OPTIMIZATION OF FUZZY MODELS FOR SYSTEM ANALYSIS, PATTERN RECOGNITION AND KNOWLEDGE ENGINEERING

1994 ◽  
pp. 245-270
Author(s):  
WITOLD PEDRYCZ
Keyword(s):  
Pattern Recognition ◽  
System Analysis ◽  
Knowledge Engineering ◽  
Fuzzy Models ◽  
Analysis Pattern

A Structured Method for Security Requirements Elicitation concerning the Cloud Computing Domain

2015 ◽  
pp. 875-896
Author(s):  
Kristian Beckers ◽  
Isabelle Côté ◽  
Ludger Goeke ◽  
Selim Güler ◽  
Maritta Heisel
Keyword(s):  
Cloud Computing ◽  
System Analysis ◽  
Business Case ◽  
Security Requirements ◽  
Cloud System ◽  
Security Requirement ◽  
Security Measures ◽  
Analysis Pattern ◽  
It Resources ◽  
Requirements Patterns

Cloud computing systems offer an attractive alternative to traditional IT-systems, because of economic benefits that arise from the cloud's scalable and flexible IT-resources. The benefits are of particular interest for SME's. The reason is that using Cloud Resources allows an SME to focus on its core business rather than on IT-resources. However, numerous concerns about the security of cloud computing services exist. Potential cloud customers have to be confident that the cloud services they acquire are secure for them to use. Therefore, they have to have a clear set of security requirements covering their security needs. Eliciting these requirements is a difficult task, because of the amount of stakeholders and technical components to consider in a cloud environment. Therefore, the authors propose a structured, pattern-based method supporting eliciting security requirements and selecting security measures. The method guides potential cloud customers to model the application of their business case in a cloud computing context using a pattern-based approach. Thus, a potential cloud customer can instantiate our so-called Cloud System Analysis Pattern. Then, the information of the instantiated pattern can be used to fill-out our textual security requirements patterns and individual defined security requirement patterns, as well. The presented method is tool-supported. Our tool supports the instantiation of the cloud system analysis pattern and automatically transfers the information from the instance to the security requirements patterns. In addition, they have validation conditions that check e.g., if a security requirement refers to at least one element in the cloud. The authors illustrate their method using an online-banking system as running example.

Fuzzy Models and Algorithms for Pattern Recognition and Image Processing

1999 ◽  
Author(s):  
James C. Bezdek ◽  
James Keller ◽  
Raghu Krisnapuram ◽  
Nikhil R. Pal
Keyword(s):  
Image Processing ◽  
Pattern Recognition ◽  
Fuzzy Models

A Structured Method for Security Requirements Elicitation concerning the Cloud Computing Domain

2014 ◽  
Vol 5 (2) ◽  
pp. 20-43 ◽  
Author(s):  
Kristian Beckers ◽  
Isabelle Côté ◽  
Ludger Goeke ◽  
Selim Güler ◽  
Maritta Heisel
Keyword(s):  
Cloud Computing ◽  
System Analysis ◽  
Economic Benefits ◽  
Cloud Services ◽  
Security Requirements ◽  
Cloud System ◽  
Security Requirement ◽  
Analysis Pattern ◽  
It Resources ◽  
Requirements Patterns

Cloud computing systems offer an attractive alternative to traditional IT-systems, because of economic benefits that arise from the cloud's scalable and flexible IT-resources. The benefits are of particular interest for SME's. The reason is that using Cloud Resources allows an SME to focus on its core business rather than on IT-resources. However, numerous concerns about the security of cloud computing services exist. Potential cloud customers have to be confident that the cloud services they acquire are secure for them to use. Therefore, they have to have a clear set of security requirements covering their security needs. Eliciting these requirements is a difficult task, because of the amount of stakeholders and technical components to consider in a cloud environment. Therefore, the authors propose a structured, pattern-based method supporting eliciting security requirements and selecting security measures. The method guides potential cloud customers to model the application of their business case in a cloud computing context using a pattern-based approach. Thus, a potential cloud customer can instantiate our so-called Cloud System Analysis Pattern. Then, the information of the instantiated pattern can be used to fill-out our textual security requirements patterns and individual defined security requirement patterns, as well. The presented method is tool-supported. Our tool supports the instantiation of the cloud system analysis pattern and automatically transfers the information from the instance to the security requirements patterns. In addition, they have validation conditions that check e.g., if a security requirement refers to at least one element in the cloud. The authors illustrate their method using an online-banking system as running example.

Preface to the special section on new trends on pattern recognition with fuzzy models

2010 ◽  
Vol 161 (1) ◽  
pp. 1-2 ◽  
Author(s):  
Oscar Castillo ◽  
Patricia Melin ◽  
Witold Pedrycz ◽  
Janusz Kacpzryk
Keyword(s):  
Pattern Recognition ◽  
Special Section ◽  
Fuzzy Models

Knowledge Engineering for Clinical Consultation Programs: Modeling the Application Area

1989 ◽  
Vol 28 (01) ◽  
pp. 28-35 ◽  
Author(s):  
Mark A. Musen ◽  
Johan van der Lei
Keyword(s):  
Pattern Recognition ◽  
Expert Systems ◽  
Knowledge Engineering ◽  
Decision Support Tools ◽  
Application Domain ◽  
Pattern Recognition Techniques ◽  
Clinical Consultation ◽  
Support Tools ◽  
Computer Based ◽  
Alternative Approaches

Abstract:Developers of computer-based decision-support tools frequently adopt either pattern recognition or artificial intelligence techniques as the basis for their programs. Because these developers often choose to accentuate the differences between these alternative approaches, the more fundamental similarities are frequently overlooked. The principal challenge in the creation of any clinical consultation program - regardless of the methodology that is used - lies in creating a computational model of the application domain. The difficulty in generating such a model manifests itself in symptoms that workers in the expert systems community have labeled “the knowledge-acquisition bottleneck” and “the problem of brittleness”. This paper explores these two symptoms and shows how the development of consultation programs based on pattern-recognition techniques is subject to analogous difficulties. The expert systems and pattern recognition communities must recognize that they face similar challenges, and must unite to develop methods that assist with the process of building of models of complex application tasks.

Description ofa Blockcourse in Medical Informatics

1989 ◽  
Vol 28 (04) ◽  
pp. 239-242 ◽  
Author(s):  
A. Hasnian
Keyword(s):  
Pattern Recognition ◽  
Information Systems ◽  
Medical Students ◽  
Expert Systems ◽  
Medical Informatics ◽  
Signal Analysis ◽  
Analysis Pattern ◽  
Hands On

Abstract:This paper describes a blockcourse in medical informatics. The course is presented to fourth, fifth and sixth year medical students and lasts one week. During the course all aspects of medical informatics are considered. Programming, databases, information systems, signal analysis, pattern recognition, expert systems, etc. are explained. Lectures alternate with hands-on experience. The subjects discussed in the lectures are rehearsed during the practical sessions, so that gaps in the student´s knowledge are immediately detected. The blockcourse has been positively rated by the students.

Neuro-fuzzy models in pattern recognition

1993 ◽  
Author(s):  
Sunanda Mitra ◽  
Yong Soo Kim
Keyword(s):  
Pattern Recognition ◽  
Fuzzy Models ◽  
Neuro Fuzzy
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