Filling the Experience gap in the Drilling Optimization Continuous Improvement Cycle Through a Self-Learning Expert System

2013 ◽  
Author(s):  
Cliff Kirby ◽  
Andreas Sadlier ◽  
Christopher Wood ◽  
Morten Vinther
Keyword(s):  
Expert System ◽  
Continuous Improvement ◽  
Drilling Optimization ◽  
Self Learning

ANFIS-based Self-learning Expert System for Weapon Target Assignment Problem

2021 ◽  
Author(s):  
Changcheng Wang ◽  
Lisi Chen ◽  
Wencai Li ◽  
Kan Zeng
Keyword(s):  
Expert System ◽  
Assignment Problem ◽  
Target Assignment ◽  
Self Learning

Automated Generation of Course Improvement Plans Using Expert System

2018 ◽  
pp. 1232-1243
Author(s):  
Muhammad Hasan Imam ◽  
Imran Ali Tasadduq ◽  
Abdul-Rahim Ahmad ◽  
Fahd Aldosari ◽  
Haris Khan
Keyword(s):  
Expert System ◽  
Continuous Improvement ◽  
Inference Engine ◽  
Assessment Tools ◽  
Target Level ◽  
Web Based ◽  
Automated Generation ◽  
Improvement Plan ◽  
Improvement Criterion ◽  
Cloud Implementation

To satisfy ABET's continuous improvement criterion, an instructor, teaching a course suggests, at the end of the course, an improvement plan to be implemented when the same course is taught next time. Preparation of such a course improvement plan may be mandatory if a pre-specified target level of students' learning is not attained. Since, manual preparation of a course improvement plan is difficult, an idea of generating it using an expert system is presented. The objective is to make the task of improvement plan preparation easier and enjoyable. The proposed expert system has a set of remedies and a set of rules in a data base. A web-based interface queries the instructor about teaching and assessment tools used in the course. The inference engine selects the most appropriate remedy based on instructor's preferences. A cloud implementation of the expert system has been used to test it for a course.

scholarly journals Self Learning Real Time Expert System

2014 ◽  
Vol 3 (2) ◽  
pp. 13-25 ◽  
Author(s):  
Latha B Kaimal ◽  
Abhir Raj Metkar ◽  
Rakesh G
Keyword(s):  
Expert System ◽  
Real Time ◽  
Self Learning

Oncoguide System- a Computerized Interactive Assistance System for the Diagnosis and Treatment of CML / MPN and MDS

Blood ◽  
2015 ◽  
Vol 126 (23) ◽  
pp. 5145-5145
Author(s):  
Dirk Hempel
Keyword(s):  
User Interface ◽  
Expert System ◽  
Learning System ◽  
Assistance System ◽  
Diagnosis And Treatment ◽  
Current Evidence ◽  
Decision Criteria ◽  
Research Results ◽  
Intuitive User Interface ◽  
Self Learning

The project aims to develop and implement a computerized interactive assistance system for the diagnosis and treatment of CML / MPN and MDS. The system is based on modulated, established medical guidelines with reference to the individual patient information. The user is navigated through the complex recommendations of current guidelines and decision trees of the CML/MPN and MDS, similarly to a car navigation system. The system was developed in cooperation between the Steinbeis Institute for Clinical haematooncology and the Fraunhofer IOSB in Karlsruhe. In addition to the guidelines, recommendations on current evidence-based research results will be incorporated into the expert system. The medical user is hereby offered a systematic decision support. In addition, the decisions are documented and visualized in an intuitive user interface. The expert system is also able to point to currently recruiting therapy studies. The future system is planned to be self-learning by weighing the decision criteria. It will adjust the decision criteria by increasing usage and possibly readjusted criteria. Technical implementation and advantages of this solution: From a technical perspective, the knowledge-based system should be implemented as a client-server architecture. The server acts as a central data storage, for example, in the form of a database. As a client, for example, Internet browsers can be used. So it is possible to centrally reproach the accumulated knowledge and to present the current results to each user. The advantage of such a technical solution compared to guidelines-books is rapidly adaptable knowledge on new research results, as well as the implementation of a self-learning system. The system design has to pay particular attention to an intuitive user interface. On the server's side, the knowledge of guidelines and interviews with experts have to be formalized in an appropriate manner. There are approaches based on an ontological or logic-based modeling. On the client's, side the system suggests the user appropriate decision for the diagnosis and further treatment of diseases. The underlying methodology is based on approaches from artificial intelligence, such as the Bayesian inference or machine learning methods. Disclosures Hempel: Novartis and Celgene: Research Funding.

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