytime anywhere.

four componenent of ITS:

Student model.

pedagogical module.

Domain knowledge module.

communication module.

An additional component called Expert model.

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Description: This paper gives an overview of industrial applications of real-time knowledge based expert systems (KBESs) for process control. After a brief overview of the features of a KBES useful in process control, several case studies are reviewed. Online KBESs are making significant contributions to process control and management. They are economically justified. The applications and benefits are often in areas which complement traditional process control technology, for instance, in handling abnormal situations, and in overall quality management. The KBES integrates new techniques with conventional controls. Problem Domain: Industry

Knowledge Domain: Real Time Process Control

Language: LISP

Inference Engine: Forward Chaining

Developed by: G.M. Stanley Gensym Corporation, 125 CambridgePark Dr., Cambridge, MASS., 02140 (USA) *

By: Puah Mei Chin (220874)

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Description:

PXDES which is a pneumoconiosis, a lung disease, X-ray diagnosis. This expert system incorporates the inference engine to examine the shadows on the X-ray. The shadows are used to determine the type and the degree of pneumoconiosis. This system also includes three other modes: the knowledge base, the explanation interface, and the knowledge acquisition modes. The knowledge base mode contains the data of X-ray representations of various stages of the disease. These elements are in the form of fuzzy production rules discussed in the previous paragraphs. The explanation interface details the conclusions, and the knowledge acquisition mode allows medical experts to add or change information in the system.

Problem domain:

medical

Knowledge domain:

medical/x-ray diagnosis (used to diagnosis various levels of pheumoconiosis)

Inference engine:

backward chaining

Language:

Lisp

Develop by:

Julie Herzner and Miriam Kubiska,

Dec 1992.

By:

Aina Saleha Binti Abdul Rahim (219885).

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Description : This paper surveys existing work in the field of knowledge-based systems that assist plant/process operators in the task of monitoring and control.

REACTOR is an expert system developed to assist operators in the diagnosis and treatment of nuclear reactor accident.

Problem domain :

To monitor a nuclear reactor facility, detect deviation from normal operating condition determine the significant of the situation and recommended an appropriate respond.

Knowledge domain :

Function-oriented knowledge and event-oriented knowledge.

Language : C

Inference engine :

Forward and backward chaining process.

Develop by :

Willian R. Nelso EG&G Idaho Inc

by Norliyana Shafiqah binti Khairul Anuar (220080)

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Description

The development of expert system is implemented
in CLIPS programming environment (C Language
Integrated Production System) .This
programming tool is designed to facilitate the
development of software to model human
knowledge or expertise for medical therapy.

This work presents the design of an expert system that aims to provide the patient with background for
suitable diagnosis of some of the eye diseases. The eye has always been viewed as a tunnel to the inner
workings of the body. There are many disease states that may produce symptoms from the eye.

Problem domain:

Medical.

Knowledge domain:

eye disease

inferences engine:

foward chaining

Develop By:

1Associate Prof., Samy S. Abu Naser, 1Abu Zaiter A. Ola
1Faculty of Engineering & Information Technology, Al-Azhar University, Gaza, Palestine

By:

MEK KECHUM A/P EH WAD

(219856)

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Description:
National Institute of Agricultural Extension Management (MANAGE) has developed an expert system to diagnose pests and diseases for rice crop and suggest preventive/curative measures. The rice crop doctor illustrates the use of expert-systems broadly in the area of agriculture and more specifically in the area of rice production through development of a prototype, taking into consideration a few major pests and diseases and some deficiency problems limiting rice yield.
The following diseases and pests have been included in the system for identification and suggesting preventive and curative measures. The diseases included are rice blast, brown spots, sheath blight, rice tungro virus, false smut fungi, bacterial leaf blight, sheath rot and zinc deficiency disease. The pests included are stem borers, rice gall midge, brown plant hopper, rice leaf folder, green leaf hopper and Gundhi bug

Problem Domain:

Agriculture

Knowledge Domain:
Diagnose disease and Pets

Language:
LIPS

Inference Engine:
Forward Chaining

Develop By:

National Institute of Agricultural Extension Management (MANAGE)

BY:

OIEL A/P EMIN

(221002

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Description:

The R1 (later called XCON, for eXpert CONfigurer) program was a production-rule-based

system written in OPS5 by John P. McDermott of CMU in 1978 to assist in the ordering of DEC's VAX computer systems by automatically selecting the computer system components based on the customer's requirements.

Problem domain:  Computer

Knowledge domain:Configure computer hardware.

