free download complete expert system source code in php
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 please i need a free download Medical expert system major in php  and its source code
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Expert System is an artificial system or application where we can consult the experts of a field through short questions contained in a program that ended in the questions we answer will result in a conclusion and a solution that can solve our problems. Almost as much as we consult the doctor, the doctor will ask us what our complaints are and what we feel and will tell us what we are and what the cure is. So does the Expert Systems Applications.


Expert systems are designed to solve complex problems by reasoning about knowledge, represented primarily as if-then rules rather than through conventional procedural code. The first expert systems were created in the 1970s and then proliferated in the 1980s. Expert systems were among the first truly successful forms of artificial intelligence (AI) software.

An expert system is divided into two subsystems: the inference engine and the knowledge base. The knowledge base represents facts and rules. The inference engine applies the rules to known facts to deduce new facts. Inference engines can also include explaining and debugging capabilities.

Advantages

The goal of knowledge-based systems is to make the critical information required for the system to work to be explicit rather than implicit. In a traditional computer program logic is embedded in a code that normally can only be checked by an IT specialist. With an expert system, the aim was to specify the rules in an intuitive and easily understandable format, reviewed and even edited by domain experts instead of IT experts. The benefits of this explicit representation of knowledge were the rapid development and ease of maintenance.

Ease of maintenance is the most obvious benefit. This was achieved in two ways. First, by eliminating the need to write conventional code, many of the normal problems that can be caused by even small changes in a system could be avoided with expert systems. Essentially, the program's logical flow (at least at the highest level) was simply a die for the system, just invoking the inference engine. This was also a reason for the second benefit: rapid prototyping. With an expert system shell it was possible to introduce some rules and have a prototype developed in days instead of the months or year typically associated with complex IT projects.

One claim for expert system shells that was often made was that they eliminated the need for trained programmers and that the experts could develop systems themselves. In reality, this was rarely or never. While the rules for an expert system were more understandable than typical computer code, they still had a formal syntax where a hesitated comma or other character could wreak havoc as with any other computer language. In addition, as expert systems moved from prototypes in the laboratory to deployment in the business world, issues of integration and maintenance became much more critical. Inevitably, the need to integrate and take advantage of large databases and legacy systems was raised. To achieve this, integration requires the same skills as any other type of system.

Disadvantages

The most common disadvantage cited for expert systems in academic literature is the problem of knowledge acquisition. Getting the domain expert time for any software application is always difficult but for expert systems it was especially difficult because the experts were by definition highly valued and constantly in demand by the organization. As a result of this problem, a lot of research in recent years expert systems focused on knowledge acquisition tools, to help automate the process of designing, debugging and maintaining rules defined by experts. However, in examining the lifecycle of expert systems in real use, other problems - essentially the same problems as any other large system - seem at least as critical as the acquisition of knowledge: integration, access to large databases Data and performance.

Performance was especially problematic because early expert systems were built using tools such as Lisp, which implemented interpreted (rather than compiled) code. The interpretation provided an extremely powerful development environment, but with the drawback that it was virtually impossible to match the efficiency of the faster compiled languages such as C. The integration of systems and databases was difficult for early expert systems because the tools Were mainly in languages and platforms that were not familiar or welcome in most corporate IT environments: programming languages such as Lisp and Prolog, and hardware platforms such as Lisp machines and personal computers. As a result, many efforts in the later stages of the development of expert system tools focused on integration with legacy environments such as COBOL and large database systems, and porting to more standard platforms. These problems were solved mainly by the client-server paradigm shift, as PCs were gradually accepted in the IT environment as a legitimate platform for the development of serious business systems and as affordable minicomputer servers provided the processing power needed to AI applications.
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