You are previewing International Journal of Rough Sets and Data Analysis (IJRSDA) Volume 1, Issue 2.
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International Journal of Rough Sets and Data Analysis (IJRSDA) Volume 1, Issue 2

Book Description

The International Journal of Rough Sets and Data Analysis (IJRSDA) is a multidisciplinary journal that publishes high-quality and significant research in all fields of rough sets, granular computing, and data mining techniques. Rough set theory is a mathematical approach concerned with the analysis and modeling of classification and decision problems involving vague, imprecise, uncertain, or incomplete information. Rough sets have been proposed for a variety of applications, including artificial intelligence and cognitive sciences, especially machine learning, knowledge discovery, data mining, expert systems, approximate reasoning, and pattern recognition. The journal extends existing research findings (theoretical innovations and modeling applications) to provide the highest quality original concepts, hybrid applications, innovative methodologies, and development trends studies for all audiences. This journal publishes original articles, reviews, technical reports, patent alerts, and case studies on the latest innovative findings of new methodologies and techniques.

This issue contains the following articles:

  • Identification of Heart Valve Disease using Bijective Soft Sets Theory
  • A New Heuristic Function of Ant Colony System for Retinal Vessel Segmentation
  • A Hybrid Approach to Diagnosis of Hepatic Tumors in Computed Tomography Images
  • Algebraic Properties of Rough Set on Two Universal Sets based on Multigranulation
  • Image Segmentation Using Rough Set Theory: A Review

Table of Contents

  1. Cover
  2. Masthead
  3. Call For Articles
  4. Identification of Heart Valve Disease using Bijective Soft Sets Theory
    1. ABSTRACT
    2. 1. INTRODUCTION
    3. 2. RELATED WORK
    4. 3. RESEARCH MOTIVATION
    5. 4. PRELIMINARIES
    6. 5. PROPOSED METHODOLOGY
    7. 6. RESULT AND DISCUSSION
    8. 7. CONCLUSION
    9. ACKNOWLEDGMENT
    10. REFERENCES
  5. A New Heuristic Function of Ant Colony System for Retinal Vessel Segmentation
    1. ABSTRACT
    2. 1. INTRODUCTION
    3. 2. RELATED WORK
    4. 3. BACKGROUND
    5. 4. BASELINE APPROACH AND ITS IMPROVEMENTS
    6. 5. EXPERIMENTAL RESULTS
    7. 6. CONCLUSION AND FUTURE WORK
    8. REFERENCES
  6. A Hybrid Approach to Diagnosis of Hepatic Tumors in Computed Tomography Images
    1. ABSTRACT
    2. 1. INTRODUCTION
    3. 2. SURVEY ON CAD FOR LIVER
    4. 3. MATERIALS AND METHODS
    5. 4. CONNECTED COMPONENT LABELING ALGORITHM (CCL)
    6. 5. TUMOR SEGMENTATION METHODS
    7. 6. FEATURES EXTRACTION AND TUMOR CLASSIFICATION
    8. 7. FRACTAL DIMENSION METHOD (FD)
    9. 8. GREY LEVEL CO-OCCURRENCE MATRICES (GLCM)
    10. 9. k-NEAREST NEIGHBORS (K-NN)
    11. 10. CLASSIFIER EVALUATION
    12. 11. PROPOSED SYSTEM
    13. 12. EXPERIMENTAL RESULTS
    14. 12. CONCLUSION AND FUTURE WORK
    15. REFERENCES
  7. Algebraic Properties of Rough Set on Two Universal Sets based on Multigranulation
    1. ABSTRACT
    2. INTRODUCTION
    3. FOUNDATIONS OF ROUGH SET
    4. ROUGH SET ON TWO UNIVERSAL SETS
    5. ROUGH SET ON TWO UNIVERSAL SETS BASED ON MULTIGRANULATION
    6. ALGEBRAIC PROPERTIES OF ROUGH SETS ON TWO UNIVERSAL SETS BASED ON MULTIGRANULATION
    7. GENERALIZATION OF ALGEBRAIC PROPERTIES
    8. A REAL LIFE APPLICATION
    9. MEASURES OF UNCERTAINTY AND ROUGHNESS
    10. CONCLUSION
    11. REFERENCES
  8. Image Segmentation Using Rough Set Theory:
    1. ABSTRACT
    2. 1. INTRODUCTION
    3. 2. METHODOLOGY
    4. 3. REVIEW OF IMAGE SEGMENTATION USING ROUGH SETS
    5. 4. DIFFERENT FRAMEWORKS FOR ROUGH SET IMAGE SEGMENTATION
    6. 5. COMPARATIVE STUDY
    7. 6. CONCLUSION
    8. REFERENCES
  9. Call For Articles