Language:OPS5 a production system tool

Inference engine:forward chaining

Develop by:   John P. McDermott and DEC

By: Saili Surianie bt Mohd Ilias (220062)

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The Heart Disease Program (HDP) is a large diagnostic program covering most areas of heart disease. The physician can enter patient information about the history, physical examination, and laboratory tests, and then the program generates detailed explanations of differential diagnoses indicating the clinical data items which support each diagnosis. Its' Purpose is to assist physicians in the diagnosis of patients with heart disease

PROBLEM DOMAIN : Medical

KNOWLEDGE DOMAIN: Heart disease

LANGUAGE: LISP

INFERENCE ENGINE : Backward chaining

DEVELOP BY : Amani Al-Ajlan

KING SAUD UNIVERSITY COLLEGE OF COMPUTER & INFORMATION SCIENCES DEPARTMENT OF COMPUTER SCIENCE

BY: Norfaezah bt shak

220061

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Description:

It was a medical expert system that begun in 1970s and finished in the mid-1980s. It is an expert system that use to diagnose a diseases that focused on blood. This expert system eventually could diagnose up to 1000 different diseases. This expert system made a number of changes like incorporating abductive reasoning to deal with the additional complexity of internal disease there can be a number of simultaneous diseases, and it data is generally flawed and scarce. CADUCEUS has been described as the "most knowledge-intensive expert system in existence".

Problem Domain: Medicine diagnostic

Knowledge Domain:

this expert system was intent to improve on MYCIN-which focused on blood-borne infectious bacteria - to focus more comprehensive issues than a narrow field like blood poisoning, instead embracing all internal medicine.

Language: LISP

Inference Engine: Backward Chaining

Develop By: Harry Pople, building on Pople's years of interviews with Dr. Jack Meyers (top internal medicine diagnosticians)

From: University of Pittsburgh

By: Ng Xin Ning (219363)

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Description: ADVISE is an integrated set of tools for the devolopment of and experimentation with expert systems in different specific application domains. It functions as a multi-purpose inferences system which it allows use of three different form of knowledge representations such as a rule-based, a conceptual network and a relational database. Besides, it also includes learning capabilities by incorporating the inductive learning program GEM and CLUSTER.

Problem Domain: Agriculture and Medical

Knowledge Domain: Diagnosing soybean diseases, predicting cutworm damage to corn and a consultant for the neo-natal intensive care unit.

Language: PASCAL

Inference Engine: Backward Chaining

Developed by: A. Boulanger, M. Seyler, L. Rodewald, K. Spackman, C. Uhrik and R. Reinke from Department of Computer Science University of Illinois Urbana, Illinois

By: Lee Tsu Ping (219089)

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Description:

In those developing countries boasting near-saturation of mobile phones, the potential benefits of mHealth strategies are the greatest.
Microsoft and the University of Botswana are taking advantage of mobile telephony’s broad reach in the country to develop an Integrated Healthcare Information System through Mobile (IHISM). The system serves both health workers and the general public. It uses a mobile phone-based software application to allow health workers to capture, store, process, transmit, and access patient records. This results in lower costs and greater efficiency by eliminating redundancy and reducing the amount of
time devoted to data input.

The public can also turn to IHISM for information: individuals pose frequently asked questions about HIV/AIDS via SMS messages and receive a reply straight to their mobile phones. The project partners have identified several challenges, including localization and customization for illiterate users, but overall feel that the system has the potential
to become a valuable tool and take on increased scope.
Problem Domain: Medical

Knowledge Domain: HIV and AIDS Information

Language: Natural Language Processing

Inference Engine: Backward Chaining

Develop by: A team of computer scientists at the University of Botswana, led by Professor Henry Nyongesa (Professor of Computer Science at University of Botswana).

And sponsored by Microsoft, USA, as part of their Digital Inclusion.

By: Woo Ching Yin (218968)

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Description:

The system has been operational since June, 1972. At present we are attempting to improve the accuracy and performance of the system by adding to and improving the knowledge base. This is being done by an analysis of errors made by the system on seven sets of data from five male speakers in four different task domains. This process of modification and improvement is expected to continue for several years, using increasingly complex vocabularies, syntax, and task environments. The Hearsay system will be used primarily as a research tool to evaluate the contributions of various sources of knowledge, as well as serving as an information processing model of speech perception.

Problem domain:

Speech recognition and understanding

Knowledge domain:

The Hearsay system is not restricted to any particular recognition task. Given the syntax and the vocabulary of a language and the semantics of the task, it attempts recognition of utterances in that language. It is designed to serve as a research tool in which the contributions of various sources of knowledge towards recognition can be clearly evaluated. Since each source of knowledge is represented as an Independent process, it can be removed without crippling the system

Inference engine:

Backward and forward chaining

Develop By:

D.R. Reddy, LD. Erman, R.O. FennelI, and R.B. Neely Computer Science Department Carnegie -Mellon University Pittsburgh, Pa. 15213

By:

Nurul Syafirah binti Yusof (220879)

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Description:
SHYSTER-MYCIN is a hybrid legal expert system created by combining rule-based and case-based reasoning. The MYCIN part uses a system of rules to reason with provisions of an Act of a parliament; the SHYSTER part uses analogy to reason with cases that explain “open-textured” concepts encountered in legislation.The construction of the expert system is focused upon: creating and evaluating a model of legal reasoning, and improving the reporting made by the MYCIN part. The model of legal reasoning is supported by jurisprudential discussion. The model holds that rules (in the strict sense of the word) cannot be extracted from cases. Cases should therefore be argued by analogy. The only rules that exist in law are those in legislation.

