Knowledge-Based Expert Systems

In this expert system will be created an expert system that will be used to diagnose diseases of bone fractures in humans using forward chaining and it is an application designed with engineering reasoning and tracking forward the start of a set of facts (data) to look for rules (indications) were matched with allegations / hypothesis that there towards the conclusion. An expert system developed web-based technology client and PHP (Hypertext Proprocessor) and MySQL based on the server side. The expert system is designed by using the method of Forward Chaining wherein expert system will display the questions in the form of indications that are often experienced by the user who will mengarkan user to reach conclusions and treatment that may be experienced by user (patient). I. INTRODUCTION The bones are the framework of the body that causes the body to be able to stand up straight, place of attachment of muscles thus allowing the course of the blood vessels, where the bone marrow and nerve protect the soft tissues, and bone is also an organ that humans need to lift and carry heavy goods. The Bone is an organ that we need to perform daily activities, so we can not imagine how disruption when there is a damage that happens to our bones. Some people think the bone is dead tissue that is passive, when in fact not. Bone is a living tissue and grow, as well as the constantly unload, reshape and improve its network. Bone Fracture is a condition that damages the frame and makes the bones weak and prone to fracture. Weak bones are not a natural part of aging. While strong bones begin in childhood, people of all ages can improve their bone health. Difficulties in diagnosing fractures, the authors make the application of expert systems to diagnose broken bones in humans by using forward chaining method which sourced from an expert who is in the Broken Bones Medical Clinic, the expected ability of an expert who specializes in health issues, particularly regarding diseases of the bones (in this case is an expert fractures) can be substituted into the computer in the form of a program that can be used by many people and can be used to resolve problems experienced independently without the presence of an expert directly so that we can conclude the diagnosis.

Machine Intelligence plays a crucial role in the design of expert systems in medical diagnosis. In India most of the people suffering from some sort of diseases like asthma, diabetics, cancer and many more. We consider the disease asthma for diagnosis. The diagnosis of asthma can be done in two ways 1) through questionnaire and 2) through clinical data. We considered both approaches to design the expert system for diagnosis of asthma. We have chosen some machine learning algorithms such as Context sensitive auto-associative memory neural network model[1] (CSAMM), Backpropogation model, C4.5 algorithm, Bayesian Network, Particle Swarm Optimization [7]. We present a performance study on these algorithms in terms of accuracy and some outstanding characteristics.

This book provides a set of important contributions presenting a number of expert systems that deal with modern engineering applications. The book is divided in five parts. Part 1--General issues. Part 2--Expert systems in engineering domains. Pat 3--Expert systems in fault diagnosis. Part 4--Expert systems in robotics and manufacturing. Part 5--Expert systems catalogs ( AI and expert systems tools).

