Manuscript Topics

Recent developments in computer vision and machine learning have contributed to great advances in many areas of interest to the engineering community. Data-driven or domain-oriented engineering applications can benefit significantly from the latest developments in computer vision and machine learning theories and methods (including deep, reinforcement, transfer and extreme learning), but may also promote the development of learning algorithms, optimization approaches, fusion techniques for multimodal data, novel hardware and network architectures.

The goal of this Special Issue is to provide a forum for engineers, data scientists, researchers and practitioners to present new academic research and industrial development on computer vision and machine learning for engineering applications. The Special Issue aims at original research papers in the field, covering new theories, algorithms, systems, as well as new implementations and applications incorporating state-of-the-art machine learning techniques. Review articles and works on performance evaluation and benchmark datasets are also solicited.

Indicative domains of application of interest to the Special Issue include:
• Electrical and mechanical engineering, production management and optimization, manufacturing, failure detection, energy management, smart grid
• Robotics and automation, computer vision and pattern recognition applications, critical infrastructure protection
• Civil engineering, construction management and optimization, structural health monitoring, earthquake engineering, urban planning
• Transportation, hydraulics, water power and environmental engineering
• Surveying and geospatial engineering, spatial planning, and remote sensing
• Materials science and engineering
• Biomedical engineering

Instructions for authors
https://www.aimspress.com/era/news/solo-detail/instructionsforauthors
Please submit your manuscript to online submission system
https://aimspress.jams.pub/