Proceedings of SAI Intelligent Systems Conference (IntelliSys) 2016

Volume 1
 
 
Springer (Verlag)
  • erschienen am 19. August 2017
  • |
  • XIII, 1149 Seiten
 
E-Book | PDF mit Adobe-DRM | Systemvoraussetzungen
E-Book | PDF mit Wasserzeichen-DRM | Systemvoraussetzungen
978-3-319-56994-9 (ISBN)
 

These proceedings of the SAI Intelligent Systems Conference 2016 (IntelliSys 2016) offer a remarkable collection of chapters on a wide range of topics in intelligent systems, artificial intelligence and their applications to the real world. Authors hailing from 56 countries on 5 continents submitted 404 papers to the conference, attesting to the global importance of the conference's themes. After being reviewed, 222 papers were accepted for presentation, and 168 were ultimately selected for these proceedings. Each has been reviewed on the basis of its originality, novelty and rigorousness.

The papers not only present state-of-the-art methods and valuable experience from researchers in the related research areas; they also outline the field's future development.

1st ed. 2018
  • Englisch
  • Cham
  • |
  • Schweiz
Springer International Publishing
  • 100 farbige Tabellen, 520 s/w Abbildungen
  • |
  • 520 schwarz-weiße Abbildungen, 100 farbige Tabellen, Bibliographie
  • 155,23 MB
978-3-319-56994-9 (9783319569949)
10.1007/978-3-319-56994-9
weitere Ausgaben werden ermittelt
  • Intro
  • Editor's Preface
  • Contents
  • On the Possibility to Resolve the Scientific Paradoxes in Artificial Cognitive System
  • Abstract
  • 1 Introduction
  • 2 On the Nature of Paradoxes
  • 2.1 General Paradoxes
  • 2.2 Scientific Paradoxes
  • 2.3 Some Examples of Scientific Paradoxes
  • 3 Basic Elements of NCA
  • 3.1 Dynamical Theory of Information
  • 3.2 Neurophysiology Reasons
  • 3.3 Neural Computing
  • 4 Cognitive Architecture Within NCA
  • 4.1 The Processes of Recording and Memorization of the Image Information
  • 4.2 Symbolic Infrastructure
  • 4.3 Equations for Interaction of the Whole Neuron Ensemble
  • 4.4 The Scheme of the Cognitive System Within NCA
  • 4.5 Appearance and Resolution of Symbolic Paradoxes
  • 5 Discussion and Conclusions
  • References
  • A Framework for Optimization of Pattern Sets for Financial Time Series Prediction
  • 1 Introduction
  • 2 Data
  • 2.1 Candlestick Representation of Financial Time Series
  • 2.2 Data Selection
  • 3 Method
  • 3.1 Pattern Types
  • 3.2 Encoding
  • 3.3 Evaluation
  • 3.4 Evolutionary Algorithm
  • 4 Results
  • 4.1 Tuning an Existing Pattern Set
  • 4.2 Discovering New Pattern Sets
  • 5 Discussion
  • 6 Conclusion
  • References
  • Fuzzy Modelling of Diffuse Solar Radiation
  • Abstract
  • 1 Introduction
  • 2 Building a Fuzzy Model of Diffuse Solar Radiation
  • 2.1 Fuzzy Clustering and Partitioning of the Output Space
  • 2.2 Input Structure Identification
  • 2.3 Fuzzy Rule Structure Identification
  • 2.4 Parameter Identification
  • 2.5 The Identification Algorithm
  • 3 Results and Discussion
  • 3.1 Position Type Fuzzy Model of the Solar Diffuse Fraction
  • 3.2 A Position-Gradient Type Fuzzy Model of the Diffuse Solar Fraction
  • 4 Conclusion
  • Acknowledgements
  • References
  • Application of Cellular Genetic Algorithms and Space Efficient Chromosomes to Wells Placement in Oil Fields
  • 1 Introduction
  • 2 Problem Description
  • 3 Methodology
  • 3.1 Mathematical Formulation
  • 3.2 Solution to the Optimization Problem
  • 4 Results
  • 5 Conclusions
  • References
  • Delay and Area Efficient Sound Wave Decomposition by Nonuniform Filter Bank for Digital Hearing Aids
  • Abstract
  • 1 Introduction
  • 2 Constrained Least Square FIR Filters
  • 3 Hearing Loss and Audiogram
  • 4 Methodology
  • 5 Experimental Results
  • 5.1 Audiogram for Mild Hearing Loss at High Frequencies
  • 6 Conclusion
  • References
  • Ideo-Dynamic Diagnostic Expert Systems
  • Abstract
  • 1 Introduction
  • 2 The Components of Diagnostics Expert Systems
  • 2.1 Knowledge Base
  • 2.2 Interface Engine and User Interface
  • 3 The Results of Test-Retest Reliability
  • 3.1 The Nomothetic Diagnostics Expert Systems of Mental Resources Assessment (Self-evaluation of Hierarchical Structure of Abilities)
  • 3.2 The Ideographic Diagnostics Expert Systems of Mental Resources Assessment (Self-evaluation of Hierarchical Structure of Abilities)
  • 3.3 The Ideo-Dynamic Diagnostics Expert Systems of Mental Resources Assessment (Self-evaluation of Hierarchical Structure of Abilities)
  • 4 Conclusion
  • References
  • Adaptive NeuroFuzzy Sliding Mode Based Damping Control for SSSC
  • Abstract
  • 1 Introduction
  • 2 SMIB System Installed with SSSC
  • 3 Overview of Sliding Mode Control
  • 4 Proposed Control Strategy
  • 4.1 Adaptive NeuroFuzzy Sliding Mode Control
  • 4.2 Adaptation Mechanism
  • 5 Simulation Results and Discussion
  • 5.1 Case 1. Nominal Loading ( P_{e} = 0.75\,p.u. )
  • 5.2 Case 2. Heavy Loading ( P_{e} = 1\,p.u. )
  • 5.3 Case 3. Series of Faults
  • 6 Conclusion
  • References
  • Micro Aerial Vehicle Path Planning and Flight with a Multi-objective Genetic Algorithm
  • 1 Introduction
  • 2 Related Work
  • 3 The Genetic Algorithm
  • 3.1 Chromosome Representation
  • 3.2 Multi-objective Optimization
  • 3.3 Population
  • 3.4 The Next Generation
