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Networking and informatics volume 1

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Cấu trúc

  • Foreword

  • Message from the Honorary General Chair

  • Preface

  • Organization

  • Contents

  • Application of Bilinear Recursive Least Square Algorithm for Initial Alignment of Strapdown Inertial Navigation System

    • 1 Introduction

    • 2 SINS Initial Alignments

      • 2.1 Coarse Alignment

    • 3 Theoretical Background

      • 3.1 Bilinear RLS Algorithm (BRLS)

    • 4 Dynamic Modeling

    • 5 Dynamic Simulation of Fine Alignment

    • 6 Results and Discussions

    • 7 Conclusions

    • References

  • Time-Domain Solution of Transmission through Multi-modeled Obstacles for UWB Signals

    • 1 Introduction

    • 2 Propagation Environment

    • 3 Proposed Transmission Model

      • 3.1 TD Transmission Coefficient

      • 3.2 Transmitted Field through the Propagation Environment

    • 4 Results and Discussions

    • 5 Conclusion

    • References

  • Indexing and Retrieval of Speech Documents

    • 1 Introduction

    • 2 Proposed Speech Indexing and Retrieval System

    • 3 Performance Evaluation

    • 4 Summary and Conclusions

    • References

  • An Improved Filtered-x Least Mean Square Algorithm for Acoustic Noise Suppression

    • 1 Introduction

    • 2 Problem Formulation

    • 3 Simulation Setup

    • 4 Simulation and Results

    • 5 Conclusion

    • References

  • A Unique Low Complexity Parameter Independent Adaptive Design for Echo Reduction

    • 1 Introduction

    • 2 Existing Algorithms for AEC

    • 3 Proposed Algorithm

    • 4 Results and Discussion

    • 5 Conclusion

    • References

  • On the Dissimilarity of Orthogonal Least Squares and Orthogonal Matching Pursuit Compressive Sensing Reconstruction

    • 1 Introduction

    • 2 Orthogonal Matching Pursuit

    • 3 Orthogonal Least Squares

    • 4 Simulation Results and Analysis

    • 5 Conclusions

    • References

  • Machine Learning Based Shape Classification Using Tactile Sensor Array

    • 1 Introduction

    • 2 Related Work

    • 3 Methodology

      • 3.1 Tactile Image Enhancement

      • 3.2 Feature Extraction

      • 3.3 Machine Learning System

    • 4 Experimental Set Up

    • 5 Result and Discussion

    • 6 Conclusion and Future Work

    • References

  • Multi-view Ensemble Learning for Poem Data Classification Using SentiWordNet

    • 1 Introduction

    • 2 Related Work

    • 3 Preprocessing

    • 4 Classification Using Multi-view Ensemble Learning

    • 5 Experimental Setup and Results

    • 6 Analysis

    • 7 Conclusion

    • References

  • A Prototype of an Intelligent Search Engine Using Machine Learning Based Training for Learning to Rank

    • 1 Introduction

    • 2 Related Work

    • 3 Proposed System m

    • 4 Experimental Results

    • 5 Case Study

    • 6 Conclusions and Future Work

    • References

  • Vegetable Grading Using Tactile Sensing and Machine Learning

    • 1 Introduction

    • 2 Background

    • 3 Proposed Methodology

    • 4 Parameter Selection

    • 5 Background of the Classifier Used

      • 5.1 Support Vector Machine

      • 5.2 K-Nearest Neighbor

    • 6 Experimental Setup

    • 7 Results and Discussions

    • 8 Conclusion

    • References

  • Neural Networks with Online Sequential Learning Ability for a Reinforcement Learning Algorithm

    • 1 Introduction

    • 2 Approximation of Value Function Using mRAN

    • 3 mRAN Learning Algorithm

    • 4 Simulation Experiments

      • 4.1 Two-Link Robot Manipulator

      • 4.2 Controller Setup

      • 4.3 Network Topologies and Learning Details

    • Results and Discussion

    • 6 Conclusions

    • References

  • Scatter Matrix versus the Proposed Distance Matrix on Linear Discriminant Analysis for Image Pattern Recognition

    • 1 Introduction

    • 2 Idea Behind the Proposed Distance Matrix

    • 3 Formulation of LDA Using Distance Based Symmetric Matrix

    • 4 Experiments

    • 5 Results and Conclusion

      • 5.1 Experiment 1: Case Study on the Comparison of the

      • 5.2 Experiment 2: Study on the Effect of Selection of Images on the Performance of Distance Matrix on Keeping the Vector

    • References

  • Gender Recognition Using Fusion of Spatial and Temporal Features

    • 1 Introduction

    • 2 Proposed Method

      • 2.1 Face Detection

      • 2.2 Preprocessing

    • 3 Feature Extraction Techniques

      • 3.1 Spatial Feature Extraction

      • 3.2 Temporal Feature Extraction

      • 3.3 Fusion of Features

    • 4 Experimental Studies

    • 5 Conclusions

    • References

  • The Use of Artificial Intelligence Tools in the Detection of Cancer Cervix

    • 1 Introduction

    • 2 Segmentation of CSV

      • 2.1 Elimination of Inflammatory Cells

      • 2.2 Image Segmentation in Experimental Phase

    • 3 Shape Parameters

    • 4 Methodologies of Classification per the Tools of Artificial Intelligence of the Vaginal Smears Cervico

      • 4.1 The Technique of Neural Networks (Multilayer Perceptron (MP))

      • 4.2 The Technique of Fuzzy Logic

      • 4.3 The Hybrid Approach: Neuro-Fuzzy

    • 5 Simulation Results

    • 6 Discussion

    • 7 Conclusion

    • References

  • A Scalable Feature Selection Algorithm for Large Datasets – Quick Branch & Bound Iterative (QBB-I)

    • 1 Introduction

    • 2 Feature Selection Algorithms

      • 2.1 Feature Selection

      • 2.2 Quick Branch and Bound Iterative (QBB-I) Algorithm

    • 3 Experimental Results of QBB-I

      • 3.1 Dataset Used for Feature Selection

      • 3.2 Experimental Setup

      • 3.3 Results

    • 4 Conclusion

    • References

  • Towards a Scalable Approach for Mining Frequent Patterns from the Linked Open Data Cloud

    • 1 Introduction

    • 2 Related Work

    • 3 Mining Data from LOD Cloud Using COFI Tree Algorithm

    • 4 Experimental Evaluation and Results

    • 5 Conclusion and Future Work

    • References

  • Automatic Synthesis of Notes Based on Carnatic Music Raga Characteristics

    • 1 Introduction

      • 1.1 Basic Terms and Definitions

      • 1.2 Fundamental Idea

    • 2 Related Work

    • 3 The Proposed Method

      • 3.1 First Order Markov Model

      • 3.2 Hidden Markov Model

    • 4 Assumptions and Constraints

    • 5 Results and Analysis

    • 6 Conclusion and Future work

    • References

  • Smart Card Application for Attendance Management System

    • 1 Introduction

    • 2 Related Work

    • 3 Entities in The Smart Card System

    • 4 Set up

    • 5 Implementation

    • 6 Conclusion

    • References

  • Performance Evaluation of GMM and SVM for Recognition of Hierarchical Clustering Character