SHYSTER-MYCIN has been developed to demonstrate that a useful legal expert system can be based upon a pragmatic approach to the law. SHYSTER-Mycin has a simple representation structure which simplifies the problem of knowledge acquisition. Yet this structure is complex enough for SHYSTER to produce useful advice. SHYSTER is a case-based legal expert system.

Problem Domain:

Advise on authorization of ownership copyrights based on court law.

Knowledge Domain:

Advise on authorization on ownership copyrights based on court law enforced by Australian government.

Language:

bespoke specification language + LISP

Inference Engine:

Forward and backward Chaining

Develop by:

Thomas A. O’Callaghan, James Popple and Eric McCreath May 2003

By:

NORFARHANA BINTI MOHD ASRI

219959

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Description: Expert system technology has captured the interest of professionals in a number of fields in recent years. Systems have been developed in such diverse areas as science.  Almost every professional and computer society currently has a special interest group for expert systems technology. This widespread interest can be attributed to the ability of the expert system to aid various organizations in solving practical, real-world problems. Scientific and technology-oriented organizations have applied expert systems underground to find oil or mineral deposits (Duda et al. 1977).

Problem domain:  One of the principal attractions of expert systems is that they enable computers to assist humans in many fields of endeavor with the processes of analyzing and solving complex problems. Experts use their knowledge about a given domain coupled with specific information about the current problem to arrive at a solution

Knowledge domain: A physician would possess knowledge about a variety of possible diseases and, coupled with specific information about a given patient, would be able to diagnose the patient's problem.

Inference engine: Backward and forward chaining may be integrated to solve a given problem. For example, a physician may initially gather information about a patient and use this information to form a hypothesis of the possible disease.

Rule : A rule is an IF/THEN type structure which relates some known information contained in the IF part to other information. This information can then be concluded to be contained in the THEN part.

Develop By : J Durkin
By: Saili Surianie Binti Mohd Ilias (220062)

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Description :

This paper describes an evaluation and several applications of a knowledge-based system, the PROSPECTOR consultant for mineral exploration. PROSPECTOR is a rule-based judgmental reasoning system that evaluates the mineral potential of a site or region with respect to inference network models of specific classes of ore deposits. Knowledge about a particular type of ore deposit is encoded in a computational model representing observable geological features and the relative significance thereof.

Problem Domain :

Geological Exploration

Knowledge Domain :

Representing observable geological features and the relative significance thereof.

Language :

INTERLISP/Visual Basic

Inference Engine :

Backward Chaining

Develop By :

J. Gaschnig Artificial Intelligence Center SRI International, USA

By :

Salsabeela Bt Mohd Yasin

221584

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Description:

Pople and Myers begin work on INTERNIST, one of the first clinical decision support systems, designed to support diagnosis, in 1970.INTERNIST was a rule-based expert system designed at the University of Pittsburgh in 1974 for the diagnosis of complex diagnosis of complex problems in general internal medicine. It uses patient observations to deduce a list of compatible disease states (based on a tree-structured database that links diseases with symptoms). By the early 1980s, it was recognized that the most valuable product of the system was its medical knowledge base. This was used as a basis for successor systems including CADUCEUS and Quick Medical Reference (QMR), a commercialised diagnostic DSS for internists.

Problem domain:  Medical.

Knowledge domain:

Medical diagnosis given interactive input.

Language:

Lisp

Inference engine:

forward chaining

Develop By:  Harry Pople.

By:

Siti Aishah Bt Mohamad @ Mat Nor (219067)

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MYCIN was an early expert system which was developed to capture the knowledge of an expert on infectious blood disease. MyCIN was developed by Stanford University in the early 1970. MyCIN had success rate of 69% which was better than human expert in the same criteria. But it was never used in practice due to some ethical and legal issues.

Problem Domain: Medical

Knowledge domain: Medical Diagnosis

Language: LISP

Inference Engine:

The backward chaining process

Conflict resolution strategies

Certainty factor computations (if desired)
Developed by:
Edward Shortliffe as his doctoral dissertation in Standford University under the supervision of Bruce Buchanan, Stanley N. Cohen and others.

By: Mohammad Amimul Ihsan Aquil (218784)

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Description... EACH posting worth 2 marks. Due date 2 Mac 2014. NO DUPLICATE.

Problem Domain: <general>
Knowledge Domain: <specificly...>
Language: <programming language>
Inference Engine: <Forward Chaining or backward>
Develop by:  <developer name> from <university/institution>

By: <your name>(<matric no>)

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DENDRAL (DENDRitic ALgorithm) a pioneer project in artificial intelligence (AI) of the 1960s. Claim to be the first expert system that automate the decision-making process and problem-solving behavior of organic chemists.  Consists two main programs Heuristic Dendral and Meta-Dendral.

Problem Domain: Chemistry
Knowledge Domain: Organic Chemistry
Language: Lisp
Inference Engine: Forward Chaining
Develop by:  Edward Feigenbaum, Bruce Buchanan,  Joshua Lederberg, and Carl Djerassi from Stanford University

By: Wan Hussain Wan Ishak
(1855)