Special Issue: Machine Learning-Based Information Processing
Guest Editors
Dr. Guanqiu Qi
Computer Information Systems Department, State University of New York at Buffalo State, Buffalo, NY 14222, USA
Email: qig@buffalostate.edu
Dr. Zhiqin Zhu
College of Automation, Chongqing University of Posts Telecommunications, Chongqing 400065, China
Email: zhuzq@cqupt.edu.cn
Dr. Yu Liu
Department of Biomedical Engineering, Heifei University of Technology, Heifei 230009, China
Email: yuliu@hfut.edu.cn
Dr. Yinong Chen
School of Computing and Augmented Intelligence, Arizona State University, Tempe, AZ 85287, USA
Email: Yinong.Chen@asu.edu
Manuscript Topics
Information processing is an essential component of modern daily life. Various types of information such as sound, images, text, vibrations, and more, contain rich intrinsic details. With the rapid development of sensor technology, the avenues for information collection have become even more diverse. To better extract useful insights from these signals, methods from machine learning are frequently employed. In recent years, a variety of machine learning-based techniques such as Support Vector Machines, Random Forests, and Deep Learning have been increasingly applied, leading to significant advancements. However, the relevant fields still face a series of critical challenges, including algorithm real-time performance, interpretability, small-sample learning, and transfer learning.
This special issue aims to present innovative machine learning-based information processing methods. It encompasses, but is not limited to, the latest machine learning algorithms in areas such as image segmentation, image recognition, natural language processing, machine translation, vibration signal processing, biomedical information processing, medical big data analysis, industrial big data analysis, and more. It also includes applications of related machine learning methods in medical image processing, natural image processing, industrial on-site diagnostics, medical disease diagnosis, and other aspects. Other research pertaining to machine learning and information processing is also welcomed for submission.
Topics covered include but not limited to:
• New theories in machine learning
• New theories in neural networks
• Machine learning-based signal processing
• Machine learning-based image processing
• Machine learning-based natural language processing
• Machine learning-based biomedical information processing
• Machine learning-based industrial data processing
• Machine learning-based big data analysis
Keywords:
machine learning, deep learning, neural networks, signal processing, image processing, natural language processing, biomedical information processing, industrial data processing, big data analysis
Instructions for authors
https://www.aimspress.com/mbe/news/solo-detail/instructionsforauthors
Please submit your manuscript to online submission system
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