    • 1 Introduction

      • 1.1 Related Work

      • 1.2 Outline of the Work

    • 2 Proposed Hierarchical Character Recognition

      • 2.1 Dataset

      • 2.2 Pre-processing

    • 3 Character Intensity Vector

    • 4 Hierarchical Character Clustering

    • 5 Gaussian Mixture Model

    • 6 Support Vector Machine

    • 7 Experimental Results

      • 7.1 Handwritten Dataset

      • 7.2 Evaluation Metrics

      • 7.3 Classifier

    • 8 Conclusion

    • References

  • Data Clustering and Zonationof Earthquake Building Damage Hazard Area Using FKCN and Kriging Algorithm

    • 1 Introduction

    • 2 FKCN Algorithm: Development and Its Application

    • 3 Methodology

    • 4 Analysis and Result

      • 4.1 Resulting Cluster Data Validation

      • 4.2 Clusterization of Building Damage Hazard Data

      • 4.3 Zonation of Earthquake Building Damage Hazard Area

    • 5 Conclusion

    • References

  • Parametric Representation of Paragraphs and Their Classification

    • 1 Introduction

    • 2 Classification of Paragraph

    • 3 Parameterizations of a Paragraph

      • 3.1 Identification of Parameters

      • 3.2 Extraction of Parameter Values

      • 3.3 Data Preprocessing

      • 3.4 Identification of Outliers and Removal

      • 3.5 Training and Testing

    • 4 Experimental Result

    • 5 Conclusion and Future Scope

    • References

  • LDA Based Emotion Recognition from Lyrics

    • 1 Introduction

    • 2 Literature Survey

    • 3 System Design

      • 3.1 System Architecture

      • 3.2 Steps in Emotion Recognition

    • 4 Implementation and Evaluation

      • 4.1 LDA Parameters

      • 4.2 Emotion Recognition

      • 4.3 Evaluation

    • 5 Conclusion and Future Work

    • References

  • Efficient Approach for Near Duplicate Document Detection Using Textual and Conceptual Based Techniques

    • 1 Introduction

    • 2 Related Work

    • 3 Proposed Approach

    • 4 Experimental Results

      • 4.1 Determination of Optimum Values for Parameters Involved in Algorithm

      • 4.2 Comparison of Proposed Approach with Traditional Simhash Implementation

    • 5 Conclusion and Future Work

    • References

  • Decision Tree Techniques Applied on NSL-KDD Data and Its Comparison with Various Feature Selection Techniques

    • 1 Introduction

    • 2 Methods and Materials

      • 2.1 Decision Tree

      • 2.2 Feature Selection

      • 2.3 NSL-KDD Data

    • 3 Experimental Work

    • 4 Conclusion

    • References

  • Matra and Tempo Detection for INDIC Tala-s

    • 1 Introduction

    • 3 PastWork

    • 4 Proposed Methodology

      • 4.1 Extraction of Peaks from Amplitude Envelope

      • 4.2 Detection of

    • 5 Experimental Results

    • 6 Conclusion

    • References

  • Modified Majority Voting Algorithm towards Creating Reference Image for Binarization

    • 1 Introduction

    • 2 The Proposed Methodology

      • 2.1 Proposed Method

    • 3 Experimental Verification

    • 4 Conclusions

    • References

  • Multiple People Tracking Using Moment Based Approach

    • 1 Introduction

    • 2 Methodology

      • 2.1 Robust Multiple People Tracking

      • 2.2 HSV Range Identif fication

      • 2.3 Moment Calculation

      • 2.4 Calculation of Away and Toward Parameter

      • 2.5 Calibration Techniques

    • 3 Experiment Results

      • 3.1 Single Person Tracking

      • 3.2 Multiple Person(s) Tracking

    • 4 Discussion

    • 5 Conclusion

    • References

  • Wavelets-Based Clustering Techniques for Efficient Color Image Segmentation

    • 1 Introduction

    • 2 Haar Wavelet Transformation

    • 3 Clustering Technique on Images

    • 4 Proposed Image Segmentation Technique Using Color Feature

    • 5 Proposed Segmentation Technique Using Color and Texture

    • 6 Experimental Results

    • 7 Future Scope and Conclusion

    • References

  • An Approach of Optimizing Singular Value of YCbCr Color Space with q-Gaussian Function in Image Processing

    • 1 Introduction

    • 2 Architecture of Radial Basis Function Neural Network

    • 3 Analysis

    • 4 Simulation

    • 5 Conclusion

    • References

  • Motion Tracking of Humans under Occlusion Using Blobs

    • 1 Introduction

    • 2 Related Works

    • 3 Object Tracking

      • 3.1 Background Modelling

      • 3.2 Segmentation

    • Object Representation and Feature Selection

    • 5 Proposed Tracking Algorithm

      • 5.1 Blob Tracking Approach

      • 5.2 Particle Filter Trac cking Approach

    • 6 Results and Discussion

    • 7 Conclusion

    • References

  • Efficient Lifting Scheme Based Super Resolution Image Reconstruction Using Low Resolution Images

    • 1 Introduction

    • 2 Super Resolution Image Reconstruction

    • 3 Lifting Schemes

      • 3.1 Lifting Scheme of the Daubechies4 Wavelet Transforms

      • 3.2 SPHIT Technique

    • 4 Proposed Super Resolution Reconstruction

    • Measurement of Performance

    • 6 Results

    • 7 Conclusion

    • References

  • Improved Chan-Vese Image Segmentation Model Using Delta-Bar-Delta Algorithm

    • 1 Introduction

    • 2 The C-V Algorithm

    • 3 The GDS Method and the Delta-Bar-Delta Algorithm

    • 4 The MDBR Algorithm for Level Set Based Image Segmentation

    • 5 Implementation and Results

    • 6 Conclusions

    • References

  • Online Template Matching Using Fuzzy Moment Descriptor

    • 1 Introduction

    • 2 Required Tools

      • 2.1 Arduino Based Mobile Robot with Camera

      • 2.2 Template Matching Using Fuzzy Moment Descriptor

      • 2.3 Fuzzy Membership Distributions

      • 2.4 Fuzzy Production Rules

      • 2.5 Fuzzy Moment Descriptors

    • 3 Tracking Algorithm

    • 4 Experiment on Mobile Robot

    • 5 Conclusion and Future Work

    • References

  • Classification of High Resolution Satellite Images Using Equivariant Robust Independent Component Analysis

    • 1 Introduction

    • 2 A Proposed Framework for Satellite Image Classification

      • 2.1 Equivariant Robust Independent Component Analysis (ERICA)

    • 3 Results and Discussion

      • 3.1 Performance Index and Accuracy Assessment

    • 4 Conclusion and Future Scope

    • References

  • 3D Face Recognitionacross Pose Extremities

    • 1 Introduction

    • 2 An Overview of the Proposed System

      • 2.1 Select the 3D-Database Directory

      • 2.1.1 Cropping

      • 2.2 Preprocess 2.5D Range Image and Register by Hausdorff’s Distance Metric

      • 2.3 Registrationby Hausdorff’s Distance Metric

      • 2.4 Feature Extraction and Classification by Normal Points

    • 3 Our Contribution and a Comparative Analysis with other 3D Registration Systems

