The Role and Applications of Machine Learning in Future Self-Organizing Cellular Networks

2019 ◽  
pp. 1-23
Author(s):  
Paulo Valente Klaine ◽  
Oluwakayode Onireti ◽  
Richard Demo Souza ◽  
Muhammad Ali Imran
Keyword(s):  
Machine Learning ◽  
Cellular Networks ◽  
Research Trends ◽  
Future Research ◽  
Wireless Cellular Networks ◽  
Intelligent Algorithms ◽  
Current State ◽  
Future Networks ◽  
Applications Of Machine Learning ◽  
Self Organizing

In this chapter, a brief overview of the role and applications of machine learning (ML) algorithms in future wireless cellular networks is presented, more specifically, in the context of self-organizing networks (SONs). SON is a promising and innovative concept, in which future networks are expected to analyze and use historical data in order to improve and adapt themselves to the network conditions. For this to be possible, however, algorithms that are capable of extracting patterns from data and learn from previous actions are necessary. This chapter highlights the utilization and possible applications of ML algorithms in future cellular networks. A brief introduction of ML and SON is presented, followed by an analysis of current state of the art solutions involving ML in SON. Lastly, guidelines on the utilization of intelligent algorithms in SON and future research trends in the area are highlighted and conclusions are drawn.

scholarly journals A Survey on the Applications of Machine Learning in the Early Phases of Product Development

2019 ◽  
Vol 1 (1) ◽  
pp. 2437-2446 ◽  
Author(s):  
Seyed Sina Shabestari ◽  
Michael Herzog ◽  
Beate Bender
Keyword(s):  
Machine Learning ◽  
Product Development ◽  
Product Development Process ◽  
Future Research ◽  
Concept Design ◽  
Survey Techniques ◽  
Current State ◽  
State Of Research ◽  
Applications Of Machine Learning ◽  
Early Phases

AbstractMachine learning has shown its potential to support the knowledge extraction within the development processes and particularly in the early phases where critical decisions have to be made. However, the current state of the research in the applications of the machine learning in the product development are fragmented. A holistic overall view provides the opportunity to analyze the current state of research and is the basis for the strategic planning of future research and the actions needed. Hence, implementing the systematic literature survey techniques, the state of the applications of machine learning in the early phases of the product development process namely the Requirements, functional modelling and principal concept design is reviewed and discussed.

Trends in Blockchain Technologies

2022 ◽  
pp. 208-238
Author(s):  
Mariacarmela Passarelli ◽  
Alfio Cariola ◽  
Giuseppe Bongiorno
Keyword(s):  
Intellectual Property ◽  
Bibliometric Analysis ◽  
Research Trends ◽  
Future Research ◽  
Evidence Based ◽  
Scientific Communities ◽  
Blockchain Technology ◽  
Current State ◽  
Barriers And Challenges

The aim of this work is to investigate emerging research trends and propose an evidence-based roadmap for encouraging further research into the management of blockchain technology. A bibliometric analysis is proposed, with a focus on intellectual property (IP) issues, in the field of blockchain technology. Then, the study highlights the main benefits that blockchain provides as well as the main difficulties, barriers, and challenges that emerge from the literature. The present study provides a reference for scientific communities to understand the current state of blockchain technology, thereby contributing to future research in the area. Moreover, it offers industrial implications and recommendations for entrepreneurs, managers, and practitioners.

scholarly journals Application of Machine Learning in Supply Chain Management: A Comprehensive Overview of the Main Areas

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Erfan Babaee Tirkolaee ◽  
Saeid Sadeghi ◽  
Farzaneh Mansoori Mooseloo ◽  
Hadi Rezaei Vandchali ◽  
Samira Aeini
Keyword(s):  
Machine Learning ◽  
Supply Chain ◽  
Supply Chain Management ◽  
Inventory Management ◽  
Future Research ◽  
Comprehensive Overview ◽  
Chain Management ◽  
Efficiency And Effectiveness ◽  
Applications Of Machine Learning ◽  
Supply Chain Risks

In today’s complex and ever-changing world, concerns about the lack of enough data have been replaced by concerns about too much data for supply chain management (SCM). The volume of data generated from all parts of the supply chain has changed the nature of SCM analysis. By increasing the volume of data, the efficiency and effectiveness of the traditional methods have decreased. Limitations of these methods in analyzing and interpreting a large amount of data have led scholars to generate some methods that have high capability to analyze and interpret big data. Therefore, the main purpose of this paper is to identify the applications of machine learning (ML) in SCM as one of the most well-known artificial intelligence (AI) techniques. By developing a conceptual framework, this paper identifies the contributions of ML techniques in selecting and segmenting suppliers, predicting supply chain risks, and estimating demand and sales, production, inventory management, transportation and distribution, sustainable development (SD), and circular economy (CE). Finally, the implications of the study on the main limitations and challenges are discussed, and then managerial insights and future research directions are given.

scholarly journals A Survey of Machine Learning Techniques Applied to Self-Organizing Cellular Networks

