Finding new framework for resolving problems in various dimensions by the use of es an efficient and effective computer oriented artificial intelligence approach
This document provides an overview of expert systems, including their components, development lifecycle, applications, advantages, and limitations. It describes the basic modules of an expert system including the knowledge acquisition subsystem, knowledge base, inference engine, explanation subsystem, and user interface. It also discusses expert system tools, characteristics, and some examples of expert system applications in domains like monitoring, diagnosis, design, and more. Overall, the document presents a broad introduction to expert systems, their architecture and uses.
Application of VLSI In Artificial IntelligenceIOSR Journals
This document discusses the application of VLSI (Very Large Scale Integrated) circuits in artificial intelligence. It begins with a brief history of the development of microelectronics and integrated circuits. It then provides definitions of artificial intelligence and describes how VLSI technology has enabled more powerful computer architectures for AI. The document focuses on how expert systems, which apply reasoning to knowledge bases, have been important early applications of AI to VLSI chip design. It provides examples of expert systems used for tasks like circuit simulation and assisting with VLSI design. In closing, it emphasizes that knowledge-based approaches using rules have advantages for incremental improvements and explaining reasoning.
In computer domain the professionals were limited in number but the numbers of institutions looking for
computer professionals were high. The aim of this study is developing self learning expert system which is
providing troubleshooting information about problems occurred in the computer system for the information
and communication technology technicians and computer users to solve problems effectively and efficiently
to utilize computer and computer related resources. Domain knowledge was acquired using semistructured
interview technique, observation and document analysis. Domain experts were purposively
selected for the interview question. The conceptual model of the expert system was designed by using a
decision tree structure which is easy to understand and interpret the causes involved in computer
troubleshooting. Based on the conceptual model, the expert system was developed by using ‘if – then’ rules.
The developed system used backward chaining to infer the rules and provide appropriate
recommendations. According to the system evaluators 83.6% of the users were satisfied with the prototype.
Face recognition for presence system by using residual networks-50 architectu...IJECEIAES
Presence system is a system for recording the individual attendance in the company, school or institution. There are several types presence system, including the manually presence system using signatures, presence system using fingerprints and presence system using face recognition technology. Presence system using face recognition technology is one of presence system that implements biometric system in the process of recording attendance. In this research we used one of the convolutional neural network (CNN) architectures that won the imagenet large scale visual recognition competition (ILSVRC) in 2015, namely the Residual Networks-50 architecture (ResNet-50) for face recognition. Our contribution in this research is to determine effectiveness ResNet architecture with different configuration of hyperparameters. This hyperparameters includes the number of hidden layers, the number of units in the hidden layer, batch size, and learning rate. Because hyperparameter are selected based on how the experiments performed and the value of each hyperparameter affects the final result accuracy, so we try 22 configurations (experiments) to get the best accuracy. We conducted experiments to get the best model with an accuracy of 99%.
An Expert System in artificial intelligence is a computer application to solve complex problems in a particular domain and make a right decision in order to implement corrections. Arjumand Ali "Expert Systems and Decision–Making" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-3 , April 2021, URL: https://www.ijtsrd.com/papers/ijtsrd38678.pdf Paper URL: https://www.ijtsrd.com/engineering/computer-engineering/38678/expert-systems-and-decision–making/arjumand-ali
Expert Systems in Banking and InsuranceMahesh Karane
This document provides an overview of expert systems, including:
- Expert systems are computer applications that solve complex problems at an expert human level using domain-specific knowledge. They advise, derive solutions, explain, predict, and suggest options.
- Key components include a knowledge base, interface engine, and user interface. The knowledge base contains domain expertise. The interface engine uses this to arrive at solutions. The user interface facilitates interaction.
- Examples of expert systems include ATMs, which perform bank teller functions, and underwriting systems that help assess insurance risks. Various industries have implemented expert systems to improve decisions, availability, and consistency compared to human experts.
Report-An Expert System for Car Failure Diagnosis-ReportViralkumar Jayswal
This document describes a proposed expert system for car failure diagnosis. It explains that car failure detection requires expertise and is a complex process. The proposed system would have a knowledge base of 150 rules for different failure types and causes, and could detect over 100 failure types. It was tested and showed promising results. The system would help less experienced mechanics and drivers diagnose issues by capturing expertise in a computer application. It would have components like a knowledge base, inference engine, and user interface to identify failures based on user inputs about symptoms.
An expert system is a computer program that contains knowledge about a specific domain that allows it to solve problems or provide advice like a human expert. Expert systems are made up of a knowledge base, inference engine, and user interface. They are developed through knowledge engineering, which involves knowledge engineers working with domain experts to gather knowledge about a problem domain and represent it in a way that a computer can understand. Some key applications of expert systems include medical diagnosis, mineral prospecting, and configuring computer systems.
The document discusses different approaches to developing expert systems, including object models, reformulation approaches, and low road, high road, and middle road approaches. It analyzes the advantages of each approach and concludes that the reformulation approach is best. This approach separates knowledge from problem solving procedures and uses transformation processes to map the expert's knowledge structure to input problems.
Application of VLSI In Artificial IntelligenceIOSR Journals
This document discusses the application of VLSI (Very Large Scale Integrated) circuits in artificial intelligence. It begins with a brief history of the development of microelectronics and integrated circuits. It then provides definitions of artificial intelligence and describes how VLSI technology has enabled more powerful computer architectures for AI. The document focuses on how expert systems, which apply reasoning to knowledge bases, have been important early applications of AI to VLSI chip design. It provides examples of expert systems used for tasks like circuit simulation and assisting with VLSI design. In closing, it emphasizes that knowledge-based approaches using rules have advantages for incremental improvements and explaining reasoning.
In computer domain the professionals were limited in number but the numbers of institutions looking for
computer professionals were high. The aim of this study is developing self learning expert system which is
providing troubleshooting information about problems occurred in the computer system for the information
and communication technology technicians and computer users to solve problems effectively and efficiently
to utilize computer and computer related resources. Domain knowledge was acquired using semistructured
interview technique, observation and document analysis. Domain experts were purposively
selected for the interview question. The conceptual model of the expert system was designed by using a
decision tree structure which is easy to understand and interpret the causes involved in computer
troubleshooting. Based on the conceptual model, the expert system was developed by using ‘if – then’ rules.
The developed system used backward chaining to infer the rules and provide appropriate
recommendations. According to the system evaluators 83.6% of the users were satisfied with the prototype.
Face recognition for presence system by using residual networks-50 architectu...IJECEIAES
Presence system is a system for recording the individual attendance in the company, school or institution. There are several types presence system, including the manually presence system using signatures, presence system using fingerprints and presence system using face recognition technology. Presence system using face recognition technology is one of presence system that implements biometric system in the process of recording attendance. In this research we used one of the convolutional neural network (CNN) architectures that won the imagenet large scale visual recognition competition (ILSVRC) in 2015, namely the Residual Networks-50 architecture (ResNet-50) for face recognition. Our contribution in this research is to determine effectiveness ResNet architecture with different configuration of hyperparameters. This hyperparameters includes the number of hidden layers, the number of units in the hidden layer, batch size, and learning rate. Because hyperparameter are selected based on how the experiments performed and the value of each hyperparameter affects the final result accuracy, so we try 22 configurations (experiments) to get the best accuracy. We conducted experiments to get the best model with an accuracy of 99%.