This research practically demonstrates how to use data mining technology to supply knowledge to the rule based system. It lays down a framework for utilization of data mining concepts to provide a sustained supply of knowledge to a rule based system at runtime. A novel ‗record couple‘ based production rule mining algorithm has been proposed to extract production rules from large datasets. Production rules thus extracted are added to the knowledge base of a rule based system.
Conventional programming techniques are not efficient for performing knowledge oriented data consistency checks, especially in the domain involving large body of regularly updating knowledge. ‗Rule based systems‘, an established technique of Artificial Intelligence (AI), is well suited to perform data consistency checks on large datasets and perform corrective measures accordingly. A problem that limits the use of rule based systems is their implementation environment, which differs from data storage environment of the business world. Rule based systems and other AI related systems are normally developed in their specific environments such as Prolog, LISP, OPS-5 etc., while data of the business world is stored in relational environment.
This research proves that rule based programming is more efficient than conventional programming techniques. Further, to increase the utility of rule based systems, relational database environment has been used for implementation. A Structured Query Language (SQL) based representation of production rules has been proposed. Rule based engine has been designed, developed, and evaluated in relational database environment.
Moreover, the problem of knowledge acquisition has been persistent in the area of rule based systems. Manual knowledge editing techniques have been used so far to add knowledge to rule based systems. Although machine learning techniques have been used to overcome knowledge acquisition bottlenecks, yet limited success has been reported so far. In recent years, data mining has emerged as a useful technique to solve knowledge acquisition problems. Most of the data mining work produces very useful information for business executives and decision makers; however it leaves to the choice of decision makers either to use it or to disregard that information which limits the utilization of data mining technology considerably. Additionally, the area of ‗production rule mining‘ is not adequately explored by the data mining research community and often it is mixed up with ‗association rule mining‘.
A system integrated with machine learning module is termed as ‗Learning Apprentice System‘. Configuration of the system emerging as result of this research is termed as the ‗architecture of a learning apprentice system‘ as it involves a ‗rule based system module‘ integrated with ‗data mining based learning module‘. ‗Production rule mining algorithm‘ and ‗feeding the extracted production rules to a rule based system designed in relational database environment‘ are novel contributions of this research.
Medical billing compliance has been used as testing domain for concepts and ideas developed during this research. The rule based system is populated with the knowledge of medical claim processing rules using a knowledge editor. The proposed production rule mining algorithm has been utilized to mine medical claim processing rules which are then applied by the system to scrub medical claims i.e. detecting errors and performing minor, legitimate, and corrective actions.
Performance of the proposed system has been proved to be efficient and has problem-solving ability as compared to the systems based on conventional programming. Conventional programming based systems are good enough to perform small and simple checks, however where complex and knowledge oriented checks are involved, the techniques proposed have proven to be much better. Evaluation of data mining driven rule based learning apprentice system proved that a lot of time were saved by prompt identification of knowledge oriented data consistency errors from the medical billing data.
Although the current system has been tested in medical billing compliance domain, it can be applied to many real life problem domains which involve large numbers of knowledge oriented data consistency checks, such as credit card processing system, loan approval system, examination system etc.

The interdependence and complexity of socio-technical systems and availability of a wide variety of policy measures to address policy problems make the process of policy formulation difficult. In order to formulate sustainable and efficient transport policies, development of new tools and techniques is necessary. One of the approaches gaining ground is policy packaging, which shifts focus from implementation of individual policy measures to implementation of combinations of measures with the aim of increasing efficiency and effectiveness of policy interventions by increasing synergies and reducing potential contradictions among policy measures. In this paper, we describe the development of a virtual environment for the exploration and analysis of different configurations of policy measures in order to build policy packages. By developing systematic approaches it is possible to examine more alternatives at a greater depth, decrease the time required for the overall analysis, provide real-time assessment and feedback on the effect of changes in the configurations, and ultimately form more effective policies. The results from this research demonstrate the usefulness of computational approaches in addressing the complexity inherent in the formulation of policy packages. This new approach has been applied to the formulation of policies to advance sustainable transportation.

The purpose of this study is to explore the new facial biometric authentication feature of the new iPhone X with future research geared towards using this technology as a key component for traditional Chinese medical (TCM) medical diagnosis.

(1) To develop a rigorous and repeatable method for building effective Bayesian network (BN) models for medical decision support from complex, unstructured and incomplete patient questionnaires and interviews that inevitably contain examples of repetitive, redundant and contradictory responses; (2) To exploit expert knowledge in the BN development since further data acquisition is usually not possible; (3) To ensure the BN model can be used for interventional analysis; (4) To demonstrate why using data alone to learn the model structure and parameters is often unsatisfactory even when extensive data is available. The method is based on applying a range of recent BN developments targeted at helping experts build BNs given limited data. While most of the components of the method are based on established work, its novelty is that it provides a rigorous consolidated and generalised framework that addresses the whole life-cycle of BN model development. The method is based on two original and recent validated BN models in forensic psychiatry, known as DSVM-MSS and DSVM-P. When employed with the same datasets, the DSVM-MSS demonstrated competitive to superior predictive performance (AUC scores 0.708 and 0.797) against the state-of-the-art (AUC scores ranging from 0.527 to 0.705), and the DSVM-P demonstrated superior predictive performance (cross-validated AUC score of 0.78) against the state-of-the-art (AUC scores ranging from 0.665 to 0.717). More importantly, the resulting models go beyond improving predictive accuracy and into usefulness for risk management purposes through intervention, and enhanced decision support in terms of answering complex clinical questions that are based on unobserved evidence. This development process is applicable to any application domain which involves large-scale decision analysis based on such complex information, rather than based on data with hard facts, and in conjunction with the incorporation of expert knowledge for decision support via intervention. The novelty extends to challenging the decision scientists to reason about building models based on what information is really required for inference, rather than based on what data is available and hence, forces decision scientists to use available data in a much smarter way.