2017 ◽  
Vol 19 (4) ◽  
pp. 2392-2431 ◽  
Author(s):  
Paulo Valente Klaine ◽  
Muhammad Ali Imran ◽  
Oluwakayode Onireti ◽  
Richard Demo Souza
Keyword(s):  
Machine Learning ◽  
Cellular Networks ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Self Organizing

scholarly journals Overview of current state of research on the application of artificial intelligence techniques for COVID-19

2021 ◽  
Vol 7 ◽  
pp. e564
Author(s):  
Vijay Kumar ◽  
Dilbag Singh ◽  
Manjit Kaur ◽  
Robertas Damaševičius
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Drug Interaction ◽  
Future Research ◽  
Rapid Testing ◽  
Learning Models ◽  
Open Area ◽  
Current State ◽  
State Of Research ◽  
Novel Drug

Background Until now, there are still a limited number of resources available to predict and diagnose COVID-19 disease. The design of novel drug-drug interaction for COVID-19 patients is an open area of research. Also, the development of the COVID-19 rapid testing kits is still a challenging task. Methodology This review focuses on two prime challenges caused by urgent needs to effectively address the challenges of the COVID-19 pandemic, i.e., the development of COVID-19 classification tools and drug discovery models for COVID-19 infected patients with the help of artificial intelligence (AI) based techniques such as machine learning and deep learning models. Results In this paper, various AI-based techniques are studied and evaluated by the means of applying these techniques for the prediction and diagnosis of COVID-19 disease. This study provides recommendations for future research and facilitates knowledge collection and formation on the application of the AI techniques for dealing with the COVID-19 epidemic and its consequences. Conclusions The AI techniques can be an effective tool to tackle the epidemic caused by COVID-19. These may be utilized in four main fields such as prediction, diagnosis, drug design, and analyzing social implications for COVID-19 infected patients.

Autonomous Flight

2018 ◽  
Vol 1 (1) ◽  
pp. 29-52 ◽  
Author(s):  
Sarah Tang ◽  
Vijay Kumar
Keyword(s):  
High Speed ◽  
State Of The Art ◽  
Low Cost ◽  
Research Trends ◽  
Multirobot Systems ◽  
Future Research ◽  
Full Vehicle ◽  
Current State ◽  
Tremendous Progress ◽  
New Research

This review surveys the current state of the art in the development of unmanned aerial vehicles, focusing on algorithms for quadrotors. Tremendous progress has been made across both industry and academia, and full vehicle autonomy is now well within reach. We begin by presenting recent successes in control, estimation, and trajectory planning that have enabled agile, high-speed flight using low-cost onboard sensors. We then examine new research trends in learning and multirobot systems and conclude with a discussion of open challenges and directions for future research.

Applications of Machine Learning in Real-Time Tumor Localization

2012 ◽  
pp. 419-431
Author(s):  
Ruijiang Li ◽  
Steve B. Jiang
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Principal Component ◽  
Machine Learning Techniques ◽  
Future Research ◽  
Tumor Localization ◽  
Linear Discriminant ◽  
Learning Techniques ◽  
Domain Specific Knowledge ◽  
Applications Of Machine Learning

Recently, machine learning has gained great popularity in many aspects of radiation therapy. In this chapter, the authors will demonstrate the applications of various machine learning techniques in the context of real-time tumor localization in lung cancer radiotherapy. These cover a wide range of well established machine learning techniques, including principal component analysis, linear discriminant analysis, artificial neural networks, and support vector machine, etc. Respiratory gating, as a special case of tumor localization, will also be discussed. The chapter will demonstrate how domain specific knowledge and prior information can be useful in achieving more accurate and robust tumor localization. Future research directions in machine learning that can further improve the accuracy for tumor localization are also discussed.

scholarly journals Applications of Machine Learning to Reciprocating Compressor Fault Diagnosis: A Review

Processes ◽  
2021 ◽  
Vol 9 (6) ◽  
pp. 909
Author(s):  
Qian Lv ◽  
Xiaoling Yu ◽  
Haihui Ma ◽  
Junchao Ye ◽  
Weifeng Wu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Fault Diagnosis ◽  
Research Direction ◽  
Machine Learning Techniques ◽  
Future Research ◽  
Reciprocating Compressor ◽  
Operating Condition ◽  
Practical Applications ◽  
Learning Techniques ◽  
Applications Of Machine Learning

Operating condition detection and fault diagnosis are very important for reliable operation of reciprocating compressors. Machine learning is one of the most powerful tools in this field. However, there are very few comprehensive reviews which summarize the current research of machine learning in monitoring reciprocating compressor operating condition and fault diagnosis. In this paper, the recent application of machine learning techniques in reciprocating compressor fault diagnosis is reviewed. The advantages and challenges in the detection process, based on three main monitoring parameters in practical applications, are discussed. Future research direction and development are proposed.

Sign in / Sign up

Export Citation Format

Share Document