An Expert System in artificial intelligence is a computer application to solve complex problems in a particular domain and make a right decision in order to implement corrections. Arjumand Ali "Expert Systems and Decision–Making" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-3 , April 2021, URL: https://www.ijtsrd.com/papers/ijtsrd38678.pdf Paper URL: https://www.ijtsrd.com/engineering/computer-engineering/38678/expert-systems-and-decision–making/arjumand-ali
Expert Systems in Banking and InsuranceMahesh Karane
This document provides an overview of expert systems, including:
- Expert systems are computer applications that solve complex problems at an expert human level using domain-specific knowledge. They advise, derive solutions, explain, predict, and suggest options.
- Key components include a knowledge base, interface engine, and user interface. The knowledge base contains domain expertise. The interface engine uses this to arrive at solutions. The user interface facilitates interaction.
- Examples of expert systems include ATMs, which perform bank teller functions, and underwriting systems that help assess insurance risks. Various industries have implemented expert systems to improve decisions, availability, and consistency compared to human experts.
Report-An Expert System for Car Failure Diagnosis-ReportViralkumar Jayswal
This document describes a proposed expert system for car failure diagnosis. It explains that car failure detection requires expertise and is a complex process. The proposed system would have a knowledge base of 150 rules for different failure types and causes, and could detect over 100 failure types. It was tested and showed promising results. The system would help less experienced mechanics and drivers diagnose issues by capturing expertise in a computer application. It would have components like a knowledge base, inference engine, and user interface to identify failures based on user inputs about symptoms.
An expert system is a computer program that contains knowledge about a specific domain that allows it to solve problems or provide advice like a human expert. Expert systems are made up of a knowledge base, inference engine, and user interface. They are developed through knowledge engineering, which involves knowledge engineers working with domain experts to gather knowledge about a problem domain and represent it in a way that a computer can understand. Some key applications of expert systems include medical diagnosis, mineral prospecting, and configuring computer systems.
The document discusses different approaches to developing expert systems, including object models, reformulation approaches, and low road, high road, and middle road approaches. It analyzes the advantages of each approach and concludes that the reformulation approach is best. This approach separates knowledge from problem solving procedures and uses transformation processes to map the expert's knowledge structure to input problems.
1. The document describes an expert system and its components.
2. It defines an expert system as an intelligent computer program that uses knowledge and reasoning to solve problems that usually require human expertise.
3. The key components of an expert system are the knowledge base, inference engine, explanation facility, and knowledge acquisition facility.
Sep2009 Introduction to Medical Expert Decision Support Systems for Mayo Clinicdoc_vogt
This document discusses expert systems and their potential application to medical decision support. It provides background on expert systems, describing their components like knowledge bases, inference engines, and explanation facilities. It also discusses different approaches to building expert systems, such as production rules, pattern recognition, fuzzy logic, and imagery analysis. The document then discusses some examples of medical expert systems from the past and potential benefits of developing new expert decision support systems.
Comparative Study on Machine Learning Algorithms for Network Intrusion Detect...ijtsrd
Network has brought convenience to the earth by permitting versatile transformation of information, however it conjointly exposes a high range of vulnerabilities. A Network Intrusion Detection System helps network directors and system to view network security violation in their organizations. Characteristic unknown and new attacks are one of the leading challenges in Intrusion Detection System researches. Deep learning that a subfield of machine learning cares with algorithms that are supported the structure and performance of brain known as artificial neural networks. The improvement in such learning algorithms would increase the probability of IDS and the detection rate of unknown attacks. Throughout, we have a tendency to suggest a deep learning approach to implement increased IDS and associate degree economical. Priya N | Ishita Popli "Comparative Study on Machine Learning Algorithms for Network Intrusion Detection System" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-1 , December 2020, URL: https://www.ijtsrd.com/papers/ijtsrd38175.pdf Paper URL : https://www.ijtsrd.com/computer-science/computer-network/38175/comparative-study-on-machine-learning-algorithms-for-network-intrusion-detection-system/priya-n
Problem Decomposition: Goal Trees, Rule Based Systems, Rule Based Expert Systems. Planning:
STRIPS, Forward and Backward State Space Planning, Goal Stack Planning, Plan Space Planning,
A Unified Framework For Planning. Constraint Satisfaction : N-Queens, Constraint Propagation,
Scene Labeling, Higher order and Directional Consistencies, Backtracking and Look ahead
Strategies.
Expert systems are a type of artificial intelligence application that uses knowledge about a specialized domain to provide expert-level advice. An expert system consists of a knowledge base containing facts and rules about the domain, and an inference engine that reasons about the knowledge base like a human expert would. Expert systems were developed to help solve complex problems in domains like medicine, engineering, and business by embodying the knowledge and reasoning of human experts.
IRJET- Chest Abnormality Detection from X-Ray using Deep LearningIRJET Journal
This document proposes using a convolutional neural network (CNN) to detect abnormalities in chest x-rays. It discusses developing a CNN model with an input of chest x-ray images labeled as normal or abnormal. The model would use techniques like pre-processing, data augmentation, and a network architecture with convolutional and pooling layers to classify images as normal or abnormal. The goal is to build an accurate system for detecting various chest diseases from x-ray images to help doctors with diagnosis.
Expert systems are a type of artificial intelligence application that aims to emulate the decision-making ability of human experts. They are designed to analyze complex problems through reference to a large body of knowledge and provide advice or solutions to problems. An expert system consists of a knowledge base, strategy component, and implementation programs. They are used in applications like medical diagnosis, financial forecasting, and vehicle routing. Some key benefits of expert systems include their ability to imitate human reasoning, facilitate knowledge sharing, provide expert-level recommendations, and handle uncertain information. An example of an Indian expert system is the Rice Crop Doctor developed by MANAGE to diagnose pests and diseases affecting rice production.
This document is a term paper submitted by Hannah Gurung and Rajesh Paneru to their professor at Kathmandu University School of Management about expert systems and their applications in Nepal. The paper includes an introduction on expert systems, a literature review on expert systems and their impacts in various sectors, examples of expert system success and failure stories, the potential for expert systems in Nepal, and conclusions and recommendations.
Expert systems are a type of artificial intelligence application that aims to emulate the decision-making ability of human experts. They are designed to analyze complex problems through reference to a large body of knowledge and provide advice or solutions to problems. An expert system consists of a knowledge base, strategy component, and implementation programs. They are used in applications like medical diagnosis, financial forecasting, and vehicle routing. Some key benefits of expert systems include their ability to imitate human reasoning, facilitate knowledge sharing, provide expert-level recommendations, and handle uncertain information. An example of an Indian expert system is the Rice Crop Doctor developed by MANAGE to diagnose pests and diseases affecting rice production.
