The use of Machine Learning techniques is constantly growing in several research fields such as computer science, engineering, medical sciences, economics and other scientific fields. The large datasets that have to be processed have exponential increased in size terms. Hence it requires computational resources and ad hoc techniques to deal with these problems efficiently and effectively, for example Convolutional Neural Networks. The latter can represent data using sparse methods that requires more time and resource complexity, due to both limited to canonical software modeling of Machine Learning algorithms and huge data to be processed. In recent years new parallel and distributed architectures has been introduced. In fact, the use of GP-GPU combining with virtualization systems and framework such as Hadoop are able to solve large problems. For example, Patter Recognition in several medical fields is able to detect illnesses better than human doctors. Natural Language Processing can benefit from ML combining HPC to solve problems related to Speech Recognitions, Automatic Translation and Vocal Assistants. The aims of this Special Issue include capturing the latest achievements in covered topics, building a scientific background to create new or improve existing Machine Learning techniques and combining them with high efficiency and most recently techniques of High Performance Computing.

Aims and Scope:

1. High Performance Computing Algorithm
2. Machine Learning Theory and Algorithms
3. Scientific Computing
4. Embedded Systems
5. Distributed Operating Systems
6. Image Processing