Name: Multi-disciplinary Trends in Artificial Intelligence
Price: 320.14 RON
Availability: OnlineOnly
Author: M. Narasimha Murty
ISBN: 9783319133645

Multi-disciplinary Trends in Artificial Intelligence: Lecture Notes in Computer Science, cartea 8875

M. Narasimha Murty

Xiangjian He

Raghavendra Rao Chillarige

Paul Weng

This book constitutes the refereed conference proceedings of the 8th International Conference on Multi-disciplinary Trends in Artificial Intelligence, MIWAI 2014, held in Bangalore, India, in December 2014. The 22 revised full papers were carefully reviewed and selected from 44 submissions. The papers feature a wide range of topics covering both theory, methods and tools as well as their diverse applications in numerous domains.

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Cuprins

“Potential Interval of Root” of Nonlinear Equation: Labeling Algorithm.- Stochastic Leaky Integrator Model for Interval Timing.- Multi-objective Exploration for Compiler Optimizations and Parameters.- Association Rule Mining via Evolutionary Multi-objective Optimization.- Distance-Based Heuristic in Selecting a DC Charging Station for Electric Vehicles.- Automated Reasoning in Deontic Logic.- Image Processing Tool for FAE Cloud Dynamics.- N-gram Based Approach for Opinion Mining of Punjabi Text.- Application of Game-Theoretic Rough Sets in Recommender Systems.- RGB - Based Color Texture Image Classification Using Anisotropic Diffusion and LDBP.- A Knowledge-Based Design for Structural Analysis of Printed Mathematical Expressions.- A New Preprocessor to Fuzzy c-Means Algorithm.- Domain Specific Sentiment Dictionary for Opinion Mining of Vietnamese Text.- Support Vector–Quantile Regression Random Forest Hybrid for Regression Problems.- Clustering Web Services on Frequent Output Parameters for I/O Based Service Search.- IntelliNavi : Navigation for Blind Based on Kinect and Machine Learning.- A Trust Metric for Online Virtual Teams and Work Groups.- Web Service Composition Using Service Maps.- Integrated Representation of Spatial Topological and Size Relations for the Semantic Web.- Using Bayesian Networks to Model and Analyze Software Product Line Feature Model.- A Content-Based Approach for User Profile Modeling and Matching on Social Networks.