    • 4 Experimental Results

      • 4.1 Recognition of Registered Images

    • 5 Conclusion and Future Scope

    • References

  • Indexing Video Database for a CBVCD System

    • 1 Introduction

    • 2 Past Work

    • 3 Proposed Methodology

      • 3.1 Selection of Shot Representative

      • 3.2 Indexing the Database of Representative Frames

    • 4 Experimental Result

    • 5 Conclusion

    • References

  • Data Dependencies and Normalization of Intuitionistic Fuzzy Databases

    • 1 Introduction

      • 1.1 An Example

    • 2 Intuitionistic Fuzzy Sets and Human Logic

    • 3 Normalization

    • 4 Related Work

    • 5 Proposed Intuitionistic Fuzzy Normalization

    • 6 Intuitionistic Fuzzy Normal Forms

      • 6.1 Intuitionistic Fuzzy Equality

      • 6.2 Intuitionistic Fuzzy Functional Dependency

      • 6.3 Normalization Process

    • 7 Conclusion

    • References

  • Fuzzy Logic Based Implementation for Forest Fire Detection Using Wireless Sensor Network

    • 1 Introduction

    • 2 Related Works

    • 3 Problem Statement

    • 4 Proposed Solution for Forest Fire Detection

    • 5 Network Topology for Energy Efficient WSN

    • 6 Simulation and Results

    • 7 Conclusion

    • References

  • Fuzzy Connectedness Based Segmentation of Fetal Heart from Clinical Ultrasound Images

    • 1 Introduction

    • 2 Methodology

      • 2.1 Probabilistic Patch Based Weighted Maximum Likelihood

      • 2.2 Fuzzy Connectedness Based Image Segmentation

    • 3 Quantitative Validation of Segmentation

    • 4 Results and Discussion

    • 5 Conclusion

    • References

  • An Improved Firefly Fuzzy C-Means (FAFCM) Algorithm for Clustering Real World Data Sets

    • 1 Introduction

    • 2 Fuzzy C-Means Algorithm

    • 3 Firefly Algorithms (FA)

    • 4 Particle Swarm Optimization

    • 5 Proposed Clustering Approach

      • 5.1 Firefly Based Fuzzy C-Means Clustering (FAFCM)

      • 5.2 Improved Firefly Based Fuzzy C-Means Clustering

    • 6 Experimental Analysis

      • 6.1 Parameter Set Up

      • 6.2 Experimental Results

    • 7 Conclusion and Future Work

    • References

  • On Kernel Based Rough Intuitionistic Fuzzy C-means Algorithm and a Comparative Analysis

    • 1 Introduction

    • 2 Definitions and Notations

      • 2.1 Performance Indexes

      • 2.1.1 Davis-Bouldin (DB) Index

      • 2.1.2 Dunn (D) Index

    • 3 Kernel Methods

      • 3.1 Types of Distance Functions

    • 4 The c-Means Clustering Algorithms

      • 4.1 Intuitionistic Fuzzy C-Means

    • 5 Kernel Based Rough Intuitionistic Fuzzy C-Means

      • 5.1 The KRIFCM Algorithm

    • 6 Experimental Analysis

      • 6.1 Image Dataset

      • 6.2 Numeric Dataset

    • 7 Conclusion

    • References

  • FuSCa: A New Weighted Membership Driven Fuzzy Supervised Classifier

    • 1 Introduction

    • 2 Preliminary

    • 3 Proposed Algorithm and Implementation

    • 4 Experimental Analysis

      • 4.1 Performance Evaluation

      • 4.2 Friedman Test

    • 5 Conclusion

    • References

  • Choice of Implication Functions to Reduce Uncertainty in Interval Type-2 Fuzzy Inferences

    • 1 Introduction

    • 2 Selection of the Most Efficient Implication Function that Minimizes the Span of Uncertainty

      • 2.1 Type-1 and Type-2 Inference Technique

      • 2.2 Metric of Span of Uncertainty (SOU) in Terms of UMF and LMF

      • 2.3 Measure of SOU for Various Fuzzy Implication Function

      • 2.4 Comparative Study

    • 3 Conclusion

    • References

  • Detection of Downy Mildew Disease Present in the Grape Leaves Based on Fuzzy Set Theory

    • 1 Introduction

    • 2 Preliminaries

      • 2.1 Diseases in Grape L Leaves

      • 2.2 Featured used in th he Proposed Techniques

      • 2.3 Fuzzy Value

    • 3 Proposed Technique

      • 3.1 Feature Selection Based on Fuzzy Value

      • 3.2 Detection of Diseased Image

    • 4 Experimental Results

    • 5 Conclusion

    • References

  • Facial Expression Synthesis for a Desired Degree of Emotion Using Fuzzy Abduction

    • 1 Introduction

    • 2 Determination of Desired Facial Features for a Given Degree of Emotions by Fuzzy Abduction

      • 2.1 Principles of Fuzzy Abduction

      • 2.2 Determining Features for a Desired Degree of Emotion for Emotion Synthesis Applications

    • 3 Experiments

      • 3.1 Membership Function Construction

      • 3.2 Implication Relatio on Construction

      • 3.4 Results

    • 4 Conclusions

    • References

  • A Novel Semantic Similarity Based Technique for Computer Assisted Automatic Evaluation of Textual Answers

    • 1 Introduction

    • 2 Previous Work

    • 3 Proposed Methodology

      • 3.1 Preprocessing Tasks

      • 3.2 Steps for the Evaluation of the Learner Response

    • 4 Experiments and Results

      • 4.1 Set 1

      • 4.2 Set 2

    • 5 Conclusion

    • References

  • Representative Based Document Clustering

    • 1 Introduction

    • 2 Boundary Entropy and Frequency as Segmentation Measures

    • 3 The Sequence Segmentation Algorithm

    • 4 Representative Based Document Clustering

    • 5 Experiments

    • 6 Conclusions and Future Work

    • References

  • A New Parallel Thinning Algorithm with Stroke Correction for Odia Characters

    • 1 Introduction

    • 2 Existing Thinning Algorithms

    • 3 Stroke Preserving Thinning Algorithm

    • 4 Stroke Corrections

    • 5 Experimental Results

    • 6 Conclusions

    • References

  • Evaluation of Collaborative Filtering Based on Tagging with Diffusion Similarity Using Gradual Decay Approach

    • 1 Introduction

    • 2 Related Work

      • 2.1 Collaborative Filter ring

      • 2.2 Collaborative Filter ring Based on Tagging

      • 2.3 Neighborhood Formation Using Diffusion Similarity

      • 2.4 Prediction and Recommendation

    • 3 Proposed Work CF Framework Based on Tagging with Diffusion Similarity Using Gradual Decay Approach

      • 3.1 Collaborative Filtering Based on Tagging Using Gradual Decay Approach

    • 4 Experiments

      • 4.1 Datasets

      • 4.2 Results

      • 4.3 Analysis of Results

    • 5 Conclusion

    • References

  • Rule Based Schwa Deletion Algorithm for Text to Speech Synthesis in Hindi

    • 1 Introduction

      • 1.1 Hindi Writing System and Schwa

    • 2 Terminology Used

    • 3 Algorithm for Schwa Deletion

      • 3.1 For 1 Block Length Word

      • 3.2 For 2 Block Length Word

      • 3.3 For 3 Block Length Word

      • 3.4 For 4 Block Length Word

    • 4 Results

    • 5 Conclusion and Future Work

    • References

  • Unsupervised Word Sense Disambiguation for Automatic Essay Scoring

    • 1 Introduction

    • 2 Existing Systems

      • 2.1 Project Essay Grader (PEG)

      • 2.2 E-Rater

      • 2.3 Intelligent Essay Assessor (IEA)

      • 2.4 Corpus-Based Word Sense Disambiguation (WSD)

