Data security is an important area of concern for every computer system owner. An intrusion detection system is a device or software application that monitors a network or systems for malicious activity or policy violations. Already various techniques of artificial intelligence have been used for intrusion detection. The main challenge in this area is the running speed of the available implemen...

This research is related to the usefulness of different machine learning methods in forecasting time series on financial markets. The main issue in this field is that economic managers and scientific society are still longing for more accurate forecasting algorithms. Fulfilling this request leads to an increase in forecasting quality and, therefore, more profitability and efficiency. In this pa...

In order to know if new electronic voting systems truly represent a gain in usability, it is necessary to have information about the usability of more traditional voting methods. The usability of three different voting methods was evaluated using the metrics recommended by the National Institute of Standards and Technology: efficiency, effectiveness, and satisfaction. Participants voted with tw...

AIs playing strategic games have always fascinated humans. Specifically, the reinforcement learning technique Alpha Zero (D.Silver, 2016) has gained much attention for its capability to play Go, which was hard crack problem AI a long time. Additionally, we see rise of explainable (xAI), tries address that many modern decision techniques are black-box approaches and incomprehensible Combining bo...

Social media allows people interact to express their thoughts or feelings about different subjects. However, some of users may write offensive twits to other via social media which known as cyber bullying. Successful prevention depends on automatically detecting malicious messages. Automatic detection of bullying in the text of social media by analyzing the text "twits" via one of the machine l...

The main idea of a priori machine learning is to apply a machine learning method on a machine learning problem itself. We call it “a priori” because the processed data set does not originate from any measurement or other observation. Machine learning which deals with any observation is called “posterior”. The paper describes how posterior machine learning can be modified by a priori machine lea...

The fields of machine learning and mathematical programming are increasingly intertwined. Optimization problems lie at the heart of most machine learning approaches. The Special Topic on Machine Learning and Large Scale Optimization examines this interplay. Machine learning researchers have embraced the advances in mathematical programming allowing new types of models to be pursued. The special...