![]() Finally, neural networks make free the people from monotonous computing operations and enable creative implementation. With long-term high loads, the efficiency of solving problems by the neural network does not sag. ![]() Unlike humans, neural networks are more stable. While working with big data, the probability of error remains relatively low. This is easily explained by the fact that systems based on neural networks are able to perform complex business tasks more efficiently and cheaper than the people. And just two years ago, many leading IT companies showed the world already created smart applications in the field of neural networks, which indicates the uniqueness and relevance of this technology. The approach used was a content analysis review of the literature to create a use-able framework for reputation risk management.read more read lessĪbstract: Over the last decade, print and online publications have published a huge number of articles with proposals for the introduction and use of neural networks in existing business and research. This costly oversight has caused organizational losses, including customers, industry standing, and revenue. Organizations also fail to implement viable risk management programs that enable proactive responses that effectively address the challenges that they face. While reputation risk has grown in magnitude, organizations continue to ineptly manage reputation by failing to appropriately integrate this highly prized asset into their risk management programs. ![]() With mounting oversight and regulatory requirements, stakeholder influence, and the ability for social media to largely impact consumer opinion, it has become imperative to identify and mitigate risks that underscore reputational damage and impede the ability to achieve projected profitability. It has become an essential issue in the financial stability and long-term sustainability of businesses. It is shown that the use of proposed technique allows quantifying the risk assessment that can be obtained using the factor analysis of information risks methodology.read more read lessĪbstract: Over the past decade, reputation risk has grown in significance in corporate environments. ![]() ![]() As an example, it was calculated the amount of risk the company may be exposed to in case of violation of information confidentiality according to the standard factor analysis of information risks methodology and the developed methodology. During the analysis, the cause and effect relationships of the confidentiality violation have been formed, defined and given in the corresponding table and in the form of the Ishikawa diagram. A comparative analysis of the standard factor analysis of information risks methodology and the developed methodology using statistical data was carried out. The development factor analysis of information risks methodology of the methodology was carried out using the mathematical apparatus of probability theory, namely Bayesian networks. Abstract: The paper suggests methods to the assessment of information risks, which makes the transition from a qualitative assessment of information risks (according to the factor analysis of information risks methodology) to a quantitative assessment. ![]()
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |