A Fuzzy Association Rule Mining Expert-Driven (FARME-D) approach to Knowledge Acquisition

Fuzzy Association Rule Mining Expert-Driven (FARME-D) approach to knowledge acquisition is proposed in this paper as a viable solution to the challenges of rule-based unwieldiness and sharp boundary problem in building a fuzzy rule-based expert system. The fuzzy models were based on domain experts’ opinion about the data description. The proposed approach is committed to modelling of a compact Fuzzy Rule-Based Expert Systems. It is also aimed at providing a platform for instant update of the knowledge-base in case new knowledge is discovered. The insight to the new approach strategies and underlining assumptions, the structure of FARME-D and its practical application in medical domain was discussed. Also, the modalities for the validation of the FARME-D approach were discussed

Flexible Fuzzy Rule Bases Evolution with Swarm Intelligence for Meta-Scheduling in Grid Computing

Fuzzy rule-based systems are expert systems whose performance is strongly related to the quality of their knowledge and the associated knowledge acquisition processes and thus, the design of effective learning techniques is considered a critical and major problem of these systems. Knowledge acquisition with a swarm intelligence approach is a recent learning strategy for the evolution of fuzzy rule bases founded on swarm intelligence showing improvement over classical knowledge acquisition strategies in fuzzy rule based systems such as Pittsburgh and Michigan approaches in terms of convergence behaviour and accuracy. In this work, a generalization of this method is proposed to allow the simultaneous consideration of diversely configured knowledge bases and this way to accelerate the learning process of the original algorithm. In order to test the suggested strategy, a problem of practical importance nowadays, the design of expert meta-schedulers systems for grid computing is considered. Simulations results show the fact that the suggested adaptation improves the functionality of knowledge acquisition with a swarm intelligence approach and it reduces computational effort; at the same time it keeps the quality of the canonical strategy

A fuzzy expert system for small business loan processing

In recent years, managerial applications of artificial intelligence, especially in the area of financial services, has received considerable attention. In this paper, a fiizzy logic expert system is developed for approvals of small business loans. Previous studies have used non-fuzzv expert systems. But the fuzzification of the variables used in a business loan approval decisionmaking promises more efficient results. Furthermore, another distinct feature of this paper is its focus on small business loans. CubiCalc fuzzy expert system shell is used to develop the expert system. Knowledge acquisition is made using the resources and expertise of a Small Business Development Center. Tests to establish generalizability and validity of the results are conducted

Development of medical expert systems with fuzzy concepts in a PC environment.

