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IJFSA 2013 : Special Issue On: Fuzzy and Rough Hybrid Intelligent Techniques in Medical Diagnosis

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Link: http://www.igi-global.com/calls-for-papers-special/international-journal-fuzzy-system-applications/41876
 
When N/A
Where N/A
Submission Deadline Feb 28, 2013
Notification Due Apr 30, 2013
Final Version Due Jun 15, 2013
Categories    computer sience   artificial intelligence   machine learning   biomedical engineering
 

Call For Papers

Calls for Papers (special): International Journal of Fuzzy System Applications (IJFSA)
Special Issue On: Fuzzy and Rough Hybrid Intelligent Techniques in Medical Diagnosis
Submission Due Date
2/28/2013

Guest Editors
Assistant Professor Dr. Ahmad Taher Azar, Faculty of Engineering, Misr University for Science & Technology, Egypt
Assistant Professor Dr. Hala Own, Computer Science Department, Kuwait University, Kuwait
Associate professor Dr. Soumya Banerjee, Birla Institute of Technology, Mesra, India
Professor Aboul Ella Hassanien, Information Technology Department, Cairo University, Egypt


Introduction
Over the past few decades, machine leaning plays an essential role in the medical imaging field, including medical image analysis, computer-aided diagnosis, organ/ lesion segmentation, image fusion, image-guided therapy, image annotation and image retrieval, because objects such as lesions and anatomy in medical images cannot be modeled accurately by simple equations; thus, tasks in medical imaging require learning from examples. ML provides methods, techniques, and tools that can help solving diagnostic and prognostic problems in a variety of medical domains. ML is being used for the analysis of the importance of clinical parameters and of their combinations for prognosis, e.g., prediction of disease progression, for the extraction of medical knowledge for outcomes research, for therapy planning and support, and for overall patient management.ML is also being used for data analysis, such as detection of regularities in the data by appropriately dealing with imperfect data, interpretation of continuous data used in the Intensive Care Unit, and for intelligent alarming resulting in effective and efficient monitoring. Machine learning systems are a well-established paradigm with current systems having many of the characteristics of biological computers and being capable of performing a variety of tasks that are difficult or impossible to do using conventional techniques. The combination or integration of more distinct intelligent methodologies can be done in any form, either by a modular integration of two or more intelligent methodologies, which maintains the identity of each methodology, or by integrating one methodology into another, or by transforming the knowledge representation in one methodology into another form of representation, characteristic to another methodology. Neural networks and rough sets are widely used for classification and rule generation. Instead of solving a problem using a single machine learning technique like neural network, rough sets, fuzzy image processing alone. On the other hand, rough fuzzy hybridization is a new approach of hybrid intelligent system or machine learning technologies where fuzzy set theory is used for linguistic representation of patterns, leading to a fuzzy granulation of the feature space. Rough set theory is used to obtain dependency rules which model informative regions in the granulated feature space.

Objective
The objective of this special issue is to showcase the most recent developments and research in the field of medical diagnosis using rough sets and fuzzy set hybridization approaches with other intelligent as well as to enhance its state-of-the-art. This special issue solicits original, full length original articles on new findings and developments from researchers, academicians and practitioners from industries, in the area of rough set theory for medical diagnosis.

Recommended Topics
Topics to be discussed in this special issue include (but are not limited to) the following:

Connectionist expert systems in medical diagnosis
Evolutionary neural networks in medical diagnosis
Fuzzy expert systems in medical diagnosis
Genetic fuzzy systems in medical diagnosis
Granular rough-fuzzy networks in medical diagnosis
Hybrid connectionist-symbolic models
Neuro-fuzzy systems in medical diagnosis
Rough fuzzy hybridization in medical diagnosis
Rough sets and near sets in medical imaging


Submission Procedure
Researchers and practitioners are invited to submit papers for this special theme issue on Fuzzy and Rough Hybrid Intelligent Techniques in Medical Diagnosis on or before February 28, 2013. All submissions must be original and may not be under review by another publication. INTERESTED AUTHORS SHOULD CONSULT THE JOURNAL’S GUIDELINES FOR MANUSCRIPT SUBMISSIONS at http://www.igi-global.com/Files/AuthorEditor/guidelinessubmission.pdf. All submitted papers will be reviewed on a double-blind, peer review basis. Papers must follow APA style for reference citations. Manuscripts must be sent electronically to all guest editors with the subject title as: “Special Issue – IJFSA” in Word document (Microsoft Word 2003/2007/2010 version).

Important Dates
Submission deadline: February 28, 2013
First decision notification: April 30, 2013
Submission of revised papers: May 31, 2013
Final decision notification: June 15, 2013

All submissions and inquiries should be directed to the attention of:
Assistant Professor Ahmad Taher Azar (Ahmad_t_azar@ieee.org)
Dr. Soumya Banerjee (soumyabanerjee@bitmesra.ac.in)
Assistant Professor Hala Own (h_own@hotmail.com)
Professor Aboul-Ella Hassanien (aboitcairo@gmail.com)

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