Dear Colleagues,

Recent years have demonstrated the importance of computational power in developing science and technology. This influence was even more clearly visible in the last decade, as the effectiveness of computational hardware such as graphical processing units (GPUs) has increased enormously. These devices, previously appreciated mainly by video game players, have become an important tool in various fields of science in which computational capabilities are essential. The most vivid example is the recent progress in artificial intelligence and machine learning, especially in the field of deep learning. The computational power of modern GPUs has enabled the training of deep neural networks in sensible amounts of time. As a consequence, difficult tasks such as image understanding, speech recognition, and natural language processing became accessible for machines, reaching a beyond-human level in some cases. These kinds of tasks can often positively affect important aspects of human lives, such as in medicine (e.g., machine learning algorithms able to analyze ECG signals or CT images help doctors in making accurate diagnoses).

Deep learning and complex data analysis are not the only fields in which strong computational resources play a crucial role. They are also essential in areas such as big data processing or in data stream mining, where the computational power is required to handle huge amounts of data. They are also commonly used in various problems of computer graphics (e.g., scene rendering, points cloud processing, or virtual and augmented realities). Fast computing devices like GPUs have also led to the increased effectiveness of various numerical methods, used for solving differential equations or in finite element methods. The high-performance numerical and big data mining algorithms play a significant role in various fields of science, such as in particle physics experiments or in weather forecasting and atmospheric data processing.

Although the above-mentioned disciplines are now developing extremely quickly, there are still many areas for improvement. The aim of this Special Issue is to consolidate research efforts towards improving computational intelligence algorithms used for complex data of various kinds, with emphasis on data fusion and image analysis tasks.

Potential topics include but are not limited to the following:

• Deep neural networks and their applications;
• Big data analysis;
• Data-stream mining algorithms;
• Image recognition and understanding;
• Medical data processing;
• Parallelization of machine learning algorithms;
• Computationally demanding numerical algorithms;
• Virtual and augmented reality;
• Point cloud processing;
• GPU computing in science and technology;
• Data fusion algorithms for scientific data.

Dr. Paweł Pławiak
Dr. Maciej Jaworski
Guest Editors

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• Computational intelligence
• Data fusion
• Data mining
• Machine learning
• Deep neural networks
• Point cloud processing