scholarly journals Leveraging Machine-Learning for D2D Communications in 5G/Beyond 5G Networks

Electronics ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 169
Author(s):  
Sherief Hashima ◽  
Basem M. ElHalawany ◽  
Kohei Hatano ◽  
Kaishun Wu ◽  
Ehab Mahmoud Mohamed
Keyword(s):  
Machine Learning ◽  
Network Architecture ◽  
D2d Communication ◽  
Neighbor Discovery ◽  
Research Directions ◽  
Fifth Generation ◽  
Research Activities ◽  
Comprehensive Survey ◽  
Network Overhead

Device-to-device (D2D) communication is a promising paradigm for the fifth generation (5G) and beyond 5G (B5G) networks. Although D2D communication provides several benefits, including limited interference, energy efficiency, reduced delay, and network overhead, it faces a lot of technical challenges such as network architecture, and neighbor discovery, etc. The complexity of configuring D2D links and managing their interference, especially when using millimeter-wave (mmWave), inspire researchers to leverage different machine-learning (ML) techniques to address these problems towards boosting the performance of D2D networks. In this paper, a comprehensive survey about recent research activities on D2D networks will be explored with putting more emphasis on utilizing mmWave and ML methods. After exploring existing D2D research directions accompanied with their existing conventional solutions, we will show how different ML techniques can be applied to enhance the D2D networks performance over using conventional ways. Then, still open research directions in ML applications on D2D networks will be investigated including their essential needs. A case study of applying multi-armed bandit (MAB) as an efficient online ML tool to enhance the performance of neighbor discovery and selection (NDS) in mmWave D2D networks will be presented. This case study will put emphasis on the high potency of using ML solutions over using the conventional non-ML based methods for highly improving the average throughput performance of mmWave NDS.

scholarly journals Leveraging Machine-Learning for D2D Communications in 5G/Beyond 5G Networks

2021 ◽  
Author(s):  
Sherief Hashima ◽  
Basem Elhalwany ◽  
Kohei Hatano ◽  
Kaishun Wu ◽  
Ehab Mahmoud Mohammed
Keyword(s):  
Machine Learning ◽  
Network Architecture ◽  
D2d Communication ◽  
Neighbor Discovery ◽  
5G Networks ◽  
Research Directions ◽  
Fifth Generation ◽  
Research Activities ◽  
Network Overhead

Device-to-device (D2D) communication is a promising paradigm for the fifth generation 2 (5G) and beyond 5G (B5G) networks. Although D2D communication provides several benefits, 3 including limited interference, energy efficiency, reduced delay, and network overhead, it faces a lot 4 of technical challenges such as network architecture, and neighbor discovery, etc. The complexity 5 of configuring D2D links and managing their interference, especially when using millimeter-wave 6 (mmWave), inspire researchers to leverage different machine-learning (ML) techniques to address 7 these problems towards boosting the performance of D2D networks. In this paper, a comprehensive 8 survey about recent research activities on D2D networks will be explored with putting more 9 emphasis on utilizing mmWave and ML methods. After exploring existing D2D research directions 10 accompanied with their existing conventional solutions, we will show how different ML techniques 11 can be applied to enhance the D2D networks performance over using conventional ways. Then, still 12 open research directions in ML applications on D2D networks will be investigated including their 13 essential needs. A case study of applying multi-armed bandit (MAB) as an efficient online ML tool 14 to enhance the performance of neighbor discovery and selection (NDS) in mmWave D2D networks 15 will be presented. This case study will put emphasis on the high potency of using ML solutions 16 over using the conventional non-ML based methods for highly improving the average throughput 17 performance of mmWave NDS.

scholarly journals Machine Learning for Neonatal Mortality Risk Assessment: A Case Study Using Public Health Data from São Paulo

