Intelligent Systems and Applications
Intelligent Systems Conference (IntelliSys) 2018 is the fourth research conference in the series. This conference is a part of SAI conferences being held since 2013. The conference series has featured keynote talks, special sessions, poster presentation, tutorials, workshops, and contributed papers each year.
The conference focus on areas of intelligent systems and artificial intelligence (AI) and how it applies to the real world. IntelliSys is one of the best respected Artificial Intelligence (AI) Conference.
The machine learning sphere is one of the most important and growing science spheres nowadays. Many algorithms dealing with images exist, but attention to music is growing as well. Some algorithms classify music by genre while others developed recently deal with music synthesis. The goal of this paper is to present an algorithm, which syntheses a music audio file on the basis of two certain fragments of different music compositions. This paper provides a theoretical basis of neural networks and convolutional neural networks in particular, describing how to construct a working model to create an audio output from two inputs, and shows how the network parameters can be evaluated and changed to fit the model better and generate better results.
Day by day data volumes are increasing, and most of the data are stored in the databases after manual transformations and derivations. The behavior of those stored data is unpredictable. Furthermore, the data are collected from various sources such as physical, geological, environmental, chemical, and biological. A relational database management system (RDBMS) provides a high level data interface. Inside RDBMS sources and intermediate data items are relations, tuples, and attributes. In the context of data provenance, this paper describes how data are produced. When data needs to be retrieved from RDBMS using queries, sometimes it is necessary to check the output data product back to its source values if that particular output seems to have an unexpected value. The aim of this paper is to show the source values for output data using query inversion approach, and to propose the technique for creating an inverse query for queries with aggregation functions, multiple (join, set) operations, and sub-queries.