by So Yuen Tai.Thesis (M.Phil.)--Chinese University of Hong Kong, 1990.Bibliography: leaves [144]-[146].ACKNOWLEDGEMENTSTABLE OF CONTENTS --- p.T.1ABSTRACTChapter 1. --- INTRODUCTION --- p.1.1Chapter 1.1 --- Inexact Knowledge in Medical Expert Systems --- p.1.1Chapter 1.2 --- Fuzzy Expert System Shells --- p.1.2Chapter 1.2.1 --- SPII-2 --- p.1.3Chapter 1.2.2 --- Fuzzy Expert System Shell for Decision Support System --- p.1.4Chapter 1.3 --- Medical Expert Systems --- p.1.6Chapter 1.3.1 --- EXPERT --- p.1.6Chapter 1.3.2 --- DIABETO --- p.1.8Chapter 1.4 --- Impact from Micro-computer --- p.1.10Chapter 1.5 --- Approach --- p.1.11Chapter 2. --- SYSTEM Z-ll --- p.2.1Chapter 2.1 --- General Description --- p.2.1Chapter 2.2 --- Main Features --- p.2.2Chapter 2.2.1 --- Fuzzy Concepts --- p.2.2Chapter 2.2.2 --- Fuzzy Certainty --- p.2.3Chapter 2.2.3 --- Fuzzy Comparison --- p.2.5Chapter 2.2.4 --- Rule Evaluation --- p.2.7Chapter 2.2.5 --- Certainty Factor Propagation --- p.2.9Chapter 2.2.6 --- Linguistic Approximation --- p.2.10Chapter 2.3 --- Limitations and Possible Improvements --- p.2.11Chapter 3. --- A FUZZY EXPERT SYSTEM SHELL (Z-lll) IN PC ENVIRONMENT --- p.3.1Chapter 3.1 --- General Description --- p.3.1Chapter 3.2 --- Programming Environment --- p.3.1Chapter 3.3 --- Main Features and Structure --- p.3.3Chapter 3.3.1 --- Knowledge Acquisition Module --- p.3.5Chapter 3.3.1.1 --- Object Management Module --- p.3.5Chapter 3.3.1.2 --- Rule Management Module --- p.3.6Chapter 3.3.1.3 --- Fuzzy Term Management Module --- p.3.7Chapter 3.3.2 --- Consultation Module --- p.3.8Chapter 3.3.2.1 --- Fuzzy Inference Engine --- p.3.8Chapter 3.3.2.2 --- Review Management Module --- p.3.11Chapter 3.3.2.3 --- Linguistic Approximation Module --- p.3.11Chapter 3.3.3 --- System Properties Management Module --- p.3.13Chapter 3.4 --- Additional Features --- p.3 14Chapter 3.4.1 --- Weights --- p.3.15Chapter 3.4.1.1 --- Fuzzy Weight --- p.3.16Chapter 3.4.1.2 --- Fuzzy Weight Evaluation --- p.3.17Chapter 3.4.1.3 --- Results of Adding Fuzzy Weights --- p.3.21Chapter 3.4.2 --- Fuzzy Matching --- p.3.24Chapter 3.4.2.1 --- Similarity --- p.3.25Chapter 3.4.2.2 --- Evaluation of Similarity measure --- p.3.26Chapter 3.4.3 --- Use of System Threshold --- p.3.30Chapter 3.4.4 --- Use of Threshold Expression --- p.3.33Chapter 3.4.5 --- Playback File --- p.3.35Chapter 3.4.6 --- Database retrieval --- p.3.37Chapter 3.4.7 --- Numeric Variable Objects --- p.3.39Chapter 3.5 --- Implementation Highlights --- p.3.41Chapter 3.5.1 --- Knowledge Base --- p.4.42Chapter 3.5.1.1 --- Fuzzy Type --- p.4.42Chapter 3.5.1.2 --- Objects --- p.3.45Chapter 3.5.1.3 --- Rules --- p.3.49Chapter 3.5.2 --- System Properties --- p.3.53Chapter 3.5.2.1 --- System Menu --- p.3.53Chapter 3.5.2.2 --- Option Menu --- p.3.54Chapter 3.5.3 --- Consultation System --- p.3.55Chapter 3.5.3.1 --- Inference Engine --- p.3.56Chapter 3.5.3.2 --- Review Management --- p.3.60Chapter 3.6 --- Comparison on Z-lll and Z-ll --- p.3.61Chapter 3.6.1 --- Response Time --- p.3.62Chapter 3.6.2 --- Accessibility --- p.3.62Chapter 3.6.3 --- Accommodation of Large Knowledge Base --- p.3.62Chapter 3.6.4 --- User-Friendliness --- p.3.63Chapter 3.7 --- General Comments on Z-lll --- p.3.64Chapter 3.7.1 --- Adaptability --- p.3.64Chapter 3.7.2 --- Adequacy --- p.3.64Chapter 3.7.3 --- Applicability --- p.3.65Chapter 3.7.4 --- Availability --- p.3.65Chapter 4. --- KNOWLEDGE ENGINEERING --- p.4.1Chapter 4.1 --- Techniques used in Knowledge Acquisition --- p.4.1Chapter 4.2 --- Interviewing the Expert --- p.4.2Chapter 4.3 --- Knowledge Representation --- p.4.4Chapter 4.4 --- Development Approach --- p.4.6Chapter 4.5 --- Knowledge Refinement --- p.4.7Chapter 4.6 --- Consistency Check and Completeness Check --- p.4.12Chapter 4.6.1 --- The Consistency and Completeness in a nonfuzzy rule set --- p.4.13Chapter 4.6.1.1 --- Inconsistency in nonfuzzy rule-based system --- p.4.13Chapter 4.6.1.2 --- Incompleteness in nonfuzzy rule-based system --- p.4.18Chapter 4.6.2 --- Consistency Checks in Fuzzy Environment --- p.4.20Chapter 4.6.2.1 --- Affinity --- p.4.21Chapter 4.6.2.2 --- Detection of Inconsistency and Incompleteness in Fuzzy Environment --- p.4.24Chapter 4.6.3 --- Algorithm for Checking Consistency --- p.4.25Chapter 5. --- FUZZY MEDICAL EXPERT SYSTEMS --- p.5.1Chapter 5.1 --- ABVAB --- p.5.1Chapter 5.1.1 --- General Description --- p.5.1Chapter 5.1.2 --- Development of ABVAB --- p.5.2Chapter 5.1.3 --- Computerisation of Database --- p.5.4Chapter 5.1.4 --- Results of ABVAB --- p.5.7Chapter 5.1.5 --- From Minicomputer to PC --- p.5.15Chapter 5.2 --- INDUCE36 --- p.5.17Chapter 5.2.1 --- General Description --- p.5.17Chapter 5.2.2 --- Verification of INDUCE36 --- p.5.18Chapter 5.2.3 --- Results --- p.5.19Chapter 5.3 --- ESROM --- p.5.21Chapter 5.3.1 --- General Description --- p.5.21Chapter 5.3.2 --- Multi-layer Medical Expert System --- p.5.22Chapter 5.3.3 --- Results --- p.5.25Chapter 6. --- CONCLUSION --- p.6.1REFERENCES --- p.R.1APPENDIX I --- p.A.1APPENDIX II --- p.A.2APPENDIX III --- p.A.3APPENDIX IV --- p.A.1

