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This book provides a technical approach to a Business Resilience
System with its Risk Atom and Processing Data Point based on fuzzy
logic and cloud computation in real time. Its purpose and
objectives define a clear set of expectations for Organizations and
Enterprises so their network system and supply chain are totally
resilient and protected against cyber-attacks, manmade threats, and
natural disasters. These enterprises include financial,
organizational, homeland security, and supply chain operations with
multi-point manufacturing across the world. Market shares and
marketing advantages are expected to result from the implementation
of the system. The collected information and defined objectives
form the basis to monitor and analyze the data through cloud
computation, and will guarantee the success of their
survivability's against any unexpected threats. This book will be
useful for advanced undergraduate and graduate students in the
field of computer engineering, engineers that work for
manufacturing companies, business analysts in retail and
e-Commerce, and those working in the defense industry, Information
Security, and Information Technology.
This book provides a technical approach to a Business Resilience
System with its Risk Atom and Processing Data Point based on fuzzy
logic and cloud computation in real time. Its purpose and
objectives define a clear set of expectations for Organizations and
Enterprises so their network system and supply chain are totally
resilient and protected against cyber-attacks, manmade threats, and
natural disasters. These enterprises include financial,
organizational, homeland security, and supply chain operations with
multi-point manufacturing across the world. Market shares and
marketing advantages are expected to result from the implementation
of the system. The collected information and defined objectives
form the basis to monitor and analyze the data through cloud
computation, and will guarantee the success of their
survivability's against any unexpected threats. This book will be
useful for advanced undergraduate and graduate students in the
field of computer engineering, engineers that work for
manufacturing companies, business analysts in retail and
e-Commerce, and those working in the defense industry, Information
Security, and Information Technology.
The last several years have seen a dramatic increase in the number
of neurobiologists building or using computer-based models as a
regular part of their efforts to understand how different neural
systems function. As experimental data continue to amass, it is
increasingly clear that detailed physiological and anatomical data
alone are not enough to infer how neural circuits work.
Experimentalists appear to be recognizing the need for the
quantitative approach to explore the functional consequences of
particular neuronal features that are provided by modeling. This
combination of modeling and experimental work has led to the
creation of the new discipline of computational neurosciences. More
than the use of models per se, the authors believe that
computational neuroscience is most distinguished from classical
neurobiology due to an explicit focus on how the nervous system
computes. Thus, instead of obtaining experimental information about
the structure of the nervous system for its own sake, a
computational approach involves collecting the information most
relevant to the advancement of functional understanding. In our
hands, models, especially those based on the detailed physiology
and anatomy of the brain region in question, capture what is known
about this region while also directing further experimental
investigations. These same models can then provide an
interpretation for the obtained data. Thus, the interaction between
experiments and computer modeling is increasingly iterative and
interdependent.
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