This document provides an overview of artificial intelligence and expert systems. It defines artificial intelligence as computers with the ability to mimic human intelligence. The major branches of AI discussed are perceptive systems, vision systems, robotics, expert systems, learning systems, natural language processing, and neural networks. Expert systems are defined as systems that store knowledge and make inferences. The components of an expert system are described as the knowledge base, inference engine, rules, and other elements. Examples are provided of rules for a credit application and the roles of explanation facilities and knowledge acquisition in expert systems.
The document discusses expert systems, which are computer programs that use artificial intelligence to solve complex problems that usually require human expertise. An example is a medical diagnosis expert system that allows a user to diagnose a disease without seeing a doctor. The key components of an expert system are the knowledge base, inference engine, and user interface. The knowledge base contains facts and rules acquired from human experts. The inference engine uses the rules to deduce conclusions. It can work forward or backward from the facts. The user interface allows interaction between the user and the system. The document provides examples of code for a medical diagnosis expert system and discusses some limitations of expert systems.
ANALYSIS OF SYSTEM ON CHIP DESIGN USING ARTIFICIAL INTELLIGENCEijesajournal
Automation is a powerful word that lies everywhere. It shows that without automation, application will not get developed. In a semiconductor industry, artificial intelligence played a vital role for implementing the chip based design through automation .The main advantage of applying the machine learning & deep learning technique is to improve the implementation rate based upon the capability of the society. The main objective of the proposed system is to apply the deep learning using data driven approach for controlling the system. Thus leads to a improvement in design, delay ,speed of operation & costs. Through this system, huge volume of data’s that are generated by the system will also get control.
This document discusses expert systems, including their structure, characteristics, and development process. It describes expert systems as programs that use knowledge and reasoning to solve complex problems like human experts. The key components of an expert system are the knowledge base, inference engine, and user interface. The document also outlines the typical phases of expert system development including assessment, knowledge acquisition, design, testing and maintenance. It identifies the main roles of domain experts, knowledge engineers and users in building expert systems.
This document discusses expert systems and their application in road transport. It begins with definitions of expert systems and how they emulate human decision making. It then outlines the typical design of rule-based expert systems, including the knowledge base and inference engine. Next, it describes the six phase development process for building an expert system and provides comments on each phase. It also discusses rule-based reasoning approaches, including goal-driven and data-driven reasoning. Finally, it lists some advantages of expert systems and examples of their applications.
This document summarizes a research paper that proposes using an ensemble of k-nearest neighbor (k-NN) classifiers with genetic programming to improve network intrusion detection. The researchers trained classifiers on the KDD Cup 1999 dataset, which contains network traffic labeled as normal or an attack of various types. They preprocessed the data to remove redundancy and applied feature selection before training. The ensemble of k-NN classifiers classified data into five categories - one normal and four attack types - and achieved 99.97% accuracy on testing after genetic programming optimized the ensemble.
An expert system is an interactive computer-based decision tool that uses both facts and heuristics to solve difficult decision problems based on knowledge acquired from an expert.
IRJET- Design an Approach for Prediction of Human Activity Recognition us...IRJET Journal
The document proposes a framework for human activity recognition using smartphones. It involves collecting data from a smartphone's accelerometer and gyroscope sensors worn on the waist during various activities of daily living. The data is preprocessed and classified using machine learning algorithms like Naive Bayes, logistic regression, and SVM. The proposed framework first loads and preprocesses the sensor data, then generates features before splitting the data into training and test sets. Various classifiers are applied and evaluated to select the best performing one for activity recognition. The authors conclude that implementing tri-axial acceleration from sensors provides different accuracy for different algorithms, with SVM achieving maximum accuracy in previous work.
An expert system is a type of artificial intelligence program that uses a knowledge base of human expert knowledge to advise or make decisions like a human expert. It consists of a user interface, knowledge base containing facts and rules provided by experts, and an inference engine that examines the knowledge base to match queries to answers. The non-expert user asks a question through the interface, the inference engine searches the knowledge base and provides a response drawing on the stored human expertise. Expert systems are used for tasks like medical diagnosis, game strategies, and identification of objects.
Due to diagnosis problem in detecting lung Cancer, it becomes the most dangerous cancer seen in human being. Because of early diagnosis, the survival rate among people is increased. The prediction of lung cancer is the most challenging cancer problem, due to its structure of cells in human body. In which most of tissues or cells are overlapping on one another. Now-a-days, the use of images processing techniques is increased in growing medical field for its disease diagnosis, where the time factor plays important role. Detecting cancer within a time, increases the survival rate of patients. Many radiologists still use MRI only for assessment of superior sulcus tumors and in cases where invasion of spinal cord canal is suspected. MRI can detect and stage lung cancer and this method would be excellent of lung malignancies and other diseases.
A PROPOSED EXPERT SYSTEM FOR EVALUATING THE PARTNERSHIP IN BANKSjares jares
Expert systems are no longer just a technology, but they have entered many fields of decision-making from these medical fields, for example, as they help in diagnosing the disease and giving treatment, and also in the field of administration, where they give the manager a rational decision to solve a problem and other fields, DSS is an interactive information system that provides information, models, and data processing tools to assist decision-making. Islamic banks such as commercial banks offer products and services to customers, but these banks face many problems and the most important ones are the problems financing where Islamic banks seek to participate in money rather than lending and interest the participatory financing system is one of the most important sources of financing within Islamic banks This system is based on the agreement between the Bank and the customer to participate in a new project or project already in place in the proportions that agree to by the bank and the client but this funding takes a long time and many actions so the researcher has built an expert system to reduce the time it takes to award Funding and also to reduce procedures as the expert systems have the ability to help the human element in making decisions. This paper presents expert systems in Islamic banks in the system of co-financing in order to save time and effort and maximize profit.
Multiagent Based Methodologies have become an
important subject of research in advance Software Engineering.
Several methodologies have been proposed as, a theoretical
approach, to facilitate and support the development of complex
distributed systems. An important question when facing the
construction of Agent Applications is deciding which
methodology to follow. Trying to answer this question, a
framework with several criteria is applied in this paper for the
comparative analysis of existing multiagent system
methodologies. The results of the comparative over two of them,
conclude that those methodologies have not reached a sufficient
maturity level to be used by the software industry. The
framework has also proved its utility for the evaluation of any
kind of Multiagent Based Software Engineering Methodology
Knowledge or Rule based Expert systems systems are widely used in engineering applications and in problem-solving. Rapid development today has brought with it environmental problems that cause loss or destruction of natural resources. Environmental impact assessment (EIA) has been acknowledged as a powerful planning and decisionmaking tool to assess new development projects. It requires qualified personnel with special expertise and responsibility in their domain. Rule-based EIA systems incorporate expert’s knowledge and act as a device-giving system. The system has an advantage over human experts and can significantly reduce the complexity of a planning task like EIA.