      • 2.5 Graph-Based Word Sense Disambiguation Using Measures of

    • 3 Automated Essay Scoring with WSD

      • 3.1 WSD Process

      • 3.2 Scoring Model Using Multiple Regression Analysis

    • 4 Results and Performance Evaluation

      • 4.1 Kappa Score

    • 5 Conclusion

    • References

  • Disjoint Tree Based Clustering and Merging for Brain Tumor Extraction

    • 1 Introduction

    • 2 Related Work

    • 3 Proposed Approach

      • 3.1 Disjoint Tree Generation

      • 3.2 Tree Merging

    • 4 Experimental Results

      • 4.1 Dataset Used

      • 4.2 Results and Discussion

    • 5 Conclusion

    • References

  • Segmentation of Acute Brain Stroke from MRI of Brain Image Using Power Law Transformation with Accuracy Estimation

    • 1 Introduction

    • 2 Proposed Methodology

    • 3 Results and Discussion

    • 4 Quantification and Accuracy Estimation

    • 5 Conclusion

    • References

  • A Hough Transform Based Feature Extraction Algorithm for Finger Knuckle Biometric Recognition System

    • 1 Introduction

    • 2 The Proposed System Design

      • 2.1 Preprocessing and ROI Extraction

      • 2.2 Elliptical Hough Transform Based Feature Extraction Method

      • 2.3 Feature Information Representation

      • 2.4 Classification

      • 2.5 Matching Score Level Fusion

    • 3 Experimental Analysis and Results Discussion

    • 4 Conclusion

    • References

  • An Efficient Multiple Classifier Based on Fast RBFN for Biometric Identification

    • 1 Introduction

      • 1.1 Classifier Performance Evaluation

    • 2 Overview of the System and Approach

      • 2.1 Preprocessing

      • 2.2 Theory of Operation

    • 3 Result and Performance Analysis

      • 3.1 Training Set

      • 3.2 Test Set

    • 4 Conclusion

    • References

  • Automatic Tortuosity Detection and Measurement of Retinal Blood Vessel Network

    • 1 Introduction

    • 2 Background

      • 2.1 Determination of Furcation and Branches

      • 2.2 Relation Between Arc and Chord:

      • 2.3 Tortuosity Features:

    • 3 Proposed Method

      • 3.1 Vessel Skeleton Extraction

      • 3.2 Breaking Into Branches

      • 3.3 Rotating Vessel Branches and Partitioning into Segments by Finding Maximum Distance from Chord

      • 3.4 Tortuosity Measurement

      • 3.5 Tortuosity Detection

    • 4 Experimental Results

    • 5 Conclusion

    • References

  • A New Indexing Method for Biometric Databases Using Match Scores and Decision Level Fusion

    • 1 Introduction

    • 2 Proposed Indexing Methodology

      • 2.1 Selection of Sample Image Set

    • 3 Retrieval of Best Matches (Identification)

      • 3.1 Fusion of Decisions Output

    • 4 Experimental Results

      • 4.1 Retrieval Time

      • 4.2 Comparison with Other Indexing Techniques

    • 5 Conclusions

    • References

  • Split-Encoding: The Next Frontier Tool for Big Data

    • 1 Motivation

    • 2 Introduction

    • 3 Related Work

    • 4 Design and Implementation

      • 4.1 Split-Encoding Scheme # 1

    • 5 Experimental Results

      • 5.1 Experimental Setup

      • 5.2 Performance Measurement

    • 6 Conclusion

    • References

  • Identification of Lost or Deserted Written Texts Using Zipf’s Law with NLTK

    • 1 Introduction

    • 2 Background Theory

    • 3 Motivation

    • 4 Getting Started

    • 5 Result Analysis

    • 6 Conclusion and Future Work

    • References

  • An Efficient Approach for Discovering Closed Frequent Patterns in High Dimensional Data Sets

    • 1 Introduction

    • 2 Related Work

    • 3 Preliminaries

    • 4 Proposed Approach for Discovering Frequent Pattern

    • 5 Results and Discussion

    • 6 Conclusion and Future Scope

    • References

  • Time-Fading Based High Utility Pattern Mining from Uncertain Data Streams

    • 1 Introduction

    • 2 Preliminaries

    • 3 Related Work

      • 3.1 Proposed Work

      • 3.2 Complexity Analysis of Landmark and Sliding Window Tree Structures

    • 4 Experimental Results

    • 5 Conclusions

    • References

  • Classification for Multi-Relational Data Mining Using Bayesian Belief Network

    • 1 Introduction

    • 2 Related Work

      • 2.1 Inductive Logic Programming (ILP)

      • 2.2 Probabilistic Approaches

      • 2.3 Graph-Based Approach

    • 3 Background Theory

      • 3.1 Bayesian Belief Network

      • 3.2 Semantic Relationship Graph (SRG)

      • 3.3 Tuple-ID Propagation

    • 4 Proposed Approach

    • 5 Conclusion and Future Work

    • References

  • Noise Elimination from Web Page Based on Regular Expressions for Web Content Mining

    • 1 Introduction

    • 2 Preliminaries

    • 3 Proposed Method

      • 3.1 Filtering

      • 3.2 Regular Expression

      • 3.3 Noise Detection in SST [13]

      • 3.4 Proposed Algorithm

    • 4 Experimental Results

    • 5 Conclusion

    • References

  • Modified Literature Based Approach to Identify Learning Styles in Adaptive E-Learning

    • 1 Introduction

    • 2 Related Works

    • 3 Methodology

      • 3.1 Felder-Silverman Learning Style Model (FSLSM)

      • 3.2 E-learning Framework

    • 4 Experimentation Details

    • 5 Experimentation Design

    • 6 Results and Discussion

    • 7 Conclusion

    • References

  • A Concept Map Approach to Supporting Diagnostic and Remedial Learning Activities

    • 1 Introduction

    • 2 System Overview

    • 3 Implementation in M-Learning Environment

    • 4 Experiments

    • 5 Conclusion

    • References

  • Data Prediction Based on User Preference

    • 1 Introduction

    • 2 Model for Collaborative Filtering

    • 3 The Aspect Model

    • 4 Implementation Details

      • 4.1 Dataset

    • 5 Results and Analysis

    • 6 Conclusion

    • References

  • Automatic Resolution of Semantic Heterogeneity in GIS: An Ontology Based Approach

    • 1 Introduction

    • 2 Proposed Semantic Matching Framework

    • 3 Implementation and Results

    • 4 Conclusion

    • References

  • Web-Page Indexing Based on the Prioritized Ontology Terms

    • 1 Introduction

    • 2 Related Works

    • 3 Proposed Approach

      • 3.1 Extraction of Dominating and Sub-Dominating Ontology Terms

      • 3.2 Proposed Algorithm of Web-Page Indexing

      • 3.3 Web-page Retrieval Mechanism

    • 4 Experimental Analyses

      • 4.1 Time Complexity to Produce Resultant Web-Page List

      • 4.2 Experimental Result

    • 5 Conclusions

    • References

  • A Hybrid Approach Using Ontology Similarity and Fuzzy Logic for Semantic Question Answering

    • 1 Introduction

    • 2 Background

      • 2.1 Question and Answering System

      • 2.2 Ontology

      • 2.3 Data Clustering

    • 3 Methodology Description

    • 4 Conclusion and Future Work

    • References

  • Ontology Based Object-Attribute-Value Information Extraction from Web Pages in Search Engine Result Retrieval

