Rapid improvements in ultrasound imaging technology have made it much more useful for screening and diagnosing breast problems. Local-speckle-noise destruction in ultrasound breast images may impair image quality and impact observation and diagnosis. It is crucial to remove localized noise from images. In the article, we have used the hybrid deep learning technique to remove local speckle noise from breast ultrasound images. The contrast of ultrasound breast images was first improved using logarithmic and exponential transforms, and then guided filter algorithms were used to enhance the details of the glandular ultrasound breast images. In order to finish the pre-processing of ultrasound breast images and enhance image clarity, spatial high-pass filtering algorithms were used to remove the extreme sharpening. In order to remove local speckle noise without sacrificing the image edges, edge-sensitive terms were eventually added to the Logical-Pool Recurrent Neural Network (LPRNN). The mean square error and false recognition rate both fell below 1.1% at the hundredth training iteration, showing that the LPRNN had been properly trained. Ultrasound images that have had local speckle noise destroyed had signal-to-noise ratios (SNRs) greater than 65 dB, peak SNR ratios larger than 70 dB, edge preservation index values greater than the experimental threshold of 0.48, and quick destruction times. The time required to destroy local speckle noise is low, edge information is preserved, and image features are brought into sharp focus.

One of the most challenging tasks for clinicians is detecting symptoms of cardiovascular disease as earlier as possible. Many individuals worldwide die each year from cardiovascular disease. Since heart disease is a major concern, it must be dealt with timely. Multiple variables affecting health, such as excessive blood pressure, elevated cholesterol, an irregular pulse rate, and many more, make it challenging to diagnose cardiac disease. Thus, artificial intelligence can be useful in identifying and treating diseases early on. This paper proposes an ensemble-based approach that uses machine learning (ML) and deep learning (DL) models to predict a person’s likelihood of developing cardiovascular disease. We employ six classification algorithms to predict cardiovascular disease. Models are trained using a publicly available dataset of cardiovascular disease cases. We use random forest (RF) to extract important cardiovascular disease features. The experiment results demonstrate that the ML ensemble model achieves the best disease prediction accuracy of 88.70%.

The scientific paper deals with a part of the business sector, which is made up of family businesses. The paper presents the current status of such businesses from the perspective of positive and negative factors which will be linked to the problem areas. In this article, we have focused our attention on some aspects of family business, especially the managerial aspects, because the management of a family business has various differences and specifics compared to other types of businesses. In the theoretical part, we present the current state of the issue, while the empirical part of the article is based on a survey conducted among family businesses using a questionnaire. This article does not aim to highlight the contentious areas of family business. However, it brings valuable findings of business practice. More than 400 enterprises were approached, the resulting sample consisted of 185 family enterprises. Therefore, we understand the results as a case study from Slovakia. Our findings were subject to statistical analysis using several quantitative methods (t-test, regression models) and we present them in the empirical part. Based on our results, we bring the most valuable findings and ideas for further research.

Communication professionals have been facing various challenges and one of them is how to win the audience. Past studies suggest that credibility could be the key. Therefore, credibility can be suggested as one of the key factors driving the traffi c of individuals to certain media. By gaining a better understanding of how Millennials perceive credibility, companies can more appropriately plan and execute successful media campaigns directed to this very important public. A survey with 190 respondents -Millennials -was conducted to determine how they perceive the credibility of various media types. To measure the perception of media credibility, 12 characteristics like objectiveness, activity, intelligence, professionalism, etc. were examined. The results of the study revealed general moderate credibility of newspapers and television. The most credible medium for the Millennials is the Internet, especially because of its activity, ability to act fast, independence and objectivity. On the other hand, this cohort sees both television and newspapers as better presented than the Internet. The worst rated feature of television and newspapers was their passivity and political background. When examining statistically signifi cant difference in overall perception, based on the results from the Wilcoxon signed-rank test, we can conclude that the difference in perception of television, newspapers and the Internet was unlikely to occur by chance and the Millennials perceive the Internet as signifi cantly more credible than television and newspapers.JEL classifi cation: M310; M390

In the field of communication, researchers are primarily interested in finding out about people"s choices of media such as television, newspapers, magazines, online news, etc. Perceptions of the reliability and trustworthiness may be significantly affected by the selection of information sources and credibility can be suggested as one of the factors driving the traffic of individuals to certain media. The concept of credibility is not new and has been studied in the ancient Greece -how the speakers persuade audience members. However, studies of the credibility of mass media began interesting in times when the rising number of people started turning to radio for news instead of newspapers. Another change was brought by television and in the last decade of the 20th century, rise of the Internet has led to recent credibility studies comparing traditional sources with this emerging medium.The purpose of this paper is to examine the differences in advertising credibility perception across different media channels -television, newspapers and the Internet and determine if the medium of delivery has an impact on credibility assessment of advertising. The results showed overall moderate credibility of all the media but newspapers have shown the highest overall credibility, followed by the Internet and television, respectively. Advertising credibility was higher in traditional media than in the Internet. Negative attitudes were the highest in the online channels and the most credible advertising channel was the television. The results indicated there is no relationship between the medium credibility and credibility of advertising. Communication to audiences requires an exploration of trustworthiness in order to formulate correct strategies. By recognizing the credibility of the advertisements and the media in which they are placed, the findings can be considered for attracting audiences.