Advances in Machine Learning and Cognitive Computing for Industry Applications

Submission Deadline: 31 May 2020

IEEE Access invites manuscript submissions in the area of Advances in Machine Learning and Cognitive Computing for Industry Applications.

Over the past few years, great progress has been made due to advances in machine learning and cognitive computing. For example, with the adoption of Convolutional Neural Networks (CNNs), computer vision has surpassed human vision in the task of image recognition. Moreover, the improvement in Natural Language Processing (NLP) makes machine translation, speech recognition, and other sequence applications more powerful than ever. It is the significant progress of machine learning algorithms, computing capability, and big data that makes machine learning and cognitive computing increasingly powerful in many applications. Compared to machine learning, cognitive computing places more emphasis on how the human brain works. Cognitive computing simulates human thought processes with self-learning algorithms that utilize data mining, pattern recognition, and natural language processing. In industrial scenarios, the data amount, as well as data generation speed, is very different compared to standard machine learning data sets. It is a challenge to utilize these heterogeneous data and find meaningful insights for practical applications.

As the basis, the Internet of Things (IoT) middleware platforms, communication, and network ecosystems should be involved. Considering the heterogeneity of industrial data, we are expected to inspect, clean, transform, and model data with the goal of specific industrial applications. Moreover, specialized algorithms, computing architectures, and feature engineering are needed to review, analyze, and present information. With the support of machine learning and cognitive computing, the significant insights and knowledge hidden behind industrial data can be capitalized for process optimization, anomaly detection, energy management, and so on. This Special Section focuses on consolidating research efforts that aim at machine learning and cognitive computing for industrial applications.

The topics of interest include, but are not limited to:

• Development of new machine learning algorithms for industrial applications
• Big data analytics, new algorithms, and approaches
• IoT system engineering
• Cognitive computing, affective computing (artificial emotional intelligence), and other innovative approaches for industrial scenarios
• Context-aware, emotion-aware, and other novel information and communication technologies based on machine learning and cognitive computing
• Machine learning for human, computer and machine interface
• Multi-modal sensor fusion, unstructured data mining and knowledge discovery in industrial applications
• Big data analytics software architectures
• Developing reusable and analytic tools and frameworks
• Development of autonomous systems for industrial applications
• New theories and applications of deep learning in industrial informatics

We also highly recommend the submission of multimedia with each article as it significantly increases the visibility, downloads, and citations of articles.

Associate Editor: Min Xia, Lancaster University, United Kingdom

Guest Editors:

1. 1. Zheng Liu, University of British Columbia, Canada
   2. Hong-Ning Dai, Macau University of Science and Technology, China
   3. Yu Zhang, University of Lincoln, UK
   4. Jixiang Yang, Huazhong University of Science and Technology, China
   5. Tsuyoshi Ide, IBM T. J. Watson Research Center, USA
   6. Jiong Jin, Swinburne University of Technology, Australia

Relevant IEEE Access Special Sections:

1. Data Mining for Internet of Things
2. Artificial Intelligence in CyberSecurity
3. Intelligent Data Sensing, Collection and Dissemination in Mobile Computing

IEEE Access Editor-in-Chief:  Prof. Derek Abbott, University of Adelaide

Article submission: Contact Associate Editor and submit manuscript to:
http://ieee.atyponrex.com/journal/ieee-access

For inquiries regarding this Special Section, please contact: m.xia3@lancaster.ac.uk.