    • 1 Introduction

      • 1.1 Semantic Web

      • 1.2 Ontology

      • 1.3 Knowledge Representation

      • 1.4 WordNet

      • 1.5 RDF and RDFS

    • 2 Related Work

    • 3 Ontology Based Information Extraction from Web Pages

      • 3.1 Proposed Architecture for O-A-V Evaluation

      • 3.2 Algorithms

    • 4 A Light Weight Ontology Based Search Engine

      • 4.1 Proposed Architecture for O-A-V Representation

    • 5 Conclusions and Future Work

    • References

  • Effects of Robotic Blinking Behavior for Making Eye Contact with Humans

    • 1 Introduction

    • 2 Related Work

    • 3 System Overview

    • 4 Experiments

      • 4.1 Experiment 1: Effect of Eye Blinks

      • 4.2 Experiment 2: Effect of Blink Duration

    • 5 Conclusion

    • References

  • Improvement and Estimation of Intensity of Facial Expression Recognition for Human-Computer Interaction

    • 1 Introduction

    • 2 Optical Flow Based Feature Extraction

    • 3 Rule Based Generation by Training a Decision Tree

    • 4 Improvement in Intensity of Expressions Based on Component Based Analysis

    • 5 Estimation of Expression Intensity Based on Manhattan Distance Metric

    • 6 System's Performance Compared to Human Perception

    • 7 Conclusion

    • References

  • Cognitive Activity Recognition Based on Electrooculogram Analysis

    • 1 Introduction

    • 2 Principles and Methodology

      • 2.1 Electrooculogram Signal Acquisition and Pre-processing

      • 2.2 Feature Extraction

      • 2.3 Classification

    • 3 Experiments and Results

    • 4 Conclusions and Future Scopes

    • References

  • Detection of Fast and Slow Hand Movements from Motor Imagery EEG Signals

    • 1 Introduction

    • 2 Experiments and Methods

      • 2.1 Visual Cue

      • 2.2 Experimental Setup

      • 2.3 Pre-processing

      • 2.4 Feature Extraction

      • 2.5 Classification

    • 3 Results and Discussions

    • 4 Conclusion and Future Direction

    • References

  • A Solution of Degree Constrained Spanning Tree Using Hybrid GA with Directed Mutation

    • 1 Introduction

    • 2 Degree Constrained Spanning Tree

    • 3 Solution Methodology

      • 3.1 Representation of Chromosomes in GA in ST Problems

      • 3.2 Generation of Initial Population

      • 3.3 Graphical Edge-Set t Crossover

      • 3.4 Graphical Edge-Set t Directed Mutation

    • 4 Experimental Result and Comparison

    • 5 Conclusion

    • References

  • Side Lobe Reduction and Beamwidth Control of Amplitude Taper Beam Steered Linear Array Using Tschebyscheff Polynomial and Particle Swarm Optimization

    • 1 Introduction

    • 2 Beam Steered Linear Array and Tschebyscheff Polynomial

    • 3 PSO on Linear Array Design

    • 4 Proposed Method

    • 5 Experimental Setup and Results

    • 6 Conclusion

    • References

  • Pervasive Diary in Music Rhythm Education: A Context- Aware Learning Tool Using Genetic Algorithm

    • 1 Introduction

    • 2 Related Works

    • 3 Fundamentals of Music Rhythm

    • 4 Experimental Details

      • 4.1 Overview of Pervasive Diary in Music Rhythm Education

      • 4.3 Generation of Rhythm Matrix

      • 4.4 Fitness Function

      • 4.5 Parent Music Rhythm Selection Strategy Using Linear Rank Method

    • 5 Result Set Analysis and User Interface Design

    • 6 Conclusion

    • References

  • An Elitist Binary PSO Algorithm for Selecting Features in High Dimensional Data

    • 1 Introduction

    • 2 Preliminaries

      • 2.1 Binary Particle Swarm Optimization (BPSO)

      • 2.2 Non-dominated Sorting Algorithm

      • 2.3 k-NN Classifier

    • 3 Proposed Approach

      • 3.1 Preprocessing Data

      • 3.2 Fitness Function

      • 3.3 Elitist BPSO Method

    • 4 Results and Discussions

      • 4.1 Gene Expression Data Sets

      • 4.2 Results

    • 5 Conclusion

    • References

  • An Immune System Inspired Algorithm for Protein Function Prediction

    • 1 Introduction

    • 2 Background of the Problem

      • 2.2 Formulation of the Problem

      • 2.3 Solution Representation and Cost Function Evaluation

    • 3 An Overview of Clonal Selection Algorithm (CSA)

    • 4 Experiments and Results

    • 5 Conclusion

    • References

  • Fast Approximate Eyelid Detection for Periocular Localization

    • 1 Introduction

    • 2 Related Works

    • 3 Proposed Fast Eyelid Detection

    • 4 Experimental Results

    • 5 Conclusion

    • References

  • Prediction of an Optimum Parametric Combination for Minimum Thrust Force in Bone Drilling: A Simulated Annealing Approach

