For this study, the researchers conducted a systematic literature review to answer complex questions about the field of blockchain technology. We used an unbiased systematic review process to find works on blockchain-based applications and developed a Python code that searched various online databases. This paper provides an overview of the characteristics, mode of operation, and applications of blockchains in various domains such as transportation, commerce and industry, privacy and security, the financial sector, government, education, healthcare, and the Internet of Things (IoT). The aim was to identify the key research themes addressed in existing articles within each application domain and suggest future research directions for these domains. We analyzed a set of 750 articles published between 2015 and 2021 that dealt with blockchain applications. We found that financial management and security issues have been the main research focus since 2015. However, the use of blockchain in education has become a central research theme in 2021. Healthcare, IoT, and government applications have also grown in popularity. We furthermore analyzed some of the implementations of privacy mechanisms, as well as the challenges and future directions that need to be addressed for effective blockchain deployment. This study contributes to existing research by providing a comprehensive overview of blockchain application themes and their emerging areas for stakeholders in diverse sectors.

Presumptions of each data analysis are data themselves, regardless of the analysis focus (visit rate analysis, optimization of portal, personalization of portal, etc.). Results of selected analysis highly depend on the quality of analyzed data. In case of portal usage analysis, these data can be obtained by monitoring web server log file. We are able to create data matrices and web map based on these data which will serve for searching for behaviour patterns of users. Data preparation from the log file represents the most time-consuming phase of whole analysis. We realized an experiment so that we can find out to which criteria are necessary to realize this time-consuming data preparation. We aimed at specifying the inevitable steps that are required for obtaining valid data from the log file. Specially, we focused on the reconstruction of activities of the web visitor. This advanced technique of data preprocessing belongs to time consuming one. In the article we tried to assess the impact of reconstruction of activities of a web visitor on the quantity and quality of the extracted rules which represent the web users’ behaviour patterns

In recent years, there has been an increasing interest in text sentiment analysis and speech emotion recognition in finance due to their potential to capture the intentions and opinions of corporate stakeholders, such as managers and investors. A considerable performance improvement in forecasting company financial performance was achieved by taking textual sentiment into account. However, far too little attention has been paid to managerial emotional states and their potential contribution to financial distress prediction. This study seeks to address this problem by proposing a deep learning architecture that uniquely combines managerial emotional states extracted using speech emotion recognition with FinBERT-based sentiment analysis of earnings conference call transcripts. Thus, the obtained information is fused with traditional financial indicators to achieve a more accurate prediction of financial distress. The proposed model is validated using 1278 earnings conference calls of the 40 largest US companies. The findings of this study provide evidence on the essential role of managerial emotions in predicting financial distress, even when compared with sentiment indicators obtained from text. The experimental results also demonstrate the high accuracy of the proposed model compared with state-of-the-art prediction models.

Data pre-processing presents the most time consuming phase in the whole process of knowledge discovery. The complexity of data pre-processing depends on the data sources used. The aim of this work is to determine to what extent it is necessary to carry out the time consuming data pre-processing in the process of discovering sequential patterns in e-documents. We used the transaction/sequence model for text representation and sequence rule analysis as a method of modelling. We compare four datasets of different quality obtained from texts and pre-processed in different ways: data with identified the paragraph sequences, data with identified the sentence sequences, data with identified the paragraph sequences without stop words and data with identified the sentence sequences without stop words. We try to assess the impact of these advanced techniques of data pre-processing on the quantity and quality of the extracted rules. The results confirm some initial assumptions, but they also show that the stop words removal has a substantial impact on the quantity and quality of extracted rules in case of paragraph sequence identification. Contrary, in case of sentence sequence identification, removing the stop words has not any significant impact on the quantity and quality of extracted rules