1. The document describes an expert system and its components.
2. It defines an expert system as an intelligent computer program that uses knowledge and reasoning to solve problems that usually require human expertise.
3. The key components of an expert system are the knowledge base, inference engine, explanation facility, and knowledge acquisition facility.
Sep2009 Introduction to Medical Expert Decision Support Systems for Mayo Clinicdoc_vogt
This document discusses expert systems and their potential application to medical decision support. It provides background on expert systems, describing their components like knowledge bases, inference engines, and explanation facilities. It also discusses different approaches to building expert systems, such as production rules, pattern recognition, fuzzy logic, and imagery analysis. The document then discusses some examples of medical expert systems from the past and potential benefits of developing new expert decision support systems.
Comparative Study on Machine Learning Algorithms for Network Intrusion Detect...ijtsrd
Network has brought convenience to the earth by permitting versatile transformation of information, however it conjointly exposes a high range of vulnerabilities. A Network Intrusion Detection System helps network directors and system to view network security violation in their organizations. Characteristic unknown and new attacks are one of the leading challenges in Intrusion Detection System researches. Deep learning that a subfield of machine learning cares with algorithms that are supported the structure and performance of brain known as artificial neural networks. The improvement in such learning algorithms would increase the probability of IDS and the detection rate of unknown attacks. Throughout, we have a tendency to suggest a deep learning approach to implement increased IDS and associate degree economical. Priya N | Ishita Popli "Comparative Study on Machine Learning Algorithms for Network Intrusion Detection System" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-1 , December 2020, URL: https://www.ijtsrd.com/papers/ijtsrd38175.pdf Paper URL : https://www.ijtsrd.com/computer-science/computer-network/38175/comparative-study-on-machine-learning-algorithms-for-network-intrusion-detection-system/priya-n
Problem Decomposition: Goal Trees, Rule Based Systems, Rule Based Expert Systems. Planning:
STRIPS, Forward and Backward State Space Planning, Goal Stack Planning, Plan Space Planning,
A Unified Framework For Planning. Constraint Satisfaction : N-Queens, Constraint Propagation,
Scene Labeling, Higher order and Directional Consistencies, Backtracking and Look ahead
Strategies.
Expert systems are a type of artificial intelligence application that uses knowledge about a specialized domain to provide expert-level advice. An expert system consists of a knowledge base containing facts and rules about the domain, and an inference engine that reasons about the knowledge base like a human expert would. Expert systems were developed to help solve complex problems in domains like medicine, engineering, and business by embodying the knowledge and reasoning of human experts.
IRJET- Chest Abnormality Detection from X-Ray using Deep LearningIRJET Journal
This document proposes using a convolutional neural network (CNN) to detect abnormalities in chest x-rays. It discusses developing a CNN model with an input of chest x-ray images labeled as normal or abnormal. The model would use techniques like pre-processing, data augmentation, and a network architecture with convolutional and pooling layers to classify images as normal or abnormal. The goal is to build an accurate system for detecting various chest diseases from x-ray images to help doctors with diagnosis.
Expert systems are a type of artificial intelligence application that aims to emulate the decision-making ability of human experts. They are designed to analyze complex problems through reference to a large body of knowledge and provide advice or solutions to problems. An expert system consists of a knowledge base, strategy component, and implementation programs. They are used in applications like medical diagnosis, financial forecasting, and vehicle routing. Some key benefits of expert systems include their ability to imitate human reasoning, facilitate knowledge sharing, provide expert-level recommendations, and handle uncertain information. An example of an Indian expert system is the Rice Crop Doctor developed by MANAGE to diagnose pests and diseases affecting rice production.
This document is a term paper submitted by Hannah Gurung and Rajesh Paneru to their professor at Kathmandu University School of Management about expert systems and their applications in Nepal. The paper includes an introduction on expert systems, a literature review on expert systems and their impacts in various sectors, examples of expert system success and failure stories, the potential for expert systems in Nepal, and conclusions and recommendations.
Expert systems are a type of artificial intelligence application that aims to emulate the decision-making ability of human experts. They are designed to analyze complex problems through reference to a large body of knowledge and provide advice or solutions to problems. An expert system consists of a knowledge base, strategy component, and implementation programs. They are used in applications like medical diagnosis, financial forecasting, and vehicle routing. Some key benefits of expert systems include their ability to imitate human reasoning, facilitate knowledge sharing, provide expert-level recommendations, and handle uncertain information. An example of an Indian expert system is the Rice Crop Doctor developed by MANAGE to diagnose pests and diseases affecting rice production.
This document provides an overview of artificial intelligence and expert systems. It defines artificial intelligence as computers with the ability to mimic human intelligence. The major branches of AI discussed are perceptive systems, vision systems, robotics, expert systems, learning systems, natural language processing, and neural networks. Expert systems are defined as systems that store knowledge and make inferences. The components of an expert system are described as the knowledge base, inference engine, rules, and other elements. Examples are provided of rules for a credit application and the roles of explanation facilities and knowledge acquisition in expert systems.
The document discusses expert systems, which are computer programs that use artificial intelligence to solve complex problems that usually require human expertise. An example is a medical diagnosis expert system that allows a user to diagnose a disease without seeing a doctor. The key components of an expert system are the knowledge base, inference engine, and user interface. The knowledge base contains facts and rules acquired from human experts. The inference engine uses the rules to deduce conclusions. It can work forward or backward from the facts. The user interface allows interaction between the user and the system. The document provides examples of code for a medical diagnosis expert system and discusses some limitations of expert systems.
ANALYSIS OF SYSTEM ON CHIP DESIGN USING ARTIFICIAL INTELLIGENCEijesajournal
Automation is a powerful word that lies everywhere. It shows that without automation, application will not get developed. In a semiconductor industry, artificial intelligence played a vital role for implementing the chip based design through automation .The main advantage of applying the machine learning & deep learning technique is to improve the implementation rate based upon the capability of the society. The main objective of the proposed system is to apply the deep learning using data driven approach for controlling the system. Thus leads to a improvement in design, delay ,speed of operation & costs. Through this system, huge volume of data’s that are generated by the system will also get control.
This document discusses expert systems, including their structure, characteristics, and development process. It describes expert systems as programs that use knowledge and reasoning to solve complex problems like human experts. The key components of an expert system are the knowledge base, inference engine, and user interface. The document also outlines the typical phases of expert system development including assessment, knowledge acquisition, design, testing and maintenance. It identifies the main roles of domain experts, knowledge engineers and users in building expert systems.
This document discusses expert systems and their application in road transport. It begins with definitions of expert systems and how they emulate human decision making. It then outlines the typical design of rule-based expert systems, including the knowledge base and inference engine. Next, it describes the six phase development process for building an expert system and provides comments on each phase. It also discusses rule-based reasoning approaches, including goal-driven and data-driven reasoning. Finally, it lists some advantages of expert systems and examples of their applications.
