scholarly journals Machine Learning Meets Communication Networks: Current Trends and Future Challenges

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 223418-223460
Author(s):  
Ijaz Ahmad ◽  
Shariar Shahabuddin ◽  
Hassan Malik ◽  
Erkki Harjula ◽  
Teemu Leppanen ◽  
...  
Entropy ◽  
2021 ◽  
Vol 23 (7) ◽  
pp. 831
Author(s):  
Vaneet Aggarwal

Due to the proliferation of applications and services that run over communication networks, ranging from video streaming and data analytics to robotics and augmented reality, tomorrow’s networks will be faced with increasing challenges resulting from the explosive growth of data traffic demand with significantly varying performance requirements [...]


2021 ◽  
Vol 22 (3) ◽  
pp. 313-320
Author(s):  
Dana Petcu

This position paper aims to identify the current and future challenges in application, workload or service deployment mechanisms in Cloud-to-Edge environments. We argue that the adoption of the microservices and unikernels on large scale is adding new entries on the list of requirements of a deployment mechanism, but offers an opportunity to decentralize the associated processes and improve the scalability of the applications. Moreover, the deployment in Cloud-to-Edge environment needs the support of federated machine learning.


2021 ◽  
Vol 2 ◽  
Author(s):  
Kai-Cheng Yan ◽  
Axel Steinbrueck ◽  
Adam C. Sedgwick ◽  
Tony D. James

Over the past 30 years fluorescent chemosensors have evolved to incorporate many optical-based modalities and strategies. In this perspective we seek to highlight the current state of the art as well as provide our viewpoint on the most significant future challenges remaining in the area. To underscore current trends in the field and to facilitate understanding of the area, we provide the reader with appropriate contemporary examples. We then conclude with our thoughts on the most probable directions that chemosensor development will take in the not-too-distant future.


2021 ◽  
Vol 5 (1) ◽  
pp. 1-15
Author(s):  
Rubina Shaheen ◽  
Mir Kasi

The report gives a presents use of artificial intelligence in few administrative agencies. In-depth thematic analysis of some institution, have been conducted to review the current trends. In thematic analysis, 12 institutions have been selected and described the details of the institutions using artificial intelligence in different departments. These analyses yielded five major findings. First, the government has a wide application of Artificial Intelligence toolkit traversing the federal administrative and state. Almost half of the federal agencies evaluated (45%) has used AI and associated machine learning (ML) tools. Also, AI tools are already enhancing agency strategies in  the full span of governance responsibilities, such as keeping regulatory assignments bordering on market efficiency, safety in workplace, health care, and protection of the environmental, protecting the privileges and benefits of the government ranging from intellectual properties to disability, accessing, verifying and analyzing all risks to public  safety and health, Extracting essential data from the data stream of government including complaints by consumer and the communicating with citizens on their rights, welfare, asylum seeking and business ownership. AI toolkit owned by government span the complete scope of Artificial Intelligence techniques, ranging from conventional machine learning to deep learning including natural language and image data. Irrespective of huge acceptance of AI, much still has to be done in this area by the government. Recommendations also discussed at the end.


2018 ◽  
Vol 179 ◽  
pp. 165-182 ◽  
Author(s):  
Shady Attia ◽  
Senem Bilir ◽  
Taha Safy ◽  
Christian Struck ◽  
Roel Loonen ◽  
...  

Sign in / Sign up

Export Citation Format

Share Document