    • 1 Introduction

    • 2 Experimental Pr rocedure

      • 2.1 Experimental Desig gn Based on Response Surface Methodology

      • 2.2 Experimental Deta ils

    • 3 Development of Mathematical Model

    • 4 Optimization of the Thrust Force with SA

    • 5 Conclusions

    • References

  • Author Index

Nội dung

27 Editors SMART INNOVATION, SYSTEMS AND TECHNOLOGIES Malay Kumar Kundu Durga Prasad Mohapatra Amit Konar Aruna Chakraborty Advanced Computing, Networking and Informatics – Volume Advanced Computing and Informatics Proceedings of the Second International Conference on Advanced Computing, Networking and Informatics (ICACNI-2014) 13 Smart Innovation, Systems and Technologies Volume 27 Series editors Robert J Howlett, KES International, Shoreham-by-Sea, UK e-mail: rjhowlett@kesinternational.org Lakhmi C Jain, University of Canberra, Canberra, Australia e-mail: Lakhmi.jain@unisa.edu.au For further volumes: http://www.springer.com/series/8767 About this Series The Smart Innovation, Systems and Technologies book series encompasses the topics of knowledge, intelligence, innovation and sustainability The aim of the series is to make available a platform for the publication of books on all aspects of single and multi-disciplinary research on these themes in order to make the latest results available in a readily-accessible form Volumes on interdisciplinary research combining two or more of these areas is particularly sought The series covers systems and paradigms that employ knowledge and intelligence in a broad sense Its scope is systems having embedded knowledge and intelligence, which may be applied to the solution of world problems in industry, the environment and the community It also focusses on the knowledge-transfer methodologies and innovation strategies employed to make this happen effectively The combination of intelligent systems tools and a broad range of applications introduces a need for a synergy of disciplines from science, technology, business and the humanities The series will include conference proceedings, edited collections, monographs, handbooks, reference books, and other relevant types of book in areas of science and technology where smart systems and technologies can offer innovative solutions High quality content is an essential feature for all book proposals accepted for the series It is expected that editors of all accepted volumes will ensure that contributions are subjected to an appropriate level of reviewing process and adhere to KES quality principles Malay Kumar Kundu · Durga Prasad Mohapatra Amit Konar · Aruna Chakraborty Editors Advanced Computing, Networking and Informatics – Volume Advanced Computing and Informatics Proceedings of the Second International Conference on Advanced Computing, Networking and Informatics (ICACNI-2014) ABC Editors Malay Kumar Kundu Machine Intelligence Unit Indian Statistical Institute Kolkata India Durga Prasad Mohapatra Dept of Computer Science and Engineering National Institute of Technology Rourkela Rourkela India ISSN 2190-3018 ISBN 978-3-319-07352-1 DOI 10.1007/978-3-319-07353-8 Amit Konar Dept of Electronics and Tele-Communication Engineering Artificial Intelligence Laboratory Jadavpur University Kolkata India Aruna Chakraborty Dept of Computer Science and Engineering St Thomas’ College of Engineering & Technology Kidderpore India ISSN 2190-3026 (electronic) ISBN 978-3-319-07353-8 (eBook) Springer Cham Heidelberg New York Dordrecht London Library of Congress Control Number: 2014940383 c Springer International Publishing Switzerland 2014 This work is subject to copyright All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed Exempted from this legal reservation are brief excerpts in connection with reviews or scholarly analysis or material supplied specifically for the purpose of being entered and executed on a computer system, for exclusive use by the purchaser of the work Duplication of this publication or parts thereof is permitted only under the provisions of the Copyright Law of the Publisher’s location, in its current version, and permission for use must always be obtained from Springer Permissions for use may be obtained through RightsLink at the Copyright Clearance Center Violations are liable to prosecution under the respective Copyright Law The use of general descriptive names, registered names, trademarks, service marks, etc in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use While the advice and information in this book are believed to be true and accurate at the date of publication, neither the authors nor the editors nor the publisher can accept any legal responsibility for any errors or omissions that may be made The publisher makes no warranty, express or implied, with respect to the material contained herein Printed on acid-free paper Springer is part of Springer Science+Business Media (www.springer.com) Foreword The present volume is an outcome, in the form of proceedings, of the 2nd International Conference on Advanced Computing, Networking and Informatics, St Thomas’ College of Engineering and Technology, Kolkata, India, June 24–26, 2014 As the name of the conference implies, the articles included herein cover a wide span of disciplines ranging, say, from pattern recognition, machine learning, image processing, data mining and knowledge discovery, soft computing, distributed computing, cloud computing, parallel and distributed networks, optical communication, wireless sensor networks, routing protocol and architecture to data privacy preserving, cryptology and data security, and internet computing Each discipline, itself, has its own challenging problems and issues Some of them are relatively more matured and advanced in theories with several proven application domains, while others fall in recent thrust areas Interestingly, there are several articles, as expected, on symbiotic integration of more than one discipline, e.g., in designing intelligent networking and computing systems such as forest fire detection using wireless sensor network, minimizing call routing cost with assigned cell in wireless network, network intrusion detection system, determining load balancing strategy in cloud computing, and side lobe reduction and beam-width control, where the significance of pattern recognition, evolutionary strategy and soft computing has been demonstrated This kind of interdisciplinary research is likely to grow significantly, and has strong promise in solving real life challenging problems The proceedings are logically split in two homogeneous volumes, namely, Advanced Computing and Informatics (vol 1) and Wireless Networks and Security (vol 2) with 81 and 67 articles respectively The volumes fairly represent a state-of-the art of the research mostly being carried out in India in these domains, and are valued-additions to the current era of computing and knowledge mining VI Foreword The conference committee, editors, and the publisher deserve congratulations for organizing the event (ICACNI-2014) which is very timely, and bringing out the archival volumes nicely as its output Kolkata, April 2014 Sankar K Pal Distinguished Scientist and former Director Indian Statistical Institute Message from the Honorary General Chair It gives me great pleasure to introduce the International Conference on Advanced Computing, Networking and Informatics (ICACNI 2014) which will be held at St Thomas’ College of Engineering and Technology, Kolkata during June 24–26, 2014 ICACNI is just going to cross its second year, and during this small interval of time it has attracted a large audience The conference received over 650 submissions of which only 148 papers have been accepted for presentation I am glad to note that ICACNI involved top researchers from 26 different countries as advisory board members, program committee members and reviewers It also received papers from 10 different countries ICACNI offers an interesting forum for researchers of three apparently diverse disciplines: Advanced Computing, Networking and Informatics, and attempts to focus on engineering applications, covering security, cognitive radio, human-computer interfacing among many others that greatly rely on these cross-disciplinary research outcomes The accepted papers are categorized into two volumes, of which volume includes all papers on advanced computing and informatics, while volume includes accepted papers on wireless network and security The volumes will be published by SpringerVerlag The conference includes plenary lecture, key-note address and four invited sessions by eminent scientists from top Indian and foreign research/academic institutes The lectures by these eminent scientists will provide an ideal platform for dissemination of knowledge among researchers, students and practitioners I take this opportunity to thank all the participants, including the keynote, plenary and invited speakers, reviewers, and the members of different committees in making the event a grand success VIII Message from the Honorary General Chair Thanks are also due to the various Universities/Institutes for their active support towards this endeavor, and lastly Springer-Verlag for publishing the proceedings under their prestigious Smart Innovation, Systems and Technologies (SIST) series Wish the participants an enjoyable and productive stay in Kolkata Kolkata, April 2014 Dwijesh Dutta Majumder Honorary General Chair ICACNI -2014 Preface The twenty first century has witnessed a paradigm shift in three major disciplines of knowledge: 1) Advanced/Innovative computing ii) Networking and wireless Communications