This document summarizes a research paper that proposes using an ensemble of k-nearest neighbor (k-NN) classifiers with genetic programming to improve network intrusion detection. The researchers trained classifiers on the KDD Cup 1999 dataset, which contains network traffic labeled as normal or an attack of various types. They preprocessed the data to remove redundancy and applied feature selection before training. The ensemble of k-NN classifiers classified data into five categories - one normal and four attack types - and achieved 99.97% accuracy on testing after genetic programming optimized the ensemble.
An expert system is an interactive computer-based decision tool that uses both facts and heuristics to solve difficult decision problems based on knowledge acquired from an expert.
IRJET- Design an Approach for Prediction of Human Activity Recognition us...IRJET Journal
The document proposes a framework for human activity recognition using smartphones. It involves collecting data from a smartphone's accelerometer and gyroscope sensors worn on the waist during various activities of daily living. The data is preprocessed and classified using machine learning algorithms like Naive Bayes, logistic regression, and SVM. The proposed framework first loads and preprocesses the sensor data, then generates features before splitting the data into training and test sets. Various classifiers are applied and evaluated to select the best performing one for activity recognition. The authors conclude that implementing tri-axial acceleration from sensors provides different accuracy for different algorithms, with SVM achieving maximum accuracy in previous work.
An expert system is a type of artificial intelligence program that uses a knowledge base of human expert knowledge to advise or make decisions like a human expert. It consists of a user interface, knowledge base containing facts and rules provided by experts, and an inference engine that examines the knowledge base to match queries to answers. The non-expert user asks a question through the interface, the inference engine searches the knowledge base and provides a response drawing on the stored human expertise. Expert systems are used for tasks like medical diagnosis, game strategies, and identification of objects.
Due to diagnosis problem in detecting lung Cancer, it becomes the most dangerous cancer seen in human being. Because of early diagnosis, the survival rate among people is increased. The prediction of lung cancer is the most challenging cancer problem, due to its structure of cells in human body. In which most of tissues or cells are overlapping on one another. Now-a-days, the use of images processing techniques is increased in growing medical field for its disease diagnosis, where the time factor plays important role. Detecting cancer within a time, increases the survival rate of patients. Many radiologists still use MRI only for assessment of superior sulcus tumors and in cases where invasion of spinal cord canal is suspected. MRI can detect and stage lung cancer and this method would be excellent of lung malignancies and other diseases.
A PROPOSED EXPERT SYSTEM FOR EVALUATING THE PARTNERSHIP IN BANKSjares jares
Expert systems are no longer just a technology, but they have entered many fields of decision-making from these medical fields, for example, as they help in diagnosing the disease and giving treatment, and also in the field of administration, where they give the manager a rational decision to solve a problem and other fields, DSS is an interactive information system that provides information, models, and data processing tools to assist decision-making. Islamic banks such as commercial banks offer products and services to customers, but these banks face many problems and the most important ones are the problems financing where Islamic banks seek to participate in money rather than lending and interest the participatory financing system is one of the most important sources of financing within Islamic banks This system is based on the agreement between the Bank and the customer to participate in a new project or project already in place in the proportions that agree to by the bank and the client but this funding takes a long time and many actions so the researcher has built an expert system to reduce the time it takes to award Funding and also to reduce procedures as the expert systems have the ability to help the human element in making decisions. This paper presents expert systems in Islamic banks in the system of co-financing in order to save time and effort and maximize profit.
A PROPOSED EXPERT SYSTEM FOR EVALUATING THE PARTNERSHIP IN BANKS
Similar to Finding new framework for resolving problems in various dimensions by the use of es an efficient and effective computer oriented artificial intelligence approach
Multiagent Based Methodologies have become an
important subject of research in advance Software Engineering.
Several methodologies have been proposed as, a theoretical
approach, to facilitate and support the development of complex
distributed systems. An important question when facing the
construction of Agent Applications is deciding which
methodology to follow. Trying to answer this question, a
framework with several criteria is applied in this paper for the
comparative analysis of existing multiagent system
methodologies. The results of the comparative over two of them,
conclude that those methodologies have not reached a sufficient
maturity level to be used by the software industry. The
framework has also proved its utility for the evaluation of any
kind of Multiagent Based Software Engineering Methodology
Knowledge or Rule based Expert systems systems are widely used in engineering applications and in problem-solving. Rapid development today has brought with it environmental problems that cause loss or destruction of natural resources. Environmental impact assessment (EIA) has been acknowledged as a powerful planning and decisionmaking tool to assess new development projects. It requires qualified personnel with special expertise and responsibility in their domain. Rule-based EIA systems incorporate expert’s knowledge and act as a device-giving system. The system has an advantage over human experts and can significantly reduce the complexity of a planning task like EIA.
This document provides an overview of expert systems, including their history, types, components, applications, and development process. Some key points:
- Early expert systems included DENDRAL for chemical analysis and MYCIN for medical diagnosis. Expert systems were pioneered in the 1970s and 1980s.
- Expert systems can advise on complex problems like a human expert through rule-based or frame-based knowledge representation.
- The main components are the knowledge base containing domain expertise, an interface engine that uses rules to solve problems, and a user interface.
- Expert systems have various applications in fields like design, medicine, monitoring, and finance. However, they also have limitations like knowledge acquisition difficulties
A Review on Reasoning System, Types, and Tools and Need for Hybrid ReasoningBRNSSPublicationHubI
This document summarizes a review article about reasoning systems, types of reasoning, and the need for hybrid reasoning systems. It discusses expert systems and how they use knowledge representation and reasoning to emulate expert decision making. The main types of reasoning discussed are deductive, inductive, and abductive reasoning. It also introduces the concept of a hybrid reasoning system that integrates two different types of reasoning to provide both qualitative and quantitative assessments.
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Development of Intelligence Process Tracking System for Job SeekersIJMIT JOURNAL
At the present time to getting a good job is very intricate task for any job seekers. The same problem also a company can face to acquire intelligent and qualified employees. Therefore, to minimize the problem, there are many management systems were applied and out of them, computer based management system is one of an appropriate elucidation for this problem. In the computer management system, software are made for jobseekers to find their suitable companies and as well as made for companies for finding their suitable employees. However, the available software in the market are not intelligent based, and to make privacy, security and robustness, the software should made with the application of expert system. In this proposed study, an attempt has been made for finding the solution for job seekers and the companies with the application of expert systems.
This document provides an agenda on expert systems that includes an introduction, definition, history, components, advantages, disadvantages and applications. It defines an expert system as a computer program that simulates human judgment to solve complex problems. The key components are a knowledge base that stores information and rules, and an inference engine that applies rules to deduce answers. Expert systems emerged in the 1970s and proliferated in the 1980s, being among the earliest successful forms of artificial intelligence. They are used in fields like healthcare, manufacturing and games.
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Genetic fuzzy process metric measurement system for an operating systemijcseit
Operating system (Os) is the most essential software of the computer system,deprived ofit, the computer
system is totally useless. It is the frontier for assessing relevant computer resources. It performance greatly
enhances user overall objective across the system. Related literatures have try in different methods and
techniques to measure the process matric performance of the operating system but none has incorporated
the use of genetic algorithm and fuzzy logic in their varied techniques which indeed is a novel approach.
