Aims and Scope

Machine Intelligence are areas in software engineering and computer science where soft computing and machine intelligence are solutions to computationally hard tasks. Soft computing differs from conventional (hard) computing in that, unlike hard computing, it is tolerant of imprecision, uncertainty, partial truth, and approximation. Machine Intelligence explores virtually all aspects of machine learning, including deep learning and more classical algorithms. Machine Learning is based on Artificial Neural Networks, Support Vector Machines, Classification and Regression Trees, and some more similar methods. Important is that the resulting mathematical models are built by training from example data instead of being constructed analytically. This means that they are example based or empirical mathematical models instead of analytical ones. This special issue provides a new forum for dissemination of knowledge on both theoretical and applied research on machine learning with an ultimate aim to bridge the gap between these two disciplines of knowledge. It focuses technologies that support interaction between the above two bodies of knowledge, and fosters unified development in next generation computational models for machine intelligence.

Topics Covered

The topics covered by Journal of Machine Systems, Intelligence and Simulations include the following but not limited to:
Analog Circuits and Signal Processing, Analysis and Stochastic Modeling Techniques and Applications, Biological Circuits and Systems, Circuit Simulation and Modeling, Equipment Simulation and Modeling, Modeling and Simulation in Science and Engineering Calculations, Modeling, Simulation and Control of Technical Processes, Radio Frequency and Wireless Circuits and Systems, Simulation of Complex Systems, Intelligent System Simulation, Testing and Reliability, Safety and Risk Management in Engineering Design, Reliability, Availability and Serviceability in Engineering Design, Reliability and Safety Theory of Aging and Longevity, Quality Control in Engineering Design for Reliability and Safety, Engineering Design for Manufacturing Reliability and Safety, Engineering Design for Machines and Tools Reliability and Safety, Innovative Technology for Reliability and Safety Management, Civil Engineering for Reliability and Safety Design, Computational Intelligence, Computational Intelligence in Bioinformatics and Computational Biology, Computer Vision Systems, Intelligent Classification, Intelligent Control, Intelligent Distributed Sensor Networks, Intelligent Hybrid Systems, Intelligent Image Processing, Intelligent Information Retrieval, Intelligent Information Systems, Intelligent Measurement, Intelligent Signal Processing, Machine Learning, Natural Language Processing, Neural Networks, Syntactic and Semantic Processing, Pattern Recognition, Data Mining and Data Fusion, Data Mining and Knowledge Discovery, Intelligent Search, Automated Reasoning and Logic Programming, Intelligent Planning, Visual/linguistic Perception, Evolutionary and Swarm Algorithms, Derivative-free Optimization Algorithms, Fuzzy Sets and Fuzzy Logic; Rough Sets, Multi-agent Systems, Data and Web Mining, Emotional Intelligence, Hybrid Intelligent Models and Algorithms, Parallel and Distributed Intelligent Algorithms and Systems.


Volume 1, No. 1, 2020 


  • TBD

Editorial Boards

  • Stefano Mariani, Politecnico di Milano, Italy
  • Jan Awrejcewicz, Lodz University of Technology, Poland