Aims

Applied Sciences (ISSN 2076-3417) provides an advanced forum on all aspects of applied natural sciences. It publishes reviews, research papers and communications. Our aim is to encourage scientists to publish their experimental and theoretical results in as much detail as possible. There is no restriction on the length of the papers. The full experimental details must be provided so that the results can be reproduced. Electronic files and software regarding the full details of the calculation or experimental procedure, if unable to be published in a normal way, can be deposited as supplementary electronic material.

Scope

The journal covers all aspects of applied physics, applied chemistry, engineering, environmental and earth sciences, and applied biology. It has 7 broad subjects and 33 narrower Sections:

Physical Sciences

- Optics and Lasers
- Energy
- Acoustics and Vibrations
- Applied Physics
- Quantum Science and Technology
- Applied Thermal Engineering
- Applied Adhesion
- Fluid

Chemistry

- Chemistry
- Membrane Science and Technology 
- Food Science and Technology
- Surface Sciences and Technology

Material Sciences

- Materials
- Nanotechnology and Applied Nanoscience

Environmental Science and Engineering

- Environmental and Sustainable Science and Technology

Engineering

- Mechanical Engineering
- Computing and Artificial Intelligence
- Electrical, Electronics and Communications Engineering
- Civil Engineering
- Applied Industrial Technologies
- Robotics and Automation
- Agricultural Engineering
- Additive Manufacturing Technologies
- Transportation and Vehicles

Earth-Aerospace-Marine Science and Engineering

- Earth Sciences and Geography
- Marine Engineering 
- Aerospace Science and Engineering

Biological Science and Engineering

- Applied Biosciences and Bioengineering
- Applied Dentistry
- Applied Microbiology
- Applied Micromedicine
- Biomedical Engineering
- Neurosciences

Special Issue on Advances in Big Data and Machine Learning 
Submission Date: 2021-10-20

Dear Colleagues,

Deep learning is a branch of machine learning. It uses linear or non-linear transforms in multiple processing layers to automatically extract features that are sufficient to represent the characteristics of the data. In traditional machine learning, features are usually analyzed and researched by manpower to produce useful and good features. Compared with the traditional method, deep learning has the ability to automatically extract features. Because of the powerful ability of the automatic feature extraction, the applications of deep learning that machine learning has not been able to break through in the past are now possible. With the development of the Internet, more and more digital data are generated in the social network. How to extract useful knowledge from big data becomes more important. There are many methods of data mining. The use of deep learning technology in data mining is also one of the mainstream. Any research papers on big data and deep learning are welcome for submission.

Prof. Shi-Jinn Horng
Guest Editor