ARTIFICIAL INTELLIGENCE: APPROACHES, TOOLS AND APPLICATIONS -- ARTIFICIAL INTELLIGENCE: APPROACHES, TOOLS AND APPLICATIONS -- Library of Congress Cataloging-in-Publication Data -- CONTENTS -- PREFACE -- Chapter 1 APPLICATION OF ARTIFICIAL INTELLIGENCE IN THE UPSTREAM OIL AND GAS INDUSTRY -- ABSTRACT -- 1. NEURAL NETWORKS AND THEIR BACKGROUND -- 1.1. A Short History of Neural Networks -- 1.2. Structure of a Neural Network -- 1.3. Mechanics of Neural Networks Operation -- 2. EVOLUTIONARY COMPUTING -- 2.1. Genetic Algorithms -- 2.2. Mechanism of a Genetic Algorithm -- 3. FUZZY LOGIC -- 3.1. Fuzzy Set Theory -- 3.2. Approximate Reasoning -- 3.3. Fuzzy Inference -- 4. APPLICATIONS IN THE OIL AND GAS INDUSTRY -- 4.1. Neural Networks Applications -- 4.1.1. Reservoir Characterization -- 4.1.2. Virtual Magnetic Resonance Imaging Logs -- 4.2. Genetic Algorithms Applications -- 4.3. Fuzzy Logic Applications -- 4.3.1. Results -- REFERENCES -- Chapter 2 AN ARTIFICIAL INTELLIGENCE APPROACH FOR MODELING AND OPTIMIZATION OF THE EFFECT OF LASER MARKING PARAMETERS ON GLOSS OF THE LASER MARKED GOLD -- ABSTRACT -- 1. INTRODUCTION -- 2. ANFIS, ANNS, GA AND PSO -- 2.1. Adaptive Neuro-Fuzzy Inference System -- 2.1.1. Anfis Architecture -- 2.1.2. ANFIS Learning Algorithm -- 2.2. Artificial Neural Networks -- 2.2.1. Network Types -- 2.2.2. Training Algorithm -- 2.3. Genetic Algorithm -- (a) Population Initialization -- (b) Operators -- (c) Chromosome Evaluation -- 2.4. Particle Swarm Optimization -- 3. INPUT/OUTPUT VARIABLES -- 4. ANFIS AND ANNSIMPLEMENTATION -- 4.1.Model Building Methodology -- 4.2. ANFIS Modeling -- 4.3. ANNs Modeling -- 4.4. Results and Discussion -- 5. GA AND PSO IMPLEMENTATION -- 5.1. Optimization -- 5.2. Optimization Using GA -- 5.3. Optimization Using PSO -- 5.4. Results and Discussion -- 6. METHODOLOGY VALIDATION -- CONCLUSION.

APPENDIX A. COMPARISONOF SOMEOF ANFIS MODELING AND ANN MODELINGRESULTSBEFORE AND AFTERCLEANING THE DATA -- REFERENCES -- Chapter 3 AI APPLICATIONS TO METAL STAMPING DIE DESIGN -- ABSTRACT -- 1. INTRODUCTION -- 1.1. Sheet Metal Operations and Press Tools -- 1.2. Design of Press Tools -- 1.3. Artificial Intelligence (AI) -- Knowledge Based System (KBS) /Expert System (ES) -- Neural Network (NN) -- Case Based Reasoning (CBR) -- Hybrid System -- 2. REVIEW OF APPLICATIONS OF AI TECHNIQUES TO METAL STAMPING DIE DESIGN -- 2.1. Manufacturability Evaluation of Sheet Metal Parts -- 2.2. Process Planning and Metal Stamping Die Design -- 2.3. Comments on Reviewed Literature -- 3. PROCEDURE FOR DEVELOPMENT OF KNOWLEDGE BASE SYSTEM (KBS) FOR DESIGN OF METAL STAMPING DIE -- 3.1. Knowledge Acquisition -- Literature Reviews -- Die Design Experts -- Industrial Visits -- Industrial Brochures -- 3.2. Framing of Production Rules -- 3.3. Verification of Production Rules -- 3.4. Sequencing of Production Rules -- 3.5. Identification of Suitable Hardware and a Computer Language -- 3.6. Construction of Knowledge Base -- 3.7. Choice of Search Strategy -- 3.8. Preparation of User Interface -- 4. AN INTELLIGENT SYSTEM FOR DESIGN OF PROGRESSIVE DIE: INTPDIE -- 4.1. Organization of the System -- 4.2. Validation of the Proposed System INTPDIE -- 4.3. Scope of Further Research Work -- CONCLUSION -- REFERENCES -- Chapter 4 STRUCTURAL FEATURES SIMULATION ON MECHANOCHEMICAL SYNTHESIS OF AL2O3-TIB2 NANOCOMPOSITE USING ANN WITH BAYESIAN REGULARIZATION AND ANFIS -- ABSTRACT -- 1. INTRODUCTION -- 2. EXPERIMENTALPROCEDURES -- 3. MODELING INTENSITYIN XRD -- 3.1. Pre-Processing of the Data -- 3.2. ANFIS -- 3.3. ANN with Bayesian Regularization with Full Sampling -- 3.4. Optimizing ANN by Taguchi Method -- 4. RESULTS AND DISCUSSION -- 4.1. Experimental Data -- 4.2. Modeling by ANFIS.

4.3. Modeling by ANN -- 4.3. Simulation of Structural Features -- CONCLUSIONS -- REFERENCES -- Chapter 5 AN ARTIFICIAL INTELLIGENCE TOOL FOR PREDICTING EMBRYOS QUALITY -- ABSTRACT -- 1. INTRODUCTION -- 2. PROPOSED SYSTEM -- 2.1. Segmentation and Pre-Processing -- 2.2. Feature Extraction -- 2.3. Classification -- 3. EMBRYOS DATASET -- 4. RESULTS -- DISCUSSION -- REFERENCES -- Chapter 6 PASSIVE SYSTEM RELIABILITY OF THE NUCLEAR POWER PLANTS (NPPS) USING FUZZY SET THEORY IN ARTIFICIAL INTELLIGENCE -- ABSTRACT -- 1. INTRODUCTION -- 2. METHOD -- 3. CALCULATION -- 4. RESULT AND DISCUSSION -- CONCLUSIONS -- ACKNOWLEDGMENTS -- REFERENCES -- Chapter 7 EMERGENT TOOLS IN AI -- ABSTRACT -- 1. REPRESENTATION PROBLEMS -- 2. RULES -- 3. INFERENCE IN SBR -- 4. FRAMES -- 5. SCRIPTS -- 6. SEARCHING METHODS -- 7. INTRODUCTION TO FUZZINESS -- 8. ROUGHNESS -- 9. COMPARISON BETWEEN FUZZINESS AND ROUGHNESS -- 10. NETWORKS -- REFERENCES -- Chapter 8 NEURAL NETWORKS APPLIED TO MICRO-COMPUTED TOMOGRAPHY -- ABSTRACT -- 1. INTRODUCTION -- 2. SYNCHROTRON RADIATION MICRO-COMPUTED TOMOGRAPHY FOR BIOMEDICAL IMAGING -- 3. ARTIFICIAL NEURAL NETWORKS TRAINING STRATEGIES -- 4. VALIDATION METHODOLOGY: THE LEAVE-ONE-OUT CROSS VALIDATION -- 5. COMPUTATIONAL EXPERIMENTAL RESULTS -- 6. DISCUSSION -- CONCLUSION -- ACKNOWLEDGMENTS -- REFERENCES -- INDEX.