Soft Computing Based Risk Management

2018 ◽

pp. 1-34

Author(s):

Yuriy V. Kostyuchenko ◽

Yulia Stoyka ◽

Iurii Negoda ◽

Ivan Kopachevsky

Keyword(s):

Water Quality ◽

Risk Management ◽

Decision Support ◽

Soft Computing ◽

Deep Uncertainty ◽

Fuzzy Algorithm ◽

Quality Degradation ◽

Spatially Distributed ◽

Distributed Measurement ◽

Model Approach

Task of soft computing for decision support in field of risk management is analyzed in this chapter. Multi-model approach is described. Interrelations between models, remote sensing data and forecasting are described. Method of water quality assessment using satellite observation is described. Method is based on analysis of spectral reflectance of aquifers. Correlations between reflectance and pollutions are quantified. Fuzzy logic based approach for decision support in field of water quality degradation risk is discussed. Decision on water quality is making based on fuzzy algorithm using limited set of uncertain parameters. It is shown that this algorithm allows estimate water quality degradation rate and pollution risks. Using proposed approach, maps of surface water pollution risk from point and diffuse sources are calculated. Conclusions concerned soft computing in risk management are proposed and discussed. It was demonstrated, that basing on spatially distributed measurement data, proposed approach allows to calculate risk parameters with resolution close to observations.

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Design for Software Risk Management Using Soft Computing and Simulated Biological Approach

International Journal of Security and Privacy in Pervasive Computing ◽

10.4018/ijsppc.2020040104 ◽

2020 ◽

Vol 12 (2) ◽

pp. 44-54

Author(s):

Alankrita Aggarwal ◽

Kanwalvir S. Dhindsa ◽

P. K. Suri

Keyword(s):

Risk Management ◽

Soft Computing ◽

Prediction Models ◽

Multiple Parameters ◽

Simulated Environment ◽

Microbiological Process ◽

Software Risk Management ◽

Software Risk ◽

Defect Prediction Models ◽

Architecture And Design

The process of software evaluation is one of the key tasks that are addressed by the quality assurance teams so that the risks in the software suite can be identified and can be removed with prior notifications. Different types of metrics can be used in defect prediction models, and widely used metrics are source code and process metrics. A simulated environment for the entire process shall be generated for multiple scenarios and parameters so that the results and conclusion can be depicted in an effective way. The focus of research is to develop a narrative architecture and design for software risk management using soft computing and nature-inspired approach. The proposed approach titled simulated biological reaction (SBR) is expected to have the effectual results on multiple parameters with the flavor of soft computing-based optimization. The proposed approach shall be integrating the simulation of microbiological process in different substances and elements to produce a new substance.

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Decision Making Under Deep Uncertainty With Fuzzy Algorithm in Framework of Multi-Model Approach

Environmental Information Systems ◽

10.4018/978-1-5225-7033-2.ch020 ◽

2019 ◽

pp. 456-489

Author(s):

Yuriy V. Kostyuchenko ◽

Yulia Stoyka ◽

Iurii Negoda ◽

Ivan Kopachevsky

Keyword(s):

Water Quality ◽

Risk Management ◽

Decision Support ◽

Soft Computing ◽

Deep Uncertainty ◽

Fuzzy Algorithm ◽

Quality Degradation ◽

Spatially Distributed ◽

Distributed Measurement ◽

Model Approach

Task of soft computing for decision support in field of risk management is analyzed in this chapter. Multi-model approach is described. Interrelations between models, remote sensing data and forecasting are described. Method of water quality assessment using satellite observation is described. Method is based on analysis of spectral reflectance of aquifers. Correlations between reflectance and pollutions are quantified. Fuzzy logic based approach for decision support in field of water quality degradation risk is discussed. Decision on water quality is making based on fuzzy algorithm using limited set of uncertain parameters. It is shown that this algorithm allows estimate water quality degradation rate and pollution risks. Using proposed approach, maps of surface water pollution risk from point and diffuse sources are calculated. Conclusions concerned soft computing in risk management are proposed and discussed. It was demonstrated, that basing on spatially distributed measurement data, proposed approach allows to calculate risk parameters with resolution close to observations.

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E-Banking Operational Risk Management using Soft Computing Tools

Strategic and Pragmatic E-Business - Advances in E-Business Research ◽

10.4018/978-1-4666-1619-6.ch008 ◽

2012 ◽

pp. 176-202 ◽

Cited By ~ 1

Author(s):

Rita E Ochuko ◽

Andrea Cullen ◽

Daniel Neagu

Keyword(s):

Risk Management ◽

Soft Computing ◽

Fuzzy Inference ◽

Operational Risk ◽

Management Tools ◽

Modeling Tools ◽

Market Opportunities ◽

Inference Systems ◽

Tools And Techniques ◽

Improved Technology

Electronic banking (E-banking) systems provide a promising solution for breaking geographical, industrial, and regulatory barriers. Improved technology could help with creating anytime, anywhere services and new market opportunities, but does not necessarily ensure a risk-free transaction environment. A main aim for E-banking adopters is to include E-banking risk management to their overall risk management strategy. They must identify the tools and techniques available for managing such risk. In this chapter we provide an overview of E-banking and identify the various risks which exist within the system. The chapter focuses on analyzing state-of-the-art risk management tools and techniques, paying attention to models for internally managing E-banking operational risk. It discusses several soft computing techniques applied to E-banking operational risk as causal modeling tools. The tools include: Decision Trees, Artificial Neural Networks (ANN), Fuzzy Inference Systems, and Bayesian Networks. Some examples are presented to describe the models developed.

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EARLY QUALITATIVE SOFTWARE RELIABILITY PREDICTION AND RISK MANAGEMENT IN PROCESS CENTRIC DEVELOPMENT THROUGH A SOFT COMPUTING TECHNIQUE

International Journal of Reliability Quality and Safety Engineering ◽

10.1142/s0218539309003551 ◽

2009 ◽

Vol 16 (06) ◽

pp. 521-532 ◽

Cited By ~ 5

Author(s):

K. KRISHNA MOHAN ◽

A. K. VERMA ◽

A. SRIVIDYA

Keyword(s):

Risk Management ◽

Soft Computing ◽

Computing Technique ◽

Fuzzy Membership Functions ◽

Soft Computing Techniques ◽

Management Schemes ◽

Soft Computing Technique ◽

Unified Process ◽

Orthogonal Defect Classification

A unique take on strengthening the role of a prototype of a software system without actually realizing it, would be to arrive at predictions using historical information from similar PoCs or the permeating experience of those involved in projects of comparable nature. Abundance of soft computing techniques should make this crucial bypassing feasible. The purpose of the this work is to demonstrate the same. Validation of this approach could be obtained by comparing the results with the ones obtained on realized prototypes at module level. In a work of the first of its kind involving studies at the PoC level, qualititave predictions for the metric 'number of defects' are obtained using a generic Fuzzy Logic based modeling. A sound mathematical base for the calculation of slopes of various Fuzzy membership functions employed is explained in detail for the case studies considered. This framework is applicable to any of the process oriented developmental systems like rational unified process. Pivotal risk management schemes are put forward. Significance of orthogonal defect classification method is explained in the context of the case study considered earlier.

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