and iii) informatics While the first two are complete in themselves by their titles, the last one covers several sub-disciplines involving geo-, bio-, medical and cognitive informatics among many others Apparently, the above three disciplines of knowledge are complementary and mutually exclusive but their convergence is observed in many real world applications, encompassing cyber-security, internet banking, healthcare, sensor networks, cognitive radio, pervasive computing and many others The International Conference on Advanced Computing, Networking and Informatics (ICACNI) is aimed at examining the convergence of the above three modern disciplines through interactions among three groups of people The first group comprises leading international researchers, who have established themselves in one of the above three thrust areas The plenary, the keynote lecture and the invited talks are organized to disseminate the knowledge of these academic experts among young researchers/practitioners of the respective domain The invited talks are also expected to inspire young researchers to initiate/orient their research in respective fields The second group of people comprises Ph.D./research students, working in the cross-disciplinary areas, who might be benefited from the first group and at the same time may help creating interest in the cross-disciplinary research areas among the academic community, including young teachers and practitioners Lastly, the group comprising undergraduate and master students would be able to test the feasibility of their research through feedback of their oral presentations ICACNI is just passing its second birthday Since its inception, it has attracted a wide audience This year, for example, the program committee of ICACNI received as many as 646 papers The acceptance rate is intentionally kept very low to ensure a quality publication by Springer This year, the program committee accepted only 148 papers from these 646 submitted papers An accepted paper has essentially received very good recommendation by at least two experts in the respective field To maintain a high standard of ICACNI, researchers from top international research laboratories/universities have been included in both the advisory committee and the program committee The presence of these great personalities has helped the 702 S.Z.N Hazarika et al Algorithm Eyelid detection Require: im Ensure: cc2 A ← smooth(im) B ← edge(A) cc1 ← labelled edge(B) no of edges ← maximum(cc1) for i = to no of edges hor spr ← horizontal spread of the edge nop ← number of pixel in ith edge spr horizontality(i) ← hor nop end for 10 cc2 ← cc1 11 for i = to max(cc1) 12 if horizontality(i) < 90% of horizontality of other edges then 13 cc2 ← cc1 − ith edge 14 end if 15 end for 16 return cc2 Fig Proposed periocular localization algorithm : (a) Coloured input image; (b) Noisy image of the iris; (c) Grayscale image of the iris; (d) Smoothed image of the iris; (e) Detection of edges of the image; (f) Horizontal edges retained; (g) Upper and lower eyelids detected through checking hf ; (h) 2-means clustering performed on detected eyelid points; (i) Periocular region localized Fast Approximate Eyelid Detection for Periocular Localization 703 Table Detail of database for evaluation of proposed approach Database Developer Version Images Subjects Resolution Color Model Spectrum Soft Computing and Image Analysis (SOCIA) v1 [9] 1,877 241 800 × 600 RGB Group, Department of UBIRIS VS Computer Science, University of Beira v2 [10] 11,102 261 400 × 300 sRGB Interior, Portugal Table Localization results on UBIRISv1 and UBIRISv2 databases Accuracy → Databases ↓ UBIRISv1 UBIRISv2 Upper Eyelid Mislocalization Partial (%) Localization (%) 22.48 68.13 30.07 65.86 Lower Eyelid Mislocalization Partial (%) Localization (%) 35.79 55.22 40.18 50.28 Conclusion Iris is considered to be one of the most reliable biometric traits, making it one of the most widely used in the present biometric scenario However, in cases where unconstrained images are acquired, iris recognition fails to deliver desired accuracy Hence, to achieve recognition in unconstrained images, the periocular region is considered as an alternative Upon implementing the proposed eyelid detection algorithm on publicly available visual spectrum (VS) image database UBIRISv1 and UBIRISv2, it is observed that both the upper and lower eyelids show partial localization for most of the images Even with the partial success in detecting the eyelids, we are able to approximately localize the periocular region References Adam, M., Rossant, F., Amiel, F., Mikovikova, B., Ea, T.: Eyelid Localization for Iris Identification Radioengineering 17(4), 82–85 (2008) Bakshi, S., Sa, P.K., Majhi, B.: Optimised periocular template selection for human recognition BioMed Research International 2013, 1–14 (2013) Bakshi, S., Tuglular, T.: Security through human-factors and biometrics In: 6th International Conference on Security of Information and Networks, pp 463–463 (2013) Cui, J., Wang, Y., Tan, T., Ma, L., Sun, Z.: A Fast and Robust Iris Localization Method Based on Texture Segmentation In: Biometric Authentication and Testing, National Laboratory of Pattern Recognition, Chinese Academy of Sciences (2004) He, Z., Tan, T., Sun, Z., Qiu, X.: Robust eyelid eyelash and shadow localization for iris recognition In: 15th IEEE International Conference on Image Processing, pp 265–268 (2008) Ling, L.L., de Brito, D.F.: Fast and efficient iris image segmentation Journal of Medical and Biological Engineering 30(6), 381–392 (2010) 704 S.Z.N Hazarika et al Mahlouji, M., Noruzi, A.: Human Iris Segmentation for Iris Recognition in Unconstrained Environments IJCSI International Journal of Computer Science Issues 9(1), 3, 149–155 (2012) Masek, L.: Recognition of Human Iris Patterns for Biometric Identification In: Bachelor of Engineering Thesis at The University of Western Australia (2003) Proen¸ca, H., Alexandre, L.A.: UBIRIS: A noisy iris image database In: Roli, F., Vitulano, S (eds.) ICIAP 2005 LNCS, vol 3617, pp 970–977 Springer, Heidelberg (2005) 10 Proena, H., Filipe, S., Santos, R., Oliveira, J., Alexandre, L.: The UBIRISv2: A database of visible wavelength iris images captured on-the-move and at-a-distance IEEE Transactions on Pattern Analysis and Machine Intelligence 32(8), 1529–1535 (2010) 11 Radman, A., Zainal, N., Ismail, M.: Efficient Iris Segmentation based on eyelid detection Journal of Engineering Science and Technology 8(4), 399–405 (2013) 12 Thalji, Z., Alsmadi, M.: Iris Recognition Using Robust Algorithm for Eyelid, Eyelash and Shadow Avoiding World Applied Sciences Journal 25(6), 858–865 (2013) 13 Valentina, C., Hartono, R.N., Tjahja, T.V., Nugroho, A.S.: Iris Localization using Circular Hough Transform and Horizontal Projection Folding In: Proceedings of International Conference on Information Technology and Applied Mathematics (2012) 14 Xu, G., Zhang, Z., Ma, Y.: Improving the Performance of Iris Recognition System Using Eyelids and Eyelashes Detection and Iris Image Enhancement In: 5th IEEE conference on Cognitive Informatics, pp 871–876 (2006) Prediction of an Optimum Parametric Combination for Minimum Thrust Force in Bone Drilling: A Simulated Annealing Approach Rupesh Kumar Pandey and Sudhansu Sekhar Panda* Department of Mechanical Engineering, Indian Institute of Technology Patna, India rupeshiitp@gmail.com, sspanda@iitp.ac.in Abstract Minimally invasive drilling of bone has a great demand in orthopaedic surgical process as it helps in better fixation and quick healing of the damaged bones The aim of the present study is to find out the optimal setting of the bone drilling parameters (spindle speed and feed rate) for minimum thrust force during bone drilling using simulated annealing (SA) The bone drilling experiments were carried out by central composite design scheme and based on the results obtained, a response surface model for thrust force as a function of drilling parameters is developed This model is used as an objective function in the SA approach The results of the confirmation experiments showed that the SA can effectively predict the optimal settings of spindle speed and feed rate for minimum thrust force during bone drilling The suggested methodology can be very useful for orthopaedic surgeons to minimize the drilling induced bone tissue injury Keywords: Bone drilling, Thrust force, Response surface methodology, Simulated annealing Introduction Drilling of bone is commonly employed during orthopaedic surgery to produce hole for the insertion of screws and wires to fix the damaged bone or for the installation of prosthetic device [1] The thrust force induced to the bone during drilling is one of the major concerns as the exposure of the bone to their higher magnitudes can damage the bone cells or can even result in their death (osteonecrosis) leading to the loosening of the fixation and increased healing time [1-4] Moreover, micro cracks and the drill bit breakage can also occur as an additional disadvantage of the higher mechanical forces The micro cracks can lead to the further fracture of the damaged bone which can initiate large number of cracks resulting in the misalignment of the fixation or permanent failure [2] The broken drill bit can obstruct the placement of other fixating devices and can cause adverse histological effects if it undergoes corrosion thus, demands for additional procedures to remove the broken drill bit [5-9] thereby increasing the operative time The rate of heat generation is also high with higher drilling forces [10] which can result in