  • 3.5 Mass Extinction
  • 3.6 Computational Complexity
  • 4 The Micro Aerial Vehicle
  • 5 Flying a Mission
  • 6 Results
  • 7 Conclusion
  • References
  • Intellectualization of the Data Processing in the Industrial Automatization on the Basis of Modern Equipment
  • Abstract
  • 1 Introduction
  • 2 Principles of the AIS Construction
  • 2.1 Mathematical Model of the Formal Peptide
  • 3 Description of the Method
  • 3.1 Statement of the Problem
  • 4 Data Collection from Complex Objects
  • 5 Simulation Results
  • 5.1 Pre-processing Data
  • 5.2 Solving the Image Recognition Problem
  • 6 Conclusion
  • Appendix
  • References
  • A Framework for Collaborative Human-Computer Interaction E-learning
  • Abstract
  • 1 Introduction
  • 2 Literature Review
  • 2.1 Theoretical Background
  • 2.1.1 Behaviourism Theory
  • 2.1.2 Cognitivism Theory
  • 2.1.3 Constructivism Theory
  • 2.1.4 Connectivism Learning Theory
  • 2.1.5 Social Cognitive Development Theory
  • 2.1.6 Social Interdependence Theory
  • 2.1.7 Cognitive Elaboration Perspectives
  • 2.2 Related Work
  • 3 The Proposed CEL Model
  • 3.1 Levels of the Proposed CEL Model
  • 3.1.1 Social Networking
  • 3.1.2 Contribution
  • 3.1.3 Rooting Social Interaction
  • 3.1.4 Knowledge Development
  • 3.1.5 Cognitive Evaluation
  • 3.2 Tasks of the Proposed CEL Model
  • 3.2.1 Shared Space
  • 3.2.2 Knowledge Preparation
  • 3.2.3 Knowledge Negotiation
  • 3.2.4 Knowledge Reusable
  • 3.2.5 Knowledge Evolving
  • 3.2.6 Knowledge Transferring
  • 4 Implementation of the CEL Model
  • 4.1 Group Posting Module
  • 4.1.1 Teacher Group Activity
  • 4.1.2 Student Group Activity
  • 4.2 Group Communication Module
  • 4.3 Individual Commenting Module
  • 4.4 Knowledge Base Module
  • 4.5 Directional Shifting Module
  • 5 Technical Infrastructure for CEL Environment
  • 6 CEL Model Evaluation
  • 6.1 Evaluation of CEL Model Framework
  • 6.1.1 Learners have Individual Responsibility and Accountability
  • 6.1.2 Learning Interaction Takes Place in Small Groups
  • 6.1.3 Communication During Learning is Interactive and Dynamic
  • 6.1.4 Learners can Identify Their Role in the Learning Task
  • 6.1.5 Participants have a Shared Understanding Within the Learning Environment
  • 6.2 Evaluate Usability and Accessibility Among Different Student Stages
  • 6.3 Evaluation of CEL Model Features
  • 6.4 Evaluation Based on an Adaptable Usability
  • 7 Conclusion
  • References
  • Search-Based Requirements Traceability Recovery
  • Abstract
  • 1 Introduction
  • 2 Foundational Concepts
  • 2.1 Requirements Engineering (RE)
  • 2.2 Requirements Traceability (RT)
  • 2.3 Requirements Traceability Recovery (RTR)
  • 2.4 Heuristic Search (HS)
  • 3 Application of Heuristic Search to Requirements Traceability Recovery
  • 3.1 General Idea
  • 3.2 Application of the Genetic Algorithm to Requirements Traceability Recovery
  • 3.3 Representation of Individuals
  • 3.4 Description of the Genetic Operators
  • 3.5 Decoding of Individuals
  • 4 Implementation and Experimental Settings
  • 4.1 Research Questions to Investigate
  • 4.2 Experimental Settings
  • 4.3 Projects Used in the Evaluation
  • 4.4 Use of Precision and Recall
  • 5 Results and Discussion
  • 5.1 Precision and Recall
  • 5.2 Threats to Validity
  • 6 Related Work
  • 7 Future Work
  • 8 Conclusions
  • Acknowledgements
  • References
  • Ensemble of Trees for Classifying High-Dimensional Imbalanced Genomic Data
  • 1 Introduction
  • 2 Mining Genomic Data
  • 3 Ensemble Learning
  • 3.1 Random Forest
  • 3.2 Bagging
  • 3.3 Boosting
  • 4 Proposed Method
  • 4.1 Data Balancing Method
  • 4.2 Ensemble of Trees
  • 5 Experiments
  • 5.1 Experimental Setup
  • 5.2 Experimental Results
  • 6 Conclusion
  • References
  • Performance Analysis of CPW Fed Multiband Microstrip Patch Antenna
  • Abstract
  • 1 Introduction
  • 2 Methodology
  • 2.1 Antenna Design
  • 2.1.1 Edge Fed Multiband Microstrip Patch Antenna
  • 2.1.2 Coaxial Fed Multiband Microstrip Antenna
  • 2.1.3 Coplanar Waveguide (CPW) Coupled Multiband Antenna
  • 3 Results and Explanations
  • 3.1 Return Losses
  • 3.2 Radiation Pattern
  • 3.3 3-D Radiation Pattern
  • 3.4 Radiation Characteristics
  • 4 Conclusion
  • References
  • An Improved Algorithm Based on ROMP for Compressive Sensing
  • Abstract
  • 1 Introduction
  • 2 Improved Algorithm Called MCC-ROMP
  • 2.1 Review of ROMP
  • 2.2 Modify the Regularization Procedure
  • 3 Experimental Simulations
  • 4 Conclusions
  • Acknowledgements
  • References
  • Using Machine Learning to Predict Length of Stay and Discharge Destination for Hip-Fracture Patients
  • Abstract
  • 1 Introduction
  • 2 Motivation
  • 3 Application Domain
  • 4 Data Description
  • 5 Data Anomalies
  • 5.1 Outliers Removal
  • 5.2 Dealing with Data Imbalances
  • 6 Learning Algorithm: Random Forests
  • 7 Feature Selection
  • 8 Experimental Results
  • 9 Related Work
  • 10 Limitations of the Study
  • 11 Conclusions
  • References
  • Chunking in Dependency Model and Spelling Correction in Russian and English
  • Abstract
  • 1 Introduction
  • 2 About Syntax
  • 3 Chunking Algorithm for Russian
  • 3.1 Pre-processing
  • 3.2 Tokenization
  • 3.3 Segmentation by Sentences
  • 3.4 Morphological Analysis
  • 3.5 Synthesis of Potential Corrections
  • 3.6 Building an Expanded Grammar Word Tuples
  • 3.7 Search for the Main Parts of the Sentence
  • 3.8 Forming a Set of Potential Chunks
  • 3.9 Building a Set of Chunk Trees
  • 3.10 Choosing the Best Chunk Tree
  • 3.11 Output of Results
  • 4 Example of Work of Algorithm
  • 5 Chunking in English
  • 6 Example of Work of Algorithm for English Chunking
  • 7 Conclusion
  • Appendix A
  • Appendix B
  • Appendix C
  • Appendix D
  • References
  • Neurophenomenology of Social Tension: A Theoretical Framework for Modelling Prospective Scenarios
  • Abstract
  • 1 Introduction: The Systemic Approach to Social Organization
  • 2 The Systemic Method
  • 3 Time Frame for Decision-Making: The Temporal Components
  • 4 Human Behaviour as a Complex Adaptive System
  • 5 Primary Conditions of Behavioural Stability
  • 6 The MOSIG Model Apply to the Chilean Political Case (1969-1992)
  • 7 Chilean Political Historiography (1969-1992)
  • 8 Scenarios of Interaction: Political Chilean Case 1969-1993
  • 8.1 Scenario of Interaction: Critical - Collapse (1969-1973)
  • 8.2 Scenario of Interaction: Reactive - Transitional (1973-1989)
  • 8.3 Scenario of Interaction: Proactive/Development (1989-1993)
  • 9 Scenarios of Interaction MOSIG/Chile Case of Study (1969-1993)
  • 9.1 Representation of the Variables
  • 10 Conclusion
  • 10.1 Scientific
  • 10.2 Academical
  • References
  • Event Abstraction for Process Mining Using Supervised Learning Techniques
  • 1 Introduction
  • 2 Related Work
  • 3 Preliminaries
  • 3.1 XES Event Logs
  • 3.2 Petri Nets
  • 3.3 Conditional Random Field
  • 4 Motivating Example
  • 5 Event Abstraction as a Sequence Labeling Task
  • 5.1 From a XES Log to a Feature Space
  • 5.2 Evaluating High-Level Event Predictions for Process Mining Applications
  • 6 Case Study 1: Smart Home Environment
  • 6.1 Experimental Setup
  • 6.2 Results
  • 7 Case Study 2: Artificial Digital Photocopier
  • 7.1 Experimental Setup
  • 7.2 Results
  • 8 Conclusion
  • References
  • A Conceptual Framework for Integrating Scientific Tacit Knowledge
  • Abstract
  • 1 Introduction
  • 2 Theoretical Background
  • 2.1 SKM
  • 2.2 KA Process
  • 2.3 KETs
  • 3 Conceptual Framework for Integrating Knowledge
  • 3.1 EMMs Elicitation (E1)
  • 3.2 EMMs Formalization (E2)
  • 3.3 FKBs and OntoBio Correspondence (E3)
  • 3.4 CSS Specification (E4)
  • 3.5 Framework Evaluation (E5)
  • 4 Conclusions
  • Acknowledgments
  • References
  • Oil Whirl Fault Detection in Induction Motors Using Orbital Analysis and Neural Networks
  • Abstract
  • 1 Introduction
  • 2 Oil Whirl Motor Fault
  • 3 Sample Measurement
  • 3.1 Positioning
  • 4 Pre-processing
  • 5 Orbital Patterns
  • 5.1 Data Processing
  • 5.2 Orbits Building
  • 6 Artificial Neural Network Architecture
  • 7 Experimental Results and Discussions
  • 8 Conclusion and Future Work
  • References
  • Simulated Annealing and Cloud Computing Applied to Forest Planning
  • Abstract
  • 1 Introduction
  • 2 Methodologies
  • 3 Demonstrations of Target Forest Landscape Patching
  • 3.1 Demonstrations of Blocking and Scheduling
  • 3.2 Demonstrations of Target Forest Landscape Patching
  • 3.2.1 Results of Scenario S1 (All Polygons Are 160 Years Old in the Initial Forest)
  • 3.2.2 Results of Scenario S2 (Polygon Ages Are Randomly 0-160 Years in the Initial Forest)
  • 3.2.3 Results of Scenario S3 (All Polygons Are 50 Years Old in the Initial Forest)
  • 3.2.4 Results of Scenario S4 (Polygon Ages Are Randomly 0-50 Years in the Initial Forest)
  • 4 Conclusions
  • Acknowledgements
  • References
  • A Forecasting Model for Data Center Bandwidth Utilization
  • 1 Introduction
  • 2 Methodology
  • 2.1 Design of Data Center
  • 2.2 Traffic Generation
  • 2.3 Data Collection
  • 2.4 Traffic Observation
  • 3 Experimental Results
  • 3.1 Auto-Regressive Integrated Moving Average (ARIMA) Model
  • 3.2 Exponential Smoothing Functions
  • 3.3 Vector Autoregression (VAR) (p) Model
  • 3.4 Implementation Tools and Specifications
  • 4 Background and Related Work
  • 5 Conclusion and Future Work
  • References
  • A Belief Rule-Based Expert System to Assess Bronchiolitis Suspicion from Signs and Symptoms Under Uncertainty
  • Abstract
  • 1 Introduction
  • 2 Uncertainty Phenomenon of Bronchiolitis
  • 3 Related Work
  • 4 Methodology
  • 4.1 Belief Rule Base Represents the Domain Knowledge
  • 4.2 BRBES Inference Procedure
  • 5 Bronchiolitis Suspicion System
  • 5.1 BRBES Architecture and Implementation
  • 5.2 System Components
  • 5.2.1 Input Facts of Bronchiolitis Suspicion
  • 5.2.2 Knowledge Base Constructed Using BRB
  • 5.2.3 Inference Engine
  • 6 Results and Discussion
  • 7 Conclusion and Future Work
  • Acknowledgment
  • References
  • Intelligent Hamilton Path: Using Artificial Intelligent A* Algorithm and Hamilton Path to Navigate Multiple Destinations
  • Abstract
  • 1 Introduction
  • 1.1 Heuristics
  • 1.2 Spatial Databases
  • 1.3 Geographical Information Systems and Driving Path Applications
  • 1.4 Navigating Using Heuristic Functions and Hamilton Path
  • 2 Background and Related Work
  • 2.1 Artificial Intelligent Heuristic Algorithm A*
  • 2.2 Graph Definitions and Notations
  • 2.3 Related Work: Multi-destinations Using Google Maps