Extending the work of Michalis, this research focuses on measuring the process matrix performance of an
operating system utilizing set of operating system criteria’s while fusing fuzzy logic to handle
impreciseness and genetic for process optimization.
GENETIC-FUZZY PROCESS METRIC MEASUREMENT SYSTEM FOR AN OPERATING SYSTEMijcseit
Operating system (Os) is the most essential software of the computer system,deprived ofit, the computer system is totally useless. It is the frontier for assessing relevant computer resources. It performance greatly
enhances user overall objective across the system. Related literatures have try in different methods and techniques to measure the process matric performance of the operating system but none has incorporated the use of genetic algorithm and fuzzy logic in their varied techniques which indeed is a novel approach. Extending the work of Michalis, this research focuses on measuring the process matrix performance of an
operating system utilizing set of operating system criteria’s while fusing fuzzy logic to handle impreciseness and genetic for process optimization.
IRJET- Factoid Question and Answering SystemIRJET Journal
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The document discusses expert systems and their components. It describes the three main components of most expert systems: the knowledge base, inference engine, and user interface. The knowledge base contains facts and rules. The inference engine applies rules to solve problems. The user interface allows communication between the user and system. It also discusses the stages of developing expert systems, including identifying the problem, conceptualizing the problem, formalizing it, implementing a prototype, and testing the system. Finally, it lists features of a good expert system such as being useful, usable, and able to explain its advice.
A PROPOSED EXPERT SYSTEM FOR EVALUATING THE PARTNERSHIP IN BANKSJaresJournal
Expert systems are no longer just a technology, but they have entered many fields of decision-making from
these medical fields, for example, as they help in diagnosing the disease and giving treatment, and also in
the field of administration, where they give the manager a rational decision to solve a problem and other
fields, DSS is an interactive information system that provides information, models, and data processing
tools to assist decision-making. Islamic banks such as commercial banks offer products and services to
customers, but these banks face many problems and the most important ones are the problems financing
where Islamic banks seek to participate in money rather than lending and interest the participatory
financing system is one of the most important sources of financing within Islamic banks This system is
based on the agreement between the Bank and the customer to participate in a new project or project
already in place in the proportions that agree to by the bank and the client but this funding takes a long
time and many actions so the researcher has built an expert system to reduce the time it takes to award
Funding and also to reduce procedures as the expert systems have the ability to help the human element in
making decisions. This paper presents expert systems in Islamic banks in the system of co-financing in
order to save time and effort and maximize profit.
This document discusses information technology and database concepts. It covers relational, hierarchical, and network database models. It also discusses two-tier and three-tier architecture. The document then discusses system analysis and design, including defining a system, the software development life cycle, and the different phases of system analysis, design, coding, testing, implementation, and maintenance.
ANALYSIS OF SYSTEM ON CHIP DESIGN USING ARTIFICIAL INTELLIGENCEijesajournal
Automation is a powerful word that lies everywhere. It shows that without automation, application will not
get developed. In a semiconductor industry, artificial intelligence played a vital role for implementing the
chip based design through automation .The main advantage of applying the machine learning & deep
learning technique is to improve the implementation rate based upon the capability of the society. The
main objective of the proposed system is to apply the deep learning using data driven approach for
controlling the system. Thus leads to a improvement in design, delay ,speed of operation & costs.
Through this system, huge volume of data’s that are generated by the system will also get control.
ANALYSIS OF SYSTEM ON CHIP DESIGN USING ARTIFICIAL INTELLIGENCEijesajournal
Automation is a powerful word that lies everywhere. It shows that without automation, application will not
get developed. In a semiconductor industry, artificial intelligence played a vital role for implementing the
chip based design through automation .The main advantage of applying the machine learning & deep learning technique is to improve the implementation rate based upon the capability of the society. The main objective of the proposed system is to apply the deep learning using data driven approach for controlling the system. Thus leads to a improvement in design, delay ,speed of operation & costs.Through this system, huge volume of data’s that are generated by the system will also get control.
A Mobile Expert System Application for Solving Personal Computer Problems.pdfKayla Smith
This document describes a mobile expert system application developed to solve personal computer problems. The application uses an Android-based expert system with a knowledge base of rules to diagnose computer issues through a question-and-answer interface. It detects problems by asking users questions and offers solutions depending on their answers. The output provides clear descriptions and images to help users understand the issue. The application was designed to accurately diagnose computer problems through an interactive question-based approach on smartphones.
The document discusses knowledge-based systems and knowledge acquisition. It defines a knowledge-based system as a computer program that uses an explicit knowledge base and inference engine to solve complex problems. The knowledge base represents facts about the world, and the inference engine allows new knowledge to be inferred through rules and reasoning approaches. Knowledge acquisition is the process of transforming knowledge from sources like experts, documents and databases into forms usable by knowledge-based systems. It addresses challenges like acquiring knowledge from busy experts and handling differing expert opinions. Common knowledge acquisition methods include handcrafting, knowledge engineering and machine learning.
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3) For design, the document discusses using intelligent agents to recommend patterns and designs to satisfy quality attributes from requirements and assist with assigning responsibilities to components.
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Finding new framework for resolving problems in various dimensions by the use of es an efficient and effective computer oriented artificial intelligence approach
1. Innovative Systems Design and Engineering www.iiste.org
ISSN 2222-1727 (Paper) ISSN 2222-2871 (Online)
Vol.4, No.11, 2013
1
Finding New Framework for Resolving Problems in Various
Dimensions by the Use of ES: An Efficient and Effective
Computer Oriented Artificial Intelligence Approach
Vaibhav Kant Singh, Amit Baghel,Satish Kumar Negi
Abstract
In this paper we will see how an expert system could be created. Expert system is a set of programs that
manipulate encoded knowledge to solve problems in a specialized domain that normally requires human
expertise. In this paper some of the applications of expert systems in different domains are discussed. Also we
will look into the characteristics, advantages, and limitations of expert systems. The users and life cycle of
Expert systems is also discussed.
Keywords— Expert system (ES), Production System (PS).
INTRODUCTION
This document comprises of a brief description of Expert system development. Expert systems are made to make
things simpler. Although ES are complex and sophisticated systems but there utility makes them such important
in current environment that the acceptability has reached the current high level. Expert systems are developed for
a wide range of domains. There are some fields where the deployment is currently easily possible, whereas there
are fields where it is still very difficult. Expert system like other customized software products should satisfy the
properties which should be there in every good software product. The properties include efficiency, effectiveness,
portability, usability, reusability, timeliness, nice user interfaces etc. Figure 1 explains ES. Expert System helps
the users to solve the problems by the application of processes that are analogous to human reasoning processes.
