Risk-based efficiency assessment of information systems

Abstract

The implementation of information systems is aimed at improving the financial performance of a company, creating a transparent reporting system and improving many other competitive factors. However, the acquisition of these benefits does not negate the complexity of making a decision whether or not to implement a particular IT project. The total cost of ownership of the information system throughout the life cycle is usually not considered in comparison with the expected benefits from the use of the system, due to the uncertainty of such benefits. Comparative certainty of approaches and methods is present only in terms of costs, both for a priori (planned) and a posteriori (actual) assessment. It is possible to determine both capital and operating costs accurately enough. Indirect definition of the positive influence of an information system on the activity of the organization also seems possible. However, there are currently no generally recognized methods for analyzing the expected positive effect of an IT project. At the same time, large companies, in accordance with the requirements of the respective regulators and / or due to internal management considerations, build a risk management system to determine the level of capabilities, losses and to prevent adverse events. This study considers the feasibility of an approach to analyze the effectiveness of the implementation of the information system on the basis of the company’s risk reduction, leading to a decrease in economic benefits. It takes into account the internal risks of the information system that occur during the installation of the system, its operation and the termination of work with the system.

Revisiting the Pushchino RRAT search using a neural network

“…Neural networks can be used to reduce the number of interferences in visually verified images. For example, convolutional neural networks (CNN), which were previously used to analyze data obtained from LPA LPI, have shown a high efficiency (Isaev et al 2019). When processing data using the CNN model, the time series was divided into fixed-length frames, which made it possible to use CNN, which works well in a search for objects in images.…”