  • 3 Proposed Approach: A*Hamilton
  • 4 Results, Conclusions and Future Work
  • 4.1 Results
  • 4.2 Conclusions
  • 4.3 Future Work
  • References
  • Two Prediction Models for Some Economic Indicators of the Russian Arctic Zone
  • 1 Introduction
  • 2 Datasets
  • 3 Two New Prediction Models
  • 3.1 ARIMA Model
  • 3.2 VAR Model
  • 4 Conclusion
  • References
  • Enhanced WalkSAT with Variable Neighborhood Search for MAX-SAT Problems
  • 1 Introduction
  • 2 The Maximum Satisfiability Problem
  • 3 Variable Neighborhood Walksat Based Algorithm (VNS-WS)
  • 4 Experimental Results
  • 4.1 Test Suite and Parameter Settings
  • 4.2 Experimental Results
  • 5 Conclusions
  • References
  • The Development and Preliminary Applications of Semantic Information Knowledge-Base of Mongolian
  • Abstract
  • 1 Introduction
  • 2 New Progress of Knowledge-Base
  • 2.1 Expansion of Scale
  • 2.2 Development of General Base
  • 2.3 Improvement of Semantic Classification
  • 2.4 Perfection of the Attribute Description
  • 3 The Preliminary Application of SIKM
  • 3.1 The Function on Phrase Research
  • 3.2 Value on the Development of Semantic Network
  • 3.3 Significance on Corpus Tagging
  • 4 Conclusion
  • References
  • Multiobjective Clonal Selection Algorithm for the Forecasting Models on the Base of the Strictly Binary Trees
  • Abstract
  • 1 Introduction
  • 2 The Main Ideas of the Modified Clonal Selection Algorithm
  • 3 Multiobjective Modified Clonal Selection Algorithm
  • 4 Experiments
  • 5 Conclusions
  • References
  • RP-AG-SOM: A Growing Self-organizing Map with Assymetric Neighborhood Function and Variable Radius
  • Abstract
  • 1 Introduction
  • 2 Rp-Ag-Som
  • 2.1 SOM
  • 2.2 GSOM
  • 2.3 ASOM
  • 2.4 RPSOM
  • 2.5 RP-AG-SOM
  • 3 A Voice Instruction Learning System Using PR-AG-SOM
  • 4 Experiment
  • 4.1 Results
  • 4.2 Discussions
  • 5 Conclusions
  • Acknowledgement
  • References
  • A Bibliometric Analysis of Human Action Recognition
  • Abstract
  • 1 Introduction
  • 2 Method
  • 3 Result
  • 4 Conclusions
  • Acknowledgment
  • References
  • Solving MaxSAT by Successive Calls to a SAT Solver
  • 1 Introduction
  • 2 Linear Search Algorithms
  • 3 Binary Search Based Algorithms
  • 4 Core-Guided Algorithms
  • 4.1 Fu and Malik's Algorithm
  • 4.2 WPM1
  • 4.3 Improved WPM1
  • 4.4 WPM2
  • 4.5 WMSU4
  • 5 Core-Guided Binary Search Algorithms
  • 6 Portfolio MaxSAT Techniques
  • 7 Experimental Investigation
  • 7.1 Random Category
  • 7.2 Crafted Category
  • 7.3 Industrial Category
  • 8 Conclusion
  • References
  • Genomic Variant Classifier Tool
  • Abstract
  • 1 Introduction
  • 2 Default Classification Algorithm
  • 3 Knowledge Bases Generation
  • 4 Final Remarks
  • Acknowledgments
  • References
  • ARTool- Augmented Reality Platform for Machining Setup and Maintenance
  • 1 Introduction
  • 1.1 AR, New Vision in Manufacturing
  • 1.2 Recent Works
  • 1.3 Paper Structure
  • 2 Platform Description
  • 2.1 Information Flow Explained
  • 2.2 User Device
  • 2.3 The SCADA and Per-Machine Server
  • 3 Marker Library Description
  • 3.1 ARSceneDetector Library Structure
  • 3.2 Library Benchmarking
  • 4 Usability Assessment
  • 4.1 Tasks Definition
  • 4.2 Part Program Description
  • 4.3 Results
  • 5 Conclusions
  • References
  • Computational Intelligence Applications to Crisis Management in Power Systems
  • Abstract
  • 1 Introduction
  • 2 Basics of Power System Analysis
  • 3 Intelligent System for Clearing Overloads
  • 3.1 Background
  • 3.2 Loading Capability Assessment of a Transmission Line
  • 3.3 Sensitivity Factors
  • 3.4 Knowledge Base and Database
  • 3.5 Production Rules
  • 3.6 Case Study
  • 4 Expert System for Voltage Control
  • 4.1 Problem Statement and Sensitivity Tree Method
  • 4.2 Database and Knowledge Base
  • 4.3 Performance Evaluation
  • 5 Neural Network for On-Line Identification of Multiple Failures
  • 5.1 Fault Identification Problem
  • 5.2 The Problem Decomposition
  • 5.3 Structure of the Neural Network Identification System
  • 5.4 Case Studies
  • 6 Conclusion
  • References
  • Emergency Management: Exploring Hard and Soft Data Fusion Modeling with Unmanned Aerial Systems and Non-governmental Human Intelligence Mediums
  • Abstract
  • 1 Introduction
  • 2 Data Fusion Model
  • 3 Emergency Management Framework
  • 4 UAS Operations and Sensor Data Analysis
  • 4.1 UAS Operations in Emergency Management
  • 4.2 Sensors for Emergency Management UAS
  • 4.3 Sensor Data Analysis
  • 5 Human Reliability in Emergency Management
  • 6 A Crisis of Information, Not Data
  • 7 Emergency Management Data Fusion System
  • 7.1 Comparing DF and EM Systems
  • 7.2 Framing a EMDF System
  • 8 Summary
  • 9 Conclusions and Recommendations
  • Acknowledgements
  • References
  • A Rapid Detection of Meat Spoilage Using an Electronic Nose and Fuzzy-Wavelet Systems
  • Abstract
  • 1 Introduction
  • 2 Experimental Case
  • 2.1 Sample Preparation and Microbiological Analysis
  • 2.2 Volatile Samples Acquisition
  • 3 CFWNN Architecture
  • 3.1 Clustering-Based Initialization
  • 3.2 CFWNN Learning Phase
  • 4 Decision Support System Development
  • 5 Results and Discussion
  • 5.1 Aerobic Storage Case Study
  • 5.2 Modified Atmosphere Packaging Case Study
  • 6 Conclusions
  • Acknowledgment
  • References
  • Providing and Adapting Information Assistance for Smart Assembly Stations
  • 1 Introduction
  • 2 Related Work
  • 3 Conceptual Approach
  • 3.1 Information Assistance for the Assembly Workplace
  • 3.2 Situation-Aware Information Assistance
  • 4 Recognising Assembly Tasks
  • 4.1 Formal Task-Models to Capture Work Flows
  • 4.2 From Task Models to Finite State Machines
  • 4.3 From Task Models to Hidden Markov Models
  • 5 Providing Assistance
  • 5.1 Cognitive Architectures
  • 5.2 Knowledge Processing with Soar
  • 5.3 State Detection
  • 5.4 Working with Contextual Knowledge
  • 5.5 Interaction
  • 5.6 Learning
  • 6 Reducing the Technical Error
  • 6.1 Sensor-Based Errors
  • 6.2 Knowledge-Based Errors
  • 7 The Industrial Prototype
  • 8 Conclusions and Future Work
  • References
  • Brain-Controlled Wheelchair Through Discrimination of Two Mental Tasks
  • Abstract
  • 1 Introduction
  • 2 Method and Materials
  • 2.1 Participant and Data Acquisition
  • 2.2 Robotic Wheelchair
  • 2.3 Initial Training
  • 2.4 Signal Processing
  • 2.5 Navigation Paradigm
  • 2.6 Experimental Procedure
  • 3 Results and Discussion
  • 3.1 Discrete Movements
  • 3.2 Continuous Movements
  • 4 Conclusion
  • Acknowledgments
  • References
  • Strategic Location Models and Their Impact on Operational Level
  • 1 Introduction
  • 2 Literature Review
  • 3 Location Models and Simulation
  • 3.1 Notation
  • 3.2 Model1
  • 3.3 Model2
  • 3.4 Model3
  • 4 Experimental Results
  • 4.1 Models Results Comparison
  • 4.2 Response Time
  • 4.3 Busyness Rate
  • 5 Conclusion
  • References
  • Hybrid Intelligence Nano-enriched Sensing and Management System for Efficient Water-Quality Monitoring
  • Abstract
  • 1 Introduction
  • 2 Data Sensing and Forwarding Subsystem (DSFS)
  • 2.1 Hybrid Intelligence-Based WSN-Based Data Classification and Forwarding Scheme
  • 2.2 Overview of the Technical Insights of the Proposed Scheme for Efficient Water Quality Monitoring
  • 2.2.1 Introduction
  • 2.2.2 Weighted Classifiers Using Fuzzy Logic, Entropy and Decision Trees
  • 2.2.3 Case Study and Illustrative Example
  • 3 Experimental Section
  • 4 Data Visualization and Operation Management System
  • 5 Evaluation and Discussion
  • 5.1 Evaluation of HI-Based Data Classification and Forwarding Scheme
  • 5.1.1 Simulation Setup
  • 5.2 Optical Nanoparticles Characterization
  • 5.3 Detection Effectiveness
  • 6 Conclusion
  • Acknowledgements
  • References
  • Visualizing Large Graphs Out of Unstructured Data for Competitive Intelligence Purposes
  • Abstract
  • 1 Introduction
  • 2 Big Data
  • 3 Significance and Methods of Managing Unstructured Data
  • 3.1 Using Relational Databases
  • 3.2 Using XML
  • 3.3 Using NoSQL
  • 4 Mining Unstructured Data Approach in the Competitive Intelligence System XEW
  • 4.1 XEW Sourcing Service (XEW-SS)
  • 4.2 Meta-model of Unstructured Data
  • 4.3 Homogenization of the Information Source
  • 4.4 XEW Big Data Analytics Service (XEW-BDAS)
  • 5 Graph Visualization in CIS XEW
  • 5.1 Graph Visualization
  • 5.2 Benchmarking
  • 5.3 XEWGraph
  • 6 Conclusion and Perspectives
  • References
  • NRCS: Neutrosophic Rule-Based Classification System
  • 1 Introduction
  • 2 Neutrosophic Logic
  • 3 The Proposed Neutrosophic Rule-Based Classification System
  • 3.1 Neutrosophic Membership Function
  • 3.2 Extracting Information Phase
  • 3.3 Neutrosophication Phase
  • 3.4 Generating a Set of Rules Phase
  • 3.5 Classification Phase
  • 3.6 Performance Measure Phase
  • 4 Results and Discussion
  • 4.1 Dataset
  • 4.2 Evaluation Results
  • 5 Conclusions and Future Enhancement
  • References
  • Developing a Real-Time ITS Using VANETs: A Case Study for Northampton Town
  • Abstract
  • 1 Introduction
  • 2 Literature Review
  • 3 The Proposed Approach
  • 4 Intellegent Road Congestion Avoidance Algorithm (IRCA)
  • 5 Simulation
  • 6 Results and Discussion
  • 7 Conclusion and Future Works
  • Acknowledgment
  • References
  • Large-Scale Traffic Grid Signal Control Using Decentralized Fuzzy Reinforcement Learning
  • 1 Introduction
  • 2 Problem Statement
  • 3 Method
  • 3.1 Fuzzy Sets and Fuzzification of Queue Length
  • 3.2 Coordination Between Agents
  • 3.3 Rules for Algorithm Selection
  • 3.4 Greedy Algorithm
  • 3.5 Neighborhood Approximate Q-Learning (NAQL)
  • 4 Experiments
  • 4.1 Traffic Simulation
  • 4.2 Results and Comparisons
  • 5 Conclusion
  • References
  • Intelligence: The Interdependence of Independent Members of Teams
  • Abstract
  • 1 Introduction
  • 2 Macro Effects: Conflict
  • 3 Macro Effects: Physics Model
  • 3.1 Macro Effects: Team Models
  • 3.2 Macro Effects: Tradeoffs
  • 3.3 Hypothesis
  • 3.4 Macro Effects: Discussion of Prior Research
  • 4 Macro Effects: Future Research
  • 4.1 Merger Data
  • 4.2 Future Research. Emotion
  • 5 Conclusions
  • Acknowledgements
  • References
  • Proactive Business Intelligence to Give Best Customer Experience to Valued Social Networks in Telecoms
  • Abstract
  • 1 Introduction
  • 2 Methodology
  • 2.1 Data Source
  • 2.2 Feature Extraction
  • 2.3 Communities Detection
  • 2.4 Call Drop Rate Analysis
  • 3 Results
  • 4 Conclusion