Artificial Intelligence field is a very old field which has given the user the liberty to solve their problems by the
use of Computer System. The word Artificial Intelligence comprise of two words where Artificial means copy of
something natural whereas Intelligence means the ability to apply knowledge and skills. Artificial Intelligence is
the art of creating machines that perform functions that require intelligence when performed by people. Artificial
Intelligence is the branch of Computer Science that is concerned with the automation of intelligent behavior.
Artificial Intelligence is the study of how to make computers do things which at the moment people do better.
Figure 1:- Block Diagram of ES
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Figure 2:- Block Diagram representing personals having interaction with the ES
personals having interaction with expert system
The users of ES include users of the domain for whom the ES is being constructed; they are having interaction
with the external interface. The second category of the personals having interaction with the ES include Domain
Expert; they are responsible for placing experiential knowledge pattern which knowledge engineer the
constructer may not predict as he may not belong to the domain for which he is constructing the system. There
also one more person associated with the ES, which is system maintenance personal. They pursue maintenance
activity for the Expert System. It is the knowledge engineer who makes coordination with others to deploy the
ES for the domain. Figure 2 explains the interaction.
work already done
In [1] the importance of creating knowledge base with the help of domain expert is shown. The utility of
having simple knowledge representation in the ES framework is explained. The research paper shows that only a
very little amount of research is done to examine the predictive validity of expert systems. It is proposed that ES
are more accurate than econometric models in one study and tied in two. It is shown that Protocol analysis is
especially useful if the area to be modeled is complex or if expert lack an awareness of their process. It is
proposed that in forecasting, the most promising applications of expert systems are to be replacing unaided
judgment in cases requiring many forecasts, to model complex problems where data on the dependent variable
are of poor quality, and to handle semi-structured problems. It is now bare fact that ES are used to forecast time
series and to predict outcomes on the basis of cross-sectional data. In [2] how to use sensor web for making a
monitoring and detection of hazard condition in near-real-time is specified. The paper shows how the
combination of sensor technology, wireless technology, GIS software, and rule-based logic techniques for
organizing and analyzing hazard –related data provides a powerful approach for hazard monitoring in real time
environment. Also the use of the generated data for decision making is advocated. In [3] a program which is
designed to assist in medical diagnosis depending on blood test results is proposed. The program merely lists an
estimated order of likelihood and estimates probabilities of diseases or condition based on internal logics and
answers given by physicians. In [4] the development of a legal expert system to carry out transfer of property act,
a domain within the Indian legal system which is often in demand is discussed. The VisiRule software is used to
attain the objective desired. It is felt in the paper that TAP-Expert proposed can benefit both the non-law literate
who intended to purchase property and also for experts in the field of law for production and fast decision
making. In [5] a unique approach for designing an ES that is going to help the Electronics technicians is
proposed. El. Tech. (Electronic Technicians) is created for the purpose described above. The work contains an
overview of knowledge elicitation methods that can be used for construction of model of the knowledge domain.
In [6] first principles is explained, it refers to the understanding the structure and function of problem solving
most of the system are utilizing empirical models relationship or models of the tax laws. The research paper
focuses on the application of first principle to construct ES in the taxation based paradigm. This paper employs a
theory based approach to make tax researchers and knowledge engineers aware of the benefits and limitations of
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working in taxation based ES. In [7] a simulat
focus is on finding solution of the resource sizing problems for the production system(PS), defined as the
specification of the number of each type of resources to be used in a production p
The problem is tackled by a simulation expert
made. A number of simulation ES optimizations are utilized for the purpose. It is an enhanced version of SESA
coupling. The current version uses performance measures that are adapted to the Due Dates (DD) characterized
make to order production context. In [8] a system for clinical performance improvement through rule
analysis of medical practice patterns and ind
quality feedback expert system (QFES) is developed to perform the objective.
There are five basic modules in the ES interfa
A. Knowledge Acquisition Subsystem
B. Knowledge base
C. Inference Engine
D. Explanation Subsystem.
E. User Interface
Knowledge Acquisition Subsystem
Knowledge Acquisition System is the module utilizing which the experts makes entry onto the knowledg
base. Interfaces are there in the knowledge acquisition sub
patterns to the knowledge engineer who is responsible for the creation of the ES.
Knowledge Base
This module is responsible for storing knowled
knowledge could be stored in the knowledge base.
Inference Engine
Inference engine are there in the ES utilizing which the ES evaluates the queries with the help of the
knowledge base. In other words inference engine is the module which produces answer to the queries of the user.
Explanation Subsystem
This module is there to help the user to have answers of their queries in there understandable form. It is
formulated to implement high user and system in
User Interface
The user interface is the module from where the user makes interaction with the developed ES.
2871 (Online)
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working in taxation based ES. In [7] a simulation expert system is proposed. In the research work prescribed the
focus is on finding solution of the resource sizing problems for the production system(PS), defined as the
specification of the number of each type of resources to be used in a production process for a given time period.
The problem is tackled by a simulation expert-system approach, where coupling an ES with a simulation tool is
made. A number of simulation ES optimizations are utilized for the purpose. It is an enhanced version of SESA
ling. The current version uses performance measures that are adapted to the Due Dates (DD) characterized
make to order production context. In [8] a system for clinical performance improvement through rule
analysis of medical practice patterns and individualized distribution of published scientific evidence is made. A
quality feedback expert system (QFES) is developed to perform the objective.
architecture of expert system
Figure 3:- Architecture of ES
There are five basic modules in the ES interface. The modules are:-
Knowledge Acquisition Subsystem
Knowledge Acquisition Subsystem
Knowledge Acquisition System is the module utilizing which the experts makes entry onto the knowledg
base. Interfaces are there in the knowledge acquisition sub-system which helps the experts to convey knowledge
patterns to the knowledge engineer who is responsible for the creation of the ES.
This module is responsible for storing knowledge patterns. There are various models utilizing which
knowledge could be stored in the knowledge base.
Inference engine are there in the ES utilizing which the ES evaluates the queries with the help of the
nference engine is the module which produces answer to the queries of the user.
This module is there to help the user to have answers of their queries in there understandable form. It is
formulated to implement high user and system interactivity.
The user interface is the module from where the user makes interaction with the developed ES.
www.iiste.org
ion expert system is proposed. In the research work prescribed the
focus is on finding solution of the resource sizing problems for the production system(PS), defined as the
rocess for a given time period.
system approach, where coupling an ES with a simulation tool is
made. A number of simulation ES optimizations are utilized for the purpose. It is an enhanced version of SESA
ling. The current version uses performance measures that are adapted to the Due Dates (DD) characterized
make to order production context. In [8] a system for clinical performance improvement through rule-based
ividualized distribution of published scientific evidence is made. A
Knowledge Acquisition System is the module utilizing which the experts makes entry onto the knowledge
system which helps the experts to convey knowledge
ge patterns. There are various models utilizing which
Inference engine are there in the ES utilizing which the ES evaluates the queries with the help of the
nference engine is the module which produces answer to the queries of the user.
This module is there to help the user to have answers of their queries in there understandable form. It is
The user interface is the module from where the user makes interaction with the developed ES.