2020 ◽  
Author(s):  
Carlos Eduardo Beluzo ◽  
Luciana Correia Alves ◽  
Rodrigo Bresan ◽  
Natália Arruda ◽  
Ricardo Sovat ◽  
...  
Keyword(s):  
Public Health ◽  
Machine Learning ◽  
Risk Assessment ◽  
Network Architecture ◽  
Health Data ◽  
Sao Paulo ◽  
São Paulo ◽  
Public Health Data ◽  
Tools And Techniques

AbstractInfant mortality is a reflection of a complex combination of biological, socioeconomic and health care factors that require various data sources for a thorough analysis. Consequently, the use of specialized tools and techniques to deal with a large volume of data is extremely helpful. Machine learning has been applied to solve problems from many domains and presents great potential for the proposed problem, which would be an innovation in Brazilian reality. In this paper, an innovative method is proposed to perform a neonatal death risk assessment using computer vision techniques. Using mother, pregnancy care and child at birth features, from a dataset containing neonatal samples from São Paulo city public health data, the proposed method encodes images features and uses a custom convolutional neural network architecture to classification. Experiments show that the method is able to detect death samples with accuracy of 90.61%.

A Cognitive Machine-Learning System to Discover Syndromes in Erythemato-Squamous Diseases

2014 ◽  
pp. 66-101
Author(s):  
Francesco Gagliardi
Keyword(s):  
Machine Learning ◽  
Support System ◽  
Early Phase ◽  
Syndromic Surveillance ◽  
Scientific Discovery ◽  
Clustering Algorithms ◽  
Human Mind ◽  
Learning System ◽  
Research Activities

A syndrome is a set of typical clinical features that appear together often enough to suggest they may represent a single, as yet unknown, disease. The discovery of syndromes and relative taxonomy formation is the critical early phase of the process of scientific discovery in the medical domain. The author proposes a machine learning system to discover syndromes (seen as prototypes of clinical cases) that is based on the Eleanor Rosch’s prototype theory on how the human mind categorizes and infers prototypes from observations. A comparison on a case study in erythemato-squamous diseases of the proposed system against three hierarchical clustering algorithms shows that the system obtains performances which are averagely better. The system implemented can be considered a “scientific discovery support system” because it can discover unknown syndromes to the advantage of research activities and syndromic surveillance.

scholarly journals A Survey of State-of-the-Art on Blockchains

2021 ◽  
Vol 54 (2) ◽  
pp. 1-42
Author(s):  
Huawei Huang ◽  
Wei Kong ◽  
Sicong Zhou ◽  
Zibin Zheng ◽  
Song Guo
Keyword(s):  
State Of The Art ◽  
Future Research ◽  
High Quality Research ◽  
Research Directions ◽  
Business Applications ◽  
Research Activities ◽  
Comprehensive Survey ◽  
Future Research Directions ◽  
Analytic Models ◽  
Open Issues

To draw a roadmap of current research activities of the blockchain community, we first conduct a brief overview of state-of-the-art blockchain surveys published in the past 5 years. We found that those surveys are basically studying the blockchain-based applications, such as blockchain-assisted Internet of Things (IoT), business applications, security-enabled solutions, and many other applications in diverse fields. However, we think that a comprehensive survey toward the essentials of blockchains by exploiting the state-of-the-art theoretical modelings, analytic models, and useful experiment tools is still missing. To fill this gap, we perform a thorough survey by identifying and classifying the most recent high-quality research outputs that are closely related to the theoretical findings and essential mechanisms of blockchain systems and networks. Several promising open issues are also summarized for future research directions. We hope this survey can serve as a useful guideline for researchers, engineers, and educators about the cutting-edge development of blockchains in the perspectives of theories, modelings, and tools.

scholarly journals Machine Learning for Physical Layer in 5G and beyond Wireless Networks: A Survey