thermal osteonecrosis Therefore, minimization of the * Corresponding author M.K Kundu et al (eds.), Advanced Computing, Networking and Informatics - Volume 1, Smart Innovation, Systems and Technologies 27, DOI: 10.1007/978-3-319-07353-8_81, © Springer International Publishing Switzerland 2014 705 706 R.K Pandey and S.S Panda thrust force generated during bone drilling will result in better fixation of the broken bones and their quick recovery postoperatively Previously, many researchers have investigated the bone drilling process to study the effect of the various drilling parameters on the thrust force produced [1-4] The early researches in this area were reported in late 1950s [3] Spindle speed and feed rate were the main drilling parameters analyzed in most of the studies [3-4, 10-14] There is a consensus among the researchers that the thrust force decreases with an increase in spindle speed [3-4, 10-14] But, the drilling of bone with higher spindle speeds were reported with increased trauma [3, 14] The analysis on the effect of the feed rate showed that the increase in feed rate increases the thrust force induced in bone drilling [1-2, 4, 13] Despite of the above mentioned studies, there is a lack of a clear suggestion on the optimal settings of the feed rate and spindle speed for minimum thrust force generation during bone drilling In this work, a statistical model for bone drilling process to predict the thrust force as a function of feed rate (mm/min) and spindle speed (rpm) is developed using response surface methodology (RSM) Next, the model is used as a fitness function in SA algorithm to determine the optimal setting of feed rate and spindle speed for minimum thrust force during bone drilling The adopted approach is then validated through the confirmation experiment RSM is a collection of mathematical and statistical tools which are easy, quick and effective for modeling the process in which several variables influence the response of interest [15-16] In most of the real problems the relationship between the response and the independent variable is unknown In RSM the relationship between the response and the independent process variables is represented as (1) Y = f ( A, B, C ) + ε (1) Where Y is the desired response, f is the response function and ε represents the error observed in the response A second order model is generally employed if the response function is nonlinear or not known, shown in (2) [15-16] k k i =1 i =1 Y = β +  β i xi +  βii xi2 +  β ij xi x j + ε i (2) j Where β is the coefficient for constant term β i , β ii and β ij are the coefficients for linear, square and interaction terms respectively Simulated annealing algorithm mimics the process of annealing which involves heating of a metal to a temperature beyond its melting point followed by a gradual cooling In molten state, the particles are in random motion and when it is cooled gradually the particles rearrange themselves to attain minimum energy state As the cooling is done gradually, lower and lower energy states are obtained until the lowest energy state is reached [17] In the process of annealing the probability Pr (E ) of being at energy state is given by the Boltzmann distribution as (3): Pr ( E ) = (3) Z (T ) exp(− E / KT ) Prediction of an Optimum Parametric Combination for Minimum Thrust Force 707 Where Z (T ) = normalized factor depending upon the temperature T and K is the Boltzmann constant The probability Pr (E ) approaches one when the temperature T is high for all energy states The probability of the higher energy states decreases as compared to the lower energy states as the temperature decreases Metropolis et al [18] used the above criteria and proposed that if a random perturbation is applied to the present energy state of a solid with temperature T for the generation of the perturbed energy states and the difference of the energy ΔE between the two states is negative then the particles rearrange themselves such that they attain the low energy state The probability of the acceptance of perturbed energy state as the new energy state is given as (4) Pr ( E ) = exp(−ΔE / KT ) (4) This criterion of acceptance for the new state is known as the Metropolis acceptance criteria Kirkpatrick et al [19] used the sequence of Metropolis algorithm evaluated by the sequence of reducing temperatures as simulated annealing minimization of an objective function The objective function corresponds to the energy function used in Metropolis acceptance criteria Recently, SA has been used successfully for the optimization of the various engineering problems [20-21] It uses a number of points ( N ) to test the thermal equilibrium at a particular temperature before acquiring a reduced temperature state The algorithm is stopped when the desirable minimum change in the value of the objective function is obtained or the temperature obtained is sufficiently small The initial starting temperature T and the number of iterations N to be performed at each temperature are the two user defined parameters that governs the effective working of SA algorithm The step by step procedure of SA algorithm is discussed below [17] • Initialize the starting temperature T and the termination temperature Tmin Randomly generate initial starting point X Also define the number of iterations N to be performed at each Temperature Set the iteration counter as i = • Calculate the value of the objective function E = f ( X ) • Update E Best = E and X Best = X • Generate the random neighborhood point X ∗ and calculate the objective function E∗ = f ( X ∗) • Evaluate the change in energy ΔE = E ∗ − E • If the change in energy ΔE < , then update the point X = X ∗ and if E < E best ,then E best = E and X Best = X Update the iteration as i = i + and go to next step Else go to step number and repeat the process • If i > N go to the next step • Reduce the temperature by a factor α and update T = αT • If T ≤ Tmin then terminate the process and print X best and E best else, move to step 708 R.K Pandey and S.S S Panda Experimental Prrocedure 2.1 Experimental Desig gn Based on Response Surface Methodology The bone drilling parameteers considered are feed rate (mm/min) and spindle sppeed (rpm) (shown in Table 1) and a the response taken is thrust force (N) The central coomposite design (CCD) of RS SM was employed to design the plan of experiments for studying the relationship beetween the response and the bone drilling parameters F Full factorial design for factors at two levels i.e high (+1) and low (-1) corresponding tto a face centered design with th hirteen runs (four factorial points, four axial points and ffive central points) was used as shown in Table The bone drilling parameters and thheir levels are considered based on the wide range of experiments reported in the literatture [1-4, 10-14] Table Factor F and levels considered for bone drilling A B 2.2 Control facto or Feed rate (mm m/min) Spindle speed d (rpm) Low level (-1) 30 500 High level (+ +1) 150 2500 Experimental Details The work material used fo or conducting the bone drilling experiments was bovvine femur, as the human bones are not easily available and it closely resembles the hum man bone, allowing the results to be extrapolated in the real surgical situations [19-220] The bovine femur was obttained from a local slaughter house immediately after the slaughter and the experiments were done within few hours to maintain minimum lloss in thermo-physical propertiies of the fresh bone [22-23] No animal was scarified sspecifically for the purpose of this t research Fig Experimental set up Prediction of an Optimum Parametric Combination for Minimum Thrust Force 709 The experiments were carried out on axis MTAB flex mill using 4.5mm HSS (high speed steel) drill bit The drilling depth was 6mm The drilling thrust force signals were measured using Kistler 9257B piezo electric dynamometer The signals were acquired using 5070 multichannel charge amplifier and Dynoware software The thrust force obtained for each experimental run is listed in the last column of Table 2.The experimental set up is shown in the Fig Table Experimental condition and result Exp No 10 11 12 13 Feed rate (mm/min) 90 150 90 150 30 90 30 90 90 90 150 90 30 Spindle speed (rpm) 1500 1500 500 500 2500 2500 1500 1500 1500 1500 2500 1500 500 Thrust Force (N) 16.93 24.55 29.25 45.43 4.72 14.31 6.155 16.79 17.41 17.29 20.33 17.01 11.32 Development of Mathematical Model A mathematical model correlating the thrust force and drilling process parameters is developed based on (2) using design expert software version 8.0.1 [24] A quadratic model is selected based on low standard deviation and high R squared value as mentioned in Table [24] Table Model summary statistics Source Linear 2FI Quadratic Standard deviation 4.08 3.00 1.44 R-Squared 0.8721 0.9378 0.9888 Adjusted R-Squared 0.8466 0.9171 0.9808 Predicted R-Squared 0.6912 0.7966 0.8880 Suggested The model is given by (5) as: Force = 8.796 + 0.3817 × Feed rate − 0.0155 × spindle speed − 7.7083 × E − × Feed rate × spindle speed − 4.2713 × E − × Feed rate + 4.8898 × E − × spindle speed (5) 710 R.K Pandey and S.S Panda Analysis of variance (ANOVA) carried out to find the significance of the developed model and individual model coefficients at 95% confidence interval is shown in Table Table ANOVA table for the proposed model Source Model A-Feed rate B-Spindle speed AB A2 B2 Residual Total DOF 1 1 12 SS 1287.77 773.28 362.55 85.56 6.53 66.04 14.58 1302.35 MS 257.55 773.28 362.55 85.56 6.53 66.04 2.08 P value

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