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life cycle development of expert system
Figure 4:- Life cycle of ES development
. The life cycle development of ES basically comprises of five basic steps:-
Step1:-Problem Specification after Identification
Step2:-Fixation of the mode of Development
Step3:-Prototype Development
Step4:-Planning of full scale development
Step5:-Implementation, Maintenance and Evaluation of the system to be deployed
characteristics of expert system
In this section we will discuss some of the characteristics of Expert System. The basic characteristics are given
below:-
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The main theme of Expert system is in the knowledge lies the power. This theme is exploited to construct the
system.
The basic idea in ES designing is to construct a system for a domain where a human expert is required to
solve the problem.
The domain specific knowledge should be placed in the knowledge base in such a manner that it contains all
the minute details of the domain. The knowledge acquired by the professionals during their practice known as
heuristic knowledge should be properly conveyed by the knowledge engineer to the system along with the
conventional knowledge
The expert system facilitates the provision of heuristic search mechanism along with the conventional
searches.
The ES shell program provides facilities to run the same control program with run with multiple knowledge
bases. It also provides platform where addition is possible in the knowledge bases without recompilation.
ES is enabled to provide an answer as to how it came to the evaluation of a prescribed query.
ES are able to accept advice, modify its previous knowledge structure and update and expand the knowledge
base.
ES uses symbolic notation to construct its knowledge base.
ES are able to deal with uncertain and irrelevant data.
ES should provide nice interfaces through which it is convenient for the users to make interaction with the ES.
ES is constructed with a lot of investment thus every ES is having nice framework for ROI.
L. ES is capable enough to provide solution for individual desires
M.ES often makes use of meta-knowledge to arrive to the solution of a given problem, although this facility is
in very few systems.
expert system tools
Four basic types of Software tools for ES construction are briefly explained in this section. The four types are
algorithmic languages, symbolic languages, Development environments and expert system shells.
Algorithmic Languages
The first category is algorithmic language under this come the conventional programming languages like C,
Pascal etc.
Symbolic Languages
Under this category come languages like PROLOG, LISP etc. It works on the symbolic paradigm.
Development Environments
Under this category come ART, KEE, LOOPS etc.
Expert System Shells
Under this category come products like CRYSTAL, XPERT RULE, LEONARDO, XI-PLUS etc.
advantages of expert system
In this section we will discuss some of the advantages of Expert System. The basic advantages are given below:-
Domain specific knowledge of ES helps to play dual role of knowledge archiever and knowledge disseminator.
ES could be accessed “round the clock”.
ES plays a triple role of problem solver, a tutor and a knowledge archiever.
ES provides solution to the “why’s & how’s” of the user.
ES of current days provide facilities utilizing which the domain expert without taking help of the knowledge
engineer may directly insert knowledge patterns onto the knowledge base.
ES are capable to handle uncertain and contradictory data.
ES does not suffer from stresses which a human may undergo from. There is no case of moods in ES
For creating one human expert it takes a large amount of time, in this terms also ES are beneficial
ES works well in interdisciplinary fields
ES has features of prioritizing the task assigned.
limitations of expert system
In this section we will look into some of the limitations of ES. The limitations are described below:-
ES are designed for specific domains
ES are represented using Knowledge representation schemes which are limited in number and have there
customized specifications.
Lack of meta-knowledge
ES are brittle in nature
Lack of flexibility
Lack of proper validation facility
G. Knowledge boundaries not precise
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H. Lack of understandability
I. Lack common sense
J. Most frameworks miss direct knowledge insertion facility for domain expert.
expert system applications
The applications of ES are prescribed in this section. The applications are elaborated below:-
Control and Monitoring
Debugging
Design
Diagnosis
Instruction
Interpretation
Planning
Prediction
Conclusion
After going through all the sections of this paper we conclude that ES are the demand of the coming era which
should be accepted with open hands. In current world of globalization ES is the need of the time. It has provided
assistance to the human being in various aspects. It is of high importance to the professionals working in real
time places to make quality and efficient operation. This paper is a theory based approach towards expert system
development.
ACKNOWLEDGMENT
We would like to place my sincere thanks to Dr. Vinay Kumar Singh for the help provided by him during the
creation of this paper.
REFERENCES
[1] F. Collopy, Monica Adya and J. S. Armstrong, “Expert systems for forecasting,” Pinciples of forecasting : A
handbook for researchers and practitioners, Kluwer Academic Publishers, 2001.
[2] J.D. Macarthy, P.A. Graniero and S. M. Rozic, “An integrated GIS-expert system framework for live hazard
monitoring and detection,” Sensors ISSN 1424-8220, 8, 830-846, 2008.
[3] S. Alshaban and A.K. Taher, “Building a proposed expert system using blood testing,” Journal of
Engineering and Technology Research, Vol.1 (1), pp. 001-006, April 2009.
[4] N.B. Bilgi, R.V. Kulkarni and C. Spenser, “An expert system using a decision logic charting approach for
Indian legal domain with specific reference to transfer of property act,” International Journal of Artificial
Intelligence and Expert systems (IJAE), Volume (1) : Issue (2), pp. 32-39.
[5] J.W. Coffey, A.J. Canas, T. Reichherzer, G. Hill, N. Suri, R. Carff, T. Mitrovich and D. Eberle, “Knowledge
modeling and the creation of El-Tech: A performance support and training system for electronic
technicians,” Published in Expert System with Applications, 25(4), 2003, pp. 1-19.
[6] S.S. Karlinsky and D.E.O. Leary, “Tax based expert systems: A First principles approach,” Expert System
in Finance, Elsevier science publishers, B.V. rights reserved.
[7] W. Masmoudi, H. Chtourou and A.Y. Maalej, “A simulation – expert – system – based approach for
machine sizing of production system,” Journal of Manufacturing Technology and Management, Vol. 17, No.
2, 2006, pp. 187-198, Emerald Group Publishing Limited.
[8] E.A. Balas and F. Jaffrey, “An Expert system for performance- based direct delivery of published clinical
evidence,” Journal of the American Medical Informatics Associations, Vol. 3, No. 1, Jan/Feb 1996.
V.K. Singh is Assistant Professor, in the Department of Computer Science and Engineering, Institute of
Technology, Guru Ghasidas Vishwavidyalaya, Central University, Bilaspur, Chhattisgarh, India, Email:
vibhu200427@gmail.com
Amit Kumar Baghel is Assistant Professor, in the Department of Computer Science and Engineering, Institute of
Technology, Guru Ghasidas Vishwavidyalaya, Central University, Bilaspur, Chhattisgarh, India, Email:
amit_kumar_baghel@rediffmail.com , Mobile +919826339264
Satish Kumar Negi is Assistant Professor, in the Department of Computer Science and Engineering, Institute of
Technology, Guru Ghasidas Vishwavidyalaya, Central University, Bilaspur, Chhattisgarh, India, Email:
satish_kumar_negi@yahoo.co.in
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