Spatial reasoning is a relevant topic in artificial intelligence with applications in geographical Information System, robotics, content-based image retrieval, traffic engineering. Additionally formal representation of knowledge allows the processing in a computer. Prolog is a programming language used in artificial intelligence that is useful to represent knowledge and perform a search, by asking questions in the knowledge base. Prolog can be used to develop a variety of applications like check the consistency or to perform any kind of reasoning. This article proposes the use of Prolog as a representation model and a reasoning engine to describe the topological relations between several objects in a geographic space, using the RCC model. The application of this simplifies the constructionprogram, allows us to focus on the spatial problem.

The model of RISK ANALYZER was implemented as Knowledge-based System for the purpose of undertaking risk analysis for proposed construction projects in a selected domain. The Fuzzy Decision Variables (FDVs) that cause differences between initial and final contract sums of building projects were identified, the likelihood of the occurrence of the risks were determined and a Knowledge-Based System that would rank the risks was constructed using JAVA programming language and Graphic User Interface. The Knowledge-Based System is composed a Knowledge Base for storing data, an Inference Engine for controlling and directing the use of knowledge for problem-solution, and a User Interface that assists the user retrieve, use and alter data in the Knowledge Base. The developed Knowledge-Based System was compiled, implemented and validated with data of previously completed projects. The client could utilize the Knowledge-Based System to undertake proposed building projects

Models of consensus are used in the validation process to develop a basis for system performance. This paper develops two analytic models of consensus that can be useful in the validation process. The first model employs the binomial model to study the probability that the consensus judgment is correct or incorrect. This problem is critical for the validation of expert systems, since the model leads us to conclude that in some cases consensus judgment is not appropriate. That basic model is extended to account for both different levels of expertise and unequal prior odds. The second model is a Bayesian model of the use of consensus judgment and the validation process. There are two applications of the Bayesian model The first application ofthe Bayesian model finds that, in some cases, consensus judgment is not appropriate and to generate a hypothesis as to the conditions for the use of the consensus judgment. The second application compares an expert systems performance to the consensus judgment. It can be used to assist in deriving stopping criteria for the: validation process.

Most of the data mining projects generate information (summarized in the form of graphs and charts) for business executives and decision makers; however it leaves to the choice of decision makers either to use it or disregard it. The manual use of the extracted knowledge limits the effectiveness of data mining technology considerably. This chapter proposes an architecture, in which data mining module is utilized to provide continuous supply of knowledge to a rule based expert system. Proposed approach solves the knowledge acquisition problem of rule based systems and also enhances effective utilization of data mining techniques (i.e. by supplying extracted knowledge to rule based system for automated use). The chapter describes the details of a data mining driven rule based expert system applied in medical billing domain. Main modules of the system along with the final analysis of performance of the system have also been presented.

Models ofconsensus are used in the validation process to develop a basis for system performance. This paper develops two analytic models ofconsensus that can be useful in the validation process. The first model employs the binomial model to study the probability that the consensus judgment is correct or incorrect. This problem is critical for the validation of expert systems, since the model leads us to conclude that in some cases consensus judgment is not appropriate. That basic model is extended to account for both different levels of expertise and unequal prior odds. The second model is a Bayesian model of the use of consensus judgment and the validation process. There are two applications of the Bayesian model The first application ofthe Bayesian model finds that, in some cases, consensus judgment is not appropriate and to generate a hypothesis as to the conditions for the use of the consensus judgment. The second application compares an expert systems performance to the consens...