  • 5 Limitation and Future Works
  • References
  • Pricing European Options Using a Novel Multi-objective Firefly Algorithm
  • 1 Introduction and Background
  • 2 Financial Options
  • 3 Firefly Algorithm
  • 4 Related Work
  • 5 Option Pricing as a Multi-objective Optimization Problem
  • 5.1 Firefly for Evaluating an Option Contract
  • 5.2 Probability Computation
  • 5.3 Weighted-Sum Approach
  • 5.4 Option Pricing Firefly Algorithm
  • 6 Experimental Results
  • 6.1 Experimental Setup
  • 6.2 Opion Contract Result as a Pareto Front
  • 6.3 Risk-Aware Application of the Model
  • 6.4 Error Analysis
  • 7 Conclusions
  • References
  • Performance Evaluation of Relay Deployment in Long-Term Evolution Advanced (LTE-A) Network
  • Abstract
  • 1 Introduction
  • 2 Literature Review
  • 3 Research Methodology
  • 4 Results, Analysis and Discussion
  • 5 Conclusion
  • 6 Recommendations for Future Work
  • Acknowledgements
  • References
  • Adaptive Associative Classifier for Mammogram Classification
  • Abstract
  • 1 Introduction
  • 2 Related Work
  • 2.1 Mammogram Classification
  • 2.2 Associative Classifier for Mammogram
  • 2.3 Rule Refinement
  • 3 Adaptive Associative Classifier
  • 3.1 Associative Classifier for Mammogram
  • 3.2 Rule Refinement Based on Incremental Modification
  • 4 Experimental Results
  • 4.1 Experimental Setting
  • 5 Results and Discussion
  • 6 Conclusion
  • References
  • Surrogate Reservoir Model for Average Reservoir Pressure
  • 1 Introduction
  • 2 Related Work
  • 3 Methodology
  • 4 Results and Discussion
  • 5 Conclusion
  • References
  • Comparative Study of Different Data Mining Techniques in Predicting Forest Fire in Lebanon and Mediterranean
  • Abstract
  • 1 Introduction
  • 2 Data Mining Fields
  • 3 Lebanese Current Situation and Place of Study
  • 4 Applying Neural Networks
  • 5 Applying Decision Tree
  • 6 Applying Fuzzy Logic
  • 7 Applying Discriminate Analysis
  • 8 Applying Support Vector Machine
  • 9 Interpretations and Results
  • 10 Conclusion
  • References
  • An Interactive Mobile Augmented Reality for Advertising Industry
  • Abstract
  • 1 Introduction
  • 2 Related Work
  • 3 Proposed Architecture
  • 4 Case Studies
  • 4.1 Point of Interest Locater
  • 4.2 Menara Kuala Lumpur Advertisement
  • 4.3 HyppTV Advertisement
  • 5 Conclusion and Future Work
  • Acknowledgements
  • References
  • New Strange Chaotic Attractors in Dynamical Systems of Multi-spin Spacecraft and Gyrostats
  • Abstract
  • 1 Introduction
  • 2 Main Dynamical Systems
  • 3 Evaluating the MSSC Parameters Delivering the Dynamics with Strange Chaotic Attractors
  • 4 New Strange Chaotic Attractors
  • 5 Systems with Complex Behavior Close to Strange Chaotic Attractors
  • 6 Conclusion
  • Acknowledgement
  • References
  • A Credit Risk Model Based on Contour Subspaces for Decision Support Systems in Loan Granting
  • 1 Introduction
  • 2 A Credit Risk Model
  • 2.1 Credit Risk Functions
  • 2.2 A Method to Construct a Credit Risk Function
  • 2.3 Applications of the Credit Risk Model
  • 3 Results
  • 4 Conclusion
  • References
  • Intelligent Predictive Maintenance System
  • Abstract
  • 1 Introduction
  • 2 Required Data Format
  • 3 Machine Learning Approach
  • 3.1 Models Comparision
  • 3.2 K Nearest Neighbors
  • 3.3 Naive Bayes Classifier
  • 3.4 Decision Trees Method
  • 3.5 Random Forest Classifier
  • 3.6 Supportive Vectors Machines
  • 3.7 Generalized Linear Models
  • 3.8 Models Accuracy Comparsion
  • 4 Data Transformation Problem
  • 4.1 Time Series Typed Data
  • 5 Real Data Results
  • 6 Conclusions
  • References
  • Smartphone-Based Vehicular Tire Pressure and Condition Monitoring
  • 1 Introduction
  • 2 Related Work
  • 3 Detection Theory
  • 4 Practical Implementation
  • 4.1 Experimental Setup
  • 4.2 Data Processing
  • 4.3 Feature Selection
  • 4.4 Machine Learning Techniques
  • 5 Experimental Results
  • 5.1 Classification Results
  • 5.2 Effect of Road Conditions
  • 6 Tread Depth Monitoring
  • 7 Conclusions
  • 8 Extensions and Future Work
  • References
  • An Introduction to the NMPC-Graph as General Schema for Causal Modeling of Nonlinear, Multivariate, Dynamic, and Recursive Systems with Focus on Time-Series Prediction
  • Abstract
  • 1 Motivation for Introducing a New Modeling Method
  • 2 Component Definitions
  • 2.1 Parameter
  • 2.2 Bilateral Couplings
  • 2.2.1 Integrative Coupling
  • 2.2.2 Synchronous Coupling
  • 2.2.3 Differential Coupling
  • 2.3 Operators
  • 2.3.1 Summator
  • 2.3.2 Modulator
  • 3 Nodal Matrix Definition of the NMPC-Graph
  • 3.1 Definition of the State Equation
  • 3.2 Example of a NMPC-Graph
  • 4 Converting the NMPC-Graph into a NMPC-Model by Calibration with Historic Data
  • 4.1 The Principle of Calibration
  • 4.2 Proposing an Algorithm for Calibrating NMPC-Functions
  • 4.3 Calculating Inverse Functions of Functions with Multiple Input Variables
  • 4.3.1 Calculating Inverse Function of a NMPC-Modulator
  • 4.3.2 Calculating Inverse Function of a NMPC Summator
  • 5 Calibration as Hypothesis Testing
  • 6 Solving the State Equation
  • 7 Outlook
  • APPENDIX - GLOSSARY
  • References
  • Fast-and-Fit: An Intelligent Auto-Pricing System for Airlines Travel Agencies
  • Abstract
  • 1 Introduction
