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Simple Summary: A novel approach for automatic detection of neoplastic lesions in lymph nodes is presented, which incorporates machine learning methods and the new LN-RADS scale. The presented solution incorporates different network structures with diverse datasets to improve the overall effectiveness. Final findings demonstrate that incorporating the LN-RADS scale labels improved the overall diagnosis, especially when compared with current, standard practices. The presented solution is meant as an aid in the diagnosis process. The paper presents a novel approach for the automatic detection of neoplastic lesions in lymph nodes (LNs). It leverages the latest advances in machine learning (ML) with the LN Reporting and Data System (LN-RADS) scale. By integrating diverse datasets and network structures, the research investigates the effectiveness of ML algorithms in improving diagnostic accuracy and automation potential. Both Multinominal Logistic Regression (MLR)-integrated and fully connected neuron layers are included in the analysis. The methods were trained using three variants of combinations of histopathological data and LN-RADS scale labels to assess their utility. The findings demonstrate that the LN-RADS scale improves prediction accuracy. MLR integration is shown to achieve higher accuracy, while the fully connected neuron approach excels in AUC performance. All of the above suggests a possibility for significant improvement in the early detection and prognosis of cancer using AI techniques. The study underlines the importance of further exploration into combined datasets and network architectures, which could potentially lead to even greater improvements in the diagnostic process. [ABSTRACT FROM AUTHOR]

Beta parameter is one of the commonly used measures of the investment risk of individual stock or portfolio. It plays a crucial role in modern portfolio theory particularly in management of financial investment portfolios. In the field of beta parameter, numerous studies have been conducted, especially beta properties stability in the context of the stock market cycle phases, measuring frequency of rate of return, and the length of a sample period. There are much fewer studies concerned beta parameter in the countries of Central and Eastern Europe which have undergone systemic transformation at the end of the previous century. From a scientific point of view, it is interesting to know how the beta parameter behaves in these countries.

In the modern portfolio theory investment risk plays a crucial role. It is the subject of numerous studies and publications, in particular in relation to the management of investment portfolios. Commonly used measure of investment management in equities is a beta parameter, which is used to estimate individual stock risk and portfolio risk. In particular, numerous studies the subject of which are the beta parameter properties such as stability in the context of the stock market cycle phases, intervalling effect, length estimation sample etc. The main objective of this paper is to investigate the intervalling effect on the beta parameter. The empirical analysis is carried out for the 33 largest companies of the Warsaw Stock Exchange (WSE) on a sample from the years 2005 to 2012 on the basis of daily, weekly and monthly rates of return. Statistical verification of the hypothesis of the importance of the frequency measuring the return of shares will be based on the single-index Sharpe’s model.

The aim of the paper is to recognize and extract the milk duct in haematoxylin and eosin (HE) stained breast cancer tissues. The paper presents the modified K-means approach to segmentation of the milk duct in HE stained images. Instead of using single pixels we propose to consider the defined region of pixels in the process. Thanks to such modification more accurate extraction of the milk ducts has been achieved. To compare the results in a numerical way the GT images prepared by the medical expert have been subtracted from the corresponding images created by the segmentation methods. The numerical experiments performed for many preparations have confirmed the superiority of such approach. The proposed method has allowed reducing significantly the error of duct segmentation in comparison to the classical K-means approaches. The results show, that our method is superior to the standard K-means and to the K-means preceded by averaging or Gaussian filtration at different size of filtration mask.