Steganography Approach to Image Authentication Using Pulse Coupled Neural Network

This paper introduces a model for the authentication of large-scale images. The crucial element of the proposed model is the optimized Pulse Coupled Neural Network. This neural network generates position matrices based on which the embedding of authentication data into cover images is applied. Emphasis is placed on the minimalization of the stego image entropy change. Stego image entropy is consequently compared with the reference entropy of the cover image. The security of the suggested solution is granted by the neural network weights initialized with a steganographic key and by the encryption of accompanying steganographic data using the AES-256 algorithm. The integrity of the images is verified through the SHA-256 hash function. The integration of the accompanying and authentication data directly into the stego image and the authentication of the large images are the main contributions of the work

Regression Analysis and Modeling of Local Environmental Pollution Levels for the Electric Power Industry Needs

Reliability, longevity, and maintenance costs of electric power industry installations and equipment depend strongly on the extent to which their design reflects relevant environmental factors, such as expected levels of local environmental pollution. These factors guide the choice of specific types of components – insulators, towers, conductors, etc. – and are often estimated through complex and tedious long-term field measurements of pollution deposits. In Slovakia, such field measurements were mandated by the national standard STN 33 0405. This standard was retired in 2015 without replacement, which opened the way for developing alternative and less cumbersome methods. One such alternative is to apply artificial intelligence techniques to atmospheric pollution and other relevant data, which is already routinely monitored and collected in many countries. In this paper, we explore the strength of the relationships between the field measurements performed in various regions of Slovakia according to STN 33 0405 and atmospheric pollution data monitored and collected by the Slovak Hydrometeorological Institute (SHMÚ). The paper is focused on input attributes significance, in relation to output attributes. It represents the first phase of our long-term research aiming at the creation of reliable regression models of local pollution in order to replace the cumbersome field measurements mandated by STN 33 0405

Using Satellite Imagery to Improve Local Pollution Models for High-Voltage Transmission Lines and Insulators

This paper addresses the regression modeling of local environmental pollution levels for electric power industry needs, which is fundamental for the proper design and maintenance of high-voltage transmission lines and insulators in order to prevent various hazards, such as accidental flashovers due to pollution and the resultant power outages. The primary goal of our study was to increase the precision of regression models for this application area by exploiting additional input attributes extracted from satellite imagery and adjusting the modeling methodology. Given that thousands of different attributes can be extracted from satellite images, of which only a few are likely to contain useful information, we also explored suitable feature selection procedures. We show that a suitable combination of attribute selection methods (relief, FSRF-Test, and forward selection), regression models (random forest models and M5P regression trees), and modeling methodology (estimating field-measured values of target variables rather than their upper bounds) can significantly increase the total modeling accuracy, measured by the correlation between the estimated and the true values of target variables. Specifically, the accuracies of our regression models dramatically rose from 0.12–0.23 to 0.40–0.64, while their relative absolute errors were conversely reduced (e.g., from 1.04 to 0.764 for the best model)