  • 2 Related Works
  • 2.1 Airline-Viewed Auto-Pricing Systems
  • 2.2 Passenger-Viewed Auto-Pricing Systems
  • 2.3 Discussion
  • 3 System Architecture
  • 4 Mining Sequence Pattern for Frequent Route Finding
  • 5 Enhanced HMM System for Auto-Pricing
  • 5.1 Making Decision with Typical HMM Technique
  • 5.2 Enhanced HMM for Valuation of Price Changes
  • 6 Experiments
  • 6.1 Simulation System
  • 7 Conclusion
  • Acknowledgements
  • References
  • Providing Intelligent Assistance for Product Configuration in Manufacturing: A Learning-to-Rank Approach
  • 1 Introduction
  • 2 Ranking SVM Approach to Product Configuration
  • 2.1 Problem Statement
  • 2.2 Formulation as a Learning-to-Rank Problem
  • 2.3 Feature Extraction
  • 2.4 Acquiring Training Data
  • 3 Experiments
  • 3.1 Industrial Connectors: Background and Dataset
  • 3.2 Evaluation Measures
  • 3.3 Results
  • 3.4 Discussion
  • 4 Related Work
  • 5 Conclusion and Future Work
  • References
  • A Review of Animal Behavior-Inspired Methods for Intelligent Systems
  • Abstract
  • 1 Introduction
  • 2 Emergent Behavior Paradigm
  • 2.1 Definition
  • 2.2 Single-Agent Emergent Behavior
  • 2.3 Multi-agent Emergent Behavior
  • 3 Bat Algorithm
  • 3.1 Definition
  • 3.2 Algorithm
  • 3.3 Application
  • 3.3.1 Optimization Problem
  • 3.3.2 Scheduling
  • 3.3.3 Image Processing (Computer Vision)
  • 4 Krill Herd Algorithm
  • 4.1 Definition
  • 4.2 Algorithm
  • 4.3 Application
  • 4.3.1 Engineering Optimization Problem
  • 4.3.2 Route Optimization
  • 4.3.3 Machine Learning and Data Mining
  • 5 Artificial Bee Colony Algorithm
  • 5.1 Definition
  • 5.2 Algorithm
  • 5.3 Application
  • 6 Conclusion
  • References
  • 5G as Intelligent System: Model and Regulatory Consequences
  • Abstract
  • 1 Introduction
  • 2 What Is 5G?
  • 3 The Proposed 5G Intelligent System Model
  • 3.1 Intelligent Network
  • 3.2 Intelligent Spectrum
  • 3.3 Intelligent Applications: Smart Data
  • 4 Implications to Regulatory Policies
  • 4.1 Intelligent Network: Management and Cost Structure
  • 4.2 Intelligent Spectrum: Refarming, Spectrum Pool and Open Spectrum Access
  • 4.3 Intelligent Applications: Privacy and Security
  • 5 Future Scope
  • 6 Conclusions
  • References
  • Suitability of IEEE 802.11ac/n/p for Bandwidth Hungry and Infotainment Applications for Cities
  • 1 Introduction
  • 2 IEEE 802.11p for Vehicular Networks
  • 2.1 Literature Survey
  • 3 IEEE 802.11n
  • 3.1 Literature Survey
  • 4 IEEE 802.11ac
  • 4.1 Literature Survey
  • 5 Simulation Setup, Results and Discussion
  • 5.1 Application Traffic Load
  • 5.2 Effect of Increasing Transmitting Nodes
  • 5.3 High Node Density
  • 5.4 Simulation for Voice Quality
  • 5.5 Inter-node Distance
  • 5.6 BER (Bit-Error Rate) Effect on Aggregation
  • 6 Conclusions
  • References
  • Human Emotion Interpreter
  • Abstract
  • 1 Introduction
  • 2 Related Work
  • 2.1 Facial Features
  • 2.2 Vocal Features
  • 2.3 Audiovisual Recognition Systems
  • 2.4 Databases and Contests
  • 3 Objectives
  • 4 Proposed System
  • 5 Impact of Study
  • 6 Conclusion and Future Work
  • References
  • Probabilistic Occupancy Level Estimation Based on Opportunistic Passive Wi-Fi Localisation
  • 1 Introduction
  • 2 Related Work
  • 2.1 Indoor Localisation
  • 2.2 Occupancy Detection
  • 3 Passive Wi-Fi Localisation Approach
  • 3.1 Offline Phase: Deployment and Fingerprint Acquisition
  • 3.2 Online Phase: Performing Localisation
  • 3.3 Localisation Accuracy
  • 4 Occupancy Level Estimation
  • 4.1 Inferring the Level of Occupancy
  • 4.2 Occupancy Level Estimation Quality
  • 5 Discussion
  • 5.1 Localisation Limitations
  • 5.2 Privacy Concerns
  • 5.3 Adaptability
  • 5.4 Adding Data to the System
  • 6 Conclusion
  • References
  • Location-Based Content Delivery Using iBeacon Technology
  • Abstract
  • 1 Introduction
  • 2 Location-Based Technologies
  • 2.1 RFID
  • 2.2 NFC
  • 2.3 QR-code
  • 2.4 GPS
  • 2.5 iBeacon Technology
  • 3 Comparison Between the Location-Based Technologies and Discussion
  • 4 Related Works
  • 5 Comparison Between Mobile Applications that Use iBeacon Technology and Findings
  • 6 Conclusion and Future Work
  • Appendix
  • References
  • Inference Engine Based on a Hierarchical Structure for Detecting Everyday Activities Within the Home
  • Abstract
  • 1 Introduction
  • 2 Related Work
  • 3 ADL Model Structure
  • 3.1 Knowledge Base of ADL Characteristics
  • 3.1.1 Sub-ADL and Action Attributes
  • 3.1.2 ADL Attributes
  • 4 ADL Recognition
  • 4.1 Feature Detection - Sensor Event Detection
  • 4.2 Windows Segmentation
  • 4.3 Utility Function Algorithm
  • 4.4 Aggregate Windows Algorithm
  • 5 Experimental Setup
  • 6 Results
  • 7 Conclusion
  • References
  • CASA: Safe and Green Driving Assistance System for Real-Time Driving Events
  • Abstract
  • 1 Introduction
  • 2 Related Works
  • 3 The CASA System Architecture
  • 4 The Driving Context
  • 4.1 The Vehicle Cockpit
  • 4.2 The Scenario Simulator
  • 4.3 Context of Interaction
  • 5 CASA's Multimodal Signal Processing
  • 5.1 The Principle of Multimodal Signal Processing
  • 5.2 Multimodal Fusion and Fission
  • 5.3 Human-Machine Interface
  • 6 Sample Simulation
  • 6.1 Knowledge Representation Implementation
  • 6.2 Rules for Fusion of Multimodal Signals
  • 6.3 Messages and Message Ranking
  • 6.4 Multimodal Fission
  • 6.5 Laboratory Experimental Results
  • 7 Conclusion and Future Works
  • Acknowledgment
  • References
  • Data Driven Monitoring of Rolling Stock Components
  • 1 Introduction
  • 2 Case Study: Train Door Monitoring
  • 2.1 Data Foundation
  • 2.2 Learning Approach
  • 2.3 Unsupervised Learning
  • 2.4 Synthetic Label Generation
  • 2.5 Supervised Training of a Sequence Classifier
  • 2.6 Sequence Monitoring
  • 2.7 Classification Results
  • 3 Conclusions
  • References
  • A Virtual Assistive Companion for Older Adults: Design Implications for a Real-World Application
  • Abstract
  • 1 Introduction
  • 2 Related Work
  • 2.1 Embodied Conversational Agents for Older Adults
  • 3 Design Methodology
  • 3.1 Understanding the Role of a Daily Life Companion
  • 3.2 Requirements for an Assistive Companion Agent
  • 4 System Architecture Design
  • 4.1 Hardware Platform
  • 4.2 Perception Components
  • 4.3 Decision-Making Components
  • 4.4 Assistive Care Services
  • 4.5 Synthesis Components
  • 5 Multimodal Interaction Paradigm
  • 5.1 Natural Language Interface
  • 5.2 Graphical User Interface (GUI)
  • 6 Evaluation Study
  • 6.1 Instruments and Measures
  • 7 Results and Lessons Learned
  • 7.1 How Older Adults Perceive and Interact with a Companion
  • 7.2 A Companion in Real-World Settings
  • 8 Conclusion and Future Work
  • Acknowledgment
  • References
  • Emotional Domotics: Inhabitable Space Variable Control for the Emotions Modulation
  • Abstract
  • 1 Motivation and Objectives
  • 2 Introduction
  • 3 Investigations Progress
  • 4 State of the Art and Precedents
  • 5 Experiment Description
  • 6 Results and Analysis
  • 7 Conclusions and Future Work
  • References
  • Modeling the Dynamic Context of Ambient Systems
  • Abstract
  • 1 Introduction
  • 2 Ambient Systems and Context
  • 2.1 Ambient System
  • 2.2 Context
  • 2.2.1 Changes in the Context
  • 2.2.2 Context Categories
  • 3 Related Work
  • 4 Proposed Approach
  • 4.1 Functional Analysis
  • 4.2 Decomposition of the System
  • 4.3 Context Definition
  • 4.4 Modeling Internal Dynamic Context
  • 5 Case Study
  • 5.1 Functional Analysis
  • 5.2 Decomposition of System
  • 5.2.1 Area 1: The Quay
  • 5.3 Defining the Risk Categories
  • 5.4 Modeling Dynamic Context
  • 6 Discussion
  • 7 Conclusion
  • References
  • WheelScout - Barrier-Free Navigation
  • 1 Introduction
  • 2 Indoor Computation
  • 2.1 Indoor Positioning/Tracking
  • 2.2 Indoor Routing
  • 3 Outdoor Computation
  • 4 Combined Route Calculation
  • 5 Basic Map Data
  • 6 Customization of Wheel Chair Profiles
  • 7 Voice Control
  • 8 Responsive Web Design
  • 9 Outlook
  • References
  • Investigating Off-Angle Iris Recognition in Unconstrained Acquisition
  • Abstract
  • 1 Introduction
  • 2 Background
  • 3 Iris Acquisition Methodology
  • 4 Preliminary Review of Iris Acquisition Samples
  • 5 Discussion
  • 6 Conclusion
  • Acknowledgment
  • References
  • A Novel Approach of Protein Secondary Structure Prediction by SVM Using PSSM Combined by Sequence Features
  • Abstract
  • 1 Introduction
  • 2 Data and Method
  • 2.1 Dataset
  • 2.2 DSSP
  • 2.3 PSSM
  • 2.4 Sequence Features
  • 2.5 SVM (Support Vector Machine)
  • 3 The Experimental Results
  • 3.1 Evaluation Measure
  • 3.2 Feature Selection
  • 4 Conclusions
  • Acknowledgements
  • References
  • Classification of Liver Fibrosis Patients by Multi-dimensional Analysis and SVM Classifier: An Egyptian Case Study
  • Abstract
  • 1 Introduction
  • 2 Machine Learning and Knowledge Management
  • 2.1 SVM: Support Vector Machine
  • 2.2 KNN: K-Nearest Neighbor
  • 3 Knowledge-Based and Multidimensional Model
  • 4 Data Collection and Preprocessing
  • 5 Securing Data
  • 6 Data Warehouse
  • 7 Feature Selection and Case Retrieval
  • 8 Knowledge Acquisition and Prediction
  • 9 Conclusion
  • References
  • Modeling of Cognitive Brain Activity Through the Information Images Theory in Terms of the Bilingual Stroop Test
  • Abstract
  • 1 Introduction
  • 2 Information Images Theory
  • 2.1 Dominant
  • 2.2 Active
  • 2.3 Passive
  • 2.4 Deferred
  • 3 The Stroop Test
  • 4 The Experiment Methodology
  • 5 The Experiment Results
  • 6 Interpretation of the Experiment Results in Terms of the IIT
  • 7 The Results of Modeling
  • 8 Conclusion
  • Acknowledgements
  • References
  • Self-regulation and Covariance of Intermittent DNA Activity in the Major Networks Inside Cells
  • Abstract
  • 1 Introduction
  • 2 Fractals and Networks in DNA Activity
  • 3 Intermittency in DNA Activity Inside Cells
  • 4 Covariance in DNA Activity Inside Cells
  • 5 Self-regulation of Informational Homeostasis
  • Acknowledgment
  • References
  • Corroborating Quality of Data Through Density Information
  • 1 Introduction
  • 2 Related Work
  • 3 Density-Based Partitioning
  • 4 Confidence Model
  • 5 Corroborating of Attribute Values
  • 6 Tuple Merging
  • 7 Corroborating Algorithm
  • 8 Experimental Evaluation
  • 8.1 Scalability Evaluation
  • 8.2 Qualitative Evaluation
  • 8.3 Comparative Evaluation
  • 9 Conclusions
  • 10 Future Work
  • References
  • Author Index

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