Machine Learning for Cognitive Networks: Technology Assessment and Research Challenges

Machine learning (ML) has evolved to the point that this technique enhances communications and enables fifth-generation (5G) wireless networks. ML is great to get insights about complex networks that use large amounts of data, and for predictive and proactive adaptation to dynamic wireless environments. ML has become a crucial technology for mobile broadband communication. Special case goes to deep learning (DL) in immersive media. Through this chapter, the goal is to present open research challenges and applications of ML. An exploration of the potential of ML-based solution approaches in the context of 5G primary eMBB, mMTC, and uHSLLC services is presented, evaluating at the same time open issues for future research, including standardization activities of algorithms and data formats.

The Adversarial Machine Learning Conundrum: Can the Insecurity of ML Become the Achilles' Heel of Cognitive Networks?

IEEE Network ◽

10.1109/mnet.001.1900197 ◽

2020 ◽

Vol 34 (1) ◽

pp. 196-203 ◽

Cited By ~ 2

Author(s):

Muhammad Usama ◽

Junaid Qadir ◽

Ala Al-Fuqaha ◽

Mounir Hamdi

Keyword(s):

Machine Learning ◽

Cognitive Networks

Towards Energy Efficient 5G Networks Using Machine Learning: Taxonomy, Research Challenges, and Future Research Directions

IEEE Access ◽

10.1109/access.2020.3029903 ◽

2020 ◽

Vol 8 ◽

pp. 187498-187522

Author(s):

Amna Mughees ◽

Mohammad Tahir ◽

Muhammad Aman Sheikh ◽

Abdul Ahad

Keyword(s):

Machine Learning ◽

Energy Efficient ◽

Future Research ◽

5G Networks ◽

Research Directions ◽

Research Challenges ◽

Future Research Directions

Machine Learning Healthcare Applications (ML-HCAs) Are No Stand-Alone Systems but Part of an Ecosystem – A Broader Ethical and Health Technology Assessment Approach is Needed

The American Journal of Bioethics ◽

10.1080/15265161.2020.1820104 ◽

2020 ◽

Vol 20 (11) ◽

pp. 46-48 ◽

Cited By ~ 1

Author(s):

Helene Gerhards ◽

Karsten Weber ◽

Uta Bittner ◽

Heiner Fangerau

Keyword(s):

Machine Learning ◽

Health Technology Assessment ◽

Technology Assessment ◽

Health Technology ◽

Healthcare Applications ◽

Assessment Approach

Machine Learning with IoMT: Opportunities and Research Challenges

Internet of Things - Internet of Medical Things ◽

10.1007/978-3-030-63937-2_13 ◽

2021 ◽

pp. 235-252

Author(s):

M. Bharathi ◽

A. Amsaveni

Keyword(s):

Machine Learning ◽

Research Challenges

Multi-sensor information fusion based on machine learning for real applications in human activity recognition: State-of-the-art and research challenges

Information Fusion ◽

10.1016/j.inffus.2021.11.006 ◽

2021 ◽

Author(s):

Sen Qiu ◽

Hongkai Zhao ◽

Nan Jiang ◽

Zhelong Wang ◽

Long Liu ◽

...

Keyword(s):

Machine Learning ◽

Activity Recognition ◽

Human Activity ◽

Information Fusion ◽

State Of The Art ◽

Human Activity Recognition ◽

Research Challenges ◽

Sensor Information

A Review On Utilizing Machine Learning Technology in The Fields of Electronic Emergency Triage and Patient Priority Systems in Telemedicine: Coherent Taxonomy, Motivations, Open Research Challenges and Recommendations for Intelligent Future Work

Computer Methods and Programs in Biomedicine ◽

10.1016/j.cmpb.2021.106357 ◽

2021 ◽

pp. 106357

Author(s):

Omar. H. Salman ◽

Zahraa Taha ◽

Muntadher Q. Alsabah ◽

Yaseein S. Hussein ◽

Ahmed S. Mohammed ◽

...

Keyword(s):

Machine Learning ◽

Learning Technology ◽

Research Challenges ◽

Open Research ◽

Priority Systems ◽

Future Work

A survey of visual analytics techniques for machine learning

Computational Visual Media ◽

10.1007/s41095-020-0191-7 ◽

2020 ◽

Author(s):

Jun Yuan ◽

Changjian Chen ◽

Weikai Yang ◽

Mengchen Liu ◽

Jiazhi Xia ◽

...

Keyword(s):

Machine Learning ◽

Visual Analytics ◽

Model Building ◽

Future Research ◽

Research Topics ◽

Research Opportunities ◽

Research Challenges

AbstractVisual analytics for machine learning has recently evolved as one of the most exciting areas in the field of visualization. To better identify which research topics are promising and to learn how to apply relevant techniques in visual analytics, we systematically review 259 papers published in the last ten years together with representative works before 2010. We build a taxonomy, which includes three first-level categories: techniques before model building, techniques during modeling building, and techniques after model building. Each category is further characterized by representative analysis tasks, and each task is exemplified by a set of recent influential works. We also discuss and highlight research challenges and promising potential future research opportunities useful for visual analytics researchers.

A TinyMLaaS Ecosystem for Machine Learning in IoT: Overview and Research Challenges

2021 International Symposium on VLSI Design, Automation and Test (VLSI-DAT) ◽

10.1109/vlsi-dat52063.2021.9427352 ◽

2021 ◽

Author(s):

Hiroshi Doyu ◽

Roberto Morabito ◽

Martina Brachmann

Keyword(s):

Machine Learning ◽

Research Challenges

A Survey on Traffic Prediction Techniques Using Artificial Intelligence for Communication Networks

Telecom ◽

10.3390/telecom2040029 ◽

2021 ◽

Vol 2 (4) ◽

pp. 518-535

Author(s):

Aaron Chen ◽

Jeffrey Law ◽

Michal Aibin

Keyword(s):

Machine Learning ◽

Communication Networks ◽

Network Performance ◽

Research Effort ◽

Cognitive Networks ◽

Wide Area Networks ◽

Traffic Prediction ◽

Short Term ◽

Prediction Techniques

Much research effort has been conducted to introduce intelligence into communication networks in order to enhance network performance. Communication networks, both wired and wireless, are ever-expanding as more devices are increasingly connected to the Internet. This survey introduces machine learning and the motivations behind it for creating cognitive networks. We then discuss machine learning and statistical techniques to predict future traffic and classify each into short-term or long-term applications. Furthermore, techniques are sub-categorized into their usability in Local or Wide Area Networks. This paper aims to consolidate and present an overview of existing techniques to stimulate further applications in real-world networks.