Electronics ◽  
2021 ◽  
Vol 11 (1) ◽  
pp. 121
Author(s):  
Jawad Tanveer ◽  
Amir Haider ◽  
Rashid Ali ◽  
Ajung Kim
Keyword(s):  
Machine Learning ◽  
Wireless Networks ◽  
Network Architecture ◽  
Spectrum Sharing ◽  
High Mobility ◽  
Physical Layer ◽  
Use Cases ◽  
Security And Privacy ◽  
Fifth Generation ◽  
5G Network

Fifth-generation (5G) technology will play a vital role in future wireless networks. The breakthrough 5G technology will unleash a massive Internet of Everything (IoE), where billions of connected devices, people, and processes will be simultaneously served. The services provided by 5G include several use cases enabled by the enhanced mobile broadband, massive machine-type communications, and ultra-reliable low-latency communication. Fifth-generation networks potentially merge multiple networks on a single platform, providing a landscape for seamless connectivity, particularly for high-mobility devices. With their enhanced speed, 5G networks are prone to various research challenges. In this context, we provide a comprehensive survey on 5G technologies that emphasize machine learning-based solutions to cope with existing and future challenges. First, we discuss 5G network architecture and outline the key performance indicators compared to the previous and upcoming network generations. Second, we discuss next-generation wireless networks and their characteristics, applications, and use cases for fast connectivity to billions of devices. Then, we confer physical layer services, functions, and issues that decrease the signal quality. We also present studies on 5G network technologies, 5G propelling trends, and architectures that help to achieve the goals of 5G. Moreover, we discuss signaling techniques for 5G massive multiple-input and multiple-output and beam-forming techniques to enhance data rates with efficient spectrum sharing. Further, we review security and privacy concerns in 5G and standard bodies’ actionable recommendations for policy makers. Finally, we also discuss emerging challenges and future directions.

scholarly journals A Comprehensive Survey of Privacy-preserving Federated Learning

2021 ◽  
Vol 54 (6) ◽  
pp. 1-36
Author(s):  
Xuefei Yin ◽  
Yanming Zhu ◽  
Jiankun Hu
Keyword(s):  
Machine Learning ◽  
Data Privacy ◽  
Rapid Development ◽  
Privacy Preserving ◽  
Future Research ◽  
Research Directions ◽  
Privacy Concerns ◽  
Privacy Leakage ◽  
Comprehensive Survey ◽  
Future Research Directions

The past four years have witnessed the rapid development of federated learning (FL). However, new privacy concerns have also emerged during the aggregation of the distributed intermediate results. The emerging privacy-preserving FL (PPFL) has been heralded as a solution to generic privacy-preserving machine learning. However, the challenge of protecting data privacy while maintaining the data utility through machine learning still remains. In this article, we present a comprehensive and systematic survey on the PPFL based on our proposed 5W-scenario-based taxonomy. We analyze the privacy leakage risks in the FL from five aspects, summarize existing methods, and identify future research directions.

MATERIAL HANDLING MACHINES AND SYSTEMS – UMI-TWINN PROJECT CONTRIBUTION

2019 ◽  
Vol 12 (1) ◽  
pp. 7-20
Author(s):  
Péter Telek ◽  
Béla Illés ◽  
Christian Landschützer ◽  
Fabian Schenk ◽  
Flavien Massi
Keyword(s):  
Material Handling ◽  
Digital Data ◽  
Flow Process ◽  
Research Directions ◽  
Scientific Excellence ◽  
Research Activities ◽  
New Research ◽  
Eu Project ◽  
The University

Nowadays, the Industry 4.0 concept affects every area of the industrial, economic, social and personal sectors. The most significant changings are the automation and the digitalization. This is also true for the material handling processes, where the handling systems use more and more automated machines; planning, operation and optimization of different logistic processes are based on many digital data collected from the material flow process. However, new methods and devices require new solutions which define new research directions. In this paper we describe the state of the art of the material handling researches and draw the role of the UMi-TWINN partner institutes in these fields. As a result of this H2020 EU project, scientific excellence of the University of Miskolc can be increased and new research activities will be started.

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