Keberkesanan modul infusi kemahiran berfikir aras tinggi pembelajaran luar bilik darjah (iKBAT-PLBD) bagi bidang pembelajaran sukatan dan geometri

Kemahiran berfikir aras tinggi (KBAT) merupakan satu kemahiran berfikir yang sangat diperlukan dalam mendepani cabaran kehidupan masa kini terutama dalam bidang matematik. Oleh itu, kajian ini dijalankan untuk mengkaji sama ada KBAT matematik pelajar dapat ditingkatkan dengan menggunakan modul infusi Kemahiran Berfikir Aras Tinggi - Pembelajaran Luar Bilik Darjah (iKBAT–PLBD) atau tidak? Justeru itu, satu kerangka perancangan telah dibuat terhadap empat kemahiran tertinggi dalam Taksonomi Bloom semakan semula yang juga merupakan konstruk utama dalam KBAT. Konstruk KBAT tersebut ialah konstruk menganlisis, mengaplikasi menilai dan mencipta. Sampel kajian ini melibatkan 120 pelajar tingkatan 1 di empat buah sekolah yang berbeza di negeri Johor. Dalam menjalankan kajian kuasi eksperimental ini, data dikumpul melalui kajian keputusan ujian pra dan ujian pos sebelum dan selepas menggunakan modul bagi kumpulan rawatan. Manakala pendekatan PdP tradisional pula digunakan bagi kumpulan kawalan. Hasil daripada analisis data menunjukkan bahawa aktiviti pembelajaran dan pemudahcaraan (PdPc) yang bertunjangkan modul iKBAT–PLBD telah dapat meningkatkan penguasaan matematik pelajar dalam kempat-empat tahap KBAT serta bagi keseluruhan tahap. Dapatan kajian ini menunjukkan terdapat perbezaan yang signifikasi antara kumpulan kawalan dan kumpulan rawatan terhadap peningkatan KBAT pelajar dalam matematik dengan menggunakan pendekatan iKBAT–PLBD bagi tahap mengaplikasi, menganalisis, menilai, mencipta juga secara keseluruhan. Kesimpulannya, kajian ini dapat memberi manfaat kepada semua pihak termasuk pihak Kementerian Pendidikan Malaysia (KPM), pihak pentadbiran sekolah, ibubapa, guru matematik malah bagi pelajar itu dari segi pengubalan dasar yang berkaitan, pengaplikasian dan sebagai satu bukti keberkesanan dalam proses pemerkasaan KBAT matematik di Malaysia